Qb.‘ 7 5 P 5 ' a changing world 'no. 1681 wart 7 ~ UA‘)’ I”; logical Survey Toxic Substances Hydrology Program _ Toxic Substances in Surface Waters and Sediments—A Study to Assess the Effects of ' Arsenic-Contaminated Alluvial Sediment in - Whitewood Creek, South Dakota Professional Paper 1681 ‘. U.S. Department of the Interior U.S. Geological Survey Photographs on the cover and within site maps of this report depict sampling sites along Whitewood Creek, South Dakota, in downstream order. Top leftphotograph, Headwater site, 7 kilometers upstream from mining activities. Top center photograph, Sewage—Treatment Plant site located 0.25 kilometer downstream from the wastewater- treatment plant in Deadwood, South Dakota. Top rightphotograph, A sampling site along Whitewood Creek above the town of Whitewood, South Dakota. High gradient reach of stream below historic inputs of mine tailings. Photograph is near location of site C described in section II of this report. Bottom leftphotograph, A site above Vale, South Dakota, showing a wider channel and slower velocities relative to upgradient, steeper sloping sites (note the thick algal and macrophytic mats lining the channel). AtangentiaI-flow filtration device was used to collect and chemically characterized suspended sediments in the water column. Bottom rightphotograph, Sheeler Seep site where ground-water seeps were marked along the channel banks by ferrihydrite precipitation. Photograph is near location of site A described in section II of this report. Photographs were taken by J. Kuwabara and C. Fuller in August 1986. 1 '. lla‘M Toxic Substances in Surface Waters and Sediments—A Study to Assess the Effects of Arsenic-Contaminated Alluvial Sediment in Whitewood Creek, South Dakota Edited by James S. Kuwabara and Christopher C. Fuller Secfionl Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota By James S. Kuwabara, Cecily C.Y. Chang, and Sofie P. Pasilis Section II Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota By Christopher C. Fuller and James A. Davis US. Geological Survey Toxic Substances Hydrology Program RECORDS/DOCUMENTS MAR I 2 2004 Professional Paper 1681 U follVERS‘!7"/r3y'E.T('-A 57L anRA/[A U.S. Department of the Interior US. Geological Survey U.S. Department of the Interior Gale A. Norton, Secretary U.S. Geological Survey Charles G. Groat, Director U.S. Geological Survey, Reston, Virginia: 2003 For sale by U.S. Geological Survey, Information Services Box 25286, Denver Federal Center Denver, CO 80225 For more information about the USGS and its products: Telephone: l»888—ASK-USGS World Wide Web: http://wwwusgsgov/ Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply endorsement by the U.S. Government. Library of Congress Cataloging-in-Publication Data Toxic substances in surface waters and sediments : a study to assess the effects of arsenic—contaminated alluvial sediment in Whitewood Creek, South Dakota : US. Geological Survey Toxic Substances Hydrology Program / by James S. Kuwabara [et al.]. p. cm, -- (Professional paper; 1681) Includes bibliographical references. ISBN 0-607-93722—X (alk. paper) 1. Arsenic—-Environmenta| aspects-South Dakota-Whitewood Creek. 2. Water--Pollution—-South Dakota-Whitewood Creek. 3, Contaminated sediments-South Dakota-Whitewood Creek. 4. US. Geological Survey Toxic Substances Hydrology Program. I. Kuwabara, James S. ll. Geological Survey (U.S.) lll. Geological Survey professional paper; 1681. TD427.A77T58 2003 363.738'4-—dc22 2003049476 Contents SECTION I. Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota, byJames S. Kuwabara, Cecily C.Y. Chang, and Sofie P. Pasilis Abstract ..................................................................................................... 1 Introduction ................................................................................................. 1 Purpose and Scope ................................................................................... 2 Acknowledgments .................................................................................... 2 Study Site ................................................................................................... 2 Methods of Study ........................................................................................... 2 Field Studies .......................................................................................... 4 Laboratory Studies .................................................................................... 5 Results and Discussion ..................................................................................... 16 Field Experiments .................................................................................... 16 Laboratory Experiments .............................................................................. 17 Effects of Benthic Flora on Physico-Chemical Processes ............................................. 22 Summary and Conclusions ................................................................................. 24 References ................................................................................................. 25 Figures 1—1. Map ofthe study area along Whitewood Creek, South Dakota ................................. 3 l—2—I—5. Graphs showing: 1—2. Diel trends in pH and irradiance along Whitewood Creek at (Althe Headwater site, (B) the Sewage-Treatment—Plant site, ( Cl the Above Vale site, and (D) the Sheeler Seep site ................................................ 18 1—3. Diel trends in specific conductance and laboratory determinations for dissolved-arsenic species for (A) the Above Vale site and (B) the Sheeler Seep site ..................................................................... 19 l—4. Diel trends in pH and concentrations of arsenic species for (Althe Above Vale site and (B) the Sheeler Seep site ......................................... 19 1—5. Dieltrends in concentrations ofdissolved orthophosphate and arsenic species at (A) the Above Vale site and (B) the Sheeler Seep site ....................... 20 Tables |—1. Estimated biomass forfour sampling sites along Whitewood Creek, August 4—9, 1986 ............................................................................... 4 1—2. Field data collected monthly during the summer of 1987 from four sites along Whitewood Creek ............................................................................. 5 1—3. Physical and chemical characteristics in Whitewood Creek atthe Headwater site, monitored between August 29 and 30, 1988, to examine diel fluctuations in these characteristics ........................................................................... 6 iv l—4. Physical and chemical characteristics in Whitewood Creek atthe Sewage- Treatment-Plant site, monitored between August30 and 31, 1988, to examine diel fluctuations in these characteristics ........................................................ 8 Hi. Physical and chemical characteristics in Whitewood Creek atthe Above Vale site, monitored between August 31 and September 1, 1988, to examine diel fluctuations in these characteristics .......................................................... 11 l—6. Physical and chemical characteristics in Whitewood Creek atthe Sheeler Seep site, monitored between September 1 and 2, 1988, to examine diel fluctuations in these characteristics .......................................................... 13 H. Algal cell sorption of arsenate and orthophosphate at 24 and 48 hours by living and heat-killed cells using three Stichococcus isolates from three sites along Whitewood Creek ..................................................................... 15 I—8. Total arsenic concentrations in dominant benthic plant species collected from four sites along Whitewood Creek ............................................................ 17 l—9. Site comparison for parameters monitored along Whitewood Creek ......................... 20 HO. Lag times, in hours, between selected water-quality variables monitored along Whitewood Creek ............................................................................ 21 H 1. Results from experiments examining arsenate sorption by heat—killed Achnanthes minutissima cells isolated from the Headwater site and the Above Vale site .................. 21 l—12. Results from experiments examining orthophosphate sorption by cell surfaces of Achnanthes minutissima isolated from the Headwater site and the Above Vale site ........... 22 SECTION II. Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota, by Christopher C. Fuller and James A. Davis Abstract .................................................................................................... 27 Introduction ................................................................................................ 27 Purpose and Scope .................................................................................. 27 Description of Study Area ............................................................................ 28 Acknowledgments ................................................................................... 28 Methods of Sample Collection and Analysis ................................................................ 28 Diurnal Sampling ..................................................................................... 28 Statistical Analysis of Diurnal Surface—Water Chemistry Data ......................................... 30 Conservative-Tracer Injection ........................................................................ 30 Sampling of Ground-Water Inflows ................................................................... 31 Solid-Phase Characterization and Adsorption Properfies ............................................. 31 Results and Discussion ..................................................................................... 31 Diurnal Fluctuations in Surface-Water Chemistry ..................................................... 31 Ground-Water Chemistry ............................................................................. 35 Variations in Stream Discharge ....................................................................... 35 Dissolved Arsenic in Synoptic Samples ............................................................... 38 Arsenate Adsorption and Isotopic Exchange on Iron nyhydroxides .................................. 38 Processes that Contribute to the Diurnal Cycle of Dissolved Arsenic in Surface Water ...................... 39 Ground-Water Sources of Streamflows .............................................................. 41 Molecular Diffusive Flux from Bed Sediments ........................................................ 42 Algal Uptake of Arsenate from Surface Water ........................................................ 42 Potential for Desorption of Arsenic from Suspended Sediments and Bed Sediments .................. 43 Additional Controlling Processes ..................................................................... 44 Summary ................................................................................................... 45 Selected References ....................................................................................... 45 Figures ”—1. Map showing location of study area and location of sampling sites in Whitewood Creek ........................................................................................ 29 ||—2—||—10. Graphsshowing: ”—11. Tables ”—1 . ”—2. ”—3. ||~2. Incident-light intensity, dissolved-arsenate concentration, and pH compared to time of day at site A, August 1 1—13, 1987 .......................................... 32 ”—3. Dissolved- arsenate concentration and pH compared to time ofday at site B, August12—13,1987 .................................................................. 33 11—4. Dissolved- arsenate concentration and pH compared to time ofday at srte C, August 12—13, 1987 .................................................................. 33 ”—5. Total alkalinity concentration and pH compared to time ofday at site A, August 11—13, 1987 .................................................................. 35 IN)“. Sulfate and bromide concentrations compared to time of day at site A, August 1 1—13, 1987 .................................................................. 37 ”—7. Bromide and sulfate concentrations compared to distance downstream for lithium-bromide injection site, August 13, 1987, 9 am. to 1 pm. . . .. ............... 37 “—8. Synoptic dissolved-arsenate concentration related to distance from lithium- bromide injection site, August 13, 1987, 9 a.m.to 1 pm. .............................. 38 ”—9. Uptake of arsenate at pH 8.0 from surface water byferrihydr'rte formed from ground-water seep, as a function oftime ............................................ 39 ”—10. Arsenic-isotope exchange as a function oftime on ferrihydrite following 96 hours of uptake .................................................................. 40 Generalized diagram showing dissolved-arsenate cycle ..................................... 41 Cross-correlation analysis of diurnal time-series data ........................................ 34 Ground-water chemistry data for Whitewood Creek near site A .............................. 36 Estimated sources and sinks of dissolved arsenic (CA5) in Whitewood Creek, South Dakota ................................................................................ 43 vi Conversion Factors, Datum, and Abbreviated Water-Duality Units Multiply By To obtain millimeter (mm) 0.03937 inch centimeter (cm) 0.3937 inch cubic centimeter (cm3) cubic inch meter (m) 3 .281 foot kilometer (krn) 0.6214 mile square kilometer (kmz) 0.3861 square mile cubic meter per second (m3/s) 35.3107 cubic foot per second cubic meter per second per meter (m3/s/m) cubic foot per second per foot liter per second (US) 15.85 gallon per minute megagram (Mg) 106 grams 2,204.6 pounds microgram (pg) 10$ gram 2.2046><10_9 pound gram (g) 0.0022046 pound micrometer (um) 10‘6 meter 3.937><10_5 inch nanometer (nm) 10‘9 meter 3.937><10_8 inch milligram (mg) 10‘3 gram 2.2046><10'6 pound liter (L) 0.3785 gallon milliliter (m/L) 0.03785 gallon cubic meter per second (m3/s) 35.3198 cubic feet per second square meter (m2) 10.7650 square feet square centimeter (cmz) 0.1550 square inch gravitational constant (G) parts per million (ppm) micromoles per square meter per day (umol/mz/d) square centimeter per second (cm2/s) micromoles per liter per day (umol/L/d) milligrams per liter (mg/L) micromoles per gram (umol/g) milliequivalents per liter (meq/L) and (umol/L) micromoles per liter liter per day (L/d) rnicroeinsteins microsiemens per centimeter at 25 degrees Celsius (uS/cm) Airtemperatures are given in degrees Celsius (°C), which can be converted to degrees Fahrenheit (°F) by the following equation: °F =1.8(°C)+32 Vertical coordinate information is referenced to the National Geodetic Vertical Datum of 1929 (NGVD 29). Horizontal coordinate information is referenced to the North American Datum of 1927 (NAD 27). Chemical concentration and watertemperature are given only in metric units. Chemical concentration in water is given in millimoles per liter (mmol/L) or micromoles per liter (umol/L). Millimoles per liter is a unit expressing the solute per unit volume (liter) of water. One thousand micromoles per liter is equivalent to l millimole per liter. Molar concentrations can be converted to mass concentration by multiplying the molar concentration (moles per liter) by the molecular weight of the solute (grams per mole). For example, 1 micromole per liter (umol/L) dissolved arsenic is equivalent to 7.49x10‘5 grams per liter or 74.92 rig/L. Total alkalinity is given in units of milliequivalents per liter (meg/L). Chemical cencentration of solid-phase samples is given in moles per gram (mol/g). SECTION I. Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota By James S. Kuwabara, Cecily C.Y. Chang, and Sofie P. Pasilis Abstract Field measurements and bioassay experiments were done to investigate the effects of arsenic and phosphorus interactions on sorption of these solutes by the benthic flora (periphyton and submerged macrophytes) in Whitewood Creek, a stream in western South Dakota. Short-term (24-hour) sorption experi- ments were used to determine arsenic transport characteristics for algae (first-order rate constants for solute sorption, biomass, and accumulation factors) collected in the creek along a transect beginning upstream from a mine discharge point and down- gradient through a 57-kilometer reach. Temporal changes in biomass differed significantly between and within sampling sites. Arsenic concentrations in plant tissue increased with distance downstream, but temporal changes in concentrations in tissues differed considerably from site to site. Cultures of Achnanthes minutissima (Bacillariophyceae) and Stichococcus sp. (Chlorophyceae) were isolated from four sites along a lon- gitudinal concentration gradient of dissolved arsenic within the study reach and were maintained at ambient solute concen- trations. Arsenic accumulation factors and sorption-rate con- stants for these isolates were determined as a function of dis— solved arsenate and orthophosphate. Cell surfaces of algal isolates exhibited preferential orthophosphate sorption over arsenate. Initial sorption of both arsenate and orthophosphate followed first-order mass transfer for each culturing condition. Although sorption—rate constants increased slightly with increased dissolved-arsenate concentration, algae, isolated from a site with elevated dissolved arsenic in the stream channel, had a significantly slower rate of arsenic sorption compared with the same species isolated from an uncontaminated site upstream. In diel studies, amplitudes of the pH cycles increased with measured biomass except at a site immediately down- stream from water—treatment—plant discharge. Inorganic pen- tavalent arsenic dominated arsenic speciation at all sites—not a surprising result for the well-oxygenated water column along this reach. Concentration fluctuations in dissolved-arsenic spe- cies lagged pH fluctuations by approximately 3 hours at the most downstream site, but no discernible lag was observed at an artificially pooled area with an order of magnitude higher biomass. Furthermore, the amplitudes of diel fluctuations in arsenic species were greater at the pooled area than at the most downstream site. Lack of correspondence between changes in dissolved-orthophosphate concentrations and arsenic species may have resulted from preferential sorption of orthophosphate over arsenate by the biomass. Based on carbon-fixation esti- mates, the phosphorus demand from photosynthetic activity required water-column concentrations to be supplemented by another source such as phosphate regeneration within the benthic community or desorption of particle-bound phosphate. Introduction Biological uptake and chemical transformations can affect transport of reactive solutes in streams and, consequently, the complexity of water-quality modeling and monitoring (Kuwabara and others, 1984; Cain and others, 1988; Kuwabara and others, 1988; Kuwabara and Helliker, 1988). For example, solute uptake by organisms may retard downstream transport, whereas metabolic reactions might change chemical speciation (Andreae, 1977; Sanders, 1985) and thereby effect changes in surface activity and transport characteristics. The effects of bio- logical processes can be difficult to quantify because character- istics of the benthic and planktonic communities (for example, species composition and biomass) can change temporally and spatially. These changing characteristics can influence, and be affected by, interacting chemical and hydrologic processes. Although biological processes have long been considered in water—quality and nutrient—cycling models for macronutri— ents, the importance of integrating biological models into trace- contaminant transport models has only recently been acknowl— edged (Zison and others, 1978; Jorgensen, 1983; Kuwabara and others, 1984). This can be due, in part, to the complexity of quantifying or even identifying the pertinent mechanisms that affect transport and distribution of a given solute. Biological processes that need to be considered in solute-transport models include solute uptake and release, toxicity and adaptation, metabolism and storage, community structure, and species interactions. In particular, attempts to model the influence of the benthic flora on arsenic transport within an aquatic environ- ment have been hampered by a number of factors: (1) arsenate 2 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota inhibits the growth of certain algal species at submicromolar concentrations, presumably because of interference with phos- phate metabolism (Button and others, 1973; Sanders, 1979); (2) the effects of arsenic speciation on uptake rates and solute accumulation by periphyton are not well known; (3) possible changes in metabolic reaction rates as a consequence of pro- longed exposure at elevated arsenic concentrations (and further uptake of arsenic) are poorly understood; and (4) data to quan- tify the effects of physical and chemical factors (for example, light intensity and surface reactions with inorganic particles) on arsenic uptake are sparse. Purpose and Scope The dependence of arsenic transport and speciation in Whitewood Creek on algal growth was investigated. This report presents results of a field and laboratory study of arsenic trans— port characteristics of periphyton and submerged macrophytes along a 57-km reach of Whitewood Creek in South Dakota, where both dissolved—arsenate and orthophosphate concentra— tions varied substantially. A first-order mass transfer equation was tested as a model to describe solute sorption on cell sur- faces of the benthic flora. The variables in the equation (for example, algal biomass, sorption and release rates, and accumu- lation factors) may be used as a biological term in a comprehen— sive transport model consisting of hydrologic, chemical, and biological terms. Results of a subsequent study done between August 30 and September 2, 1988, also are presented that examine diel relationships among benthic flora and pH, specific conduc- tance, water temperature, and photosynthetically active radiation. The effects of these relations on concentration trends for dissolved-orthophosphate and arsenic species are discussed. Acknowledgments The authors wish to thank Christopher Fuller for com— ments and discussions about this manuscript and for alkalinity measurements. Contributions to manuscript revisions by James Carter, James Davis, Ronald Harvey, and Harry Leland also are gratefully acknowledged. Thanks are extended to CB. Hellquist, Biology Department, North Adams State College in North Adams, Massachusetts, and Raymond Wong of the Math/Science Nucleus in Fremont, California, for taxonomic analyses of submerged macrophytes and benthic algae. Logistical support from the staff of the US. Geological Survey South Dakota District office is very much appreciated. We also thank Cyndi Azevedo, Herbert Buxton, Keith Kirk, Gail Mallard, David Morganwalp, and Brent Topping for their critical administrative and editorial efforts. The USGS Toxic Substances Hydrology Program is gratefully acknowledged for long-term support of this interdisciplinary research. Study Site Whitewood Creek, a perennial, snow—fed stream in the Black Hills of South Dakota, has received effluents from gold mining since 1876 and from municipal activities such as sewage treatment and power-generator cooling. Four sites along the creek were selected for this study, beginning upstream from the mining activities and continuing approximately 1 km upstream from the confluence of Whitewood Creek and the Belle Fourche River (fig. I—l). Although direct discharge of mine tailings into the creek ended in 1977, residual mine tailings still form most of the bank and bed sediment over the 57-km study reach. These accumulated tailings contribute to a dissolved-arsenic concen— tration gradient that increases in the downstream direction from less than 0.1 micromolar (umol/L) upstream from the mining activities to 1.5 umol/L at the most downstream site (Goddard, 1988). Conversely, inputs of phosphorus and other macro- nutrients from a municipal wastewater-treatment facility at Deadwood, downstream from major mining activities, provides a dissolved-orthophosphate gradient that decreases in the down- stream direction from 20 umol/L immediately downstream from the wastewater facility to less than 1 umol/L at 57 km downstream (Goddard, 1988). A dense community of attached algae and submerged macrophytes quickly forms in Whitewood Creek after snowmelt and remains throughout the summer months. Dissolved arsenic (primarily as arsenate) in the streamwater also reaches maximal concentrations during the summer downstream from mining activities, possibly because of ( 1) elevated water-column pH during the summer that causes desorption of arsenic and (2) summer inputs of arsenic-laden ground water influenced by both percolation of irrigation water and lower summer surface-water flow after snowmelt (Goddard and others, 1988; Christopher C. Fuller, section II of this report). The study reach, therefore, represents idea] field conditions for the examination and quantification of potentially important contributions of the benthic flora to arsenic mobiliza- tion and attenuated transport. Methods of Study Arsenic transport characteristics were determined for algae and macrophytes collected at four sampling sites (fig. I—l): (1) 7 km upstream from the mining activities (1,700-m eleva- tion); (2) 15 km downstream from the first site and within the town of Deadwood (0.25 km downstream from the municipal wastewater-treatment plant, 1,390—m elevation); (3) 39 km farther downstream at a US. Geological Survey gaging station (870-m elevation); and (4) approximately 1 km upstream from the confluence of Whitewood Creek and the Belle Fourche River (850-m elevation). These sites are hereinafter referred to as the Headwater (HW) site, the Sewage-Treatment—Plant (STP) site, the Above Vale (AV) site, and the Sheeler Seep (SS) site, respectively (Kuwabara and others, 1988). Abundant ground—water seepage occurs as springs along the banks of the creek at the farthest down- stream site. 103°40' SOUTH DAKOTA Study area 103°30' 44°40' 44°30' — Whitewood Deadwood D 44°20' - Headwater site (elevation 1,700 meters) Sewage-treatment- plant site (elevation 1,390 meters) Belle Fourche River Sheeler Seep site (elevation 850 meters) Above Vale site (elevation 870 meters) l Elevations based on 0 NGVD of 1929 0 Figure H. 10 KILOMETERS 10 MILES Map of the study area along Whitewood Creek, South Dakota. Methods of Study 4 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota Estimated transport characteristics included biomass (p b) or the accessible biomass per unit streambed area, net sorption-rate constant (lb), and accumulation factor (that is, biological parti- tioning coefficient, K b)- The estimates were used to describe a bio- logical component of a comprehensive transport model describing hydrologic, chemical, and biological processes. Assuming a first- order process, the rate of change of dissolved arsenic resulting from plant tissue accumulation would take the form (phi/Dj)ij _(pbj/Dj)(6ij/5t) _>"bj(pbj/Dj)(cbj — ijCj) where ( pbj le) = biomass factor that scales changes in solute concentrations in plant communities to changes in water-column solute concentration, in units of biotic mass per cubic length; Rb = temporal change in Cb, in units of solute mass per biotic mass per unit time; j = finite interval of the stream reach (dimensionless); C b = concentration associated with the benthic plant material, in units of solute mass per biotic mass; D = mean interval channel depth, in units of length; t = time; and C = solute concentration in the stream as a function of time, in units of solute mass per cubic length. Field Studies During an initial reconnaissance of Whitewood Creek, August 4-9, 1986, algal biomass (periphyton and macrophyte abundance in grams ash-free dry weight per square meter) was estimated from cobble scrapings (number of sample replicates, n=6) taken from the four sites illustrated in figure I—l (table I—l). Acetone (99.5 percent) extractions of chlorophyll-a were ana- lyzed fluorometrically (Franson, 1985). Ash-free dry weights from the scrapings also were taken in order to calculate an autotrophic index (mass ratio of plant biomass to chlorophyll-a) for each site as a potential indicator of environmental stress. Table l—1. Estimated biomass for four sampling sites along Whitewood Creek, August 4-9, 1986. [Headwater site, HW; Sewage—Treatment—Plant site, STP; Above Vale site, AV; and Sheeler Seep site, SS; Biomass, Pb, in grams per square meter; and temper- ature in degrees Celsius. The mass ratio of periphyton and macrophytes to chlorophyll—a, the autotrophic index, has been used as an indicator of environ- mental stress with increasing values indicating greater abundance of senescent algal cells or increased heterotrophic growth (Weber, 1973)] Autotrophic Site Temperature . Biomass Index HW 13.2 120 21+14 STP 17.5 210 34:13 AV 19.0 140 77:27 SS 24.2 190 25i11 Subsequent field studies during the summer of 1987 examined temporal fluctuations in characteristics of the benthic flora. Benthic plant abundance (pb) at each of the four sites was estimated monthly from late May to September 1987 by using ash—free dry weight and chlorophyll-a measurements (n29) of streambed areas (Franson, 1985). Measurements of channel width (W, in meters, n=3), depth (D, in meters, n=9), and channel velocity (V, in meters per second, n=9) were made at all sites, as were temperature (TEMP, in degrees Celsius), specific conductance (COND, in microsiemens per centimeter at 25 degrees Celsius), dissolved oxygen, and pH (table 1—2). Periphyton and submerged macrophytes were collected for arsenic analyses. Tissues were rinsed four times in stream- water and then four times in deionized water (18 megohms). Macrophyte root tissue was excluded from the samples in order to minimize arsenic contamination by attached inorganic parti- cles that could not efficiently be removed by rinsing (that is, to improve precision in measurements of plant-tissue arsenic concentrations). The macrophyte sampling procedure did not exclude epiphytic algal cells. Lyophilized plant tissue was prepared and analyzed using a dry-ash procedure (Johns and Luoma, 1988). Reconstituted samples also were analyzed for total iron by flame atomic absorption spectroscopy to check for contamination by attached inorganic particles. Error bars presented in tables represent 95-percent confidence intervals for the specified replicate measurements. Diel variations in biologically significant characteristics were examined using data loggers (Campbell, Model CR—10) at the four sampling sites. Data loggers were used with various probes to monitor the following variables: photosynthetically active radiation between 400 and 700 nm (LICOR probe, Model LI—192SA), water temperature (Campbell Scientific Probe, Model 107), air temperature (copper constantan thermo- couple), specific conductance (4-lead probe fabricated by the Hydrologic Instrumentation Facility, US. Geological Survey), and pH (Innovative Sensors probe, Model M—12 with 9-volt preamplifier). Parameters monitored by data loggers and pre- sented in tables 1—3 to 1—6 represent 15-minute averages of measurements taken at 10—second intervals. Data from the irradi- ance probe were verified against an irradiance meter (LICOR, Model L1185B) before the sampling study. Water and air temper- ature, and temperature corrected specific-conductance measure— ments (Cole Parmer, Model 1500—20) were taken manually every 3 hours during the sampling period. Measurements from the pH probe, designed for long-term data-logging applications, also were calibrated every 3 hours against temperature-compensated pH determinations (Orion Research, Model SA250). Therefore, with the exception of the irradiance measurements, the plotted data—logger values are identical to the manually determined measurements taken at 3-hour intervals (fit to manually deter— mined measurements is imposed). Analysis of time—series data was performed using a statistical computer program (Minitab; Ryan and others, 1985) to produce correllograms for a range of lag times. During the die] study, periphyton and macrophytes were sampled at noon at each site (n=9) for ash-free dry mass and spectrophotometric determination of chlorophyll-a corrected Methods of Study 5 Table l—Z. Field data collected monthly during the summer of 1987 from four sites along Whitewood Creek. [Headwater site, HW; Sewage—Treatment—Plant site, STP; Above Vale site, AV; and Sheeler Seep site, SS. Measured characteristics include: specific conductance, COND, in microsiemens per centimeter at 25 degrees Celsius; water temperature, TEMP, in degrees Celsius; pH; stream—channel width, W, in meters with 95-percent confidence intervals (n=6); mean channel depth, D, in meters with 95-percent confidence intervals (21:9); mean velocity, V, in meters per second with 95—percent confidence intervals (71:9); biomass, p1,, in grams per square meter with 95-percent confidence intervals (n=9)‘, and autotrophic index, AI, in grams ash—free dry mass per gram chlorophyll—a. Sampling times are given in military format. The symbol “—” in the pl, or Al column indicates that the benthic—plant community at that time was not present in sufficient mass for analysis] Date Time Site COND TEMP pH W D V pl, Al 05/27 0930 HW 420 8.2 8.3 26:01 0.12:0.05 0.82:0.02 37:15 120 05/27 1430 STP 545 15.2 8.1 7.7:0.5 0.38:0.09 0.94:0.12 34:17 110 05/28 1300 AV 890 18.4 8.2 21.6:1.6 0.35:0.12 0.30:0.11 — — 05/28 1550 SS 980 20.0 8.2 39:04 0.38:0.15 0.77:0.20 — — 07/07 1255 HW 450 16.0 8.3 2.4:0.3 0.10:0.02 0.58:0.10 52:19 110 07/08 1040 STP 760 17.0 8.4 7.3:0.5 0.24:0.04 0.74:0.11 71:51 120 07/09 1300 AV 1,180 21.5 8.6 17.9:0.7 0.05:0.02 0.39:0.14 469:65 90 07/10 1610 SS 1,310 28.0 8.6 3.4:0.5 0.24:0.11 0.86:0.16 28:5 80 08/19 0940 HW 429 9.0 8.7 25:03 0.15:0.03 0.51:0.16 51:27 140 08/19 1300 STP 899 17.6 8.3 7.1:0.3 0.23:0.06 0.65:0.19 54:19 100 08/26 1400 AV 1,108 17.0 8.0 18.0:0.5 0.21:0.18 0.13:0.03 280:30 180 08/26 1015 SS 1,245 14.5 8.1 3.6:0.3 0.27:0.04 0.72:0.20 29:14 160 09/16 1315 HW 563 11.1 8.3 1.7:0.2 0.10:0.02 0.25:0.05 60:34 170 09/16 1730 STP 930 16.8 7.9 6.5:0.7 0.19:0.04 0.53:0.07 82:60 120 09/28 1745 AV 1,167 18.3 8.1 17.8:0.8 0.33:0.06 0.07:0.01 237:25 310 09/28 1435 SS 1,222 19.5 8.3 37:04 0.24:0.05 0.46:0.09 7:4 20 for pheophytin-a (Franson 1985; Kuwabara and others, 1990). Laboratory Studies Streamwater sampled (n=5) at 3—hour intervals was filtered (Nuclepore, 0.2-ttm polycarbonate membranes) for dissolved- arsenic and orthophosphate analyses. Water samples were simultaneously taken at 6-hour intervals for alkalinity determi- nation by titration (Franson, 1985). Filtered orthophosphate samples (n=2 per sampling time) were preserved with mercuric chloride (l-mL mercury-saturated solution per 250-mL sample), and filtered arsenic samples were acidified to pH 2 with 6 normal, quartz-distilled hydrochloric acid. All processed water samples were refrigerated (approximately 5°C) in darkness. Arsenic speciation was determined in the laboratory by the following steps. The acidified sample was buffered to pH 5 by using an acetate buffer (Tallman and Shaikh, 1980) and ana- lyzed directly for trivalent arsenic [As(III)] by hydride genera- tion, atomic absorption spectrometry (AAS). The sample was then reduced with potassium iodide in 40-percent hydrochloric acid for 1.5 hours and analyzed again to determine the total of inorganic As(III) and pentavalent arsenic [As(V)] (Tallman and Shaikh, 1980; Glaubig and Goldberg, 1988). The reduced sample then was analyzed by graphite furnace AAS to provide a measure of total dissolved arsenic. Dissolved orthophosphate was determined colorimetrically by the molybdate method using an autoanalyzer (Technicon, Model 11) with an extended cell-path length (50 mm) for greater sensitivity (Murphy and Riley, 1962; Merle Shockey, oral commun., September 6, 1988). Algal species common to each of the four sampling sites were isolated for use in arsenate and orthophosphate sorption studies. Algal suspensions generated from periphyton scrapings were streaked on 1-percent agar plates containing an algal growth medium (Kuwabara and others, 1985) enriched with silica. Algal clusters that formed on the plates were transferred to liquid media for one week to increase algal density. This agar plate streaking and resuspension procedure was repeated until unialgal cultures were achieved. Stichococcus spp. and Scene- desmus spp. (Chlorophyceae), common to the four sampling sites, were initially isolated. It is not clear why common diatoms were not obtained from this isolation procedure because an abundance of diatoms was observed in the initial scrapings. One possibility is that chlorophyte growth was more rapid than diatoms on these agar plates. Another possibility is that the agar color obscured diatom clusters on the plates while chlorophyte clusters were readily visible. Stichococcus isolates were selected for use in subsequent sorption experiments over Scene- desmus because in agitated cultures, Stichococcus typically remained unicellular and therefore could be quickly and pre- cisely enumerated with a particle counter. Algal isolates were maintained in a chemically defined growth medium with added solute concentrations similar to ambient levels at their respec- tive collection sites (0.0 umol/L for HW, 0.5 umol/L for STP, and 1.0 umol/L for AV and SS isolates). 6 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota Table l-3. Physical and chemical characteristics in Whitewood Creek at the Headwatersite, monitored between August 29 and 30,1988, to examine diel fluctuations in these characteristics. [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet- ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second] Date Time E".""°" ““3“" pH com: IHBADIANCE time temperature 08/29/88 1645 0 16.9 8.77 216 160.5 1700 15 16.8 8.76 222 184.5 1715 30 16.8 8.75 228 160.7 1730 45 16.8 8.73 232 139.8 1745 60 16.7 8.73 236 151.9 1800 75 16.6 8.72 236 150.8 1815 90 16.6 8.71 242 123.6 1830 105 16.6 8.69 248 66.8 1845 120 16.3 8.66 252 42.1 1900 135 16.1 8.63 255 31.6 1915 150 15.8 8.60 252 22.4 1930 165 15.5 8.57 255 9.7 1945 180 15.4 8.53 248 3.4 2000 195 15.0 8.50 255 1.5 2015 210 14.7 8.47 255 1.5 2030 225 14.4 8.44 265 1.6 2045 240 14.2 8.41 261 1.7 2100 255 14.0 8.39 268 1.5 2115 270 13.6 8.37 268 1.6 2130 285 13.4 8.36 271 1.5 2145 300 13.2 8.34 268 1.6 2200 315 12.8 8.33 274 1.4 2215 330 12.6 8.33 271 1.4 2230 345 12.4 8.32 284 1.4 2245 360 12.2 8.31 278 1.4 2300 375 12.0 8.31 274 1.4 2315 390 11.7 8.31 281 1.4 2330 405 11.5 8.30 278 1.4 2345 420 11.3 8.31 281 1.4 08/30/88 0 435 11.1 8.31 281 1.4 15 450 10.9 8.31 287 1.4 30 465 10.8 8.31 287 1.4 45 480 10.7 8.31 281 1.4 100 495 10.6 8.31 284 1.4 115 510 10.4 8.31 290 1.4 130 525 10.3 8.31 281 1.4 145 540 10.0 8.31 287 1.4 200 555 9.9 8.31 300 1.4 215 570 9.8 8.32 300 1.4 230 585 9.8 8.32 284 1.4 245 600 9.7 8.32 294 1.4 300 615 9.4 8.32 303 1.4 315 630 9.4 8.32 300 1.4 330 645 9.3 8.32 290 1.4 Methods of Study 7 Table l—3. Physical and chemical characteristics in Whitewood Creek atthe Headwater site, monitored between August 29 and 30,1988, to examine diel fluctuations in these characteristics.——Continued [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet— ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second] Date Time “3.95"" wa'e' pH com) IRRADIANCE tlme temperature 08/30/88—Continued 345 660 9.2 8.32 294 1.4 400 675 9.0 8.32 310 1.4 415 690 9.0 8.33 307 1.4 430 705 9.1 8.33 294 1.4 445 720 9.0 8.33 313 1.4 500 735 8.9 8.33 297 1.4 515 750 8.9 8.33 300 1.4 530 765 8.8 8.33 307 1.4 545 780 8.7 8.33 316 1.4 600 795 8.7 8.33 316 1.4 615 810 8.6 8.33 313 1.7 630 825 8.5 8.34 303 3.3 645 840 8.5 8.34 303 6.5 700 855 8.5 8.34 307 9.7 715 870 8.3 8.35 310 14.1 730 885 8.3 8.36 313 19.4 745 900 8.3 8.36 317 25.4 800 915 8.3 8.37 318 34.5 815 930 8.4 8.39 320 55.3 830 945 8.5 8.41 307 76.5 845 960 8.5 8.44 317 107.9 900 975 8.8 8.47 307 111.3 915 990 9.0 8.49 314 112.6 930 1,005 9.1 8.52 317 136.8 945 1,020 9.4 8.55 317 146.4 1000 1,035 9.7 8.57 304 159.5 1015 1,050 10.0 8.60 310 173.9 1030 1,065 10.5 8.63 301 189.0 1045 1,080 10.9 8.65 310 210.2 1100 1,095 11.5 8.67 294 240.7 1115 1,110 12.0 8.69 290 318.0 1130 1,125 12.5 8.70 291 348.6 1145 1,140 13.1 8.71 278 465.3 1200 1,155 13.6 8.72 271 641.8 1215 1,170 14.2 8.73 261 421.8 1230 1,185 14.8 8.74 261 327.3 1245 1,200 15.4 8.74 252 594.7 1300 1,215 15.9 8.75 248 762.3 1315 1,230 16.4 8.75 236 831.7 1330 1,245 16.7 8.76 232 535.4 1345 1,260 17.1 8.77 232 626.4 1400 1,275 17.5 8.78 232 925.9 1415 1,290 17.8 8.77 229 1,026.6 1430 1,305 18.2 8.77 229 987.1 8 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota Table l—3. Physical and chemical characteristics in Whitewood Creek atthe Headwater site, monitored between August 29 and 30,1988, to examine diel fluctuations in these characteristics.—Continued [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet- ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second] Date Time 5'39”“ wa‘e' pH cono IBRADIANCE tlme temperature 08/30/88—Continued 1445 1,320 18.4 8.78 226 402.5 1500 1,335 18.5 8.78 223 451.6 1515 1,350 18.6 8.78 223 212.4 1530 1,365 18.8 8.78 223 198.8 1545 1,380 18.8 8.78 226 91.7 1600 1,395 18.6 8.78 223 113.3 1615 1,410 18.5 8.78 232 138.9 1630 1,425 18.5 8.78 232 156.0 1645 1,440 18.3 8.77 226 166.3 1700 1,455 18.2 8.77 232 153.3 Table l—4. Physical and chemical characteristics in Whitewood Creek atthe Sewage-Treatment-Plant site, monitored between August 30 and 31, 1988, to examine diel fluctuations in these characteristics. [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet- ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second. Symbol “—” indicates the malfunction of the water-temperature probe] Date Time ”“3”“ Mt" pH cono IRRADIANCE tlme temperature 08/30/88 845 0 12.4 8.26 820 152.5 900 15 12.6 8.26 817 172.3 915 30 12.6 8.27 817 195.9 930 45 12.7 8.28 814 210.6 945 60 12.9 8.29 812 241.0 1000 75 13.0 8.31 813 255.9 1015 90 13.3 8.32 807 338.3 1030 105 13.3 8.33 811 384.8 1045 120 13.6 8.34 806 412.9 1100 135 13.6 8.36 802 393.9 1115 150 13.8 8.38 794 443.8 1130 165 14.2 8.41 787 478.0 1145 180 14.4 8.42 777 454.0 1200 195 14.6 8.44 776 419.4 1215 210 14.9 8.45 771 393.9 1230 225 14.9 8.45 763 422.4 1245 240 15.3 8.46 758 429.4 1300 255 15.3 8.46 761 387.9 1315 270 15.5 8.47 761 301.4 1330 285 15.7 8.49 762 260.4 1345 300 16.0 8.50 757 258.1 1400 315 16.0 8.51 751 248.3 1415 330 16.2 8.52 745 243.3 1430 345 16.3 8.53 744 239.7 Methods of Study 9 Table I—4. Physical and chemical characteristics in Whitewood Creek atthe Sewage-Treatment—Plant site, monitored between August 30 and 31, 1988, to examine diel fluctuations in these characteristics.—Continued [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet- ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second. Symbol “—” indicates the malfunction of the water—temperature probe] Date Time Helps“ wa‘e' pH coun IRRADIANCE time temperature 08/30/88—Continued 1445 360 16.4 8.53 739 229.9 1500 375 16.6 8.54 733 229.0 1515 390 17.1 8.55 722 225.5 1530 405 17.1 8.56 723 224.9 1545 420 17.1 8.56 723 188.4 1600 435 17.1 8.55 726 209.0 1615 450 17.1 8.54 720 164.0 1630 465 17.0 8.54 724 146.3 1645 480 16.7 8.53 730 143.8 1700 495 16.7 8.53 732 128.3 1715 510 16.6 8.52 734 131.2 1730 525 16.5 8.51 733 95.0 1745 540 16.7 8.50 728 93.6 1800 555 16.4 8.49 734 103.3 1815 570 16.7 8.47 729 101.2 1830 585 16.5 8.46 735 60.4 1845 600 16.4 8.45 734 37.7 1900 615 16.3 8.43 730 21.8 1915 630 16.3 8.41 722 17.0 1930 645 16.1 8.40 724 7.4 1945 660 16.0 8.39 728 1.1 2000 675 16.0 8.38 734 0.1 2015 690 15.8 8.37 736 0.1 2030 705 15.6 8.36 740 0.1 2045 720 15.6 8.34 738 0.1 2100 735 15.3 8.33 745 0.1 2115 750 15.3 8.32 748 0.1 2130 765 15.2 8.32 752 0.1 2145 780 15.0 8.31 757 0.1 2200 795 14.9 8.31 762 0.1 2215 810 14.9 8.30 754 0.1 2230 825 14.6 8.29 755 0.1 2245 841 14.6 8.28 752 0.1 2300 856 14.6 8.28 753 0.1 2315 871 14.4 8.27 753 0.1 2330 886 14.2 8.26 756 0.1 2345 901 14.2 8.25 757 0.1 08/31/88 0 916 14.0 8.25 762 0.1 15 931 13.8 8.24 766 0.1 30 946 13.6 8.23 774 0.1 45 961 13.6 8.23 775 0.1 100 976 13.6 8.23 775 0.1 115 991 13.5 8.22 766 0.1 130 1,006 13.2 8.21 761 0.1 145 1,021 13.2 8.20 755 0.1 200 1,036 13.2 8.20 759 0.1 10 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota Table l—4. Physical and chemical characteristics in Whitewood Creek atthe Sewage-Treatment-Plant site, monitored between August 30 and 31, 1988, to examine diel fluctuations in these characteristics.—Continued [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet- ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second. Symbol “—” indicates the malfunction of the water-temperature probe] Date Time ””59”“ “M" pH cono IRRADIANCE tlme temperature 08/31/88—C0ntinued 215 1,051 13.0 8.19 762 0.1 230 1,066 12.9 8.19 771 0.1 245 1,081 12.8 8.18 775 0.0 300 1,096 12.6 8.18 783 0.1 315 1,111 12.6 8.17 784 0.0 330 1,126 12.6 8.17 784 0.0 345 1,141 12.4 8.16 783 0.2 400 1,156 12.3 8.15 793 0.2 415 1,171 12.3 8.14 792 0.3 430 1,186 12.3 8.14 789 0.4 445 1,201 12.1 8.15 783 0.3 500 1,216 12.0 8.15 786 0.3 515 1,231 12.0 8.15 786 0.5 530 1,246 12.0 8.15 789 0.5 545 1,261 12.0 8.16 789 0.6 600 1,276 12.0 8.16 791 0.6 615 1,291 12.0 8.16 791 1.3 630 1,306 12.0 8.17 795 1.7 645 1,321 12.0 8.17 796 4.4 700 1,336 12.0 8.18 805 7.9 715 1,351 — 8.19 804 10.8 730 1,366 — 8.19 799 15.7 745 1,381 — 8.20 794 20.7 800 1,396 — 8.21 791 24.7 815 1,411 — 8.22 787 35.1 830 1,426 — 8.23 791 55.4 845 1,441 — 8.23 789 80.9 900 1,456 — 8.25 792 75.9 915 1,471 — 8.26 795 116.2 930 1,486 — 8.27 802 144.2 945 1,501 —- 8.28 817 153.6 1000 1,516 —— 8.29 830 166.2 1015 1,531 - 8.31 830 308.9 1030 1,546 — 8.32 819 474.6 1045 1,561 — 8.33 814 517.8 1100 1,576 — 8.35 810 529.0 1115 1,591 — 8.37 812 552.3 1130 1,606 — 8.39 814 631.6 1145 1,621 — 8.41 821 686.0 1200 1,636 — 8.43 816 816.9 1215 1,651 — 8.45 808 941.1 1230 1,666 — 8.47 803 931.4 1245 1,681 ‘ — 8.50 804 808.8 1300 1,696 — 8.53 800 711.9 1315 1,711 — 8.55 780 388.2 Methods of Study 11 Table l—4. Physical and chemical characteristics in Whitewood Creek atthe Sewage-Treatment-Plant site, monitored between August 30 and 31, 1988, to examine diel fluctuations in these characteristics.—-Continued [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet- ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second. Symbol “—” indicates the malfunction of the water-temperature probe] Date Time ”1”“ wa‘e' pH couo IRBADIANCE tlme tempefatul’e 08/31/88—Continued 1330 1,726 — 8.56 762 134.9 1345 1,741 — 8.56 748 90.8 1400 1,756 — 8.57 741 91.5 1415 1,771 — 8.57 737 102.2 1430 1,786 _ 8,56 736 102.4 1445 1,801 — 8.54 730 91.4 1500 1,816 — 8.51 730 79.7 1515 1,831 — 8.49 727 92.0 1530 1,846 — 8.46 728 121.2 1545 1,861 _ 8.43 729 130.2 1600 1,876 — 8.41 735 127.9 1615 1,891 _ 8.39 731 123.8 1630 1,906 — 8.36 726 120.8 Table l—5. Physical and chemical characteristics in Whitewood Creek at the Above Vale site, monitored between August 31 and September 1, 1988, to examine diel fluctuations in these characteristics. [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet- ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second] Date Time ”1”“ wa‘e' pH cone lRBADIANCE time temperature 08/31/88 700 0 15.1 8.37 1,177 9.7 715 15 15.1 8.38 1,177 19.8 730 30 15.1 8.40 1,177 32.3 745 45 15.1 8.41 1,178 43.4 800 60 14.9 8.43 1,179 53.5 815 75 14.8 8.44 1,180 69.7 830 90 14.9 8.47 1,180 75.9 845 105 14.8 8.49 1,180 92.2 900 120 14.9 8.52 1,179 129.8 915 135 15.0 8.55 1,179 175.3 930 150 15.1 8.58 1,178 286.2 945 165 15.1 8.62 1,177 376.3 1000 180 15.3 8.66 1,176 423.6 1015 195 15.4 8.70 1,174 486.0 1030 210 15.7 8.74 1,172 531.5 1045 225 16.0 8.77 1,170 580.1 1100 240 16.2 8.80 1,168 599.6 1115 255 16.4 8.84 1,166 646.8 1130 230 16.7 8.88 1,163 675.1 1145 285 17.1 8.90 1,160 685.3 1200 300 17.3 8.92 1,158 698.3 12 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota Table l—5. Physical and chemical characteristics in Whitewood Creek at the Above Vale site, monitored between August31 and September 1, 1988, to examine diei fluctuations in these characteristics.—Continued [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet- ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second] Elapsed Water Date Time . pH COND IBRADIANCE time temperature 08/31/88—Continued 1215 315 17.6 8.93 1,155 723.2 1230 330 18.0 8.95 1,152 732.5 1300 360 18.7 9.00 1,147 831.51 1315 375 19.0 9.01 1,145 450.1 1330 390 19.4 9.03 1,141 354.4 1345 405 19.8 9.03 1,139 252.1 1400 420 20.1 9.03 1,136 281.9 1415 435 20.5 9.03 1,134 799.8 1430 450 20.9 9.04 1,131 813.5 1445 465 21.1 9.04 1,130 655.1 1500 480 21.4 9.05 1,128 429.7 1515 495 21.7 9.03 1,126 361.5 1530 510 22.1 9.02 1,124 348.9 1545 525 22.2 8.98 1,123 211.6 1600 540 22.5 8.94 1,122 172.8 1615 555 22.6 8.93 1,121 179.1 1630 570 22.8 8.92 1,120 182.4 1645 585 22.9 8.91 1,120 204.4 1700 600 23.0 8.89 1,120 254.8 1715 615 23.2 8.85 1,120 232.4 1730 630 23.1 8.80 1,120 157.7 1745 645 23.0 8.77 1,121 127.1 1800 660 22.9 8.76 1,122 117.6 1815 675 22.8 8.75 1,122 80.9 1830 690 22.6 8.74 1,123 45.8 1845 705 22.5 8.73 1,124 37.6 1900 720 22.3 8.72 1,125 27.9 1915 735 22.1 8.71 1,126 22.7 1930 750 22.1 8.71 1,126 11.2 1945 765 21.9 8.70 1,128 3.3 2000 780 21.5 8.69 1,130 1.4 2015 795 21.4 8.69 1,130 1.3 2030 810 21.1 8.67 1,132 1.4 2045 826 21.1 8.67 1,132 1.4 2100 841 20.8 8.66 1,134 1.6 2115 856 20.6 8.66 1,135 1.9 2130 871 20.4 8.65 1,136 1.6 2145 886 20.2 8.64 1,137 1.7 2200 901 10.0 8.63 1,138 2.0 2215 916 19.7 8.62 1,140 1.9 2230 931 19.7 8.60 1,140 1.8 2245 946 19.4 8.59 1,141 1.8 2300 961 19.4 8.57 1,142 1.7 2315 976 19.1 8.56 1,144 1.5 2330 991 18.8 8.54 1,145 1.7 2345 1,006 18.7 8.54 1,146 1.4 Methods of Study Table l—5. Physical and chemical characteristics in Whitewood Creek at the Above Vale site, monitored between August 31‘ and September 1, 1988, to examine diel fluctuations in these characteristics.—Continued 13 [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet- ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second] Elapsed Water Date Time . pH COND IRBADIANCE time temperature 09/01/88 0 1,021 18.5 8.54 1,147 1.5 15 1,036 18.5 8.51 1,147 1.4 30 1,051 18.3 8.51 1,149 1.4 45 1,066 18.2 8.50 1,150 1.5 100 1,081 18.0 8.49 1,152 1.4 115 1,096 17.9 8.49 1,152 1.4 130 1,111 17.7 8.49 1,153 1.4 145 1,126 17.4 8.49 1,155 1.4 200 1,141 17.4 8.49 1,156 1.3 215 1,156 17.2 8.48 1,157 1.3 230 1,171 17.2 8.48 1,158 1.3 245 1,186 17.1 8.48 1,158 1.3 300 1,201 16.9 8.48 1,160 1.3 315 1,216 16.6 8.47 1,161 1.6 330 1,231 16.6 8.46 1,162 1.6 345 1,246 16.5 8.45 1,164 1.7 Table I—6. Physical and chemical characteristics in Whitewood Creek at the Sheeler Seep site, monitored between September 1 and 2, 1988, to examine diel fluctuations in these characteristics. [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet- ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second] Date Time Hall’s“ “’3‘” pH couo IBBADIANCE time temperature 09/01/88 645 0 15.3 7.98 1,367 34.3 700 15 15.3 7.98 1,369 45.8 715 30 15.2 7.99 1,370 62.4 730 45 15.2 8.00 1,374 78.9 745 60 15.2 8.00 1,377 97.5 800 75 15.2 8.01 1,378 135.2 815 90 15.2 8.03 1,380 186.9 830 105 15.2 8.04 1,383 133.3 845 120 15.3 8.05 1,383 366.0 900 135 15.3 8.07 1,386 485.9 915 150 15.5 8.08 1,385 549.7 930 165 15.6 8.09 1,390 624.8 945 180 15.9 8.10 1,388 696.7 1000 195 16.2 8.12 1,387 771.1 1015 210 16.5 8.14 1,385 850.5 1030 225 16.8 8.16 1,385 935.1 1045 240 17.3 8.17 1,383 980.9 1100 255 17.7 8.19 1,381 1,018.7 1115 270 18.1 8.20 1,378 1,091.0 1130 285 18.6 8.22 1,370 1,122.9 1145 300 19.3 8.23 1,370 1,146.0 14 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota Table l—6. Physical and chemical characteristics in Whitewood Creek at the Sheeler Seep site, monitored between September 1 and 2, 1988, to examine diel fluctuations in these characteristics.—Continued [Elapsed time in minutes; Water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet— ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second] Elapsed Water Date Time time temperature pH COND IBRADIANCE 09/01/88—C0ntinued 1200 315 19.8 8.24 1,368 1,177.4 1215 330 20.3 8.26 1,365 490.7 1230 345 21.0 8.27 1,364 663.8 1245 360 21.5 8.28 1,359 1,152.1 1300 375 22.1 8.29 1,355 1,218.8 1315 390 22.6 8.30 1,354 1,237.5 1330 405 23.0 8.31 1,349 1,256.4 1345 420 23.5 8.32 1,346 1,246.7 1400 435 24.0 8.32 1,344 1,214.4 1415 450 24.6 8.33 1,342 1,189.8 1430 465 24.9 8.34 1,341 1,167.1 1445 480 25.1 8.34 1,338 1,128.1 1500 495 25.4 8.34 1,338 1,071.4 1515 510 25.7 8.34 1,333 1,045.5 1530 525 25.8 8.34 1,325 986.5 1545 540 25.8 8.34 1,323 907.8 1600 555 26.1 8.33 1,325 838.3 1615 570 26.2 8.32 1,325 781.0 1630 585 26.2 8.32 1,325 717.5 1645 600 26.0 8.31 1,326 619.2 1700 615 25.8 8.31 1,325 545.4 1715 630 25.8 8.29 1,326 483.7 1730 645 25.5 8.28 1,326 279.4 1745 660 25.3 8.27 1,329 137.4 1800 675 25.0 8.25 1,326 82.6 1815 690 24.9 8.23 1,329 73.9 1830 705 24.6 8.21 1,331 60.5 1845 720 24.2 8.19 1,332 38.5 1900 735 23.9 8.18 1,334 33.9 1915 750 23.7 8.16 1,332 26.1 1930 765 23.5 8.15 1,334 10.8 1945 780 23.4 8.13 1,335 2.7 2000 795 23.0 8.11 1,336 1.7 2015 810 22.8 8.09 1,337 1.5 2030 825 22.5 8.08 1,337 1.4 2045 840 22.3 8.07 1,338 1.3 2100 855 22.0 8.05 1,339 1.3 2115 870 21.8 8.04 1,342 1.4 2130 885 21.4 8.03 1,344 1.3 2145 900 21.2 8.02 1,344 1.3 2200 915 20.9 8.01 1,344 1.3 2215 930 20.7 8.00 1,347 1.3 2230 945 20.3 8.00 1,351 1.3 2245 960 20.1 7.99 1,351 1.3 2300 975 19.8 7.99 1,349 1.3 2315 990 19.7 7.98 1,351 1.3 2330 1,005 19.4 7.97 1,349 1.3 2345 1,020 19.2 7.96 1,349 1.3 Methods of Study 15 Table l—6. Physical and chemical characteristics in Whitewood Creek at the Sheeler Seep site, monitored between September 1 and 2, 1988, to examine diel fluctuations in these characteristics.—Continued [Elapsed time in minutes; water temperature in degrees Celsius; COND, specific conductance in microsiemens per centimeter at 25 degrees Celsius; photosynthet- ically active radiation, IRRADIANCE, between 400 and 700 nanometers in microeinsteins per square meter per second] Elapsed Water Date Time . pH COND IRBADIANCE time temperature 09/01/88—Continued 0 1,035 19.0 7.96 1,354 1.3 15 1,050 18.7 7.96 1,351 1.3 30 1,065 18.5 7.95 1,354 1.3 45 1,080 18.2 7.95 1,357 1.3 100 1,095 18.2 7.95 1,357 1.3 115 1,110 18.0 7.95 1,360 1.3 130 1,125 17.7 7.94 1,359 1.3 145 1,140 17.6 7.94 1,360 1.3 200 1,155 17.4 7.94 1,360 1.3 215 1,170 17.4 7.94 1,360 1.3 230 1,185 17.2 7.93 1,362 1.2 245 1,200 17.1 7.93 1,355 1.3 300 1,215 16.9 7.93 1,360 1.3 315 1,230 16.7 7.93 1,354 1.3 In initial experiments, sorption of arsenate and phosphate was observed in living and heat—killed cells from three collection sites (HW, STP, and AV). The SS isolate was not initially used because of the similarity in media formulations for the AV and SS isolates. Algal suspensions (105 cells per milliliter) in 40-mL fluoroethyl- ene polymer Oakridge tubes were spiked with arsenic-73 and phosphorus-32—1abeled stock solutions yielding a 0.5-umol/L con- centration for both total dissolved arsenate and orthophosphate. Suspensions then were placed on a mechanical shaker and agi— tated. After 24 and 48 hours of exposure, suspensions were centri- fuged at 10,000 revolutions per minute for 10 minutes to remove the algal cells from suspension. Five-milliliter volumes of the resulting supernatant, the algal suspension before centrifugation, and an internal arsenic—73 and phosphorus-32 standard were counted by liquid scintillation to determine the extent of arsenic and orthophosphate sorption by these cultures (table I—7). In subsequent experiments, arsenic sorption by algal cells was studied in laboratory experiments as a function of dissolved— arsenate and orthophosphate concentrations in media formula- tions. Unialgal cultures of the diatom Achnanthes minutissima (isolated by micropipetting from all four sampling sites) were used to determine sorption-rate constants. A. minutissima was selected for this series of experiments over the initially isolated chloro- phytes (Stichococcus spp. and Scenedesmus spp.) because A. minutissima were present in greater abundance in periphyton samples and also were easily enumerated with a particle counter. Sorption-rate constants for arsenate and phosphate were deter- mined from arsenic-73 and phosphorus—32—1abeled experiments using heat-killed cells as described by Fisher and others (1984). Previous experiments (Kuwabara and others, 1988) indicated that use of heat-killed cells for short-term arsenate and orthophosphate sorption experiments yielded results comparable to living cell cul- tures and avoided the need for biomass corrections. A 32 full- factorial experimental design (n=3) was used to examine the interactive effects of arsenic and phosphorus on sorption rates and accumulation in A. minutissima. Isolates from the HW and AV sites were used because of contrasting ambient dissolved- arsenic concentrations. Arsenic-73 and phosphorus-32-labeled stock solutions were used to achieve 0.0—, 0.5-, and 1.0-umol/L initial concentrations of dissolved arsenate and orthophosphate in the nine chemically defined formulations (representative of ambi- ent concentration ranges observed at these two sites). An inoculum of heat-killed cells was then added (106 cells per milliliter). After periods of 0.5, 1, 2, 4, 8, and 24 hours, supernatant from centri- fuged cell suspensions were counted by liquid scintillation to determine solute removal from solution by the cells. Table l—7. Algal cell sorption of arsenate and orthophosphate at 24 and 48 hours by living and heat-killed cells using three Stichococcus isolates from three sites along Whitewood Creek. [Headwater site, HW; Sewage-Treatment-Plant site, STP; and the Above Vale site, AV. Calculated 95—percent confidence intervals are given for three repli- cate cultures. Culture media additions of dissolved arsenate and dissolved orthophosphate at 0.5 micromole per liter were used] Percent Percent Elapsed arsenate orthophosphate Sample lime sorbed sorbed (hours) . . Heat- . . Heat- L'V'"9 killed ““9 killed HW 24 25:12 29:1 1 93:2 94:2 48 23:8 20:6 96:2 95:2 STP 24 19:3 27:10 93:2 90:4 48 20:4 18:6 96:2 92:2 AV 24 24:6 21:10 88:2 84:2 48 16:5 19:3 94:2 84:2 16 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek. South Dakota Results and Discussion Field Experiments Biomass in August 1986 increased downstream from the Headwater site (HW) to the Above Vale (AV) site and then decreased at the Sheeler Seep site (SS). Estimates for benthic plant biomass abundance (table I—l) represent the ash—free dry weights per unit area of scrapings multiplied by the percent cov— erage of algal growth at the sampling site. Autotrophic indices (AI) ranged from 120 at the HW site to 210 downstream from the municipal water—treatment plant (STP). This index has been used as an indicator of environmental stress, whereby increas- ing values indicate increasing abundance of senescent or stressed cells or an increasing proportion of heterotrophic growth (Leland and Carter, 1985; Weber, 1973). The HW and AV sites displayed AI values typical of unstressed communities (about 100:50), but elevated values at the STP and SS sites were observed. Temporal changes in biomass observed in 1987 monthly samples differed among sites. Measured plant mass per unit streambed area was similar at the HW and STP sites, highest at the AV site once the mat was established, and lowest at the most downstream (SS) site (table 1—2). At the HW site, which was dominated throughout the summer by a submerged macrophyte (Rammculus longirostris), biomass increased from an average of 37 grams per square meter in late May to 60 grams per square meter in September. However, differences between monthly biomass measurements at the HW site were not significant at the 95-percent confidence level (table I—2). The large confi— dence intervals relative to average pb values, even with nine replicates per site, is indicative of a patchy distribution of the benthic plant communities. At the STP site, a community dom— inated by Ranunculus during June and July changed during August and September to a dense growth of the pondweed Zannichellia galustris and the chlorophytes Cladophora spp. and Ulothrix spp. Monthly biomass at the STP site did not differ significantly (table 1—2). Because of streambed scouring, dense plant growth was not established at the AV site in June. How— ever, by July, a thick (approximately 20 cm) mat of Ranunculus and epiphytic diatoms blanketed more than half the stream channel. Similar to the STP site, August and September samples showed a shift toward Zarmichellia and filamentous chloro— phyte species at the AV site. Once a dense periphyton and macrophyte assemblage was established, biomass was most abundant at the AV site, where estimated biomass was an order of magnitude higher than at other sites. Elevated stream discharge in late May also inhibited the establishment of a dense assemblage at the SS site, but Zannichellia was observed in abundance by July under much lower flow conditions (table 1—2). Epiphytic diatoms and filamentous chlorophytes dominated benthic plant communities in August and September samples. Biomass was consistently lower at the SS site than at the three upstream sites. AI ranged from 80 at the SS site in July to 310 at the AV site in September (table I—2). All four sites in late May and July displayed AI values typical of unstressed or nonsenescent benthic plant communities, whereas elevated values were mea- sured at the AV and SS sites in August and at the HW, AV, and SS sites in September. Dissolved oxygen was consistently at or above saturation in water-column samples. Arsenic concentrations in algal tissues consistently increased between the late May and August sampling dates at the HW and STP sites and between July and August at the AV and SS sites except for the macroalgae at the SS site, which showed not significant concentration difference between July and August samples (table 1—8). Concentrations measured in August and September samples were similar or showed a decrease in September. There also was a discernible increase in tissue arsenic concentration with distance downstream. However, the range of concentrations observed during the sampling period at the upstream (HW) site was greater than that observed at the downstream sites (table 1—8). Arsenic in submerged macrophyte tissue was slightly higher than in macroalgae, although general concentration trends at each site were similar between plant groups. As noted in the “Methods of Study” section, arsenic concentrations for macrophyte tissue include contributions from epiphytic algal cells that are not excluded in the sampling procedure. Iron concentrations in digested samples indicate minimal effects on arsenic concentra- tions due to residual inorganic particles attached to plant tissues (table I—8). Data from the die] study done between August 30 and September 2, 1988, at the four sampling sites are shown in tables I—3 to I—6 for the HW to SS sites, respectively, and in figures 1—2 to I—5 plotted with dissolved-arsenic and orthophos— phate concentrations. A comparison of measured parameters in this late summer sampling (data summarized in table I—9) shows trends in biomass and autotrophic indices (ratio of ash- free dry mass to chlorophyll-a) that were typical of the study sites for this period of the algal growth season (table I—2). Algal abundance increased from the HW to the AV site and decreased at the SS site. The highest estimated autotrophic index (210) at the AV site (the artificially pooled area) coincided with observed senescence of the benthic plant community at that site (for example, degraded macrophyte fronds and algal fila- ments, and visually observed organic detrital material within the mat). The ranges for irradiance (table I—9) and the irradiance time course for the four sites (fig. I—2) were indicative of inter- mittent cloud cover, especially during the afternoons at the HW, STP, and AV sites, but indicated a consistently clear sampling day at the SS site. The water-temperature cycle typically lagged by 2 to 3 hours behind the irradiance (table I—lO), and mean water temperature increased downgradient (that is, with decreased altitudes). With the exception of the STP site, the amplitude of the pH cycle increased with increasing biomass, as one would expect of sites with metabolically active flora (the highest and lowest val- ues for both pH amplitudes and biomass at the AV and SS sites, respectively). The pH cycle at each of the four sites lagged the irradiance cycle by 1 to 2 hours, which is similar to observed fluctuations in water temperature (table I—lO). An increase in total dissolved arsenic was observed between the HW and AV sites; a decrease occurred at the SS site (table 1—9)—resu1ts are consistent with water years 1985 and 1986 (Goddard, 1988). The predominant arsenic species at all four sites were inorganic. Our analytical protocol for arsenic Table l—8. Total arsenic concentrations in dominant benthic plant species collected from four sites along Whitewood Creek. [Arsenic concentrations in plant tissues in micrograms solute per gram dry tis- sue with 95-percent confidence intervals (n=4 replicates). Iron concentrations in plant tissues in micrograms solute per gram dry tissue also were checked (":1 replicate) for possible effects of residual inorganic particles (that is, parti— cles remaining on plant tissue samples after the rinsing procedure) on reported arsenic concentrations. The Headwater and most upstream site, HW; Sewage- Treatment—Plant site, STP; Above Vale site, AV; and Sheeler Seep site (and most downstream sampling site for this study, SS). Symbol “—” indicates that the benthic plant community had not yet been established at that site and there- fore tissues could not be sampled in adequate quantities for analysis] Sampling site Arsenic lron Tissues sampled 05/27—28/87 HW Macroalgael 1,465i38 2,180 Macrophytez 1,552i16 2,070 STP Macroalgae 1,709i22 1,960 Macrophyte 1,757i14 1,850 AV Macroalgae — — Macrophyte —- _ SS Macroalgae — — Macrophyte — — Tissues sampled 07/07—10/87 HW Macroalgae 1,633i33 2,470 Macrophyte 1,866.19 2,140 STP Macroalgae 1,819i31 2,090 Macrophyte 1,97118 1,690 AV Macroalgae 2,051i45 4,040 Macrophyte 2,3901'8 5,170 SS Macroalgae 2,358i91 3,570 Macrophyte 2,480i16 3,150 Tissues sampled 08/19—26/87 HW Macroalgae 1,884i43 2,630 Macrophyte 2,1801-52 2,860 STP Macroalgae 1,903i18 1,970 Macrophyte 2,030149 2,210 AV Macroalgae 2,151i15 5,370 Macrophyte 2,479i39 4,830 SS Macroalgae 2,418i24 3,340 Macrophyte 2,5721'42 3,020 Results and Discussion 17 Table 1—8. Total arsenic concentrations in dominant benthic plant species collected from four sites along Whitewood Creek. —Continued Sampling site Arsenic lron Tissues sampled 09/16—28/87 HW Macroalgae 1,931150 2,960 Macrophyte 1,9911-83 2,340 STP Macroalgae 1.933150 2,550 Macrophyte 2,099i1 12 2,070 AV Macroalgae 2,117i94 5,460 Macrophyte 2,184195 5,160 SS Macroalgae 2,317i189 4,120 Macrophyte 2,352123 4,760 lSamples dominated by filamentous chlorophytes and epiphytic diatoms. Specific changes in community structure are described in the text. 2Early summer samples typically dominated by the submerged macro- phyte, Ranunculus longirostris and associated algal epiphytes, whereas later samples commonly dominated by the pondweed, Zarmichellia palurtris. Specific changes in dominant macrophyte species are discussed in the text. species indicated that inorganic trivalent arsenic represented less than 30 percent of the total dissolved arsenic at all sites, and significant dissolved organic arsenic was not detected as the dif- ference between total dissolved arsenic and dissolved inorganic trivalent and pentavalent arsenic. There is a controversy as to whether the preservation technique prescribed by Glaubig and Goldberg (1988) does in fact prevent significant oxidation of trivalent arsenic to pentavalent arsenic. Therefore, the presence of reduced arsenic species may be even more significant than indicated by the analytical protocol used here. As expected, dissolved—orthophosphate concentrations were highest at the STP site and decreased downstream (table 1—9). Concentration cycles in dissolved-arsenic species and dissolved orthophos- phate lagged approximately 3 hours and 6 hours, respectively, behind pH cycles at the SS site, but no lag was resolved at the AV site for the major arsenic species (table I—lO). Specific con- ductance at these sites cycled out of phase with fluctuations in arsenic species (fig. 1—3). Diel cycles for dissolved arsenic and orthophosphate were not evident at the HW site upstream from mining activities because of low total dissolved-arsenic concen- trations (less than 0.05 umol/L) nor at the STP site downstream from water-treatment-plant discharge (table 1—9). Laboratory Experiments In experiments using the chlorophyte Stichococcus, arsenate and orthophosphate sorption was similar for living and dead cells over the first 24 hours of exposure (table I—7). Concentrations accumulated (for both arsenic and phosphorus) were similar for algae collected from different sites and maintained at ambient sol- ute concentrations. A preference of orthophosphate sorption over arsenate was observed for all species isolates. 18 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek. South Dakota 9-5 I I I I I I I I I A B — - 1,500 9.0 ‘ _ 1,000 IRRADIANCE, IN MICROEINSTEINS PER SQUARE METER PER SECOND A A f —500 ,_ A _. A 8.0 A AA 15%. A AAA A A . A A I? Irradiance , Irradiance A“ , M f f a, .4 w. 75 IE I 1 we; | I 1%.. I 1.x: | 0 1800 2400 0600 1200 1800 1200 1800 2400 0600 1200 1800 08/29/88 08/30/88 08/30/88 08/31/88 TIME, IN HOURS 9.5 g C D o o R — — 1.500 E 9.0 — .m I“. DH _ n. E" “a. E an an E Bun z E u E LU : — — 1,000 I”) 0: :I: — =° Z 5 C. 8.5 J A “hm s O .1“ A «a 9: m f g: E AAA A E 0. AA Irradiance _ _ 500 2 8.0 _ A A A E A A AA 3 A AA A 5: A . LTJ A AKA A Irradiance O A AA AA A 5 A _ 7.5 E I ”4%” ' “EA | m I 0 2 0600 1200 1800 2400 0600 0600 1200 1800 2400 0600 08/31/88 09/01/88 09/01/88 09/02/88 TIME, IN HOURS Figure l—2. DieI trends in pH and irradiance along Whitewood Creek at (A) the Headwater site, (B) the Sewage-Treatment- Plant site, (C) the Above Vale site, and (D) the Sheeler Seep site. 1,400 I I 1,300 - 1,200 Specific conductance ......... Results and Discussion qrq‘i: Specific B “in conductance REMWDDD ‘ / As(V) 1 DISSOLVED ARSENIC, IN MICROMOLES PER LITER SPECIFIC CONDUCTANCE, IN MICROSIEMENS PER CENTIMETER 1,100 ....... .......... .I. ....... -\u-. /5. ~ 2visa”) ‘1] I I As(III) __u___u___n___¢.--—fl--—l--g 1200 1800 08/31/88 2400 0600 09/01/88 1200 09/01/88 Figure [—3. Above Vale site and (B) the Sheeler Seep site. TIME, IN HOURS 1800 2400 09/02/88 0 0600 Diel trends in specific conductance and laboratory determinations for dissolved-arsenic species for (A) the 1.5 9.5 1 DISSOLVED ARSENIC, IN MICROMOLES PER LITER — 0.5 8.0 — ,A A 111 ‘ 1:. 4/" ‘\S( 1/3“ As(lll) ‘fl" r . __n___u___n___n___n___u._§ 7.5 I 1 1 1 | 1 1 0 1200 1800 2400 0600 1200 1800 2400 0600 08/31/88 09/01/88 09/01/88 09/02/88 TIME, IN HOURS Figure I—4. Diel trends in pH and concentrations of arsenic species for (A) the Above Vale site and (B) the Sheeler Seep site. 19 20 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota 0.6 I I I I I 1.5 0.4 - 0.2 — ‘1-.._! As(V) IN MICROMOLES PER LITER I I > U) _| I DISSOLVED ARSENIC, IN MICROMOLES PER LITER Orthophosphate DISSOLVED ORTHOPHOSPHATE, Orthophosphate ,AASUII) As(III) - —r:- - .5 - —n- - —u- - -'fl' ' —rz- ' I l I o 1200 1800 2400 0600 1200 1800 2400 0600 08/31/88 09/01/88 09/01/88 09/02/88 TIME, IN HOURS Figure l—5. Diel trends in concentrations of dissolved orthophosphate and arsenic species at (A) the Above Vale site and (B) the Sheeler Seep site. Table I—9. Site comparison for parameters monitored along Whitewood Creek. [Headwater site, HW; Sewage—Treatment—Plant site, STP; Above Vale site, AV; and Sheeler Seep site, SS. Mass concentrations of benthic flora, biomass, in grams per square meter are tabulated with 95-percent confidence intervals (n29). Autotrophic indices reflect the ratio of mean ash—free dry mass to chlorophyll-a. Photo— synthetically active radiation, irradiance, represents solar radiation between 400 and 700 nanometers in microeinsteins per square meter per second. Water and air temperatures are tabulated in degrees Celsius. Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius. Concentrations for total dissolved arsenic, pentavalent arsenic, As(V), trivalent arsenic, As(III), and orthophosphate are given in micromoles per liter. Alkalinity measurements are given in milliequivalents per liter. Values tabulated for data—logger parameters represent 15—minute averages of measurements taken at lO-second intervals. Brackets denote inclusive ranges for monitored parameters. Abbreviation “ND.” indicates that parameter values were not determined] Pa'a'l‘eler HW STP Av ss description Biomass 48:19 57i26 3 16153 32:17 Autotrophic index 120 110 210 190 Data-logger parameter ranges Irradiance [0, 1030] [0,940] [0, 830] [0, 1240] Air temperature [3.1, 28.1] [7.7, 25.5] [10.1, 37.3] [6.3, 37.9] Water temperature [8.5, 18.8] [12.0, 17.1] [14.8, 23.2] [15.2, 26.3] pH [8.30, 8.78] [8.14, 8.57] [8.37, 9.02] [7.93, 8.34] Specific conductance [220, 320] [720, 830] [1120, 1180] [1320, 1390] Ranges for laboratory analysis1 Total dissolved arsenic <0.05 [0.23, 0.28] [0.91, 1.28] [0.45, 0.69] Dissolved As(V) <0.05 [0.14, 0.18] [0.65, 1.12] [0.36, 0.59] Dissolved As(III) <0.05 [0.06, 0.07] [0.11, 0.23] [0.08, 0.1 1] Dissolved orthophosphate [0.04, 0.41] [13.1, 15.5] [0.19, 0.44] [0.06, 0.25] Alkalinity ND. ND. [2.84, 3.24] [2.89, 3.40] 1Tabulated ranges indicate the minimum and maximum lS-minute average values for the site monitoring period (1 day for the Headwater, Above Vale, and Sheeler Seep sites; 2 days for the Sewage-Treatment—Plant site). 2Arsenic, orthophosphate, and alkalinity determinations by S.P. Pasilis, M.W. Shockey, and CC. Fuller, respectively. Table l—10. Lag times, in hours, between selected water-quality variables monitored along Whitewood Creek. [Total dissolved arsenic, AsT; dissolved pentavalent arsenic, As(V); and dissolved trivalent arsenic, As(III). Lag times in hours; cross correlation coeffi- cient, r, is dimensionless, as is the number of time-corresponding measure- ments, n. Abbreviation “N.D.” indicates that values were not determined because diel fluctuations in dissolved-arsenic species and orthophosphate were not evident at the Headwater and Sewage-Treatment-Plant sites as described in the “Results” section] Water-quality Lag Maximal characteristic time rvalue Headwater site pH lags irradiance 1.5 0.75 98 Water temperature lags irradiance 2.0 0.77 98 AsT lags le N.D. As(V) lags pH N.D. As(III) lags pH N.D. Orthophosphate lags pH N.D. Sewage-Treatment-Plant site pH lags irradiance 2.0 0.80 126 Water temperature lags irradiance 2.0 0.77 98 AST lags pH N.D. As(V) lags pH N.D. As(III) lags pH N.D. Orthophosphate lags pH N.D. Above Vale site pH lags irradiance 1.0 0.82 82 Water temperature lags irradiance 3.0 0.71 82 AsT lags pH 0 0.80 8 As(V) lags pH 0 0.77 8 As(III) lags pH 6 0.55 8 Orthophosphate lags pH 6 0.55 4 Sheeler Seep site pH lags irradiance 1.5 0.88 83 Water temperature lags irradiance 3.2 0.70 83 AsT lags pH 3 0.58 8 As(V) lags pH 3 0.69 8 As(III) lags pH 6 0.55 8 Orthophosphate lags pH 6 0.42 4 1Lower significant figures in lag estimates involving arsenic and ortho- phosphate data reflect the lower sampling frequency for laboratory-analyzed concentrations relative to data-logged parameters. Short-term sorption—kinetics experiments using the diatom Achnanthes minutissima indicated that a first—order rate equation closely describes the observed algal sorption of arsenate. Fit of the kinetic data for HW and AV isolates at the nine treatment combinations to a first-order model (Dixon, 1985) yielded consistently high coefficients of determination 0220.84, table I—l 1). Sorption-rate constants were significantly increased by elevating dissolved-arsenate concentrations. A comparison of rate constants for HW and AV isolates shows significantly lower constants for the apparently arsenic—tolerant Results and Discussion 21 AV isolate. The accumulation of arsenic by both isolates was inhibited by increasing orthophosphate concentrations. The AV isolate had slightly lower K b values than did the HW isolate. Increasing dissolved-arsenate concentrations in the media did not result in a corresponding increase in biologically sorbed arsenic (note in table I—ll that the Kb values significantly decreased at higher dissolved-arsenic concentrations). As with arsenic sorption, orthophosphate sorption also was well described by first-order mass transfer (table I—12). Sorption-rate constants for orthophosphate were consistently lower than for arsenate, and unlike arsenate, sorption did not vary significantly between isolate or with variations in dis- solved arsenate or orthophosphate (table I—12). Accumulation of phosphorus decreased with increase in dissolved arsenate. As observed in initial chlorophyte experiments, orthophosphate was preferentially accumulated over arsenate by A. minutis- sima, as clearly indicated by the orders of magnitude larger Kb scale for phosphorus relative to arsenic. Large Kb error bars for orthophosphate reflect 10w solution concentrations at equilibrium in these batch experiments. Table l—11. Results from experiments examining arsenate sorption by heat-killed Achnanthes minutissima cells isolated from the Headwater site and the Above Vale site. [Headwater (HW) site with low (less than 0.1 micromole per liter) dissolved arsenic; Above Vale (AV) site with elevated (approximately 1 micromole per liter) dissolved arsenic. Sorption-rate constants, 7% in reciprocal hours; accu- mulation factors, Kb, ratio of micromoles solute per gram algae to micromoles solute per gram media. Confidence intervals (95 percent) reflect the multivari- ate regression from a 32 full factorial design (n=3) sampled at elapsed times of 0.5, 1.0, 2.0, 4.0, 8.0, and 24.0 hours after inoculation. The coefficients of determination (r2) are given for a fit of the kinetic data to a first—order model (Dixon, 1985). Details of the experimental design are provided in the text] Treatments (micromolar solute added) Arsenate transport characteristics Arsenate phglstxate Ab K,, r2 HW isolate 0.5 0.0 0.73i'0.08 1,7901'70 0.96 0.5 0.5 0.56i0.08 1,340i90 0.92 0.5 1.0 0.94i0.13 990i50 0.92 1.0 0.0 1.08i0.11 9901'20 0.96 1.0 0.5 1.37i0.22 800i30 0.90 1.0 1.0 1.77i0.l7 500i30 0.84 AV isolate 0.5 0.0 0.39i0.02 1,750i30 0.99 0.5 0.5 0.48i0.06 1,240i30 0.98 0.5 1.0 0.571‘007 960i30 0.99 1.0 0.0 0.741008 870i30 0.98 1.0 0.5 0.81i0.11 630i30 0.94 1.0 1.0 1.09i0.19 500i20 0.97 22 Eflects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota Table l—12. Results from experiments examining orthophosphate sorption by cell surfaces of Achnanthes minutissima isolated from the Headwater site and the Above Vale site. [Headwater (HW) site with low (less than 0.1 micromole per liter) dissolved arsenic; Above Vale (AV) site with elevated (approximately 1 micromole per liter) dissolved arsenic. Sorption-rate constants, 7th, in reciprocal hours; accu— mulation factors, K b, ratio of micromoles solute per gram algae to micromoles solute per gram media. Confidence intervals (95 percent) reflect the multivari- ate regression from a 32 full factorial design (n=3) sampled at elapsed times of 0.5, 1.0, 2.0, 4.0, 8.0, and 24.0 hours after inoculation. The coefficients of determination (r2) are given for a fit of the kinetic data to a first-order model (Dixon, 1985). Details of the experimental design are provided in the text] Treatments Orthophosphate transport (mrcromolar . . solute added) characteristics phg'strffate Arsenate 7th Kb(><1,000) r2 HW isolate 0.5 0.0 0.43i0.02 28i10 0.97 0.5 0.5 0.36:0.02 28:7 0.98 0.5 1.0 0.35i0.02 11i4 0.96 1.0 0.0 0.41:0.02 581-13 0.99 1.0 0.5 0.32i0.03 38i9 0.97 1.0 1.0 0.30i0.17 7.9i2.1 0.98 AV isolate 0.5 0.0 0.44i0.03 58i17 0.97 0.5 0.5 0.33i0.02 7.24.11 0.99 0.5 1.0 0.41i0.01 1.8i0.3 0.98 1.0 0.0 0.351r0.03 120i48 0.95 1.0 0.5 0.38i0.03 28:10 0.97 1.0 1.0 0.44i0.03 18i7 0.97 Effects of Benthic Flora on Physico-Chemical Processes Our observation that arsenic accumulation (Kb values in table I—1 1) in A. minutissima decreased with increasing dissolved-orthophosphate concentrations is interesting but not surprising. Preferential sorption of orthophosphate over arsenate was observed in initial chlorophyte experiments (table I—7) and for marine diatoms (Morris and McCartney, 1984). Sanders (1985) suggested that the arsenic reactivity and toxicity to estua- rine phytoplankton species could be altered by algal transforma- tion of arsenic and subsequent release of methylated forms of arsenic. The mechanism of cell-surface preference for orthophos- phate sorption in Whitewood Creek algal isolates has not been identified. However, the fact that preference was observed in experiments using cells from the HW site, not previously exposed to elevated dissolved-arsenate concentrations, suggests that the mechanism is not acquired by previous arsenic exposure. Differences between algal isolates in arsenic sorption sug- gest that A. minutissima from the AV site exhibited arsenic toler— ance in two ways. First, this isolate accumulated significantly less arsenic than did controls (the HW isolate) when initial dissolved- arsenate concentrations were high (1.0 umol/L) and dissolved- orthophosphate concentrations were low (0.0 and 0.5 umol/L) (table I—11). Second, the AV isolate consistently demonstrated slower arsenic sorption kinetics than did the HW isolate. Both observations represent distinctive advantages for cells exposed to elevated and fluctuating dissolved—arsenic concentrations. Conway (1978) observed that arsenic sorption by Asterionella formosa in continuous culture was linearly dependent on ambient dissolved-arsenate concentrations less than 1.8 umol/L but reached a plateau at higher ambient concentrations. He hypothe- sized that this “leveling off’ of cellular arsenic was due to satura- tion of arsenic sorption sites on cell surfaces. Our results using heat-killed cells support this hypothesis. However, Kb values for Whitewood Creek isolates suggest saturation above 0.5 umol/L dissolved arsenate (table 1—1 1). Arsenic saturation by A. minutis- sima at lower ambient concentrations relative to Asterionella formosa suggests a contributory factor toward the survival of A. minutissima over the arsenic concentration range of the four sampling sites (less than 0.1 to approximately 1 umol/L). Algal accumulation factors measured here (table I—l 1) are within the range of values observed in other laboratory studies (Conway, 1978; Giddings and Eddlemon, 1977). Increasing algal abundance with distance downstream may be attributed to inputs of macronutrients from the water- treatment-plant effluent, ground—water inputs, and irrigation runoff from fertilized land. In addition, increased algal growth rates (Jorgensen, 1983) as a result of increasing water tempera- ture in the downstream direction may contribute to the down— stream increase in algal abundance (tables I—1 and 1—2). At the SS site, decreased abundance may be caused by scarcity of firm substrate needed to establish a dense algal community. Visibly high turbidity also may have hindered photosynthetic activity at this site. Although periphyton and macrophyte growth was patchy, especially at the SS site, replicate ash-free, dry-weight measurements indicated similar biomass concentrations within the patches. Elevated AI values, particularly at the AV and SS sites in August and September 1987, suggest a change in the condition of the submerged plant community. Senescence was visibly evi- dentin these samples and probably represents typical growth pro- gression and recession within these mats. It is interesting to note that the elevated AI observed in August 1986 at the STP site (Kuwabara and others, 1988) was not observed in 1987, probably because of a shift in community structure of benthic plants at the STP site in 1987 to include new growth of Z. palustris and vari- ous filamentous chlorophytes. Without development of models describing physical and chemical transport of arsenic in this reach, it is difficult to directly apply the results of this modeling study to accurately determine the effects of the benthic flora on arsenic transport. However, epi— sodic significance of the plant community can be demonstrated. In the spring, the most discernible change in plant abundance occurs with the establishment of the periphyton and macrophyte mat. Once the mat is formed, changes in the community structure principally observed at the STP and SS sites, and continued periphyton growth to maintain relatively constant Pb values as observed at the HW and STP sites, contribute to dynamics of the mat. In the fall, senescent tissues are scoured off and transported downstream or are decomposed and buried beneath settling sedi- ment. These fluxes in the biomass parallel corresponding arsenic fluxes into and out of the mat as indicated by the temporal changes in biomass (pb) and arsenic concentrations in the plant tissues (Cb) (tables I—2 and I—8). For example, at the SS site between May 28 and July 10, a 28 gram-per—square-meter change in p b was observed. This corresponds to an average doubling time of 8.9 days. Using that 8.9-day doubling time and a 28 gram— per-square-meter final biomass value in July, the greatest daily increase in biomass for this 43-day period would be 2.1 g/m2/d. Given an average channel depth of 0.24 m (table 1—2) and an algal tissue concentration of about 2,300 ug/g dry weight (table 1—8), this daily mass flux during the establishment of the mat corre- sponds to a 0.3-umol/L arsenic per day water-column flux. This is similar to the diurnal arsenic fluctuations observed by Fuller and others (1988) at the SS site. However, those measured fluc- tuations were made in August after the mat had been established. A similar calculation for the AV site results in a maximum daily mass flux and corresponding water—column arsenic flux of 62.5 g/mZ/d and 7.8 umol/L arsenic per day, respectively. This arsenic flux is an order of magnitude greater than the diurnal arsenic fluctuation observed at the AV site by Fuller and others (1988). The SS site was consistently dominated by epiphytic and filamentous algae that sorb solutes over the entire thallus (that is, water-column uptake). In contrast, at the AV site, 50 to 80 per- cent of the plant biomass is composed of submerged macrophytes that take up solutes from pore water through root systems in addi- tion to water-column uptake. The effect on water—column solute concentrations for the AV site may therefore be overestimated by a factor of 2 to 5 but nevertheless is environmentally significant. An inconsistency should be noted between the laboratory and field—measured values for arsenic accumulation by algal cells. Given the Kb values for the AV—site isolate (630 at 1 umol/L dissolved arsenate and 0.5 umol/L orthophosphate, table I—l 1), algal concentrations at that site should be approximately 400 pg arsenic per gram dry weight. Yet algal tissue concentrations of greater than 2,000 ug/g were measured. This discrepancy initially was believed to be due to arsenic associated with inorganic particles attached to plant tissues that remained after plants were washed. However, low iron concentrations in leachate used to measure algal arsenic concentrations do not support this hypothesis (table I—8). On the basis of arsenic-to—iron ratios reported for Whitewood Creek sediment (approximately 1 to 70, Goddard and others, 1988), attached particles made up less than 10 percent of the total dry weight of the plant samples. Conway (1978) found that arsenic taken up by the diatom Asterionellaformosa was concentrated in an organic layer sur- rounding the frustule. Enhanced accumulation by algae at the field sites could have been due to sorption and retention of partic— ulate arsenic in this organic surface layer that was not effectively removed during washing. Results from laboratory algal cultures in the absence of particles would consequently underestimate measured algal accumulation. Results and Discussion 23 Maintaining data-logger monitoring at the STP site for more than 24 hours allowed a comparison of pH trends during days when the site was exposed to different time courses in light intensity. Table I—4 indicates that the gradual irradiance fluctu- ations during the first day of monitoring relative to the second resulted in a corresponding gradual pH fluctuation on the first day relative to observed pH changes during the second. That is, pH lagged irradiance (table I—10) and showed similar trends in their time course over the two sampling days. The STP site, unlike the other three sites, exhibited a low pH amplitude relative to the biomass estimate (less than the HW site; 0.43 pH unit). This inconsistency may result at least in part from effects of the effluent from the municipal water-treatment plant on rates of photosynthesis or the buffer- ing capacity at the STP site. This effluent represents a signifi- cant portion (approximately 10 percent) of the creek discharge at the STP site during the summer. Diel fluctuations in specific conductance were examined as an indicator of ground-water effects on observed changes in solute concentrations. The observation that specific conduc— tance was out of phase with irradiance is explained by the fact that the creek flows over a perched aquifer with high sulfate concentrations that contribute to high specific conductance of ground water (approximately 5,000 uS/cm with no diel fluctua- tions at the SS site, K.E. Goddard, oral commun.). Hem (1948) reported diel fluctuations in chloride concentration in the Gila River as a result of changes in the hydraulic gradient. Diel variations in specific conductance have been reported (Kuwabara, 1992) as an indicator of trends in ground-water inflow. These trends are typical of streams where evapotranspi- ration in the riparian zone causes diel fluctuations in the hydrau- lic gradient (Rantz and Eakin, 1971; Wood, 1975; Winter and others, 1988). Changes in the hydraulic gradient also were evident in the increase in specific conductance with decrease in elevation between the HW and SS sites (table I—9). Maximal specific conductance, corresponding to maximal ground-water inflow into the creek, was not in phase with maximal dissolved— arsenic concentrations (figs. I—4 and I—5), thus supporting the hypothesis that the ground water was not the direct source driv- ing observed fluctuations in dissolved arsenic, as observed by Fuller and others (1989), or dissolved orthophosphate. How- ever, As(III), present at elevated concentrations in the ground water, oxidizes upon atmospheric or surface—water exposure (maximal channel inflow of ground water at night) then coprecipitates or adsorbs onto mineral surfaces. The ground water thereby may serve as an important source of particle- bound arsenic that desorbs in response to photosynthetically driven increases in pH (Cherry and others, 1986; Goddard, 1988; Fuller and Davis, 1989). The observed lag in the arsenic species relative to the pH cycle observed at the SS site has been previously observed for total arsenic at the SS site and at another site 7 km upstream from the AV site (Fuller and others, 1988). A discussion of the significance of these lags was previously presented and involves shifts in sorption equilibria in response to pH shifts (Fuller and Davis, 1989; Kuwabara, 1992). The 24 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota lag of dissolved—arsenic species behind the observed pH cycle at the SS site was more pronounced than at the AV site, which had a greater biomass and a higher pH range than the SS site. In fact, the statistical analysis (table I—10) indicates that at a 3-hour sampling interval, a lag at the AV site cannot even be resolved for the dominant arsenic species. The amplitude of the cycles for dissolved-arsenic species at the AV site also were greater than at the SS site. In spite of lower cumulative irradiance at the AV site relative to the SS site on the respective sampling days for each site, the pH fluctuation was greater at the AV site, the artificially pooled area with higher biomass, than at the SS site. Kuwabara (1992) discussed possible abiotic and biotic explanations for the lack of correspondence between temporal changes in dissolved orthophosphate and arsenic species observed at the AV and SS sites. Based on carbon-fixation esti— mates, Kuwabara (1992) determined that the demand for phos- phorus by photosynthetic activity far exceeded the solution phase pool. Possible sources of supplementary phosphorus included solute regeneration within the mat or particle-bound phosphorus. Preferential sorption of orthophosphate over arsen- ate and the demand for orthophosphate imposed by photosyn- thetic activity provided a reasonable explanation for the dis- solved orthophosphate lag behind changes in arsenic species. These observations for orthophosphate were contrasted by Fuller and Davis (1989) who concluded that periphyton uptake in Whitewood Creek was not a major process affecting arsenic diel cycles. These contrasting dissolved-orthophosphate and dissolved-arsenic observations might be expected based upon preferential phosphorus sorption by the benthic flora and con— sistently greater dissolved arsenic than dissolved orthophos- phate at the AV and SS sites (table I—9), making the effects of the benthic flora on arsenic retention more difficult to resolve. It is clear from these studies that algal isolates from Whitewood Creek have the ability to sorb orthophosphate over arsenate preferentially. Given the arsenic-transport characteris- tics reported here for A. minutissima, the rapid growth of biom- ass between late May and July at the AV and SS sites represents both a rapid and significant accumulation of arsenic (table I—8) within this single biological component. Furthermore, A. minut- issima, which was exposed to elevated concentrations of dis- solved arsenic, seems to be able to slow down the kinetics of cellular arsenic sorption. The results presented here suggest a number of extensions to this work that address complex ques- tions related to the modeling of the effects of the benthic flora on stream transport of arsenic: What causes large annual varia— tions in community structure of the type seen between 1986 and 1987 samples? How do solute sorption processes differ between the algae and submerged macrophytes seen in abundance in 1987? How does the state of the periphyton and macrophyte communities affect arsenic sorption and accumulation (that is, what other physical and chemical perturbations that account for changes in observed autotrophic indices, in addition to elevated orthophosphate concentrations, affect arsenic sorption)? How long does it take for plant species to adapt to ambient arsenic concentrations by way of changing sorption characteristics, and how does this vary among benthic plant species? Given the apparent importance of effects of cell sorption on solute trans- port, it is believed that a quantitative response to the above questions may be critical for incorporation into more sophisti- cated and ultimately predictive transport models for reactive solutes. Summary and Conclusions Effects of the benthic flora on arsenic transport in a mining-affected stream have been examined by estimating parameters for a kinetic sorption model. Using a first-order mass transfer equation, the effects of dissolved arsenate, orthophosphate, and previous solute exposure on transport parameters were determined for an algal species present at all four sampling sites along the 57-km study reach. Test algae took up orthophosphate preferentially over arsenate. Algae isolated from an area of elevated dissolved arsenate concen- trated less arsenic than did the same species isolated upstream from mining activities when dissolved arsenate was high (1.0 umol/L) and dissolved orthophosphate was low (0.0 and 0.5 umol/L). Lower arsenic sorption—rate constants also were determined for the apparently arsenic-tolerant isolate. These factors represent obvious advantages for species exposed to elevated and fluctuating dissolved-arsenic concentrations. Field and laboratory results reported here suggest that increases in plant biomass during mat development represent both a rapid and significant accumulation of arsenic within this single bio- logical component and therefore may have significant effects on water-column concentrations. In diel studies, characteristic shapes for diel pH fluctua- tions followed irradiance, while pH and water temperature lagged behind irradiance by 1 to 3 hours. Amplitudes of the pH cycles increased with biomass except at the STP site immedi- ately downstream from water-treatment-plant discharge. Inor- ganic pentavalent arsenic dominated arsenic speciation at all sites. Concentration fluctuations in dissolved-arsenic species lagged pH fluctuations by approximately 3 hours at the Sheeler Seep site, but no discernible lag was observed at the Above Vale site, which has an order of magnitude higher biomass. Furthermore, the amplitudes of diel fluctuations in arsenic species were greater at the Above Vale site than at the Sheeler Seep site. The lack of phase correspondence between dissolved— orthophosphate concentrations and arsenic species may have resulted from preferential sorption of orthophosphate over arsenate by the biomass. The phosphorus demand from photo— synthetic activity, based on carbon—fixation estimates, suggests a supplemental source, such as phosphate regeneration within the mat or desorption of particle-bound phosphate. The observed relationships provide further evidence for the need for a process-interactive approach to describe stream systems and for consideration of diel variations in water-quality monitoring programs. References Andreae, M.O., 1977, Determination of arsenic species in natu- ral waters: Analytical Chemistry, V. 49, p. 820—823. Button, D.K., Dunker, SS, and Morse, ML, 1973, Continuous culture of Rhodorula rubra—Kinetics of phosphate-arsenate uptake, inhibition and phosphate limited growth: Journal of Bacteriology, v. 113, p. 599—611. Cain, D.J., Fend, S.V., and Carter, J .L., 1988, Arsenic concen- trations of selected benthic insects in Whitewood Creek and the Belle Fourche River, South Dakota, in US. Geological Survey Toxic Substances Program—Surface-Water Contam- ination: Proceedings of the Technical Meeting, Denver, Colo., February 2-4, 1987, GE. Mallard, ed., U.S. Geologi— cal Survey Open-File Report 87—764, p. 55—60. Cherry, J.A., Morel, F.M.M., Rouse, J.V., Schnoor, J. L., and Wolman, M.G., 1986, Hydrogeochemistry of sulfide and arsenic-rich tailings and alluvium along Whitewood Creek, South Dakota: Golden, Colorado School of Mines, Mineral and Energy Resources Series, v. 29, 40 p. Conway, H.L., 1978, Sorption of arsenic and cadmium and their effects on growth, micronutrient utilization, and photosyn- thetic pigment coposition of Asterionellaformosa: Journal Fisheries Resources Board Canada, v. 35, p. 286—294. Dixon, W.J., 1985, BMDP Statistical Software: Berkeley, University of California Press, 734 p. Fisher, N .S., Bohe, M., and Teyssie, J ., 1984, Accumulation and toxicity of Cd, Zn, Ag and Hg in four marine phytoplankters: Marine Ecological Progress Series, v. 18, p. 201—213. Franson, M.A.H., 1985, Standard methods: 16th ed., American Public Health Association, American Works Association and Water Pollution Control Federation, Washington, DC, 1268 p. Fuller, C.C., Claypool—Frey, R.G., Davis, J .A., and Goddard, K.E., 1988, The role of iron oxides in diurnal fluctuations of dissolved arsenic in Whitewood Creek, South Dakota: EOS, v. 69, p. 368. Fuller, CC, and Davis, J .A., 1989, Influence of coupling of sorption and photosynthetic processes on trace element cycles in natural waters: Nature. v. 340, p. 52—54. Fuller, C.C., Davis, J .A., Zellweger, G.W., and Goddard K.E., 1989, Coupled chemical, biological and physical processes—Evaluation of the controls on dissolved arsenic in Whitewood Creek, South Dakota, in Mallard, GE, and Ragone, S.E., eds., US. Geological Survey Water-Resources Investigations Report 88420, p. 235—246. Giddings, J .M., and Eddlemon, GK, 1977, The effects of microcosm size and substrate type on aquatic microcosm behavior and arsenic transport: Environmental Contamina— tion and Toxicology, v. 6, p. 491—505. Glaubig, RA, and Goldberg, S., 1988, Determination of inorganic arsenic(III) and arsenic(III + V) using automated hydride— generation atomic absorption spectrometry: Soil Sci- ence Society of America Journal, v. 52, p. 536—537. References 25 Goddard, K.E., 1988, US. Geological Survey applied research studies of the Cheyenne River System, South Dakota— Description and collation of data, 1985 and 1986 water years: US. Geological Survey Open-File Report 88—484, 84 p. Goddard, K.E., Fuller, CC, and Davis, J .A., 1988, Seasonal and diurnal fluctuations of dissolved arsenic in Whitewood Creek, South Dakota: EOS, v. 69, p. 368. Hem, J .D., 1948, Fluctuations in concentrations of dissolved solids of some southwestern streams: Transactions of the American Geophysical Union, v. 29, p. 80—84. Johns, Cary, Luoma, SN, and Elrod, V., 1988, Selenium accu- mulation in benthic bivalves and fine sediments of San Fran- cisco Bay, the Sacramento-San Joaquin Delta, and selected tributaries: Estuarine, Coastal and Shelf Science, v. 27, p. 381—396. J orgensen, SE, 1983, Modeling the ecological processes, in Orlob, G.T., ed., Mathematical modeling of water quality— Streams, lakes, and reservoirs: John Wiley and Sons, Chiches- ter, Mass., p. 116—149. Kuwabara, J .S., 1992, Associations between benthic flora and diel changes in dissolved arsenic, phosphorus, and related physio-chemical parameters: Journal of the North American Benthological Society, v. 11, p. 218—228. Kuwabara, J.S., Chang, C.C.Y., and Pasilis, SP, 1988, Effects of algal growth on arsenic transport in Whitewood Creek, South Dakota—Preliminary results, in US. Geological Survey Toxic Substances Program—Surface-Water Contam- ination: Proceedings of the Technical Meeting, Denver, Colo., February 2—4, 1987, GE. Mallard, ed., U.S. Geologi— cal Survey Open—File Report 87—764, p. 33—37. Kuwabara, J.S., Chang, C.C.Y., and Pasilis, SP, 1990, Effects of benthic flora on arsenic transport: Journal of Environmen— tal Engineering, V. 116, p. 394—409. Kuwabara, J.S., Davis, J.A., and Chang, C.C.Y., 1985, Cultur- ing Selenastrum capricornutum (Chlorophyta) in a synthetic algal nutrient medium with defined mineral particulates: Hydrobiologia, v. 122, p. 23—27. Kuwabara, J .S., and Helliker, Paul, 1988, Trace contaminants in streams—Encyclopedia of civil engineering practice: Lancaster, Pa., Water Resources, Environmental, Technomic Publishers, v. 5, p. 739—766. Kuwabara, J .S., Leland, H.V., and Bencala, K.E., 1984, Copper transport along a Sierra Nevada stream: Journal of Environ- mental Engineering, v. 110, p. 646—655. Leland, H.V., and Carter, J .L., 1985, Effects of copper on pro- duction of periphyton, nitrogen fixation and processing of leaf litter in a Sierra Nevada, California, stream: Freshwater Biology, V. 15, p. 155—173. Morris, R.J., and McCartney, M.J., 1984, The ability of a field population of diatoms to discriminate between phosphate and arsenate: Marine Chemistry, v. 14, p. 259—265. Murphy, J ., and Riley, J .P., 1962, A modified single-solution method for the determination of phosphate in natural waters: Analytica Chimica Acta, v. 27, p. 31—36. 26 Effects of Benthic Flora on Arsenic Transport in Whitewood Creek, South Dakota Rantz, SE, and Eakin, TE, 1971, A summary of methods for the collection and analysis of basic hydrologic data for arid regions: US. Geological Survey Open-File Report 72—305, 125 p. Ryan, T.A., Jr., Joiner, B.L., and Ryan, BE, 1985, Minitab handbook, 2d ed.: Boston, Duxbury Press, 374 p. Sanders, J.G., 1979, Effects of arsenic speciation and phosphate concentration on arsenic inhibition of Skeletonema costatum (Bacillariophyceae): Journal of Phycology, V. 15, p. 424—428. Sanders, J.G., 1985, Arsenic geochemistry in Chesapeake Bay—Dependence upon anthropogenic inputs and phy- toplankton species composition: Marine Chemistry, V. 17, p. 329—340. Tallman, DE, and Shaikh, A.U., 1980, Redox stability of inor- ganic arsenic(III) and arsenic(V) in aqueous solution: Ana- lytical Chemistry, V. 52, p. 196—199. Weber, CL, 1973, Biological field and laboratory methods for measuring the quality of surface waters and effluents: Envi— ronmental Protection Agency, 670/4——73—001, 187 p. Winter, T.C., LaBaugh, J.W., and Rosenberry, DO, 1988, The design and use of a hydraulic potentiomanometer for direct measurement of differences in hydraulic head between groundwater and surface water: Limnology and Oceanogra- phy, v. 33, p. 1209—1214. Wood, SH, 1975, Holocene stratigraphy and chronology of mountain meadows, Sierra Nevada, CA: Earth Resources Monographs, US. Department of Agriculture—Forest Ser- vice, v. 4, 180 p. Zison, S.W., Mills, W.B., Deimer, D., and Chen, C.W., 1978, Rates, constants and kinetics formulations in surface water quality modeling: US. Environmental Protection Agency, EPA 600/3—78—105., 455 p. SECTION II. Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota By Christopher C. Fuller and James A. Davis Abstract Coupled physical, chemical, and biological processes affect the concentration of dissolved arsenic in Whitewood Creek, South Dakota. In the lower reaches of the stream, dissolved-arsenic concentrations were controlled primarily by adsorption and coprecipitation with iron oxyhydroxides (ferrihydrite) as ground water enriched in arsenic entered the stream. Periphyton photosynthesis induced a diurnal pH fluctua- tion of 0.25 to 0.5 in surface water that had a pH of 8.1 to 8.7 and a concomitant diurnal cycle in arsenate (30- to 40-percent varia— tion). The fluctuation in arsenate reflects the dynamic equilibrium of adsorption-desorption processes occurring in response to the pH cycle. Kinetics of the sorption processes are slow, which results in a cycle of dissolved arsenic that lags 2 to 4 hours behind the pH cycle. A mass balance for dissolved arsenate indicates that adsorption-desorption of arsenate on ferrihydrite surfaces in and on streambed sediments was the primary control of dissolved arsenic. Uptake of arsenate by algae and input from reducing sediments were of secondary importance to the dissolved-arsenic budget. The results demonstrate the importance of adsorption~ desorption processes in controlling trace—element concentrations in aquatic systems and the need to incorporate sorption kinetics into transport models. The effect of diurnal pH cycles on trace- element and nutrient cycling and availability may be of signifi- cance in other surface—water systems. Introduction Development of transport models to adequately describe and predict the fate of reactive solutes requires knowledge of the time dependence of chemical processes, such as adsorption, ion exchange, and precipitation, that control the partitioning between solution and solid phases (Rubin, 1983; Jaanasch and others, 1988). Incorporation of kinetic terms for such reactions is of particular importance in systems where the equilibrium that controls dissolved constituents fluctuates on time scales similar to or shorter than the partitioning process (reaction). For example, adsorption commonly is treated as an equilibrium process in partition-and-transport models, although kinetic studies of adsorption have shown a slow approach to equilib- rium, particularly for the oxyanions (Hingston, 1981). Adsorption processes commonly are invoked as a control on the concentrations of dissolved inorganic trace elements and con- taminants in natural waters at concentrations below their solubil- ities (Morel, 1983; Drever, 1982). Recognition of the importance of this process of trace-element partitioning is the result of many laboratory studies on the uptake of metals and oxyanions by syn- thetic oxides (Davis and Leckie, 1980; Pierce and Moore, 1982; Anderson and others, 1976), natural solids (Lions and others, 1982; Frost and Griffin, 1977), and from partial extractions of sediments from natural waters (Belzile and Tessier, 1990; Aggett and Roberts, 1986; Tessier and others, 1985). Interest in the geochemical behavior of arsenic has devel— oped because of growing concerns about contamination of sur- face water and ground water from agricultural and mining activ- ities and as a consequence of power generation from fossil- fuel combustion (Cullen and Reimer, 1989). The cycling of dissolved arsenic in surface-water systems is influenced by pro- cesses of absorption and desorption by chemical, physical, and biological constituents (Holm and others, 1980; Ferguson and Gavis, 1972; Aggett and O’Brien, 1985). Recent studies have suggested that iron is effective in the deposition, removal, and control of arsenic concentrations (Aggett and O’Brien, 1985; Belzile and Tessier, 1990; DeVitre and others, 1991). The arsenate anion is strongly sorbed by iron oxyhydroxides and, as in adsorption of other oxyanions by oxides, increases with decreasing values of pH (Leckie and others, 1980; Pierce and Moore, 1982; Goldberg, 1986; Fuller and others, 1993). pH is a determining factor in sorption equilibrium, and diurnal cycles of pH in surface water of upward of :1 unit have been reported by Dunn (1967) and Turk (I988). Trace-element concentrations are controlled by sorption reactions. The effect of fluctuations of pH on trace-element cycles, however, has received little attention. Purpose and Scope The purpose of this report is to describe a mass balance for dissolved arsenic in order to evaluate the importance of sorption and other instream processes that control concentrations of dissolved arsenic. A field study was conducted to investigate the effect of adsorption processes on concentrations of dis- solved arsenic in a small, perennial stream that was heavily con- taminated by gold-mine tailings. A diurnal cycle in pH in sur- face water was observed that results from algal photosynthesis 28 Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota (Fuller and Davis, 1989). The concentration of dissolved arsenic also fluctuates diumally in response to changes in the adsorption—desorption equilibrium with changing pH. A lag in the diurnal-arsenic cycle was attributed to the time depen- dence of the adsorption-desorption process. Kuwabara (1992) also observed diurnal cycles in pH, dissolved phosphate, and arsenic in this stream; the magnitude of the pH cycle was corre- lated to the standing crop of periphyton. Description of Study Area Between 1876 and 1977, about 100 million Mg of finely ground mill tailings were discharged into Whitewood Creek in South Dakota. These tailings contain 0.25 to 0.75 percent arse- nopyrite and other metallic—sulfide minerals (Goddard, 1987). This discharge has resulted in the deposition of contaminated flood-plain sediments that contain as much as 11,000 ug of arsenic per gram of sediment (Goddard, 1987). Field and mineralogic data indicate that a substantial fraction of the arsenic now is associated with iron oxyhydroxides and oxides. Transfer of arsenic from tailings to adjacent alluvial deposits has occurred when sulfide oxidation and flood-plain erosion and deposition were more active processes (Cherry and others, 1986). As of 1987, most of the arsenic in fine-grained (<63 mm in diameter) suspended and bed sediments and in oxidized flood-plain deposits is associated with light mineral fractions in which density is less than 2.89 g/cm3. In contrast, the arsenic in reduced flood-plain sediments is mainly in the heavy mineral fraction in which density is <2.89 g/cm3, perhaps as unoxidized arsenopyrite (McKallip and others, 1988). Oxidized flood-plain deposits mainly are coarse grained (>63 pm in diameter), and the bulk of the arsenic is in iron-oxide coatings on sand grains (Horowitz and others, 1988). Horowitz and others (1988) found arsenopyrite grains both in suspended— and bed—sediment sam- ples that were not fractionated by density. However, attempts were not made to quantify the abundance of the unoxidized mineral. Iron-oxide rinds were on the surfaces of arsenopyrite and other sulfide mineral grains and may provide shielding from further oxidation (Horowitz and others, 1988). Release of arsenic from the flood-plain deposits into ground water and surface water occurs by dissolution of, or desorption from, arsenic-rich ferrihydrite in alluvium (Cherry and others, 1986). Oxidation of arsenopyrite in tailings deposits is of minor importance because the abundance of carbonate minerals in flood-plain deposits (Marron, 1988) acts to buffer the acidity generated during oxidation of sulfides within the contaminated sediments. As a result, ground water in the flood-plain aquifers generally is of neutral pH (Cherry and others, 1986; Fuller and others, 1987). In the lower 35 km of the stream, input of arsenic to the stream results from seepage of arsenic-bearing and ferrous—iron-bearing ground water from the flood-plain aquifers after periods of high stream discharge (Cherry and others, 1986; Goddard, 1987). Dissolved arsenic in the ground water is present in the more reduced +3 oxidation state, arsenic(III); and in surface water, dissolved arsenic is essentially all in the +5 oxidation state, arsenic(V) (Fuller and others, 1987). Oxidation and precipitation of ferrous iron in the ground water occurs rapidly on contact with the atmosphere and the stream remove most of the dissolved arsenic through co— precipitation with iron or subsequent adsorption onto the ferri- hydrite (Fuller and Davis, 1989). The buildup of arsenic-rich iron oxides in the streambed and a general downstream increase in dissolved arsenic result from the continuous discharge of contaminated ground water (Goddard, 1987). In addition, an increase in dissolved arsenic at a given site is observed at low stream stages during summer and fall because of the buildup of arsenic-bearing iron oxides in the streambed (Goddard and oth- ers, 1988). The abundance of iron oxides in the bed sediments and flood-plain sediments of this stream and the strong affinity of arsenate for iron oxyhydroxides (Pierce and Moore, 1982; Goldberg, 1986) suggest that control of dissolved arsenic is from reactions with the ferrihydrite surfaces. The small range in the molar ratio of arsenic to iron in iron precipitates, in coarse- and fine-grained bed sediments (average arsenic/iron: 0022:0006), and in oxidized and unoxidized flood-plain deposits suggests that arsenic largely remains associated with iron throughout this system (Fuller and others, 1987). Acknowledgments G.W. Zellweger contributed his expertise and efforts in conducting the conservative-tracer injection. J .S. Kuwabara provided suspended—sediment samples and data on concentra- tions of arsenic in algae. Methods of Sample Collection and Analysis Diurnal Sampling In order to define the role of sorption processes in arsenic partitioning, hourly samples were collected in a 3—day period at the Sheeler Seep site (site A) in August 1987 (fig. II—l). This site is in the lower gradient reach of the stream about 2 km upstream from the confluence with the Belle Fourche River and 1 km upstream from the Sheeler site studied by Cherry and others (1986). Site A is 100 m downstream from a large, abandoned meander that contains unoxidized tailings and oxidized, coarser grained alluvium into which the stream was cutting as of 1987. Many seeps discharge ground water from this cutbank, and iron oxyhydroxide precipitates form as the ground water discharges into the stream. In this part of the stream, the flood plains are extensively contaminated with arsenic, and many visible ground- water seeps issue from the streambank during low flow. At sites B and C (fig. II—l), hourly sampling also was con- ducted during the second and third days. Site B (Custer Camp) is 11.8 km upstream from site A in an area of extensive flood- plain contamination and visible ground-water discharge. Site C (Whitewood Creek above Whitewood) is in the higher gradient Methods of Sample Collection and Analysis 29 SOUTH DAKOTA Study area / 103°40' 103°30' l l 44°40' Belle Fourche River Sheeler Seeps site A Custer Camp site B 44°30' —- Whitewood Whitewood Creek above Whitewood site C Deadwood El 44°20' — — I I O 10 KILOMETERS 0 10 MILES Figure "—1. Map showing location of study area and location of sampling sites in Whitewood Creek. 30 Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota part of the stream and had no apparent inflow of arsenic-bearing or iron-bearing ground water. At sites B and C, hourly water samples for determination of dissolved inorganic arsenic and other dissolved constituents were collected in a single container from the center of flow. At site A, samples were composited from four subsamples collected across the channel. Samples were filtered through 0.1-um membrane filters and preserved by acidification with trace-metal grade hydrochloric acid to a pH of 1. A threefold greater iron concentration was measured in <0.45-um filtrates than in <0.1- and <0.01-um filtrates, which indicates a large component of colloidal iron (Fuller and others, 1988a). The pH electrodes were calibrated at hourly intervals with buffers at surface—water temperature. Concurrent measure— ments of pH and water temperature also were made at one- half—hour intervals. At site A, integrated incident-light intensity (400- to 700-nm wavelength) was measured at the water surface at one-half—hour intervals. Alkalinity samples were collected at 3-hour intervals during the first 36 hours at site A. Total alka- linity was determined by Gran titration (Franson, 1985). Dis- solved calcium was determined by flame atomic-absorption spectroscopy. Dissolved sulfate was determined by ion chromatography. Partial pressure of carbon dioxide of surface water was calculated with the chemical-equilibrium program MINEQL (Westall and others, 1976) from the measured pH and alkalinity during the first 24 hours at site A. Temperature- corrected stability constants from Plummer and Busenberg (1982) were used. Total dissolved inorganic arsenic was determined by hydride-generation atomic-absorption spectroscopy after reduction of arsenic(V) to arsenic(III) in stron acid by the addi— tion of an excess of potassium iodide. The avErage uncertainty in analyses was i003 umol/L. Preliminary sampling indicated that dissolved arsenic in surface water is greater than 98 percent in the +5 oxidation state as the arsenate oxyanion (Fuller and others, 1987). Arsenic speciation was determined by graphite- furnace atomic-absorption spectroscopy after separation by anion exchange in the field immediately after filtration and acidification (Ficklin, 1983). The agreement between hydride generation and graphite-furnace analyses indicates that the concentrations of organic arsenic species are negligible. For the purpose of this report, dissolved—arsenic concentrations reported here are assumed to be entirely arsenic(V) as arsenate oxyanion species and will be referred to as C As' Statistical Analysis of Diurnal Surface-Water Chemistry Data A cross-correlation analysis was conducted on the diurnal time-series data to determine if observed variations in one con- stituent were in phase or lagging behind another constituent. The statistical computer program, Minitab (Ryan and others, 1985), was used to generate correlograms of a range of lag times and their associated correlation coefficients (r). The correlation interval with the maximum r value was accepted as the lag time provided that the correlation was significant (P<0.05). The program required data sets with fixed time intervals. Missing intervals in time-series data were filled in by linear interpolation of adjacent time intervals. The resolution of the correlogram is equal to the sampling-time interval (i0.5 or :1 hour). Conservative-Tracer Injection In small streams, conservative-tracer injections are used to accurately determine stream discharge, fluctuations in dis- charge, and ground-water inflows (Bencala and others, 1987; Jackman and others, 1984/1985; Zellweger and others, 1989; Bencala and others, 1990). Traditional methods of stream gag- ing are inappropriate in streams that have a significant compo- nent of the total discharge flowing through streambed deposits (Zellweger and others, 1989). These subsurface zones have been described as transient-storage zones (Bencala and others, 1990). The stream discharge determined by conservative- tracer injection includes flow through the transient—storage zones, assuming that sufficient time is allowed for the tracer to equilibrate between the surface flow and the transient—storage zones. In this study, a conservative tracer was injected 2.8 km upstream from site A on the second and third days of diurnal sampling. Results from this conservative-tracer injection pro- vided estimates of the magnitude of concentrations of dissolved arsenic in ground—water inflow to the stream along this reach. A concentrated solution of bromide (4.02:0.14x105 mg/L) was prepared by dissolving technical-grade lithium bromide into 100 L of streamwater and injecting the solution into the stream for 28 hours at a flow rate of 69il mL/min from a constant-flow- rate metering pump. At this concentration and flow rate, about 1 mg/L of bromide in surface water was expected on dilution. This amount is above the background level for bromide concen- tration in surface water and ground water (<0.02 mg/L). During the first 3 hours of injection, samples were collected at 5- to 10-minute intervals at four sites along the reach to determine the mean traveltime of the stream through the reach. Sampling times at these points were determined from a traveltime of about 170 minutes for the reach on the basis of a dye test conducted before the injection. After the injected bromide had reached a pla- teau in concentration, stream discharge at any point downstream from the injection can be calculated from the bromide concentra- tion at that point by the following relation (Zellweger and others, 1989): Q = Qi(Ci_Cb) (1) S (C a — C b) where Q = stream discharge below injection point in liters per second; Q, = tracer-injection flow rate in liters per second; Ca = bromide concentration in surface water, in milli- grams per liter; C,- = bromide concentration in injectate, in milligrams per liter; and C b = bromide concentration above injection point, in milligrams per liter. Synoptic samples were collected 24 hours after the start of the injection at 20 points along this reach to determine the stream discharge as a function of distance. In addition, surface-water samples were collected at 10 of these sites for dissolved-arsenic analysis concurrent with the synoptic samples for bromide anal- ysis. At the downstream end of the reach (the diurnal C As sam— pling site), hourly samples for bromide analysis were collected throughout the duration of the injection to identify diurnal fluctu- ations in stream discharge. All surface-water samples for bro- mide analysis were filtered through 045—th membrane filters. Concentrations of dissolved bromide, Chloride, and sulfate were determined by ion chromatography. This method had a detection limit of 0.02 mg/L and a precision of :00] mg/L. Sampling of Ground-Water Inflows Samples were collected at four seeps from the cutbank of the abandoned meander upstream from site A. A wide-mouth bottle was used to sample directly from the seep outflow, and the samples were filtered and acidified for determination of dis- solved arsenic and iron. Separate samples were collected for anions and alkalinity. The other seeps along the 2.8-km reach were not suitable for sampling. In June 1987, at one of the four seeps, a 30—cm section of perforated 1.3-cm-diameter PVC tub- ing covered with 60-ttm nylon screen was inserted horizontally into the streambank at the seep outlet, at the contact between the overlying flood-plain alluvium and the Pierre Shale. The outlet of the pipe was fitted with 0.64—mm (inside diameter) nylon tub- ing. Water was allowed to flow for 24 hours before sample col- lection. The seep was sampled in June and July 1987 as well as during the diurnal sampling. Ground water from this seep was filtered through a nitrogen-purged filtration tower and acidified before exposure to the atmosphere (Fuller and others, 1988a). Arsenic speciation after separation by anion exchange (Ficklin, 1983) and iron speciation (Stookey, 1970) were determined for this sample. pH was measured in a flow—through cell that was fitted to the outlet of the horizontal well. Solid-Phase Characterization and Adsorption Properties To characterize the iron precipitate that forms as a result of oxygenation of ground water, a 20-L sample was collected in a glass carboy from the horizontal well seep. This sample was purged with air to oxidize and precipitate dissolved iron. The resulting precipitate was concentrated by settling and centrifuga- tion. The iron oxyhydroxide precipitate was aged for 1 week at a pH of 8.0. An aliquot was dried and ground for X-ray diffraction characterization and for total arsenic and iron analyses following acid dissolution. The time dependence of arsenate adsorption onto this iron oxyhydroxide precipitate was studied by transfer— ring 1.0-mL aliquots of the precipitate suspension into centrifuge tubes containing 30 mL of surface water collected at a pH of 8.4 and filtered through a 0.1-um filter. These tubes then were mixed on an end-over-end mixer at 12 revolutions per minute. Results and Discussion 31 At various time intervals through 120 hours, the solution phase from pairs of tubes was sampled for dissolved arsenic after pH measurement and centrifugation at 16,000 gravita- tional constant (G). To determine the amount of arsenic associated with the solid that is available for desorption, arsenic isotopic exchange was measured on an additional set of tubes of the surface-water precip— itate. The isotopic—exchange technique is based on the attainment of isotopic equilibrium between the solution phase (dissolved arsenic) and the fraction of adsorbate (arsenic) available for des- orption (Davis and others, 1987). The radioisotope arsenic—73 in the +5 oxidation state was added to an aliquot of supernatant from one tube and mixed for 24 hours to ensure isotopic equilibrium in the solution phase. A 1.00—mL aliquot of the labeled solution then was added to each of 10 tubes that were equilibrated for 120 hours before the addition of the isotope. During the next 4 days, pairs of tubes were sampled, and the arsenic—73 activity that remained in solution was determined by gamma spectroscopy of the super- natant. The amount of exchangeable sorbed arsenic was calculated by mass balance from the amount of arsenic-73 removed from solution (Davis and others, 1987). This calculation assumes the total dissolved-arsenic concentration did not change significantly during isotopic exchange. Results and Discussion Diumal Fluctuations in Surface-Water Chemistry A diurnal fluctuation in the pH cycle of surface water varied as much as 0.5 unit at all three sites (figs. 11—2, 11—3, and 11—4). The pH cycle results from algal photosynthesis that lowers the partial pressure of carbon dioxide during the day (Dunn, 1967; Turk, 1988). The diurnal cycle of pH is attributed to photosynthe- sis because of the correlation of pH maxima with light-intensity maxima at site A (fig. II—2A, table II—l) and because of the diur— nal variations of dissolved oxygen (Fuller and Davis, 1989) and calculated partial pressure of carbon dioxide. At all three sites, the cycle of pH either preceded change in water temperature by as much as 2.5 hours or was in phase with the measured diurnal variation in water temperature (table II—l). The amplitude of the pH fluctuation generally was constant during the two or three cycles measured at each site, and the amplitude of the tempera- ture cycle varied as much as 50 percent between cycles at each site. These differences between pH and water-temperature cycles suggest that increases in pH did not result from a decrease in carbon dioxide solubility as surface-water temperature increased. Partial pressure of carbon dioxide in surface water ranged from 680 ppm at high pH to 1,200 ppm at low pH and always was supersaturated with respect to equilibrium with the atmosphere. Stream reaeration probably is insufficient for equilibrium with the atmosphere. This disequilibrium results in the fluctuations of pH in response to changes in partial pressure of carbon dioxide as the rate of photosynthesis varies diumally. Diurnal pH fluctu- ations observed in other surface-water systems have been attrib- uted to photosynthetic activity (Dunn, 1967; Turk, 1988). 32 Evaluation ol the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota A 0.20 I l I I I I I | I I I 8-5 (D LIGHT INTENSITY H a ‘ p . >_~ l— - 8.4 F.u1 0.15 — _ E “A (I) LIJ A — Z A A.AAA AAAA m m AA A AA A < _ A“ Q ‘ A AA 2 = e . .. .. . . G _ a 3 —(3 1% A AA A F-03 Qgfi A AA AA 9 I I — _ (D E O 10 Q 0 AA ‘ Q AA A Ge 0 D. j a . Q A A AA ‘.A if AA 9 O $53 A A A A.A AA A C) G —-82 __ _ A A G u D E Go . A A: G e (a _ _ (I) Q Q G) ‘2’ Z 0.05 — A A Q A — UJ A Q) G 6 AA — 81 Z A <99 _ Q A (369 % _ (Q G GB 9 G e e o 0 I I i | 040 I I (D I b I I 8 0 B 09 I I I I I | r I I I | 8-5 O DISSOLVED ARSENATE ” —I g A pH '2 CE ‘ — 8.4 E m 0.8 — z t . MA _ N.J A Afi‘A AfiA L’m ‘.A A AA A AA 2 m - AA A A o A AA AA 0 O A —‘83 0 1 AA A ‘ . AA 1 w ° A AA .uP m o F—fl 07 — 0A 0 A o A o — %i < C) 0“ A At 0 o O E E l A AA A“ A? “ o 00 03c) A A A A A AA A — 82 E m _ o . ‘ .. o A o o 0 d19 A ‘ 5: . o. o O o 2 _ m o 5 Z 06 — O o “ o o A o o o o o o 0 AA — 81 (n A 0 O . . 2 — o D A . - o 05 I I I I I I I I I 8.0 AUGUST11 AUGUST12 AUGUST13 0000 0600 1200 1800 0000 0600 1200 1800 0000 0600 1200 1800 0000 TIME Figure "-2. Incident-light intensity, dissolved-arsenate concentration, and pH compared to time of day at site A, August 11—13, 1987. DISSOLVED-ARSENATE CONCENTRATION, IN MICROMOLES PER LITER DISSOLVED-ARSENATE CONCENTRATION, IN MICROMOLES PER LITEFI Results and Discussion 1.1 I l | I I T ' | ' ' = ' 9'0 o DISSOLVED ARSENATE A pH _ O O... O . O 1.0 — 0 . . ’ 8'8 O O O x O . _ ‘ AIA _ o :5 o 15““ o 0.9 — A“ ‘5‘ A‘ ‘A ~ 8.6 AA 0 ‘; A A “A . $ A ' A A A . _ . O A o o 0‘ A 0.8 — A o o g A — 8.4 _ é“ ‘ .‘A A _ “A A ‘A AA AA AA A 0.7 I I I I | I AAA I I | ‘ 8.2 AUGUST 11 AUGUST 12 AUGUST 13 0000 0600 1200 1800 00.00 0600 1200 1800 0000 0600 1200 1800 0000 TIME Figure "—3. Dissolved-arsenate concentration and pH compared to time of day at site B, August 12—13, 1987. 1-1 I I I I I I I I I I I 9-2 o DISSOLVED ARSENATE _ ‘ A H p A A A — 9.0 A A A A 1.0 — t ‘m A ‘ ‘8 — A A A A A A ‘ — s 8 A . _ A AA ‘AIA ‘ AVA ‘ _ A A AA A A 0.9 — ‘A *“ — 8.6 O O O. _ O . o O o o o o — 8-4 O . O I O O 0.8 — O O . . ... _ ° — 8.2 07 I I I | | I I I l 8.0 AUGUST 11 AUGUST 12 AUGUST 13 0000 0600 1200 1800 00.00 0600 1200 1800 0000 0600 1200 1800 0000 TIME Figure "—4. Dissolved-arsenate concentration and pH compared to time of day at site C, August 12—13, 1987. pH pH 34 Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota Table "—1. Cross-correlation analysis of diurnal time-series data. Cross correlation of first component Time interval, Lag of first component Maximum correlation coefficient used to Number of pairs time series behind of data used in time series to second component time series in hours second component1 define lag time2 cross-correlation (hours) (r) analysis I. Site A Arsenic, dissolved to pH ................................ 1 4 0.62 55 pH to light ...................................................... 0.5 0.5 0.79 110 Water temperature to light .............................. 0.5 3.0 0.86 110 Water temperature to pH ................................ 0.5 1.5 0.66 110 Sulfate to pH ................................................... 1 10 0.76 52 Bromide to sulfate .......................................... 1 —1 0.77 23 ll. Site B Arsenic, dissolved to pH ................................ 1 2 0.83 37 Water temperature to pH ................................ 0.5 2.5 0.83 73 I”. Site C Arsenic, dissolved to pH ..... 2 (3) (4) 18 Water temperature to pH ................................ 0.5 0.5 0.80 71 IV. Intersite Comparison 1. Sites A and B Arsenic, dissolved, site B to arsenic, 1 —2 0.67 33 dissolved, site A .......................................... pH, site B to pH, site A 0.5 —1.5 0.92 67 pH, site B to light, site A 0.5 —0.5 0.83 67 Temperature, site B to temperature, 0.5 0 0.93 67 site A .......................................................... 2. Sites A and C pH, site C to pH, site A .................................. 0.5 0 0.73 67 pH, site C to light, site A ................................ 0.5 —1.0 0.79 67 Temperature, site C to temperature, 0.5 0 0.96 67 site A ........................................................... lNegative lag indicates second component proceeds first. 2Resolution equals length of data-time intervals. 3No correlation observed. 4Maximum r value insignificant (P<0.05). A diurnal cycle in dissolved arsenate was observed at sites A and B (figs. 11—23 and 11—3). The fluctuation in CA3 of as much as 40 percent lagged behind the cycle of pH by 2 to 4 hours (table 11—] ). Fluctuations in both pH and C A s were greater at site B than at site A. Additionally, a shorter lag time of C A s behind pH also was observed at site B. The general trend of CA3 fluctuation with pH at these sites is consistent with the pH depen- dence of arsenate adsorption by iron oxyhydroxides (Pierce and Moore, 1982; Goldberg, 1986). The higher C AS at site B was because of higher pH range and a slightly higher arsenic to iron molar ratio in bed sediments in this reach. The shorter lag time of the C A 3 cycle for pH cycles of greater amplitude also was observed by Kuwabara (1992). Although a diurnal fluctuation in pH of 0.5 unit also was observed at site C, no significant variation in C As was observed (fig. II—4). CA5, pH, and water—temperature data for the three sites are given in Fuller and others (1989). Total alkalinity ranged from a minimum of 2.93 meq/L to a maximum of 3.42 meq/L and appeared to fluctuate in phase with pH (fig. 11—5). A significant cross correlation of alkalinity with pH was not possible because of the small number of alka- linity samples. Sulfate fluctuated diurnally by 0.35 mmol/L but was out of phase with pH cycle by about 10 hours (fig. 11—5 and table II~1). Dissolved calcium also fluctuated diurnally and ranged from 3.05 to 3.38 mmol/L and was in phase with sulfate (data not shown). Dissolved ferrous and total iron both were below the detection limit of 0.1 umol/L in all samples. Results and Discussion 35 4 | I I I I I I j I r | 8'5 U) 0 TOTAL ALKALINITY |_ _ _ Z 9 A PH lilJ O ‘ O O 8 4 < . § . . . — . 2 3 ’— . 3 AMA ‘ _ O A A AAA AAA 'i" A A A AA ‘ AA j m _ A “AA AA AA A A — 8-3 E If; A ‘A A A A A I E E 2 — AA A AAA A _ 0- >: Lu A A A I: n. A A‘ AAA ‘ AAA A n A — 8.2 2 ' A A A j A A < ‘ ‘ _ E 1 _ r ‘ r < A ._l M — 8.1 E _ A +- ‘ ‘ 0 l I I | I I I I I 80 AUGUST 11 AUGUST 12 AUGUST 13 0000 0600 1200 1800 0000 0600 1200 1800 0000 0600 1200 1800 0000 TIME Figure "—5. Total alkalinity concentration and pH corn Ground-Water Chemistry The results of analyses of ground—water data collected from seeps in the abandoned meander upstream from site A (table 11—2) are similar to results of analysis of ground-water data collected by piezometer in tailings and oxidized alluvium at several sites along Whitewood Creek (Cherry and others, 1986; Fuller and others, 1988a). The concentration of dissolved arsenic was five to eight times higher in the ground- water seeps than in surface water and, in contrast to surface water, was predominantly in the +3 oxidation state. The pre— dominance of +5 oxidation state in surface water suggests that oxidation of arsenic is rapid. Belzile and Tessier (1990) have argued that only arsenic in the +5 oxidation state should be associated with iron oxyhydroxides. Concentrations of dis- solved iron—essentially all as ferrous iron—in ground water were more than three orders of magnitude greater than concen- trations of dissolved iron in surface water. Sulfate and alkalinity also were elevated in ground water. Data for each constituent from the various sampling dates were averaged for use in mod- eling ground-water inflow to the stream (table II—2). Variations in Stream Discharge The increase in dissolved bromide from background level (<0.02 mg/L) to the plateau concentration (1.1 mg/L) at site A yielded a mean traveltime of 147 minutes through the 2.8-km injection reach (fig. II—6). Mean traveltime is defined as the time between the start of the bromide injection and the point at which pared to time of day at site A, August 1 1—13, 1987. bromide reaches 50 percent of plateau concentration. Triska and others (1989) have defined this as the nominal traveltime. An overall velocity of water in the reach of 0.32 m/s is calculated by dividing the length of the reach by the mean traveltime (J ackman and others, 1984/1985). Velocities within each subreach between adjacent sampling points were 0.34 m/s for the subreach 0 to 1,518 m downstream from the injection point; 0.36 m/s for the subreach between 1,518 to 2,267 m, 0.22 m/s for the subreach between 2,267 to 2,543 m, and 0.32 m/s for the subreach between 2,543 and 2,842 m. The lowest velocity was in a 276-m reach that included a ponded section of lower gradient stream reach that had thick, fine— grained bed sediments. The ponded section is not char- acteristic of the rest of the study reach. The overall traveltime and velocity for the 2.8-km reach are used in modeling ground—water inflows and rates of instream processes. A linear decrease (r=0.9 l) in bromide concentration of 7 percent through the 2.8-km injection reach (fig. II—7A) was measured in the synoptic samples. An increase in the stream dis- charge from 0.453 m3/s at the injection point to 0.484 m3/s at the downstream end of the reach was calculated from equation II—l and the bromide concentrations from the linear regression for the upstream and downstream ends of the reach. The increase in stream discharge of 0.031 m3/s is attributed to ground-water dis- charge from perched aquifers in the flood plain to the stream along the reach. This increase also is evident from a linear (1:098) downstream increase in dissolved sulfate (fig. II—7B) because ground water that discharges from flood-plain aquifers and the underlying shale is enriched in sulfate. The linear downstream decrease in bromide and the increase in sulfate indicate that 36 Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota Table "—2. Ground-water chemistry data for Whitewood Creek near site A. Sulfate, Alkalinity, Arsenic, total, Arsenic(lll), Arsenic(V), Iron, total, lronlll), Date pH in millimoles in millimoles in micromoles in micromoles in micromoles in micromoles in micromoles per liter per liter per liter per liter per liter per liter per liter August 1986 6.6 12.0 (I) 3.63 3.39 0.24 151 125 June 1987 7.1 12.3 (1) 24.481005 3.711012 0.791006 147 142 June 1987 7.1 12.9 5.3 40610.07 (1) (1) 142 143 July 1987 7.0 12.7 (1) 3.5710.13 3.501005 0261011 191 190 July 1987 7.0 12.3 (1) 39310.03 37610.09 04810.13 202 200 August 1987 (I) 13.7 (I) 2.42:0.11 (I) (I) (I) (I) August 1987 (1) 12.8 (1) 41110.11 (1) (1) (1) (1) August 1987 (I) 12.1 (I) 4.22:0.24 (I) (I) (I) (I) August 1987 (1) 12.9 (1) 37310.18 (1) (1) (1) (1) Average concentrations 7.0101 127108 5.3 3810.6 3.6102 0.4103 167128 160133 1Not analyzed. 2Plus minus sign represents standard error of mean of replicate analyses. ground-water inflow to the stream is distributed evenly along the length of the reach. The fractional increase in stream discharge also can be calculated from the increase in sulfate throughout the reach by assuming a value of sulfate in ground water that is equal to the average concentration of sulfate determined in the seeps near site A (12.7 millimoles per liter [mmol/L], table II—2) as follows: Qd = ng + Q,- (11—2) and [SO41de = [SO4]nggw+ [SO4]ij (II—3) where Qd = discharge at the diurnal sampling site A, in cubic meters per second; ng = discharge from ground-water inflow, in cubic meters per second; Q = discharge at the injection point, in cubic meters per second; SO4d = sulfate concentration at diurnal sampling site A, in millimoles per liter; S04gw = sulfate concentration in ground water, in millimoles per liter; and S04j = sulfate concentration at injection point, in millimoles per liter. If Qd is set to 1 and equation 11—2 is substituted into equation 11—3, solving for ng yields the fraction of discharge at site A because of ground-water inflow along the injection reach. This inflow results in a fractional increase in stream dis— charge of 0.11 in the 2.8-km reach that is significantly greater than discharge determined from the bromide data (0.07). Based on the range of sulfate—7 to 200 mmol/L— in ground water from piezometer measurements in the flood—plain aquifers of this stream (Cherry and others, 1986), the disparity probably results from ground water that contains higher sulfate concen- trations than values of sulfate measured near site A. If values of stream discharge from bromide are used, an average value of sulfate in ground water of 18.7 mmol/L is required for the observed downstream increase in sulfate in surface water. Because of the uncertainty in concentration of sulfate in ground-water inflows, the fractional increase in stream dis- charge calculated from the injected bromide will be used for estimating the ground—water contribution to CAS. Coincident diurnal fluctuations in bromide and sulfate were observed. Maximum concentrations for both constituents occurred at night, and minimal concentrations for sulfate occurred in early afternoon (fig. 11—6). Cross-correlation analysis of diurnal bromide with sulfate time series yielded a 1-hour lag of sulfate behind bromide (table II—1). Because this diurnal fluc- tuation was observed both for the injected bromide and for sul- fate, which is increased by ground-water inflow, the diurnal fluctuation in both constituents must be the result of an increase in stream discharge from sources upstream from the lithium- bromide injection site. The increase in stream discharge dilutes both constituents during the day. An increase in ground-water inflow only within the reach at night would result in the observed increase in sulfate but would cause a decrease in bromide from greater dilution. Concentrations of sulfate ranged from 5.93 to 6.33 mmol/L, and concentrations of bromide ranged from 0.93 to 1.18 mg/L. The greater relative fluctuation in bromide com- pared to sulfate may be attributed to a variation in sulfate in water entering the reach that dampens out the observed diurnal fluctuation in sulfate at the downstream end of the reach. On the basis of the concentrations of bromide in surface water, stream discharge at site A ranged from a minimum 0.391 m3/s at night to a maximum of 0.497 m3/s at midday. The diurnal cycles of sulfate and bromide and, therefore, the stream-discharge min- ima, lag behind the pH cycle by 9 to 10 hours (table II—1). Results and Discussion D: D: E 7 I I | I I r I T r j I 2-0 g I _I n: 5 - _ m 0' ° 0 o 3: a) o O 0 00° 0 o L“ o o o 0 o o o 2 _ o _ 5 6 o W ° owoooo 1-5 g E 9 3 _ — j E mum” 5 Z Ir fun” E - 5 _ El E — 1.0 - z Z 9 9 ,2 _ '2: m T II I- I— z Z LU El LLI o 4 — — 0.5 0 Z Z 0 8 O o SULFATE I:I _ Lu LIJ n D E I: BROMIDE E. D l l J 1 mI l I n I O _I 3 0 a; 0) AUGUST 11 AUGUST 12 AUGUST 13 m 0000 0600 1200 1800 00100 0600 1200 1800 00.00 0600 1200 1800 0000 TIME Figure "—6. Sulfate and bromide concentrations compared to time of day at site A, August I I—I3, 1987. I: c: E 8 I | I I I | I | I I I 1.25 g j _I D: — II E ' E I: I: :I I: a) (D D D E n n D D an — 1.00 I: I:I=I D E 5 7 _ :I E I: D an I: < O n: E ‘ (5 3 3 _l _ _I E — 0.75 3 Z Z Z" 6 — oo 0 ‘ C2)“ 0 O _ o d) o O O — I; o 0 ° ° .9 — 0.50 E a: - O E Q) I I— o o 0 ° 3 g '2 E 0 o 0 ° 0 o R: an ‘ IJJ O 5 ‘ a E 0 Z c D. Z O ‘2- E — 0.25 8 8 _ o SULFATE o 8 m I— " 9 E I: BROMIDE E 3 4G I | I I I l I I I l I o g ‘0 O 500 1,000 1,500 2,000 2,500 3,000 m DISTANCE DOWNSTREAM FFIOM INJECTION POINT, |N METERS Figure "—7. Bromide and sulfate concentrations compared to distance downstream for lithium-bromide injection site, August 13, 1987, 9 am. to 1 pm. 37 38 Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota Diurnal fluctuations in stream discharge have previously been observed in tracer studies by Kennedy and others (1984/ 1985) and Bencala and others (1987), although no driving mechanism was proposed. Others have attributed these fluctuations to diur- nal effects of evapotranspiration on ground-water discharge (Rantz and Eakin, 1972). Many large cottonwood trees growing in the flood plains of the stream may account for the observed fluctuation in ground-water inflow. Dissolved Arsenic in Synoptic Samples Concentrations of dissolved arsenic in samples collected during synoptic sampling along the injection reach were variable but fell within the range of dissolved arsenic measured at site A (fig. II—8). If the synoptic sample C AS and the C As measured at site A during the synoptic sampling period are plotted in relation to time instead of distance, the synoptic samples follow the trend of increasing C A S that was measured at site A. The measured vari- ation then can be explained by the diurnal fluctuation in C A s. The results indicate that the processes that control the diurnal arsenic cycle occur throughout the reach and are present because of instream processes rather than variable point-source inflows and downstream transport. This hypothesis also is indicated by the concurrent cycles of C As occurring at sites A and B (table II—l ), which are 11.8 km apart. If the cycle was a result of transport from an upstream point source, the cycle of C A s at site A should lag behind the cycle at site B by 9.5 hours, assuming the travel- time per kilometer is the same as measured in the 2.8-km reach. Instead, the C AS cycle at site A lagged the C As cycle at site B by 1 hour (table II—l). Arsenate Adsorption and Isotopic Exchange on Iron Oxyhydroxides The reddish-orange iron oxyhydroxide precipitate that was formed and was isolated from ground water yielded an X-ray diffraction pattern characteristic of 2-line ferrihydrite (Carlson and Schwertmann, 1981). This least crystallized form of ferri— hydrite results from the hydrolysis of ferric iron solutions at neutral pH and consists of primary crystallites of 0.8 to 1.5 nm in length (Waychunas and others, 1993). The ferrihydrite crys- tallites aggregate and coalesce rapidly under favorable condi- tions to form large, porous gelatinous floccules of 100 nm or greater length (Bottero and others, 1991). Ferrihydrite has a high reactive surface area for the adsorption of cations and anions (Dzombak and Morel, 1990). The field samples had a total iron concentration of l.77><10_3 mol/g and total arsenic concentration of 2.36x10‘5 mol/g that was determined by acid dissolution of the precipitate. Inductively coupled plasma- emission spectroscopic analysis of the acid leachate yielded, in addition to arsenic and iron, significant concentrations for man- ganese (4.9)(10‘5 mol/g) and cobalt (5.5><10_6 mol/g). In laboratory studies of arsenate sorption by synthesized 2-line ferrihydrite designed to simulate ground—water inflow to the stream, arsenic uptake during coprecipitation of arsenic with iron was rapid and was followed by constant CA; concentration during the next 24 hours (Fuller and Davis, 1989). The magni- tude of uptake by coprecipitation decreased with increasing pH and resulted in a linear increase in C As from a pH of 8.0 to a pH of 9.0. This pH dependence was similar to pH dependence in the sorption of oxyanions on oxides (Davis and Leckie, 1980; E a RANGE OF VALUES OF DIURNAL CA3 DURING SYNOPTIC-SAMPLE COLLECTION 0.5 I I 1 | l 0.8 I I I | I | | I ‘ I I o SYNOPTIC ARSENATE _ A DIURNAL ARSENATE _ I RANGE OF VALUES 0.7 — _ z; — ARSENATE CONCENTRATION, IN MICROMOLES PER LITER 0 500 1,000 1,500 2,000 2,500 3,000 DISTANCE DOWNSTREAM FROM INJECTION POINT, IN METERS Figure "—8. Synoptic dissolved—arsenate concentration related to distance from lithium-bromide injection site, August 13, 1987, 9 am. to 1 pm. Processes that Contribute to the Diurnal Cycle of Dissolved Arsenic in Surlace Water 39 Belzile and Tessier, 1990). In contrast, lower uptake and a slow approach to equilibrium were observed for the adsorption of arsenic when it was added subsequent to precipitation and aging of ferrihydrite (adsorption). Adsorption was characterized by a rapid (<5 minutes) uptake in arsenic that continued for 8 days as the arsenic diffused to adsorption sites on ferrihydrite sur- faces within aggregates of colloidal particles (Fuller and others, 1993). Desorption of arsenic from both adsorbed and coprecip- itated systems with an increase in pH also exhibited this slow approach to equilibrium. Arsenate adsorbs to ferrihydrite pri- marily as an inner-sphere bidentate—surface complex in both coprecipitation and adsorption systems (Waychunas and others, 1993). Adsorption is rapid in coprecipitation systems because arsenate adsorbs to ferrihydrite crystallite surfaces before aggregation. The rate of adsorption or desorption is limited by diffusion to or from sorption sites buried within ferrihydrite aggregates (Fuller and others, 1993). A slow approach to adsorption equilibrium often is observed for phosphate uptake onto ferrihydrite and soils (Barrow, 1983; Bolan and others, 1985; Crosby and others, 1984). The rate-controlling process is attributed to the diffusion of phosphate into ferrihydrite aggregates (Willett and others, 1988). A slow approach to equilibrium was measured for the uptake of arsenic at a pH of 8.0 from filtered surface water (<0.1-um filter, collected at a pH of 8.4) onto the ferrihydrite precipitate formed from ground water (fig. II—9). This slow approach to equilibrium also was observed for arsenic adsorp- tion and desorption by synthesized ferrihydrite (Fuller and others, 1993). After 5 days of uptake, the reversible fraction of sorbed arsenic was determined by arsenic-isotopic exchange. The exchange of arsenic-73 with particle-bound arsenic also exhibited a slow approach to equilibrium following an initial rapid exchange (fig. 11—10). After 96 hours of arsenic-73 uptake by the ferrihydrite, the calculated fraction of exchange- able arsenic was small (about 5 percent) compared with the total arsenic associated with the solid phase. The exchangeable frac- tion of sorbed arsenic would result in a solution concentration of 0.8 umol/L if all the arsenic was desorbed. This amount is equivalent to about two times the uptake measured from pH 8.4 to pH 8.0. These experiments indicate that the arsenate- sorption behavior of iron precipitates from the field is similar to arsenate-sorption behavior in laboratory-synthesized 2-line fer- rihydrite (Fuller and others, 1993). Although the ferrihydrite precipitates and coatings on streambed sediments have high arsenic concentrations, the isotopic-exchange study indicates that, at most, only about 5 percent of the total arsenic is avail- able for desorption. Processes that Contribute to the Diurnal Cycle of Dissolved Arsenic in Surface Water Diurnal cycles of C As resulted from adsorption-desorption of arsenic from iron oxyhydroxides in the streambed (Fuller and Davis, 1989). A mass balance for dissolved arsenic at site A is presented to demonstrate the importance of adsorption pro- cesses in the streambed in controlling C AS in surface water. Processes contributing to the C As cycle are estimated in units of micromoles per liter per day for comparison with the measured 0.7 I 0.6 0.2 ' 1 Total iron = 1.4 x10‘4 mole per liter Total arsenate = 15.7 micromoles per liter 0.64 micromole per liter of dissolved arsenate added — ARSENIC CONCENTRATION, lN MICROMOLES PER LlTER 72 96 120 144 TIME, IN HOURS Figure "—9. Uptake of arsenate at pH 8.0 from surface water by ferrihydrite formed from ground-water seep, as a function of time. 40 Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota fluctuation in C A S of 0.21 umol/L/d. A schematic representation of the dissolved-arsenic cycle illustrates the processes that may contribute to the diurnal cycle of C As (fig. 11—1 1). The fluctua- tion in concentrations of dissolved arsenate in surface water can be described by a one-dimensional advection-dispersion equa- tion that includes the contribution to C AS from ground-water inflows. The equation was modified from Bencala (1983) to include other sources and sinks for C As (fig. 11—1 1) as follows: 8:,” = __Q fl + la_(ADaCAS at A 3x Aax Bx (II—4) + qflarcgwu —fc)]8p_H A apH at + JsedAC— FER + PS + BS where C AS = concentrations of dissolved arsenate in surface water, in micromoles per liter; 1‘ = time, in seconds; Q = volumetric streamflow, in cubic meters per second; x = distance, in meters; A = cross sectional area of streamflow, in square meters; D = dispersion coefficient, in square meters per second; ng = inflow of ground water per length of streamflow, in cubic meters per second per meter; 100 I l C gw = C AS of ground-water inflow, in micromoles per liter; fc = fraction of arsenic adsorbed during coprecipita- tion with iron from ground-water inflow; J sed = molecular diffusive flux of arsenic out of reducing fine-grained sediments, in micromoles per square meter per second; AC = contact area of streamflow with streambed, in liters per square meter; F C = net carbon fixation by algae, in moles of carbon per liter per second; and R ratio of arsenic uptake by algae to carbon fixed, in micromoles of arsenic per moles of carbon. The last two terms of equation II—4—PS and BS—describe the time-dependent adsorption and desorption of arsenate on fer- rihydrite in suspended sediments and in bed sediments in contact with streamflow, respectively, as a function of pH and time as follows: = S a7"tz(SAs — KSCAén)QLH P II—5 S S BpH at ( ) and at (B —K C“")a H B = A z 1 _ B ML 11—6 3 c b( ¢)pb As apH at ( ) where SS 2 suspended-sediment concentrations, in grams per cubic meter; 7t“ = adsorption—desorption rate constant (s‘l), in recip- rocal seconds; PERCENT ARSENIC-73 EXCHANGED WITH ARSENIC ON SOLID O 12 24 36 48 60 72 84 96 TIME, IN HOURS Figure "—10. Arsenic-isotope exchange as a function of time on ferrihydrite following 96 hours of uptake. Processes that Contribute to the Diurnal Cycle of Dissolved Arsenic in Surlace Water 41 Coprecipitation of As with iron oxides StreamfloW “ Desorption S Sorption-desortion onto sediments Figure "—11. S A S = concentration of arsenic in suspended sediments, in micromoles per gram; n, K S = Freundlich isotherm adsorption coefficients; Zb = depth of bed sediment in contact with streamflow, 1n centimeters; ¢ = porosity of bed sediments; p b = dry sediment density, in grams per cubic centimeter; and BAS = total arsenic concentration of bed sediment, in micromoles per gram. Arsenate adsorption and desorption by ferrihydrite are described by a nonlinear Freundlich adsorption isotherm (Fuller and others, 1993). The rate of adsorption and desorption is lim- ited by diffusion within aggregates of ferrihydrite (Fuller and others, 1993). For simplicity, a first-order rate constant is used to approximate the adsorption-desorption kinetics. Instead of solving the complex transport equation (II—4), the individual input, reaction, and loss terms are evaluated over one diurnal cycle to illustrate the contribution of each process to the cycle in C A S. The source and loss terms are determined from the data collected in the 2.8-km study reach (2.4-hour travel- time) and are used to represent conditions along a reach with a 24-hour traveltime. The various components contributing to the C As cycle are estimated in units of micromoles per liter per day for compari- son with the observed fluctuation in C As of 0.21 umol/L in 1 day. The coincidence of pH and CAS cycles between sites High arsenic [As] and iron [Fe+2] ground-water discharge Oxidation As(lll) —->As(V) Biological uptake by algae and vascular plants Streamflow Generalized diagram showing dissolved-arsenate cycle. (table II—l) indicates that the cycles are the result of processes that occur throughout the stream rather than the result of transport from upstream point sources. The measured variation in C A s with time in the 2.8-km reach also provides evidence that instream processes control the CAS cycle. Because the inflow of ground water is uniform throughout the 2.8-km reach as indicated by the linear decrease in bromide and increase in sul- fate (fig. 11—7), inflow of ground water per length of streamflow is assumed to be constant and equals ground-water discharge divided by reach length. The following mass balance is limited to arsenic(V) species that comprises more than 98 percent of the total dissolved inorganic arsenic. In contrast, Kuwabara (1992) observed con- centrations of arsenic(III) of 0.08 to 0.1 1 umol/L at this site in the late summer of 1988; a slight fluctuation of arsenic(III) appears out of phase with arsenic(V). Possible reduction of arsenic(V) in acidified samples that occurs between sampling and analysis may account for the greater concentrations of arsenic(III) (Kuwabara, 1992). In this study, arsenic(III) and arsenic(V) were separated immediately upon filtration by anion exchange (Ficklin, 1983) to eliminate reduction at low pH during sample storage. Ground-Water Sources of Streamflows Results of the bromide injection indicate that a 7—percent increase in stream discharge in the length of reach was attrib- uted to ground—water inflow. The average concentration of dissolved arsenic in ground water at this site (3.8 umol/L, 42 Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota table 11—2) and the results of arsenic and iron coprecipitation experiments (Fuller and Davis, 1989) were used to estimate dissolved arsenic in ground-water inflow to the stream. By assuming that ground-water inflow to the surface water is uni- form and constant in a distance equivalent to a 24-hourtrave1- time, the change in ground-water contribution to C AS is esti— mated by evaluating the third term in equation H—4 over one 24-hour pH cycle as follows: ,w _ q WM pH8.35 _ ACAEY — —5X~ng(1—fc)|pH&15 (II 7) where ACfiYW = change in C AS from ground-water inflow, in micromoles per liter per day; At = change in time, in seconds; and fc = fraction of arsenic adsorbed with iron during coprecipitation from ground—water inflow. Assuming that arsenic was not removed during precipita- tion of iron from ground water entering the stream (12:0), a. ground—water inflow of 2.37 umol/L/d was calculated. Values of f0 are determined from laboratory data for coprecipitation of arsenic under conditions simulating ground-water inflow to the stream as a function of pH (Fuller and Davis, 1989). Inputs of dissolved arsenic of 0.17 and 0.22 umol/L/d are calculated when arsenic uptake is accounted for during coprecipitation with iron at a pH of 8.10 (fc=0.93) and 8.35 (fc=0.91), respec- tively. The difference in uptake of arsenic at a pH of 8.15 and 8.35 results in a net change in C As of 0.05 umol/L/d (ACflObecause of input from ground water during a pH cycle. Because diurnal samples for arsenic and anion analyses were not collected at the upstream end of the injection reach, the effect of the measured diurnal fluctuations (fig. 11—6) on ground-water inflows and on C AS cannot be determined. If inflow of ground water per length of streamflow increases by an amount equivalent to the observed day-to-night increase in sul- fate of 7 percent, however, a ACAé’SW of 0.04 umol/L/d in a pH cycle is estimated by equation II—7. Molecular Diffusive Flux from Bed Sediments Release of arsenic to sediment interstitial water during the reductive dissolution of iron oxides and degradation of organic matter (Aggett and O’Brien, 1985; Peterson and Carpenter, 1986; Cullen and Reimer, 1989) results in a diffusive flux to the overlying water column that contributes to C A s. A concentration of dissolved arsenic in pore water of 4.7 umol/L was determined at a depth of 4 cm in a deposit of fine—grained reducing sedi- ment (US. Geological Survey, unpub. data, 1987). Molecular- diffusive flux of arsenic from reducing fine-grained sediments (J 39d) was calculated from the gradient between C A S in pore water at 4 cm and the overlying surface water by using Fick’s first law (Bemer, 1980) and evaluating the following equation over 4 cm: = 92395?” sed (1)2 32 (II—8) where D = self-diffusion coefficient, in square centimeters per second; 0 = sediment tortuosity, unitless; 4) = porosity, unitless; C/fsw = concentrations of dissolved arsenic in pore water, in micromoles per liter; and Z = depth in sediment, in centimeters. A J 58d of 1.65 umol/mZ/d is calculated using D for arsen— ate of 9.05><10_6 cmZ/s at 25°C (Li and Gregory, 1974) and fine-grained sediment values for (1) and 9 of 0.8 and 2.0, respec— tively. This flux of arsenic is an upper limit because removal of arsenic by adsorption near the sediment/water interface is not accounted for (Aggett and O’Brien, 1985; Peterson and Carpenter, 1986). Because the areal distribution of reducing sediments is unknown, quantifying the molecular-diffusive flux of arsenic in the length of the stream reach in the mass balance is difficult. An upper limit of the flux, however, can be calculated by converting the J sad to micromoles per liter per day by multiply- ing by the effective contact area of surface water with the streambed, Ac. AC is estimated from the overall mean-flow velocity for the 2.8—km reach of 0.32 m/s. First, a cross- sectional area of flow (A) of 1.51 m2 is calculated by dividing the discharge (0.48 m3/s) by the velocity (J ackman and others, 1984/1985). In 1 second, therefore, a volume of 0.48 m3 of water overlies 0.32 m of streambed. A vertically well-mixed stream that has a rectangular stream-channel geometry of 0.3 m in depth and 5.0 m in width for a cross—sectional area (A) of 1.5 m2 is assumed. For these cross-sectional dimensions and a 0.32—m length traversed in 1 second, a 0.48-cubic meter volume is in contact with 1.8 m2 of streambed for a contact area (AC) of 270 L/mz. Multiplying Jsed by Ac (equation 11—4) results in a volumetric input of 0.006 umol/L/d. This value is an upper limit because the assumption is made that the streambed along the reach is fine—grained reducing sediments. The streambed, how- ever, typically consists of sand, gravel, cobbles, and limited areas of fine-grained (<63 tun) sediments that would reduce the magnitude of this input of C As from the sediments by an unknown amount. The sediment—diffusive flux is assumed to be constant during the diurnal period and accounts for, at most, 3 percent of the measured diurnal fluctuation in C As of 0.21 umol/L/d. Algal Uptake of Arsenate from Surface Water The similarity between the arsenate and phosphate mole- cules results in the uptake of arsenate by algae during photo- synthesis (Apte and others, 1986; Andreae, 1978). Kuwabara and others (1990) observed preferential uptake of phosphate over arsenate by several orders of magnitude for algal cultures that were isolated from contaminated reaches of Whitewood Creek and that included site A. Despite this preferential uptake of phosphate, elevated arsenic content in algae at site A has been Processes that Contribute to the Diurnal Cycle of Dissolved Arsenic in Surface Water 43 measured in the algal species, Cladophora and Ulothrix, that dominated the plant biomass. The rate of algal arsenic uptake from surface water (FOR in eq. 11—4) is calculated by assuming that arsenic is incorporated during carbon fixation at the arsenic— to-carbon molar ratio, R, observed in algae collected at this site 1 week before the diurnal sampling (R=1.8><10‘5; J.S. Kuwabara, hydrologist, US. Geological Survey, oral commun., 1987). Because nitrate uptake, which is light dependent (Triska and others, 1989), results in a concomitant increase in alkalinity (Stumm and Morgan, 1981), the observed diurnal fluctuation in alkalinity is attributed to algal productivity. The carbon—fixation rate, F C, was estimated from the change in alkalinity (Stumm and Morgan, 1981) and the Redfield nitrogen-to-carbon-molar ratio. Multiplying the resulting F c of 1.8><10'3 moles of carbon per liter per day by R yields an arsenic uptake rate by algae of 0.033 umol/L/d. This estimate may be low because alkalinity, like sulfate, is about two times greater in ground water than in surface water (table II—2). Because ground-water inflow may fluctuate diumally, the surface-water alkalinity should increase at night and diminish the measured surface-water alkalinity fluc- tuation. An increase in alkalinity on the order of the 7-percent increase in sulfate measured from day to night, however, would not change the magnitude of algal arsenic uptake significantly. If the uptake of arsenic is directly correlated with phosphate uptake, which may be light enhanced (Wetzel, 1975; Stewart, 1974), the phase of algal arsenic uptake would diminish the measured diur- nal fluctuation in C As by 0.033 umol/L/d. Potential for Desorption of Arsenic from Suspended Sediments and Bed Sediments The contribution to C As by adsorption and desorption on suspended sediments in a cycle of pH could not be evaluated from equation II—5 because the pH dependence on nonlinear- adsorption coefficients and the appropriate adsorptiomrate con— stant are not well known. Instead, equation 11—5 is simplified as follows to estimate the potential desorption of arsenate from suspended sediments by assuming that the fraction of SAS avail- able to desorb is equivalent to the fraction of exchangeable arsenic associated with the seep precipitate as defined by isoto- pic exchange (5 percent). Desorption of arsenic is assumed to reach equilibrium rapidly. Aer. = 5.125,... (II—9) where ACffS = change in CAS due to desorption from suspended sediments, in micromoles per liter per day; and fd = fraction of adsorbed arsenic available for desorption. Suspended sediments (55:22 mg/L) collected at this site in July 1987 had a concentration of arsenic of 5.2><10’6 mol/g (S AS) and a concentration of iron of 5.1><10‘4 mol/g. These values were determined after digestion of suspended—sediment samples in concentrated nitric acid (US. Geological Survey, unpub. data, 1987). Assuming these values for SS and SAS were the same in August 1987 and that a maximum of 5 per- cent of the arsenic can desorb (fd=0.05), a maximum AC/fi of 0.006 umol/L/d would result from desorption from suspended sediments, PS, during a pH cycle. This estimate does not account for desorption of arsenic in or on streambed sediments. The contribution to the diurnal fluctuation of C A s by adsorption and desorption from bed sediments in contact with streamflow, BS, cannot be estimated independently because the depth of sediment, 2],, in equation 11—6 is not known. The difference in the measured diurnal fluctuation in CA5 of 0.21 umol/L/d and the sum of the other sources and sinks, however, indicate that by mass balance, BS must account for 85 percent of the diurnal CA5 cycle (table II—3). Sand-size and coarser grained bed sediments have Visible iron-oxide coatings and contain high arsenic content (2.1 to 20 umol/g; Fuller and others, 1987). The arsenic-to-iron molar ratios both of fine- and coarse-grained sediments are similar to the arsenic—to-iron ratio of ferrihydrite precipitates collected at seep outflows. The continuous inflow of ground water to the stream results in a large reservoir of sorbed arsenic in the bed sediments because more than 90 percent of the dissolved—arsenic load is adsorbed to ferrihydrite that is formed during precipitation of iron in ground-water inflows. The ferrihydrite precipitates form coatings on the grains of the bed sediments. An additional source of adsorbed arsenic to the streambed occurs during high stream stages by the erosion of oxidized flood-plain deposits in which most of the arsenic is associated with iron-oxide coatings (Horowitz and others, 1988). In order to estimate the contribution of the bed sediments to the diurnal cycle of C A S, the mass of sediment in contact with the streamflow must be determined. Bencala and others (1990) observed that a significant flow of streamflow moves through the streambeds of high-gradient gravel-bed and cobble-bed streams and that an exchange of water occurs between the sur- face flow and the flow within the streambed. The areas of flow through the streambed that exchange water with streamflow are transient-storage zones. The cross-sectional area of flow through the transient-storage zones can be estimated by model- ing the increase of conservative-tracer concentration with time Table "—3. Estimated sources and sinks of dissolved arsenic (CA5) in Whitewood Creek, South Dakota. Dissolved arsenic, in micromoles per liter per day Ground-water inflow, ACgff .............................. 0.05 Sediment diffusive flux, JsedAc .......................... 0.006 Algal uptake of As, FCR ..................................... —0.033 Desorption from suspended sediments, PS ........ 0.006 Sum of inputs and loss ....................................... 0.03 Observed diurnal fluctuation in CA5 .................. 0.21 Desorption from bed sediment, BX, by difference .................................................. 0.18 44 Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota after start of an injection and the decrease to background levels at the end of the injection period (Bencala and others, 1990). This flow includes a zone in which surface water exchanges rapidly with streambed flow. Whitewood Creek is primarily a gravel-bed stream (Cherry and others, 1986), although significant amounts of sand and fines were measured within the streambed sediments. Quantifying the cross-sectional area of transient-storage zones was not possible with the present data set because bromide was not increased suf- ficiently above background levels to provide adequate resolution of the slow decrease to background levels following the rapid decrease in bromide after the injection ended. The absence of a resolvable slow decrease in the post-injection bromide and the rapid initial decrease in bromide (fig. II—6) suggest that the cross- sectional area of the transient-storage zones is limited. Triska and others (1989) used the ratio of time for a conservative tracer to reach 50 percent of the plateau to the first arrival time of the tracer above background concentration as an indicator of the hydrologic retention of solutes in transient-storage zones within the streambed. A ratio of 1.06 for the 2.8-km injection reach indi- cates that these zones make up a small but significant fraction of the cross-sectional area of flow in Whitewood Creek. Because the injection data do not allow determination of the cross-sectional area of the transient-storage zones or the mass of sediment in contact with this component of streamflow, desorption from a l-cm-thick zone of bed sediment is calculated to illustrate the potential contribution of the bed sediments to the diurnal cycle in C AS. The assumption was made that a 1-cm- thick zone is in chemical equilibrium with streamflow and that water in this zone exchanges rapidly with streamflow. The amount of arsenic that can desorb from a l-cm-thick zone of streambed sediments during an increase in pH is calculated by simplifying equation 11—6 to AC3“; = deAs(l _¢)prbsAc (H‘IO) where ACAQ = change in C AS due to desorption from bed sedi- ments, in micromoles per liter per day; and fd fraction of B As that desorbs because of increases in pH. For a pH increase from 8.15 to 8.35, fd is about 0.015 based on the arsenic adsorption and isotopic-exchange experiments (figs. II—9 and II—10). The arsenic concentration (64:10 umol/g) of fine sand (63 to 210 um; US. Geological Survey, unpub. data, 1987) was used to represent arsenate sorbed onto ferrihydrite coatings of sediments, B AS. Using a value of 0.5 for fine-sand bed-sediment porosity, (1), and a density, pl), of 2.5 g/cm3, a ACl’fi of 4 umol/L was calculated from equa- tion II—lO. Although this estimate is much greater than the mea- sured fluctuation in C A s of 0.21 umol/L/d, the result suggests that the abundant sorbed arsenate in the bed sediments accounts for the cycle in CA5. Additional Controlling Processes Other physical and chemical processes, such as variation in stream discharge and ground-water inflow, also may contrib— ute to fluctuation of C A S. The coincident diurnal variations in sulfate and the injected bromide measured at site A suggest an increase in stream discharge from upstream sources that dilutes both constituents during the day rather than an increase in ground—water inflow along the reach at night. The magnitude of the fluctuation in discharge (7 to 25 percent on the basis of sulfate and bromide, respectively), however, is insufficient to result in the magnitude of the measured diurnal variation in C As and is out of phase with the CA3 cycle. Reductive dissolution of ferrihydrite by photoreduction of iron (McKnight and others, 1988) would result in a release of adsorbed arsenic. The magnitude of this process was not evalu- ated because low ferrous-iron concentrations in surface water suggest that either photoreduced iron undergoes rapid reoxida- tion and precipitation (most arsenic sorbed during coprecipita- tion) or that iron photoreduction is negligible. Solubility controls of CAS are unlikely because few arsenic compounds are stable in well-oxygenated water (Crecelius and others, 1986; Cullen and Reimer, 1989). For example at site A, the log degree of saturation with respect to calcium arsenate [Ca3(AsO4)20 4H20] ranged from —8.8 to —9.4 in the range of measured C As, pH, and concentrations of dis- solved calcium. These saturation indices were calculated using MINEQL (Westall and others, 1976) and a solubility product of 1.26><10_19 (Naumov and others, 1974). Evaluation of aluminum arsenate (AlAsO4) was not possible because concentrations of dissolved aluminum were not determined. If the concentration of dissolved aluminum, however, is assumed to be in equilibrium with amorphous Al(OH)3 using the solubility and complexation constants of Lindsay and Walthall (1989), a log degree of satura- tion for AlAsO4 on the order of —9 is estimated. These calculated saturation indices, which are many orders of magnitude below saturation, agree with calculations by Belzile and Tessier (1990) and indicate a lack of solubility control on CA5. In contrast to Belzile and Tessier (1990), no solubility calculation for ferric arsenate was made because ferric arsenates are unstable with respect to ferrihydrite at a pH greater than 2 (Robins, 1987). Ferric arsenates do not form during coprecipitation at the pH of 8.0 to 8.5 in Whitewood Creek (Waychunas and others, 1993). The absence of other instream processes of sufficient mag- nitude to generate the measured fluctuation of C A s is evidence that the diurnal cycle in CAS at sites A and B are the result of arsenic adsorption and desorption processes on the surfaces of ferrihydrite on and in streambed sediments. The results of the laboratory experiments on the time and pH dependence of arsenate adsorption on synthetic 2-line ferrihydrite are consis- tent with the measured diurnal fluctuations of CA3. Summaly At the time of this study, dissolved arsenic (C A S) in the lower reaches of Whitewood Creek was controlled primarily by adsorption and coprecipitation with ferrihydrite as reducing ground water that contained dissolved arsenic contacted the atmosphere and surface water. Subsequent diurnal variations in CAS resulted from shifts in the adsorption and desorption equi- librium in response to a biologically induced diurnal pH cycle. The primary source and sink of dissolved C AS in the diurnal cycling was arsenic adsorption and desorption by the abundant ferrihydrite in bed sediments. The rate of adsorption and des— orption process caused the C A 5 cycle to lag behind the pH cycle. Thus, the dynamic equilibrium of the chemical processes that control C A s are coupled to biological processes in the stream. This cycle of pH determines the importance of adsorption and desorption in controlling C A 5. Development of an adequate model for arsenic transport in Whitewood Creek will require incorporation of adsorption kinetic terms into an advection- dispersion reaction model (for example, see eq. 11—4). The adsorption control of C A s by ferrihydrite and the large “reser- voir” of adsorbed arsenic in the streambed should act to support C AS even during high stream discharge. Despite the cycle in pH at site C, the absence of a diurnal cycle and the lower levels of C A S are difficult to resolve with the present data set. In this higher gradient reach of the stream, con- taminated flood-plain deposits and bed sediments with arsenic concentrations and molar ratios of arsenic to iron exist that are similar to the lower reaches. Ground—water seeps that supply dissolved arsenic and fresh ferrihydrite precipitates to the stream are not visible. Either the aging of the ferrihydrite or the adsorption of organics on ferrihydrite surfaces may dimin— ish the role of sorption on ferrihydrite in the cycle of C A s by decreasing the availability of sorbed arsenate for desorption and decreasing the number of available sorption sites. Evaluation of the diurnal pH cycles in other surface waters and their effect on the partitioning, cycling, and availability of trace elements and nutrients need to be made in order to ade- quately apply geochemical and biological models. For example, diurnal pH cycles generated by algal photosynthesis may increase availability of nutrients such as phosphate as well as toxic elements such as arsenate, selenium, and trace metals through the shifting of sorption equilibria. Diurnal cycles in orthophosphate concentration observed in Whitewood Creek appeared to lag the pH cycle by about 6 hours. The phase of the fluctuation of dissolved phosphate suggested the light dependence of phosphate uptake. In this study, a phosphate fluctuation of similar magnitude and phase also was measured; however, the data are not reported here because the measured concentrations of phosphate (0.05 to 0.15 umol/L) had an uncertainty of 20 percent or greater. In both studies, the net change in phosphate was more than an order of magnitude lower than the phosphate-uptake rate predicted by the carbon- fixation rate and Redfield ratios. This difference argues that Summary 45 an additional source of dissolved phosphate exists such as phosphate adsorbed on ferrihydrite. The competition of arsen— ate and phosphate for adsorption sites on ferrihydrite, greater uptake rates for phosphate by algae, and the factor of 4 or more higher C A s than phosphate all act to mask the cycle of phosphate that would result from pH fluctuations alone. Diurnal pH fluctuations should have a greater effect on the bioavailability of metals in more acidic systems in which metals are not as strongly adsorbed. The continual disturbance of the sorption equilibria may result in an increase in the resi- dence time of metals in surface water and a decrease in the effective rate of the processes that remove reactive elements from a surface-water system. Selected References Aggett, John, and O’Brien, GA, 1985, Detailed model for the mobility of arsenic in lacustrine sediments based on measure~ ments in Lake Ohakuri: Environmental Science and Technol- ogy, v. 19, no. 3, p. 231—238. Aggett, John, and Roberts, LS, 1986, Insight into the mecha- nism of accumulation of arsenate and phosphate in hydro- lake sediments by measuring the rate of dissolution with ethylenediaminetetracetic acid: Environmental Science and Technology, v. 20, no. 2, p. 183—186. Anderson, M.A., Ferguson, J .F., and Gavis, J., 1976, Arsenate adsorption on amorphous aluminum hydroxide: Journal of Colloid and Interface Science, v. 54, no. 3, p. 391—399. Andreae, M.O., 1978, Distribution and speciation of arsenic in natural waters and some marine algae: Deep-Sea Research, v. 25, no. 4, p. 391—402. Apte, S.C., Howard, A.G., Morris, R.J., and McCartney, M.J., 1986, Arsenic, antimony, and selenium speciation during a spring phytoplankton bloom in a closed experimental ecosystem: Marine Chemistry, v. 20, no. 2, p. 119—130. Barrow, N.J., 1983, A mechanistic model for describing the sorption and desorption of phosphate by soil: Journal of Soil Science, v. 34, no. 4, p. 733—750. Belzile, Nelson, and Tessier, Andre, 1990, Interactions between arsenic and iron oxyhydroxides in lacustrine sediments: Geochimica et Cosmochimica Acta, v. 54, no. 1, p. 103—109. Bencala, K.E., 1983, Simulation of solute transport in a moun— tain pool-and-riffle stream with a kinetic mass-transfer model for sorption: Water Resources Research, v. 19, no. 3, p. 732—738. Bencala, K.E., McKnight, D.M., and Zellweger, G.W., 1987, Evaluation of natural tracers in an acidic and metal-rich stream: Water Resources Research, v. 23, no. 5, p. 827—836. Bencala, K.E., McKnight, D.M., and Zellweger, G.W., 1990, Characterization of transport in an acidic and metal-rich mountain stream based on lithium tracer injection and simu- lations of transient storage: Water Resources Research, v. 26, no. 5, p. 989—1000. 46 Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek. South Dakota Berner, R.A., 1980, Early diagenesis: Princeton, New Jersey, Princeton University Press, 241 p. Bolan, N.S., Barrow, N.J., and Posner, A.M., 1985, Describing the effect of time on sorption of phosphate by iron and aluminum hydroxides: Journal of Soil Science, V. 36, no. 2, p. 187—197. Bottero, J .—Y., Tchoubar, D., Amaud, M., and Quienne, P., 1991, Partial hydrolysis of ferric-nitrate salt—Structural investigation by dynamic light scattering and small angle x-ray scattering: Langmuir, v. 7., no. 7, p. 1365—1369. Carlson, L., and Schwertmann, U., 1981, Natural ferrihydrite in surface deposits from Finland and their association with silica: Geochimica et Cosmochimica Acta, V. 45, no. 3, p. 421—429. Cherry, J.A., Morel, F.M.M., Rouse, J.V., Schnoor, J.L., and Wolman, M.G., 1986, Hydrogeochemistry of sulfide and arsenic-rich tailings and alluvium along Whitewood Creek, South Dakota: Colorado School of Mines Mineral and Energy Resource Series, V. 29, no. 5, p. 1—15. Crecelius, E.A., Bloom, N.S., Cowan, CE, and Jenne, EA, 1986, Speciation of selenium and arsenic in natural waters—v. 2, Arsenic speciation: Electrical Power Research Institute, Inc., Report EA—4621, V. 2., 55 p. Crosby, S.A., Millward, G.E., Butler, E.I., Turner, DR, and Whitfield, M., 1984, Kinetics of phosphate adsorption by iron oxyhydroxides in aqueous systems: Estuarine, Coastal and Shelf Science, V. 19, no. 2, p. 257—270. Cullen, W.R., and Reimer, K.J., 1989, Arsenic speciation in the environment: Chemical Reviews, V. 89, no. 4, p. 713—764. Davis, J .A., and Leckie, J .O., 1980, Surface ionization and complexation at the oxide/water interface 3—Adsorption of anions: Journal of Colloid and Interface Science, V. 74, no. 1, p. 32—43. Davis, J .A., Fuller, CC, and Cook, AD, 1987, A model for trace metal adsorption processes at the calcite surface—Adsorption of Cd 2+ and subsequent solid solution formation: Geochimica et Cosmochimica Acta, V. 51, no. 6, p. 1477—1490. DeVitre, R.R., Belzile, N., and Tessier, A., 1991, Speciation and adsorption of arsenic on diagenetic iron oxyhydroxides: Limnology and Oceanography, V. 36, no. 7, p. 1480—1485. Drever, J .I., 1982, The geochemistry of natural waters: Engle- wood Cliffs, New Jersey, Prentice—Hall, 388 p. Dunn, LG, 1967, Diurnal fluctuations of physiochemical con- ditions in a shallow tropical pond: Limnology and Oceanog- raphy, V. 12, no. 1, p. 151—154. Dzombak, DA, and Morel, F.M.M., 1990, Surface complex- ation modeling—Hydrous ferric oxide: New York, John Wiley, 393 p. Ferguson, J .F., and Gavis, J ., 1972, A review of the arsenic cycle in natural waters: Water Research, V. 6, no. 11, p. 1259—1274. Ficklin, W.H., 1983, Separation of arsenic (III) and arsenic (V) in ground waters by ion—exchange: Talanta, V. 30, no. 5, p. 371—373. Franson, M.A.H., 1985, Standard methods, 16th ed.: Washing- ton, D.C., American Public Health Association, American Water Works Association and Water Pollution Control Fed- eration, 1268 p. Frost, RR, and Griffin, R.A., 1977, Effect of pH on adsorption of arsenic and selenium from landfill leachate by clay miner— als: Soil Science Society of America Journal, V. 41, no. 1, p. 53—57. Fuller, CC, and Davis, J .A., 1989, The influence of coupling of sorption and photosynthetic processes on trace element cycles in natural waters: Nature, V. 340, no. 6228, p. 52—54. Fuller, C.C., Davis, J .A., Claypool-Frey, R.G., 1987, Partition- ing of arsenic by iron oxides in Whitewood Creek, South Dakota [abs.]: American Chemical Society, Division of Environmental Chemistry, Proceedings of 193d National Meeting, v. 27, no. 1, p. 550—551. Fuller, C.C., Davis, J .A., ClaypooleFrey, R.G., 1988a, Arsenic and iron versus filter pore size in Whitewood Creek and the Belle Fourche River, in Goddard, K.E., ed., U.S. Geolog- ical Survey applied research studies of the Cheyenne River system, South Dakota—Description and collation of data, water years 1985—1986: U.S. Geological Survey Open—File Report 88—484, p. 118—121. Fuller, C.C., Davis, J .A., Claypool-Frey, R.G., 1988b, Ground- water geochemistry along Whitewood Creek, in Goddard, K.E., ed., U.S. Geological Survey applied research studies of the Cheyenne River system, South Dakota—Description and collation of data, water years 1985—1986: U.S. Geological Survey Open-File Report 88—484, p. 122—124. Fuller, C.C., Davis, J .A., Zellweger, G.W., and Goddard, K.E., 1988, Coupled chemical, biological and physical processes in Whitewood Creek, South Dakota—Evaluation of the con- trols of dissolved arsenic: U.S. Geological Survey Water- Resources Investigations Report 88—4220, p. 235—246. Fuller, C.C., Goddard, K.E., and Davis, JD, 1989, Field investigations of the effect of stream pH on dissolved arsenic concentration, in Goddard, K.E., ed., U.S. Geological Survey applied research studies of the Cheyenne River sys- tem, South Dakota—Description and collation of data, water years 1987—1988: U.S. Geological Survey Open-File Report 89—580, p. 114—126. Fuller, C.C., Davis, J .A., and Waychunas, GA, 1993, Surface chemistry of ferrihydrite—II, Kinetics of arsenate adsorption and coprecipitation: Geochimica et Cosmochimica Acta, v. 57, no. 10, p. 2271—2282. Goddard, K.E., 1987, Composition, distribution, and hydro— logic effects of contaminated sediments resulting from the discharge of gold mining wastes to Whitewood Creek at Lead and Deadwood, South Dakota: U.S. Geological Survey Open-File Report 87—405 1, 76 p. Goddard, K.E., Fuller, CC, and Davis, J .A., 1988, Seasonal and diurnal fluctuations of dissolved arsenic in Whitewood Creek, South Dakota [abs.]: American Geophysical Union, EOS, V. 69, no. 15, p. 368. Goldberg, Sabine, 1986, Chemical modeling of arsenate adsorption on aluminum and iron oxide minerals: Soil Sci- ence Society of America Journal, v. 50, no. 5, p. 1154—1160. Hingston, F.J., 1981, A review of anion adsorption, in Ander- son, M.A., and Rubin, A.J., eds., Adsorption of inorganics at solid—liquid interfaces: Ann Arbor, Michigan, Ann Arbor Science Publishing Co., 357 p. Holm, T.R., Anderson, M.A., Stanforth, RR, and Iverson, D.G., 1980, The influence of adsorption on the rates of microbial degradation of arsenic species in sediments: Lim- nology and Oceanography, v. 25, no. 1, p. 23—30. Horowitz, A.J., Elrick, K.A., and Cook, RB, 1988, Source and transport of arsenic in the Whitewood Creek-Belle Fourche— Cheyenne River-Lake Oahe system, South Dakota: U.S. Geological Survey Water—Resources Investigations Report 88—4220, p. 223—233. Jaanasch, H.W., Honeyman, B.D., Balistieri, LS, and Murray, J .W., 1988, Kinetics of trace element uptake by marine particles: Geochimica et Cosmochimica Acta, v. 52, no. 2, p. 567—577. Jackman, A.P., Walters, R.A., and Kennedy, V.C., 1984/1985, Transport and concentration controls for chloride, strontium, potassium and lead in Uvas Creek, a small cobble-bed stream in Santa Clara County, California, U.S.A.——2, Mathematical modeling: Journal of Hydrology, v. 75, no. 1, p. 111—141. Kennedy, V.C., Jackman, A.P., Zand, S.M., and Zellweger, G.W., 1984/1985, Transport and concentration controls for chloride, strontium, potassium and lead in Uvas Creek, a small cobble bed stream in Santa Clara County, California, U.S.A.—1., Conceptual model: Journal of Hydrology, v.75, no. 1, p. 67—110. Kuwabara, J .S., 1992, Associations between benthic flora and diel changes in dissolved arsenic, phosphorus, and related physico-chemical parameters: Journal of North American Benthological Society, v. 11, no. 2, p. 218—228. Kuwabara, J .S., Chang, C.C.Y., and Pasilis, S.P., 1990, Effects of benthic flora on arsenic transport: Journal of Environmen— tal Engineering, v. 116, no. 2, p. 394—409. Leckie, J.O., Benjamin, M.M., Hayes, K.F., Kaufmann, G., and Altmann, S., 1980, Adsorption/coprecipitation of trace elements from water with iron oxhydroxide: Palo Alto, California, Electric Power Research Institute, EPRI Report CS—1513, 254 p. Li, Y-H. and Gregory, S., 1974, Diffusion of ions in sea water and deep-sea sediments: Geochimica et Cosmochimica Acta, v. 38, no. 6, p. 703—714. Lindsay, W.L., and Walthall, P.M., 1989, The solubility of alu- minum in soils, in Sposito, Garrison, ed., The Environmental Chemistry of Aluminum: Boca Raton, Florida, CRC Press, 317 p. Lions, L.W., Altmann, RS, and Leckie, J.O., 1982, Trace— metal adsorption characteristics of estuarine particulate matter—Evaluation of contributions of Fe/Mn oxide and organic surface coatings: Environmental Science and Tech- nology, v. 16, no. 10, p. 660—665. Selected References 47 Marron, DC, 1988, Trends in arsenic concentrations and grain-size distribution of metal-contaminated overbank sediments along the Belle Fourche River downstream from Whitewood Creek, South Dakota: U.S. Geological Survey Water—Resources Investigations Report 88—4220, p. 211-216. McKallip, T.E., Goddard, K.E., and Horowitz, A.J., 1988, Arsenic in the alluvial sediments of Whitewood Creek and the Belle Fourche and Cheyenne Rivers in western South Dakota: U.S. Geological Survey Water-Resources Investiga- tions Report 88—4220, p. 203—209. McKnight, D.M., Kimball, B.A., and Bencala, K.E., 1988, Iron photoreduction and oxidation in an acidic mountain stream: Science, v. 240, no. 4852, p. 637—640. Morel, F.M.M., 1983, Principles of aquatic chemistry: New York, John Wiley and Sons, 446 p. Naumov, G.B., Ryzhenko, B.N., and Khodakovsky, LL, 1974, Handbook of thermodynamic data, translated by Soleimani, G.J., U.S. Geological Survey, 1974: U.S. Department of Commerce National Technical Information Service Publica— tion PB 226 722, 328 p. Peterson, M.L., and Carpenter, R., 1986, Arsenic distribution in pore waters and sediments of Puget Sound, Lake Washing- ton, the Washington coast and Saanich Inlet, B.C.: Geochim- ica et Cosmochimica Acta, V. 50, no. 3, p. 353—369. Pierce, M.L., and Moore, CB, 1982, Adsorption of arsenite and arsenate on amorphous iron hydroxide: Water Research, v. 16, no. 8, p. 1247—1253. Plummet, L.N., and Busenberg, E., 1982, The solubilities of calcite, aragonite, and vaterite in COZ-HZO solutions between 0 and 90°C, and an evaluation of the aqueous model for the system CaCO3—C02-HZO: Geochimica et Cosmo— chimica Acta, v. 46, no. 6, p. 1011—1040. Rantz, SE, and Eakin, TE, 1972, A summary of methods for the collection and analysis of basic hydrologic data for arid regions: US. Geological Survey Open-File Report 72—305, 280 p. Robins, R.G., 1987, Solubility and stability of scorodite, FeAsO4-2HZO—Discussion: American Mineralogist, v. 72, no. 8, p. 842—844. Rubin, Jacob, 1983, Transport of reacting solutes in porous media—Relation between mathematical modeling nature of problem formulation and chemical nature of reactions: American Geophysical Union, Water Resources Research, v. 19, no. 5, p. 1231—1252. Ryan, T.A., Jr., Joiner, B.L., and Ryan, BE, 1985, Minitab handbook, 2d ed: Boston, Massachusetts, Duxbury Press, 154 p. Ryden, J .C., Syers, J .K., and Tillman, R.W., 1987, Inorganic anion sorption and interactions with phosphate by hydrous ferric oxide gel: Journal of Soil Science, v. 38, no. 2, p. 211—217. 48 Evaluation of the Processes Controlling Dissolved Arsenic in Whitewood Creek, South Dakota Stewart, W.D.P., 1974, Algal physiology and biochemistry: Berkeley, California, University of California Press, 832 p. Stookey, LL, 1970, Ferrozine—A new spectrophotometric reagent for iron: Analytical Chemistry, v. 42, no. 7, p. 779—78 1 . Stumm, W., and Morgan, J .J ., 1981, Aquatic chemistry: New York, Wiley Interscience, 780 p. Tessier, A., Rapin, F., and Carigan, R., 1985, Trace metals in oxic lake sediments—Possible adsorption onto iron oxyhy- droxides: Geochimica et Cosmochimica Acta, v. 49, no. 1, p. 183—194. Triska, F.J., Kennedy, V.C., Avanzino, R.J., Zellweger, G.W., and Bencala, KB, 1989, Retention and transport of nutrients in a third—order stream-channel processes: Ecology, V. 70, no. 6, p. 1877—1892. Turk, J .T., 1988, Natural variance in pH as a complication in detecting acidification of lakes: Water, Air, and Soil Pollu— tion, V. 37, no. 2, p. 171—176. Waychunas, G.A., Rea, B.A., Fuller, CC, and Davis, J .A., 1993, Surface chemistry of ferrihydrite——I EXAFS studies of the geometry of coprecipitated and adsorbed arsenate: Geochim- ica et Cosmochimica Acta, v. 57, no. 10, p. 2251—2269. Westall, J.C., Zachary, J.L., and Morel, F.M.M., 1976, MINEQL—A computer program for the calculation of chem— ical equilibrium composition of aqueous systems: Cam— bridge, Massachusetts, Massachusetts Institute of Technol- ogy, Technical Note 18, 91 p. Wetzel, KG, 1975, Limnology: Philadelphia, Pennsylvania, W.B. Saunders and Co., 743 p. Willett, I.R., Chartres, C.J., Nguyen, T.T., 1988, Migration of phosphate into aggregated particles of ferrihydrite: Journal of Soil Science, v. 39, no. 2, p. 275—282. Zellweger, G.W., Avanzino, R.J., and Bencala, KB, 1989, Comparison of tracer—dilution discharge measurements in a small gravel-bed stream, Little Lost Man Creek, California: US. Geological Survey Water-Resources Investigations Report 89—4150, 20 p. QB 75 P6 no . cart 7-DAYS 1 6 8 2- for a changing world Role of Limnological Processes in Fate and Transport of Nitrogen and Phosphorus Loads t Delivered Into Coeur d'Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana Professional Paper 1682 National Water-Quality Assessment Program US Department of the Interior US. Geological Survey Cover photo: Rainy day at Lake Pend Oreille, Idaho, 1989. Turbid snowmelt plume from Clark Fork overflowing clear, dark lake water in vicinity of Warren Island/Hope Point just downstream from Clark Fork delta. View is to the northwest. Photo reproduced with permission from Michael A. Beckwith, May 1989. Role of Limnological Processes in Fate and Transport of Nitrogen and Phosphorus Loads Delivered Into Coeur d’AIene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana By Paul F. Woods U.S. GEOLOGICAL SURVEY PROFESSIONAL PAPER 1682 NATIONAL WATER-QUALITY ASSESSMENT PROGRAM " ““7. I‘Lri-IPIL/‘x ._ 5 um“ o a 2114 ' W LIP-“ARV I . ‘ZhSI-‘VAKITEC‘ILIF 2004 EARTH SCIENCES & MAP LIBRARY UNIV. OF CALIF. BERKELEY, CA US. DEPOSITORY US. DEPARTMENT OF THE INTERIOR GALE A. NORTON, Secretary U.S. GEOLOGICAL SURVEY Charles G. Groat, Director Any use of firm, trade, and brand names in this report is for identification purposes only and does not constitute endorsement by the US. Government. Library of Congress Cataloging in Publication Data Woods, P.F. Role of limnological processes in fate and transport of nitrogen and phosphorus loads delivered into Coeur d’AIene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana / by Paul F. Woods p. cm. -- (US. Geological Survey professional paper; 1682) Includes bibliographical references (p. ). ISBN 0-607-95562-7 (alk. paper) 1. Limnology--Idaho--Coeur d’AIene Lake. 2. Nitrogen-Environmental aspects--Idaho--Coeur d’AIene Lake. 3. Phosphorus--Environmental aspects--Idaho--Coeur d’AIene Lake. 4. Limnology--Idaho--Pend Oreille, Lake. 5. Nitrogen-Environmental aspects--|daho--Pend Oreille, Lake. 6. Phosphorus--Environmental aspects--Idaho--Pend Oreille, Lake. 7. Limnology--Montana--Flathead Lake. 8. Nitrogen-Environmental aspects--Montana--Flathead Lake. 9. Phosphorus--Environmental aspects--Montana--Fiathead Lake. I. Title. II, Series QH105.I2W66 2004 551 .48’2--d022 2003071042 For sale by US. Geological Survey Information Services Box 25286, Federal Center Denver, CO 80225—0286 FOREWORD The US. Geological Survey (USGS) is committed to serve the Nation with accurate and timely scien- tific information that helps enhance and protect the overall quality of life, and facilitates effective manage- ment of water, biological, energy, and mineral resources (http:/lwww.usgs.govl). Information on the quality of the Nation’s water resources is of critical interest to the USGS because it is so integrally linked to the long-term availability of water that is clean and safe for drinking and recreation and that is suitable for industry, irrigation, and habitat for fish and wildlife. Escalating population growth and increasing demands for the multiple water uses make water availability, now measured in terms of quantity and quality, even more critical to the long-term sustainability of our communities and ecosystems. The USGS implemented the National Water-Quality Assessment (NAWQA) Program to support national, regional, and local information needs and decisions related to water-quality management and pol— icy (http://water.usgs.gov/nawqa/). Shaped by and coordinated with ongoing efforts of other Federal, State, and local agencies, the NAWQA Program is designed to answer: What is the condition of our Nation’s streams and ground water? How are the conditions changing over time? How do natural features and human activities affect the quality of streams and ground water, and where are those effects most pro— nounced? By combining information on water chemistry, physical characteristics, stream habitat, and aquatic life, the NAWQA Program aims to provide science-based insights for current and emerging water issues and priorities. NAWQA results can contribute to informed decisions that result in practical and effective water-resource management and strategies that protect and restore water quality. Since 1991, the NAWQA Program has implemented interdisciplinary assessments in more than 50 of the Nation’s most important river basins and aquifers, referred to as Study Units (http://water.usgs.gov/nawqalnawqamap.htmI). Collectively, these Study Units account for more than 60 percent of the overall water use and population served by public water supply, and are represen- tative of the Nation’s major hydrologic landscapes, priority ecological resources, and agricultural, urban, and natural sources of contamination. Each assessment is guided by a nationally consistent study design and methods of sampling and analysis. The assessments thereby build local knowledge about water-quality issues and trends in a particular stream or aquifer while providing an understanding of how and why water quality varies regionally and nationally. The consistent, multi-scale approach helps to determine if certain types of water-quality issues are isolated or pervasive, and allows direct comparisons of how human activities and natural processes affect water quality and ecological health in the Nation’s diverse geographic and environ- mental settings. Comprehensive assessments on pesticides, nutrients, volatile organic compounds, trace metals, and aquatic ecology are developed at the national scale through comparative analysis of the Study- Unit findings (http://water.usgs.gov/nawqa/natsyn.htmI). The USGS places high value on the communication and dissemination of credible, timely, and rele- vant science so that the most recent and available knowledge about water resources can be applied in man- agement and policy decisions. We hope this NAWQA publication will provide you the needed insights and information to meet your needs, and thereby foster increased awareness and involvement in the protection and restoration of our Nation’s waters. The NAWQA Program recognizes that a national assessment by a single program cannot address all water-resource issues of interest. External coordination at all levels is critical for a fully integrated under- standing of watersheds and for cost-effective management, regulation, and conservation of our Nation’s water resources. The Program, therefore, depends extensively on the advice, cooperation, and information from other Federal, State, interstate, Tribal, and local agencies, non-government organizations, industry, academia, and other stakeholder groups. The assistance and suggestions of all are greatly appreciated. Robert M. Hirsch Associate Director for Water Foreword iii iv Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake CONTENTS Foreword ................................................................................ iii Abstract ................................................................................. 1 Introduction .............................................................................. 2 Overview of National Water—Quality Assessment Program ...................................... 2 Overview of Northern Rockies Intermontane Basins study area .................................. 3 Purpose and scope ...................................................................... 6 Description of drainage basins ............................................................... 6 Coeur d’Alene Lake .................................................................... 6 Flathead Lake ......................................................................... 7 Lake Pend Oreille ...................................................................... 8 Evaluation approach ....................................................................... 9 Nutrient loads ............................................................................ 9 Overview ............................................................................. 9 Coeur d’Alene Lake .................................................................... 10 Flathead Lake ......................................................................... 11 Lake Pend Oreille ...................................................................... 12 Among—lake comparisons ................................................................ 13 Physical limnology ........................................................................ 14 Overview ............................................................................. 14 Among—lake comparisons ................................................................ 16 Coeur d’Alene Lake .................................................................... 17 Flathead Lake ......................................................................... 23 Lake Pend Oreille ...................................................................... 23 Chemical and biological limnology ........................................................... 25 Overview ............................................................................. 25 Trophic state .......................................................................... 26 Hypolimnetic nutrient storage ............................................................ 27 Nutrient partitioning .................................................................... 28 Epilimnion and hypolimnion .......................................................... 28 Inflow loads and outflow loads ........................................................ 30 Fate and transport of nutrient loads ............................................................ 37 Overview .......................................................................... 37 Coeur d’Alene Lake .................................................................. 38 Flathead Lake ....................................................................... 39 Lake Pend Oreille ................................................................... 40 Summary and conclusions ................................................................... 41 References cited .......................................................................... 42 FIGURES 1—4. Maps showing: 1. Location of the Northern Rockies Intermontane Basins study area, selected dams, selected US. Geological Survey gaging stations, Indian Reservations, and Glacier National Park, Montana, Idaho, and Washington .................................................. 4 2. Bathymetry and locations of selected limnological stations, Coeur d’Alene Lake, Idaho ....... 18 3. Bathymetry and locations of selected limnological stations, Flathead Lake, Montana .......... 19 4. Bathymetry and locations of selected limnological stations, Lake Pend Oreille, Idaho ......... 20 Contents v 5—6. 7—11. Graphs showing: 5. Annual mean percentage composition of bioavailable and particulate nitrogen and phos- phorus concentrations, in relation to total nitrogen and phosphorus concentrations, respectively, within the epilimnion and hypolimnion at the deepest limnological stations, Coeur d’Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana ......................... 6. Annual mean percentage composition of bioavailable and particulate nitrogen and phos— phorus concentrations, in relation to total nitrogen and phosphorus concentrations, respectively, for inflows and outflows, Coeur d’Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana ...................................................... Diagrams showing: 7. Relation between inputs and outputs of total nitrogen and phosphorus and nutrient partitioning of input and output loads, Coeur d’Alene Lake, Idaho, 1991 calendar year ........ 8. Relation between inputs and outputs of total nitrogen and phosphorus and nutrient partitioning of input and output loads, Coeur d’Alene Lake, Idaho, 1992 calendar year ........ 9. Relation between inputs and outputs of total nitrogen and phosphorus and nutrient partitioning of input and output loads, Flathead Lake, Montana, 1978—82 ................... 10. Relation between inputs and outputs of total nitrogen and phosphorus and nutrient partitioning of input and output loads, Lake Pend Oreille, Idaho, 1989 water year ............ 11. Relation between inputs and outputs of total nitrogen and phosphorus and nutrient partitioning of input and output loads, Lake Pend Oreille, Idaho, 1990 water year ............ TABLES 1. 2. Annual input, output, and retained loads of total nitrogen and phosphorus, Coeur d’Alene Lake, Idaho, calendar years 1991 and 1992 .............................................. Mean annual input, output, and retained loads of total nitrogen and phosphorus, Flathead Lake, Montana, 1978—82 ............................................................ Annual input, output, and retained loads of total nitrogen and phosphorus, Lake Pend Oreille, Idaho, water years 1989 and 1990 ..................................................... Average of retained load/input load for total nitrogen and phosphorus, Coeur d’Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana ..................................... Physical limnological characteristics, Coeur d’Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana ............................................................. Trophic—state classification based on open-boundary values for four limnological variables ........ Trophic state of Coeur d’Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana, based on annual mean values for four limnological variables ................................ . Annual mean concentrations of total nitrogen and phosphorus within the epilimnion and hypo- limnion at the deepest limnological stations, Coeur d’Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana .......................................................... . Summary of retained load/input load for total nitrogen and phosphoms and selected physical limnological characteristics, Coeur d’Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana ......................................................................... vi Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake 29 31 32 33 34 35 36 11 12 13 14 16 26 27 28 37 CONVERSION FACTORS, VERTICAL DATUM, AND ABBREVIATED WATER-QUALITY UNITS Multiply By To obtain centimeter (cm) 0.3937 inch (in.) cubic kilometer (km3) 0.2399 cubic mile (mi3) cubic meter per second (m3/s) 35.31 cubic foot per second (ft3/s) kilogram (kg) 2.205 pound (lb) kilometer (km) 0.6214 mile (mi) meter (m) 3 .281 foot (ft) meter per year (m/yr) 3.281 foot per year (ft/yr) square kilometer (ka) 0.3861 square mile (miz) To convert °C (degrees Celsius) to °F (degrees Farenheit), use the following equation: °F 2 (1.8 °C) + (32) Sea level: In this report, “sea level” refers to the National Geodetic Vertical Datum of 1929 (NGVD of 1929)—a geodetic datum derived from a general adjustment of the first-order level nets of the United States and Canada, for- merly called Sea Level Datum of 1929. Abbreviated water-quality units ug/L microgram per liter um micrometer mg/L milligram per liter mg/kg milligram per kilogram Contents vii viii Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake Role of Limnological Processes in Fate and Transport of Nitrogen and Phosphorus Loads Delivered Into Coeur d’Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana By Paul F. Woods Abstract The fate and transport of nutrient loads, fol- lowing their delivery into a lake, result from the integration of all hydrologic, physical, chemical, and biological processes that operate within that lake. If only empirical relations such as areal water load, hydraulic residence time, trap efficiency, and mean depth are used to estimate nutrient fate and transport without consideration of the other com— plex limnological processes involved, the magni— tude of nutrient retention by the lake may be incor— rectly predicted. The nutrient retentions empirical- ly predicted for northern Idaho’s Coeur d’Alene Lake and Lake Pend Oreille and northwestern Montana’s Flathead Lake did not agree with the nutrient retentions measured for the three lakes on the basis of quantitative differences between input and output loads. The three lakes are within the 81,600—square— kilometer Northern Rockies Intermontane Basins study area, which was added in 1996 to the US. Geological Survey’s National Water—Quality Assessment Program. The lakes were selected for evaluation of nutrient fate and transport because they are affected by nutrient enrichment, their input and output nutrient loads had been quanti- fied, and their limnological characteristics had been extensively studied. The three lakes represent a broad range in physical limnological characteristics, which can be expected to influence the fate and transport of nutrients within the lakes. Lake volumes range from 2.8 (Coeur d’Alene Lake) to 53.9 (Lake Pend Oreille) cubic kilometers. Lake Pend Oreille is the deepest (357 meters), and Coeur d’Alene Lake is the shallowest (63.7 meters). Coeur d’Alene Lake has the shortest hydraulic residence time (lake vol- ume divided by mean annual outflow volume), 0.50 year; the other two lakes have longer hydrau— lic residence times—2.2 years for Flathead Lake and 2.4 years for Lake Pend Oreille. The annual loads of nutrients delivered into the three lakes from drainage basin and atmo— spheric sources varied widely; total nitrogen loads ranged from 945,000 to 5,670,000 kilograms, and total phosphorus loads ranged from 43,600 to 408,000 kilograms. Lake Pend Oreille received and discharged the largest loads of both nutrients; Coeur d’Alene Lake received and discharged the smallest loads. Coeur d’Alene Lake and Lake Pend Oreille retained about 15 percent of the total nitrogen loads they received; Flathead Lake re— tained about one-third of the nitrogen load it re— ceived. The retention of total phosphorus was much different for Coeur d’Alene and Flathead Lakes; respectively, they retained about one—half and three—fourths of the phosphorus loads they received. Lake Pend Oreille retained less than about 17 percent of the total phosphorus load it received. If only morphometric values such as mean depth and maximum depth were considered, the lake with the largest values, Lake Pend Oreille, would be expected to retain the largest percentage of total nitrogen and phosphorus loads received. The unexpected small retention of both nutrients, particularly phosphorus, by Lake Pend Oreille indicated that limnological processes other than just physical sedimentation were affecting the fate Abstract 1 and transport of nutrient loads delivered to that lake. Nutrient retention, or the lack thereof, was strongly related to circulation processes, in a spa— tial and temporal context, in the three lakes. The inflow plumes from their primary tributaries were routed primarily as overflow, especially during snowmelt runoff, when each lake received most of its annual loads of total nitrogen and phosphorus. The long, narrow shape of Coeur d’Alene Lake, along with its short hydraulic residence time, was not empirically predictive of nutrient retention. In contrast, the other two lakes were much deeper, had much larger and wider basins, and had hy- draulic residence times longer than 2 years; all of which would predict substantial retention of nutri- ents. Inflow—plume routing in Flathead Lake dur- ing snowmelt runoff tended to follow a lengthy and somewhat circular path, both favorable to retention. Inflow—plume routing of snowmelt run- off in Lake Pend Oreille was directed primarily into the shallow northern basin that leads to the lake’s outlet, not into the deep southern basin with its large retentive capacity for nutrients. The for- mation of a thermal bar along the approximate boundary between the shallow and deep basins of Lake Pend Oreille was postulated as a major cause of its small retention of nutrients. The influence of chemical and biological pro— cesses, in addition to physical processes, was evi— dent in each of the lakes on the basis of differences in partitioning of nutrients between the bioavail— able (dissolved) and particulate fractions in input and output loads. Input and output loads of total nitrogen in Coeur d’Alene Lake differed little in their proportions of bioavailable and particulate nitrogen. Of the total nitrogen input to Flathead Lake, 42 percent was bioavailable; that proportion had declined to 23 percent at the lake’s outlet. The proportion of bioavailable nitrogen in Lake Pend Oreille was comparable to that in Flathead Lake, but the average proportion declined from 36 per— cent at the lake’s inlet to 24 percent at the outlet. The shifts in partitioning of nitrogen were attribut— able in part to phytoplankton assimilation of bio— available nitrogen in each of the lakes. In Coeur d’Alene Lake, the addition of bioavailable nitro- gen from a substantial benthic flux was an impor- tant internal source. Input and output proportions of bioavailable and particulate phosphorus differed little in Coeur d’Alene Lake, even though the lake retained about 50 percent of its total phosphorus input load. Nu— trient partitioning and retention in Flathead Lake were comparable to those in Coeur d’Alene Lake, except that Flathead Lake retained about 75 per— cent of its input load of phosphorus. The output proportion of bioavailable phosphorus in Lake Pend Oreille was slightly smaller than the input proportion. The lack of substantial shifts in parti— tioning of phosphorus between input and output loads, despite substantial sedimentation of input loads, was attributable both to the propensity of bioavailable phosphorus to sorb to inorganic and organic particulates and to the production of par— ticulate phosphorus as a result of phytoplankton assimilation of bioavailable phosphorus. The primary determinants of the fate and transport of nutrients in these three lakes were physical limnological processes such as inflow- plume routing and sedimentation. However, the evaluation of chemical and biological processes was essential to decipher how the fate and trans- port of nutrients were altered within each lake. INTRODUCTION Overview of National Water-Quality Assess- ment Program Industry and government have made substantial financial investments over the past several decades with the intent to improve water quality across the Nation; despite these efforts, numerous water-quality issues remain. To provide consistent and scientifically sound information for managing the Nation’s water resources, the US. Geological Survey (USGS) began full-scale implementation of a National Water-Quality Assess- ment (NAWQA) Program in 1991. The long—term goals of the NAWQA Program are to (1) provide a nationally 2 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake consistent description of current water-quality condi- tions for a large part of the Nation’s water resources, (2) detect long-term trends (or lack of trends) in water quality, and (3) identify and describe major factors that affect observed water-quality conditions and trends (Hirsch and others, 1988). The design of the program enables integration of information into a nationally consistent data base for comparisons of water-quality data over a large range of geographic and hydrologic conditions. Fifty-two NAWQA study areas, comprising many of the Nation’s most important river basins and aquifer systems, have been investigated since the pro- gram started in 1991. Overview of Northern Rockies lntermontane Basins Study Area The Northern Rockies lntermontane Basins (N ROK) study area was selected by the USGS for inclusion in the NAWQA Program for the following four reasons: (1) the area includes several important river systems; (2) land use/land cover in the area is a mixture of forested, agricultural, urban, and developing areas; (3) the area contains major sole—source aquifers such as the Spokane Valley/Rathdrum Prairie and Mis— soula Valley aquifers; and (4) mining practices have affected the quality of streams and aquifers (Tornes, 1997). Extensive consultation with water-resource managers, planners, State and local governments, and citizen groups identified five high—priority, regional- scale, water-quality issues within the NROK study area: (1) toxic trace elements in surface water and ground water; (2) nutrients in surface water and ground water from point and nonpoint sources; (3) degradation of surface water and ground water from urban areas and suburban development; (4) sedimentation from timber harvesting and agriculture; and (5) effects of these inputs on aquatic biological communities (Tomes, 1997). The USGS began study activities in the NROK study area in late 1996. The NROK study area is situated in western Montana, northern Idaho, and eastern Washington and encompasses 81,600 ka (fig. 1). The Clark Fork-Pend Oreille River Basin constitutes about 79 percent of the area; the Spokane River Basin constitutes the remain- der. The study area lies entirely within the Columbia River Basin and Northern Rocky Mountains physio- graphic provinces. Topography ranges from high, mountainous areas to large, flat-lying valleys. Eleva- tions range from about 3,000 m in the mountains and about 1,700 m in the mountain valleys of western Mon- tana to about 460 m along the Spokane River in eastern Washington. The intermontane basins of western Mon— tana receive as little as 38 cm of precipitation annually, whereas the area near the Continental Divide in north- western Montana receives more than 250 cm of precip— itation (Maret and Dutton, 1999). Snowmelt from April to July generates most of the annual runoff in the study area (Kendy and Tresch, 1996). The Clark Fork originates in southwestern Montana near Butte and flows northwestward about 560 km to Lake Pend Oreille in northern Idaho. The Pend Oreille River exits the lake and flows northward to join the Columbia River in Canada. The mean annual streamflow of the Pend Oreille River near the United States—Canadian border is about 790 m3/s (Tomes, 1997). The Clark Fork’s major tributaries, in downstream order, are the Blackfoot, Bitterroot, and Flathead Rivers. Within northern Idaho, the Coeur d’Alene and St. Joe Rivers are the two principal tribu- taries to Coeur d’Alene Lake, which is drained by the Spokane River. The Spokane River flows westward and enters the Columbia River in eastern Washington. The mean annual streamflow at the USGS gaging station Spokane River at Spokane, Washington (station 5, fig. 1), is about 200 m3/s (Tomes, 1997). The NROK study area contains numerous large, natural lakes and reservoirs. The Clark Fork-Pend Oreille River Basin contains Hungry Horse Reservoir, Flathead Lake, Lake Pend Oreille, and Priest Lake; the Spokane River Basin contains Coeur d’Alene Lake (fig. 1). Flathead Lake is the largest natural freshwater lake in the Western United States; Lake Pend Oreille is the 21st-largest and fifth-deepest freshwater lake in the United States (Bue, 1963). These five water bodies sig- nificantly influence the hydrology and water quality of the NROK study area; their combined volume, about 87 mm, represents more than 3 times the combined annual discharge, about 28 km3, delivered to the Columbia River by the Pend Oreille and Spokane Rivers. Several of the high-priority, regional—scale, water— quality issues listed by Tornes (1997) for the NROK study area have affected Coeur d’Alene and Flathead Lakes and Lake Pend Oreille. Nutrients from point and nonpoint sources have led to concerns about nutrient enrichment and potential eutrophication of all three lakes. Historical mining, ore-processing, and tailings- disposal activities in the study area have led to substan- lntroduction 3 49. __ |__r CANADA . l \"UNITED STATES .( (z H:c: ”L\‘ ‘ I \ ' 2 E ‘ < 7 D. A - -. l ) 3ng Priest\ 3 (Z 3 Lake I A ' KALISPELL . ° I INDIAN ‘ '33 RESERVATION (_ I E " J I " Sand oint [A ’ Priest 3 p ' River :2 L 7 “ Lake . , Pend /-‘ \fl ' Albeni Ore ill . ( Falls 0 .' \ 691% I Dam J, Lake ‘ B" Hayden N/ “ “i . :5 Lake Spokane thtle v“ Post 9- A r Falls Coeur d'Alene 0? ‘ Enaville I I we River ’ en" 8% Pinehurst B k H]: "k :3 un er I Superfund Site m B Base from U.S. Geological Survey digital data. COEIIJ’IIEISH Hydrologic unit maps, l:100,000, 1994; rivers --‘--§__ BIA \ ALEBIA_1_____.._——--’1 and streams, 1:250,000, 1994; Albers Equal- | t " i Area Projection. Standard parallels 46° 00‘, :\—‘ \ 48° 30', and 115° 00', 45° 00'. No false easting \ ‘. or false northing ”i MONTANA \ L“ . F Figure 1. Location of the Northern Rockies Intermontane Basins study area, selected dams, selected U.S. Geological Survey gaging stations, Indian Reservations, and Glacier National Park, Montana, Idaho, and Washington. 4 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake 1 1 4e EXPLANATION CANADA UNITED STATES Superfund study area _ . . _ Basin boundary A US. Geological Survey 4 gaging station and site number )—( Kerr Darn Dam and name U.S. GEOLOGICAL SURVEY GAGING STATIONS SITE GAGING STATION IDENTIFICATION NUMBER NUMBER AND NAME 12388700 Flathead River at Perma, Montana 12413500 Coeur d'Alene River near Cataldo, Idaho 12413860 Coeur d‘Alene River near Harrison, Idaho 12419000 Spokane River near Post Falls, Idaho 12422500 Spokane River at Spokane, Washington 12392000 Clark Fork at Whitehorse Rapids, near Cabinet, Idaho 12395500 Pend Oreille River at Newport, Washington N G'BU'I-BWNH , A“? is Clark Fork Superfund Site Hamilton 0 10 20 30 40 50 KILOMETERS |___lfi_l_LT___l__Tl 0 10 20 30 MILES Introduction 5 tial trace-element contamination of terrestrial, riparian, and riverine habitats (Idaho Department of Environ- mental Quality, 2001; URS Greiner Inc., and CH2M- Hill Inc., 2001a) upstream from Coeur d’Alene Lake and Lake Pend Oreille. The NROK study area contains two of the Nation’s largest Superfund sites, the Clark Fork Superfund site in the Clark Fork-Pend Oreille River Basin and the Bunker Hill Superfund site in the Spokane River Basin (fig. 1). Despite these water—quality issues and the impor— tance of these lakes to the hydrology of the NROK study area, no limnological sampling was conducted by the NAWQA Program within the NROK study area. That lack of limnological sampling reflected the NAWQA Program’s decision not to study water quality in lakes and reservoirs for any of the 52 study areas; that decision was based primarily on budgetary con- straints. The planning phase of the NAWQA Program, begun in 1986, resulted in a national program that was an aggregation of individual study areas in key river basins and aquifer systems. The program design em- phasized a multitude of physical, chemical, and biolog— ical measurements over multiyear and decadal time- scales. The high cost of conducting limnological sam- pling according to the NAWQA Program design resulted in the decision to target only freshwater streams and aquifers. The desirability for limnological evaluation of lakes in the NROK study area became evident for sev- eral reasons as the study team reviewed historical data and published reports for the study area. First, several of the lakes were likely to significantly affect down— stream water quality because of their very large volume in relation to annual discharge. Second, several of the lakes have been studied extensively over the past two decades. Substantial riverine loading data for nutrients are available for Coeur d’Alene and Flathead Lakes and Lake Pend Oreille; evaluation of these data re- vealed a distinct lake effect on the relation between inflow and outflow loads. Besides riverine loading data, these three lakes also have been sampled exten- sively for physical, chemical, and biological character- istics that would be useful for evaluating limnological effects on constituent transport. Third, each of the three lakes receives more than 85 percent of its annual inflow of water from a single tributary (Flathead Lake and Lake Pend Oreille) or two tributaries (Coeur d’Alene Lake). In lieu of NAWQA-funded limnological sam— pling of lakes in its study area, the NROK study team devised this analysis as an alternative approach for their evaluation of the effects of lakes on constituent transport. Purpose and Scope The purpose of this report is to describe the role of limnological processes in determining the fate and transport of nutrients delivered into and discharged from three large, natural lakes within the NROK study area. Coeur d’Alene and Flathead Lakes and Lake Pend Oreille represent a broad range in physical limnologi- cal characteristics, which can be expected to signifi- cantly influence the fate and transport of nutrients delivered into the lakes. The study approach was to evaluate the substantial amount of historical limnologi— cal and riverine loading data for these three lakes. The initial phase of the evaluation was quantification of in- flow and outflow loads of nutrients; subsequent phases of the evaluation focused on the influence of physical, chemical, and biological processes on in-lake distribu- tion and partioning of nutrients. Information gained from this study of the cumulative limnological pro- cesses influencing nutrients in the NROK lakes will benefit future lake-assessment studies and activities designed to manage and protect lake and reservoir quality. DESCRIPTION OF DRAINAGE BASINS Coeur d’Alene Lake Coeur d’Alene Lake, Idaho’s second largest, is located in northern Idaho within the 17,300-km2 Spokane River Basin. The lake occupies the drowned river valley of the Pleistocene Spokane River and its two principal tributaries, the Coeur d’Alene and St. Joe Rivers. The valley was dammed by infilling of the present Rathdrum Prairie-Spokane River Valley with about 100 m of coarse gravel deposits during a series of glacial outburst floods from Lake Missoula between about 18,000 and 13,000 years ago (URS Greiner Inc., and CH2M—Hill Inc., 2001b). Present—day Coeur d’Alene Lake is about 35 km long in a north-south orientation and has a maximum width of about 3.7 km and a maximum depth of about 64 m. The lake covers 129 km2 and has a volume of 2.8 km3 at its full-pool 6 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake elevation of 648.7 m. The southern end of the lake is contiguous with four shallow lakes that were flooded in 1906 by impoundment of the Spokane River by Post Falls Dam at Post Falls. The dam provides hydroelec— tric power, flood control, and irrigation supply. Coeur d’Alene Lake receives surface-water inflow from a 9,690—km2 drainage area. About 90 percent of that inflow is delivered by the Coeur d’Alene and St. Joe Rivers (Woods and Beckwith, 1997); the lake is drained by the Spokane River, a tributary to the Colum- bia River. The Coeur d’Alene and St. Joe Rivers arise within the Coeur d’Alene and St. Joe Mountains, respectively. Much of the lake’s drainage area is char- acterized by high, massive mountains and deep, inter- montane valleys. Elevations range from about 650 m above sea level at the lake outlet to about 2,090 m at the Idaho-Montana border. About 75 percent of the drain- age’s land cover is forest (coniferous, sparse, or recov- ering harvest); rangeland and agriculture account for about 7 and 5 percent, respectively (Woods and Beck- with, 1997). The lake’s drainage basin receives some of the largest amounts of precipitation in Idaho. Basin topog- raphy affects the areal distribution of precipitation. Mean annual precipitation is about 64 cm at the lake, whereas mean annual precipitation is about 97 cm at Wallace, about 75 km east and 250 In higher in eleva- tion than the lake. About 70 percent of the annual pre- cipitation is snow during October to April. The influ- ence of Pacific Maritime conditions can produce large rain-on—snow events during the winter. Although winter temperatures at Coeur d’Alene Lake are often below freezing, the lake normally does not freeze except at the shallow southern end. The Coeur d’Alene River (drainage area 3,810 kmz) discharges into the southern third of the lake. Land-use activities in the drainage basin include recreation, logging, agriculture, and mining and ore processing. Most of the mining and ore-processing activities are in the drainage basin of the South Fork Coeur d’Alene River, which contains the Bunker Hill Superfund site (fig. 1). The St. Joe River (drainage area 4,520 kmz) discharges into the extreme southern end of the lake. Recreation and logging are the dominant land uses; little mining activity has occurred in this basin. Urban land use in the two basins is less than 1 percent (Woods and Beckwith, 1997). Considerable concern exists about the potential for nutrient enrichment and subsequent eutrophication of Coeur d’Alene Lake because of land—use activities within its drainage basin and near its shoreline, in addi- tion to intensive recreational use of the lake (Woods and Beckwith, 1997). A 1975 nutrient load study, done as part of the National Eutrophication Survey, led to the conclusion that Coeur d’Alene Lake was mesotrophic, or moderately productive (US. Environmental Protec- tion Agency, 1977). A second concern is the large amount of trace elements that have been introduced into Coeur d’Alene Lake as a consequence of more than 100 years of mining and ore-processing activities in the Coeur d’Alene River Basin (Woods and Beck— with, 1997). About 85 percent of the bottom of Coeur d’Alene Lake is substantially enriched in antimony, arsenic, cadmium, copper, lead, mercury, silver, and zinc (Horowitz and others, 1993, 1995). Flathead Lake The Flathead Lake and River Basin, at the con- fluence with the Clark Fork, cover about 18,400 km2 of northwestern Montana and southeastern British Columbia, Canada. Flathead Lake lies in a tectonic graben basin that underwent extensive glaciation dur- ing the Pleistocene; the lake was formed by moraines at the western and southern boundaries of the last ice advance and is probably a remnant of a larger glacial lake system (Moore and others, 1982). The present—day lake is about 56 km long and has a maximum width of about 26 km and a maximum depth of about 113 m. The lake’s 300-km shoreline is often steep and rocky, especially along the eastern shore. The lake covers 496 km2 and has a volume of 23.2 km3 at its full-pool elevation of 879 m. The lake’s surface elevation was raised about 3.4 m by completion of Kerr Dam in the 1930’s. The dam, about 7 km downstream from the lake’s natural outlet, is operated for flood control and hydropower purposes. The Flathead River is the lake’s major tributary; it enters the lake at the shallow northern end. At its inflow to the lake, the Flathead River drains about one- half of the Flathead Lake and River Basin. Inflow from other tributaries increases the drainage area of Flathead Lake to about 11,400 km2 at its outlet. The lake is drained at the shallow southern end by the Flathead River, which is the largest tributary to the Clark Fork. Most of the basin’s northern, northwestern, and eastern regions are characterized by high, rugged mountains interspersed with deep, intermontane val- Description of Drainage Basins 7 leys. The southern and southwestern regions are domi- nated by the Flathead Valley, which contains Flathead Lake. Elevations range from about 880 m at the lake outlet to more than 3,000 m in Glacier National Park along the basin’s eastern boundary. The dominant land cover in the basin’s high mountain region is coniferous forest, whereas the dominant land cover in the Flathead Valley and adjacent foothills is grasses and, in drier areas, sagebrush. Logging is the dominant land use in the mountainous areas of the basin. Within the Flathead Valley, agriculture is an important land use, along with urban and rural development. Recreation is also an important land use in the basin because about 60 per- cent of the basin’s area is contained within Glacier National Park and National Forest wilderness and roadless areas. Climatic conditions in the basin are dominated by Pacific Maritime influences; however, cold air- masses of continental origin occasionally affect the area in the winter. At the lake, mean annual precipita- tion is about 50 cm; along the Continental Divide, it is about 250 cm. Most of the annual precipitation is snow during the winter. Flathead Lake’s substantial heat— storage capacity influences local weather conditions by moderating air temperatures and increasing precipita- tion to the east of the lake. Although winter air temper- atures are often below freezing, the lake normally does not freeze over for any appreciable period, primarily because of its very large volume and wind-induced turbulence. Eutrophication is the primary water-quality issue for Flathead Lake (Stanford and others, 1997). Nutrient loadings to the lake originate from a combination of drainage basin and nearshore sources. The extensive road network developed for logging has contributed sediment and associated nutrients to the lake. Farming and grazing in the Flathead Valley are also sources of sediment and nutrients to the lake. Residential develop— ment around the lake’s shoreline and its adjacent drain- ages has increased nutrient loadings from nonpoint sources, as well as from municipal wastewater-treat- ment facilities. Lake Pend Oreille Lake Pend Oreille is Idaho’s largest lake. The lake is located in northern Idaho and is an important feature of the 64300-ka Clark Fork-Pend Oreille River Basin. The lake lies in a glacially scoured graben, the southern end of which was plugged by massive deposition of sediments transported by catastrophic glacial outburst floods from Lake Missoula during the late Pleistocene (Molenaar, 1988). The deep, U-shaped basin of the lake separates three mountain ranges: the Cabinet, the Sel- kirk, and the Coeur d’Alene. Albeni Falls Dam, on the Pend Oreille River (fig. 1), was completed in 1952 and increased the lake’s normal surface elevation by 4.3 m. The dam is operated to provide hydroelectric power, flood control, navigation, recreation, and fish and wild- life conservation. Present-day Lake Pend Oreille, including its outlet arm behind Albeni Falls Dam, is about 110 km long and has a maximum width of about 10.8 km and a maximum depth of about 357 m. The lake and its outlet arm cover 369 km2 and have a vol— ume of 54.2 km3 at a normal full-pool elevation of 628.6 m. Excluding its outlet arm, the lake covers 329 kmz, contains 53.9 km3, and is about 50 km in length. Lake Pend Oreille, including its outlet arm, re— ceives surface—water inflow from a 62,700—km2 drain- age area, the vast majority of which is in northwestern Montana. About 85 percent of the lake’s surface—water inflow is delivered by the Clark Fork (Frenzel, 1993a), which enters the lake from the east. The Clark Fork begins near Butte, Montana, and drains an extensive area west of the Continental Divide, including the Flat- head River and Flathead Lake. The Clark Fork is impounded by Cabinet Gorge Dam about 11 km upstream from Lake Pend Oreille; the drainage area upstream from Cabinet Gorge Dam is 57,150 kmz. The dam was completed in 1951 and is operated primarily for hydroelectric power generation. Lake Pend Oreille is drained by the Pend Oreille River, a tributary to the Columbia River. Much of Lake Pend Oreille’s drainage basin is characterized by mountainous terrain interspersed with broad valleys. Elevations range from about 3,000 m in northwestern Montana and Glacier National Park to about 600 m near the Idaho-Washington border. In the lake’s southern basin, much of the shoreline rises pre- cipitously to elevations between 1,500 and 2,000 m. Most land cover in the basin is coniferous forest; range- land and agriculture together account for about 20 per- cent. Land-use activities include recreation, logging, agriculture, mining, and grazing; urban land use applies to only about 1 percent of the drainage area. Lake Pend Oreille responds to a wide range of cli- matological conditions because the drainage area of the Clark Fork is so large relative to the lake’s local drain- 8 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake age area. Most of the annual precipitation is snow dur- ing the winter months. The influence of Pacific Mari- time conditions can produce large rain-on-snow events during the winter. Mean annual precipitation at the northern end of the lake is 84 cm, whereas it is 125 cm in the adjacent mountains. Near the Continental Divide in northwestern Montana, mean annual precipitation is about 250 cm. Because of its considerable volume and heat-storage capacity, the lake does not freeze except at the shallow northern end. Similar to Coeur d’Alene and Flathead Lakes, the potential for eutrophication by excessive nutrient loads is a water-quality concern for Lake Pend Oreille. Stud— ies in the early 1990s documented that increased lake productivity was manifested largely in the littoral zone; the lake’s limnetic zone remained oligotrophic (Hoel— scher and others, 1993). The lake’s primary inflow, the Clark Fork, contributed about 80 percent of the nitro- gen and phosphorus loads annually delivered to the lake (Frenzel, 1993b). Trace-element contamination is another water—quality issue for Lake Pend Oreille. More than a century of mining, ore—processing, and tailings disposal has left the upper Clark Fork severely contaminated with trace elements (Andrews, 1987; Moore and Luoma, 1990); as a result, four Superfund sites have been listed in the upper Clark Fork. Several impoundments downstream from the Superfund sites and upstream from Lake Pend Oreille have trapped part of the trace-element-contaminated sediments intro- duced into the Clark Fork (Moore, 1997). However, bed-sediment samples collected from Lake Pend Oreille by the US. Army Corps of Engineers (P.L. Hall, US. Army Corps of Engineers, written commun., 1989) and collected in the Pend Oreille River near its confluence with the Priest River by the US. Geological Survey (Beckwith, 2002) indicated substantially ele— vated concentrations of cadmium, copper, lead, and zinc. These results suggest that trace-element-contami- nated sediments are being transported into and through the lake. EVALUATION APPROACH The fate and transport of water and associated constituents following their delivery into a lake are determined by the interactions of a myriad of physical, chemical, and biological processes operating within the lake over a wide range of spatial and temporal scales. The outcome of those interactions determines the quan- tity and nature of constituents discharged from the lake at its surface-water outlet. Nitrogen and phosphorus compounds, the principal constituents of concern in this report, occur in dissolved, colloidal, and particulate fractions. These fractions of nitrogen and phosphorus vary in their degree of participation in chemical and biological processes within the lake’s water column, as well as within the lakebed sediments. The lake’s effect on fate and transport is likely to be quite different for particulate-fraction constituents, which have high den- sities relative to that of water, than for dissolved-frac- tion and colloidal—fraction constituents, which have low densities. In general, lakes are efficient traps for sedi- ment because the significant decrease in flow velocity and turbulence within a lake inhibits advective trans- port of sediments and particulate-fraction constituents. In contrast, dissolved and colloidal constituents are transported by convection and diffusion, as well as by advection, after their delivery into a lake. The initial phase of the evaluation was to quantify the differences in nutrient loadings upstream and down- stream from each lake. The second phase was to define the combined influence of the hydrologic characteris- tics of each drainage basin and physical limnological characteristics of each lake on the fate and transport of nutrients. The third phase was to determine the spatial and temporal variability of chemical and biological constituents and partitioning of nutrients within each lake. The final phase was to combine the insight gained from the first three phases of evaluation to distinguish the effects of hydrological and physical limnological processes from the effects of chemical and biological processes in determining nutrient fate and transport through each lake’s water mass. NUTRIENT LOADS Overview The primary objective of quantifying nutrient loads in this evaluation was to demonstrate the impor— tance of understanding how limnological processes influence the quantitative differences between loads of nutrients input to and output from a lake. The initial step in gaining this understanding is to quantify those nutrient loads over equivalent timescales. Load is de- fined as the quantity of a constituent passing a riverine Nutrient Loads 9 cross section per unit of time and is calculated as the product of constituent concentration, discharge, and appropriate conversion factors for measurement units. Although the calculation of load is simple mathe- matically, the method of deriving concentration and discharge can produce substantial differences in load calculations, especially for periods longer than 1 day. Both constituent concentration and discharge vary sub- stantially over the course of a year. Perhaps more importantly, the two quantities are nonsynchronous in their temporal variation; also, concentrations of dis- solved constituents respond differently to discharge changes than do concentrations of particulate constitu— ents. Thus, constituent loads may vary on a daily, monthly, and (or) annual basis. A variety of methods, from basic to complex, have been used to process mul- tiple-date data sets of concentration and discharge into estimates of seasonal and annual loads. A basic method is to multiply the mean values of concentration and dis- charge over the period of interest to derive the load. Additional temporal resolution can be gained by linear interpolation of measured values of concentration and discharge to examine daily load variability over the period of interest. More complex methods use linear regression, either simple or multiple, to relate load or concentration to discharge and other explanatory vari- ables such as seasonality and time trend. Constituent loads typically are quite variable, both within and among years, because changes in discharge are often the dominant influence on loads. This is espe- cially true for sediment-associated constituents where the sediment supply is not limited. In this situation, the smallest loads of sediment-associated constituents within a year typically are associated with minimum discharges, owing to reduced water velocities that pro— duce less erosion and transport. Alternatively, elevated water velocities during maximum discharge within a year result in erosion and transport of the largest loads of sediment-associated constituents. The pattern is gen— erally similar for dissolved constituents, but differences in loads between high and low discharge are reduced because the transport of dissolved constituents is less dependent on stream velocity. These within-year pat- terns of variability in loads for sediment-associated and dissolved constituents are important considerations in assessing a lake’s effects on the fate and transport of nutrient loads. The foregoing logic for seasonal loads also applies to annual loads; in general, larger constitu— ent loads are transported during high-discharge years than during low—discharge years. A lake’s hydrologic budget, an accounting of the amount of water entering and exiting the lake, is an important determinant of constituent loads delivered into and discharged from the lake. The hydrologic bud- get provides an accounting of gains and losses of water associated with surface—water inflow and outflow, pre- cipitation and evaporation, ground—water inflow and outflow, wastewater—treatment facility inflows, indus- trial and municipal withdrawals, and changes in lake storage. If each water source for the lake can be quanti- fied and assigned an associated concentration, then a constituent budget can be calculated for the lake. Hy- drologic and constituent budgets are important tools for evaluating the in—lake fate and transport of particulate, colloidal, and dissolved constituents that affect lake water-quality characteristics. Coeur d’Alene Lake A nutrient load/lake response study of Coeur d’Alene Lake conducted cooperatively by the USGS and Idaho Department (formerly, Division) of Environ- mental Quality (IDEQ) quantified hydrologic, nutrient, and trace—element budgets for calendar years 1991 and 1992 (Woods and Beckwith, 1997). Numerous budget components were quantified, either by measurement or estimation; details about data sources and computa- tions were presented by Woods and Beckwith (1997). Riverine—derived constituent loads were computed using the computer program FLUX (Walker, 1996), a regression-based approach for estimating loads that stratifies streamflow and constituent concentration data to reduce prediction error. The stratified data sets then are used to compute a series of loads on the basis of five different regression equations. The regression equation and stratification method that yields the small- est coefficient of variation is considered the best esti- mate of load. FLUX provides several additional diag- nostic tools for assessing results; these include plots of residuals and hypothesis tests for various model param- eters. The hydrologic budgets reported by Woods and Beckwith (1997) indicated that the combined inflows from the lake’s two primary tributaries, the Coeur d’Alene and St. Joe Rivers, accounted for about 92 per- cent of all inflows to the lake. The St. Joe River was the larger inflow source, delivering about one-half of the inflow to the lake. Precipitation on the lake surface 10 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake accounted for less than 2.5 percent of the hydrologic budget in each year. At least 90 percent of the water exiting the lake was by way of the Spokane River, the remainder was evaporation loss from the lake surface and ground-water outflow from the lake’s northern margin. Annual loads of total nitrogen and phosphorus delivered to and discharged from Coeur d’Alene Lake during 1991 and 1992 (table 1) were calculated on the basis of constituent budgets reported by Woods and Beckwith (1997). These 2 years represented different hydrologic conditions for the lake. In 1991, the annual mean lake outflow as measured in the Spokane River was 199 m3/s, or 112 percent of the long—term mean annual outflow of 177 m3/s (Brennan and others, 2001); in contrast, annual mean lake outflow for the 1992 water year was 99 m3/s, only 56 percent of the long-term value. As expected, nitrogen and phosphorus loads for 1992 were also substantially less than those for 1991. On the basis of magnitude, the St. Joe River, and then the Coeur d’Alene River, delivered the most nitrogen and phosphorus to the lake. Combined, these two rivers delivered 83 and 82 percent, respectively, of the total nitrogen and phosphorus loads during 1991— 92. Atmospheric deposition of nutrients on the lake surface by precipitation and dryfall was minor, deliver- Table 1. Annual input, output, and retained loads of total nitrogen and phosphorus, Coeur d’Alene Lake, Idaho, calendar years 1991 and 1992 [kg, kilograms; km3, cubic kilometers; retained load values are rounded] Variable, unit 11991 1 1992 Mean annual lake outflowz, km3 6.37 3.17 Total nitrogen Input load, kg 2,150,000 945,000 Output load, kg 2,030,000 860,000 Retained load3, kg 120,000 85,000 Retained load/input load, percent 6 9 Total phosphorus Input load, kg 1 15,000 43,600 Output load, kg 36,100 27,600 Retained load3, kg 78,900 16,000 Retained load/input load, percent 69 37 1 From Woods and Beckwith (1997). 2 Measured at Spokane River near Post Falls, Idaho (station 4, fig. 1). 3 Input load-output load. ing less than 8 percent of the nitrogen and less than 15 percent of the phosphorus. The magnitude and percentage of the total nitro- gen and phosphorus loads retained by the lake also were calculated on the basis of Woods’ and Beckwith’s 1997 constituent budgets (table 1). The retained load of nitrogen was 85,000 kg in 1992, 35,000 kg less than in 1991. However, the retained loads for each year were a similar percentage of their respective input loads, 5.6 percent in 1991 and 9.0 percent in 1992. The retained load of phosphorus in 1991 was 78,900 kg, about 5 times that retained in 1992. Unlike nitrogen, the retained loads for phosphorus were not similar between the 2 years; the percentage in 1991 was 69 but, in 1992, the percentage was only 37. On this basis, the large dif- ferences in inflow and outflow volumes for 1991 and 1992 had little effect on the lake’s ability to retain nitrogen but a large effect on its ability to retain phos— phorus. Flathead Lake The primary sources of hydrologic and nutrient budget information for Flathead Lake were twofold: a doctoral dissertation examining the effects of turbidity on the lake’s biogeochemistry and trophic state (Stuart, 1983), and a 1978—82 limnological assessment (Stan— ford and others, 1983) prepared for the US. Environ— mental Protection Agency (EPA). Numerous additional years of nutrient budget information for Flathead Lake also were presented by Stanford and others (1995, 1997); however, much of their phosphorus load data could not be used for the purposes of this report be- cause of an introduced bias. These authors adjusted their nutrient load calculations for phosphorus to focus on bioavailable (dissolved) phosphorus; when the total suspended-solids concentration in a sample exceeded 10 mg/L, they reduced the associated total phosphorus concentration in that sample by 90 percent. The effect was to mathematically discount most of the sediment- associated phosphorus load from the lake’s primary inflow source, the Flathead River, and, thereby, bias the calculation of phosphorus retention in Flathead Lake. The limnological assessment (Stanford and others, 1983) presented hydrologic and nutrient budgets for 1978—82, as well as details about data sources and computations for loads delivered from riverine, atmo— spheric, wastewater, and nearshore sources. Riverine- derived constituent loads were computed using month— Nutrient Loads 11 ly mean values of streamflow and nutrient concentra- tion. The total phosphorus loads reported in the limno- logical assessment included the introduced bias de— scribed earlier, but the bias affected only the influent Flathead River because suspended—sediment concentra- tions at the lake’s outlet did not exceed 10 mg/L. Fortu- nately, the authors reported both unbiased and biased total phosphorus loads for the Flathead River for 1978— 82, thereby providing the data needed to calculate an unbiased total phosphorus budget for the lake over that period. The additional years of nutrient budgets re- ported by Stanford and others (1995, 1997) covered a wide range of hydrologic conditions; however, only biased phosphorus loads were reported. Hydrologic and nutrient budgets calculated by Stanford and others (1983) for 1978—82 were reported as a mean for that period; these are summarized in table 2. Inflow from the Flathead River accounted Table 2. Mean annual input, output, and retained loads of total nitrogen and phosphorus, Flathead Lake, Montana, 1978—82 [kg, kilograms; km3, cubic kilometers; mg/L, milligrams per liter] Variable, unit 1 1978 — 82 Mean annual lake outflow, km3 9.41 Total nitrogen Input load, kg 1,590,000 Output load, kg 1,020,000 Retained loadz, kg 570,000 Retained load/input load, percent 36 Total phosphorus, biased3 Input load, kg 1 18,000 Output load, kg 59,300 Retained loadz, kg 58,700 Retained load/input load, percent 50 Total phosphorus, unbiased4 Input 242,000 Output 59,300 Retained loadz, kg 183,000 Retained load/input load, percent 76 I From Stanford and others (1983); loads converted from metric tons to kilograms. 2 Input load—output load. Concentrations reduced 90 percent if suspended—solids concentra— tions larger than 10 mg/L for Flathead River inflow. 4 Concentrations not reduced to account for suspended-solids concen- trations larger than 10 mg/L for Flathead River inflow. for 87 percent of the lake’s hydrologic budget during 1978—82; the Swan River (fig. 1) and direct precipita- tion on the lake surface accounted for most of the remaining inflow (Stuart, 1983). About one—half of the annual inflow to the lake was delivered during April to July, the period of snowmelt runoff (Stuart, 1983). Regarding outflow from the lake, 97 percent was by way of the Flathead River, and 3 percent was evaporation loss from the lake surface (Stuart, 1983). Hydrologic conditions were slightly less than average; the mean annual lake outflow of 294 m3/s, as measured at Perma, during 1978—82 (table 2) wasabout 88 per- cent of the long-term mean of 336 m3/s (Shields and others, 2001). Similar to Coeur d’Alene Lake, the input and output loads of total nitrogen were much larger than those of total phosphorus (table 2). Details on the nitrogen budget reported by Stanford and others (1983) indicated that the Flathead River delivered 80 percent of the nitrogen load to the lake, and precipitation on the lake surface delivered 11 percent. For the biased total phosphorus loads to the lake (Stanford and others, 1983), the Flathead River delivered 64 percent, whereas precipitation on the lake surface delivered 25 percent. If unbiased total phosphorus inputs are used, the Flathead River delivered 82 percent of the lake’s total phosphorus load; precipitation delivered 12 percent. During 1978—82, the retained load of nitrogen in Flathead Lake was 570,000 kg, or 36 percent of the input load (table 2). The biased retained load of phosphorus was 58,700 kg, or 50 percent of the input load; the unbiased retained load of phosphorus was 183,000 kg, or 76 percent of the input load. Unlike the multiple-year data set available for Coeur d’Alene Lake, Flathead Lake lacks unbiased nutrient data for estimating the effects of streamflow on the magnitude of nutrient loads retained by the lake. Lake Pend Oreille As part of a nutrient load/lake response study of Lake Pend Oreille conducted cooperatively by the USGS and IDEQ, hydrologic and nutrient budgets for numerous sources were quantified for water years 1989 and 1990 (Frenzel, 1993a,b). Riverine-derived nutrient loads were computed using the same computer pro— gram, FLUX (Walker, 1996), that was used for the nutrient load/lake response study of Coeur d’Alene Lake (Woods and Beckwith, 1997). Hydrologic bud— 12 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake gets reported by Frenzel (1993a) indicated that the inflow from the lake’s primary tributary, the Clark Fork, accounted for about 85 percent of all inflows to the lake and its outlet arm. The Priest River (fig. 1), located near the downstream end of the lake’s outlet arm, accounted for 6.4 percent of inflow to the lake and its outlet arm. Precipitation on the lake and outlet arm surface accounted for about 1.3 percent of the hydro- logic budget in both years. At least 98 percent of the water discharged from the lake was by way of the Pend Oreille River; much of the remainder was evaporation loss from the lake surface. Annual loads of total nitrogen and phosphorus delivered to and discharged from Lake Pend Oreille, including its outlet arm, during 1989 and 1990 (table 3) were calculated on the basis of the constituent budgets reported by Frenzel (1993b). In 1989, the annual mean lake outflow of 627 m3/s was 88 percent of the long-term mean annual outflow of 716 m3/s (Brennan and others, 2001); the annual mean lake outflow of 799 m3/s in 1990 was 112 percent of the long-term value. Nitrogen and phosphorus loads for the above- average outflow year, 1990, were larger than those for 1989, thereby illustrating the strong positive correlation between load and streamflow. In both years, the Clark Fork delivered about 80 percent of the lake’s nitrogen load and about 70 percent of its phosphorus load (Fren- zel, 1993b). The Priest River delivered about 6 percent of the nitrogen load and about 9 percent of the phos- phorus load. Atmospheric deposition delivered about 4.3 percent of the nitrogen load and about 5.8 percent of the phosphorus load in both years. The retained load of nitrogen in Lake Pend Oreille was 670,000 kg in 1989 and 840,000 kg in 1990 (table 3). Despite a substantial difference in outflow volumes, the retained loads were similar percentages of the input load, 15.2 in 1989 and 14.8 percent in 1990. Retained loads of phosphorus also were similar percentages of the input load, 16.9 in 1989 and 13.5 in 1990. On this basis, the large difference in the lake’s hydrologic budgets for 1989 and 1990 had little effect on the percentages of nitrogen and phosphorus loads retained by the lake. Table 3. Annual input, output, and retained loads of total nitrogen and phosphorus, Lake Pend Oreille, Idaho, water years 1989 and 1990 [kg, kilograms; km3, cubic kilometers] Variable, unit 11989 1 1990 Mean annual lake outflowz, km3 20.0 25.6 Total nitrogen Input load, kg 4,410,000 5,670,000 Output load, kg 3,740,000 4,830,000 Retained load3, kg 670,000 840,000 Retained load/input load, percent 15 15 Total phosphorus Input load, kg 326,000 408,000 Output load, kg 271,000 353,000 Retained load3, kg 55,000 55,000 Retained load/input load, percent 17 14 1 From Frenzel (1993b). 2 Measured at Pend Oreille River at Newport, Wash. (station 7, fig. 1). 3 Input load - output load. Among-Lake Comparisons The annual loads of nutrients delivered to the three lakes varied widely; total nitrogen loads ranged from 945,000 to 5,670,000 kg, whereas total phosphorus loads ranged from 43,600 to 408,000 kg (tables 1—3). Lake Pend Oreille received the largest loads of both nutrients; Coeur d’Alene Lake received the smallest. Nutrient loads discharged from the lakes ranged from 860,000 to 4,830,000 kg of total nitrogen and from 27,600 to 353,000 kg of total phosphorus (tables 1—3). Similar to input loads, Lake Pend Oreille discharged the largest loads of both nutrients and Coeur d’Alene Lake discharged the smallest. The variable, retained load/input load (tables 1—3), was averaged for total nitrogen and phosphorus (table 4). Coeur d’Alene Lake retained the smallest percentage (7.3) of nitrogen and Flathead Lake re— tained the largest percentage (36). On the basis of un- biased loads, Lake Pend Oreille retained the smallest percentage (15) of phosphorus, Coeur d’Alene Lake retained 53 percent, and Flathead Lake retained the largest percentage (76). Nutrient Loads 13 Table 4. Average of retained |oad/input load for total nitrogen and phosphorus, Coeur d’Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana Average retained load 1/ input load, in percent Lake Total nitrogen Total phosphorus Coeur d’Alene2 . . . . 7.3 53 Flathead3 ......... 36 476 Pend Oreille5 ...... 15 15 I Input load - output load. 2 Based on calendar years 1991—92, see table 1. 3 Based on calendar years 1978—82, see table 2. 4 Unbiased loads from Flathead River. 5 Based on water years 1989—90, see table 3. PHYSICAL LIMNOLOGY Overview The initial phase of evaluation demonstrated sub- stantial quantitative differences in the relative amounts of nutrient loads delivered to and discharged from the three lakes. The next phase, discussed in this section, defines the quantitative differences in light of the com— bined influence of the hydrologic characteristics of each drainage basin and physical limnological Charac- teristics of each lake. One measure of the potential influence of a drain- age basin on a lake is the ratio of drainage basin area to lake surface area. As that ratio increases, a concomitant increase in the hydrologic influence of the drainage basin upon the lake receiving the basin’s runoff might be expected. However, two problems arise in this com- parison of surface areas: (1) runoff from a drainage basin is highly dependent upon the amount of precipi— tation it receives, and (2) lake surface area is a poor indicator of lake volume. Calculation of areal water load deals more effectively with the issue of drainage basin area because the volume of runoff delivered into the lake is divided by lake surface area. As areal water load increases, the drainage basin’s hydrologic influ- ence on the lake also increases. The second problem, lake surface area, can be dealt with by considering the ratio of inflow volume to lake volume, commonly termed flushing rate (Ryding and Rast, 1989). As flushing rate increases, the drainage basin’s hydrologic influence on the lake also increases because the lake’s volume is replaced more frequently. The inverse of flushing rate is termed retention time and represents the time theoretically needed to fill a lake if it were empty. If lake volume is divided by lake outflow volume in- stead of inflow volume, then the time needed to empty the lake, the hydraulic residence time, is obtained. On an annual basis, retention time and hydraulic residence time often are comparable. Hydraulic residence time was chosen for analysis in this report because outflow volumes for the three lakes have been measured for many years. Although retention time and hydraulic residence time are theoretical concepts, the processes that they incorporate are important for understanding fate and transport of constituents in lakes. The rate at which water enters and leaves a lake affects the amount of tur- bulence within the lake’s water column, both in the horizontal and vertical dimensions. Years of above—nor- mal inflow and outflow produce more water-column turbulence and increase advective transport of particu- late materials. Conversely, years of below-normal in- flow and outflow produce less water-column mixing and, hence, increase the trapping of sediment and asso- ciated constituents. The potential for trapping of sediment by water bodies such as lakes and reservoirs can be estimated empirically on the basis of a nomograph that relates the ratio of storage capacity (lake volume) to inflow vol— ume (Gray, 1973). That ratio is analogous to retention time. In the case of these three lakes, outflow volume was substituted for inflow volume; as such, hydraulic residence time was used in the nomograph to estimate trap efficiency. Given a hydraulic residence time of 0.01 year (for example, a 25-km3 volume with a 2,500- km3-per-year outflow volume), the estimated trap effi- ciency for sediment from the nomograph would be about 45 percent. If the hydraulic residence time were increased to 0.1 year (for example, a 25-km3 lake vol- ume with a 250-km3—per-year outflow volume), the estimated trap efficiency would be about 86 percent. The estimated trap efficiency would increase to 95 per— cent or greater for hydraulic residence times equal to or longer than 1.0 year (for example, a 25—km3 lake vol- ume with a 25-km3-per—year outflow volume). The foregoing discussion provides insight into the relation of inflow or outflow magnitude on the genera- tion of turbulence and advective transport within lakes. However, the concepts of retention and hydraulic resi- dence times remain theoretical because lakes rarely are 14 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake filled or emptied; the two descriptors are best suited for general comparisons among lakes representing wide ranges of retention and hydraulic residence times. In actuality, the movement of riverine inflows within a lake can be quite complex because of characteristics such as lake shape, lake depth, and temporal and spatial differences in density between riverine and lake water. Three generalized cases of inflow-plume routing were discussed by Fischer and others (1979): overflow, inter- flow, and underflow. Overflow occurs if the inflow plume is warmer (less dense) than the lake; river water floats on the lake’s surface. Interflow occurs when the inflow plume is colder than the lake’s upper water col- umn but warmer than the lower water column; thus, interflow is routed to the lake depth where the tempera- ture, or density, of the inflow plume and lake is equal. Underflow occurs when the inflow plume is colder than, or about the temperature of, the lake’s lower water column. Turbulence at the interface of the inflow plume and the lake mixes the two water masses until thermal equilibrium is reached. The spatial extent of inflow-plume routing is highly dependent on the mag- nitude of riverine discharge. Riverine inflows generated by snowmelt runoff and floods can penetrate farther into the receiving lake because the large inflow vol- umes produced by such events increase turbulence and advective transport. The physical, chemical, and biological responses of a lake to the delivery of water and associated constit- uents from its drainage basin are closely tied to mor— phometric characteristics of the lake. Calculation of such characteristics requires a bathymetric map of the lake; such maps are available for Coeur d’Alene Lake (Woods and Berenbrock, 1994), Flathead Lake (Stan- ford and others, 1997), and Lake Pend Oreille (Fields and others, 1996). Lake shape can vary from circular to elongate to dendritic. A long, narrow lake such as Coeur d’Alene Lake is more prone to channel inflow along its major axis. Flathead Lake and Lake Pend Oreille are also long but are much wider than Coeur d’Alene Lake; hence, their inflows are less prone to channeling along their major axis. Surface area affects the amount of atmospheric materials that may be directly deposited onto the lake’s surface versus those deposited onto the lake’s drainage basin. Surface area and lake shape are determinants of the lake’s exposure to wind. A large surface area in combination with long or wide reaches increases the wind’s ability to generate turbulence and mix the lake’s water column. The maxi- mum length and maximum width of a lake are useful descriptors of this effect but fail to convey the shelter- ing effects of islands or the shoreline. Maximum effec— tive length and maximum effective width (Hakanson, 1981) are better indicators of wind exposure because they represent linear reaches absent from wind shelter- ing. Increased wind exposure favors the development of large-scale turbulent processes such as surface and internal seiches, which can displace large masses of water in the horizontal and vertical dimensions and, thus, are important mechanisms for water-column mixing. Depth is also a critical lake dimension for several reasons. Deep lakes are more resistant to turbulent, full-depth mixing by wind energy, so lakebed sedi- ments are less likely to be periodically resuspended. Because of longer settling time, organic detritus in a deep lake is more likely to have undergone remineral- ization by the time it reaches the lakebed. The low fre— quency of turbulent, full-depth mixing in deep lakes also restricts the exchange of dissolved, colloidal, and particulate constituents between the upper and lower water columns. Maximum depth only conveys informa- tion about the deepest part of a lake. The mean depth, defined as lake volume divided by lake surface area, provides more information about the distribution of depth throughout a lake. A lake with a large surface area and extensive shallow areas has a relatively small mean depth because of its small volume. A similar- sized lake with extensive deep areas has a larger mean depth because of its large volume. For large lakes, mean depth is considered the primary morphometric variable because of its general inverse correlation with lake productivity (Wetzel, 1975). Mean depth also has been an important variable in the development of empirical models relating lake productivity to nutrient loadings (Reckhow and Chapra, 1983). The vertical distribution of water-quality proper— ties and constituents are closely linked to the physical limnological processes of thermal stratification and convective circulation. Thermal structure in some lakes may be established, in part, by riverine inflows routed as overflow. However, the major source of heat for most lakes is the solar radiation that impinges upon the lake’s surface (Wetzel, 1975). Wind energy distributes the surface heat into the water column until density dif— ferences impede deeper mixing. In lakes deep enough to resist full-depth convective circulation, solar heating and wind mixing during the summer vertically segre- gate the water column into three zones: epilimnion, metalimnion, and hypolimnion. The upper zone, the Physical Limnology 15 epilimnion, is the stratum in which most of the lake’s biological production occurs because light is generally sufficient to drive photosynthetic production by phy— toplankton. The metalimnion is the stratum of maxi- mum temperature change; density differences may be sufficient to impede settling of detrital material into the lower stratum, the hypolimnion. A thermocline is present within the metalimnion if the rate of tempera- ture change exceeds 1°C/m. The hypolimnion overlies the lakebed sediments and typically has more thermal stability than the epilimnion and metalimnion do. Dur- ing thermal stratification, the hypolimnion is isolated from atmospheric exchange and may develop a dis- solved oxygen deficit if biological and chemical oxy— gen demands exceed the oxygen mass available within the hypolimnion at the onset of thermal stratification. During the spring and autumn, solar radiation is less than during the summer, and windy conditions also are more prevalent. This combination facilitates convective circulation, the process whereby a weakly stratified water column undergoes vertical mixing when wind energy is sufficient to overcome the thermal gradient. A lake is termed dimictic if it undergoes convective circulation in the spring and autumn. Such mixing is an important mechanism for the vertical movement of water-quality constituents such as dissolved oxygen, nutrients, and trace elements. Among-Lake Comparisons The three lakes differ widely in many of their physical limnological characteristics, as shown in table 5. The drainage areas of Coeur d’Alene and Flat- head Lakes are comparable; that of Lake Pend Oreille is about 6 times larger. Coeur d’Alene Lake has a sur- face area about 4 times smaller than Flathead Lake’s; the surface area of Lake Pend Oreille is about midway between that of the other two lakes. These areal differ— ences yield ratios of drainage area to lake surface area of 23 for Flathead Lake, 75 for Coeur d’Alene Lake, and 190 for Lake Pend Oreille. On the basis of this ratio alone, Flathead Lake is expected to be the least affected by its drainage basin and Lake Pend Oreille to be the most affected. A similar conclusion results from comparison of areal water loads for the three lakes. Owing to the large surface area of Flathead Lake, the lake’s mean annual outflow volume yields the smallest areal water load of the three lakes. The large mean annual outflow volume for Lake Pend Oreille, coupled with its intermediate surface area, yields the largest areal water load. When mean annual outflow volume and lake vol- ume are considered together and expressed as hydraulic residence time, a more realistic appraisal of the hydro- logic effects of the drainage basins on physical mixing processes within the three lakes is obtained. Lake vol- umes range from 2.8 (Coeur d’Alene Lake) to 53.9 km3 (Lake Pend Oreille), as shown in table 5. Coeur d’Alene Lake also has the shortest hydraulic residence time, 0.50 year, and so by this measure is expected to be the most rapidly affected by runoff from its drainage basin. The other two lakes have longer and comparable hydraulic residence times; 2.2 years for Flathead Lake and 2.4 years for Lake Pend Oreille. Although Lake Pend Oreille has about twice the vol- ume of Flathead Lake, it also has about twice the mean annual outflow volume. To put these values in perspec- Table 5. Physical limnological characteristics, Coeur d’Alene Lake and Lake Pend Oreille, ldaho, and Flathead Lake, Montana [km2, square kilometers; 111, meters; km, kilometers; km3, cubic kilometers; m/yr, meters per year; yr, year] Coeur Pend Characteristic, unit d'AIene Flathead Oreille1 Drainage area at outlet, km2 9,690 1 1,400 62,700 Lake surface elevation2, m 648.7 879 628.6 Surface areaz, km2 129 496 329 Maximum length, km 35 56 50 Maximum effective length, km 12.5 56 21 Maximum width, km 3.7 26 10.8 Maximum effective width, km 3.7 18.5 10.8 Volumez, km3 2.8 23.2 539 Maximum depthz, m 63.7 113 357 Mean depth2, m 21.7 50.2 164 Drainage areazsurface area, unitless 75 23 190 Mean annual outflow volume, km3 5.6 10.6 22.6 Areal water 16ad3, m/yr 43.2 21.4 68.6 Hydraulic residence time3, yr .50 2.2 2.4 Trap efficiency3, percent 93 97 98 1 Exclusive of Pend Oreille River outlet arm, which starts at Sand- point, Idaho. 2 At normal full pool. 3 At mean annual outflow volume. 16 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake tive, note that hydraulic residence times (equivalently, retention times) may be on the order of days for rapidly flushed, run-of—the-river reservoirs to hundreds of years for large—volume lakes with relatively small drainage basins. Examples of the latter are Crater Lake, Oregon, with a retention time of about 150 years, and Lake Tahoe, California/Nevada, with a retention time of about 700 years. The trap efficiency for sediment delivered to these three lakes was estimated empirically by applying their hydraulic residence times to the previously discussed nomograph presented by Gray (1973). As listed in table 5, trap efficiencies for the three lakes exceed 90 percent. Coeur d’Alene Lake has the smallest, 93 percent; the other two lakes can be expected to trap more than 97 percent of the sediment loads delivered to them. Morphometric features for the three lakes are illus- trated in bathymetric maps (figs. 2—4) and described in table 5. None of these lakes approaches a round shape, but Coeur d’Alene Lake is the smallest in width and length (3.7 and 35 km, respectively); Flathead Lake is the largest (26 and 56 km, respectively); and Lake Pend Oreille is intermediate (10.8 and 50 km, respectively). The degree of lake surface exposure to wind can be judged on the basis of maximum effective widths and lengths. Coeur d’Alene Lake has the smallest dimen- sions for these two variables, 3.7 and 12.5 km, respec- tively. Flathead Lake’s maximum effective width is about 70 percent of its maximum width, but its maxi- mum effective length and maximum length are equal (56 km). Lake Pend Oreille’s maximum effective width and maximum width are also equal (10.8 km); how- ever, its maximum effective length is less than half of its maximum length. Given equivalent Wind conditions, Flathead Lake is most exposed to wind over both width and length; Coeur d’Alene Lake is least exposed in both dimensions. Bathymetric contours of the three lakes (figs. 2—4, table 5) illustrate Lake Pend Oreille as the deepest; it is more than 3 times deeper than Flathead Lake and about 5.5 times deeper than Coeur d’Alene Lake. Lake Pend Oreille also has the largest mean depth (164 m), where- as Coeur d’Alene Lake has the smallest (21.7 m). Ow— ing to its large mean depth and intermediate-sized sur- face area, Lake Pend Oreille has the largest volume (53.9 km3) of the three lakes. Woods (1993b) calcu— lated that about 95 percent of Lake Pend Oreille’s vol- ume is contained in the deep southern basin, south of the northernmost extent of depths greater than 200 m (fig. 4). Although not uniform, the variation of depth in the other two lakes is less dramatic than in Lake Pend Oreille (figs. 2 and 3). The foregoing section presented morphometric characteristics that indicate the extent to which runoff from the drainage basin or wind-induced mixing can produce varying degrees of water-column turbulence among the three lakes. These morphometric character- istics are fairly general and easily derived from bathy- metric maps and annual discharge data. The next three sections present additional data to evaluate in more detail how water-column turbulence in each lake is affected by interannual (among multiple years) and intraannual (within each year) variations in hydraulic residence time, inflow-plume routing, thermal stratifi- cation, and convective turnover. Coeur d’Alene Lake The hydraulic residence time of 0.50 year for Coeur d’Alene Lake (table 5) is based on a normal full- pool volume of 2.8 km3 divided by the mean annual outflow volume of 5.6 km3. The lake’s outflow volume statistics were derived for an 87-year period of record (19l3~2000) for the USGS gaging station Spokane River near Post Falls, Idaho (station 4, fig. 1) (Brennan and others, 2001). Over that period of record, however, annual mean outflow volume has varied widely. For the minimum outflow volume of 1.9 km3, the hydraulic residence time increases to 1.5 years; conversely, for the maximum outflow volume of 10.5 km3, hydraulic residence time decreases to 0.27 year. This range of hydraulic residence times indicates that, in the absence of any inflow, the lake theoretically could drain in as few as 98 days or as many as 548 days. This range also represents an index of the physical limnological pro- cess of water-column turbulence and its presumed rela- tion with hydraulic residence time. Coeur d’Alene Lake’s ability to retain constituents delivered from its drainage basin is expected to decline as hydraulic resi- dence time declines. In addition to interannual variability, outflow from the lake varies intraannually in response to climatologi- cal conditions within its drainage basin. Over the 1913—2000 period of record, the smallest monthly mean outflow of 0.07 km3 occurs in August, whereas the largest monthly mean outflow of 1.3 km3 occurs in May. On the basis of these outflow volumes, constitu- Physical Limnology 17 18 Spokane River outflow EXPLANATION _30_ Line of equal depth below lake surface at a normal full-pool elevation of 648.7 meters. Contour interval 10 meters. Datum is National Geodetic Vertical Coeur Datum of 1929 d 'A lene Lake A Limnological station and 3 number Leveas . . J _ , Coeur d'AIene szer mflow Base from US. Geological Survey 0 1 2 3 4 5 KILOMETERS Benewah Lake, 1981; Black Lake, 1981; Chatcolet, 1981; Coeur d'AIene, 1981; Fernan Lake, 1981; Harrison, 1981; 0 1 2 3 MILES Mica Bay, 1981; Mt. Coeur d'AIene, 1981; Post Falls, 1981;Wor|ey, 1981; 1:24,000 Universal Transverse Mercator (UTM) projection, Zone 11 47° 22' 30" 47° 22' 30" K‘ 5 A15 ““9“ St Joe River inflow 116° 45' Figure 2. Bathymetry and locations of selected limnological stations, Coeur d'AIene Lake, Idaho. Fate and Transport of Nitrogen and Phosphorus, Coeur d’AIene Lake, Lake Pend Oreille, and Flathead Lake Flathead River inflow 114° 07' 30" #- Fl athea d ' ‘ 30 Lake 48 00' ‘ 48° 00' EXPLANATION — so — Line of equal depth below lake - A surface at a normal full-pool 1 elevation of 879 meters. Contour interval is variable. Datum is National Geodetic Vertical Datum of 1929 A Limnological station and number Flathead Lake Contours from Stanford and others (1997) 114° 15‘ 47° 52' 31" u 470 52. 30.. 114°15' ' 47° 45' 47° 45' Base from US. Geological Survey, 1:24.000 Universal Transverse Mercator (UTM) projection, Zone 12 0 1 2 3 4 5 6 7 8 9K|LUMETERS H—rL—‘w—‘fiJ—H—L—H 0 l 2 3 4 5M|LES Flathead River outflow 114° 07' 30" Figure 3. Bathymetry and locations of selected limnological stations, Flathead Lake, Montana. Physical Limnology 19 116°30' ’ ‘ 116° 25' \ Sandpoint . 16° 20' 116° 35 5° \ 48° 15' 116° 25‘ 3 48° 15' Warren Island 116° 35' Pend Oreille River (Outlet arm of 116° 15' Lake Pend Oreille) Mama/nose EXPLANATION ’5’” ~— 350 — Line of equal depth below lake surface at a normal full-pool o . elevation of 628.6 meters. “6 25 , Contour interval 50 meters. Datum is National Geodetic 43° 10' 1 N 48° 10' Vertical Datum of 1929 250 ‘\ g I 50 Clark F ark _____ Approximate boundary separa- 30° 1 - inflow ting northern and southern ,1 “so 15. basins of Lake Pend Oreille /' A Limnological station and fid \VVV 3 number ,~// 116° 20' (Zn-v _, 116° 25' Lake 48° 05' 43° 05- Pend Oreille U 1 2 3 4 5 6 KILUMETERS 48° 00' 0 1 2 3 4 MILES Base from US. Geological Sun/ey Bayview, 1967; Clark Fork, 1989; Cocolalla, 1968; Hope, 1989; Lakeview, 1961; Minerva Peak, 1989; Oden Bay, 1989; Packsaddle Mtn., 1989; Sandpoint, 1968; Talache, 1989; Trout Peak, 1989; 1224,000 116° 30I Universal Transverse Mercator (UTM) projection, Zone 11 Figure 4. Bathymetry and locations of selected limnological stations, Lake Pend Oreille, Idaho. 20 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake ents delivered to the lake by way of inflow more likely would be retained in August when water-column turbu- lence is least. The fate and transport of nutrients in Coeur d’Alene Lake are highly dependent upon inflow-plume routing of the lake’s two primary inflow sources, the Coeur d’Alene and St. Joe Rivers. Inflow—plume rout- ing was evaluated using water—temperature data from two limnological studies of Coeur d’Alene Lake con- ducted in the 1990s. Water-temperature data for the two rivers and numerous full-depth profiles of water tem- perature collected for most months during 1991—92 at six limnological stations (fig. 2) were reported by Woods and Beckwith (1997). The 1999 water year also was evaluated using similar water-temperature data; however, lake water-column sampling was conducted only during June through October at limnological sta— tions 1, 3, 4, and 5 (fig. 2) (URS Greiner Inc., and CH2M-Hill Inc., 2001b). The 44 comparisons of inflow and lake temperatures reported for 1991 —92 and 1999 indicated that overflow was the most common mode of inflow-plume routing in about 60 percent of the comparisons (URS Greiner Inc., and CH2M-Hill Inc., 2001b). Interflow or underflow was identified in about 20 percent of the comparisons. Overflow occurred in all months except October, November, and December; during those 3 months, underflow was the most likely mode of inflow—plume routing. Interflow tended to occur during the spring or autumn when the lake was most likely to be transitioning into or out of thermal stratification. Inflow volume also was evalu- ated as part of the 44 comparisons because it affects the spatial extent of inflow-plume routing. At small inflows, the plume’s influence on the lake is reduced by rapid mixing and equilibration of riverine and lake tem- peratures; the converse is true for large inflows. Under- flows tended to be associated only with small inflows, typical for the period October through December. Underflows occurred during that 3—month period because the Coeur d’Alene and St. Joe Rivers cooled more rapidly than the lake, which has a much greater capacity to store heat. Overflows occurred over a wide range of inflows because both the Coeur d’Alene and St. Joe Rivers have lengthy backwater-affected reaches that produce heating of inflow water by solar radiation. The extent of inflow-plume routing into Coeur d’Alene Lake during the 1990s also was evaluated using unpublished information and several data sets not used in the foregoing analysis. A powerful storm dur- ing February 1996 dropped several inches of rain on a substantial snowpack and created severe flooding in northern Idaho river basins, including the Coeur d’Alene and St. Joe River Basins. This storm delivered a large volume of sediment to Coeur d’Alene Lake and produced visible turbidity throughout the lake for sev- eral months (G.F. Harvey, Idaho Department of Envi- ronmental Quality, written commun., 2000). The mag- nitude of the February 1996 flood peaks at four long- term USGS gaging stations in the Coeur d’Alene and St. Joe River Basins was within about 10 percent of the 100-year flood peak, on the basis of data reported by Beckwith and others (1996). On February 10, 1996, the suspended-sediment concentration in the Coeur d’Alene River near its point of inflow to the lake (USGS gaging station Coeur d’Alene River near Harri- son, Idaho, station 3, fig. 1) was 620 mg/L (Beckwith, 1996) and was associated with a mean daily discharge of 1,550 m3/s. In comparison, the median and range of suspended-sediment concentrations in nine samples collected at the Harrison station during the 1999 water year were, respectively, 3.7 mg/L and 1.5 to 56 mg/L (A.J. Horowitz, US. Geological Survey, written com- mun., 1999); the latter concentration was associated with a mean daily discharge of 350 m3/s. Inflow—plume routing into the lake also was evalu- ated using data collected during snowmelt runoff in the 1997 water year. Changes in water-temperature profiles and water-column transparency (G.F. Harvey, Idaho Department of Environmental Quality, written com- mun., 2000) tracked the movement of the inflow plume into and through Coeur d’Alene Lake during May and June 1997. The temperature of the inflow plume on June 6, 1997, was 11°C, as measured at Coeur d’Alene River near Harrison (station 3, fig. 1) (Brennan and others, 1998). Water-column temperature profiles recorded on May 28 at four lake stations indicated the inflow plume was routed as a combination of overflow and interflow within the upper 10 m of the water col- umn. Water-column transparencies, as measured by secchi disc on May 28, at the central and northern lake stations (stations 3 and 1, respectively, fig. 2) were 1.1 and 2.0 m, respectively. By comparison, annual mean water-column transparencies at all four stations during 1995 through 1999 ranged from 8 to 9 m (G.F. Harvey, Idaho Department of Environmental Quality, written commun., 2000). Water-quality data from these large—volume dis- charge events of 1996 and 1997 revealed the routing of intact riverine inflows into and through Coeur d’Alene Lake; these inflows had been considered infrequent and Physical Limnology 21 exceptional hydrologic events in which the lake acted as a conduit for the transport of constituents from the Coeur d’Alene and St. Joe Rivers to the Spokane River. However, this conceptual model was invalidated by the results of a limnological study, described in the follow- ing paragraph, of inflow-plume routing conducted dur- ing snowmelt runoff in the 1999 water year. Discharge and chemistry of the Coeur d’Alene and St. Joe River inflow plumes into and through Coeur d’Alene Lake were tracked by USGS scientists using specialized water-quality instrumentation and water- column sampling. The short-term study sought to answer two questions: (1) can the riverine inflows and their associated chemical nature be clearly identified within the lake? and (2) do sediment, nutrients, and trace elements carried by the riverine inflows travel far enough into the lake to be discharged out of the lake into the Spokane River? The field work was conducted during June 2 and 3, 1999, at limnological stations 1 through 5, in addition to three stations at the mouths of the Coeur d’Alene and St. Joe Rivers and at the lake’s outlet into the Spokane River (fig. 2). The study results, reported in URS Greiner Inc., and CH2M-Hill Inc. (2001b), clearly identified the riverine inflows as a combination of overflow and interflow within the upper 5 to 13 m of the lake, from limnological station 4 and northward to the lake’s outlet. Much of the lake south of limnological station 4 is shallow enough to allow full-depth mixing of the two riverine inflows. Only marginal influence from Coeur d’Alene and St. Joe River inflows was measured at limnological station 2, which is somewhat isolated from the northward flow of the two rivers. Light transmission, conductivity, and concentrations of lead, zinc, and nitrogen differed sub- stantially between the riverine inflows and lake water. Lead concentrations delivered by the Coeur d’Alene River were larger than those in lake water. Zinc con- centrations, also delivered almost exclusively by the Coeur d’Alene River, were smaller than those in lake water. Light transmission, conductivity, and nitrogen concentrations in riverine water also were smaller than those in lake water. The chemical nature of water exit- ing the lake to the Spokane River was more closely related to riverine inflows than to lake water. The January 1999 transport of sediment, nutrients, and trace elements through Coeur d’Alene Lake and into the Spokane River was measured during a snowmelt runoff event that occurs about every other year, on the basis of long-term streamflow records for the USGS gaging sta- tion Coeur d’Alene River at Cataldo (station 2, fig. 1) (Kjelstrom and others, 1996). The extent of thermal stratification and convective circulation in Coeur d’Alene Lake was evaluated on the basis of isopleth diagrams of water temperature report— ed for 1991—92 at six limnological stations (fig. 2) by Woods and Beckwith (1997) and water-temperature profiles measured during 1995—99 at limnological sta— tions 1, 3, and 4 (G.F. Harvey, Idaho Department of Environmental Quality, written commun., 2000). The lake was thermally stratified during 1991—92, com— monly between early June and mid-November. Stratifi- cation developed in early June from a combination of solar heating of the lake’s upper water column and riv- erine inflows from the Coeur d’Alene and St. Joe Riv- ers (URS Greiner Inc., and CH2M-Hill Inc., 2001b). Those riverine inflows were delivered into the lake as overflows because both rivers have lengthy, backwater- affected lower reaches that facilitate heating by solar radiation. During 1991, thermoclines had developed by mid—July and persisted until early October; thermo- clines again developed in 1992 from mid-June until early October. Although thermoclines were lost in early October, the lake remained thermally stratified into mid-November. The maximum thermocline depth was 16.5 min 1991 and 21.5 min 1992. Similarly, maxi- mum thermocline depths during 1995—99 ranged from 15 to 24 m; their duration could not be determined because water—column profiles typically were mea- sured during July through October and, thus, did not encompass the temporal extent of thermal stratification. Over those 7 years (1991—92, 1995—99), and when thermoclines were present, the epilimnion depth aver- aged about 10 in during July through September; this represents about 38 percent of the lake’s total volume, on the basis of the depth-to-volume curve presented by Woods and Berenbrock (1994). The upper depth limit of the hypolimnion over the same period averaged 15 m; thus, the hypolimnion constituted about 50 percent of the total lake volume. The remaining 12 percent of lake volume constituted the metalimnion during July through September. Coeur d’Alene Lake is dimictic in that it undergoes convective circulation twice a year. Spring circulation during 1991—92 was in April; the fall circulation was in late November of both years. The incidence of spring and fall circulation during 1995—99 could not be evaluated. 22 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake Flathead Lake The hydraulic residence time for Flathead Lake is 2.2 years (table 5), or about 4 times longer than for Coeur d’Alene Lake. That value is based on a normal full-pool volume of 23.2 km3 divided by the mean annual outflow volume of 10.6 km3. The lake’s outflow volume statistics were derived for a period of record from 1984 to 2000 for the USGS gaging station Flat- head River at Perma, Montana (station 1, fig. 1) (Shields and others, 2001). The range in hydraulic resi— dence time for this period was between 2.9 years, based on the minimum mean annual outflow of 7.9 km3, and 1.4 years, based on the maximum mean annual outflow of 16.1 km3. The variation in outflow volume within a year was evaluated on the basis of minimum and maxi- mum values for monthly mean outflow volume. For the 1984—2000 period of record, the mean minimum out— flow volume of 0.63 km3 was in August, whereas the mean maximum outflow volume of 1.7 km3 was in June. On the basis of these monthly mean outflow vol- umes, constituents delivered into the Flathead Lake by inflow would more likely be retained in August be- cause of reduced water-column turbulence. In-lake routing of the Flathead River’s inflow within Flathead Lake was reported to occur as overflow during snowmelt runoff during 1978—80 (Stuart, 1983) and during 1983 (Stanford and others, 1983). The pres— ence of overflow was determined from water-column profiles of percent light transmission (expressed as tur— bidity), spatial changes in transparency, and the lake’s thermal structure in relation to the temperature of the influent Flathead River. The vertical extent of the plume ranged from the lake surface to between 10 and 50 m in 1979 (Stuart, 1983). The augmentation of lim— nological sampling with aerial photography permitted evaluation of the advective transport of the plume with— in the lake; two generalized patterns emerged from this evaluation. In 1979, the plume (10°C) entered the lake (4°C) in early May and traveled directly south; by late June, the plume had spread throughout the lake. The second pattern, observed in 1980 and 1983, was more complicated. After entering the lake, the plume was deflected along the western shore until it met the con— striction formed by the lake’s shallow southern basin (fig. 3). From there, the plume split; part traveled south toward the lake’s outlet, and the rest traveled northward along the lake’s eastern shore. Stanford and others (1983) also noted an instance in which strong, westerly winds pushed the plume away from the western shore and toward the middle of the lake. The dynamics of inflow-plume routing in Flathead Lake have been ascribed by Stanford and others (1983) to the following five principal factors: (1) natural flowpath of the Flat- head River from north to south, (2) magnitude of snow- melt runoff, (3) density differences between influent Flathead River and the lake’s thermal structure, (4) Coriolis effect owing to the Earth’s rotation, and (5) co-occurrence of “thermal bars” adjacent to the littoral area of the lake during snowmelt runoff. Patterns of thermal stratification and convective circulation in Flathead Lake were evaluated (Stanford and others, 1983) on the basis of isopleth diagrams of water temperature reported for 1978—79 at limnologi- cal station 2 (fig. 3). Additionally, the spatial similarity in thermal stratification among seven limnological sta- tions was verified using selected temperature profiles from September 1991 through August 1993 (Stanford and others, 1994). Flathead Lake was thermally strati— fied between June and late October during 1978—79; thermocline depths ranged from about 8 to 15 m. Ther- mal stratification developed largely from solar heating of the lake’s upper water column. Riverine input of snowmelt runoff as overflow was less of a factor in the development of thermal stratification because runoff preceded the onset of thermal stratification by about 1 month. When thermoclines were present during 1978—79, the epilimnion depth averaged about 12 m; this represents about 20 percent of the lake’s total volume on the basis of the depth-to-volume curve for Flathead Lake (RF. Woods, US. Geological Survey, written commun, 2002). The upper depth limit of the hypolimnion during 1978—79 averaged 25 m; thus, the hypolimnion constituted about 5 8 percent of the total lake volume. The remaining 22 percent of lake volume constituted the metalimnion during thermal stratifica- tion. Similar to Coeur d’Alene Lake, Flathead Lake is dimictic. During 1978—79, spring Circulation was in April, about 1 month prior to snowmelt runoff; the fall circulation was in early November. Lake Pend Oreille Lake Pend Oreille has the longest hydraulic residence time, 2.4 years, of the three lakes (table 5). That value is based on a normal full-pool volume of 53.9 km3 divided by the mean annual outflow volume of 22.6 km3. The lake’s outflow volume statistics were Physical Limnology 23 derived for a 97—year period of record (1903—2000) for the USGS gaging station Pend Oreille River at New- port, Washington (station 7, fig. 1) (Brennan and others, 2001). Annual mean outflow volume varied widely over that period of record, ranging from 11.5 to 34.7 km3. The corresponding range in hydraulic resi- dence time was 4.7 to 1.6 years. In addition to interan- nual variability, outflow from the lake also varied widely during each year. Monthly mean outflows for the 1903—2000 period of record indicated that the mean minimum outflow volume of 1.0 km3 was in September, whereas the mean maximum outflow vol— ume of 4.6 km3 was in June. In-lake routing of the Clark Fork’s inflow plume within Lake Pend Oreille during snowmelt runoff was evaluated using data and observations from several sources. The nutrient load/lake response study con— ducted during 1989—90 by the USGS and IDEQ included a limnological assessment of the lake’s pelagic, or open—water, zone (Woods, 1993a). As part of that assessment, the vertical and horizontal distribu— tion of the inflow plume was tracked on May 18, 1989, using aerial photography and in—lake profiles of spe- cific conductance, water temperature, and percent light transmission (measured with an in-situ transmissome- ter). Inflow-plume tracking was performed a few days after a week of elevated Clark Fork inflow discharges, ranging from 1,440 to 1,850 m3/s (Harenberg and oth- ers, 1990), that were measured at the USGS gaging sta- tion Clark Fork at Whitehorse Rapids near Cabinet, Idaho (station 6, fig. 1). Results from the in—lake pro- files showed that the more turbid river water over- flowed the lake water to a depth of about 30 m. Aerial photographs (M.A. Beckwith, US. Geological Survey, written commun., 1989) revealed that most of the tur- bid riverine plume was routed into the lake’s northern basin. However, part of the inflow plume was routed into the lake’s southern basin, as evidenced by a de- crease in transparency measured by secchi—disc read- ings at limnological station 2 (fig. 4); transparency decreased from 10 min late April to less than 5 m dur- ing mid—May through mid-June. Runoff from snowmelt in 1990 began in late May and was of a longer duration and larger magnitude than in 1989; unfortunately, in- clement weather and hazardous lake conditions pre- vented a repeat of the aerial photography and transmis- someter profiles. However, decreased transparency throughout the lake’s pelagic zone during June 1990 clearly demonstrated that the turbid inflow plume was distributed lakewide. The shallowest transparencies during the 1990 snowmelt runoff were in the lake’s northern basin, indicating that the inflow plume’s effects were more pronounced there. The aerial photographs taken in mid—May 1989 raised the question of why the turbid inflow plume of the Clark Fork was routed so distinctly into Lake Pend Oreille’s northern basin. As shown in figure 4, the Clark Fork enters the lake near the approximate bound- ary between its northern and southern basins. Four physical limnological processes can be suggested as explanations for the plume’s northward, not southward, destination. Two processes, wind-driven surface cur- rents and outflow-induced currents, are unlikely be— cause of the transitory nature of the surface currents and the 65-km distance from the Clark Fork inlet to the lake’s outlet at Albeni Falls Dam. The third process, counterclockwise circulation induced by the Coriolis effect, is also unlikely because it would be expected to have the opposite effect: routing of the inflow plume southward along the lake’s western margin. The fourth process, development of a thermal bar near the approx— imate boundary between the northern and southern basins, is the most plausible explanation. A thermal bar, or vertical transition zone of 4°C water separating littoral and pelagic water masses, results from density gradients produced when shallow littoral water heats more rapidly than pelagic water does (Wetzel, 1975). Although water-temperature profiles for suitably located positions in Lake Pend Oreille were not avail- able with which to quantitatively evaluate this process, the bathymetry of the lake (fig. 4) can be used to postu- late the likelihood of thermal bar development. The southern basin has a mean depth of 220 m, whereas the northern basin has a mean depth of 29 m (Woods, 1993b), resulting in a very large difference in heat-stor- age capacity between these two basins. The more rapid warming of the northern basin could facilitate develop— ment of a thermal bar near the approximate boundary separating the two basins. This explanation is sup- ported by the aerial photographs of May 18, 1989 (shown on front cover of this report), which clearly show the turbid inflow plume flowing northwesterly along the approximate boundary between the northern and southern basins. In addition to inflow—plume routing during snow- melt runoff, the limnological assessment of 1989—90 (Woods, 1993a) produced data with which to assess the inflow—plume routing of the Clark Fork within Lake Pend Oreille during the 1989—90 non-snowmelt runoff periods. Frequent water-temperature measurements of 24 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake the Clark Fork’s inflow (Harenberg and others, 1990, 1991) and numerous full-depth profiles of water tem— perature at limnological station 3 (fig. 4) allowed evalu- ation of inflow—plume routing for December through September of 1989 and 1990. Fifty—seven comparisons of river and lake temperatures indicated that overflow was the most common mode of inflow-plume routing, identified in about 75 percent of the comparisons. Characteristics of thermal stratification and con- vective circulation in Lake Pend Oreille were evaluated (Woods, 1993a) on the basis of isopleth diagrams of water temperature reported for 1989—90 at limnologi— cal stations 1 through 4 (fig. 4). During 1989—90, the lake was thermally stratified at the three deepest (depth greater than 70 m) stations (1 through 3), commonly between June and mid-October. Thermocline depths ranged from 8 to 20 m in 1989 and from 8 to 16 m in 1990. In contrast, the lake at limnological station 4 was shallow (depth less than 20 m) and did not thermally stratify for any appreciable period. The development of thermal stratification by early to mid-June at the two deepest stations in the lake’s southern basin was largely the result of solar heating of the lake’s upper water col- umn and not the Clark Fork’s input of snowmelt runoff, which was routed as overflow, primarily through the lake’s shallow northern basin, during May and June. Water-column turbulence generated by such inflow- plume routing likely inhibited the development of ther- mal stratification at limnological station 4, owing to shallow depth. In contrast, the lake at limnological sta- tion 3 in the northern basin was thermally stratified, through a combination of inflow-plume routing as overflow and solar heating of the lake’s upper water column. During 1989—90 and when thermoclines were present, the epilimnion depth averaged about 15 m; this represents about 7 percent of the lake’s total volume, on the basis of the depth-to-volume curve for Lake Pend Oreille developed by Fields and others (1996). The upper depth limit of the hypolimnion over the same period averaged 30 m; thus, the hypolimnion con- stituted about 85 percent of the total lake volume. The remaining 8 percent of lake volume constituted the metalimnion. Similar to the other lakes, Lake Pend Oreille is dimictic; however, such circulation may not extend full depth during each occurrence. Spring circu- lation during 1989—90 was in April; the fall circulation was in mid-October of both years. CHEMICAL AND BIOLOGICAL LIMNOLOGY Overview Hydrologic and constituent budgets, in conjunc— tion with drainage basin and physical limnological characteristics, are important tools for evaluating fate and transport of constituents within a lake. However, the quantitative differences between input and output loads represent the net influence of limnological pro- cesses because constituent loads output from a lake result from the integration of all hydrologic, physical, chemical, and biological processes that operate within the lake. To distinguish the hydrologic and physical limnological effects on constituent fate and transport from those associated with chemical and biological processes, the third phase in the evaluation, discussed in this section, focuses on spatial and temporal varia- tions in chemical and biological characteristics within each lake’s water mass. Subsequent to their delivery into a lake as dis- solved, colloidal, and particulate fractions, nonconser— vative nutrients such as nitrogen and phosphorus can be involved in a variety of chemical and biological pro— cesses. Both nitrogen and phosphorus are involved in biological processes because they are essential for phy— toplankton production. Phytoplankton assimilation of dissolved inorganic nitrogen (nitrite, nitrate, and ammonia) and orthophosphorus converts some of the dissolved fraction to a particulate, organically bound fraction that has several possible fates. Advective trans- port physically redistributes the particles within the lake, a part of which may exit the lake as an outflow load. The particles also can be retained within the water column by turbulence and be subjected to re- mineralization or uptake by zooplankton grazing. Dis- solved and colloidally bound fractions are subject to in- lake sedimentation when converted to particles by pro- cesses such as assimilation, precipitation, complex- ation, and adsorption. Sedimentation delivers the parti- cles to the lakebed, where they may be subjected to remineralization and possible recycling back into the . water column; alternatively, subsequent sedimentation may permanently bury the particles within the lakebed. Many of these chemical and biological processes are dynamic and transient; they occur over short time- frames (seconds to days) and, thus, are difficult to quantify and evaluate accurately when limnological samples are collected over weekly or longer time- Chemical and Biological Limnology 25 frames. Limnological sampling at these three lakes was conducted over timeframes of 2 to 8 weeks. Conse— quently, much of the following evaluation of chemical and biological limnology focuses on spatial and tempo- ral comparisons of mean conditions among the three lakes. Trophic State One of the primary reasons that nutrient loading has been assessed for many lakes was to evaluate bio— logical productivity in relation to nutrient enrichment, or eutrophication; such is the case for Coeur d’Alene and Flathead Lakes and Lake Pend Oreille. Trophic state provides the initial comparison of chemical and biological limnology among the three lakes. Trophic state refers to the biological productivity of a water body and integrates the physical, chemical, and biological processes within that water body. For ease of categorization, three trophic states commonly are defined: oligotrophic (low productivity), meso- trophic (intermediate productivity), and eutrophic (high productivity). Numerous variables have been employed as a basis for trophic-state classification. Although no classification system is universally accepted, variables such as total phosphorus, total nitrogen, chlorophyll-a, and secchi-disc transparency frequently have been used to classify trophic state. The United Nation’s Organiza- tion for Economic Cooperation and Development used these four variables to develop a statistically based, open-boundary, trophic—state classification system (Ryding and Rast, 1989), which is shown in table 6. An open-boundary system compensates for the overlap in classification that commonly occurs with fixed—bound- ary, or single-value, systems. Under the open-boundary system, a water body is considered to be classified cor— rectly if three of the four upper water—column variables are within two standard deviations of their geometric mean for the same trophic state. On the basis of annual geometric mean values for upper water—column concentrations of total phosphorus, total nitrogen, chlorophyll—a, and transparency (table 7), all three lakes can be classified as oligotrophic. Even though the three lakes received very different quantities of nitrogen and phosphorus from their drainage basins (tables 1—3), their biological responses, expressed as trophic state, were quite similar. Another important water-quality characteristic used to delineate trophic state is hypolimnetic dis- solved oxygen. As lake productivity increases, decreases in hypolimnetic dissolved oxygen often result when biological and chemical oxygen demands Table 6. Trophic-state classification based on open-boundary values for four limnological variables [pg/L, micrograms per liter; m, meters; E, annual geometric mean; SD, standard deviation] Variable, unit1 Oligotrophic Mesotrophic Eutrophic E 8.0 26.7 84.4 ,3 _1SD 4.8—13.3 14.5—49.0 48.0—189 Total phosphorus (pg/L) x + 2SD 29—221 79—908 168—424 _ Ft 661 753 l,875 {:ISD 37l—l,l80 485—l,l70 86l—4,081 Total nitrogen (pg/L) x + 2SD 208—2,103 313—l,816 395—8913 E 1.7 4.7 14.3 xilSD .8—3.4 3.0—7.4 6.7—3l.0 Chlorophyll-a (pg/L) x + 25D .4-7.l 1.9—1 1.6 3.1—66.0 )_c 9.9 4.2 2.4 f :lSD 5.9— 16.5 2.4—7.4 1.5—4.0 Secchi—disc transparency (m) x i 2SD 3.6—27.5 1.4—13.0 .9—6.7 1 Modified from Ryding and Rast (1989). 26 Fate and Transport of Nitrogen and Phosphorus, Coeur d’AIene Lake, Lake Pend Oreille, and Flathead Lake Table 7. Trophic state of Coeur d’Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana, based on annual mean values for four Iimnological variables [pg/L, micrograms per liter; 111, meters; TS, trophic state; 0, oligotrop‘hic; M, mesotrophic] Total Total Secchi-disc phosphorus nitrogen Chlorophyll-a transparency (HQ/L) (Hg/L) (Hg/L) (m) Year 7:1 TS ’71 T8 7‘1 TS 7‘2 TS Coeur d’Alene 3 1991 5.6 O 282 O 0.43 O 4.0 M 1992 4.6 O 206 O .79 O 2.9 M Flathead4 1978482| 7.4 l o [120] 0| 1.2 l 0 j 8.8 | o Pend Oreille 5 1989 9.0 o 142 o .s o 7.0 o 1990 6.5 O 116 O .8 O 6.3 O l Lakewide, annual geometric mean concentration within upper water column for Coeur d’Alene Lake and Lake Pend Oreille. Annual mean con— centration at mid—lake station for Flathead Lake. 2 Lakewide, annual geometric mean for Coeur d’Alene Lake and Lake Pend Oreille. Annual mean at mid-lake station for Flathead Lake. 3 Data from Woods and Beckwith (1997). 4 Data from Stanford and others (1983) and Stuart (1983). 5 Data from Woods (1993a). exceed the oxygen mass within the hypolimnion. The oligotrophic nature of these three lakes (table 7) indi— cates a small potential for development of a hypolim- netic dissolved oxygen deficit. On the basis of water- column profiles of dissolved oxygen concentrations for Coeur d’Alene Lake (Woods and Beckwith, 1997), Flathead Lake (Stuart, 1983), and Lake Pend Oreille (Woods, 1993a), the three lakes have well—oxygenated hypolimnia. Hypolimnetic Nutrient Storage Most biological production in lakes is within the well-mixed upper water column (epilimnion), where solar radiation is sufficient to drive phytoplankton pho- tosynthesis. A notable difference among these three lakes is the percentage of epilimnion volume in relation to total lake volume: Lake Pend Oreille is 7 percent epilimnion, Flathead Lake is 20 percent epilimnion, and Coeur d’Alene Lake is 38 percent epilimnion. Rel- ative to the total volume, Lake Pend Oreille has the smallest epilimnetic volume in which to convert nutri- ents into phytoplankton biomass. Conversely, and in relation to total volume, Lake Pend Oreille has the larg- est hypolimnetic volume; Coeur d’Alene Lake has the smallest. In contrast to phytoplankton photosynthesis, remineralization throughout the water column converts particulate constituents into dissolved constituents throughout the year; however, the hypolimnion is the primary strata for remineralization. During thermal stratification, which often coincides with the period of elevated biological production, the hypolimnion re- ceives the downward “rain” of organic and inorganic constituents delivered into the lake or produced in the epilimnion and metalimnion. If convective circulation is strong enough to mix the entire water column, hypo- limnetic water can serve as a source of remineralized nutrients to augment biological production within the epilimnion. As lake depth increases, the potential for convective circulation to mix the entire water column decreases. The increase in depth also implies longer residence times for nutrient remineralization and a larger hypolimnetic volume for nutrient storage. In a large, deep lake such as Lake Pend Oreille, convective circulation of hypolimnetic nutrients into the epilim- nion may occur only sporadically. In contrast, in a shal- lower lake such as Coeur d’Alene Lake, convective cir- culation of hypolimnetic nutrients into the epilimnion may occur twice a year. Compared with Lake Pend Oreille and Coeur d’Alene Lake, Flathead Lake repre- sents an intermediate example because of its moderate depth and substantial exposure to wind. A comparison of hypolimnetic enrichment of total nitrogen among the three lakes indicates that annual mean concentrations of total nitrogen were larger in the hypolimnia than in the epilimnia (table 8). The ratios of epilimnetic to hypolimnetic concentrations ranged from 0.67 (Lake Pend Oreille in 1990) to 0.79 (Coeur d’Alene Lake in 1992). The relative difference between epilimnetic to hypolimnetic nitrogen concentrations was smallest in Coeur d’Alene Lake, the shallowest and most likely to undergo full-depth convective circulation. One possible explanation for that lake’s enriched hypolimnetic total nitrogen can be derived from the results of a 1999 study of benthic flux in Coeur d’Alene Lake done by the USGS (Kuwabara and others, 2000). Using an in-situ benthic flux chamber, the annual flux of dissolved inorganic nitrogen, a com— ponent of total nitrogen, from the lakebed sediments to Chemical and Biological Limnology 27 Table 8. Annual mean concentrations of total nitrogen and phosphorus within the epilimnion and hypolimnion at the deepest limnological stations, Coeur d'Alene Lake and Lake Pend Oreille, ldaho, and Flathead Lake, Montana [pg/L, micrograms per liter; EPI, epilimnion; HYPO, hypolimnion] Total nitrogen Total phosphorus (Hg/L) (Hg/L) E E Year EPI1 HYPO2 HYPO EPI‘ HYPO2 HYPO Coeur d’Alene 3 1991 292 375 0.78 4.6 4.8 0.96 1992 216 274 .79 2.9 2.8 1.04 Flathead 4 Sept. 1991— Aug. 1993 128 182 .70 5.1 5.8 .88 Pend Oreille 5 1989 160 212 .75 8 10 .80 1990 110 164 .67 6 10 .60 1 When water column not thermally stratified, refers to upper water column. 2 When water column not thermally stratified, refers to lower water column. 3 Limnological station 3 (fig. 2), data from Woods and Beckwith (1997). 4 Limnological station 2 (fig. 3), data from Stanford and others (1994). 5 Limnological station 2 (fig. 4), data from Woods (1993a). the overlying water column was calculated to be 270 ug/cmz. On the basis of that result and riverine loading data from Woods (2001), the contribution of nitrogen to Coeur d’Alene Lake from benthic flux was determined to exceed that delivered to the lake from its drainage basin by a factor of 1.5. Comparable studies of benthic flux have not been done for Flathead Lake or Lake Pend Oreille. Relative differences between epilimnetic and hypolimnetic total nitrogen concentrations were similar for those two lakes, regardless of large differ— ences in their depth. On the basis of epilimnetic to hypolimnetic ratios of phosphorus concentration, which ranged from 0.60 (Lake Pend Oreille in 1990) to 1.04 (Coeur d’Alene Lake in 1992), the three lakes represent a gradient in hypolimnetic enrichment of total phosphorus (table 8). Lake Pend Oreille exhibited a clearly defined case of hypolimnetic enrichment, presumably because of its large depth. Coeur d’Alene Lake exhibited the opposite condition, no enrichment of hypolimnetic total phos— phorus, presumably because of its shallow depth and short hydraulic residence time. Nutrient Partitioning Additional insight into chemical and biological limnology can be gained through analysis of the parti- tioning of nutrients into their bioavailable and particu- late fractions. The bioavailable fractions of nitrogen and phosphorus are defined here, respectively, as the sum of dissolved (<0.45 um) nitrite, nitrate, and ammo- nia and dissolved orthophosphorus. The following two comparisons were made: (1) epilimnion and hypolim- nion, and (2) inflow loads and outflow loads. EPILIMNION AND HYPOLIMNION In 1991, the mean percentages of bioavailable nitrogen and particulate nitrogen in the hypolimnion of Coeur d’Alene Lake were 25 and 75, respectively; whereas in the epilimnion, the mean percentages were 14 and 86, respectively (fig. 5). The percentage compo- sitions between the epilimnion and hypolimnion were even more dissimilar in 1992. The percentages of bio- available and particulate nitrogen between the epilim- nion and hypolimnion in Flathead Lake were compara- ble; in both strata, bioavailable nitrogen composed about 30 percent of total nitrogen. As in Coeur d’Alene Lake, percentages of bioavailable and particulate nitro— gen between the epilimnion and hypolimnion of Lake Pend Oreille also were dissimilar. However, in contrast to the other two lakes, bioavailable nitrogen in Lake Pend Oreille’s hypolimnion composed between 61 and 73 percent of total nitrogen, indicative of extensive remineralization and retention. Among the three lakes, the overall pattern for bio— available and particulate phosphorus composition, in relation to total phosphorus, was similar to that for nitrogen (fig. 5). The percentage difference between epilimnetic and hypolimnetic bioavailable phosphorus in Coeur d’Alene Lake was less than 10 percent in 1991 and 1992. Bioavailable phosphorus in both the epilimnion and hypolimnion composed a larger per- centage of total phosphorus in 1992 than in 1991 but was still less than 50 percent. In Flathead Lake, bio— available phosphorus in the epilimnion and hypolim- nion composed only 14 and 12 percent of total phos- phorus, respectively. In contrast, the percentage com- position of phosphorus in the epilimnion and hypolim- 28 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake Nitrogenl 100 , I l | ' 90—- _ Phosphorus2 ‘00 I I I I I 90— _ ANNUAL MEAN PERCENTAGE COMPOSITION .09 ‘09 .09 V. 09 .99 ‘.O9 .09 .09 .09 .09 \9 9 ($9 9 9 $9 \.\\®9 $9 $99 $9 $9 $99 99 $3 ‘(9 %*Q ‘(9 $3 <99 $4? (<9 «\Q 1991 1992 1991 —93 1989 1990 COEUR D'ALENE LAKE3 FLATHEAD LAKE4 LAKE PEND OREILLE5 EXPLANATION ! Bioavailable 3 Limnological station 3 (fig. 2), data from Woods and Beckwith (1997) l: Particulate 4 Limnological station 2 (fig. 3), data from Stanford and 1 Bioavailable nitrogen defined as sum of dissolved nitrite, nitrate, and others (1994) ammonia; particulate nitrogen defined as total minus bioavailable s Limnological station 2 (fig. 4), data from Woods (1993a) 2 Bioavailable phosphorus defined as dissolved orthophosphorus; particulate phosphorus defined as total minus bioavailable Figure 5. Annual mean percentage composition of bioavailable and particulate nitrogen and phosphorus concentrations, in relation to total nitrogen and phosphorus concentrations, respectively, within the epilimnion and hypolimnion at the deepest limnological stations, Coeur d'Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana. Chemical and Biological Limnology 29 nion of the deepest of the three lakes, Pend Oreille, was substantially different. Within the epilimnion, bioavail- able phosphorus composed 35 percent, on average, of total phosphorus; that percentage increased to 75 within the hypolimnion. As with nitrogen, this increase was indicative of significant remineralization and retention of phosphorus within the hypolimnion of Lake Pend Oreille. INFLOW LOADS AND OUTFLOW LOADS The annual mean percentage composition of bio— available and particulate nitrogen and phosphorus, in relation to total concentrations of nitrogen and phos- phorus in inflow and outflow loads for the three lakes, are listed in figure 6. The information in figure 6 was combined with that in tables 1—3 to produce figures 7—1 1, which were used to help separate physical lim— nological processes from those of a chemical and (or) biological nature. Inflow and outflow loads of bioavailable and par- ticulate nitrogen for Coeur d’Alene Lake showed little difference in their percentage composition with respect to total nitrogen; the percentages were comparable between 1991 and 1992 (figs. 7 and 8). Such results might indicate that the lake merely passed its inflow nitrogen load through to its outlet relatively unaltered; the lake retained less than 10 percent of the nitrogen it received as input in both years. However, other data suggest chemical and biological alteration of the lake’s inflow load of nitrogen. Phytoplankton assimilation of bioavailable nitrogen was evident from changes in dis- solved inorganic nitrogen concentrations periodically measured at limnological stations 1 through 6 (fig. 2) during 1991—92. Within the lake’s upper water col- umn, dissolved inorganic nitrogen concentrations ranged from less than 7 to 234 ug/L during 1991 and from less than 7 to 98 ug/L during 1992 (Harenberg and others, 1992, 1993). During both years, the mini- mum concentrations were measured during May through September, the period of thermal stratification and summer phytoplankton production. The aforemen- tioned addition of dissolved inorganic nitrogen from benthic flux from the lakebed sediments provided an internal source of bioavailable nitrogen that replaced part of the influent bioavailable nitrogen that was con- verted to particulate nitrogen within the lake. Similar to nitrogen loads, the inflow and outflow loads of bioavailable and particulate phosphorus for Coeur d’Alene Lake showed little difference in their percentage composition with respect to total phospho- rus (figs. 7 and 8). However, unlike its retention of nitrogen, the lake’s retention of phosphorus was about two-thirds of its input in 1991 and about one-third in 1992. If physical settling were the only limnological process affecting influent phosphorus loads, then the percentage of bioavailable phosphorus would have increased at the lake’s outlet; however, such was not the case. Chemical and biological processes within the lake also affected the influent phosphorus loads. The pro— pensity to sorb to particulate matter probably converted part of the bioavailable phosphorus to particulate phos- phorus. Phytoplankton assimilation also converted bio- available phosphorus into particulate phosphorus. Dis- solved orthophosphorus concentrations within the upper water column of limnological stations 1 through 6 (fig. 2) ranged from less than 1 to 11 rig/L in 1991 and from less than 1 to 6 ug/L in 1992 (Harenberg and others, 1992, 1993). The minimum concentrations of dissolved orthophosphorus were measured during the summer months of both years, indicative of phy— toplankton assimilation. In contrast to Coeur d’Alene Lake, Flathead Lake retained about 36 percent of its influent nitrogen load from drainage basin and atmospheric sources during 1978—82 (fig. 9). The percentage of bioavailable nitro- gen, in relation to total nitrogen, for Flathead Lake’s drainage basin input was 42; that declined to 23 at the lake’s outlet (fig. 9), indicative of in-lake conversion of bioavailable nitrogen to particulate nitrogen by chemi- cal and biological processes. These results are consis- tent with seasonal dynamics of dissolved nitrite plus nitrate concentrations at Flathead Lake’s deepest lim— nological station, 2 (fig. 3), as presented by Stanford and others (1997) for water years 1990—96. Within the upper water column, concentrations ranged from less than 1 to 63 ug/L; the smallest concentrations were measured during the summer months of elevated phy- toplankton production. Flathead Lake retained about three-fourths of the phosphorus it received from drainage basin and atmo- spheric sources (fig. 9). The percentage composition of inflow and outflow loads of bioavailable phosphorus in relation to total phosphorus were nearly equal—both were less than 10 percent. As mentioned previously, if physical settling were the only limnological process affecting influent phosphorus loads, then the percent- age of bioavailable phosphorus would have increased between the lake’s inlet and outlet. Given the likelihood of a substantial removal of sediment—associated phos- 30 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake ANNUAL MEAN PERCENTAGE COMPOSITION Nitrogen1 100 | Phosphorus2 100 | l i 90 ~ — so — — 70 — — 60 —~ 1 — 50 — — 4o — — 30 ~ _ 20 — _ 10 — — 0 «$0$ Q§§§A \\\°~$ %'§\€$ \x\\0$ 0&0 \K\0‘$ 6&04‘ \,\\<3“A {$0 1991 1992 1991—93 1989 1990 COEUR D'ALENE LAKE3 F LATHEAD LAKE4 LAKE PEND OREILLE5 - Bioavailable 1::1 Particulate EXPLANATION 3 Inflow concentrations from Coeur d'Alene and St. Joe Rivers; outflow concentrations from Spokane River 4 Inflow and outflow concentrations from Flathead River 1 Bioavailable nitrogen defined as sum of dissolved nitrite, nitrate, and ammonia; particulate nitrogen defined as total minus bioavailable 5 Inflow concentrations from Clark Fork; outflow con- centrations from Pend Oreille River 2 Bioavailable phosphorus defined as dissolved orthophosphorus; particulate phosphorus defined as total minus bioavailable Figure 6. Annual mean percentage composition of bioavailable and particulate nitrogen and phosphorus concentrations, in relation to total nitrogen and phosphorus concentrations, respectively, for inflows and outflows, Coeur d'AIene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana. Chemical and Biological Limnology 31 TOTAL NITROGEN Atmospheric input 75,000 kilograms 11111 W Lake output Coeur d'Alene Lake 2,030,000 kilograms 81% particulate 19% bioavailable Watershed input 2,080,000 kilograms 82% particulate 18% bioavailable (Watershed + Atmospheric inputs — Lake output): 125,000 kilograms TOTAL PHOSPHORUS Atmospheric input 6,500 kilograms 11111 W Lake output Coeur d ‘Alene Lake 36,100 kilograms 71% particulate 29% bioavailable Watershed input 108,000 kilograms 74% particulate 26% bioavailable (Watershed + Atmospheric inputs — Lake output): 78,400 kilograms Figure 7. Relation between inputs and outputs of total nitrogen and phosphorus and nutrient partitioning of input and output loads, Coeur d'Alene Lake, Idaho, 1991 calendar year. 32 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake TOTAL NITROGEN Atmospheric input 75,000 kilograms lllll Watershed input 870,000 kilograms Lake output 80% particulate Coeur d'Alene Lake 860,000 kilograms 20% bioavailable 76% particulate 24° b. 'l bl (Watershed + Atmospheric inputs — Lake output): A’ 'Oava' a e 85,000 kilograms TOTAL PHOSPHORUS Atmospheric input 6,500 kilograms lllll Watershed input 37,100 kilograms 61% particulate 39% bioavailable Lake output 27,600 kilograms 65% particulate 35% bioavailable Coeur d’Alene Lake (Watershed + Atmospheric inputs — Lake output): 16,000 kilograms Figure 8. Relation between inputs and outputs of total nitrogen and phosphorus and nutrient partitioning of input and output loads, Coeur d'Alene Lake, Idaho, 1992 calendar year. Chemical and Biological Limnology 33 TOTAL NITROGEN Atmospheric input 175,000 kilograms l l l l l W Lake output Flathead Lake 1,020,000 kilograms 77% particulate 23% bioavailable Watershed input 1,420,000 kilograms 58% particulate 42% bioavailable (Watershed + Atmospheric inputs — Lake output): 570,000 kilograms TOTAL PHOSPHORUS Atmospheric input 29,000 kilograms waterShed input W 213,000 kilograms Lake output 94% particulate Flathead Lake 59,000 kilograms 6% bioavailable 92% particulate 8% bioavailable (Watershed + Atmospheric inputs — Lake output): 183,000 kilograms Figure 9. Relation between inputs and outputs of total nitrogen and phosphorus and nutrient partitioning of input and output loads, Flathead Lake, Montana, 1978—82. 34 Fate and Transport of Nitrogen and Phosphorus, Coeur d'Alene Lake, Lake Pend Oreille, and Flathead Lake TOTAL NITROGEN Atmospheric input 190,000 kilograms Watershed input W 4,220,000 kilograms 61% particulate Lake Peml Oreille 39% bioavailable Lake output 3,740,000 kilograms 72% particulate 28% bioavailable (Watershed + Atmospheric inputs — Lake output): 670,000 kilograms TOTAL PHOSPHORUS Atmospheric input 19,000 kilograms water-Shed input W 307,000 kilograms 70% Partiw'ate Lake Pend Oreille 30% bioavailable Lake output 271,000 kilograms 85% particulate 15% bioavailable (Watershed + Atmospheric inputs - Lake output): 55,000 kilograms Figure 10. Relation between inputs and outputs of total nitrogen and phosphorus and nutrient partitioning of input and output loads, Lake Pend Oreille, Idaho, 1989 water year. Chemical and Biological Limnology 35 TOTAL NITROGEN Atmospheric input 190,000 kilograms lllll waterShed input W 5,480,000 kilograms 68% Particulate Lake Pend 0reille 32% bioavailable Lake output 4,830,000 kilograms 80% particulate 20% bioavailable (Watershed + Atmospheric inputs — Lake output): 840,000 kilograms TOTAL PHOSPHORUS Atmospheric input 19,000 kilograms lllll Watershed input —'\ 389,000 kilograms Lake Pend 0reille Lake output 353,000 kilograms 84% particulate 16% bioavailable 76% particulate 24% bioavailable (Watershed + Atmospheric inputs — Lake output): 55,000 kilograms Figure 11. Relation between inputs and outputs of total nitrogen and phosphorus and nutrient partitioning of input and output loads, Lake Pend 0reille, Idaho, 1990 water year. 36 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend 0reille, and Flathead Lake phorus by physical settling, the large percentage com— position of particulate phosphorus output from the lake indicates conversion of bioavailable phosphorus into particulate phosphorus by adsorption and phytoplank- ton assimilation. These results are consistent with the seasonal dynamics of bioavailable phosphorus concen- trations at Flathead Lake’s limnological station 2 (fig. 3) during water years 1990—96 (Stanford and others, 1997). Within the upper water column, bioavailable phosphorus concentrations ranged from 0.4 to 4.1 ug/L; the smallest concentrations were measured during the summer months of elevated phytoplankton production. During water years 1989 and 1990, Lake Pend Oreille retained about 15 percent of the nitrogen load it received from drainage basin and atmospheric inputs (figs. 10 and 11). Phytoplankton assimilation of bio- available nitrogen was indicated by the reduction in the percentage of bioavailable nitrogen at the lake outlet compared with that from drainage basin inflow (between 11 and 12 percent) (figs. 10 and 11). Again, these results are consistent with upper water-column concentrations of dissolved inorganic nitrogen at lim- nological stations 2 and 3 (fig. 4), which ranged from 10 to 144 ug/L over the 2-year period (Harenberg and others, 1991, 1992); the smallest concentrations were measured during the summer months of elevated phy- toplankton production. Of the three lakes, Lake Pend Oreille retained the smallest percentage of its input total phosphorus load; about 17 in 1989 and 13.5 in 1990 (figs. 10 and 11). In both years, the percentage of bioavailable phosphorus, in relation to total phosphorus, input to the lake was reduced at the lake outlet by 15 percent in 1989 and by 8 percent in 1990 (figs. 10 and 11). As in the other two lakes, that shift in percentage composition indi- cated conversion of bioavailable phosphorus to particu late phosphorus by processes such as adsorption and phytoplankton assimilation. Upper water-column con- centrations of dissolved orthophosphorus at limnologi- cal stations 2 and 3 (fig. 4) ranged from less than 1 to 7 ug/L over the 2-year period (Harenberg and others, 1991, 1992). As with dissolved inorganic nitrogen, the smallest concentrations of dissolved orthophosphorus were measured during the summer months. FATE AND TRANSPORT OF NUTRIENT LOADS Overview Coeur d’Alene and Flathead Lakes and Lake Pend Oreille received, discharged, and retained a wide range of nitrogen and phosphorus loads, in an absolute and relative sense (tables 1—3). The three lakes also dis- played a wide range of physical limnological character- istics (table 5); selected values from those four tables are summarized in table 9. If only morphometric and empirically derived values such as mean and maximum depth, hydraulic residence time, and trap efficiency were considered, the lake with the largest values, Lake Pend Oreille, would be expected to retain the largest percentage of the total nitrogen and phosphorus load received. However, that was not the case—Flathead Lake retained the largest loads of total nitrogen and phosphorus relative to what it received. Coeur d’Alene Lake, with the smallest values for mean and maximum Table 9. Summary of retained load/input load for total nitrogen and phosphorus and selected physical limnological characteristics, Coeur d’Alene Lake and Lake Pend Oreille, Idaho, and Flathead Lake, Montana [m, meters; cy, calendar year; wy, water year; yr, year] Coeur d’Alene 1’ 2 Flathead 1, 2 Pend Oreille 1’ 2 Variable, 1989 1990 unit 1991 cy 1992 cy 1978—82 wy wy Retained load 3linput load, percent Total nitrogen 6 9 36 15 15 Total phosphorus 69 37 476 17 14 Mean depth, m 21.7 50.2 164 Maximum depth, m 63.7 113 357 Hydraulic residence til'ne9 yr .50 2.2 2.4 Trap efficiency, percent 93 97 98 1 Data from tables 1—3. 2 Data from table 5. 3 Input load - output load. Fate and Transport of Nutrient Loads 37 depth, hydraulic residence time, and trap efficiency, retained a larger relative load of total phosphorus than did Lake Pend Oreille; Coeur d’Alene Lake’s retention of total nitrogen was slightly smaller than that of Lake Pend Oreille. The unexpected behavior of Lake Pend Oreille suggests that limnological processes other than simple physical sedimentation affected the fate and transport of nutrient loads delivered to that lake. Likewise, the large sedimentary loss of particulate phosphorus within Flathead Lake is not intuitively obvious if only the lack of change in nutrient partitioning between the lake’s inlet and outlet is considered. For each of these lakes, the influence of in-lake chemical and biological pro- cesses was evident, on the basis of shifts in nutrient partitioning between the dissolved and particulate frac— tions for input and output loads. However, a rigorous evaluation of chemical and biological processes was not possible because of the need to focus on mean con- ditions; sampling intervals were too long to adequately quantify and evaluate the temporal nature of those pro- cesses. Regardless, by coordinating the evaluation of shifts in nutrient partitioning with the evaluation of load discharged versus load received, insight can be gained into the importance of interactions among phys- ical, chemical, and biological processes that affect the fate and transport of nutrient loads delivered into the three lakes. Coeur d’AIene Lake Among the three lakes, Coeur d’Alene Lake had the smallest input and output loads of total nitrogen and phosphorus. Compared with the other two lakes, it also had the shortest hydraulic residence time, shallowest mean and maximum depths, and narrowest shape. Because these physical factors are associated with an increased potential for turbulent, advective transport of dissolved, colloidal, and particulate constituents, Coeur d’Alene Lake would be expected to retain a smaller proportion of its input loads, compared with those of the other two lakes. Indeed, Coeur d’Alene Lake retained less than 9 percent of the total nitrogen it received (table 9). An important factor in the lake’s small retention of its input load of total nitrogen was the temporal distribution of input loads. On the basis of graphical plots of daily loads delivered into Coeur d’Alene Lake over the 1999 water year, Woods (2001) reported that about 56 percent of the annual load of total nitrogen was delivered during April through June, the period of snowmelt runoff during which advective transport through the lake as overflow is most likely. The nar- rowness of the lake’s main channel (fig. 2) and the location of the two primary tributaries near the lake’s southern end are likely to enhance advective transport, compared with transport expected in a more circular lake basin. Physical sedimentation of total nitrogen does occur; the mean concentration of total nitrogen in lakebed sediments from 20 sampling locations throughout Coeur d’Alene Lake was 2,100 mg/kg (Woods and Beckwith, 1997). Even in the absence of large shifts in partitioning between bioavailable and particulate nitrogen for input and output loads (fig. 6), the biological process of phytoplankton assimilation was indicated by decreases in upper water-column con- centrations of dissolved inorganic nitrogen. Chemical processes, in the form of hypolimnetic remineralization of particulate to bioavailable nitrogen and the addition of dissolved inorganic nitrogen from the lakebed sediments by benthic flux, also affected total nitrogen concentrations and partitioning in the lake. The effect of hypolimnetic enrichment of bio- available nitrogen by remineralization and benthic flux was evaluated for the 1999 water year as part of a joint EPA/USGS assessment of trace-element and nutrient loading for Coeur d’Alene Lake (URS Greiner Inc., and CH2M—Hill Inc., 2001b). Input loads substantially exceeded output loads of bioavailable nitrogen during December and January but were about equal during May through November. During February through April, the lake discharged substantially more bioavail— able nitrogen than it received. The net loss of bioavail— able nitrogen during those 3 months was a consequence of full-depth convective mixing, which was associated with advective transport and discharge of part of the bioavailable nitrogen produced in the hypolimnion by remineralization and benthic flux. The propensity for phosphorus to sorb to inor- ganic and organic particles suggests that Coeur d’Alene Lake would retain a larger proportion of its input load for phosphorus than for nitrogen. Such was the case; the lake retained between one—third and two-thirds of its annual input load of total phosphorus (table 9). About 80 percent of that annual input was delivered during April through June, on the basis of graphical plots of daily loads delivered into Coeur d’Alene Lake over the 1999 water year (Woods, 2001). The results of 38 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake lakebed sediment analyses in Coeur d’Alene Lake clearly indicate that sedimentation of total phosphorus occurs; the mean concentration of total phosphorus in lakebed sediments was 940 mg/kg (Woods and Beck- with, 1997). Nonetheless, sedimentation and advective transport were not the only limnological processes in Coeur d’Alene Lake that affected phosphorus loads delivered to the lake. Part of the bioavailable phospho- rus was converted into particulate phosphorus by the chemical process of adsorption and the biological pro— cess of phytoplankton assimilation, as indicated by minimal shifts in partitioning between inflow and out— flow loads (fig. 6), despite the reduction in total load magnitude. The adsorption of bioavailable phosphorus in the lake was postulated as an important chemical process because the lakebed sediments are rich in iron oxides and hydroxides, for which bioavailable phosphorus has a high adsorption affinity (Wetzel, 1975; Elder, 1988). The mean concentration of total iron measured in about 150 surficial lakebed samples from Coeur d’Alene Lake was 51,000 mg/kg (Horowitz and others, 1993). Only 14 percent of 1,317 analyses of soils in the con- terminous United States (Shacklette and Boemgen, 1984) contained iron concentrations in excess of those measured in the lakebed of Coeur d’Alene Lake. The lake receives abundant amounts of iron in the inflow from the Coeur d’Alene River. According to Hem (1985), flowing, well-aerated, nonacidic surface water generally contains less than 10 ug/L dissolved iron. During the 2001 water year, however, dissolved iron concentrations in the Coeur d’Alene River ranged from 20 to 190 ug/L (O’Dell and others, 2002). The lake’s retention of one-third to two—thirds of its influent phos- phorus load, in spite of its short hydraulic residence time and shallow depths, may have been increased by its potential to form particulate phosphorus within the water column. Flathead Lake Both input and output loads of total nitrogen and phosphorus for Flathead Lake were intermediate be- tween those of the other two lakes. Flathead Lake’s hydraulic residence time and trap efficiency were com- parable to those of Lake Pend Oreille, whereas mean and maximum depths were about twice those of Coeur d’Alene Lake and only about one-third those of Lake Pend Oreille. Of the three lakes, Flathead had the long- est dimensions for length and width, both maximum and effective, and had the largest surface area. Because of its empirically determined ability to retain dissolved, colloidal, and particulate constituents, Flathead Lake would be expected to retain a large proportion of its input loads. Consistent with this expectation, Flathead Lake retained the largest proportion of its input load of total nitrogen, 36 percent (table 9). About 60 percent of that input load was delivered to the lake during April through June, the period of snowmelt runoff (Stanford and others, 1997). Although the lake’s inflow plume has been routed in a rather complex fashion in some years, in other years, the plume has been routed more directly to the lake’s southern outlet (Stuart, 1983). The predominance of overflow during snowmelt runoff favors the potential for discharge of part of the influent nitrogen load delivered by snowmelt runoff. However, the large central basin of Flathead Lake and the long distance between the lake’s primary tributary and outlet (fig. 3) are likely to hinder advective transport through the lake, especially during periods of low inflow. The lake’s large surface area also favors trapping of nitro- gen delivered by atmospheric deposition, especially when such deposition occurs in conjunction with mini- mal advective transport within the lake. During the period analyzed, atmospheric deposition accounted for about 11 percent of the total nitrogen input to the lake (Stanford and others, 1983). In addition to physical processes, differences in partitioning between bioavailable and particulate nitro- gen for both the epilimnion and hypolimnion (fig. 5) and input and output loads (fig. 6) indicate that chemi- cal and biological processes affected the in-lake fate and transport of nitrogen delivered to the lake. The lake’s moderate depth, in conjunction with long dis- tances for effective width and length, allows full-depth convective mixing of nitrogen (nitrite plus nitrate) dur— ing periods without thermal stratification (Stanford and others, 1997). When such mixing is coincident with advective transport, then part of the lake’s nitrogen could be discharged. However, the lake’s ability to dis- charge convectively circulated nitrogen is hampered by its physical characteristics of long hydraulic residence time, moderate depths, and large surface area. Of these three lakes, Flathead Lake retained the largest proportion of its input load of total phosphorus, about 75 percent (table 9). Comparable to total nitro— gen, about 60 percent of the input load of total phos- phorus was delivered to the lake during April through Fate and Transport of Nutrient Loads 39 June, the period of snowmelt runoff (Stanford and oth— ers, 1997). The retention of phosphorus resulted from a combination of physical, chemical, and biological pro- cesses. Ample evidence demonstrates that Flathead Lake is an efficient trap for particulate matter, includ- ing particulate phosphorus. The spatial distribution of mean annual concentrations of total phosphorus (verti- cally integrated data) in Flathead Lake from its inlet area to its outlet illustrates the lake’s ability to retain phosphorus (Stanford and others, 1983). On the basis of data from 1977—80, the average annual concentra- tion at the inlet was 15 ug/L; 7.5 km south, at limno- logical station 1 (fig. 3), the concentration had declined to 9 ug/L. At limnological station 2 (fig. 3), 23 km south of the inlet, the concentration had declined to about 7.5 ug/L, which was comparable to the concen- tration at the lake outlet. Additionally, Stuart (1983) presented a mass balance budget for total suspended solids (which include sediment-associated phosphorus) in Flathead Lake for 1977—79. Of the 292,000,000 kg of suspended solids input to the lake, less than 4 per- cent was discharged from the lake. Lakebed sediment analyses for Flathead Lake also clearly indicate that sedimentation of total phosphorus occurs; the mean concentration of total phosphorus from 70 surficial lakebed samples was about 2,300 mg/kg (Moore and others, 1982). That concentration was substantially larger than the mean concentration of 940 mg/kg noted previously for Coeur d’Alene Lake. Given the substantial sedimentation of particulate phosphorus, in conjunction with minimal changes in nutrient partioning between inflow and outflow loads (fig. 6), some of the bioavailable phosphorus must have been converted into particulate phosphorus by the chemical process of adsorption and the biological pro- cess of phytoplankton assimilation. As discussed previ— ously, phytoplankton assimilation of bioavailable phos- phorus was readily evident from time—series data for upper water—column concentrations of dissolved ortho- phosphorus (Stanford and others, 1997). Similar to Coeur d’Alene Lake, Flathead Lake also contains iron- rich lakebed sediments; the mean concentration of total iron measured in 70 surficial lakebed samples was 44,200 mg/kg (Moore and others, 1982). The presence of these iron—rich sediments indicates that adsorption of bioavailable phosphorus to iron oxides and hydroxides may be an important chemical process favoring the lake’s retention of phosphorus. Lake Pend Oreille Lake Pend Oreille was the outlier among these three lakes with regard to the fate and transport of its input loads of nitrogen and phosphorus. Input and out- put loads for Lake Pend Oreille were the largest, as were hydraulic residence time, trap efficiency, and both mean and maximum depths. However, the percentage of retained nitrogen was only slightly larger than that of Coeur d’Alene Lake, and the percentage of retained phosphorus was the smallest among the three lakes. The small retention of total nitrogen for Lake Pend Oreille, about 15 percent for water years 1989 and 1990 (table 9), can be attributed primarily to a combination of physical limnological processes; chem- ical and biological processes apparently were less important. About 80 percent of the lake’s annual load of total nitrogen was delivered by the Clark Fork and, on the basis of graphical output from the load model FLUX, about one-half of that load was delivered during April through June, the period of snowmelt runoff. During snowmelt runoff, two physical processes were likely to have routed most of the influent nitrogen load through the lake’s northern basin: the coincident occur- rence of inflow—plume routing as overflow and the pos— tulated presence of a thermal bar between the lake’s deep southern basin and its shallow northern basin. The role of sedimentation of nitrogen in the lake could not be evaluated because of an absence of nutrient data for lakebed sediments. Because of the lake’s large values for hydraulic residence time, trap efficiency, and depth, and the pro- pensity for phosphorus to sorb to inorganic and organic materials, it is logical to expect that Lake Pend Oreille would retain a substantial part of its input load of total phosphorus. However, the lake retained less than about 17 percent of the input load of total phosphorus (table 9). Similar to nitrogen, retention of total phosphorus was largely the result of physical limnological pro— cesses related to inflow-plume timing and routing within the lake, rather than to chemical and biological processes. Graphical output from the load model FLUX indicates that the Clark Fork, which delivered about 70 percent of the lake’s annual load of total phos- phorus, delivered about one-half of that load during April through June, the period of snowmelt runoff. Similar to nitrogen, most of the phosphorus load was routed through the lake’s northern basin by the combi- nation of inflow-plume routing as overflow and the postulated presence of a thermal bar between the lake’s 40 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and FIathead Lake deep southern basin and its shallow northern basin. Unlike Coeur d’Alene and Flathead Lakes, the adsorp- tion of bioavailable phosphorus to iron oxides and hydroxides apparently played a minimal role in reten- tion of phosphorus in Lake Pend Oreille. Surficial lake— bed samples from the outlet arm of Lake Pend Oreille contained a mean concentration of 28,500 mg/kg of total iron (G.M. Clark, US. Geological Survey, written commun., 2002), about one-half of the mean concen- trations of iron in the other two lakes. Evaluation of long—term sedimentation as an indicator of retention was not possible because lakebed sediment analyses for phosphorus were not available. SUMMARY AND CONCLUSIONS The purpose of this report was to describe the role of limnological processes in determining the fate and transport of nutrients delivered into and discharged from Coeur d’Alene and Flathead Lakes and Lake Pend Oreille. These three large, natural lakes within the NROK study area represented a broad range in physical limnological characteristics that could be expected to exert substantial influences upon the fate and transport of nutrients delivered into them. The large amount of limnological and riverine loading data historically available for these lakes was used to evaluate how interaction among physical, chemical, and biological processes within each lake produced quantitative dif- ferences between inflow and outflow loads of nutrients. The outcome of these interactions over a wide range of spatial and temporal scales determines the quantity and nature of nutrients discharged from each lake at its surface-water outlet. Each lake discharged 3 smaller nutrient load than it received; retentions of nitrogen loads tended to be smaller than retentions of phosphorus loads. Coeur d’Alene Lake, which received and discharged the smallest nutrient loads among the three lakes, retained about 8 percent and 50 percent, respectively, of its input loads of nitrogen and phospho- rus. Flathead Lake retained about one—third of its input load of nitrogen and about three-fourths of its input load of phosphorus. Unlike the other two lakes, Lake Pend Oreille retained only about 15 percent of its input load of nitrogen and less than about 17 percent of its input load of phosphorus; among the three lakes, it received and discharged the largest nutrient loads. In general, the role of physical limnological pro- cesses such as circulation and sedimentation accounted for much of the measured differences in the retention of nutrients among the three lakes. Because of its small propensity to sorb to inorganic and organic particles, nitrogen was less prone to sedimentation and was more responsive to advective circulation. Accordingly, the retention of nitrogen among the three lakes ranged from 5.6 to 36 percent. In contrast, the retention of phosphorus among the three lakes ranged from 13.5 to 75.5 percent, reflecting its strong propensity to sorb to inorganic and organic particles. Comparisons of nutri- ent partitioning between bioavailable and particulate fractions in the epilimnia and hypolimnia, as well as in input and output loads, provided evidence that chemi— cal and biological processes such as adsorption, re— mineralization, and phytoplankton assimilation affected the quantity and form of nutrients ultimately discharged from the lakes. Limnological processes were evaluated retrospec- tively and, therefore, required a fair degree of postula- tion about the interactions among physical, chemical, and biological processes because past studies of these lakes were more descriptive than process oriented. One aim of such postulation was to identify important lim- nological characteristics that might be incorporated into future studies of lakes and reservoirs. For example, the postulated existence of a thermal bar in Lake Pend Oreille and its role in that lake’s small retention of nutrients indicates the importance of delineating physi- cal processes related to inflow-plume routing, overall lake circulation, and convective circulation. The impor- tance of also addressing chemical and biological pro- cesses can be illustrated with the following example from Flathead Lake: The substantial sedimentation of particulate phosphorus within Flathead Lake, coupled with the lack of changes in partitioning between bio— available and particulate phosphorus in input and out- put loads, indicate that part of the input load of bio- available phosphorus was converted into particulate phosphorus by the chemical process of adsorption and the biological process of phytoplankton assimilation. Empirical relations were combined with extensive historical data to evaluate the role of limnological pro- cesses in the fate and transport of nutrient loads in these three lakes. If this evaluation had been under— taken with a purely empirical approach and without knowledge of historical inflow and outflow nutrient loads, the results would not have correctly predicted these lakes’ nutrient retention. Using variables such as areal water load, hydraulic residence time, trap effi- ciency, and mean depth, the empirical approach likely Summary and Conclusions 41 would have predicted that the magnitude of nutrient retention would progress from small in Coeur d’Alene Lake to large in Flathead Lake and Lake Pend Oreille. That prediction would be correct only for Flathead Lake. If the empirical approach were augmented with knowledge of nutrient loads, then the major discrep- ancy between predicted and measured nutrient reten- tion would become evident for two of the lakes. Namely, Lake Pend Oreille deviated sharply from its predicted large retention of nutrients and Coeur d’Alene Lake retained about one—half of its phosphorus input. This evaluation of the role of limnological pro- cesses in the fate and transport of nutrients delivered into and discharged from three large, natural lakes within the NROK study area provides an example of the enhanced scientific understanding to be gained if lakes and reservoirs were incorporated into basin-scale national water assessments. For example, in a report evaluating the NAWQA Program, the National Research Council (2002) stated that, “In terms of scien- tific understanding and management capability, the exclusion of lakes and reservoirs precludes an opportu- nity to understand significant physical, chemical, and biological processes that alter water quality.” Data acquisition programs designed with the capability to unravel the interplay of physical, chemical, and biolog- ical processes would benefit future limnological studies of the fate and transport of constituents, whether initi- ated by the NAWQA Program or other entities. 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Woods, P.F., 1993a, Limnology of the pelagic zone, Pend Oreille Lake, Idaho, 1989—90, in Hoelscher, Brian, Skille, Jack, and Rothrock, Glen, 1993, Phase I diagnostic and feasibility analysis: a strat- egy for managing the water quality of Pend Oreille Lake, appendices: Boise, Idaho Depart- ment of Health and Welfare, Division of Environ- mental Quality, variously paged. —1993b, Nutrient load/lake response model, Pend Oreille Lake, Idaho, 1989-90, in Hoelscher, Brian, Skille, Jack, and Rothrock, Glen, 1993, Phase I diagnostic and feasibility analysis: a strat- egy for managing the water quality of Pend Oreille Lake, appendices: Boise, Idaho Depart- ment of Health and Welfare, Division of Environ- mental Quality, variously paged. 2001, Concentrations and loads of cadmium, lead, zinc, and nutrients measured during the 1999 water year Within the Spokane River Basin, Idaho and Washington: US Geological Survey Open- File Report 00—441, 32 p. Woods, PF, and Beckwith, M.A., 1997, Nutrient and trace-element enrichment of Coeur d’Alene Lake, Idaho: US. Geological Survey Water-Supply Paper 2485, 93 p. Woods, PF, and Berenbrock, Charles, 1994, Bathy- metric map of Coeur d’Alene Lake, Idaho: US. Geological Survey Water—Resources Investiga- tions Report 94—4119, 1 sheet. 44 Fate and Transport of Nitrogen and Phosphorus, Coeur d’Alene Lake, Lake Pend Oreille, and Flathead Lake ”1' ISGS ' U 2 a changing world 96 no. 1683 eart 7 - DAY b‘ The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Professional Paper 1683 U.S. Department of the Interior U.S. Geological Survey The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment By Richard L. Bernknopf, David S. Brookshire, and Philip T. Ganderton J. lvfir’ CGLLL’I, 9i JAN Z '5’ 2304 up] ‘‘‘‘‘‘‘ L an):U will ii EAR/TH iggNoEs & MAP AHY )1va OF CALlF. BERKELEY, CA us. DEPOSITORY Professional Paper 1683 U.S. Department of the Interior U.S. Geological Survey U.S. Department of the Interior Gale A. Norton, Secretary U.S. Geological Survey Charles G. Groat, Director U.S. Geological Survey, Reston, Virginia: 2003 Available from U.S. Geological Survey information Services Box 25286, Denver Federal Center Denver, CO 80225 For more information about the USGS and its products; Telephone: l-888-ASK-USGS (1-888-275-8747) World Wide Web: http://www.usgs.gov/ Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply endorsement of the U.S. Government. Although this report is in the public domain, it contains copyrighted materials that are noted in the text. Permission to reproduce those items must be secured from the individual copyright owners. Published in the Western Region, Menlo Park, California. Manuscript approvedfor publication September 3, 2003. Text and illustrations edited by James W. Hendley || Production by Susan E. Mayfield Cataloging-in-publication data are on tile with the Library of Congress (http://www.loc.gov/). Contents Executive Summary ...................................................................................................................................... v Introduction ................................................................................................................................................... 1 Elements of Program .................................................................................................................................... 1 Experiment Design ........................................................................................................................................ 2 Empirical Model ............................................................................................................................................ 4 Data Analysis and Interpretation ............................................................................................................... 5 Program Potential ............................ References ................................................................................................................................................... 11 Glossary ........................................................................................................................................................ 13 Appendixes A. Experiment flow chart .................................................................................................................. 14 B. Game explanation text .................................................................................................................. 16 D. Screen captures ........................................................................... ,_ ................................................ 18 E. Continuation screen capture ...................................................................................................... 29 Figures 1. Maps of a natural hazard and severity of loss at two scales .................................................. 3 Tables 1. Experiment structure ...................................................................................................................... 3 2. Definitions and summary statistics for variables used in analysis ......................................... 6 3A. Bivariate probit analysis of decision to buy detailed map (simultaneous estimation) ........ 8 3B. Bivariate probit analysis of decision to buy insurance (simultaneous estimation) ............. 9 4. Marginal effect of explanatory variable on joint probabilities .............................................. 10 Executive Summary What role can geoscience information play in the assessment of risk and the value of insurance, especially for natural hazard type risks? In an earlier, related paper Ganderton and others (2000) provided subjects with relatively simple geoscience information concerning natural hazard-type risks. Their research looked at how subjects purchase insurance when faced with relatively low probability but high loss risks of the kind that characterize natu- ral hazards and now, increasingly, manmade disasters. They found evidence to support the expected utility theory (definitions of economics terms can be found in a glossary at the end of report), yet there remained the implication that subjects with excessive aversion to risk were willing to pay considerably more for insurance than the actuarially fair price plus any reasonable risk premium. Here, we report the results of additional experiments that provide further support for the basic postulates of expected utility theory. However, these new experi- ments add considerably to the decision environment facing subjects by offering an option to purchase geoscientific information that would assist them when calculating expected losses from hazards more accurately. Using an Internet—based mechanism to present information and gather data in an experi— mental setting, this research provided subjects with considerable textual and graphical infor- mation, and time to process it. Over a period of three months, almost 400 subjects participated in on—line experiments that generated approximately 22,000 usable data points for the empiri— cal analysis discussed in this report. In the design of the experiment, we modeled the decisions to purchase (1) a detailed map giving subjects more information regarding the distribution of losses from a hazard and (2) insurance to indemnify them from any losses should they occur. On the basis of this design, we find strong evidence in support of the expected utility theory. Many of the find- ings reinforce those found in the early, similar study (Ganderton and others, 2000). However, this research also finds interactions between the decision to become better informed and the decision to insure. We chose an empirical framework that allows for both explicit and implicit (unobservable) correlations between the two decisions. The results suggest that at the end of the computer game subjects recognize the benefits of greater geoscience information. They take advantage of it, but are sensitive to its cost. When subjects use the more detailed informa— tion, they are more likely to purchase insurance when it offers a net benefit. The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment By Richard L. Bernknopf‘, David S. Brookshirez, and Philip T. Ganderton3 Introduction Natural hazards present both organizations responsible for protecting public safety and organizations that protect pri— vate individuals with the most serious risk—management prob- lems. Nature is often seen as a random force, and although considerable progress has been made to model and predict natural hazard and disaster risk, events such as earthquakes, tornadoes, fires, and floods continue to wreak havoc on an increasingly dense and resource-rich social and economic environment (Kunreuther and others, 1999). Risk management is most generally a set of policies and practices designed to assess and affect risk to human life, social and economic activities, and the natural environment (Carnegie Commission, 1993). The risks from natural hazards (as well as other, manmade hazards) have the following elements: (1) A probability distribution for the natural event defined over intensity, severity, duration, magnitude, or other measures; (2) A probability distribution for the event defined over time; (3) Some process that converts natural hazard events into actions that impact human life and activity (for example, an engineering relationship that links ground movement with building collapse or rainfall and local geography with landslides and subsidence); and (4) A geographic distribution of human and economic losses attributable to the natural hazard event (Platt, 1999). The two main mechanisms for addressing the problems of natural hazards are mitigation and insurance. Insurance is most effective when the probability distribution of the event is well known, when linkage to loss is direct, and loss is well specified. Some examples are auto insurance, homeowner’s insurance, and life insurance. Insurance is least effective when the calculations required to assess the net benefits accruing from insurance cov- erage are difficult or impossible to make. Natural-hazard insur- ance is relatively uncommon because of difficulty in describing the event probability distribution over space and time. This 1 Western Geographic Science Center, U.S. Geological Survey, Menlo Park, CA. 2 Professor of Economics, University of New Mexico, Albuquerque, NM. 3Associate Professor, University of New Mexico, Albuquerque, NM. problem is exacerbated by the great variance in the losses expe- rienced across both dimensions from a single natural disaster and the relatively high premiums that insurance companies must charge given the low take—up rates for this type of insurance. This report presents the findings of a research project designed to study the role improved geoscience information might play in the assessment of risk and the value of insurance, especially for natural-hazard—type risks. Earlier, we investigated the response of subjects to relatively low probability but high— loss risks of the kind that characterize natural hazards (Gander- ton and others, 2000). Those experiments provided evidence for support of the theory that subjects are making decisions to maximize expected utility. Results from experiments reported here provide further support for the postulates of expected utility theory. In addition, the richer choice environment provided to subjects in the experimental setting allows more detailed study of the factors influencing risk assessment and insurance purchase. The research reported does not consider the decision to invest in mitigation rather than purchase insurance.4 However, it does investigate a mechanism by which people can make better decisions regarding insurance purchases by utilizing detailed information on the probability distribution of hazardous events and losses. In this sense, obtaining better information using detailed maps of either probabilities of loss or size of loss plays a complimentary role to insurance just as mitigation can. Elements of the Program The research program contains 5 basic elements: ( l) The use of maps to provide varying types of information, (2) The use of a web interface to provide and collect data, (3) The use of experimental-economics games to create sce— narios and value, (4) Investigation of the choices of mitigation and insurance, and (5) Variation of treatments to allow econometric analysis of data. The current study implements elements 1, 2, 3, and 5. “The logical next step for this research would be to study the affect of better geoscience information on the choices between insurance and mitigation. 2 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment It is essential for organizations such as the US. Geologi- cal Survey (USGS) to provide geoscience information that passes a reasonable benefit-cost test. To do this the USGS needs to assess the value placed on its geoscience informa- tion (Bernknopf and others, 2001; Bernknopf and others, 1997; Bernknopf and others, 1988). To further this agenda, the research reported here explicitly includes geoscientific information concerning the probability distribution and size of losses in a decision model of insuring against property risks. Traditionally, experimental economists have employed laboratory settings for their experiments (Hagel and Roth, 1995). Even the nomenclature encourages subjects to consider themselves part of an experiment. Despite the greater control over the environment provided by real-time laboratory set— tings, many administrative and organizational difficulties limit the size of the subject pool and the amount of data collected in a reasonable time. There is an increasing need to provide subjects with an experimental interface that is interactive, maintains their interest, gives them flexibility, and provides them control over elements of the experiment they can access. Subjects are loath to sit in laboratories waiting for slow computers to provide them with inadequate information to do the experiment properly. Although researchers must give up a certain level of control over subjects, the Internet provides a wonderful interface for supplying subjects with considerable information and it allows them to participate at their own pace, obtaining information in a controlled way, as they require it. Our experiments also suggest that the Internet is a very cost effective means of data collection. The natural progression from previous work looking at the purchase of insurance against natural—hazard—type risks with no spatial reference to the hazard is one that includes geoscience information in the decision environment of subjects. Ultimately, this information not only helps with the decision to purchase insurance, it also assists with the deci- sion to invest in mitigation activities. Further experiments will continue this progression by adding a mitigation option to the current map and insurance setting. Many observations are required to provide a statistical basis for drawing conclusions from any empirical analysis. Because of our desire to model a relatively large number of treatments in order to provide a reasonable variation in explanatory factors for the econometric analysis, and to investigate a wider range of questions, we needed to collect a large number of data points. Our results support the use of Web—based experiments as a cost-effective time-efficient mechanism for gathering large amounts of data. Experiment Design We gathered data for this analysis using the web-based experiment discussed above. The structure of the website is given below: (1) Login, or register and login. (2) Questionnaire regarding insurance use and simple demographics. (3) Miniexperiment designed to elicit independent measure of subject’s risk aversion. (4) Main experiment generating data on insurance and infor- mation purchase. (5) Generate claim check and exit from experiment. Appendix A provides screen captures from the Web site. Included are many of the introductory and welcome pages as well as the main decision page providing all relevant infor— mation to the subject. Given the nature of the Internet and people’s experience and practice with websites, the experiment was set up such that subjects were able to exit and reenter the experiment at any time, their progress through the experiment monitored to prevent retaking any previously completed sec- tion, and completion of the experiment was required to gener- ate a claim check for payment.5 The main experiment implements the study design to confront subjects with a risky scenario in which they can purchase a more detailed risk map and purchase full indemnity insurance if they choose. Subjects face the same kind of risk repeatedly, but with differing loss probabilities and loss amounts. A detailed flow chart of the design of the main experiment appears in appendix B, and table 1 provides a summary. We fixed the number of games at 15 and subjects are told this in advance. Within each game there are a random number of periods. This is chosen at the beginning of each game, as are certain parameters used as treatments in the experiment. These include the cost of the map, one from a set of two values: 10 tokens and 20 tokens, representing 5 percent and 10 percent of period income. The insurance premium is also chosen from a set of two values represent- ing, 10 percent and 20 percent of period income. In addition, the maps shown to the subject are variable; two sets were available. The subject’s location on the map was chosen at random from a possible 36 sites on the map and indicated by a dot in the center of a cell. The hazard level at that location is implied in the coarse map and displayed in the detailed map (more on this later). Each period the subject receives a constant income of 200 tokens. Appendix B gives the text of the introduction provided to subjects. There are a random number of rounds within each period, ranging between 2 and 4. The subjects are decision mak— ers within these rounds, deciding on the purchase of a more detailed map than the one shown initially. The coarse map in figure 1 shows four large cells of equal size. Each cell is colored uniformly with the color of the dominant cells that lie within that larger cell (fig. 1A). Each large cell actually contains 9 smaller cells, each colored one of three colors to indicate the amount that would be lost were a hazardous event to occur (fig. 18). For example, there may be 5 red cells of highest loss, and 4 orange cells 5This feature introduces an interesting, although subsidiary, treatment to this experiment, where some subjects completed the experiment but did not claim their payment, while others did. In real—time laboratory experiments, all partici— pating subjects are generally paid as they leave the experimental session. Experiment Design 3 Table 1. Experiment Structure. GAME PERIOD ROUND (within each game) (within each game) 15 games between 2 and 6 periods between 2 and 4 rounds Following values set: map cost (2) insurance premium (2) map (2) location on map (36) loss probability (3) loss amount (2) Income increased each period by constant amount Subject can choose to buy map or insurance, both, or neither of moderate loss. The subject may be located in an orange cell. If the subject only sees the coarse map they will get the false impression that they are in a red cell, since red is the dominant color of the cells contained within. If the subject purchases the detailed map, it remains in effect for the entire A Figure 1. Maps of a natural hazard and severity of loss at two scales. Color of zone is determined by modal hazard in that zone, yellow corresponds to low loss, orange to medium loss, and red to high loss. Highlighted point indicates subject location. This map determines actual losses in experiment. game, since the subject’s location is chosen at the start of each game, and remains fixed for that game. The subject can also purchase insurance. This insurance fully compensates the subject in the case of a loss. Insurance covers the entire period and can be purchased during any round within that period. Having purchased both the detailed map and insurance, the subject can make no more decisions that period, and the experiment progresses through the remaining rounds in the period automatically, stopping each period to inform the subject of any event that may have occurred that round, and requiring a mouse click to proceed with the experiment. Each round, the computer program draws a random num— ber from an integer set determined by the event probability. For example, if the probability of a hazardous event occurring is 0.01, the program chooses a random number from the uni- form distribution U[0,99], and if the number equals a precho— sen and fixed integer, say 11, the event occurred, otherwise no hazardous event occurred. The program shows the results of the draw to the subject each round, and the subject must click to continue (see appendix E for a screen capture of this page). The subject proceeds through the experiment accumulat- ing income each period, and spending it on map purchases or insurance premia or self-insurance. Losses without insurance can be quite large relative to both period and accumulated income. Although the probability of a hazardous event is the same for every location within a round, each location is iden- tified in the map as having a potential loss amount of small (yellow), moderate (orange), or large (red.) The two alterna— tive loss amount distributions are {10, 100, 1,000} and {100, 1,000, 10,000}. With event probabilities for each time period of (0.1, 0.01, 0.001), the expected loss can range from 0.01 token to 1,000 tokens, and actual losses can range from 10 to 10,000 tokens making bankruptcy a possibility.6 The maxi— mum possible accumulated income for a subject is 18,000 tokens at the end of the experiment, so uninsured losses of 1,000 or 10,000 can represent a considerable proportion of the subject’s accumulated income during the experiment. 6A subject who goes bankrupt during a period is required to sit out the, remaining rounds and continues the experiment with the next round income of 200 tokens. All previous income is lost. 4 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment The subject’s accumulated income at the end of any round is calculated using the following equation: New balance = max{0, old balance + NEWPER *200 — BUYMAP*MCOST — BUYINS*ICOST — (1- BUYINS)*L* [(RED)*High Loss value (for example, 10,000) + (ORANGE)*Med Loss value (for example, 1,000) + (YELLOW)*Low Loss value (for example, 100)]} where NEWPER = 1 if beginning of a new period BUYMAP = 1 if purchased detailed map this round BUYINS = 1 if purchased insurance this round MCOST = cost of map ICOST = insurance premium L = 1 if hazardous event occurred RED = 1 if subject located in red cell ORANGE = 1 if subject located in orange cell YELLOW = 1 if subject located in yellow cell Code on the website writes a comprehensive set of data to a database each round, including all parameters, subject deci— sions, and outcomes. Values from previous rounds are stored and used in calculations provided to the subjects in follow- ing rounds, even if the subject exits the experiment midway through and returns to the website later. The relatively complex scenario faced by the subjects has two important features. The first is that the experiment attempts to create a decision environment with complexity approaching that of the real world environment modeled. We can think of each game as a person’s lifetime, each period a year within that life, and each round a day within that year. Natural hazards are infrequent events (even 1/1,000 is a relatively high probability of occurrence for some hazards), but losses are often very large (and cause death or economic hardship akin to bankruptcy). The second feature of the experiment is a relatively rich treatment specification to be estimated econometrically. There are 48 possible treatment combinations in the experiment. Additionally, subjects have two decisions to make at any time during each game—(1) to purchase the detailed map and (2) to purchase insurance. In the next section, we investigate the empirical model that provides a link between the risk-event parameters and the subject’s insurance decisions. The experimental design and the website operation were extensively pretested using subjects invited into a live labora- tory session. Testers received a flat fee to compensate them for their time and were asked to login to the website and play the game. The timing of certain events was recorded in a log, and testers were asked to answer some post-experiment debriefing questions. An open discussion with the research- ers followed the experiment. An exchange rate providing adequate compensation for a subject’s time was chosen using results from the pretests. The Empirical Model In these experiments, each subject acts independently, attempting to maximize the earnings from the experiment as a return on the investment of time and effort at the website. The subject faces two decisions each round, but the consequences of each decision remain with the subject for subsequent rounds within periods or games. Having purchased insurance, it can— not be purchased again until the current period is finished. Having purchased the detailed map, it cannot be purchased again until the current game is finished. The decision to purchase insurance is based on the fol- lowing comparison for a risk neutral subject: BUY policy if Cost (C) 5 Expected Loss (EL), (1) and a risk averse subject would be prepared to pay more than the cost of the policy to avoid facing the gamble, that is BUY policy if C 3 EL + MR, W), (2) where at is the risk premium that depends upon the subject’s attitude to risk (R) and possible wealth (W). The more risk averse the subject is, the greater at will be, and the more likely a subject will be to purchase insurance even when it costs more than the expected loss of the gamble. The expected loss (EL) is the probability of loss mul-' tiplied by the amount of the loss. In most cases, the deci- sion maker does not know these two elements, particularly the probability distribution associated with the loss-caus- ing event. For some risks, the loss is well specified, such as personal property, but for others even the loss is poorly defined, such as injury in an accident or economic losses in a flood. Consequently, the calculation of expected loss when faced with a risk is determined in large part by the informa- tion available regarding the losses and probability distribu- tion of loss. The expected loss calculation is predicated on sufficient information regarding the components required to perform the calculation, otherwise the expected loss is at best an educated guess. In the current experiment, the probability of the loss event is well defined, but the loss is ill defined when the subject can only observe the coarse map (fig. 1A). The subject can draw quite incorrect conclusions from the coarse map. For example, if the location dot is inside a large RED cell, the subject may conclude that expected losses are Pr(loss) x Loss(RED), where Pr is probability, a relatively large value when compared to the actual expected loss. The actual expected loss is Pr(loss) x Loss(YELLOW) after pur- chasing the detailed map and seeing that the location was in a smaller YELLOW cell (fig. ZB). The decision to purchase the detailed map is therefore based on the potential benefits a subject expects from greater information about the spatial distribution of loss amounts and location within that space. The subject will compare the cost of purchasing the map with the benefits of a potentially more accurate calculation of expected losses from the hazard. _ The empirical model entails two equations, one to explain the decision to purchase a map, the other the decision to pur- chase insurance. What is the proper way to model the interac- tion between these two decisions? If the decisions are altema— tives, then a random utility model (RUM) framework would seem appropriate. However, they are essentially complementary, not substitute, decisions. The more detailed map aids in making the insurance decision. The decisions are not independent, but they are not alternatives, hence modeling them as simultaneous equations with possibly correlated errors seems more appropri- ate. Greene (2003) outlines the methodology for estimating a Bivariate probit model, and STATA (2001) allows the estima- tion of two alternative forms of the model, one in which both decisions are functions of the same set of variables, and another using the seemingly unrelated regression form allowing for dif- ferent sets of regressors for each decision. We can also perform a Wald test for the hypothesis that the decisions are unrelated. We model each decision as a function of the variables specifying (1) the cost of the decision, (2) the expected loss from the hazard, (3) the potential for over- or under-estima- tion of the risk, (4) historical decisions and outcomes, and (5) a measure of the wealth of the subject at the time of the decision. Expected utility theory would suggest the following impacts of these factors on the decision to purchase insurance: (1) higher premiums should decrease the probability of purchasing insur- ance and (2) higher loss amounts or higher loss probabilities should increase the probability of buying insurance. Having a map that indicates a higher loss amount than is actually the case at the location should increase the probability of buying insurance. While not explicitly indicated by the expected utility theory, other factors may play a part in determining the insur— ance purchase decision, such as past behavior. If subjects dis— play adaptive behavior or base their decisions on past behavior, past insurance purchases should increase current insurance pur- chases. The wealth of the subject may affect insurance purchase if self-insurance is more likely as wealth increases. Additionally, other factors linked to the subject’s attitudes to risk may impact the decision. We include some of these factors as measured in the survey in the empirical analysis. The decision to purchase the detailed map should be positively related to lower map costs, to a higher expected loss, and to experience because these fac- tors raise the expected net benefit from the detailed map on the expected loss calculation. Data Analysis and Interpretation Over a period of three months approximately 398 sub- jects registered for the experiment, and 362 completed the main experiment, generating 23,099 observations. We paid a total of $2,800 to 268 subjects before the experiment website was closed down. Each subject contributed an average of 58 observations to the dataset. Because each subject played 15 games, there was an average of 3.87 periods per game. As there are no decisions made at the level of rounds within peri- ods, the data were collapsed to the period level even though Data Analysis and Interpretation 5 a total of 66,221 draws of the random hazardous event were made during the experiment.7 Table 2 lists the variables used in the empirical analysis and provides definitions for these variables. The first set of variables gives some descriptive statistics for the sample of subjects participating in the experiment. More than half were female (56.7 percent) and nearly one-third were 30 years or older (29.8 percent). Slightly more than half of the subjects held health insurance (51.5 percent), 35.7 percent had either home owner’s or renter’s insurance and nearly two-thirds had auto insurance (66.5 percent).3 Just less than 3 percent of sub— jects had any form of hazard insurance, which includes flood insurance, a requirement for a mortgage in areas in or near arroyos in the desert southwest. Table 2 also gives other statistics for the experiment. The actual occurrence of hazardous events matches the mean prob- ability of a hazardous event occurring in the model (0.037). Bankruptcy was a relatively rare event (0.006). The detailed map was purchased more frequently than was insurance (0.626 verses 0.471). By comparison, the rate of purchasing insurance in a previous experiment with similar parameters (Ganderton and others, 2000) when the detailed map was not available was between 0.371 and 0.401. As discussed above, a bivariate probit model was cho- sen to model the decision to purchase the detailed map and purchase insurance against loss. The results of alternative specifications appear in table 3. The preferred model based on statistical inference is shown in column (1) of the table, with other specifications provided for comparison. There are two basic specifications of the bivariate probit model—( 1) the BiProbit (BP) where both decisions are considered functions of the same set of explanatory variables, and (2) the Seemingly Unrelated BP, where each decision equation can be specified with separate sets of explanatory variables. For most models, we give two estimates—one named Cluster, the other No Cluster. Because each subject generates more than one observation for the analysis, there is potential for nonindependent observations and correlated errors. The coefficient estimates for the Cluster and No Cluster models are identical, but the standard errors are considerably smaller for the Cluster estimates. This suggests that explicitly modeling the within-subject error correlations results in more efficient estimates of the coefficients. Despite this, the param- eter estimates are quite robust to the No Clustering/Clustering specification. Estimates for rho, the correlation of errors between the two equations, are provided in the tables. There are statisti- cally significant correlations between the errors of the map 7A statistic testifying to the power of the Internet as a mechanism for conducting experiments. It would take a considerable effort to generate more than 66,000 draws from a bingo cage in a laboratory experiment, especially with student subjects. 8This is in a state in which auto insurance is mandatory, but the sample mean is slightly above the State mean of 60 percent of drivers that have auto insurance. Table 2. Definitions and summary statistics for variables used in analysis. The Role of Geoscience information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Variable name Variable description mean1 std dev2 Idnum subject identifier Status indicates section of web completed Payout indicates if subject collected payment 0.675 Healthins indicates if health insurance held 0.515 Houseins indicates if home or contents insurance held 0.357 Cairns indicates if auto insurance held 0.665 Hazins indicates if hazard (including flood) insurance held 0.028 Insscore count of insurances held (range 0—6) 2.05 0.081 q5 indicates if subject is female 0.567 over30 indicates if subject aged 30 or older 0.298 Number count of periods in each game 4.47 0.017 Mapcost cost of purchasing detailed map 15.1 0.073 insurancecost cost of purchasing insurance (premium) 29.9 0.148 totalbalance balance of account 5234 51.7 Mapb indicates map A shown (rather than map B) 0.491 Lossamt potential loss at location 1855 64.5 Outcome hazardous event occurred 0.037 0.003 lossprobability 0.037 0.001 Bankrupt 0.006 0.001 mapbought 1=yes 0.626 0.020 insurancebought 1=yes 0.471 0.014 maphloss actual loss is higher than coarse map shows 0.021 0.002 Maplloss actual loss is lower than coarse map shows 0.110 0.007 lMean not provided for ID type variables. 2Standard deviation not given for binary variables. purchase and insurance purchase decisions in all model specifications except for the preferred model (table 3, col- umn 1). Note, however, that this is quite consistent with the proposition that insurance purchase is dependent in part on map purchase. The test is for the correlation between factors influencing the two decisions, but not included as explana- tory variables in the two equations. Equation 1 includes a sufficiently rich set of explanatory variables for the two decisions that no unexplained correlation remains between the two equations. A simple specification for the two decisions is provided in column 5 of tables 3. This provides a set of explanatory variables based on a strict interpretation of the expected utility theory. Only decision costs and expected loss variables are included. The occurrence of misleading information in the coarse map is also included in the equations as this directly affects the accuracy of the expected value calculations. The equation for the decision to purchase a detailed map shows that map cost or insurance cost has no impact on buying a map. A map is more likely to be bought the higher the prob- ability of a loss and the lower the loss amount. Potential errors from using only the coarse map (for example, by reading the map and concluding the loss is higher or lower than it actually is) reduce the likelihood of buying a detailed map. There is some difficulty in interpreting this variable, as the subject can- not know if the coarse map is revealing the true loss amount or not, and once the detailed map is purchased, the issue of any error in loss reporting in the coarse map is of no importance to the subject’s decision to purchase the map. It is therefore not surprising that the results of these variables in the map purchase equation are mixed. Map information and interpretation errors have more significance in the insurance purchase decision. Consider- ing the insurance purchase decision (table 33, column 5) we observed that although the map cost is not important, the higher the cost of insurance the less likely subjects are to buy coverage. The higher the probability of loss the more likely is the purchase of insurance, as it is when the amount of the loss is greater. The potential map errors are statistically significant in this equation, and when the actual loss is lower than what is shown by the coarse map, the subject is likely to overestimate the probability of a loss and more likely to purchase insurance (coefficient estimate is +0.182). When the actual loss is higher than what is shown by the coarse map, the subject is likely to underestimate the probability of a loss and less likely to pur— chase insurance (coefficient estimate is —0.259). In summary, the simple models in column 5 of each table perform reason- ably well as explanations of the decisions and are consistent with the predictions of the expected utility theory. Columns 3 and 4 present estimates of a BiProbit model with and without correction for the panel nature of the data gathering process. This adds to the simple model a richer specification of the decision environment facing the subjects. In particular there are variables indicating past decisions by subjects, and some demographic variables are included. Map cost is important in determining whether a subject purchases the map, and the sign of the coefficient is consistent with expectations. Once accounted for, clustering makes the cost of insurance insignificant. The higher the probability of loss the more likely is the purchase of the detailed map. The effect of uncertainty from the coarse map remains in these models of the map purchase decision. Subjects display some habitual behavior in that they are more likely to buy a map this game if they purchased one last game and more likely to buy a detailed map if they bought insurance last period. Past losses and bankruptcies are not statistically significant factors in map purchase, nor are factors indicating if the subject holds insur- ance policies outside the experiment. Age does not appear to be a factor in map purchase, but females are less likely to buy maps than males. The coefficient on the wealth variable (the natural log of accumulated experiment wealth) is negative and statistically significant. Although this result may be interpreted as evidence for less need of map information as subjects get wealthier, wealth is more likely a proxy for experience with the game, because for most subjects in this experiment, wealth increases as the game progresses. Interpreted this way, the negative coefficient indicates that subjects are less likely to buy the map the more they play the game because they see it offering little marginal benefit. This behavior may also be a reflection of increasing confidence leading to overconfidence as the game progresses and nears completion. Table 3B presents estimates for the BiProbit model for insurance purchase in columns 3 and 4. Although map cost is not important in determining the decision to purchase insurance, the cost of insurance is negative and statistically significant. Whereas higher premiums decrease the prob- ability of buying insurance, higher losses and more likely losses increase the probability of buying insurance. All these impacts are consistent with the expected utility theory. As was the case with the simple model, decision errors based on the Data Analysis and Interpretation 7 coarse map are consistent with subjects buying more insur- ance when they overestimate the size of the loss and buying less insurance when they underestimate the size of the actual loss. Insurance purchase displays some habitual behavior, while past losses and bankruptcies are not statistically impor— tant factors. Insurance coverage outside the experiment, as a measure of the subject’s risk aversion, shows a statistically significant positive impact on the decision to buy insurance in the experiment. Age does not influence insurance purchase but in contrast with the map buying decision, females are more likely to buy insurance. Could it be that females are more confident in interpreting the map information and therefore do not need the detailed map, but are more risk averse than males and hence more likely to buy insurance‘.79 Also in contrast to the impact on the map purchase decision, the wealth variable has a statistically significant but positive effect on the decision to buy insurance. In previous experiments of a similar nature, Ganderton and others (2000) found wealth to exert a negative effect on insurance purchase. Here, the subjects have better information on which to base their insurance decision in the form of the detailed map, and they do not self-insure as they become wealthier, nor do they assess the risks as being lower as their confidence builds with experience playing the game. On average, a map costs half what insurance costs, so maps could be showing an inferior income effect, whereas insurance shows a normal income effect. As stated earlier, the preferred model is shown in column 1. The model in column 2 is the same specification but does not account for within-subject correlations that are reflected in excessively large standard errors. This model finds no statisti- cal correlation between the errors in the map and insurance purchase decisions. Variables measuring map errors have been omitted from the map purchase equation since they really have no relevance as argued above. Results for these variables are mixed in this equation, and the interpretation of their impact is unclear at best. The lack of any significance for the variables indicating insurance activity outside the experiment recom— mends omitting these variables from the map buying equation. Column 1 of table 3A shows map purchase to be less likely at higher map cost, and insensitive to insurance cost and loss amount. A subject is more likely to purchase the detailed map when the probability of a loss is high. Those who previously bought maps and insurance are more likely to purchase maps. Using equation 1 for both map purchase and insurance purchase, table 4 presents estimates of the marginal effects of each variable on the joint probabilities of buying the map and insurance. Table 4, column 1 shows the influence of each vari- able on the joint probability of buying both the detailed map and insurance. Increases in both the cost of the map and insur- ance decrease this probability, but by far the strongest impact on the joint probability is the probability of loss. Increases in the size of the potential loss also increase the joint probability of purchasing the map-insurance bundle, but the effect is sub- stantially smaller than for changes in the probability of loss. 9 Or could this be evidence that females appreciate maps less than males? Table 3A. Bivariate probit analysis of decision to buy detailed map (simultaneous estimation)‘. The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Seemingly Unrelated Bivariate Bivariate Probit Probit Cluster2 No Cluster Cluster No Cluster Cluster Equation Variable (1) (2) (3) (4) (5) Buy Map Constant —0.4423 —0.442 0.129 0. 129 0.781 (—2.69) (—5.63) (0.72) (1.44) (7.02) Map cost —0.017 —0.017 —0.014 —0.014 -0.005 (—3.85) (-8.70) (—2.69) (—6.07) (—1.11) Insurance cost 0.004 0.004 0.004 0.004 0.003 (0.00) (4.06) (0.03) (3.55) (1.43) Log(loss amount) —0.001 —0.001 —0.050 —0.050 —0.043 (—0.1 1) (—0.23) (-4.70) (—8.92) (-4.12) Loss probability 1.02 1.02 0.872 0.872 0.870 (4.37) (4.56) (3.41) (3.48) (3.93) Actual loss lower than —8.58 —8.58 —7 .49 coarse map shows (~139) (0.0) (-153) Actual loss is higher than —8.76 -8.76 —7.32 coarse map shows (—45.7) (0.0) (— 1 39) Buy map last game 1.87 1.87 1.84 1.84 (28.7) (92.5) (26.5) (81.2) Buy insurance. Last period 0.137 0.137 0.177 0.177 (3.37) (6.72) (4.15) (7.71) Suffer loss last period 0.062 0.062 0.084 0.084 (1.16) (1.26) (1.40) (1.48) Bankrupt last period -0.252 —0.252 —0.292 —0.292 {—1.59) (—1.59) (—1.69) (—1.70) Log(total a/c balance) -0.031 —0.031 —0.041 —0.041 (-2.57) (—4.56) (-3.05) (—5.51) Insurance score 0.005 0.005 (0.20) (0.56) has hazard insurance —0.016 —0.016 (-0.09) (—0.23) Female —0.141 —0.141 —0.164 —0.164 (—2.17) (—6.97) (—2.43) (—7.16) Age 30 or older 0.082 0.082 0.078 0.078 (1.16) (3.75) (0.98) (2.80) ' Estimates are full information maximum likelihood. 2 Clustering allows for correlated errors within observations from the same subject, but none across subjects. 3Coefficients in bold are statistically significant at the 5 percent level. The number in parenthesis is the t-statistic for significance that is estimated as the coefficient] standard deviation of the coefficient. Table 3B. Bivariate probit analysis of decision to buy insurance (simultaneous estimation). Data Analysis and Interpretation Seemingly Unrelated Bivariate Bivan'ate Probit Probit Cluster No Cluster Cluster No Cluster Cluster Equation Variable (1) (2) (3) (4) (5) Buy Insurance Constant —2.281 —2.28 —2.62 —2.62 —1.39 (—20.7) (—33.6) (—20.9) (—33.3) (—13.3) Map cost 0.004 0.004 0.003 0.003 0.005 (1.58) (2.00) (1.24) (1.56) (1.53) Insurance cost —0.004 —0.004 —0.003 —0.003 —0.004 (—3. 16) (—3.96) (—2.99) (—3.73) (—2.68) L0g(loss amount) 0.187 0.187 0.185 0.185 0.198 (18.4) (37.9) (18.0) (37.6) (16.7) loss prob. 9.34 9.34 9.38 9.38 8.16 (19.2) (43.3) (19.3) (43.6) (17.7) Actual loss lower than 0.322 0.322 0.243 0.243 0.182 coarse map shows (7.32) (9.40) (5.23) (7.69) (2.53) Actual loss is higher than —0.075 —0.075 —0.152 —0.152 —0.259 coarse map shows (—1.00) (—1.15) (—2.09) (—2.37) (—2.63) Bought map 0.241 0.241 (3.48) (7.56) Buy map last game 0.135 0.135 (3.22) (6.66) Insured last period 1.113 1.113 1.12 1.12 (23.41) (58.4) (23.7) (59.1) Suffered loss last period —0.066 —0.066 —0.063 —0.063 (—1.34) (—1.45) (—1.26) (—1.37) Bankrupt last period 0.289 0.289 0.273 0.273 (1.83) (2.01) (1.71) (1.90) Log(total a/c balance) 0.058 0.058 0.056 0.056 (6.98) (9.13) (6.63) (8.83) Insurance score 0.041 0.041 0.042 0.042 (2.08) (5.96) (2.08) (5.99) Has hazard insurance -0_ 100 —0. 100 —0.099 -0.099 (—0.50) (—1.71) (—0.49) (—1.70) Female 0.183 0.183 0.175 0.175 (3.24) (9.59) (3.10) (9.23) Aged 30 or older —0. 1 17 —0.117 —0. 1 13 —0.113 (—1.75) (—5.08) (—1.69) (—4.92) Rho —0.043 —0.043 0.086 0.086 0. 144 Wald test for rho=0 2.13 4.01 13.56 30.43 13.58 Sample size 22981 22981 22981 22981 22981 Wald test 3000 14923 51438 12912 31453 1Figures in bold are statistically significant at the 5 percent level. 9 10 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Table 4. Marginal effect of explanatory variables on joint probabilities. Impact on joint probability: (1) (2) (3) (4) Variable buy map and buy buy map, do not do not buy map, buy buy neither map nor insurance buy insurance insurance insurance Average probability 0.334 0.320 0.186 0.159 Cost of map —0.0031 —0.004 0.004 0.003 Cost of insurance —0.0002 0.002 —0.001 —0.0002 Log(loss amount) 0.049 —0.049 0.026 -0.025 Probability of loss 2.64 —2.26 1.08 —1.46 Log(wealth) 0.009 —0.021 0.014 -0.003 Loss lower than map 0.083 —0.083 0.043 —0.043 Loss higher than map —0.020 0.020 —0.010 0.010 Bought map 0.063 —0.063 0.033 —0.033 Insurance score 0.011 -0.011 0.006 —0.006 Female 0.021 -0.073 0.052 -0.0001 Age 30 and older —0.015 0.045 —0.031 0.001 1The numbers in the table are the change in probability due to the impact of each explanatory variable from the regression equations. Just as was found in the previous research (Ganderton and oth- ers, 2000) subjects appear to be far more sensitive to changes in the probability of loss than changes in the loss amount. This is somewhat surprising given the general view that people have difficulty dealing with small probabilities of the order considered here (1/10, 1/100, 1/ 1,000). However, that View relates more to the tendency for people to either exaggerate or discount small probabilities than their sensitivity to marginal changes in these small probabilities. In summary, the results of data analysis provide strong evidence of rational behavior by subjects consistent with the expected utility theory. Subjects are less likely to pur- chase additional information (the map) the higher the cost of the map, but are insensitive to the cost of insurance in map purchase. Similarly, insurance against loss is less likely to be purchased the higher the premium but is insensitive to the cost of the map. Map purchase is more likely with an increase in the probability of loss, but is insensitive to the amount of the loss, but the decision to purchase insurance is positively impacted by both elements determining expected loss. The relationship between the two decisions is relatively strong and positive—subjects who bought a map are more likely to buy insurance, and those who bought insurance are more likely to buy a map both now and in the future. While past decisions influence current decisions, past outcomes are not statistically significant determinants of current decisions. There is some evidence that those subjects who rely on less information, in the form of a coarse map, and forego the additional information contained in the detailed map, are more likely to purchase insurance when they overestimate the size of the loss as indicated by the coarse map. Subjects are basing their insurance decision on the information provided in the coarse map, even though it is erroneous. In the case of the hazard modeled in this experiment, it is only when subjects purchase the detailed map that they realize they were overestimating the size of the loss, calculating an exaggerated expected loss, and buying too much insurance. Clearly, sub— jects are aware of the benefits that arise from the more detailed geoscience information contained in the detailed maps. In the case of this experiment, the benefit is that insurance costs to the subject can be lowered, but in the real world application , the benefit would just as likely be that the subject might real- ize that they are underinsured. As subjects accumulate earnings over the duration of the experiment, they are less likely to purchase a detailed map, but more likely to purchase insurance. Although there is no theoretical expectation regarding the marginal effect of wealth on these decisions, it could be that two distinct factors are at work. In the case of the map purchase decision, increasing wealth could be a proxy for experience with the game, and as subjects increase their experience they value the additional map information less. In the case of buy- ing insurance, subjects may be suffering from the common gambling fallacy that as the game nears its end a hazardous event is more likely to occur. It could also be that as subjects become richer they can afford more insurance as the pre- mium represents a smaller fraction of total wealth. Further investigation is required to identify the true motivations for these observed behaviors. Finally, subjects who hold insurance outside the experi- ment are more likely to buy insurance, but this behavior has no influence on their decision to buy a detailed map. Because the detailed map provides a higher level of risk-rel- evant information on which to base insurance purchase deci- sions, this suggests that subjects have little or no experience with such options in their everyday lives. Also requiring further investigation is the curious observation that females are more likely than males to purchase insurance, but less likely to purchase the additional information contained in the detailed map. Program Potential The results of this experiment suggest considerable potential for the research program of which it was a major part. The experiment demonstrates the use of the Internet as a mechanism for conducting experiments, especially of the kind requiring the delivery of considerable geoscience information of a graphical nature. The Web-based experiment is not limited geographically or temporally. Once a payment mechanism with a corresponding reach that also conforms with both the needs of human subjects and confidentiality and financial requirements that control research work is developed, we could modify existing methods employed for on-line com- merce to work in this case. Future work would entail providing maps that are more realistic to selected groups such as policy makers and stake- holders in regional organizations both public and private. Extending the coverage across the county and overseas is also a simple extension of the current work. A major exten- sion of the current experiment would provide subjects with a mitigation alternative. This would allow us to determine the impact of geoscience information on the choice between mitigation and insurance, as well as study the interaction between mitigation and insurance for these types of low- probability, high loss risks. Program Potential 11 References Bemknopf, R.L., Dinitz, LB, and Loague, K., 2001, An inter- disciplinary assessment of regional—scale nonpoint source groundwater vulnerability: theory and application: U.S. Geological Survey Professional Paper 1645. Bemknopf, R.L., Brookshire, D.S., McKee, M.J., and Soller, DR, 1997, Estimating the social value of geologic map information: a regulatory application: Journal of Environmen- tal Economics and Management, v. 32, p. 204-218. Bemknopf, R.L., Campbell, R.H., Brookshire, D.S., and Shapiro, CD, 1988, A probabilistic approach to landslide mapping in Cincinnati, Ohio, with applications for economic evaluation: Bulletin of the Association of Engineering Geolo— gists, v. 25, p. 39-56. Carnegie Commission on Science, Technology, and Govem- ment, 1993, Risk and the environment improving regulatory decision making: New York, 150 p. Feller, W., 1968, An introduction to probability theory and its applications (3rd ed., v. 1): New York, Wiley and Sons, 509 p. Ganderton, P.T, Brookshire, D.S., McKee, M., Stewart, 8., and Thurston, H., 2000, Buying Insurance for Disaster-Type Risks—Experimental Evidence, Journal of Risk and Uncer- tainty, v. 20, no. 3, p. 271—289. Greene, W.H., 2003, Econometric Analysis (5'h ed.), Englewood Cliffs, N.J., Prentice Hall, 1005 p. Hagel, J .H., and Roth, A.E., eds., 1995, The handbook of experimental economics: Princeton, N.J., Princeton Univer- sity Press, 721 p. Kunreuther, H., Platt, R., Baruch, S., Bemknopf, R., Buckley, M., Burkett, V., Conrad, D., Davison, T., Deutsch, K., Geis, D., Good, J ., Jannereth, M., Knap, A., Lane, H., Ljung, G., Mcauley, M., Mileti, D., Miller, T. Morrow, B., Myers, J ., Pielke, R., Pratt, A., and Tripp J ., 1999, The hidden cost of coastal hazards—Implications for risk assessment and mitiga— tion: The John Heinz Center for Science, Economics, and Environmental Policy, Island Press, 220 p. Platt, RH, 1999, Disasters and democracy—The Politics of Extreme Natural Events: Washington, Island Press, 320 p. STATA, 2001, STATA Reference Manual, Version 7: Press, Texas, A—G Stata. Glossary Expected utility theory Expected utility theory is a norma- tive theory for decision making under risk. Von Neumann and Morganstem axiomated expected utility theory by showing that alternative actions can be ranked by their expected utilities. The expected utility of an alternative action is the weighted average of the utilities of the possible outcomes where the weights are the objective probabilities of each outcome. Moral hazard Moral hazard is a phenomenon that occurs in insurance markets caused by an asymmetry of informa- tion between the consumer and the insurance provider. When an insurance company has a stake in the action taken by a consumer, such as self—protection (for example, maintenance of a vehicle), but the insurance company cannot observe the consumer’s action, the situation involves moral hazard. Moral hazard can be partly overcome with insurance deductibles. Adverse selection Adverse selection is a phenomenon that occurs in insurance markets that is caused by an asymmetry of information between the consumer and the insurance provider. In markets for insurance, the basic asymmetry of information is that the purchasers of insurance may well have a better idea of the relevant risks than does the insurance company. Treatment specification Treatments are experimental con- trols used to condition responses or behaviors. The method of estimation must identify them explicitly in the model specifi- cation to remove, or control, for their effect in the experiment. With sufficient sample observations, the treatment effect can be identified and measured. Glossary 13 Treatment combination The number of alternative treatment values when all possible values are allowed. If one treatment has two possible values and another three, then combined there are 6 combinations of the two treatments. To estimate treatment effects we must determine the appropriate sample size for each combination of treatment values. Wald test The Wald test is based upon the restriction imposed by the null hypothesis. If true then a specific qua- dratic form of the parameter and its mean under the null will be distributed chi-squared. This test statistic is used to test a whole range of hypotheses concerning both individual param- eters and sets of parameter restrictions. Bivariate probit (BiProbit) model The bivariate probit model is a qualitative response regression model in which the depen- dent variable assumes discrete values. The simplest of these models is that in which the dependent variable is binary (it can assume only two values which can be denoted by 0 and 1). The bivariate probit is the case where observed values of the dependent variable are realizations of a binomial process with probabilities given by Pr(y = 1) = 1— F (—,B ’x) and varying from trial to trial depending on xi, where Pr is probability, y is the dependent variable, F is the cumulative normal probability distribution, b is a regression coefficient, x is an independent variable, and i observations, 1' = 1,...,I. Income effect The income effect is a consumer’s reaction with respect to purchases of a commodity to changes in their income, prices remaining constant. 14 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Appendix A. Experiment Flow Chart Enter section If returning subject offer to bypass introduction? YES: Description of experiment, scenario, parameters, location of information, page layout, etc. QUIT: Go to QUIT section Play or Quit? PLAY: Set parameters: - income - T*: set loss prob. ' T*: loss amt - account balance : 0 - number periods, P = U(1,5) - number rounds, R = U(1,9) - T*: set map cost - T*: set insurance premium I - Select random location from set: {A, B, C, D} - Set LocA, LocB, LocC binary indicators ‘ set period = l Is period less than or equal to P? YES: Account balance + 1,000 III Set up map and decision pages 1 Show simple map with location highlighted + set MAP = 0, INS : 0 MAP = 1: show marked map with location detailed buy insurance or detailed map? YES: Buy policy or not? INS = l: subtract premium from account -A B 7‘ N0: period + 1 Is round less than or equal to R? Appendix A 15 Appendix A. Experiment Flow Chart—Continued Draw event from distribution E ~ U(0, 999) + L=0 IsE<0009? A ‘ Calculate account balance: New balance = max{ 0, old balance — (MAP) map cost — (INS) premium — L.[(LocA) 10,000 + (LocC) 1,000 + (LocB) 100] } YES: Invoke bankruptcy description. Is new balance 0? Quit chosen Record data from round in database: subj ect ID game ID treatment parameters: -- loss probabilty, loss amounts (red, orange and yellow), map cost, insurance premium) location period number eriod b innin account balan Found nifriber g Ge YES: Record the last successfully round beginning account balance completed section for subject, for later MAP return. Set session interrupted flag ON INS E Loss (round ending account balance) round + 1 Is this an interruption? Is this interruption during the main experiment? YES: Record current round for subject. Set experiment interrupted flag ON 4 Go to payout section 16 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Appendix B. Game Explanation Text New Main Game introductory text: You are about to enter the main experiment. The general structure of the game is outlined below. You play a number of GAMES. Within each game there is a random number of PERIODS. Each period you earn game income of 200 tokens. Within each period there is a random number of ROUNDS. Each round you are exposed to a potential loss event. You either suffer a loss, or not. The size and likelihood of loss depends on your location. You know how often these losses occur, but not when. The sequence above is repeated with certain experimental parameters changing each period and each game. You face the following decision each period: Buy insurance to offset any potential losses. Buy a more detailed map of your location to help in your decision making. You can buy either, or both, or none at all. At all times you will know the probability of suffering a loss, the size of that loss, and your location on either a coarse, or detailed, map. You will know how many games tokens you have and the cost of buying a map and (or) buying insurance. Once you buy insurance you have coverage for the whole period, but not for the next period. Insurance covers any losses you may suffer during the period. If you lose more tokens than you own, you are declared bankrupt, and must wait until the start of the next period to get more tokens. As you go through the experiment you will earn income and may spend it to buy maps and insurance. You may lose income if you suffer an uninsured loss. At the end of the experiment your accumulated earnings in tokens will be converted to US. dollars and a claim check will be issued for you to print. Appendix C. Maps MAPGEO = A Course Map with point D highlighted to show subject location. I II >0 w. III IV 0. UO Notes: (i) Color of zone is determined by modal hazard in zone. (ii) Location A appears to have the highest loss, B and C have medium loss, and D has lowest loss. Fine Map I II . O A B 111 IV 0 O C D Note: (i) Now location A has highest loss, C has medium loss and B and D have lowest loss. This map determines actual losses in experiment. Appendix C MAPGEO = B Course Map with point B highlighted to show subject location I II >0 DUO III IV 0. U. Notes: (i) Color of zone is determined by modal hazard in zone. (ii) Locations A and C appear to have highest loss, B and D have medium loss and no location has lowest loss. Fine Map I II . O A B 111 IV 0 O C D Note: (i) Now location A has highest loss, B and C have medium loss, and D has lowest loss. Actual losses in experiment are determined by this map. 18 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Appendix D. Screen Captures Welcome Screen Capture From Website 3 Risk Management Experiment — Microsoft Internet Explorer ,, 7 Elle Edit Mew Favorites Iools gelp . ecu - "o - e e mom hm Neel g7; , '. a a Address [in http:/Ieconld'i.m.e¢dwebunePage.cfin?RemestThe0u=200&CHD=611&CFfOKEN=22230866 it Go 7- lhis webs'ee does you to participate in an econmnics exmriment being administered bythe University 43wa Mexico new by the United States Geological Survey. Economics experiments present you. the participant with e Met-lite scenario in which you are required to make decisions The comnsaion you receive for participating in this emerinentisbaeedinpertonthedecisionsyoumake lnthisexperirnentyouwileamexperineMcmrencymdfaceenskmeehvmichyoumeyelflerlosseer havetheoptiontoinweagfimtheselossesmryoucmpurcheeeedditionatiriormationthetmayhelpyouwithyom decisions.wailbefacedwithrepededrieks.buteechtimecertainexpeflmentalparametersmaydiangeAttheend ottheexpeiimentyouwilbeiseuedaclaimcheckfixmamournbasedontheexpenmentalcunencyyouhaveeaned duirigtheexpenment Yourpartic‘paion inthis experiment is greatlyappreciated. Areport oltheresutts ofthis researchwit be available from Philip Ganderton's website in the luture. If in the meantime you have any questions or concerns regarding the experiment or your participation in the experiment, please contact the researchers at econlab@unm edu or by telephone at (505) 277-5304. MED Appendix D 19 Appendix D. Screen Captures—Continued Logon Screen Capture 3 Risk Management Experiment — Microsoft Internet Explorer Elle Edit ylewmi-‘evorites Iools Help . 9.... s a a «pm *mww «1 m Address [in me/lecorrabumedwbgircnnmmsrrmmszoo Now User? 91AM User Name Password LOGIN Forgot your password? This experiment presents you with a risky situation in which you may sufier losses of experimental currency. You can insure against these losses. or you can purchase additional information that may help you with your decisions. You will be faced with repeated risky situations. each time certain experimental parameters may change. You not individually and the objective of the experiment is to collect data on how you respond to alternative parameters as you manage the risks presented to you. At the end of the experiment you will be issued a claim check for an amount based on the experimental currency you have earned during the experiment. You should be able to complete all sections of the experiment in less than one hour. You may also leave the site. and return to complete any remaining sections. You need to complete the following sections in the order indicated. You cannot move on to the next section without completing the previous section, If you have any questions concerning the market experiments or this research you may contact the researchers at the UNM Department of Economics (505) 277-5304. or by email to econlab@unm edu "‘ You must provide a photo ID in order to receive earnings. 20 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Appendix D. Screen Captures—Continued Registration Welcome Screen Capture 3 Risk Management Experiment — Microsoft Internet Explorer file Edit yiew Favorites Iools flelp 2°?“ ' ‘9 13.] [flfilflm *Favortes cued: @gvgr . fig; Address ii} m/IemnbbmeMWcfinmewesfimzoo _ mmmmhmbehyoummlngtomtolm: NeismWinecononficdecision—making.lyouchoomtopmicipetoyounfllbemakingdecisionsm mtomwhmmmm.Youwilmkwmbeudonthedociflomthatyeumeko. mmmmmmmweuemdmdodsionswflhcmt YwflmflmmnmuthhMWinumwom.Youmustpmidoyouuocial mmmmmmmmrummzocwwnmpmicipuommmm» Wmtfism.Youcmmthewobsitoulnytimmyoucenelsontumtomwebsnomdcomm whenywkkdflouwimt.hommeinucdapeymOMcIWnchnkmhuywcomphumMW. Pmammismmmacmdotmatommmnydthodnundtocomw. peymuvuoyou.Nomwilywidimitybeusodmmmlyflsofdma.mwilmyintonnuionyoupmidomm mumymmmmmwmmm. lhuvonadundundomodmelbowaml ngycomomtoplmcipmlnflnoxpemnton Whammm. Mam Appendix D. Screen Captures—Continued Registration Screen Capture 3 Risk Management Experiment — Microsoft Internet Explorer Elle Ed'rt Mew Favorites ’Iools flelp , » gosaw o o re ogpsm *mm «M move - m Address {C3 http://oconlaburun.edu/register.cfm?RequestTmeOut=200&agree=yes W I '3 3:. -- '- Home Your Profile First Name {Sagmw - - Required 11.14 Last Name t Studemld nimmnnmmmwh £1 meadow § Address 3 . City State Zip Code PM" 32333 - 1224327 ' mammmmr’ “ em" ”6"” fiéjfl:::: ’ Your Mount I Um Nam. [ ' WW W g ' Begin with a tow. and use only m i (fit-7‘ mmm IM‘ 115."an m r -. : Appendix D 21 22 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Appendix D. Screen Captures—Continued Registration Complete Screen Capture 3 Risk Management Experiment — Microsoft Internet Explorer file Edit mew Favorites Iools Help gem. '9 L3 m«,m trams em ewe 3:3 » m 5333333333 'éjimzlleconlab.unm.edu/submmegsuamn.cfinmequesfime0ut=zoo v Go ; R. '3' I . . 3 Home Congratulations You have successfully registered: A confirmation mail has been sent to your email address containing your username and password. Please preserve the email in case you forget your usememe or password. "norm. W Appendix D 23 Appendix D. Screen Captures—Continued Post Registration, Personal Welcome and Introduction Page Screen Capture 3 Risk Management Experiment — Home — Microsoft Internet Explorer ._ file Edit Mew Favorites Iools fielp 1 A. :em- o o 33 Wow *m em a»; 3-3 W Address éifl haw/[embomxdu/seqte/homefififi "M 1 7 Logout Login Time: Tue Dec 10 2002 07:23 PM Welcome pgoea. Thisexperimhasthoeoonwnonts.waiflbeaskedtoflhabfidwwyabm‘ywsetfmdanyinsume youmaycarry.3‘laisirrfonnaionisusedinthemalysisofthedocisimsyoumakammemmmeothstwe Mcmaremdecisionswithtlmeofomer,mymous,participants.Nextyouwiltptayasmaflriskvpreference weirlvltfidiyouvnlbeaskedtommsmmms. Onceyouhavecompletedthesefisttwosedims,youwilloolbeaskedtocompletethemagain,evenlyoo motothewobexperivmtocormethemainsxperinem‘mothirdcornpmemisthema'nexpemnemcmennng riskansumemdgeogmphicidomation. Ywuoibegidedmthewebsfietocmemmmsmence,hstmctionsareprovidedtohelp youcomptetoeachsectimandtobdpyounavigatetothenensection Youneedtocmrplotethefolowingsectionsinmeordermdicaed.Youcannotmoveontothenensection Weanpldhgflteprmssection. 1M2! 2. Risk Preference Mini Experiment 3. Main Gm 24 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Appendix D. Screen Captures—Continued 7 Survey Screen Capture 3 Risk Management Experiment — Survey - Microsoft Internet Explorer §EI|e Edt 309w Favorites Ioois flelp EM *0 L309 ”m*m0m€3$"= I W3 aggresséfl WJ/eccrfliulmedw'seutelwveyform. cfm — Home Logoui WWW twididmmmpoiciesdoymom70heckdmm. Dmmmwmmwflmm Dmmmwmammtom DMWW Umuypaarfl'soroinrscoflq Dummsma Drum-rem Dmmmm Dmmm Baum Danae-um Dmmmumm.m.m BMW Dmmnmmm 2.Howc|omdoywbeieaywhwseisaocaedimahmnamlhazad? Creams-mos Om5miomies Omwmzsmes Appendix D 25 Appendix D. Screen Captures—Continued Risk Preference Mini Experiment Screen Capture 3 Risk Management Experiment - Risk Preference Mini Experiment — Microsoft In... - 3’ file Edit Mew Favorites Iools fieip 3086:!“ Q flflflaflmi'mm Owe efivmfiflr mess Ea ittpzllecwhbflumedu/seule/mfxpemwn Home Logout mmammifledw.YounnrdmkfllemiunhmoflpfihmdloS-hmm. Ywmmflamwuhmmm,mmdmm.meummdtfieMod.Note maiywchmenutoimstfluMmawioddyeaIJouhaRitospendnoumismcmmm tomspm.ywaesimiymmdtomkuualm:cmrohwwuwmdmdwchokuimudy Ywmhmmmmmwwam. Tobi Rount 2 Round 1 W The In Page! W $5,000.00 5 yrs 5021213 0.5 chance 013710151 ssnmm 5’“ «camarsnjsau invest nullity. spend $5,000.00 may 55.00000 26 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Appendix D. Screen Captures—Continued Main Experiment Welcome Screen Capture 2) Risk Management Experiment ~ Main Game — Microsoft Internet Explorer file Edit Mew Favorites Iools Help Eeaadv to - L3 to {Wm *ravms once-ea 3;: - W a. Home Logout waeabm‘tomuflreMinEmMthegemrflmdhegmeismwow e YouplayamaidnundtSGMlES.BasedonexperienceyoucanspendbetweenJOandSOmimtosontlis section. WithineachgamethereisarandommmberofPEMODS EsohpenodyouearngarnelncomeonOOtokens W‘nhineachperiodthereisarandornnurnoerotROIMS Eachroundyouareexposedtoahazardousevent Youeithersderaioeetromthehazardmrnot Thesizeandfikeiihoodotlossdependsooyourlooadon You knowhowoien these losses occur. but notwhen - 0...... Forexarnple.a9ameisalitetime.aperiodisayear,andaroundiseachday.Youcanbuyhazardinsuance.ormore intormationanydayottheyear.andtacethehazardeachdaypoundiOnceyouhaveboughtadditionalinlormation,you haveitfortherernakiderofyouriiietimetgarne).0nceyou havebougl'ltinsuranceyourpolicycoversyouloreachdayin theremainderoftheyeaflpenod). The sequence above is repeated. The following changes are possible: Each game - map cost. insurance premium. location and hazard pattern Each periodhazardprobwityand amount ollosstrom hazard, Each round - you can make decisions and your earned tokens may change. You face the following decisions. Buy hieurance to olset any potential losses Buy a more detailed map at your location to help in your decision making Youcanbuyeither.orboth. ornoneateli At all times you will know the probability of a hazard. the also of the hazard loss. and your location on either a coarse. or detailed map j You will know how many games tokens you have and the cost ot buying a map. and buying insurance Once you buy insurance you have coverage for one period Insurance covers any losses you may solar diuirig the period llyou lose more tokens than you own. you are declared bankrupt. and you will automatically go to the next period and recieve period income of 200 tokens. As you go through the experiment you will earn income. and may spend it to buy maps and insurance. You may lose income if you antler an un-insured loss. At the end at the experiment your accumulated earnings in tokens will be converted to SUS at the rate of 1000 tokens I 31.00 and a claim check will be issued for you to print Appendix D. Screen Captures—Continued Main Decision Page Screen Capture 3 Risk Management Experiment — Main Game - Microsoft Internet Explorer file Edit yiew Favorites Iools flelp 033* ' 2;) L53 {2-3 {h ):’Searcli *Favortes .Media 2)ng 3;; . fig Address 5!] http://econlab.unm.edu/secure/ma'nGame.cfrn “lie flute GIN. ' Home Logout The map at right shows your location. Each large cell contains 9 smaller cells which are hidden and only shown in the detailed map. The color ofthe cell you see is determined by the dominant color of the smaller cells within it. if a hazardous event occurs you will suifer a loss determined by the color of the small cell you are in, not necessarily the same as the large cell. Regardless of your location. you can buy insurance that fully covers any loss you may suffer during a period. Over the course of a period you will be exposed to repeated random events that may, or may not, result in hazardous less. information on loss amounts. the probability of loss. costs of the map and insurance, and your current status is provided below. Your decision (click on one) Buy Detailed Map I Buy insurance Continue You are playing GAME 1 PERIOD 1 ROUND 1 7 Your game balance is 200 tokens ? Your total balance is 200 tokens ? Detailed Map costs 10 tokens ? Insurance costs 20 tokens ? Probability of hazardous event 0001 ? Last round None ? Last period None ? Elm, 0mm . . - n J I 28 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment Appendix D. Screen Captures—Continued Main Decision Page With Detailed Map Screen Capture 3 Risk Management Experiment — Main Game - Microsoft Internet Explorer file Edit Mew Favorites Iools help §°Badt' d flfla;lsm*FavmesQMech®$V"'wak Address {mm/[WW ;uin.edtr/seoxe/marrom_.gon 7 ,, 7 M W ‘33 60 u Game Home Logout The map at right shows your location. The color ofthe cell you see indicates the loss you will experience it a hazardous event occurs Regardless of your location, you can buy insurance that fully covers any lose you may sufierdun'ngapenod. Overthecourse ota period you will beexposedto ed random events that may, or may not, result in a hazardous loss Information on loss amounts. the probability of loss. costs of the map and insurance. and your current status is provided below Buy Insurance Continue You are playing GAME 1 PERIOD 1 ROUND 1 ? Your game balance is 190 tokens ? Your total balance is 190 tokens 7 Detailed Map costs 10 tokens ? Insurance costs 20 tokens ? Probability of hazardous event 0001 ? Last round None ? Last period None ? a: Done ‘ V O mm H; Appendix E 29 Appendix E. Continuation Screen Captures— Result of Event Draw Page, Subject Must Acknowledge to Continue 3 generating an even! , Microsoft inlernel Explorer mmamtmumtb No hazardous event mum! No ioss mwnuwmuummusmmnmmmmmmwa "av-Winn mmmmdhflmwflv mum-mm. immuldmdmm Ezhwywmlyhn mm «MNMMMIMIMW Eachmmywcmnundlcnmmdyw mmm mmmnm-wmmmm-«Wm "me mmmmmuuu nan-a mummwmmmmmm-hmmamm arc-yummy: nun-mpucycmywbmmm ”W E] ‘1 GS a changing world 10. 1684 :ar‘t ’—DAYS ProféSsuonal Paper T 684 ,,( Front cover. Balanced Rock in Chiricahua National Monument, composed of outflow facies Rhyolite Canyon Tuff. Back cover. The feature known as The Fingers is located on the north side of Cave Creek and is composed of aphyric,’high-si|ica rhyolite lava. Geochemistry and Geochronology of Middle Tertiary Volcanic Rocks of the Central Chiricahua Mountains, Southeast Arizona By Edward A. du Bray, Lawrence W. Snee, and John S. Pallister :;:..-:f-".~,;»+ ' ” “fl MAP COLLE;;;¢;;;;_¢” ‘ WY 1 O 2004 __ L.l£3”’T,-'?u'-2Y “fl“: erR‘_ Professional Paper 1684 US DEPOS'TORY 0041 APR 12 2004 UNIVERSITY OF CALIFORNIA BERKELEY U.S. Department of the Interior U.S. Geological Survey U.S. Department of the Interior Gale A. Norton, Secretary U U.S. Geological Survey A Charles G. Groat, Director U.S. Geological Survey, Reston, Virginia: 2004 Version 1.0 For sale by U.S. Geological Survey, Information Services Box 25286, Denver Federal Center Denver, CO 80225 ). For more information about the USGS and its products: Telephone: 1-888-ASK-USGS World Wide Web: http://wwwtusgsgov/ Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply ‘ endorsement by the US Government. Although this report is in the public domain, it contains copyrighted materials that are noted in the text. Permission to reproduce those items must be secured from the individual copyright owners Suggested citation: du Bray, E.A., Snee, L,W., and Pallister, J.Sr, 2004, Geochemistry and geochronology of middle Tertiary volcanic rocks of the central Chiricahua Mountains, southeast Arizona: U.S. Geological Survey Professional Paper 1684, 57 p. ‘ ISBN 0-607-95559-7 Contents Abstract ............. Introduction ................................................................................................................................................... 2 Acknowledgments ........................................................................................................................................ 2 Sampling and Analytic Methods ................................................................................................................ 2 Petrographic and Stratigraphic Characteristics ..................................................................................... 7 Pre-Turkey Creek Caldera Rocks ...................................................................................................... 7 Rocks Associated with the Turkey Creek Caldera ....................................................................... 11 Post-Turkey Creek Caldera Rocks ..... Geochemistry .............................................................................................................................................. 13 Classification ...................................................................................................................................... 13 Within-UnitGeochemicalVariation ................................................................................................ 15 Pre-Turkey Creek Caldera Rocks ........................................................................................... 18 Rocks Associated with the Turkey Creek Caldera .............................................................. 18 Post-Turkey Creek Caldera Rocks .......................................................................................... 19 Geochemistry- and Petrography-Based Stratigraphic Distinctions ........................... Lavas ........................................................................................................................................ 19 Ash-Flow Tuffs and Other Pyroclastic Flow Deposits ........................................................ 22 Petrogenetic Implications ................................................................................................................ 23 Petrogenetic Evolution of the Turkey Creek Caldera Magmatic System ................................. 27 Geochronology .............................................................................................................. Miscellaneous Units ........................................................... . ............................................................... 31 Pre-Turkey Creek Caldera Rocks .................................................................................................... 40 Rocks Associated with the Turkey Creek Caldera .............................................................. - ......... 50 Post-Turkey Creek Caldera Rocks ................................................................................................... 51 Concluding Remarks .................................................................................................................................. 51 References Cited ........................................................................................................................................ 54 Figures 1. Index map showing location of central Chiricahua Mountains, Ariz. ........................................... 3 2. Simplified correlation chart of map units and identification of studied volcanic rock units... 10 3. Total alkali-silica variation diagram showing compositions of volcanic rocks ......................... 13 4. Abundance diagrams of selected major oxides and trace elements ......................................... 14 5. Chondrite-normalized extended trace-element diagrams ............................................................ 15 6. Diagrams showing stratigraphic versus compositional variation among volcanic rocks ....... 20 7. Trace-element—tectonic setting discrimination variation diagrams showing average compositions ......................................................................................................................... 24 8. Chondrite-normalized extended trace-element diagram showing average compositions ..... 26 9. Ternary variation diagram showing average relative proportions of rubidium, potassium, and strontium ................................................................................................................... 27 10. Chondrite-normalized rare earth element diagrams showing average compositions ............ 28 11. Diagrams of 40Ar/39Ar age spectra ..................................................................................................... 32 Tables 1. Compositions of volcanic rocks of the central Chiricahua Mountains ........................................... 5 2. Summary of 40Ar/39Ar age-spectrum results from the central Chiricahua Mountains ................. 8 3. 40Ar/39Ar data forvolcanic rocks of the central Chiricahua Mountains ........................................ 42 4. Diagnostic age, petrographic, and geochemical features of middle Tertiary volcanic rocks of the central Chiricahua Mountains ....................................................................................... 52 Geochemistry and Geochronology of Middle Tertiary Volcanic Rocks of the Central Chiricahua Mountains, Southeast Arizona By Edward A. du Bray, Lawrence W. Snee, and John S. Pallister Abstract Middle Tertiary volcanic rocks of the central Chiricahua Mountains in southeast Arizona are the westernmost con- stituents of the Eocene-Oligocene Boot Heel volcanic field of southwestern New Mexico and southeastern Arizona. About two dozen volumetrically and stratigraphically significant volcanic units are present in this area. These include large- volume, regionally distributed ash—flow tuffs and smaller volume, locally distributed lava flows. The most voluminous of these units is the Rhyolite Canyon Tuff, which erupted 26.9 million years ago from the Turkey Creek caldera in the central Chiricahua Mountains. The Rhyolite Canyon Tuff consists of 500—],000 cubic kilometers of rhyolite that was erupted from a normally zoned reservoir. The tuff represents sequen- tial eruptions, which became systematically less geochemi— cally evolved with time, from progressively deeper levels of the source reservoir. Like the Rhyolite Canyon Tuff, other ash-flow tuffs preserved in the central Chiricahua Mountains have equivalents in nearby, though isolated mountain ranges. However, correlation of these other tuffs, from range to range, has been hindered by stratigraphic discontinuity, structural complexity, and various lithologic similarities and ambiguities. New geochemical and geochronologic data presented here enable correlation of these units between their occurrences in the central Chiricahua Mountains and the remainder of the Boot Heel volcanic field. Volcanic rocks in the central Chiricahua Mountains are composed dominantly of weakly peraluminous, high-silica rhyolite welded tuff and rhyolite lavas of the high—potassium and shoshonitic series. Trace-element, and to a lesser extent, major-oxide abundances are distinct for most of the units studied. Geochemical and geochronologic data depict a time and spatial transgression from subduction to within-plate and extensional tectonic settings. Compositions of the lavas tend to be relatively homogeneous within particular units. In contrast, compositions of the ash-flow tuffs, including the Rhyolite Canyon Tuff, vary significantly owing to eruption from compositionally zoned reservoirs. Reservoir zonation is consistent with fractional crystallization of observed pheno- cryst phases and resulting residual liquid compositional evolu- tion. Rhyolite lavas preserved in the moat of the Turkey Creek caldera depict compositional zonation that is the reverse of that expected of magma extraction from progressively deeper parts of a normally zoned reservoir. Presuming that the source reservoir was sequentially tapped from its top downward, development of reverse zonation in the rhyolite lava sequence may indicate that later erupted, more evolved magma contains systematically less wallrock contamination derived from the geochemically primitive margins of its incompletely mixed reservoir. New 40Ar/39Ar geochronology data indicate that the principal middle Tertiary volcanic rocks in the central Chir- icahua Mountains were erupted between about 34.2 and 26.2 Ma, and that the 5.2 my. period between 33.3 and 28.1 Ma was amagmatic. The initial phase of eruptive activity in the central Chiricahua Mountains, between 34.2 and 33.3 Ma, was associated with a regional tectonic regime dominated by subduction along the west edge of North America. We infer that the magmatic hiatus, nearly simultaneous with a hiatus of similar duration in parts of the Boot Heel volcanic field east of the central Chiricahua Mountains, is related to a period of more rapid convergence and therefore shallower subduction that may have displaced subduction-related magmatic activity to a position east of the present-day Boot Heel volcanic field. The hiatus also coincides with a major plate tectonic reorganization along the west edge of North America that resulted in cessation of subduction and initiation of transform faulting along the San Andreas fault. The final period of mag- matism in the central Chiricahua Mountains, between 28.1 and 23.2 Ma, appears to be coincident with rapid westward retreat of the subducting slab hinge line and consequent redevelop- ment of an asthenospheric mantle wedge, probably associated with foundering of the Farallon plate beneath western North America. Shortly thereafter, magmatism ceased in the central Chiricahua Mountains as the position of extension—related magmatism rapidly shifted westward to the Great Basin. 2 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona Introduction This study is an outgrowth of investigations of the Turkey Creek caldera (du Bray and Pallister, 1991; Pallister and du Bray, 1997; du Bray and others, 1997), the principal volcanic edifice of the central Chiricahua Mountains east-southeast of Tucson, Ariz. (fig. 1). The Turkey Creek caldera is an Oligo- cene volcanic center that formed during eruption of the 26.9— Ma Rhyolite Canyon Tuff and partial evacuation of an under— lying rhyolitic to dacitic magma chamber (Marjaniemi, 1969; du Bray and Pallister, 1991). Caldera evolution involved three distinct phases that concluded in a span of no more than 200,000 years: (1) eruption of 500—1,000 km3 of Rhyolite Canyon Tuff and attendant caldera collapse, (2) resurgent intrusion of dacite porphyry and eruption of consanguineous dacite porphyry lava flows, (3) renewed eruption of high-silica rhyolite as lava flows. The majority of the rocks that form the topographic margin of the caldera are older, middle Tertiary volcanic rocks, and are principally rhyolite ash-flow tuffs. The Turkey Creek caldera is the westernmost and youngest source of regionally distributed ash-flow tuff sheets that are part of the Boot Heel volcanic field described by McIntosh and Bryan (2000). During geologic mapping of the Turkey Creek caldera and its eruptive products, du Bray and others (1997) defined numerous pre-caldera volcanic rock units and mapped their distributions. Earlier attempts to define stratigraphic relations in isolated parts of the central Chiricahua Mountains (Raydon, 1952; Enlows, 1951, 1955; Fernandez and Enlows, 1966; Drewes, 1982; Bryan, 1988; Drewes and Brooks, 1988) had resulted in significant stratigraphic uncertainty and volcano- logic ambiguity. Because none of the characteristics of these older units had been synthesized, we collected stratigraphic, petrographic, geochemical, and geochronological data for the older rocks; these data provide a framework for our own stud- ies as well as provide data essential in correlating these rocks with their equivalents throughout southeastern Arizona and southwestern New Mexico. In this report, we synthesize all available petrographic and stratigraphic data for volcanic rocks of the central Chirica- hua Mountains. In addition, we present and discuss geochemi- cal data for about two dozen volcano-stratigraphic rock units along with geochronologic data for 14 of these units. These units have significant implications for middle Tertiary volca- nic stratigraphy in southeastern Arizona and adjacent south- western New Mexico. Many of the units are ash-flow tuff. Because of their emplacement mode, the ash—flow tuffs are considerably more widely distributed than the lava flow units present in our study area. The distribution of the lava flow units is probably limited to the central Chiricahua Mountains; their utility in stratigraphic correlation is probably similarly restricted. In order to construct a comprehensible framework for the volcanic rocks under study, we divided them into three groups. Because the Turkey Creek caldera dominates the geology of the area, we defined the first group as “rocks associated with the Turkey Creek caldera.” The two other logically identifi- able groups are therefore referred to as “pre-Turkey Creek caldera rocks” and “post-Turkey Creek caldera rocks.” In the discussions that follow, rocks are assigned to one of the three groups, as appropriate, and data and interpretations ordered accordingly. In this report, we present a broad array of geologic data to refine knowledge of the central Chiricahua Mountains in particular and the Boot Heel volcanic field in general. First, we present basic stratigraphic setting and petrographic data acquired from the literature and from our own geologic investigations of the mountain range. Subsequently, in the geochemistry section, we present geochemical data, apply classification schemes, evaluate within—unit compositional variation, and establish diagnostic, between-unit compositional characteristics. The geochemistry section concludes with an analysis of the geochemical evolution of the Turkey Creek cal- dera and its magmatic components. Next, new geochronologic data are presented in order to refine complex stratigraphy-age relations among middle Tertiary volcanic rocks of the central Chiricahua Mountains. In the report’s concluding section, we synthesize all the data in order to constrain the large-scale magmatic—tectonic environment in which the middle Tertiary volcanic rocks of the area were erupted, and evaluate how this regime evolved through the middle Tertiary time frame. Acknowledgments Our field work was facilitated by the cooperation and assistance of the Southwestern Research Station (SWRS) of the American Museum of Natural History, Chiricahua National Monument, and the University of Arizona. We especially thank Wade and Emily Sherbrooke, Pam Limberger, and Christina Schwartz of SWRS for assistance and for providing a stimulating research environment. Dick Armstrong, Carol Kruse, Chuck Milliken, David Moore, and Alan Whalon pro- vided accommodations and assistance during our work in the national monument. We thank Joe Austin, Carol Hudson, Billie and Jean Riggs, Jim Riggs, and Robin Riggs for provid- ing access to their land. We thank D.B. Yager for preparing geochronology mineral separates and for conducting most of the trace-element analyses. R.A.Yeoman ably conducted argon analyses. Reviews by W.C. Shanks, C.A. Nutt, and DA. John improved this study. Sampling and Analytic Methods Petrographic and stratigraphic relations presented in the next section of this report are expanded versions of map Figure 1 (facing page). Location of central Chiricahua Moun- tains, Ariz. Letters show collection sites for samples whose ages were determined by the 4“Ar/”Ar method (tables 2 and 3). Sampling and Analytic Methods 3 109°30‘ 22'30" 15' 109°07‘30” 32°07‘30" l l Contact EXPLANATION 'l_l—l' Topographic margin of Turkey Creek caldera |:| Quaternary sufficia] deposits — Structural margin of Turkey Creek caldera . POST~TURKEY CREEK CALDERA ROCKS — Fault Tertiary extruslve rocks OLIGOCENE ROCKS ASSOCIATED WITH TURKEY CREEK CALDERA /: C ” Moat deposits Cochise Head Rezzigsglteinnttsrusion, ring dikes, and extrusive X Rhyolite Canyon Tuff . . . PRE-TURKEY CREEK CALDERA ROCKS L'mlt Of QEOIOQIC ' ' _ Tertiary volcanic rocks mapme\ (f I ' Tertiary intrusive rocks Mesozoic and Paleozoic rocks Proterozoic rocks 32°00' , A = P475 B = 201769 Limit of geologic C = 201771 mapping D = 201765 E = 201570 F = 201996 G = 201580 A _ H = P3 . , ) |=P272C é/ 3/ 2 .- , , W; J = 201587 5230" ~ 1/ - 5 - K = 201538 ' I- ' 2 . ,/ _ W// '-. _- L=202156 A , M=202154 _.;/j-_:¢-' N=202151 / O = P652 P = P650A O = DY91-36 R = DY92-54 S = 202064 T = DY91-11 U = 202057 V = DY91—77 W = P5 45' Limit of geologic mapping§ ‘ R Limit of geologic mapping 0 2 4 6 8 MILES l ' ' . | ' | l ' | 'I I ' 0 2 4 5 8 10 12 KILOMETERS 31°37'30" 1 4 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains. Arizona unit descriptions included on the geologic map of the central Chiricahua Mountains (du Bray and others, 1997) and contain more detailed petrographic information than that included in the map unit descriptions. Standard thin sections of selected samples were prepared and examined using a petrographic microscope. Multiple samples of each stratigraphic unit were examined; the resulting observations were synthesized and petrographic descriptions developed accordingly. Numerous samples of each ash-flow tuff stratigraphic unit were collected and chemically analyzed in order to estab— lish their compositional ranges. This procedure is essential in sampling of ash-flow tuffs derived from zoned magma res- ervoirs (Hildreth, 1979; 1981). Establishing the full range of compositional zonation within each of the ash-flow tuff units of the study area is required if composition is to be employed as a tool of stratigraphic correlation. Most ash-flow tuff samples were collected without our having specifically deter- mined their relative stratigraphic position within the sampled unit. Although we consider our sampling to have been reason- ably comprehensive, it is possible that our data do not define the full nature and extent of vertically oriented geochemical variation within some of the studied units because the unit’s complete stratigraphic extent may not be represented by the collected samples. In contrast, we were able to systematically sample complete sections of outflow facies Rhyolite Canyon Tuff and tuff of Horseshoe Canyon. Their resulting sample suites are well referenced to relative stratigraphic position, and their chemistry fully defines compositional variation within these units. The geochemistry presented pertains to samples collected at more than 500 sites. A map showing collection sites for these samples appears in du Bray and others (1997). Complete analytical results for these samples are contained in a series of reports by du Bray and others (1992a; 1992b; 1993) and du Bray and Pallister (1994; 1995). Samples collected through- out the study area provide representative areal coverage of all the units whose compositions are considered. Each sample was crushed coarsely at the outcrop and all obvious xenolithic clasts were removed. Abundances of rubidium, strontium, yttrium, zirconium, niobium, and barium were determined for all of these samples by energy dispersive X—ray fluorescence spectrometry. Major-oxide abundances were determined for more than 230 samples, and additional trace-element abun— dances in almost 100 of these samples were determined by instrumental neutron activation analysis. The number of samples for which the various types of geochemical data were obtained is listed, by stratigraphic unit, in table 1. Compositional studies of ash—flow tuffs (Boden, 1989; Fridrich and Mahood, 1987) have relied on analysis of cognate pumice inclusions because these are considered to represent quenched magma. Because most of the middle Tertiary tuffs of the central Chiricahua Mountains are indurated and moderately to densely welded, pumices are flattened to the extent that they are inseparable from enclosing tuff matrix. Therefore, to collect pumice fragments for this compositional study was not routinely possible. Lipman (1965) demonstrated that the compositions of middle Tertiary ash— flow tuff vitrophyre (including pumice) are in some cases considerably modified during postmagmatic processes, includ— ing devitrification. Consequently, devitrified pumice blocks such as those contained in the tuffs of our study area are of uncertain utility in compositional studies. The compositional study of ash—flow tuffs by du Bray (1995) demonstrated that data obtained from whole—rock samples are highly reliable and of considerable utility in characterization and correlation of lithic-poor, large—volume, high-silica ash-flow tuff strati- graphic units. We are aware that the types of whole-rock samples we collected and analyzed are subject to physical sorting that has known potential for affecting chemical compositions. However, we did not determine the magnitude of potential compositional variation resulting from glass shard elutria- tion and sorting or winnowing during ash—flow emplacement that may affect phenocryst, ash, and pumice distributions. Consequently, although the bulk compositions presented here are believed to broadly reflect magmatic values, some of the observed compositional variation must also result from emplacement dynamics. The coherence of compositional data for volcanic rocks of the mountain range suggests that data presented here represent magma compositions reason- . ably well. Regardless of the origin of intra-ash—flow chemical variation, distinctive compositions for individual ash—flow tuff units have proven to be of great value for regional correla- tion. Finally, the effects of sectoral compositional variation, as indicated by compositional overlap between samples from throughout the area, seem to be minor. To further evaluate the potential effects of sectoral variation, compositions of volcanic rocks could be more completely established from samples col— lected throughout their distributions in other isolated mountain ranges of this region. All chemical compositions were determined in analytical laboratories of the US. Geological Survey in Denver, Colo. Major—oxide abundances were determined by X—ray fluores— cence techniques (Taggart and others, 1987) (analysts, J.E. Taggart, AJ. Bartel, D.F. Siems, E.C. Robb, and KC. Stew- art). FeO:FeO*(total iron as FeO) ratios were adjusted to 0.8, and major-oxide abundances were recalculated to 100 percent volatile free. Energy—dispersive X-ray fluorescence spectros- copy, using 109Cd and 24'Am radioisotope excitation sources (Elsass and du Bray, 1982), was used to determine abundances of Rb, Sr, Y, Zr, Nb, and Ba (analysts, D.B. Yager and EA. du Bray); the precision and accuracy of these data are discussed by Sawyer and Sargent (1989) and Yager and Quick (1992). The abundances of Co, Ni, Cr, Cs, Hf, Sb, Ta, Th, U, Zn, Sc, La, Ce, Nd, Sm, Eu, Gd, Tb, Tm, Yb, and Lu were determined by instrumental neutron activation analysis (Baedecker and McKown, 1987) (analysts, R.J. Knight, J.R. Budahn, and RB. Vaughn). Thirty mineral separates (21 sanidines, 8 biotites, and 1 hornblende) from twenty-three samples representing 20 stratigraphic units of the region (fig. 1; table 2) were analyzed by the 4°Ar/39Ar incremental heating technique. Two splits Sampling and Analytic Methods 5 Table 1. Compositions of volcanic rocks of the central Chiricahua Mountains. [Major—oxide data in weight percent (normalized to 100%, anhydrous). Trace—element data in parts per million. Ferrous iron/total iron as FeO adjusted to 0.8. ND, not detected at the indicated abundance. Averages and standard deviations given for n analyses. NA, not analyzed] Pre-Turkey Creek caldera rocks Tim Th1 Tj g Tkr To Tfie Thcl Thou n= 16 6 10 0 5 6 9 12 8102 625812.19 769712.03 730211.17 NA 77.281066 73.271053 75.941130 69.751194 A1203 17.061051 12.611086 14.591081 NA 12.201037 14.911025 12.491069 15.641090 FeZO3 1.211017 0.251005 04910.05 NA 0.201005 0.261002 02810.06 04710.12 FeO 4.371060 0.891020 1.771019 NA 0.721017 0.941007 1.021023 1.871016 MgO 2.221078 0.361013 0.431013 NA 0.121007 0.381024 0.151003 04210.13 CaO 38911.08 0.931085 05610.32 NA 0.271019 1.201023 0.211013 0.911069 NaZO 3.641057 1.321043 2.601072 NA 25411.51 4.011025 2.141098 3.341079 K20 3.831152 6.481141 6.101127 NA 6.461245 4.801010 75011.36 68511.25 T101 08010.15 0.151002 0.321004 NA 01710.04 01910.01 02510.09 0.581010 120, 0271005 ND(0.01) 00810.03 NA ND(0.01) ND(0.01) 0.011002 0.121003 MnO 00810.03 0.031002 00310.01 NA 0.041003 00410.02 0.021003 0.051001 n= 24 15 13 3 11 6 23 25 Rb 152181 3141102 3121106 419125 4591147 172111 4861118 251173 Sr 4891109 1771147 122132 2616 30117 249122 40118 143182 Y 3017 40114 35112 4311 51111 2916 51110 55114 Zr 189120 10718 174132 14419 181124 16314 247188 6401182 Nb 1113 1712 1414 2715 3213 1512 3217 2315 Ba 8401220 5171290 8181163 54111 35115 829153 1971162 14961651 n= 3 0 0 0 3 2 1 2 Co 15.0115 NA NA NA 0.1661007 0.9101014 0.211 2.201039 Ni 13.913.49 NA NA NA 4.001693 8.301523 ND(0.5) 17213.0 Cr 18.1149 NA NA NA 056710.98 1.041027 1.65 1.081186 Cs 76914.18 NA NA NA 55.717135 6.521228 14.9 58413.45 Hf 5.311035 NA NA NA 7.601059 5.351033 7.44 17311.1 Sb 3.391529 NA NA NA 1.541235 013310.02 2.74 027710.12 Ta 09610.17 NA NA NA 2.701021 1.241006 2.56 1.401009 Th 14514.1 NA NA NA 29.6113 17.2109 29.0 15611.0 U 3.581084 NA NA NA 7.311055 3.881029 8.12 2.721021 Zn 59114.4 NA NA NA 60417.9 36.6109 87.5 66.61106 Sc 10211.6 NA NA NA 2.781057 2.951013 2.41 6.191048 La 36912.4 NA NA NA 45518.8 46.8129 40.2 67.1142 Ce 74314.1 NA NA NA 99.91231 91519.2 82.0 140116 Nd 33314.1 NA NA NA 38.81129 32.6135 28.8 58.3123 Sm 6,381.82 NA NA NA 8.4413.11 5.391052 5.65 11.0107 Eu 1.341022 NA NA NA 039310.24 0.7671005 0.393 3.181029 Gd 5.441039 NA NA NA 8821- 4.311091 5.31 8.911- Tb 0771010 NA NA NA 1.201032 060910.05 0.801 1.311009 Tm 0.411003 NA NA NA 0.8741- 040710.02 0.748 0.6961- Yb 2.591015 NA NA NA 5.351007 2.561013 4.79 4.401013 Lu 0.391002 NA NA NA 077710.02 03681004 0719 064610.02 Ba/Nb 76.4 30.6 56.4 2.00 1.09 566 6.13 64.9 Ba/Ta 840 NA NA NA 12.9 670 77.0 1066 La/Nb 3.3 NA NA NA 1.43 3.19 1.25 2.91 from each of two of the sanidine separates (fig. 1; 201765 Individual samples were cleaned with reagent-grade acetone, and 201769) were analyzed in order to evaluate analytical alcohol, and deionized water in an ultrasonic bath, air-dried, reproducibility for samples analyzed during the several years wrapped in aluminum capsules and sealed in silica vials along that geochronologic investigations were conducted. Mineral with monitor minerals before irradiation. Samples were irradi— separates were prepared, after crushing, grinding, and sieving, ated in two irradiation packages, one in 1995 and an earlier by magnetic separator, mica-table, and heavy liquid methods; one in 1988, at two separate TRIGA research reactor facilities. grains ranged in size between 60 and 120 mesh (250—125 um). After irradiation, the samples were progressively degassed Separates were handpicked to greater than 99 percent purity. in a double-vacuum resistance furnace in a series of 11 to 16 6 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona Table 1. Compositions of volcanic rocks of the central Chiricahua Mountains—Continued Pre-caldera Rocks associated with the Turkey Creek caldera rocks Tjj Trcb Trcl Trcm Trcu Trci Trcf po1 n= 6 6 20 17 9 39 7 24 SiOz 77.571021 77.081044 77.211039 77.501026 76.091068 765911.16 76.651136 65.421139 A1203 12.401020 11.981050 12.081016 12.101012 12.501026 12.321059 11.991067 15.541042 FeZO3 02210.04 03510.03 03410.03 0.311003 04010.08 04210.05 04110.06 0.971008 FeO 07810.14 1.261011 12110.11 1.101010 1.551011 1.491018 1.481023 3.491028 MgO 0.311008 00210.04 01610.10 00910.09 02410.14 0.161013 0.111008 1.381040 CaO‘ 0.3710.12 0.071006 0.291017 02310.07 03410.13 02310.14 0.061004 2.841074 N320 3.071062 22510.40 3.551027 3.471020 3.321072 3.101071 2.121133 3.771049 K20 5.081049 6.841050 4.961012 5.021033 5.251027 5.431071 69511.61 52611.07 TiO2 0.151003 0.101000 0.121002 01210.02 02210.01 02110.05 02110.04 0.921009 no, ND(0.01) ND(0.01) ND(0.01) ND(0.01) 0.011003 ND(0.01) ND(0.01) 0.341003 M110 0.051002 0.051001 0.071001 00610.02 0.061002 00310.03 0.031003 0.091002 n= 8 18 33 47 15 67 15 60 Rb 30418 553155 423125 394124 285123 3321100 3971122 208166 Sr 2617 2414 2216 2218 42113 38114 3417 227154 Y 4115 78111 75127 74118 6517 59111 6418 4818 Zr 175125 283114 289116 278110 366131 383168 395159 490189 Nb 3413 5913 6115 5714 4215 4317 4214 2613 Ba 33141 43114 20112 24118 77129 77138 94188 ‘ 7941117 n: 5 0 6 13 4 12 3 10 Co 028710.09 NA 0.2971023 042610.32 087010.27 0.5731071 0.2481003 8.841135 Ni 20813.81 NA 2.691433 25012.71 1.201240 2.831369 ND(0.5) 22313.6 Cr 1.351080 NA 040310.50 069310.78 1.281089 1.151148 0.6931060 16.5162 Cs 8.431168 NA 92011.67 63810.57 4.701054 62513.74 7.521143 11.71220 Hf 7.161026 NA 11.5107 11010.7 12.1109 13.2112 11.1105 11611.7 Sb 053010.46 NA 0.2481006 0.2251007 0.2281007 090510.77 1.061027 0.2441014 Ta 2.881013 NA 5.401032 4.881048 3.731014 3.791120 3.701054 1.921025 Th 34011.7 NA 48314.3 48.1129 41711.5 42816.4 40212.8 22.51301 U 6.691083 NA 11211.1 92911.41 8.491069 78912.44 8.201030 4.591094 Zn 5621156 NA 79.41132 63.51138 61.71111 70.41143 61.91113 77917.3 Sc 2.311012 NA 1.951008 2.091034 3.501025 3.851108 2.801003 9501060 La 39216.7 NA 51.31153 63.51166 96.51114 100138 87.8174 8011111 Ce 74217.9 NA 132130 146120 224127 220180 180118 175127 Nd 26.3142 NA 44.61159 58.91186 83.8188 82.51287 67313.7 72.31107 Sm 5.541113 NA 10314.1 13314.4 15212.1 13.7139 13.2105 12011.8 Eu 0.1561002 NA 010310.04 014610.05 026410.04 0.3341024 021210.03 2.001022 Gd 5.181078 NA 99713.96 13414.3 12711.3 11312.6 10410.3 10111.4 Tb 0.83 10.08 NA 1.781072 2.171072 1.951018 1.661041 1.631006 1.421015 Tm 0.8091006 NA 1.321043 1.531039 1.171015 1.081009 1081007 07401008 Yb 5.271037 NA 84212.70 9.531234 7.381044 6.741062 6.951067 4.591047 Lu 0.7701004 NA 1.231036 1.331030 1.061007 096910.10 0.9871009 0.6691008 Ba/Nb 0.98 0.72 033 0.42 1.84 1.76 2.27 31.1 Ba/Ta 11.5 NA 3.66 4.95 20.5 20.1 25.5 412 La/Nb 1.16 NA 0.84 1.11 2.32 2.30 2.11 3.14 individual, 20-minute—long temperature steps to a maximum whether the individual apparent ages yielded a “plateau” was temperature of 1,650°C. All analyses were done in the Argon made using the critical value test of Dalrymple and Lanphere Laboratory, US. Geological Survey, Denver, Colo. Decay (1969) following the plateau definition of Fleck and others constants are those of Steiger and lager (1977). The standard (1977). Plateaus that pertain to more than 50 percent of the used in these age determinations was hornblende MMhb-l gas produced during heating were achieved for all but seven with a K-Ar age of 5204 Ma (Samson and Alexander, 1987). of the mineral separates. Plateau dates were calculated using Apparent ages were calculated using decay constants recom- a weighted mean, where weighting is by the inverse of the mended by Steiger and J ager (1977). The determination of analytical variance (Taylor, 1982). Petrographic and Stratigraphic Characteristics 7 Table 1. Compositions of volcanic rocks of the central Chiricahua Mountains—Continued Rocks associated with the Turkey Creek caldera Post-caldera rocks poi Ttp Tmrb Tmrl Tm12 Tmr3 Ts Trdp n= 16 2 6 12 9 3 4 4 8102 658812.73 640015.00 73.731062 76.551047 77.321055 77.451009 77.641035 76.791040 Ale3 15.291057 15.991072 14.011041 12.811026 12.621033 12.311016 12.091033 12.731020 FeZO3 09710.20 1.021037 03910.01 0.271002 0.221001 0.241001 02510.10 02210.07 F‘eO 3.511074 3.681134 1.411003 09810.07 07810.04 0.861005 1.041015 0.951008 MgO 1.401048 1.661072 0.461008 0.171002 0.081006 0.111001 00910.07 ND(0. 10) CaO 2.591088 2.461216 1.461030 0.551024 01910.17 0.151007 04110.16 03410.10 NaZO 3.801032 4.431007 3.411047 3.841052 3.651048 3.571003 30510.27 3.981005 K10 5.201056 5.381091 4.761077 4.591088 4.931079 5.121009 5.241041 4.811009 TiO2 09410.21 0.991030 02810.02 01810.01 0.151003 0.151001 0.151001 0.111001 P105 0.331009 0.321018 00710.04 ND(0.01) 0011003 ND(0.01) ND(0.01) ND(0.01) MnO 0.091003 0.081005 00310.02 00610.01 00610.02 0.031001 0.051001 00710.02 n? 25 4 17 64 27 6 10 12 Rb 204162 206129 265157 351170 413155 391114 407144 555165 Sr 206148 1521117 162166 34114 1817 2014 34120 1713 Y 4718 6314 3916 59114 48112 38112 58114 69130 Zr 474181 8391120 175125 220139 .9317 19616 161131 150113 Nb 2414 3414 1513 4018 5317 5112 4216 64116 Ba 763196 2661182 6671109 81118 1419 1215 901106 20114 n= 9 0 6 7 7 2 0 0 Co 93512.83 NA 2.311024 0.3951005 0.3701042 0.2231001 NA NA Ni 13.2193 NA 8.231300 5.181251 11.21220 2.401000 NA NA Cr 94613.63 NA 2.261083 039810.44 058710.65 89111.29 NA NA Cs 5.021234 NA 4.911287 22.21194 11.1107 71011.13 NA NA Hf 11.8106 NA 5.681039 8.001041 8.621121 8.201021 NA NA Sb 0.2681002 NA 0.0981002 022210.07 0.2421006 0.1761002 NA NA Ta 2.101018 NA 1.451013 3.651018 4.811080 4.541016 NA NA Th 25.7157 . NA 27.8125 38.9129 51.41128 44.9106 NA NA U 4.531074 NA 3.721048 89511.29 11.212.5 8.1111.95 NA NA Zn 77.117.9 NA 39.6165 69.91242 57.81118 64.31263 NA NA Sc 94511.84 NA 3.791029 2.531022 2.291043 2.041001 NA NA La 83.81106 NA 52215.8 70519.3 36.5173 25.0109 NA NA Ce 178114 NA 114115 158118 92.11249 56.81140 NA NA Nd 72317.4 NA 46317.4 61815.5 24.2176 15.8130 NA NA Sm 12111.3 NA 84411.33 11.4109 50211.88 3.341075 NA NA Eu 1.921023 NA 1.011008 0.4871003 0.1891005 0.1761002 NA NA Gd 10.2109 NA 75011.17 10.7107 49112.82 3.371028 NA NA Tb 1.471014 NA 1.091016 1.731013 0.8801033 0.5981011 NA NA Tm 07791009 NA 060610.08 1.181004 085310.33 065210.08 NA NA Yb 4.871053 NA 3.691050 7.461022 6.611137 4.531023 NA NA Lu 072110.08 NA . 0.5241007 1.071005 0.9961022 069410.02 NA NA Ba/Nb 31.6 7.74 45.4 2.01 0.27 0.23 2.14 0.31 Ba/Ta 363 NA 460 22.1 2.96 2.57 NA NA La/Nb 3.47 NA 3.55 1.76 0.68 0.49 NA NA Petrographic and Stratigraphic denotes their Tertiary age. A simplified'correlation of map. _ . umts and a set of abbrev1ated rock unlt 1dent1f1ers are prov1ded Chara cterlstlcs in order to define stratigraphic relations among the various volcanic rock units that were studied (fig. 2). In the descriptions that follow, a three- to four-letter map unit code is identified for each of the stratigraphic units in order to aid their identification in the tables and figures. Map unit symbols designated by du Bray and others (1997) on the geologic map of the Turkey Creek caldera match those used Basement rocks on which middle Tertiary volcanic rocks here. The first letter in each of these map unit symbols, T, were deposited include interlayered Mesozoic sedimentary Pre-Turkey Creek Caldera Rocks 8 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona Table 2. Summary of 40Ar/39Ar age-spectrum results from the central Chiricahua Mountains. [Preferred ages in bold; % data in Type of apparent age column indicates percentage of total gas included in plateau steps; --. apparent age. isochron. initial 40/36. not calculated] Sample No; Unit Mineral Apparent Type of Isochron Initial unit symbol age (Ma) apparent age age 40/36 Pre-Turkey Creek caldera rocks DY91-77 Dacitic rocks of Half— Biotite 732310.11 Excess argon 74.6106 276119 moon Valley, Saddle-low intrusion. 202057 Rhyolite tuff of High Sanidine 34.161017 Plateau -- -- Th1 Lonesome Canyon. (76.5%) DY91-11 Lower member of the Biotite 33.811008 Plateau —- -- Tjg rhyolite of Joe Glenn (91.1%) Ranch. P650A Pyroclastic rocks of Biotite 333210.07 Plateau — —— Rocker Canyon, (83.5%) rhyolite lava. 202064 Pyroclastic rocks of Biotite 332110.09 Plateau -- -- Rucker Canyon. (68.9%) 202064 Pyroclastic rocks of Sanidine 330410.04 Weight 33.041004 31017 Rucker Canyon. average 201570 Granodiorite ofMackey Hornblende 32.081020 Plateau 31.421030 30916 Canyon. (50.8%) excess Ar 201570 Granodiorite of Mackey Biotite 30. 6210.15 Plateau -- — Canyon. (59.1%) P272C Rhyolite lava of Cave Sanidine 28.101012 Plateau -- -— Tc Creek. (89.3%) P475 Rhyolite of Erickson Sanidine 27 8910.09 Plateau —— -— Tfre Ridge. (78.5%) P475 Rhyolite of Erickson Biotite 282410.08 Plateau -- — Tfre Ridge. (65.5%) P652 Jesse James Canyon Sariidine 27.521006 Plateau -- -- Tjj Tuff. (61.3%) 201771 Jesse James Canyon Sanidine 27.591006 Plateau 27.711002 270110 Tl] Tuff. (55.8%) 202156 Lower member of the Biotite 27.621010 Plateau -- -- Thcl tuff of Horseshoe (88.8%) Canyon. 202156 Lower member of the Sanidine - No plateau 254210.10 29714 Thcl tuff of Horseshoe Canyon. 202154 Latite of Darnell Peak Sanidine 275810.08 Plateau -- —— (77.9%) and volcanic rocks. These Mesozoic rocks are underlain by Paleozoic marine sedimentary rocks that were deposited on a basement composed of Proterozoic granitoid rocks. These Mesozoic, Paleozoic, and Proterozoic rocks are not discussed further in this report. In many parts of the central Chiricahua Mountains, the Tertiary volcanic rocks described herein are directly under— lain by intermediate-composition lava flows, flow breccias, and near-source pyroclastic rocks (Tim) that probably were erupted from a field of coalescing composite volcanoes. These dacitic to andesitic rocks, which denote the onset of middle Tertiary volcanism in this area, are dark greenish gray to greenish black and maroon where oxidized and form massive, locally densely jointed and fractured outcrops. These rocks are aphyric to sparsely porphyritic; glassy flow margins are preserved in some places. Phenocrysts form trachytic or Petrographic and Stratigraphic Characteristics 9 Table 2. Summary of 40Ar/sgAr age-spectrum results from the central Chiricahua Mountains—Continued Sample No.; Unit Mineral Apparent Type of Isochron Initial unit symbol age (Ma) apparent age age 40/36 Rocks associated with the Turkey Creek caldera 201769 Lower member of the Sanidine 26.97:0.09 Plateau 27.09:0.04 309:2 Trel Rhyolite Canyon (60.8%) Tuff. 201769 Lower member of the Sanidine 26.93:0. 12 Plateau -- -- Trcl Rhyolite Canyon (90.8%) Tuff. DY91-36 Middle member of the Sanidine 27.03:0.1 1 Plateau 27 . 1 4:0.03 297:2 Trcm Rhyolite Canyon (75.0%) Tuff. 201765 Upper member of the Sanidine -- No plateau 26.98:0.04 309:2 Trcu Rhyolite Canyon Tuff. 201765 Upper member of the Sanidine 26.94:0.12 Plateau -- -- Trcu Rhyolite Canyon (91.3%) Tuff. 201587 Dacite porphyry, Sanidine 26 84:0 . 1 7 Weight 26.90: 0.04 296:2 poi intrusion. average P3 Dacite porphyry, lava Sanidine 26.97 :0. 13 Minimum 27.44:0.15 303:2 po1 flow. age step Turkey Creek caldera Biotite 27.1 1:0 .06 Plateau 2663:0114 366:15 201996 moat lava, biotite (67.8%) Tmrb rhyolite lava. 201996 Turkey Creek caldera Sanidine 26.74:0.05 Plateau -— -- Tmrb moat lava, biotite (65.6%) rhyolite lava. 201538 Turkey Creek caldera Sanidine 26.93:0.17 Weight 26.89:0.05 298:1 Tmrl moat lava, unit 1 average rhyolite lava. 201580 Turkey Creek caldera Sanidine 26.64:0. 13 Plateau -- —- Tmrl moat lava, unit 1 (94.3%) rhyolite lava. Post-Turkey Creek caldera rocks DY92—54 Rhyolite lava Sanidine 26.35: 0.08 Plateau -- -- (65.9%) 202151 Rhyolite lava of Sanidine 26.20i 0.07 Plateau -- -- Trdp Dobson Peak. (62.8%) P5 Rhyolite of Packsaddle Sanidine 23.233: 0.06 Plateau -— -~ Mountain. (65.0 %) intergranular intergrowths of plagioclase, pyroxene, horn- rhyolitic volcanic rocks. The onset of rhyolitic volcanism blende, and biotite in a devitrified or aphanitic groundmass; denotes the development of large, low—density magma reser- accessory phases include Fe—Ti oxide minerals and apatite. voirs in the shallow crust. Once established, these reservoirs The groundmass is variably altered to clay minerals, Fe—Ti likely inhibited the buoyant ascent and eruption of additional oxide minerals, zeolites, and calcite. intermediate-composition magma. Consequently, solidified Intermediate-composition volcanic rocks are only present masses representing unerupted parts of the rhyolitic reservoirs at the base of the Tertiary section in the map area; rocks of this are probably underplated by a considerable volume of solidi- type are not interstratified with younger, voluminous fied, intermediate-composition magma petrologically similar 10 Trd p Ts Ttp poi Td pl Trcf Trci Trcu Trcl Trcb TJ'J' Thcl Tfre Trr Tc Tkr Tjg Thl Tim Tmr3 Tmr2 Tmr1 Tmrb Trcm Thcu Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona EXPLANATION POST-TURKEY CREEK CALDERA ROCKS Rhyolite lava of Dobson Peak Pyroclastic deposits and lava flows of Swede Peak ROCKS ASSOCIATED WITH THE TURKEY CREEK CALDERA Unit 3 rhyolite lava of the Turkey Creek caldera Unit 2 rhyolite lava of the Turkey Creek caldera Unit 1 rhyolite lava of the Turkey Creek caldera Biotite rhyolite lava of the Turkey Creek caldera Trachyte porphyry lava Dacite porphyry intrusion of the Turkey Creek caldera Dacite porphyry lava of the Turkey Creek caldera Lava-flow-like phase of the Rhyolite Canyon Tuff Intracaldera facies Rhyolite Canyon Tuff Upper member, outflow facies Rhyolite Canyon Tuff Middle member, outflow facies Rhyolite Canyon Tuff Lower member, outflow facies Rhyolite Canyon Tuff Basal member, outflow facies Rhyolite Canyon Tuff PRE—TURKEY CREEK CALDERA ROCKS Jesse James Canyon Tuff Upper member of the tuff of Horseshoe Canyon Lower member of the tuff of Horseshoe Canyon Rhyolite of Erickson Ridge Pyroclastic rocks of Rucker Canyon Rhyolite lava of Cave Creek Rhyolite lava of Krentz Ranch Lower member of the rhyolite of Joe Glenn Ranch Rhyolite tuft of High Lonesome Canyon Intermediate-composition lava flows Ash-flow tuff Mixed lava flow and pyroclastic flow deposit Lava flow Intrusive Figure 2. Simplified correlation of map units and identification of studied volcanic rock units. to the intermediate—composition volcanic rocks preserved at the base of the Tertiary section in the map area. The oldest regionally extensive ash-flow tuff in the central Chiricahua Mountains was erupted from an unknown source; it is the pale-orange to yellowish—gray rhyolite tuff of High Lonesome Canyon (Thl). This ash-flow tuff, first described by Drewes and Brooks (1988), is crystal poor (contains about 5 percent crystals), pumice and lithic rich, and weakly to moderately welded. Phenocrysts include approxi- mately equal amounts of partly resorbed and embayed quartz and subhedral to anhedral sanidine, lesser amounts of anhedral albite and Fe-Ti oxide minerals, and trace amounts of oxidized biotite, all in a devitrified ash- and shard—rich matrix. In expo- sures near the mouth of Rucker Canyon, the tuff consists of two cooling units separated by a l-m-thick volcanic sandstone. Andesitic lithic fragments are common. In the study area, the tuff is restricted to a small area south of the Turkey Creek caldera. The lower member of the rhyolite of Joe Glenn Ranch (Tjg) is grayish-orange-pink to pale-red-purple ash-flow tuff erupted from an unknown source. Only the lower member of the rhyolite of Joe Glenn Ranch, first described by Drewes and Brooks (1988), is present in the area studied by do Bray and others (1997). The tuff, which overlies the rhyolite tuff of High Lonesome Canyon, is crystal rich (20—40 percent), pumice and lithic poor, and weakly to moderately welded. Crystals are resorbed quartz, subhedral sanidine, subhe- dral albite, and anhedral oxidized biotite, and include trace amounts of anhedral Fe—Ti oxide minerals and zircon in a devitrified ash matrix. This unit may be correlative with Faraway Ranch Formation member 3 of Fernandez and Enlows (1966). In the central Chiricahua Mountains, the tuff is restricted to small areas north and south of the Turkey Creek caldera. The rhyolite lava of Krentz Ranch (Tkr), defined by Drewes and Brooks (1988), is composed of light-gray to pale-orange-gray, nearly aphyric, massive to flow—laminated, high—silica rhyolite lava that contains interbedded, cogenetic pyroclastic flow deposits. These lava flows, Vitrophyric in some places, probably represent a field of coalesced rhyolite domes. Sparse phenocrysts, which are in a massive, variably devitrified groundmass, include subhedral sanidine, resorbed quartz, and trace amounts, especially in Vitrophyre, of zircon, Fe-Ti oxide minerals, and hornblende. The rhyolite lava of Krentz Ranch underlies an area of less than 10 km2 about 160 km southeast of the center of the Turkey Creek caldera. The rhyolite lava of Cave Creek (ch), defined by Raydon (1952), is light gray to pale orange gray and reddish orange, crystal-poor to aphyric, and massive to flow- laminated. Phenocrysts are mostly sanidine and quartz, but trace amounts of hornblende, biotite, magnetite, and titanite are preserved, especially in glassy rocks. The groundmass of the rhyolite is a devitrified intergrowth of quartz and feldspar that is spherulitic or granophyric in some places. The rhyolite of Cave Creek, which overlies andesite to dacite lava flows in the east part of the study area, forms numerous flow domes and contains interbedded, cogenetic pyroclastic flow deposits. These rocks probably overlie the small vents from which they were erupted. Bryan (1988) identified and mapped lower, middle, and upper members of the rhyolite in the Cave Creek watershed. The rhyolite of Erickson Ridge (Tfre), as described by Pallister and others (1994), is light-gray (devitrified) to black (glassy) biotite rhyolite. It contains phenocrysts of oscillatory- zoned, subhedral oligoclase-albite (3—7 percent) and biotite (1—2 percent). Accessory to trace titanite forms euhedral phenocrysts. The rhyolite forms small lava domes and lobate flow—layered lava flows having black glassy carapace breccias and minor interbedded, cogenetic pyroclastic flow deposits. The rhyolite overlies andesite lava flows and forms extensive overlapping flow domes that probably overlie and conceal their small vents. The lava flows are probably correlative with Faraway Ranch Formation member 7 of Fernandez and Enlows (1966), whereas interbedded pyroclastic flow deposits are probably correlative at least in part with Faraway Ranch Formation member 6 of Fernandez and Enlows (1966). The rhyolitic lava flows of the Faraway Ranch Formation, which underlie an area of about 5 km2 about 5 km west of Sugarloaf Mountain and north of the Turkey Creek caldera, probably represent a field of coalesced rhyolite domes. The tuff of Horseshoe Canyon (Bryan, 1988) crops out extensively east and southeast of the Turkey Creek caldera and consists of upper (Thcu) and lower (Thcl) members separated by a plagioclase-sanidine porphyry sill known as the latite of Darnell Peak (Bryan, 1988). This tuff, erupted from the Portal caldera (Bryan, 1988), is gray- to orange-weathering, densely welded, and strongly zoned. The lower member is zoned from a thin basal unit composed of high-silica rhyolite tuff with moderate crystal content (10—15 percent) to crystal- rich (20—35 percent) trachyte tuff containing phenocrysts of sanidine, quartz, plagioclase, biotite, clinopyroxene, titanite, and Fe-Ti oxide minerals. The upper member has moderate crystal content (10—20 percent) and is composed of low-silica rhyolite tuff that contains phenocrysts of sericitized sanidine, quartz, and accessory or trace biotite. The tuff of Horseshoe Canyon is correlative with tuff of Price Canyon (Drewes and Brooks, 1988) and the Eagle Cliffs member of the rhyolite of Cave Creek (Raydon, 1952). The Jesse James Canyon Tuff (Tjj), as described by Pal- lister and others (1994), erupted from an unknown source; it is light-gray or pinkish-gray, typically lithic poor, moder- ately crystal rich (approximately 10 percent), biotite—bearing Petrographic and Stratigraphic Characteristics 11 quartz—sanidine rhyolite ash-flow tuff. This unit is similar to middle and lower members of Rhyolite Canyon Tuff but is distinguished by trace amounts of biotite and titanite, an absence of clinopyroxene, a higher ratio of sanidine t0 quartz, less evolved chemistry, and stratigraphic position. The Jesse James Canyon Tuff is correlative, in part, with the welded tuff of Rucker Canyon (Drewes and Brooks, 1988). In the study area, the tuff is restricted to small areas north and south of the Turkey Creek caldera. Rocks Associated with the Turkey Creek Caldera The Rhyolite Canyon Tuff, as redefined by Pallister and others (1994), consists of intracaldera and outflow facies. Drewes (1982) provided a synopsis for the correlation between the Rhyolite Canyon Formation, parts of which were rede- fined to the Rhyolite Canyon Tuff, and earlier nomenclature established for these rocks by Enlows (1955) and Fernandez and Enlows ( 1966). The tuff, whose eruption caused collapse of the Turkey Creek caldera, is the volumetrically dominant eruptive product of the caldera (du Bray and Pallister, 1991). The outflow facies rocks have been divided into basal (Trcb), lower (Trcl), middle (Trcm), and upper (Trcu) members. The intracaldera facies (Trci) is dominated by a thick accumulation of homogeneous tuff but also includes a lava-flow-like phase (Trcf). All parts of the Rhyolite Canyon Tuff are petrographi— cally similar, with the exceptions noted following. The unit is light—gray to reddish-brown, high-silica rhyolite ash-flow tuff that contains 7—35 percent phenocrysts; phenocrysts are almost entirely quartz and sanidine. Sanidine forms lath- shaped crystals typically 1—4 mm long but locally as long as 1 cm in the upper member of the Rhyolite Canyon Tuff. Quartz typically is rounded and embayed, and grains are 1—3 mm in diameter. The tuff also contains accessory Fe—Ti oxide miner- als and trace augite, hornblende, zircon, apatite, and allanite. The lava-flow-like phase in the uppermost exposures of intra- caldera tuff is distinguished by large (0.5—1 cm), mostly lath shaped crystals of perthitic sanidine, large subhedral or partly resorbed quartz phenocrysts, an apparent absence of eutaxitic structure, and a few lithic inclusions. The intracaldera facies is reddish brown, red, pink, orange, or gray and lithic—poor to lithic—rich (<5—20 percent). The Rhyolite Canyon Tuff is correlative with the tuff of Shake Gulch and the tuff of Bruno Peak (Drewes and Brooks, 1988). Dacite porphyry was emplaced as a resurgent intrusion (poi) in the center of the collapsed Turkey Creek caldera. Its extrusive equivalent, dacite porphyry lava flows (pol), was erupted onto the floor of the evolving moat of the Turkey Creek caldera shortly after eruption of the Rhyolite Canyon Tuff. Dacite porphyry that forms lava flows is petrographi- cally and compositionally similar to dacite porphyry of the resurgent intrusion, except that clinopyroxene is its predomi- nant mafic mineral and its groundmass is much finer grained. Glassy margins, brecciated in places, are exposed locally 12 Middle Tertiary Volcanic Rocks. Central Chiricahua Mountains. Arizona between flows at shallow stratigraphic levels. The dacite por- phyry is gray to tan, massive, and highly jointed. Its ground- mass grades from coarse cuneiform granophyre, most common at the lowest exposed levels of the resurgent intrusion, through medium- to fine—grained granophyre higher in the intrusion. The dacite porphyry contains megacrysts (5 mm to >3 cm across) of alkali feldspar and plagioclase, and small (typically 1 cm across) hornfels inclusions. Alkali feldspar, commonly zoned, forms overgrowths on plagioclase, and is exsolved variably to microperthite; cores of some alkali feldspar phe- nocrysts are resorbed. Plagioclase phenocrysts (1—3 mm) are zoned from albite rims to andesine cores. The dacite porphyry also contains glomerocrysts of albite-andesine and phenocrysts or microphenocrysts of sanidine, quartz, biotite, hornblende, clinopyroxene, and Fe-Ti oxide minerals, and trace amounts of apatite, zircon, and titanite; phenocryst assemblages are highly variable. Phenocrysts of partly resorbed quartz are present locally, and groundmass quartz is abundant in granophyre. Trachyte porphyry lava (Ttp) crops out locally at the base of the volcanic section preserved in the moat of the Turkey Creek caldera. The unit consists of red to reddish—brown or orange trachyte lava with intersertal to granophyric ground- mass textures. The lava contains large phenocrysts (2 mm to >1 cm) of sanidine that have dusty reaction rims. Phenocrysts also include small crystals composed of Fe—Ti oxide miner- als, oxyhornblende, clinopyroxene, and plagioclase. Strained quartz xenocrysts are present in some samples. The trachyte porphyry forms very restricted outcrops in the southeastern part of the Turkey Creek caldera. A sequence of rhyolite lava flows and minor associ- ated pyroclastic flow deposits, the Fife Canyon Volcanics of Latta (1983), are stratigraphically above dacite and trachyte porphyry lava flows in the moat of the Turkey Creek cal— dera. These rocks, with a total preserved volume of about 60 km3, probably were erupted from vents along the buried ring fracture collapse system of the Turkey Creek caldera. The presence of biotite in the basal rhyolite of this sequence is distinctive. The upper three rhyolite lava flows, units 1 through 3, from oldest to youngest, are petrographically indistinguish— able, nearly aphyric, high-silica rhyolite lava flows separated by thin pyroclastic flow deposits. The biotite rhyolite (Tm rb) forms flows and domes on the north flank of the Turkey Creek caldera. The lava is gray to brownish- or yellowish—gray (devitrified) or black (glassy), moderately phenocryst rich (5—20 percent) rhyolite that contains plagioclase, sanidine, quartz, biotite, Fe—Ti oxide minerals, and trace amounts of zircon and monazite. Plagio- clase also forms small (<1 mm), oscillatory-zoned (andesine cores) crystals; a xenocrystic origin is suggested by resorption, wormy glass inclusions, and its occurrence in small crystal clots, commonly with biotite. Perlite, locally preserved at the basal contact, is spherulitic in some outcrops. Flow interiors are devitrified and locally granophyric. Unit 1 lava (Tm r1) consists of light—gray to reddish-gray or brown rock, most of which is flow layered and intricately flow folded—although some exposures are massive. The lava is devitrified, except at its base, where perlitic glass locally contains spherulitic zones and geodes. It is typically aphyric or crystal poor (<5 percent) and contains sanidine, quartz, and Fe-Ti oxide minerals, along with trace amounts of plagioclase, hornblende, and clinopyroxene. Carapace breccia is exposed locally at margins of lava flows. Flow interiors are recrys— tallized to granophyre and contain vapor-phase quartz and feldspar in amygdules. Unit 2 lava (Tm r2) is light-gray to reddish-gray, phe— nocryst-poor rhyolite lava that is flow layered and intricately flow folded, locally massive, and aphyric or sparsely (0—2 percent) porphyritic. It contains phenocrysts (<1 mm) of sanidine, quartz, and Fe—Ti oxide minerals; accessory biotite and zircon are present in some samples. The lava is devitri- fied, except at its base, where black or green glassy breccia or flow—layered perlite is locally exposed; spherulitic and axiolitic (with respect to flow layers) devitrification and granophyric recrystallization are common. Unit 3 lava (Tm r3) is light—gray to reddish-gray, typically aphyric, flow—layered and folded rhyolite. It contains trace amounts of sanidine phenocrysts and biotite microphenocrysts. Spherulitic and axiolitic (with respect to flow layers) devitrifi- cation and granophyric recrystallization are common features of this unit. Post-Turkey Creek Caldera Rocks Pyroclastic deposits and lava flows of Swede Peak (Ts), as defined by Drewes and Brooks (1988), consist of white to tan high—silica rhyolite. The rhyolite is commonly crystal rich (>20 percent); phenocrysts are sanidine, quartz, albite, and oxidized biotite and trace amounts of Fe—Ti oxide minerals, titanite, and zircon. Vitroclastic groundmass in unwelded pyroclastic deposits is composed of variably devitrified ash, glass shards, and crystal and lithic fragments. Abundances of pumice and glass shards are variable within and between individual pyroclastic flows. Abundance of lithic fragments also is widely variable. The great thickness of these areally restricted deposits near Swede Peak and decreas— ing thicknesses in surrounding areas suggest vent(s) near this feature southeast of the Turkey Creek caldera. The rhyolite lava of Dobson Peak (Trd p) is light-gray to reddish-gray, flow-layered and intricately flow folded, high- silica rhyolite lava that is crystal poor (1—4 percent); phenocrysts are resorbed quartz, sanidine, and subordinate albite and trace amounts of Fe—Ti oxide minerals and biotite. Its groundmass commonly is devitrified and recrystallized to an aggregate of microscopic spherulites and granophyre. Thickness relations are similar to those of pyroclastic deposits and lava flows of Swede Peak, which, together with compositional and temporal similarities, imply cogenesis and eruption from associated conduits. The rhyolite underlies an area of about 5 km2 about 15 km southeast of the center of the Turkey Creek caldera. Geochemistry Classification The International Union of Geological Sciences (IUGS) classification of volcanic rocks (Le Bas and others, 1986) was applied to the compositions of volcanic rocks of the central Chiricahua Mountains (table 1); most of the units studied are rhyolite (fig. 3). Because the resurgent intrusion in the Turkey Creek caldera, its associated moat—filling lava flows, and the immediately overlying lava flows contain less than 20 percent normative quartz and greater than 9 percent Na20+KZO and 64—66 percent SiOZ, they are alkaline and can be classified as trachyte using the criteria of Le Bas and others (1986). However, in the case of the resurgent intrusion and its associ- ated lava flows, we have chosen to apply the name dacite, the subalkaline variant of trachyte, because their compositions are transitional between alkaline and subalkaline compositions as defined by Irvine and Baragar (1971) and Le Bas and others (1986), and to emphasize their consanguinity with the other subalkaline products of the Turkey Creek caldera. That this igneous system is not intrinsically alkaline is another reason Geochemistry 13 that we have elected not to use the name trachyte; our choice of dacite is in accord with the absence of alkali—rich minerals, such as aegirine and alkali amphiboles, that are characteristic of alkaline series rocks. Because the trachyte porphyry lava flows (Ttp) that immediately overlie the dacite porphyry flows have compositions (fig. 3) that are alkaline, even by the definition of Irvine and Baragar (1971), we do apply the name trachyte to these rocks. However, all of the rhyolites are subalkaline, as defined by Irvine and Baragar (1971), and all but the upper member of the tuff of Horseshoe Canyon, the rhyolite of Erickson Ridge, the lower member of the rhyolite of Joe Glenn Ranch, and the biotite rhyolite lava of the Turkey Creek caldera moat sequence contain >76 percent SiO2, and are therefore high-silica rhyolite. Most volcanic rocks in the central Chiricahua Mountains are weakly peraluminous (alu- mina saturation indices range from 1.03 to 1.11); the exception is the dacite and trachyte porphyries, which are metaluminous (alumina saturation indices 0.91—0.92). The peraluminous tendencies may reflect minor, posteruptive alkali loss rela— tive to alumina rather than primary magmatic characteristics. The lower member of the rhyolite of Joe Glenn Ranch and the rhyolite tuff of High Lonesome Canyon are distinCtly more strongly peraluminous (alumina saturation indices 1.23 and 1.16, respectively). llllllllllllllllllllllll IIIIIIIIIIIIIIIII‘T Figure 3. Average (table 1) total EXPLANATION . . . . . . . _ alkali-Silica variatIon diagram showrng ‘ Pre'TurkeV creek caldera “”1“ compositions of volcanic rocks in the 15 L o Rocks associated with the Turkey Creek caldera L centra| Chiricahua Mountains, Ariz. - Post-Turkey Creek caldera rocks International Union of Geological “ /’ Sciences classification grid (Le Bas / . _ / 7 and others, 1986) IS also shown. e% \x / \ W" , \////\, _ A)? ,_ TRACHYTE / 9 z r _ Lu 2 LL, 10 e — O. A ’— I — "A _ g A‘ .3- _ . _ i A‘ Z 0‘ _ _ s? RHYOLITE + s DACITE , o fa“ z 5 — _ 0 L1 I l I 1 l I I I 1 1 I I l 1 1 I I I 1 l I 1 1 1 1 I I I 1 I I I I | I I 1 1 1 I I 35 4o 45 50 55 60 55 7o 75 SiOz, IN WEIGHT PERCENT 14 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona As is typical for most calc—alkaline rocks, abundances of A1203, total iron. MgO, CaO, P205, and TiO2 decrease with increasing SiO2 in volcanic rocks of these mountains. whereas abundances of NaZO, K20, and MnO show no clear relation— ship with SiO2 (fig. 4). Variation patterns for NaZO. K20, and MnO are poorly developed, largely because most of the volcanic rock units studied are composed of high-silica rhyo— lite; variation within this narrowly defined SiO2 compositional range is relatively limited. Exceptions to this generalization relate to the abundances of NaZO and K20. Within the group AI203, IN WEIGHT PERCENT E I . I 12— 0.6 — A e 0.4 — e 0.2 — A TIOz, IN WEIGHT PERCENT Fe0*, IN WEIGHT PERCENT 1.5 — — 0.5 ~ MgO IN WEIGHT PERCENT 8 I I 2.0 — — 1.5 7 . _ A CaO, IN WEIGHT PERCENT 0.5 — A I I I I I I I x.“ | 60 62 64 66 68 7O 72 74 76 78 80 SIOZ, IN WEIGHT PERCENT of high-silica rhyolites, those units with 76—77.6 percent SiOZ, NaZO abundances vary from about 1.3 to 4.0 weight percent, whereas K20 abundances vary from about 4.6 to 7.5 weight percent. A significant geochemical trait of volcanic rocks of the area is their elevated K20 abundances (fig. 4); all of the units are members of either the high-potassium calc—alkaline or the shoshonitic series of Ewart (1982). The K20/Na20 ratio for all of the studied units is >1.0, which indicates that all of these units are potassic as defined by Le Bas and others (1986). 0-1 I I I I I s " A O 5 - D: C 0 g o o c l— 0.05 — A 0A — (2 C L” o It 3 ‘ ‘ Z I I I I I I I I I I I I I I I I I I E 5 0.4 7 ~ 5 o " om' 0.3 ~ A .— N ‘l (I9 0.2 - ~ E A Z 0.1 7 A. e I I I I I I I I I I I I I I Hi ,_ E . g 4 — .0 A I — (5E A . O ‘ gu— 3 e 0 I - Z I <2 A A “J Q g 2 _ A. Z A I I I I I I I I I I I I I I I I I I l— A UZJ 7 Shoshonitic series 0 _ C T m A 0 ~ E O l— _ x“: 5 ‘2 LIJ E 5 — Z 1 [High-[K (caIc—alkalline) seriefl 60 62 64 66 68 70 72 74 76 78 80 Si02, IN WEIGHT PERCENT EXPLANATION A Pre-Turkey Creek caldera rocks 0 Rocks associated with the Turkey Creek caldera I Post-Turkey Creek caldera rocks Figure 4. Abundances (table I) of selected major oxides and trace elements of volcanic rocks in the central Chiricahua Mountains, Ariz. Within-Unit Geochemical Variation In order to better define the volcanic units, we used several analytical methods to study the character and extent of within-unit compositional variation, as portrayed by all Geochemistry 15 available geochemical data (du Bray and others, 1992a; 1992b; 1993; du Bray and Pallister, 1994; 1995). We first created a series of extended trace—element diagrams (Thompson and others, 1983) to evaluate Within-unit compositional variation; for each volcanic unit a separate diagram (fig. 5) displays . | | l | l I l l I I | I | I I I | I I I I | l I I I I | l I l I 1,000 I _ _ _ I I i I. I it I Lu 100 3‘: I — I: I I a: I I Q I I Z I a ,,,,, , o I I '. :c it: '. _____ Q 10 — I; I ' L LLI II I E ‘2 , < I '~.~” U) .H, -; 1 e V — — « RHYOLITE LAVA OF CAVE CREEK (Tc) RHYOLITE OF ERICKSON RIDGE (Tfre) 0.1 I I I I | | I | I I I | I I | I | | I | | I I | | | I | I I | | I | ‘ | | | | I I | I I I | I | | I I I I | I I I I I I I I I I | I I I I 1,000 I— — LL, 100 — """ — I: 1 , D: I .’ D I‘.‘ Z I; """ e =2 """"" Q 10 — ~ LLJ ._l n- v E ‘. 7 5) 1' I, I, 1 ._ _ _ _ LOWER MEMBER OF THE TUFF OF UPPER MEMBER OF THE TUFF OF HORSESHOE CANYON (Thcl) HORSESHOE CANYON (Thou) 0.1 I I I I l | I I | | I I | I I I I L | I I | | I I I | I | | I I I I RbBaTh K NbTaLaCeSrNd 195er Hf Tin YTmYb l | I l I I | I I I | l | | I I I 1,000 ~ — A I , . . . LU 100 I g _ Figure 5 (above and lollowmg pages). Chondrite- '1 . . . '3‘: I '7 normalized (modified from Thompson and others, 1983) i r); . . . . E I 3 extended trace-element diagrams showmg composutlons 2 II z; of volcanic rocks in the central Chiricahua Mountains, 0 ' v: 1 ,. . . . . . a 10 — I." [If — Ariz.; plots portray data forthe Indicated stratigraphic _| I. l.“ . . . . 51/ ,5 unit. Trace elements are arranged In order of mcreasmg 5 f geochemical compatibility to the right. A, pre-Turkey Creek 1 caldera rocks (this page). _ I a JESSE JAMES CANYON TUFF (Tijl 0.1 I I | I | | I | | l I I l RbBaTh KNbTaLaCeSrNd PSerHf -—V]fi | Y TmYb 15 SAMPLE/CHONDRITE SAMPLE/CHONDRITE SAMPLE/CHONDRITE Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona 1,000 100 0.1 UNIT 3 RHYOLITE LAVA OF THE TURKEY CREEK CALDERA (Tmr3) UNIT 2 RHYOLITE LAVA OF THE TURKEY CREEK CALDERA (Tmr2) 1,000 100 10 0.1 UNIT 1 RHYOLITE LAVA OF THE TURKEY CREEK CALDERA (Tmr1) BIOTITE RHYOLITE LAVA OF THE TURKEY CREEK CALDERA (Tmrb) 1,000 _\ O O .4 O DACITE PORPHYRY INTRUSION OF THE TURKEY CREEK CALDERA (poi) | l i i i l | i i i | i DACITE PORPHYRY LAVA OF THE TURKEY CREEK CALDERA (pol) | | i | l i | i | l i | i i | i 1 0.1 RbBaThKNbTaLaCeSrNdPSer Hf TinYTmYb B Rb BaThKNbTaLaCeSrNdPSer Hf Ti TbYTmYb SAMPLE/CHONDRITE SAMPLE/CHONDRITE SAMPLE/CHONDRITE Geochemistry 17 1,000 100 LAVA-FLOW-LIKE PHASE OF THE RHYOLITE CANYON TUFF (Trcf) INTRACALDERA FACIES RHYOLITE CANYON TUFF (Trcil 0.1 1,000 100 10 UPPER MEMBER, OUTFLOW FACIES RHYOLITE CANYON TUFF (Trcu) MIDDLE MEMBER, OUTFLOW FACIES RHYOLITE CANYON TUFF (Trcm) l l l | l l l | l l l l I | | | l 0.1 1,000 100 10 0.1 LOWER MEMBER, OUTFLOW FACIES RHYOLlTE CANYON TUFF (Trcl) l l l l | | | l l | l | i l | | Rb | BaThKNbTaLaCeSrNdPSer Hf Ti Tb Y Tm Yb Rb BaTh K NbTa LaCe SrNd PSer Hf Ti Tb YTmYb Figure 5—Continued (above and facing page). Chondrite- normalized (modified from Thompson and others, 1983) extended trace-element diagrams showing compositions of volcanic rocks in the central Chiricahua Mountains, Ariz.; plots portray data forthe indicated stratigraphic unit. B, rocks associated with the Turkey Creek caldera. 18 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona trace-element abundances for all samples analyzed by instru- mental neutron activation analysis. Additional graphical portrayal of within-unit compositional variation, for a signifi- cantly greater number of samples (those for which trace-ele— ment abundances were determined by energy-dispersive X-ray fluorescence spectroscopy), appears in figure 6. Finally, the magnitude of calculated standard deviations relative to mean abundances (table 1) for each unit was considered. These data evaluations indicate that individual volcanic rock strati— graphic units of the central Chiricahua Mountains, especially units composed of lava flows, are relatively homogeneous. However, some of the ash-flow tuff units are characterized by considerable within-unit compositional variation. Moderate, systematic compositional variation characteristic of these units is consistent with eruption from normally zoned reservoirs (Hildreth, 1981). These observations suggest that neither flow sorting, elutriation, incorporation of exotic material, nor other factors significantly skewed the whole—rock composition of these samples from magmatic values. Pre-Turkey Creek Caldera Rocks Volcanic rocks erupted prior to Turkey Creek caldera formation display variable amounts of within-unit composi- tional inhomogeneity. The rhyolite tuff of High Lonesome Canyon has considerable variation especially with respect to abundances of SiOz, K20, Ba, Sr, and Rh; given the preferen- tial partitioning of these components into feldspars (Hanson, 1978), these variations probably indicate varying amounts of feldspar fractionation. Similarly, because of preferential partitioning of Sr and Ba into feldspar (Hanson, 1978), the considerable variation of Sr and Ba abundances in samples of the lower member of the rhyolite of Joe Glenn Ranch probably results from variable amounts of feldspar fractionation. The rhyolite lavas of Cave Creek, Krentz Ranch, and Erickson Ridge, and the Jesse James Canyon Tuff display lim— ited within-unit compositional variation. Of these, the rhyolite lava of Cave Creek displays the most significant variation; moderate variation in NaZO, K20, and Rb abundances may result from non-isochemical devitrification processes. Lipman (1965) showed that post—magmatic effects can significantly modify primary compositions of glassy volcanic rocks; alkali element abundances are most strongly affected. As glassy rocks interact with ground water, they become hydrated and alkali elements are susceptible to leaching. Light REE (rare earth element) abundances in samples of the Jesse James Can— yon Tuff are moderately variable, although the tuff is other- wise quite homogeneous. Samples collected without specific regard for vertical position in both members of the tuff of Horseshoe Canyon indicate considerable compositional inhomogeneity (table 1). Samples of the lower member of the tuff of Horseshoe Canyon contain variable abundances of SiOQ, NaZO, K20, Rb, and Ba, which again may reflect varying amounts of alkali feldspar fractionation. In contrast, samples of the upper member of the tuff of Horseshoe Canyon contain variable abundances of SiOQ, CaO, Na20, K20, Rb, Sr, Zr, and Ba; these variations probably reflect control by plagioclase, biotite, and zircon, the principal residences of these components (Hanson, 1978). A reconnaissance evaluation of the nature and extent of vertical zonation within these two units was conducted by a systematic sampling of a continuous vertical section through these units along a ridge west of Dripping Spring, on the south side of Sulphur Draw in the Portal Peak 75' quadrangle. The thick section of the lower member of the tuff of Horseshoe Canyon along the ridge seems to be intact, complete, and represen- tative of the unit, whereas the upper member of the tuff of Horseshoe Canyon section is relatively thin here and may be incomplete. Geochemical abundances in a suite of samples from the lower member of the tuff of Horseshoe Canyon vary up section as follows: SiO2 decreases from 75.3 to 74.5 weight percent, NaZO increases from 1.25 to 2.98 weight percent, K20 decreases from 8.97 to 7.31 weight percent, Rb decreases from 571 to 362 ppm, Sr increases from 54 to 99 ppm, Zr increases from 262 to 467 ppm, and Ba increases from 210 to 647 ppm. Geochemical abundances in a suite of samples from the upper member of the tuff of Horseshoe Can- yon vary up section as follows: SiO2 decreases from 73.4 to 71.8 weight percent, NaZO increases from 3.79 to 3.92 weight percent, K20 decreases from 5.80 to 5.66 weight percent, Rb decreases from 270 to 142 ppm, Sr increases from 73 to 104 ppm, Zr increases from 411 to 609 ppm, and Ba increases from 714 to 1,243 ppm. Geochemical variation depicted by these two suites of samples has the same polarity, is essentially overlapping and continuous, and is consistent with the tuff having originated as a series of eruptions from progressively deeper levels of a single, normally zoned reservoir. Rocks Associated with the Turkey Creek Caldera Volcanic units associated with the Turkey Creek caldera display limited inter- and intra-unit compositional inhomo- geneity and zonation. Intracaldera facies Rhyolite Canyon Tuff is characterized by moderate compositional variation. Compositional variation within the intracaldera facies prob- ably reflects physical incorporation of relatively large and variable amounts of exotic lithic fragments from the caldera’s topographic wall during collapse. The lava-flow-like phase of intracaldera facies tuff is characterized by restricted composi- tional variation. In the lower and middle members of outflow facies Rhyolite Canyon Tuff, compositional variation, espe- cially of REE abundances, is moderate, whereas that within the basal and upper members is relatively limited. Compo- sitional variation among the four outflow facies members of the Rhyolite Canyon Tuff is continuous and systematic; as a group, the four members exhibit a trend of decreasing geo- chemical evolution with time. In particular, the upper member is characterized by lower abundances of SiOZ, Rb, Y, Nb, Ta, Th, and U, and higher abundances of total iron, MgO, CaO, TiOZ, Sr, Zr, Ba, and Eu. Compositional data for a large number of dacite porphyry samples collected from widely distributed sites indicate that this unit is relatively homogeneous. Trachyte porphyry lava flows in the moat of the Turkey Creek caldera generally dis- play even less within-unit compositional variability, although SiOz, CaO, Sr, and Ba abundances are highly variable and may reflect varying degrees of feldspar fractionation. Of the rhyolite lavas in the moat of the Turkey Creek cal- dera, those in unit 2 lava display the greatest amount of within- unit compositional variation; in particular, REE abundances vary considerably within this unit (table 1). Compositional variation among the caldera’s four rhyolite lava units indicates that the upper three units are very distinct relative to the basal biotite-bearing rhyolite lava, are considerably more evolved, and depict a trend toward increasing evolution with time. In particular, the basal lava is a low-silica rhyolite, whereas the upper three are all high—silica rhyolites. In addition, the basal biotite rhyolite is characterized by higher abundances of total iron, MgO, CaO, TiOZ, Sr, and Ba, and lower abundances of Rb, Y, Zr, Nb, Th, and U than the three high-silica rhyolites. The compositions of the two youngest high-silica rhyolites, lavas of units 2 and 3, are indistinguishable. However, these two rhyolites are compositionally distinct relative to the under- lying unit 1 lava. The most diagnostic distinction pertains to barium abundances. Lavas of units 2 and 3 contain 10—20 ppm Ba, whereas unit 1 lava contains 60—100 ppm Ba. Other dis— tinctions include higher SiOZ and lower MgO, CaO, and REE abundances in lavas of units 2 and 3 relative to unit 1 lava. Post-Turkey Creek Caldera Rocks Volcanic rocks erupted following Turkey Creek caldera formation display limited within—unit compositional inho— mogeneity. The most pronounced compositional variation in samples of the pyroclastic deposits and lava flows of Swede Peak is that of yttrium, whose abundances range from 37 to 90 ppm. Abundances of Ba and Sr are also somewhat variable in this unit, which suggests that these samples may have experi- enced varying amounts of feldspar fractionation. In samples of the rhyolite lava of Dobson Peak, Y, Nb, and Ba abundances vary considerably. Abundances of Y and Nb are probably con- trolled by accessory minerals, whereas Ba abundance varia- tions suggest that magma represented by these samples may have experienced differing amounts of biotite and feldspar fractionation. Geochemistry- and Petrography-Based Stratigraphic Distinctions As demonstrated by Hildreth and Mahood (1985), various combinations or subsets of field, petrographic, paleomagnetic, or geochronologic data considered along with major-oxide and trace-element abundance data may facilitate stratigraphic identification and correlation. Compositional ranges of major oxides and trace elements in samples of central Chiricahua Mountains volcanic rocks (fig. 6) were used to aid strati- graphic determinations when other data were either Geochemistry 19 insufficient or ambiguous. This approach is especially useful for instances in which sparse exposure or structural dismem- berment resulted in limited stratigraphic context. Because macroscopic features are sufficient to distinguish lava flows from tuffs, in most cases, compositional and petrographic comparisons presented herein are made separately for lava flows and tuffs; data for lava flows are compared only to data for other lava flow units, whereas data for tuffs were compared only to data for other tuff units. Lavas The aphyric character of the high-silica rhyolite lava of Krentz Ranch distinguishes it from crystal-bearing lavas of the study area. Relative to most other aphyric high—silica rhyolite lavas in the area, the rhyolite lava of Krentz Ranch (abbrevi— ated as KR in fig. 6) is compositionally indistinct, as indicated by limited available geochemical data. Low niobium abun- dances for the rhyolite lava of Krentz Ranch relative to those of aphyric rhyolite lavas exposed in the moat of the Turkey Figure 6 (following pages). Stratigraphic versus compositional variation among volcanic rocks in the central Chiricahua Moun- tains, Ariz. Analytical uncertainty for oxide or element is shown by error bar in bottom right corner of each plot. Units arranged from youngest (top) to oldest (bottom) in each plot. Triangle, pre- Turkey Creek caldera rocks; dot, rocks associated with the Turkey Creek caldera; square, post-Turkey Creek caldera rocks. Unit designations are abbreviated here as follows: DP, rhyolite lava of Dobson Peak SW, pyroclastic deposits and lava flows of Swede Peak R3, unit 3 rhyolite lava of the Turkey Creek caldera R2, unit 2 rhyolite lava of the Turkey Creek caldera R1, unit 1 rhyolite lava of the Turkey Creek caldera RB, biotite rhyolite lava of the Turkey Creek caldera TP, trachyte porphyry lava PI, dacite porphyry intrusion of the Turkey Creek caldera PL, dacite porphyry lava of the Turkey Creek caldera RF, lava-flow-like phase of the Rhyolite Canyon Tuff RI, intracaldera facies Rhyolite Canyon Tuff RU, upper member, outflow facies Rhyolite Canyon Tuff RM, middle member, outflow facies Rhyolite Canyon Tuff RL, lower member, outflow facies Rhyolite Canyon Tuff R0, basal member, outflow facies Rhyolite Canyon Tuff JJ, Jesse James Canyon Tuff HU, upper member of the tuff of Horseshoe Canyon HL, lower member of the tuff of Horseshoe Canyon RE, rhyolite of Erickson Ridge CC, rhyolite lava of Cave Creek KR, rhyolite lava of Krentz Ranch JG, lower member of the rhyolite of Joe Glenn Ranch HI, rhyolite tuff of High Lonesone Canyon Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona SIOZ, IN WEIGHT PERCENT A|203, IN WEIGHT PERCENT _ I __ I r | _. 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The rhyolite lava of Krentz Ranch may represent a distal part of the rhyolite dome field that also includes the rhyolite lava of Cave Creek. By extension of the preceding observations, the aphyric character of the high—silica rhyolite lava of Cave Creek dis- tinguishes it from porphyritic lavas of the study area; how- ever, the petrographic Characteristics and composition of this rhyolite are essentially indistinguishable from those of other aphyric, high-silica rhyolite lavas exposed in the area. The sole exception to this generalization may be the low niobium content of the rhyolite lava of Cave Creek relative to that of the other aphyric rhyolite lavas. As described in the preced— ing paragraph, this characteristic is common to the rhyolite lava of Cave Creek and the rhyolite lava of Krentz Ranch and hints at a possible common source. Lacking stratigraphic context, with the exception of diagnostic niobium abundances, distinguishing the rhyolite lava of Cave Creek from the other aphyric high—silica lavas is difficult. Relative to the high-silica rhyolite lavas in the area, the composition and petrography of the rhyolite of Erickson Ridge (RE in fig. 6) are quite distinctive. The phenocryst content of the rhyolite of Erickson Ridge is distinctly greater than that of any other rhyolite exposed in the central Chiricahua Moun- tains, and the presence of 1—2 percent biotite is diagnostic. In contrast to most other rhyolite lavas in the area, the rhyolite of Erickson Ridge has low silica abundances that are similar to those of the biotite rhyolite of the Turkey Creek caldera. The rhyolite of Erickson Ridge is further distinguished by higher FeO*, CaO, Sr, Ba, and Eu abundances and lower Rb, Ta, Th, and U abundances. The rhyolite of Erickson Ridge is most easily distinguished from the biotite rhyolite lava of the Turkey Creek caldera (RB in fig. 6) by a Rb/Sr ratio less than 1, higher Ba abundances, and lower Th abundances. Dacite porphyry of the Turkey Creek caldera has petro- graphic and compositional features that distinguish it from all other volcanic rocks in the study area. Dacite that forms lava flows (PL in fig. 6) and the resurgent intrusion (P1 in fig. 6) in the caldera is greenish gray to tan and contains distinctive alkali feldspar megacrysts. The most distinctive geochemi- cal characteristics of the dacite porphyry include FeO*, MgO, CaO, TiOZ, P205, Ba, Co, Cr, Ni, Sc, and Eu abundances that are elevated relative to abundances in the majority of the cen— tral Chiricahua Mountains volcanic rocks, which are consider- ably more evolved and contain significantly lower abundances of these components. Trachyte porphyry (TP in fig. 6) forms an areally restricted set of outcrops characterized by distinctive sanidine phenocrysts. The trachyte porphyry is distinguished from the dacite porphyry by its distinctive reddish-brown color and finer grained groundmass. The composition of the trachyte porphyry is similar to that of the dacite porphyry and so is similarly chemically distinct relative to all other volcanic rocks of the central Chiricahua Mountains. Elevated abundances of Na2O, Y, Zr, and Nb and lower abundances of Sr and Ba in the trachyte porphyry distinguish the trachyte porphyry from the dacite porphyry. Of the four rhyolite lavas present in the moat of the Tur- key Creek caldera, only the oldest of these, the biotite rhyolite lava (RB in fig. 6), is petrographically distinct. The biotite rhyolite contains phenocrysts of biotite, feldspar, and quartz that distinguish it from all other central Chiricahua Mountains rhyolite lavas, except the rhyolite of Erickson Ridge. The other three moat rhyolite lavas are virtually aphyric and mac- roscopically indistinguishable from one another and similarly cannot be distinguished petrographically from the aphyric rhyolite lavas of Krentz Ranch, Cave Creek, and Dobson Peak. The composition of the biotite rhyolite in the moat of the Tur— key Creek caldera is distinguished from that of other rhyolites, except the rhyolite of Erickson Ridge, by lower SiOz, Rb, Nb, Ta, U, Th, Hf, and heavy REE abundances and by higher FeO*, MgO, CaO, TiO2, Sr, Ba, Co, Sc, and Eu abundances. The biotite rhyolite in the moat of the Turkey Creek caldera is distinguished from rhyolite of Erickson Ridge by a Rb/Sr ratio greater than 1, lower Ba abundances, and higher Th abun- dances. Compositions of the three aphyric moat rhyolites are almost indistinguishable from those of other aphyric rhyolites in the central Chiricahua Mountains, except that the moat lavas have higher Nb abundances than the rhyolite lavas of Cave Creek and Krentz Ranch, and lower Rb and Nb abundances than the rhyolite of Dobson Peak. Compositions of the young- est two rhyolite lavas (R2 and R3 in fig. 6) in the moat of the Turkey Creek caldera (lavas of units 2 and 3) are indistinguish- able. They can only be differentiated in a stratigraphic context relative to the thin tuff that separates them. The composition of unit 1 lava (R1 in fig. 6) is distinct from that of units 2 and 3 lavas; unit 1 lava is distinguished by lower SiOz, Nb, Ta, and Th abundances and higher CaO, Ba, light REE, and Eu abundances. The rhyolite of Dobson Peak (DP in fig. 6) is an aphyric rhyolite that is macroscopically indistinguishable from other aphyric rhyolites in the map area. Its aphyric character distinguishes the rhyolite lava of Dobson Peak from rhyolite of Erickson Ridge and biotite rhyolite lava in the moat of the Turkey Creek caldera. Elevated Rb and Nb abundances distin- guish the rhyolite lava 0f Dobson Peak from all other rhyolites of the area. Ash-Flow Tuffs and Other Pyroclastic Flow Deposits The low phenocryst content and presence of trace amounts of oxidized biotite distinguish the rhyolite tuff of High Lonesome Canyon (H1 in fig. 6), the oldest of the l‘ r fl 7 regionally exposed ash-flow tuffs, from most other tuffs of the central Chiricahua Mountains. In addition, the rhyolite tuff of High Lonesome Canyon is characterized by unusually low zirconium and NaZO abundances relative to other high- silica rhyolite ash-flow tuffs of the study area. Although the composition of the rhyolite tuff of High Lonesome Canyon is otherwise nondistinct, these features are probably sufficient to be diagnostic. Several features of the lower member of the rhyolite of Joe Glenn Ranch (JG in fig. 6) help to distinguish this tuff from others exposed in the central Chiricahua Mountains. It is characterized by a relatively high crystal content (from 20 to 40 percent) and distinctive, 1—3 mm wide, pseudohexago- nal biotite crystals. The lower member of the rhyolite of Joe Glenn Ranch also is characterized by higher FeO*, TiOz, and Ba abundances, and lower Nb abundances than other central Chiricahua Mountains tuffs. The relatively crystal rich lower member of the tuff of Horseshoe Canyon (HL in fig. 6) contains diagnostic biotite phenocrysts in addition to quartz and sanidine. Geochemi- cal characteristics diagnostic of the lower member of the tuff of Horseshoe Canyon include elevated K20, Ba, and Eu abundances and low Zr, Nb, and Th abundances, relative to ,other central Chiricahua Mountains high-silica rhyolite tuffs. In contrast, the relatively crystal poor upper member of the 'tuff of Horseshoe Canyon (HU in fig. 6) also is character— ized by biotite phenocrysts as well as by relatively elevated A1203, FeO*, TiO2, P205, Sr, Zr, Ba, Co, Ni, Cr, Sc, Hf, and Eu abundances, and low SiOz, Nb, Ta, U, and Th abundances; another characteristic of the upper member of the tuff of Horseshoe Canyon is its dramatic within—unit compositional variation. As such, the upper member of the tuff of Horseshoe Canyon is probably the most geochemically distinct ash-flow tuff exposed in the study area. Its most distinguishing features relative to the lower member of the tuff of Horseshoe Canyon are its higher A1203, FeO*, TiOZ, Ba, and Eu abundances, and lower SiO2, U, and Th abundances. The Jesse James Canyon Tuff (JJ in fig. 6) is macro— scopically almost indistinguishable from the Rhyolite Canyon Tuff. Its only diagnostic petrographic characteristic is the presence of trace amounts of biotite. The composition of the Jesse James Canyon Tuff is distinguished from those of other ash-flow tuffs of the central Chiricahua Mountains, includ- ing the Rhyolite Canyon Tuff, by its slightly lower FeO*, Zr, Ta, Th, and Hf abundances, none of which distinguish it from the lower member of the tuff of Horseshoe Canyon. Slightly lower K20, Rb, Ba, and Eu abundances of the Jesse James Canyon Tuff may distinguish it from the lower member of the tuff of Horseshoe Canyon. The Rhyolite Canyon Tuff, the youngest regionally extensive ash-flow tuff in the central Chiricahua Mountains, is a high-silica rhyolite tuff that has been subdivided into six geologic map units. All these units contain diagnostic quartz and sanidine phenocrysts and, in contrast to most of the other ash-flow tuffs present in the study area, only rarely contain macroscopically identifiable mafic silicate minerals. The four Geochemistry 23 outflow facies members are macroscopically indistinguish- able. The intracaldera facies member is distinguished from the outflow facies members by its reddish-brown color and considerably greater lithic fragment content. The lava-flow- like phase is similar in appearance to intracaldera facies tuff, but it includes unbroken 0.5—1 cm sanidine phenocrysts, lacks eutaxitic structure, and contains very few lithic fragments. The basal (R0 in fig. 6), lower (RL in fig. 6), and middle (RM in fig. 6) members of the Rhyolite Canyon Tuff form a geochemically distinct subset of the Rhyolite Canyon Tuff, whereas the upper (RU in fig. 6), intracaldera (R1 in fig. 6), and lava—flow-like phase (RF in fig. 6) form another. Elevated Zr and Nb abundances distinguish all parts of the Rhyolite Canyon Tuff from almost all other tuffs in the area. Zirconium abundances of 280—400 ppm in the Rhyolite Canyon Tuff are unusually elevated for subalkaline rhyolites and are distinc- tive relative to abundances of similar volcanic rocks exposed in the western United States. Although Zr abundances for the Rhyolite Canyon Tuff slightly overlap those of both members of the tuff of Horseshoe Canyon, Nb abundances of the former distinguish it from the latter two units. Similarly, elevated abundances of Ta, U, Th, Hf, and the heavy REE distinguish the Rhyolite Canyon Tuff from other study area tuffs. As a group, the basal, lower, and middle members of the Rhyolite Canyon Tuff are distinguished by higher SiOz, Rb, Nb, and Ta abundances and lower TiOz, Zr, Ba, and La abundances rela- tive to the upper member, intracaldera facies, and lava-flow- like phase of the Rhyolite Canyon Tuff. The lower and middle members of the Rhyolite Canyon Tuff are essentially indistin- guishable (a distinctive parting, and in some places a distinc- tive white ash deposit at the top of the lower member of the outflow facies tuff can be used to mark the boundary between these units), whereas the basal member of the Rhyolite Can- yon Tuff is distinguished by higher K20 and Rb abundances. The upper member of the Rhyolite Canyon Tuff and the intracaldera facies are indistinguishable, but the lava-flow-like phase is distinguished by its higher K20 and Rb abundances. The pyroclastic deposits and lava flows of Swede Peak (SW in fig. 6) are present in a relatively limited area in the southeast part of the central Chiricahua Mountains. Dis- tinctive macroscopic characteristics of this unit include the fact that thick sections are composed of numerous separate pyroclastic flow deposits that (l) are essentially unwelded, (2) contain variable and frequently abundant lithic fragments, (3) are crystal rich (including phenocrysts of quartz, sanidine, albite, and biotite), and (4) are interbedded with volcanic sandstone, as well as cogenetic high—silica rhyolite lava flows. Fortunately, these features are diagnostic; compositional char- acteristics of the pyroclastic deposits and lava flows of Swede Peak are not diagnostic. Petrogenetic Implications Pearce and others (1984) recognized that granitoid rocks generated in various tectonic settings have distinctive 24 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona geochemical signatures. Trace-element abundance variations in coeval volcanic and plutonic rocks generated in a given terrane should be similar. Consequently, compositions of volcanic rocks from the central Chiricahua Mountains can be compared to the trace—element—tectonic setting diagrams developed by Pearce and others (1984). Trace-element data for most of these rock units plot in the within-plate field, near its boundary with the volcanic arc field (fig. 7). These trace—element characteristics indicate that genesis of magmas represented by this region’s volcanic rocks involved a signifi- cant crustal component and processes different than those that result in trace-element characteristics diagnostic of subduc- tion—related processes. Compositions plotting in or near the volcanic arc field may indicate that the associated magmas had somewhat distinct sources and (or) evolved by different genetic processes. Gill (1981) determined that Ba/Nb, Ba/Ta, and La/Nb ratios of modern arc rocks are >26, >450, and 2—7 respec- tively, whereas Pearce and others (1984) indicated that values of these ratios are generally <<(much less than) 26, <<450, and <3, respectively, for within-plate granites. For example, Ba/Nb and La/Nb ratios for subduction-related Tertiary volca- nic rocks of the Bolivian Altiplano are about 30 and 3, respec- tively (du Bray and others, 1995), whereas Ba/Nb, Ba/Ta, and La/Nb ratios for Late Proterozoic within—plate granites of the Arabian Shield are about 2, 5, and, 1 respectively (du Bray and others, 1988). Most, but not all, of central Chiricahua Mountains volcanic rock units (table 1) have values of Ba/Nb, Ba/Ta, and La/Nb that are <3, <50, and <2, respectively; these values are more like those of within-plate rocks than subduc— tion—related arc rocks. However, the biotite rhyolite lava in the moat of the Turkey Creek caldera, dacite porphyry, upper member of the tuff of Horseshoe Canyon, rhyolite of Erickson Ridge, lower member of the rhyolite of Joe Glenn Ranch, and rhyolite tuff of High Lonesome Canyon consistently yield arc- like values for these ratios. Average chondrite—normalized extended trace-element patterns (fig. 8) for volcanic units of the study area are gently negatively sloping, and have superposed, well-developed nega— tive Ba, Sr, P, and Ti anomalies; these patterns also include weakly developed, negative Nb-Ta anomalies. A striking feature of trace—element patterns for volcanic rocks of the area is their parallelism and relatively limited compositional range. The greatest amounts of compositional variation are among phosphorus, titanium, barium, and strontium, which probably reflect simple fractionation of varying amounts of apatite (P), iron-titanium oxide minerals (Ti), and plagioclase (Ba and Sr). The shape, slope, and abundance levels of chondrite—nor- malized extended trace—element patterns for volcanic rocks of the area are somewhat distinct from those characteristic of Figure 7 (alongside and facing page). Trace-element—tectonic setting discrimination variation diagrams showing average compositions of vol- canic rocks of the central Chiricahua Mountains, Ariz. Tectonic setting— composition boundaries from Pearce and others (1984); compositions from 100 A 10 , WITHIN PLATE _ S o 3 0 E: Q o: ‘A LlJ n. U) i.— n: 1 _ g E Z g OROGENIC _. V0 LCANIC ARC E <2: I— 0.1 - % EXPLANATION A Pre-Turkey Creek caldera rocks . Rocks associated with the Turkey Creek caldera 0,01 ' ' 0. 1 1 10 YTTERBIUM, IN PARTS PER MILLION 100 table 1. A, tantalum versus ytterbium. B, niobium versus yttrium. V rl n ‘ subduction-related arc volcanic rocks. In particular, for the rocks under study, the magnitudes of negative Ba, Sr, P, and Ti anomalies are greater, negative Nb-Ta anomalies are smaller, and, as indicated by heavy REE abundances that are greater than those of subduction-related arc rocks (fig. 8), the patterns are less steeply sloping. Negative Nb-Ta anomalies are con- sidered (Wood and others, 1979; Gill, 1981; Pearce and others, 1984) a hallmark of subduction-related, arc volcanic rocks. The weakly developed nature of Nb-Ta anomalies in the central Chiricahua Mountains rocks (fig. 8) further emphasizes the transition from subduction-related magmatism, character— istic of the oldest volcanic rocks in the area, to within-plate magmatism epitomized by younger volcanic rocks in this area. A measure of the magnitude of the negative Nb—Ta anomaly derives from the chondrite-normalized K/Nb ratio, (K/Nb)CN. The average value of (K/Nb)CN for 18 middle Tertiary, demonstrably subduction-related ash-flow tuffs of southeast— ern Nevada is 80:14 (du Bray, 1995), whereas values of this ratio are <4 for most of the volcanic rocks of the study area. Rocks with (K/Nb)CN values >4 are the biotite rhyolite lava in the moat of the Turkey Creek caldera (7.8), dacite porphyry (4.5), both members of the tuff of Horseshoe Canyon (7.1 and 5.6), rhyolite of Erickson Ridge (7.9) and rhyolite lava of Cave Creek (4.9), the lower member of the rhyolite of Joe Glenn Ranch (10.1), and the rhyolite tuff of High Lonesome Canyon Geochemistry 25 (9.3); this ratio increases among progressively older rocks. All these compositional features suggest that volcanic rocks of the study area are principally of the within—plate type, but record the transition from an older subduction-related setting to a younger, within-plate setting. Furthermore, compositions of most volcanic rocks in the area are remarkably similar to the average obsidian composition for igneous systems situated in continental interior settings (Macdonald and others, 1992). Zirconium abundances for most of the study area volca- nic rocks are commensurate with the experimentally deter— mined zirconium saturation threshold of 100—200 ppm for subalkaline compositions at typical magmatic temperatures (Watson and Harrison, 1983). Thus, magmas represented by these rocks did not equilibrate with peralkaline liquids, nor did they equilibrate at a temperature greater than about 860°C. Zirconium abundances in rocks derived from the Turkey Creek caldera and both members of the tuff of Horseshoe Canyon are variably elevated and imply final pre-eruption equilibra- tion at greater than about 860°C and (or) an association with magma having an alkaline affinity (Watson and Harrison, 1983). Of these units with elevated zirconium abundances, rhyolite lava flows in the moat of the Turkey Creek caldera have the lowest zirconium abundances (175—220 ppm) and appear to have been least affected by the processes that caused pronounced zirconium enrichments in the other caldera-related 1,000 B WITHIN PLATE 6 3 10° _ VOLCANIC ARC _ _l E 5 E ' ' w '2 IE ‘ M E 0 A Z 2* A 3 ‘ A. OROGENIC E Q 10 , — Z EXPLANATION A Pre-Turkey Creek caldera rocks 0 Rocks associated with the Turkey Creek caldera I Post-Turkey Creek caldera rocks 1 I I ‘I 10 100 1,000 YTTRIUM, IN PARTS PER MILLION 26 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona Figures. Chondrite-normalized 11000 ‘ extended trace-element diagram showing average compositions of volcanic rocks in the central Chirica- hua Mountains, Ariz. Trace elements ~ are arranged in order of increasing geochemical compatibility to the right. Modified from Thompson and others w 100 (1983). Lu t C: a Z 9 Q 10 . L|.l _l n. E < (I) 1 EXPLANATION """""""" Pre-Turkey Creek caldera rocks r Rocks associated with the Turkey Creek caldera 0_1 l l l l | I | | | | l l l l l | l RbBaThKNbTaLaCeSrNdPSerHfTinYTmYb ‘ rocks. The remaining rock units that have unusually high zirconium abundances are also characterized by elevated NaZO and especially K20 abundances and were previously identified (fig. 4) as members of the shoshonite series defined by Ewart (1982). These elevated zirconium abundances may be related to elevated alkali abundances in magmas represented by the associated volcanic units, because elevated alkali abundances stabilize zircono-alkali silicate complexes (Watson and Harri- son, 1983), which inhibit zircon nucleation, fractionation, and zirconium concentrations from reaching maximum thresholds in silicate liquids. Feldspar-melt distribution coefficients for strontium, potassium, and rubidium (Hanson, 1978) are such that feld- spar fractionation preferentially removes strontium and then potassium from the melt phase, relative to rubidium, causing residual liquids to become progressively enriched in rubidium relative to potassium and strontium. In volcanic rocks of the central Chiricahua Mountains, rubidium abundances increase systematically relative to those of strontium and potas- sium (fig. 9). Rubidium enrichment in most of these units is significantly better developed than in subduction-related volcanic arc rocks, including, for example, ash—flow tuffs of southeastern Nevada (du Bray, 1995), and is strikingly similar to that characteristic of highly evolved, within-plate granites of the Arabian Shield (du Bray and others, 1988). Rb/Sr ratios range from a low of 0.69 in the rhyolite of Erickson Ridge to a highly evolved value of 32 in the rhyolite lava of Dobson Peak; the average Rb/Sr ratio for the 23 volcanic units is 11. These ratios contrast dramatically with those for subduc— tion—related dacite to rhyolite ash-flow tuffs of southeastern Nevada, whose average Rb/Sr ratio is 0.68 (du Bray, 1995). Average chondrite-normalized REE patterns for volcanic rock units of the study area (fig. 10A) mimic relations portrayed by chondrite—normalized extended trace- element patterns, in that the REE patterns are remarkably parallel and define a relatively narrow compositional range. The light REE parts of the patterns are moderately negatively sloping, whereas the heavy REE parts are essentially flat; the patterns include small to moderately well developed negative europium anomalies that probably indicate modest to con- ' siderable amounts of feldspar fractionation (Hanson, 1978). Chondrite-normalized La/Lu ratios range from 3.73 to 13.2 and average 8.1:33; units with geochemical characteristics most like those of subduction—related arc volcanic rocks have the steepest patterns. Total REE contents range from 110 to 444 (average 276:103) ppm. Petrogenetic Evolution of the Turkey Creek Caldera Magmatic System Hildreth (1979, fig. 13) documented systematic composi— tional variation within the Quaternary Bishop Tuff (associated with the Long Valley caldera of east-central California), one of the first ash-flow tuffs for which systematic compositional variation was well characterized. In particular, cooler, earlier erupted parts of Bishop Tuff, extracted from the top of the magma reservoir, have lower light REE abundances and higher heavy REE abundances than hotter, later erupted material extracted from deeper within the reservoir. In addition, each sequentially erupted fraction of Bishop Tuff magma is charac- terized by a progressively smaller negative europium anomaly. These abundance variations for sequential eruptions from a zoned reservoir yield a clockwise rotation of chondrite nor- malized REE patterns and smaller negative europium anoma— lies. Hildreth (1981) also demonstrated that a broad suite of additional incompatible trace elements is more abundant in earlier erupted parts of the Bishop Tuff, whereas compatible trace-element abundances are greatest in its latest erupted parts. This same type of compositional variation is preserved among outflow facies members of the Rhyolite Canyon Tuff. K/lOO HIGHLY EVOLVED GRANITES (du Bray and others, 1988) ASH-FLOW TUFFS OF THE SOUTHERN GREAT BASIN (du Bray, 1995) EXPLANATION A Pre-Turkey Creek caldera rocks 0 Rocks associated with the Turkey Creek caldera I Post-Turkey Creek caldera rocks Geochemistry 27 Sanidine, Clinopyroxene, and zircon are the principal phe- nocrysts in the Rhyolite Canyon Tuff and thus are most likely to have controlled trace—element abundance variations in the evolving liquid represented by the tuff. Mineral—melt distribu- tion coefficients (KD’s) for these minerals in rhyolitic melts are as follows (Hanson, 1978, 1980): Sanidine: light REE KD’s (0.02—0.04) are greater than heavy REE KD’s (0.006); except Eu (1—2). Values for KD’s of Sr, Ba, and Rb are about 4, 6, and 0.7, respectively. Sanidine fractionation causes counterclockwise REE pattern rotation (light REE depletion), results in negative europium anomaly development, increases overall REE and Rb abundances, and decreases Sr and Ba abundances in the residual liquid. Clinopyroxene: heavy REE KD’s (1—2) are slightly greater than light REE KD’s (0.5—1). Values for KD’s of Sr, Ba, and Rb are less than 1. Clinopyroxene fractionation causes slight clockwise REE pattern rotation (heavy REE depletion), results in overall REE depletion, and increases Sr, Ba, and Rb abundances in the residual liquid. Zircon: heavy REE KD’S (as high as 300) are signifi— cantly greater than light REE KD’s (about 1). Values for KD’s of Sr, Ba, and Rb are less than 1. Zircon fractionation causes dramatic clockwise REE pattern rotation (heavy REE Figure 9. Ternary variation diagram showing average relative proportions of rubidium, potassium, and stron- tium in volcanic rocks of the central Chiricahua Mountains, Ariz. Rb Sr 28 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona 1000 l l l I I Figure 10 (alongside and facing A page). Chondrite-normalized (modified from Anders and Ebihara, 1982) rare earth element diagrams. A, average compositions of volcanic rocks in the central Chiricahua Mountains, Ariz. B, compositions of the lower (Trcl), middle (Trcm), and upper (Trcu) members of outflow facies Flhyolite Canyon Tuff. C, compositions of the biotite rhyolite (Tmrb) and units 1 (Tmrl), 2 (Tmr2), and 3 (Tmr3) of rhyolite lava preserved in moat of Turkey Creek caldera. 100 “ 10 SAMPLE/CHONDRITE EXPLANATION ................ prequrkey Creek caldera rocks Rocks associated with the Turkey Creek caldera SAMPLE / CHONDRITE 0.1 I I I I I I I I Geochemistry 29 1,000 I I 100 10 SAMPLE / CHONDRITE 0.1 I l l l depletion), results in overall REE depletion, and increases Sr, Ba, and Rb abundances in the residual liquid. REE abundance variations within outflow facies members of the Rhyolite Canyon Tuff (fig. 108; table 1) are similar to those of the Bishop Tuff as well as those of many other high- silica rhyolite systems. The upper member of outflow facies Rhyolite Canyon Tuff is the least geochemically evolved of the three members, and it therefore is considered to represent a composition similar to that from which the other two members could have evolved. REE patterns for sequentially erupted members of outflow facies Rhyolite Canyon Tuff rotate in a clockwise sense, have progressively smaller negative euro— pium anomalies, and depict compositional evolution of earlier erupted members from later erupted members. Abundances of other trace elements also vary consistently relative to strati— graphic position within the Rhyolite Canyon Tuff. In particu- lar, early erupted Rhyolite Canyon Tuff, represented by strati- graphically lower deposits, contain higher abundances of Rb, Y, Nb, Ta, Th, and U, and lower abundances of Sr, Zr, and Ba. Consequently, evolution of trace-element abundance variations within the reservoir represented by outflow facies Rhyolite Canyon Tuff is qualitatively consistent with fractionation of the phenocryst phases contained therein. Sanidine crystalliza— tion and fractionation from the least evolved, parental magma, represented by the upper member of the Rhyolite Canyon Tuff, caused the residual liquid to contain lower light REE abundances relative to heavy REE abundances, lower Sr, Eu, and Ba abundances, and higher Rb abundances. Clinopyrox- ene crystallization and fractionation caused the residual liquid to contain slightly lower REE abundances overall. Zircon crystallization and fractionation caused the residual liquid to contain lower Zr abundances and partially counteracted heavy REE enrichment caused by sanidine fractionation. These mineral-melt distribution relations and crystal fractionation processes caused the residual liquid to differentiate to more evolved compositions and more evolved liquids accumulated at the top of the reservoir prior to eruption. Subsequent top— down eruptions from the normally zoned reservoir represented by the Turkey Creek caldera and its eruptive products depict compositional gradients that developed in the reservoir as a consequence of these crystal liquid equilibria. The four rhyolite moat lava units (units 1, 2, and 3 and the biotite rhyolite), considered together, are zoned from less evolved (biotite rhyolite) to more evolved (unit 3) composi- tions, which is the reverse of that which results from progres- sive, top-down magma extraction from a normally zoned reservoir. Each subsequently erupted unit is more evolved than the previously erupted unit. For example, the abundances of compatible elements, such as Ti, Ba, and Sr, are greater in the biotite rhyolite relative to those in the three successively erupted rhyolite lavas of units 1, 2, and 3, in which their abun— dances are progressively lower. In contrast, abundances of 30 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains. Arizona incompatible elements, such as Rb, Th, and Nb, are lowest in the first erupted rhyolite lava and progressively higher in sub- sequently erupted moat rhyolite lavas (table 1). As described by Duffield and Ruiz (1992) for the Oligocene Taylor Creek Rhyolite of New Mexico, this type of zoning is a logical consequence of a reservoir’s being progressively contami- nated by incorporation of variable amounts of geochemically less evolved rock. In an incompletely mixed reservoir, with increasing distance downward, away from the upper, enclos- ing roof rock interface, the amount of incorporated wallrock, and therefore contamination, is predictably less. Progressive, top-down magma extraction from a reservoir so contaminated yields eruptions of progressively more evolved compositions. Reversed compositional variation preserved by the rhyolite lavas in the moat of the Turkey Creek caldera suggests that the upper parts of their source reservoir were progressively less evolved as a consequence of having incorporated relatively greater amounts of roof rock. To identify other geochemical processes by which the observed reversed geochemical zona— tion preserved in the four rhyolite moat lava units could have developed is difficult. REE abundance variations (fig. 10C) between the biotite rhyolite and unit 1 lava imply that wallrock contaminant unmixing was not the sole control on the compositional evolu- tion depicted by these rocks. The biotite rhyolite has slightly lower REE abundances, has a slightly less well developed negative Eu anomaly, and is characterized by a REE pat- tern slightly less steeply negatively sloped than that of unit 1 lava. In order for unit 1 lava to have evolved from the biotite rhyolite, overall REE enrichment, which requires removal of material with REE abundances lower than those of the biotite rhyolite, must have occurred. However, the biotite rhyolite REE composition could not have evolved to that of unit 1 lava by unmixing of a lithologic contaminant, because all of the lithologies plausibly present within the intracaldera environ- ment have overall REE abundances greater than those of the biotite rhyolite (fig 10A); their separation from the biotite rhyolite would have caused REE depletion in unit 1 lava relative to the biotite rhyolite. Fractionation of phenocrysts characteristic of the biotite rhyolite, sanidine and biotite, could have caused REE abundances to evolve to those of unit 1 lava. As described in the second paragraph of this section, REE mineral-melt distribution coefficients for sanidine are such that sanidine fractionation causes counterclockwise REE pattern rotation, results in negative europium anomaly development, and increases overall REE abundances in the residual liquid. Similarly, REE KD’s for biotite (0.23—0.44) are uniformly low, so that biotite fractionation causes REE enrichment (Hanson, 1980). Consequently, fractionation of sanidine and biotite phenocrysts from the biotite rhyolite could have caused the residual liquid, represented by unit 1 lava, to become REE enriched, to have a more well developed negative Eu anomaly, and to cause slight counterclockwise REE pattern rotation. Compositional evolution from unit 1 lava to unit 2 lava and from unit 2 lava to unit 3 lava was quite different than the evolution from the biotite rhyolite to unit 1 lava. In particular, REE abundances in units 2 and 3 lavas are successively lower, negative Eu anomalies are of constant magnitude, and chon- drite-normalized REE patterns are slightly counterclockwise rotated relative to that for unit 1 lava. This type of REE deple- tion requires removal of material with REE abundances greater than that of unit 1 lava. Because unit 1 lava has REE abun- dances between 100 and 10 times chondrite values, phenocryst fractionation seems improbable because very few common minerals contain sufficiently elevated REE abundances. Alter- natively, addition of material, possibly including xenocrysts, with REE abundances less than that of unit 1 lava may have caused the observed REE abundance variations. However, the aphyric character of these rocks indicates that crystals were not added. As described in the previous paragraph, all of the lithologies plausibly present within the intracaldera environ- ment are inappropriate contaminant additions, because they have REE abundances equal to or greater than those of unit 1 lava (fig. 10A); their addition to unit 1 lava would have caused REE enrichment in units 2 and 3 lavas relative to unit 1 lava. In contrast, removal of one of these lithologic contaminants could have caused the observed, systematic REE depletion. Constraints on the rock unit(s) responsible for the hypothesized contamination of the moat rhyolite reservoir include the following: 1. The near contemporaneity of Turkey Creek caldera forma- tion with eruption of the moat rhyolites, 2. The intracaldera—scale structural and lithologic configura— tion that prevailed immediately following Turkey Creek caldera formation, and 3. The compositions of rocks that may have been involved in the hypothesized contamination. The fact that the Rhyolite Canyon Tuff and the moat rhyolite lavas are of essentially the same age (du Bray and Pallister, 1991) indicates that the rocks that contaminated the moat rhyolite reservoir must have been present in the intracal- dera environment immediately following caldera formation. Structural relations immediately following caldera formation (du Bray and Pallister, 1991) indicate that major amounts of intracaldera Rhyolite Canyon Tuff and dacite porphyry domi- nated the shallow crust in the environs of the moat rhyolite reservoir. Other rocks that may have enclosed and hosted the caldera, and therefore may have been available as possible constituents of the shallow crustal roof for the moat rhyo- lite reservoir, include Tertiary volcanic rocks erupted before development of the Turkey Creek caldera. Volumetrically dominant among these are the intermediate composition lava flows (TI m) that are presumed to have been erupted from a set of coalescing stratovolcanoes and onto which younger, more evolved outflow ash-flow tuffs and lava were subsequently erupted. The fact that negative Eu anomalies characteristic of rhyolite lavas of units 1, 2, and 3 are of constant magnitude indicates that the material being removed was characterized by, at most, a very small negative Eu anomaly; removal of material with a significant negative europium anomaly would have caused progressively less contaminated moat rhyolite to have a progressively smaller negative anomaly. Of the rocks present in the central Chiricahua Mountains, only the upper member of the tuff of Horseshoe Canyon, the dacite porphyry 0f the Turkey Creek caldera, and the intermediate—composi- tion lava flows at the base of the Tertiary volcanic section have small negative Eu anomalies (fig. 10). Geologic relations sug- gest that the upper member of the tuff of Horseshoe Canyon is not present in significant volumes, if at all, in the subsurface of the intracaldera environment; it is therefore an unlikely can- didate for the unmixing lithologic contaminant. In contrast, geologic relations suggest that both the dacite porphyry and the intermediate lava flows were plausibly significant constitu- ents of the intracaldera environment and therefore are likely candidates for the material that is inferred to have variably contaminated the rhyolite moat lava reservoir; top-down tap- ping of this variably but systematically contaminated reservoir could have produced the observed reversed compositional zonation. Consideration of broader geochemical systematics is consistent with the dacite porphyry and the intermediate lava flows as plausible contaminant lithologies. Mixing consid- erations require that for moat lava compositional variation to be a consequence of variable contamination involving magma and an exotic constituent, the trace-element compositions of the contaminated products (the biotite rhyolite, unit 1 lava, and unit 2 lava) must be intermediate between those of the two parental components (uncontaminated rhyolite, represented by unit 3 lava and a combination of contaminants, represented by the dacite porphyry and the intermediate lava flows). An examination of compositional data for these six lithologies (table 1) indicates that, in general, the compositions of the biotite rhyolite and rhyolite lavas of units 1 and 2 are in fact intermediate between those of unit 3 lava and the inferred contaminants. However, these relations are not perfectly well developed. For example, zirconium abundances among these six units indicate that other processes, perhaps including minor fractionation, contributed to and modified compositional rela— tions that seem largely to derive from variable magma contam- ination. In summary, compositional considerations indicate that the reversed compositional zonation depicted by the moat rhyolite lavas is consistent with the rhyolite reservoir’s having being progressively roofward contaminated through entrain- ment and assimilation of dacite porphyry and intermediate- composition lava flow inclusions. Additional consideration of REE data suggests that evolution of the most contaminated moat rhyolite (the biotite rhyolite) to unit 1 lava also involved selective phenocryst fractionation. Geochronology Most of the samples whose ages were determined are among those used in the volcanic rock stratigraphic/ composition investigation, a principal focus of this study. However, several samples (referred to as the miscellaneous Geochronology 31 units) of other igneous rocks in the study area were collected and dated by the 40Ar/39Ar method in order to augment the existing time-stratigraphic framework for this region. These samples and the map units that they represent, from oldest to youngest, are identified; and new geochronologic data are presented. Subsequently, geochronologic data and interpreta- tions are presented for the principal volcanic rocks, from old- est to youngest. 40Ar/39Ar results are presented as age spectra (fig. 11); ages are summarized in table 2; analytical data are in table 3. Miscellaneous Units Sample DY9l—77 (fig. 1, locality V) represents a hyp— abyssal dacite intrusion, about 16 km south of the Turkey Creek caldera; the sample site is within the dacitic rocks of Halfmoon Valley mapped by Drewes and Brooks (1988). The age spectrum for biotite from this sample (fig. 11A) is com- plicated, does not include a plateau, and shows the contrast- ing effects of both excess argon and apparent argon loss in the low-temperature steps. The isochron age for 80 percent of released 3g’Ar, representing the least disturbed part of the age spectrum, is 74.6106 Ma with (“‘)Ar/3"Ar)I = 276119. Although disturbed, the age spectrum for this sample confirms the presence of Cretaceous-age igneous rocks in the central Chiricahua Mountains; igneous rocks of this age have not been previously identified in this area. Samples P650A and 202064 (fig. 1, localities P and S) were analyzed in order to establish the age of tuff and lavas that constitute the pyroclastic rocks of Rucker Canyon (du Bray and others, 1997), which are correlative, in part, with the welded tuff of Rucker Canyon (Drewes and Brooks, 1988). Although volumetrically significant, the petrographic and compositional characteristics of these rocks have not been included in the present study, because these rocks, largely composed of many separate pyroclastic flows, are lithologi- cally heterogeneous and contain large amounts of exotic lithic fragments that would render definition of their petrography and composition somewhat meaningless. The age spectrum for biotite from sample P650A (fig. 118), a rhyolite lava interbedded in the pyroclastic flows, shows the effects of a minor amount of excess argon in the low—temperature steps but yields a plateau age of 33.321007 Ma. The age spectra for biotite and sanidine from a sample (fig. 11C and D, respec- tively) of the pyroclastic flows (202064) also show the effects of excess argon in the low—temperature steps, but both yield relatively undisturbed results. The biotite gives a plateau age of 33.211009 Ma. The sanidine gives a weighted mean age of 330410.04 Ma and an identical isochron age with (4"Ar/36Ar)l = 31017, reflecting the relatively minor amount of excess 40Ar in this sample. The similarity of ages for the biotite separates from the pyroclastic flow and lava samples suggests that emplacement of pyroclastic flows and lava flows was approximately simultaneous. The best age estimate for these rocks, based on the three available analyses, is 332710.08 Middle Teniary Volcanic Rocks, Central Chmcahua Mountains, Arizona 32 8238. v.38 55$“. 8_ 8 8 8 8 8 8 8 8 8 c 3 SM n 55:5: 32 83 n 3% u was 5:58. llllv 52:5 83.25 B 938 2328;; Q mEECmm EONON 82de 52 25$; 8_ 8 8 8 8 8 8 8 8 2 o A|| as. 8.8 H 5.8 M 8m :38: ||Iv .558 836:: B 3.2: 2528;; D 385 «OONON ON mm on mm Ow ON mm Om mm ow (BW) 39V lNEIHVddV (9W) 39V lNEIHVddV 82de via :83: OO_ OO Om ON OO Om Ow OO ON O _ O | 22 BO H «0% n man :88: llv cacao L933. 8 932 2828;; m 8:05 o 2: OO OO ON cm on Ow Om ON O F O ON mm on ow Om mm OO mm On 3. OO (9W) 39V lNElHVddV (9W) 39V iNElHVddV 33 ea“ 5 86286 05:5 2: Em 8E ES 26$ :28 3 t2 5%: E EtEmE E: B 383:8 mm zuzofiuq‘ ESE 32> .23 558.522 «5:8ch E528 2:8 $69 2523 E». 28me mam $332 .7992. 233:8 25 «>33 2 2:9”. Geochronology mum/Eda v:;m :3ch >382 B 3:23:20 I mEEcmm vm..~m>a UN 3:05 cum—ON ow ow 825$ v.33 ESE“. 825mm 5% 55$“. 8— cm cm on on om ow om ow E 0 cm 2: cm on on 8 om ow om ow S 9 cm . - , - a - a All 22 3.7% Rum? ism: |lv V V d d W. E All 22 Ed I 3% mac 332; M + H J 1 cm W. - cm W. V V m . m m H~|. JL m w Inu.‘ I mm _ mm m H mam HmAh932). .8 8:23:80 6 22:3 ENE m 383:5: 258 9» av Middle Tertiary Volcanic Rocks, Central Chmcahua Mountains, Arizona 34 825% via 58$“. 2: 8 8 E 8 3 3 on 8 2 o a _. Alllll MEN—.DHO—wwnmwm swam—n— |l||V I mN '— " . 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Geochronology owm;m BaEmE 52:: W wsgcmm 8:3 om mm on mm ow om mm om mm 9‘ (9W) 39V iNHHVddV (BWHEJV lNEIHVddV 8— am on E 823: 3? 55$; om om ow om ow S a an H NmN H _f_ 9—6 H NwKN u mmm saga—n— llllffij/ cacao mozwmmEI .6 tztmnEmE $33 0 Exam mESN om mm on mm ow ON om mm ow (9W) 39V lNEUVddV (9W) EIEJV lNEHVddV 37 68“ E comm—Ema EVE: 2: 2m 02m 25 32> 58 U8 :2 3%: E EEEmE as. S ucoammtoo mm sanefi< ESE 32> .N:< ~9:35.22 «2:835 RES @520 332 2.523 58 23QO mam 3&3: .=u====oul: 2:9“. Geochronology mum/Eds v.33. H255; mum/Eda v:mffilcem§E :5an 86mm :3. 52:5 mESEm EnEmE 5%: x mEEcmm $98 > 8.25m 82% 3 av Qmm;m EnEmE 3%: tsp :3ch 263$ .mnEmE 2252 NS wEuEmm $28 D mEuEmm mmIaE 3 2V Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona 38 co— ommm_ 262$ 2:255 meSN 2: 825mm 5% ESE; ow E 8 cm 3 A| 22 mod H 3.8 u mam $82". |v «528 E35 Karate 82: E m>m_ 8:95. 3:95 mEEcmm mmmSN ow mm om mm ow om mw 8 mm ow (EWHEJV .LNEIHVddV (9W) 39V iNElHVcldV 8— 3 825mm 5? ESE“. 8 cm ow om ow E o e H 8” u féais. _ I 22 Nod u :3 u mam 5:502 IV SEES Emma >82: 8 59: E 8:2 8:35. 3:05 £55 823 oo— Bmfidm 5a 255; cc om ow on ow o _ o m; H 88 u QBm mom E=E_E_>_ A|v 82:8 :35 karate 59: E m>m_ BEES. 26mm mEuEmm mm low mm om mm ow ow mm on mm 3 WW) 39V lNElHVddV (9W) 39V lNElHVddV 39 Geochronology as: E 562.85 B VE__ m5 Em 02m 25 52> some U5 t2 3%: E 3:5:me 2t 2 ucoamwtoo mm 398:: q‘ 28%: >55 .23 £535.22 mzsmoEsu RES 3: E $.09 223:; 5. £8wa mam Eat/3 ._.¢==_=_oul.:. 9:3: 823$ v.33 55$“. 2: 8 8 E 8 8. S 8 a S o A|| 22 8.0 H 3.8 u mam .5ng Ilv _H is; 888 .6 m>m_ 8:35: mEuEmw EFSN QQ am mm on mm 3 (9W) 39V iNElHVddV 825mm 52 ESE“. 2: am cm on 8 am ow am cm 3 o om Al 22 m; « $8 u mg :83: Illv - mN .— 1l. .I. W LII d V H m cm I. V a 3 W m‘ . mm 22:8 VBEQ Exits “SE E CE: 6 mi: 8:025 9.65m 822 OD cc 40 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona Ma, the average of the two biotite ages. In the study area, the pyroclastic rocks of Rucker Canyon represent the last mani— festation of a short pulse of volcanic activity that began with eruption of the tuff of High Lonesome Canyon. This mag- matic pulse ended at about 33.3 Ma and was followed by a 5.2 m.y. eruptive hiatus, which ended with eruption of the rhyolite lava of Cave Creek. Sample 201570 (fig. 1, locality E) is from the grano- diorite of Mackey Canyon (Pallister and others, 1994), also known as the granodiorite of J hus stock (Drewes and others, 1995). The granodiorite is medium to coarse grained and contains subequal amounts of biotite and hornblende, which together constitute about 10 percent of the rock. Previous K—Ar analyses yielded ages of 30.9:12 Ma (Marvin and others, 1978, no. 100) and 29.59i0.90 Ma (Shafiqullah and others, 1978, no. 20). The age spectrum for hornblende from sample 201570 (fig. 11E) shows evidence of excess argon in the low— and high-temperature steps but gives a plateau age of 32.16:0.20 Ma and an isochron age 31.42:0.30 Ma with (“°Ar/3"Ar)I = 309:6. Considering the effect of excess argon on the plateau age, we interpret the isochron age as more likely reflecting the crystallization age of the hornblende. The age spectrum for biotite from this sample (fig. 11F) is slightly disturbed but gives a plateau age of 30.62i0.15 Ma. Given the magnitude of uncertainties characteristic of K—Ar analyses and argon retention problems typical of biotite, we consider that the 40Ar/39Ar hornblende age for sample 201570 represents the best approximation of the age of this intrusion and the biotite age may reflect a brief period of cooling to the argon-in—biotite retention temperature. The presence of coarsely holocrystal— line rocks, such as the granodiorite of Mackey Canyon, nearly adjacent to volcanic rocks as little as 5 my. younger implies rapid uplift, perhaps involving faulting, and unroofing of the terrane that includes the granodiorite prior to the onset of volcanism in this area at about 28 Ma. Sample 202154 (fig. 1, locality M) represents the latite of Darnell Peak (Bryan, 1988), which forms a sill that intruded between the upper and lower members of the tuff of Horseshoe Canyon. The sill, which according to the nomenclature of Le Bas and others (1986) is composed of trachyte (11 percent Na20+K20 at 68.6 percent SiOZ), is crystal rich and contains distinctive feldspar and biotite phenocrysts. The age spectrum (fig. l 16) for sanidine from this sample is simple, although slight effects of argon loss are evident in the low—temperature steps and excess argon in the two highest temperature steps. It gives a plateau age of 27.58i0.08 Ma. The age of the sill is indistinguishable from that of the lower member of the tuff of Horseshoe Canyon, which indicates that the sill was emplaced immediately after tuff emplacement. Bryan (1988) suggested that the considerable thickness of tuff of Horseshoe Canyon, southeast of the Turkey Creek caldera, forms an intracaldera accumulation within what he called the Portal caldera. In this context, the sill might represent resurgent magmatism within the Portal caldera and emplacement of less evolved magma up into the thick intracaldera tuff accumulation from lower within a zoned magma reservoir whose eruption created the Portal caldera. The stratigraphic and compositional relations between the sill and the tuff indicate that the reservoir from which these products were erupted was strongly and discontin— uously zoned, a feature that is known to have prevailed in the adjacent and 0.5 m.y.-younger Turkey Creek caldera as well. Sample DY92-54 represents an isolated rhyolite lava that overlies outflow facies Rhyolite Canyon Tuff about 13 km southwest of the Turkey Creek caldera (fig. 1, locality R). The age spectrum for sanidine from this sample (fig. 11H) yields a plateau with an apparent age of 26.35i0.08 Ma. The age for this sample indicates that the underlying Rhyolite Canyon Tuff here is at least 26.35 Ma. The age and stratigraphic position of this rhyolite are similar to those of the rhyolite of Dobson Peak, exposed about 16 km to the east. The rhyolite repre— sented by DY92—54 could be correlative with the rhyolite of Dobson Peak. If such a correlation were substantiated, then the lava dome field represented by rhyolite of Dobson Peak must have been considerably more extensive than its present erosional remnants. Sample P5 (fig. 1, locality W) represents the rhyolite of Packsaddle Mountain (Drewes and Brooks, 1988), an isolated, small, garnet-bearing rhyolite plug about 16 km south of the Turkey Creek caldera. Drewes and Brooks (1988) presented a K-Ar age of 29:08 Ma (R.F. Marvin, H.H. Mehnert, and EL. Brandt, written commun., 1985) on sanidine from this unit. The age spectrum for sanidine from this sample is simple (fig. 111) and gives a plateau age of 2323:008 Ma, which confirms that this is one of the youngest Tertiary—age igneous rocks known in the central Chiricahua Mountains. Pre-Turkey Creek Caldera Rocks Sample 202057 (fig. 1, locality U) represents the rhyo- lite tuff of High Lonesome Canyon, a regionally distributed ash-flow tuff for which no geochronologic data have been previously reported. The age spectrum for sanidine from this sample is relatively simple (fig. 11]), although some effects of argon loss are evident in the low—temperature steps; it gives a plateau age of 34.16i0.17 Ma, which we interpret to represent the eruption age of the tuff. The rhyolite tuff of High Lone- some Canyon is thus the oldest ash—flow tuff in the study area with a well-established age. Unfortunately, the age of the tuff does not match that of any other regionally distributed ash-flow tuff from the Boot Heel volcanic field. As such, the source, distribution, and stratigraphic correlation of this unit remain unknown. Sample DY91—ll (fig. 1, locality T) represents the lower member of the rhyolite of Joe Glenn Ranch, another regionally distributed ash-flow tuff. A sanidine K—Ar age of 29.6:19 Ma has been reported for this unit, but the collection site for the analyzed sample is uncertain (Marjaniemi, 1969; Drewes, 1982). Drewes and Brooks (1988) presented a fission track (zircon) age of 30.4:30 Ma for this unit. The age spectrum for biotite from this sample is relatively simple (fig. 11K), although it shows minor disturbance in the low—temperature steps; it gives a plateau age of 338110.08 Ma. The new geochronologic data indicate that the lower member of the rhyolite of Joe Glenn Ranch was therefore erupted within about 350,000 years of the eruption of the rhyolite tuff of High Lonesome Canyon. Like the tuff of High Lonesome Canyon, the lower member of the rhyolite of Joe Glenn Ranch does not have an age correlative among units of the Boot Heel volcanic field. Consequently, the source, distribution, and stratigraphic correlation of this unit also remain unknown. Sample P272C (fig. 1, locality I) represents the rhyolite lava of Cave Creek which forms an extensive flow dome field, possibly including the rhyolite lava of Krentz Ranch, in the eastern part of the central Chiricahua Mountains. McIntosh and Bryan (2000) reported an 40Ar/39Ar age of 27.7610.16 Ma for this unit. The age spectrum for sanidine from this sample is relatively simple (fig. 11L), although some effects of excess argon are evident in both the low- and high—temperature steps; it gives a plateau age of 28.101012 Ma. This age indicates that following eruption of the pyroclastic rocks of Rucker Can- yon at about 33.3 Ma, no volcanic rocks were erupted in the study area until the rhyolite lava of Cave Creek was erupted about 28.1 Ma. These three rock units therefore define a mag— matic hiatus of about 5.2 my. in the area. Sample P475 (fig. 1, locality A) represents the rhyolite of Erickson Ridge, which forms a set of coalesced rhyolite lava domes in the western part of the central Chiricahua Mountains; Drewes (1982) reported a biotite K-Ar age of 28.7110 Ma for this rhyolite. Age spectra for coexisting sanidine and biotite are relatively simple, although the effects of excess argon and argon loss are evident, especially in the low—temperature steps, in spectra for both minerals. The sanidine (fig. 11M) gives a plateau age of 278910.09 Ma, whereas the biotite (fig. llN) gives a discordant plateau age of 282410.08 Ma. Harlan and others (1998) and Kellogg and others (1994) have suggested that age discordance between coexisting sanidine and biotite is common in volcanic rocks and that in some cases biotite yields 40Ar/3"Ar ages older than those indicated by coexisting sanidine. We consider the sanidine age to be more representa- tive and reliable, because sanidine is less susceptible to the diversity of geologic processes that can disturb argon sys- tematics in biotite and the age spectrum of the biotite clearly is more affected by excess argon. Our preferred age for the rhyolite of Erickson Ridge is 27.891009 Ma, which, given the analytical uncertainties, suggests that the rhyolite of Erickson Ridge is the same age as the rhyolite lava of Cave Creek. As such, the re-initiation of volcanism in the region shortly before 28 Ma and following the 5.2 m.y. hiatus is dominated by the voluminous, effusive rhyolite lava dome field development preserved in the rhyolites of Erickson Ridge, Cave Creek, and Krentz Ranch. Samples 201771 and P652 (fig. 1, localities C and 0) represent the Jesse James Canyon Tuff. The two samples were collected and their ages determined to help verify the inferred correlation of these two isolated exposures of similar ash-flow tuff. Age spectra for sanidine from both of these samples are relatively simple, although that for sample P652 shows some Geochronology 41 effects of excess argon in the low- and high-temperature steps. The spectrum for sample P652 (fig. 110) gives a plateau age of 275210.06 Ma, Whereas that for sample 201771 (fig. 1 IF) gives a plateau age of 275910.06 Ma. An isochron analysis of 201771 yields an apparent age of 276310.04 Ma, which is identical to the plateau age, with (‘10Ar/35Ar)I = 270110. That these two plateau ages are statistically indistinguishable sub- stantiates the inferred correlation between these exposures of biotite-bearing high—silica rhyolite tuff exposed immediately beneath outflow facies Rhyolite Canyon Tuff in the northern and southern parts of the central Chiricahua Mountains. These ages suggest another correlation. Biotite-bearing high—silica rhyolite tuff exposed in the eastern and southeastern parts of the study area, and immediately beneath the Rhyolite Canyon Tuff, is the tuff of Horseshoe Canyon, which, as described following, has a preferred age of 27.621010 Ma. All three of these ages are statistically indistinguishable. Therefore, because the Jesse James Canyon Tuff and the lower member of the tuff of Horseshoe Canyon occupy identical stratigraphic positions and are the same age, it seems probable that the Jesse James Canyon Tuff is the eruptive correlative of part, if not all, of the lower member of the tuff of Horseshoe Canyon. The near indistinguishability of geochemical data for these two units further substantiates the inference that these two units are, at least in part, volcanologic and therefore stratigraphic correlatives. Sample 202156 (fig. 1, locality L) represents the lower member of the tuff of Horseshoe Canyon, a regionally dis- tributed rhyolite ash-flow tuff. Bryan (1988) summarized the scant K-Ar age data available for this unit. He documented the attempt by Marjaniemi (1969) to determine the age of the tuff by K-Ar analysis of biotite, which resulted in an age of 26410.7 Ma. Given that the tuff 0f Horseshoe Canyon is stratigraphically beneath the Rhyolite Canyon Tuff, and that the age of the latter (see next section) is now known to be 26.9 Ma, we conclude that the earliest attempts to date the tuff of Horseshoe Canyon resulted in a slightly too young age. Using the 40Ar/39Ar method and having analyzed 17 samples of the tuff, McIntosh and Bryan (2000) established its age as 27.6 Ma. Age spectra for sanidine and coexisting biotite from sample 202156 give significantly different ages. The spectrum for the biotite separate is relatively simple (fig. 11Q), although it shows some evidence of excess argon and argon loss in the low-temperature steps; it gives a plateau age of 276210.10 Ma. The spectrum for the sanidine separate (fig. 11R) is significantly disturbed and is characterized by a progressive stepping-up pattern in the low-temperature steps, and then by a more gradual stepping—down pattern in the medium- to high- temperature steps. The sanidine analysis gives an isochron age of 254210.10 Ma with (40Ar/36Ar)I = 29714. Given the preceding comments concerning early K-Ar analyses of this unit, clearly this age is significantly too young and therefore meaningless. Consequently, our preferred age for the lower member of the tuff of Horseshoe Canyon is 276210.10 Ma, which is in good agreement with the findings of McIntosh and Bryan (2000). This age is also consistent with intrusion of the 42 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona Table 3. 40Ar/39Ar data for volcanic rocks of the central Chiricahua Mountains.” Temp 40Ar 2/ 39Ar 2/ 40ArR/agAr 2/ 39Ar/37A r23/ %40Ar %39Ar ApparentageA’S/ o R K K R ( Cl (Ma 31:10”) Pre-Turkey Creek caldera rocks Sample: DY 91-77, biotite from dacite intrusion, J = 0.007306, sample wt = 62.4 mg Latitude: 31 °38’O4”N. Longitude: 109°22’40”W. 800 .33645 .10407 3.233 -——- 30.5 2.3 42.11 :1: .07 900 .56337 .14027 4.016 -—-- 49.0 3.1 52.17 i .33 950 .60808 .13195 4.609 ----- 53.4 2.9 59.74 :t .25 1000 .46566 .09300 5.007 ----- 62.6 2.0 64.82 :1: .45 1050 1.0832 .20101 5.389 ---— 74.5 4.4 69.67 :t .18 1100 1.6785 .29570 5.676 — 83.1 6.5 73.31 :1: .29 1150 1.7835 .30919 5.768 ----- 87.4 6.8 7447 :k .21 1200 1.8113 .31623 5.728 ----- 88.5 6.9 73.96 :1: .11 1250 21122 .36772 5.744 ----- 89.2 8.1 74.163: .11 1300 2.3476 .41295 5.685 ————— 86.3 9.1 73.41 :1: .11 1350 4.4704 .78842 5.670 ----- 85.6 17.3 73.23 i .11 1400 4.1792 .73630 5.676 -~-—— 89.0 16.2 73.30 :1: .11 1500 3. 7589 .65902 5. 704 --—- 90.6 14.5 73.65 :1: .11 No plateau; total gas age= 71 46 :t 0.15 Ma Isochron age (steps 7- 13)= 74. 6 i 0.6 Ma; (40AIF5AI)I= 276 i 19 Sample: 202057 sanidine from rhyolite tuff ole High Lonesome Canyon, J = 0.007288, sample wt = 40.2 mg Latitude. 31° 43’07”N. Longitue 109°22’10”W. 850 .0712 .0310 2. 29 ----- 36.2 .9 30. :t 2 950 .21400 .08730 2.451 ----- 85.3 2.5 31.94 :t .30 1050 .56327 .22273 2.529 ----- 74.7 6.4 32.95 i .33 1100 .56931 .22136 2.572 ----- 95.4 6.4 33.50 :1: .07 1150 .64957 .25317 2.566 ----- 96.2 7.3 33.42 i .10 1200 .75008 .28761 2.608 ----- 97.7 8.3 33.97 i .31 1250 1. 0224 .38878 2.630 ----- 98.3 11.2 34.25 i .17 1300 .89940 .34377 2.616 ----- 97.8 9.9 34.07 i .15 1350 .86144 .32854 2.622 ----- 98.4 9.5 34.15 :t .16 1400 .90951 .34579 2.630 ----- ‘ 98.6 10.0 34.25 i .07 1450 .82037 .31169 2.632 ----- 98.1 9.0 34.28 i .16 1500 .99938 .37954 2.633 ----- 98.2 10.9 34.29 :t .22 1600 .70223 .26936 2. 607 ----- 94. 8 7.8 33. 95 d: .09 Totalgas age= 33.89 :tO. 19 Ma Plateau age (steps 6- -=13) 34.16 i 0.17 Ma (for 76. 5 percent of the gas produced during heating) Sample: DY 91-11, biotite from rhyolite of Joe Glenn Ranch J= 0.007228 sample wt= 54 mg Latitude: 31 °43’45”N. Longitude. 109° 22’42”W. 800 .0228 .0077 2.96 ——- 35.9 .2 38. i 5 900 .17104 .06623 2.582 --- 49.8 1.5 33.4 i .6 1000 .12715 .05194 2.448 ----- 29.6 1.1 31.6 i .7 1050 .09694 .03672 2.640 ----- 50.11 .8 34.1 :1: .8 1100 .12553 .04807 2.611 ----- 58.0 1.1 33.7 i .5 1150 .22775 .08666 2.628 ——--- 69.3 1.9 34.0 i .8 1200 .27804 .10413 2.670 ----- 79.1 2.3 34.5 i .3 1250 .40447 .15428 2.622 ----- 83.1 3.4 33.9 i .2 1300 .60641 .23109 2.624 —-—-— 85.0 5.1 33.90 i: .05 1350 8.6287 .32912 2.622 — 84.1 7.3 33.87 i .05 1400 1. 3297 .50658 2.625 —-—-- 81.4 11.2 33.91 i .10 1450 1. 5075 .57509 2.621 ----- 79.1 12.7 33.86 i .05 1500 2.1946 .83986 2.613 ————— 78.1 18.6 33.75 i .06 1550 2.1471 .82183 2.613 ----- 74.8 18.2 33 75 i .07 1650 1.7231 .65907 2.615 —- 71.2 14.6 33.77 i .06 Total gas age =33.81fl: 0.12 Ma Plateau age (steps 8-15) = 33.81 :b 0.08 Ma (for 91.1 percent of the gas produced during heating) Sample. P650A, biotite from lava 1n pyroclastic rocks of Ruoker Canyon J= 0. 007273, sample wt =58 3 mg Latitude: 31°45' 17 ”.N Longitude: 109°23’18”W. 800 .0119 .0046 2.585 ----- 12.0 .1 34. i 7 900 .0606 .0220 2.749 ----- 22.5 .5 36. :t 2 1000 .0131 .0043 3.023 ----- 19.1 .1 39. i 9 1100 .25449 .09350 2.722 ----- 43.2 2.0 35.36 i .10 1200 .78074 .30083 2.595 ----- 79.7 6.4 33.73 i .11 1250 .91525 .35400 2.585 ----- 85.0 7.5 33.61 i .12 1300 1.4587 .56869 2.565 ——-- 83.2 12.0 33.35 i .08 1350 2. 0019 .78026 2.566 —— 75.5 16.5 33.35 5: .05 1400 2.5372 .99023 2.562 — 70.3 20.9 33.31 i .05 1450 2.4490 .95833 2.555 ----- 70.7 20.3 33.22 i .06 1600 1.6833 .65491 2.570 ----- 73.6 13.8 33.41 i .11 Total gas age = 33.42 :1: 0.10 Ma . . Plateau age (steps 7-11) = 33.32 i 0.07 Ma (for 83.5 percent of the gas produced during heating) Geochronology 43 Table 3. 40Ar/39Ar data for volcanic rocks of the central Chiricahua Mountains.V—Continued Temp 40ArR2/ 3E’ArKZ/ 40ArR/39ArK2/ 3gAr/37Ar2'3/ “/u40ArR %39Ar Apparent age4’5/ 1° C) (Ma atrlo) Pre-Turkey Creek caldera rocks (continued) Sample: 202064, sanidine from pyroolastic rocks of Rucker Canyon, J = 0.007211, sample wt = 58.3 mg Latitude: 31 °43’45”N. Longitude: 109°23’01 ”W. 850 .11280 .08128 1.388 ----- 60.8 .9 17.96 :1: .29 950 .39018 .15653 2.493 —— 76.0 1.8 32.14 i .14 1050 .73384 .28528 2.572 -—-- 88.7 3.3 33.16 i .29 1150 1.6880 .66193 2.550 ----- 97.9 7.6 32.87 i .09 1200 1.8456 .72179 2.557 ----- 98.3 8.3 32.96 3: .06 1250 1.7673 .69141 2.556 ---- 98.3 8.0 32.95 i .07 1300 2.0964 .82207 2.550 --—— 98.2 9.5 32.87 i: .06 1350 2.5270 .99487 2.540 --—- 98.3 11.5 32.74 i .05 1400 2.6241 1.0284 2.552 ----- 98.6 11.8 32.89 i .05 1450 2.8236 1.1014 2.564 —---- 99.0 12.7 33.04 i .05 1500 2.8690 1.1192 2.564 ----- 98.7 12.9 33.04 :1: .06 1550 2.2979 .89018 2.581 ----- 98.7 10.3 33.27 :t .05 1650 .32974 .13024 2.532 ----- 86.0 1.5 32.64 i .45 No plateau; total gas age = 32.81 i 0.08 Ma Weighted mean age (steps 3-12) = 33.04 i: 0.04 Ma; (”ArF‘Arh = 310 i 7 Sample: 202064, biotite from pyroclastic rocks of Rucker Canyon, J = 0.007291 , sample wt = 36.1 mg Latitude: 31 °43’45”N. Longitude: 109°23’01”W. 800 .0119 .0044 2.729 —-—- 13.2 .1 36. i 5 900 .05414 .03385 1.599 -—— 13.5 1.1 20.91 :t .15 1000 .21606 .09988 2.163 ----- 20.7 3.4 28.23 i .38 1100 .49025 .19608 2.500 ----- 48.5 6.6 32.59 :I: .05 1150 .44536 .17204 2.589 ----- 61.2 5.8 33.73 i .49 1200 .64025 .24651 2.597 ----- 67.4 8.3 33.84 i .12 1250 .44545 .16993 2.621 ----- 70.4 5.7 34.15 i .33 1300 .67533 .26433 2.555 ----- 66.9 8.9 33.29 :1: .07 1350 .72277 .28346 2.550 ----- 64.8 9.6 33.23 a: .14 1400 .94128 .36886 2.552 ----- 72.7 12.4 33.26 i .07 1450 1.1522 .4521] 2.549 ----- 79.1 15.2 33.2121: .07 1550 1.7176 .67533 2.543 ----- 84.5 22.8 33.15 :1: .07 Total gas age = 33.00 :1: 0.14 Ma Plateau age (steps 8-12) = 33.21 i 0.09 Ma (for 68.90 percent of the gas produced during heating) Sample: 201570, biotite from granodiorite of Maokey Canyon, J = 0.007202, sample wt = 44.7 mg Latitude: 31 °58’21”N. Longitude: 109°14’04”W. 500 .0428 .0294 1.45 10.5 2.8 .4 1.9 I 2 700 3.8412 1.61536 2.378 270 64.4 20.6 30.63 i .10 750 1.6872 .70572 2.391 257 86.3 9.0 30.80 i .11 800 1.0393 .43799 2.373 186 84.8 5.6 30.57 i .25 850 .99738 .41005 2.432 86 82.3 5.2 31.33 i .32 900 1.4464 .60469 2.392 105 88.0 7.7 30.81 i: .18 950 3.5422 1.49360 2.372 130 90.9 19.0 30.55 3: .08 1000 4.9721 2.09179 2.377 214 92.4 26.6 30.62 i .08 1050 1.0862 .45726 2.375 59 90.1 5.8 30.60 i .24 1150 .0158 .0064 2.45 4.41 20.1 .1 32. i 13 1300 .0205 .0058 3.52 .68 12.7 .1 45. i 16 Total gas age = 30.64 i 0.14 Ma Plateau age (steps 6-9) = 30.62 i 0.15Ma (for 59.1 percent of the gas produced during heating) Sample: 201570, hornblende from granodiorite of Mackey Canyon, J = 0.007173, sample wt = 416.4 mg Latitude: 31 °58’21 ”N. Longitude: 109°14’04”W. 500 .0737 .0097 7.6 .27 11.9 .3 96. :t 6 600 .13673 .04461 3.065 1.01 27.9 1.2 39.23 i .74 700 .20655 .07720 2.676 2.14 28.2 2.1 34.29 A: .79 750 .19676 .08145 2.416 1.73 55.3 2.2 30.99 i .85 800 .23621 .09726 2.429 .97 60.0 2.7 31.16 i .26 850 .24370 .10093 2.415 .44 55.3 2.8 30.98 3: .58 900 .44623 .17561 2.541 .13 60.1 4.8 32.59 i .36 950 2.0647 .81976 2.519 .09 61.0 22.6 32.30 i .15 1000 2.1878 .87670 2.496 .10 66.7 24.2 32.01 :b .09 1050 1.6697 .66814 2.499 .09 64.7 18.4 32.05 i .21 1100 .74820 .29713 2.518 .08 70.1 8.2 32.29 i .26 1150 .93612 .36408 2.571 .07 57.9 10.0 32.97 :t .24 1200 .04219 .01489 2.83 .08 7.4 .4 36. i 4 1350 .0145 .0010 14.0 .09 3.5 0.0 173. 3:44 Total gas age = 32.53 :I: 024 Ma Plateau age (steps 7-11) = 32.16 :1: 0.20 Ma (for 78.2 percent of the gas produced during heating) Isochron age (steps 7-11) = 31.42 i 0.30 Ma; (4°Ar/3‘Ar)I = 309 i 6 44 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona Table 3. 40Ar/SSAr data for volcanic rocks of the central Chiricahua Mountains.1/—Continued Temp 40ArR2/ 39ArK2/ 40ArR/39ArK2/ 39Ar/37Ar2’3/ %40AI'R %39Ar Apparent age4’5/ (° C) (Ma 31:10) Pre-Turkey Creek caldera rocks (continued) Sample: P272C, sanidine from rhyolite lava of Cave Creek, J = 0.007213, sample wt = 64 mg Latitude: 31 °53’26”N. Longitude: 109°09’25”W. 500 .48519 .19673 2.466 26.6 16.6 3.7 31.81 i- .20 600 .37072 .16771 2.211 28.0 53.4 3.1. 28.54 3: .52 700 .89466 .41087 2.177 31.0 91.2 7.7 28.11 :t .25 800 1.4218 .65488 2.171 32.4 93.5 12.2 28.03 i .13 850 1.1244 .51623 2.178 32.8 93.6 9.6 28.12 i .15 900 ' 1.1607 .53686 2.162 32.8 94.2 10.0 27.92 i .14 950 1.8113 .83389 2.172 29.8 94.9 15.6 28.05 i: .11 1000 2.3568 1.07976 2.183 32.5 95.4 20.2 28.18 a: .11 1050 1.63749 .74934 2.185 29.8 94.1 14.0 28.21 3c .10 1150 .36169 .16462 2.197 16.9 74.5 3.1 28.4 :1: .5 1250 .09375 .04219 2.22 18.7 38.7 .8 29. :1: 2 Total gas age = 28.26 i 0.17 Ma Plateau age (steps 3-9) = 28.10 i 0.12 Ma (for 89.3 percent of the gas produced during heating) Sample: P475, sanidine from rhyolite of Erickson Ridge, J = 0.007303, sample wt = 62.9 mg Latitude: 32 ° 00 ’25 ”N. Longitude: 109 " 21 ’5 8 ”W. 850 .0411 .0240 1.713 — 33.4 .5 22.4 i 1.0 950 .21870 .09741 2.245 ————— 62.3 1.9 29.34 :t .27 1050 .65125 .30046 2.168 ----- 79.9 5.8 28.33 i .22 1150 1.6564 .77897 2.126 ----- 96.1 15.1 27.80 i. .05 1200 1.5143 .71406 2.121 ————— 96.5 13.8 27.72‘i .05 1250 1.4656 .68496 2.140 ----- 97.1 13.2 27.97 i: .12 1300 1.7516 .81850 2.140 —-— 97.4 15.8 27.98 i: .05 1350 1.3516 .63031 2.144 ----- 97.1 12.2 28.03 i .18 1400 .92589 .43440 2.131 ----- 94.3 8.4 27.86 :t .09 1450 .54858 .25939 2.115 ————— 88.3 5.0 27.65 i .15 1500 .55001 .25775 2.134 ----- 83.7 5.0 27.90 i .24 1550 .32143 .14595 2.202 ----- 75.8 2.8 28.78 i .45 1600 .0533 .0217 2.46 ----- 47.5 .4 32. i 3 1700 0281 .0058 4.86 ----- 30.1 .1 63. i 5 Total gas age. = 27.99 i 0.13 Ma Plateau age (steps 4-9) = 27.89 i 0.09 Ma (for 78.5 percent of the gas produced during heating) Sample: P475, biotite from rhyolite of Erickson Ridge, J = 0.007249, sample wt = 58.3 mg Latitude: 32°00’25 ”N. Longitude: 109°21 ’58”W. 800 .02905 .03459 .840 ----- 11.7 .8 11.0 i .6 900 .13580 .07591 1.789 ----- 20.6 1.7 23.2 i .5 950 .14824 .06720 2.206 -—-—- 50.9 1.5 28.6 i .7 1000 .2074] .08754 2.369 ----- 71.8 1.9 30.7 i .6 1050 .32763 .14511 2.258 ----- 77.9 3.2 29.29 i .06 1100 .39272 .17782 2.209 ----- 80.1 3.9 28.65 :1: 16 1150 .35925 .16389 2.192 -—-- 81.3 3.6 28.44 i 33 1200 .22424 .10199 2.199 ---- 77.6 2.2 28.53 i 31 1250 .67488 .30564 2.208 ---— 83.9 -6.7 28.65 3: O9 1300 .93652 .42528 2.202 —-- 83.6 9.3 28.57 i 12 1350 1.3938 .63856 2.183 -— 839 13.9 28.32 i 09 1400 1.8408 .84285 2.184 —— 84.0 18.4 28.34 3: 05 1450 1.5656 .72078 2.172 ----- 84.6 15.7 28.18 i 07 1550 1.7362 .80073 2.168 ----— 86.8 17.5 28.13 i 11 Total gas age = 28.20 i 0.14 Ma Plateau age (steps 11-14) = 28.24 i 0.08 Ma (for 65.5 percent of the gas produced during heating) Sample: P652, sanidine from Jesse James Canyon Tuff, J = 0.007264, sample wt = 69.2 mg Latitude: 31 °45’05”N. Longitude: 109°23’51 ”W. 850 .0105 .0037 2.86 ----- 38.8 .1 37. :t 15 950 .07493 .03117 2.404 ----- 89.2 .5 31.2 1‘ 1.4 1050 .28184 .12708 2.218 —--- 54.7 2.2 28.8 i— .4 1150 .61017 .28434 2.146 ----- 96.6 5.0 27.90 i .10 1250 1.3877 .65676 2.113 ----- 97.4 11.5 27.483: .04 1300 1.2584 .59257 2.124 ----- 97.4 10.4 27.62 :1: .10 1350 1.5180 .71705 2.117 ----- 97.5 12.6 27.53 i .04 1400 1.5856 .74795 2.120 ----- 98.7 13.1 27.57 i .09 1450 1.6488 .7815] 2.110 ----- 98.1 13.7 27.44i .04 1500 1.7162 .80214 2.139 ----- 98.9 14.1 27.82 i .05 1550 1.7090 .77221 2.213 ----- 98.5 13.5 28.77 i .04 1650 .41918 .18834 2.226 ----- 96.6 3.3 28.93 i .12 Total gas age = 27.85 i 0.08 Ma Plateau age (steps 5-9) = 27.52 i 0.06Mn (for 61.3 percent of the gas produced during heating) Geochronology 45 . . . . 1 . Table 3. 40Ar/39Ar data for volcanic rocks of the central Chlrlcahua Mountains. /—Contlnued Temp 40Ar 2/ 39Ar 2/ 40Ar I39Ar 2/ 39Ar/37Ar2’3/ %40Ar %39Ar Apparentage4’ R K R K R 1° C) (Ma 31:10) Pre-Turkey Creek caldera rocks (continued) Sample: 201771, sanidine from Jesse James Canyon Tuff, J = 0.007077, sample wt = 82.3 mg 5/ Latitude: 32 ° 00’44 ”N. Longitude: 109 °21’43 ”W. 950 .11532 .0539 2.138 ----- 91.1 .8 27.1 d: 1.0 1050 .53649 .23574 2.276 ----- 69.7 3.7 28.82 i .13 1150 1.1595 .53383 2.172 ---- 98.1 8.3 27.52 i .06 1200 .87493 .40236 2.174 —— 98.3 6.3 27.55 :1: .08 1250 1.0288 .47172 2.181 ——-- 98.2 7.4 27.63 :h .10 1300 .49043 .22584 2.172 ----- 95.6 3.5 27.51 3: .04 1350 18168 .83416 2.178 ————— 98.3 13.0 27.59 i .04 1400 2.4049 1.10278 2.181 ----- 98.6 17.2 27.63 i .04 1450 2.0050 .91537 2.190 -—— 98.9 14.3 27.75 :t .08 1500 1.7793 .81286 2.189 ----- 98.7 12.7 27.73 :1: .05 1600 1.7859 .81488 2.192 ----- 98.2 12.7 27.77 i .04 Total gas age = 27.69 i 0.07 Ma . . Plateau age (steps 3-8) = 27.59 i 0.06 Ma (for 55.8 percent of the gas produced during heating) Isochron age (steps 3-11) = 2763 d: 0.04 Ma; (4°ArF Ar)I = 270 i 10 Sample: 202156, biotite from tuff of Horseshoe Canyon, .1 = 0.007167, sample wt = 36.1 mg Latitude: 31 °48’41 ”N. Longitude: 109°07’54”W. 800 .0011 .0047 .24 ----- 1.3 .1 3. :1: 13 900 .0959 .0523 1.833 ----- 27.9 1.3 23.6 :t .6 1000 .2390 .1209 1.976 ----- 39.3 3.0 25.4 :1: .8 1050 .2098 .09215 2.276 ----- 74.6 2.3 29.3 i .7 1100 .39023 .17880 2.183 ----- 81.5 4.4 28.08 :t .23 1150 .56686 .26338 2.152 ----- 85.4 6.6 27.69 :t: .13 1200 .87541 .40572 2.158 ----- 88.0 10.1 27.76 t .05 1250 .78254 .36346 2.153 --——— 89.0 9.0 27.70 i .08 1300 .76022 .35360 2.150 ----- 86.3 8.8 27.66 i .05 1350 .89572 .41781 2.144 -—— 80.9 10.4 27.58 i .22 1400 .63020 .29524 2.134 -—- 70.8 7.3 27.46 i .15 1450 .63368 .29453 2.151 ----- 63.5 7.3 27.68 i .04 1600 2.5219 1.1780 2.141 ----- 69.7 29.3 27.55 i .04 Total gas age : 27.53 i: 0.26 Ma Plateau age (steps 6-13) = 27.62 :t 0.10Ma (for 88.8 percent of the gas produced during heating) Sample: 202156, sanidine from tuff of Horseshoe Canyon, J = 0.007283, sample wt = 64.1 mg Latitude: 31 °48’41”N. Longitude: 109°07’54”W. 750 .00577 .00112 5.163 ————— 8.1 0.0 67. :1: 13 850 .47512 .29321 1.620 ----- 44.1 4.7 21.16 i .10 950 1.5388 .85001 1.810 ----- 84.9 13.6 23.63 :t .07 1000 .58965 .30905 1.908 ----- 74.2 5.0 24.90 i: .08 1050 .74928 .39174 1.913 —-- 87.4 6.3 24.96 i .07 1100 .66632 .34314 1.942 ----- 86.6 5.5 25.33 i .13 1150 .58334 .29686 1.965 ----- 82.3 4.8 25.64 i .14 1200 .56378 .28303 1.992 --- 75.1 4.5 25.98 1‘ .05 1250 .62872 .31318 2.008 ----- 67.7 5.0 26.18 i .24 1300 .69119 .34868 1.982 -— 62.2 5.6 25.86 :1: .24 1350 .71097 .36393 1.954 ----- 62.6 5.8 25.49 i .15 1400 .64823 .33239 1.950 ----- 63.5 5.3 25.44 i .16 1450 .64756 .33920 1.909 ----- 63.6 5.4 24.91 i .21 1500 1.4407 .74702 1.929 ----- 64.3 12.0 25.16 i .08 1600 1.9596 1.0212 1.919 —— 56.4 16.4 25.04 i .05 No lateau; total gas age =24.91 :t 0.11 Ma Isoc ron age (steps 4-15) = 25.42 i 0.10 Ma; (”Ar/"A0I = 297 i 4 Sample: 202154, sanidine from dacite sill of Darnell Peak, .1 = 0.00722, sample wt = 112.5 mg Latitude: 31 °48’32”N. Longitude: 109°08’09”W. 850 .02393 .04891 .489 ----- 8.2 .6 6.36 i .09 900 .33132 .18543 1.787 ----- 59.2 2.2 23.12 :t .24 950 .69998 .33580 2.085 ----- 91.5 4.0 26.95 :t .11 1000 .88721 .41841 2.120 ----- 78.4 5.0 27.41i .08 1050 .91390 .43340 2.109 — 95.5 5.1 27.26 :1: .08 1100 1.0905 .51235 2.128 ----- 97.3 6.1 27.51% .04 1150 1.3174 .61978 2.126 ———-- 97.6 7.3 27.48 :1: .04 1200 1.8225 .85116 2.141 ----- 98.5 10.1 27.68 i .04 1250 2.5723 1.2051 2.135 ----- 98.3 14.3 27.59 i .10 1300 1.6878 .79301 2.128 ----- 97.6 9.4 27.51 i .10 1350 1.4432 .6806] 2.121 ----- 96.8 8.1 27.41 i .04 1400 11857 .55472 2.137 —— 96.7 6.6 27.63 i .08 1450 1.0973 .50892 2.156 ----- 90.0 6.0 27.87 i .22 1500 1.8167 .85044 2.136 ----- 69.2 10.1 27.61 i .04 1550 .86657 .39935 2.170 ----- 47.4 4.7 28.04 i .11 1650 .09555 .04191 2.280 ----- 21.3 .5 29.46 :‘c .29 Total gas age = 27.34 i 0.09 Ma Plateau age (steps 6-14) = 27.58 i 0.08 Ma (for 77.9 percent of the gas produced during heating) 46 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona . . . . 1/ . Table 3. 40Ar/39Ar data for volcanic rocks of the central Chmcahua Mountains. —Contmued Temp 40Ar 2/ 39Ar 2/ 4'oAr I39Ar 2/ 39Ar/37Ar2’3/ %40Ar %39Ar Apparent age4' R K R K R 1° C) (Ma at: 10) Rocks associated with the Turkey Creek caldera 5/ Sample: 201769, sanidine from Rhyolite Canyon Tuff—lower member, J = 0.007129, sample wt = 70.3 mg Latitude: 32 ° 00 ’46 ”N. Longitude: 109 ° 21 ’46 ”W. 500 .2131 .1072 1.99 50.6 18.0 1.1 25.4 :t .5 600 .55956 .25904 2.160 81.5 58.1 2.7 27.6 :1: .3 700 1.9368 .91631 2.114 101 94.5 9.4 26.98 i .09 800 4.2472 2.0124 2.110 100 97.8 20.7 26.94 i .09 850 3.4981 1.6581 2.110 100 98.0 17.0 26.93 :t .08 900 2.7618 1.3112 2.106 103 97.6 13.5 26.89 i .09 950 2.6773 1.2700 2.108 100 88.9 13.0 26.91 i .13 1000 1.5311 .7292 2.100 89.9 92.6 7.5 26.81 i .26 1050 1.9843 .93790 2.116 94.7 95.8 9.6 27.01 i .11 1150 .93865 .43976 2.134 89.4 91.0 4.5 27.24 i .15 1300 .16154 .07816 2.067 39.4 60.2 .8 26.4 :1: 1.1 1375 .0330 .0158 2.09 6.37 20.5 .2 27. :t 7 Total gas age = 26.94 i 0.13 Ma Plateau age (steps 3-9) = 26.93 :t 0.12Ma (for 90.8 percent of the gas produced during heating) Sample: 201769, sanidine from Rhyolite Canyon Tuff—lower member, I = 0.007172, sample wt = 55.1 mg Latitude: 32 ° 00’46 ”N. Longitude: 109 °21’46”W. 850 .00011 .00249 0.04 ----- 0.0 .1 0. :1: 16 950 .11672 .06072 1.922 ----- 61.6 1.4 24.7 i 1.2 1050 .58507 .27485 2.129 ---—- 75.1 6.4 27.33 i .04 1100 .76053 .35883 2.119 ————— 97.8 8.3 27.22 i .12 1150 1.2153 .57467 2.115 ——- 99.2 13.3 27.16i .09 1200 1.2029 .57099 2.107 --- 98.3 13.2 27.05 :t .04 1250 .71461 .34090 2.096 ----- 97.3 7.9 26.92 i .17 1300 1.0934 .52163 2.096 ----- 98.2 12.1 26.92 i .09 1350 1.0051 .47980 2.095 ————— 98.2 11.1 26.90 i .06 1400 .78775 .37560 2.097 -——-- 96.8 8.7 26.93 i .10 1450 .72266 .34272 2.109 «~— 87.3 7.9 27.08 i .08 1500 .51758 .24488 2.114 —--- 89.5 5.7 27.14i .12 1600 .38239 17886 2.138 ----- 91.6 4.1 27.4 i .5 Total gas age = 27.02 i 056 Ma Plateau age (steps 6—1 1) = 26.97 i 0.09 Ma (for 60.8 percent of the gas produced during heating) Isochron age (steps 3-13) = 27.09 i 0.04 Ma; (“"Ar/35Ar)I = 309 d: 2 Sample: DY91-36, sanidine from Rhyolite Canyon Tuff—middle member, I = 0.007115, sample wt = 64.7 mg Latitude: 31 °44’24”N. Longitude: 109°24’06”W. 850 .0069 .0023 3.07 ----- 11.8 .1 39. :1: 5 950 .11256 .05347 2.105 ----- 82.6 1.2 26.8 i 1.1 1050 .43294 .20245 2.139 ----- 71.5 4.6 27.24 d: .18 1150 1.0974 .51373 2.136 ---- 98.7 11.8 27.21 i .07 1200 1.1427 .53753 2.126 --—-- 98.5 12.3 27.08 d: .06 1250 1.4135 .66592 2.123 ----- 98.2 15.2 27.04 :1: .10 1300 1.2435 .58527 2.125 ----- 98.9 13.4 27.07 d: .04 1350 1.1975 .56797 2.108 ---— 98.2 13.0 26.86 :1: .15 1400 1.1498 .54152 2.123 --— 98.7 12.4 27.05 i .14 1450 .80364 .37822 2.125 — 97.9 8.7 27.07 i .17 1500 .49925 .23369 2.136 -— 95.7 5.4 27.22 :t .06 1600 .18656 .08566 2.178 ----- 89.9 2.0 27.7 :1: .6 Total gas age = 27.09 :t 0.12 Ma Plateau age (steps 5-10) = 27.03 i 0.11 Ma (for 75.0 percent of the gas produced during heating) Isochron age (steps 2—1 1) 1 27.14 :t 0.03 Ma; (”Ar/”A0I = 297 :1: 2 Sample: 201765, sanidine from Rhyolite Canyon Tuff-upper member, J = 0.007111, sample wt = 61.3 mg Latitude: 32 ”00’47 ”N. Longitude: 109° 19’23”W. 500 .11023 .05888 1.87 31.9 6.5 .7 23.9 i1.2 600 .25977 .12202 2.129 44.2 36.0 1.5 27.1 d: .4 700 1.1332 .53671 2.111 54.0 82.9 6.5 26.88i .19 800 2.0009 .94622 2.115 54.5 92.6 11.4 26.93i .14 850 2.0728 .97892 2.117 53.5 94.5 11.8 26.963: .13 900 1.9046 .89961 2.117 53.1 95.3 10.8 26.96i .14 950 2.4967 1.1773 2.121 53.7 95.4 14.2 27.00i .08 1000 2.4659 1.1679 2.111 56.0 95.6 14.1 26.88i .08 1050 3.6877 1.7425 2.116 57.2 96.6 21.0 26.951 .07 1150 1.2413 .58051 2.138 53.7 91.2 7.0 27.22:t .15 1250 .17004 .08177 2.08 32.8 57.5 1.0 26.5 i .8 Total gas age = 26.94 i 0.13 Ma Plateau age (steps 3-9) = 26.94 i 0.12 Ma (for 89.8 percent of the gas produced during heating) Geochronology Table 3. 40Ar/39Ar data for volcanic rocks of the central Chiricahua Mountains.1/—Continued Temp 40ArR2/ 39 Aerl 40ArR/39ArKZ/ 39Ar/37Ar2’3/ "/u40ArR %39Ar Apparent age 4,5/ 1° C) (Ma at: 10) Rocks associated with the Turkey Creek caldera (continued) Sample: 201765, sanidine from Rhyolite Canyon Tuff-upper member, I = 0.007262, sample wt = 110.8 mg .92 .13 .14 .13 .13 .08 .08 .12 Latitude: 32°00’47”N. Longitude: 109°19’23”W. 850 .0304 .0155 1.96 ----- 10.7 .2 26. i 4 950 .11556 .05246 2.203 —— 38.1 .7 28.63 i 1050 .36519 .17234 2.119 --- 62.5 2.2 27.55 :1: 1150 .99459 .47862 2.078 ----- 90.7 6.1 27.02 i 1200 1.0537 .51024 2.065 ‘ ----- 94.1 6.5 26.85 :1: 1250 1.3150 .63665 2.065 ----- 95.0 8.1 26.86 :1: 1300 1.5626 .76140 2.052 —— 95.8 9.7 26.69 :I: 1350 2.4327 1.1826 2.057 ----- 97.5 15.0 26.75 :1: 1400 2.3763 1.1468 2.072 ----- 98.4 14.5 26.94 :1: 1450 2.4623 1.1872 2.074 ----- 98.4 15.0 26.97 :1: 1500 1.7665 .84937 2.080 ----- 97.7 10.8 27.04 :t 1550 1.4424 .69194 2.085 ----- 96.8 8.8 27.10 :1: 1650 .42926 .20363 2.108 ----- 86.9 2.6 27.41 :1: No plateau; total gas age = 26.94 i 0.08 Ma Isochron age (steps 3-13) = 26.98 :t 0.04 Ma; (“Ar/”Ar)I = 309 i: 2 Sample: P3, sanidine from dacite porphyry lava, J = 0.007123, sample wt = 67.1 mg Latitude: 31 °55’31 ”N. Longitude: 109° 16’22 ”W. 500 .10369 .04227 2.453 4.76 5.9 .5 31.2 a: 600 .51280 .22671 2.262 .96 40.7 2.6 28.8 i 700 .87410 .4022] 2.173 4.90 76.9 4.7 27.7 :1: 800 1.38411 .64407 2.149 11.3 85.4 7.5 27.41 :I: 850 1.57630 .73405 2.147 14.6 81.9 8.5 27.38 :I: 900 1.39669 .65051 2.147 18.9 79.1 7.6 27.38 :1: 950 2.53404 1.19844 2.114 21.7 72.9 13.9 26.97 :1: 1000 3.07313 1.44533 2.126 19.4 74.9 16.8 27.12 i 1050 4.92151 2.27911 2.159 23.1 78.3 26.5 27.54:}: 1150 1.93264 .82957 2.330 17.8 65.4 9.6 29.69 i: 1250 .36452 .15550 2.344 7.66 43.0 1.8 29.9 i . Total gas age = 27.66 :1: 0.12,Ma; no plateau Tmin = 26.97 :t 0.13 Ma (950°C); isochron age = 27.44 i 0.15 Ma; (”Ar/“A10, = 303 i 2 Sample: 201587, sanidine from dacite porphyry of resurgent intrusion, J = 0.00712, sample wt = 82.9 mg Latitude: 31 °52’25”N. Longitude: 109°22’04”W. 500 .57035 .27009 2.112 20.2 25.1 2.2 26.9 i 600 .79823 .37890 2.107 14.9 60.0 3.1 26.86 3: . 700 1.8771 .89706 2.092 14.7 85.0 7.3 26.68 :1: . 800 3.1487 1.49111 2.112 22.4 92.6 12.1 26.92zt . 850 2.1960 1.03651 2.119 27.4 92.0 8.4 27.01 :t . 900 1.33200 .62910 2.117 26.0 91.4 5.1 26.99 i . 950 2.4557 1.17063 2.098 20.3 87.6 9.5 26.75 :1: 1000 4.0596 1.94263 2.090 18.0 85.8 15.7 26.64 i . 1050 8.4536 4.01325 2.106 19.8 88.3 32.5 26.86 i . 1150 .8959 .42092 2.129 8.03 78.7 3.4 27.13 i . 1250 .23026 .10900 2.112 5.83 63.8 9 26.9 3‘: No plateau; total gas age = 26.84 :1: 0.09 Ma; weighted mean age (all steps) = 26.84 :1: 0.17 Ma Isochron age (all steps) = 26.90 :t 0.04 Ma; (4°Ar/ 6Ar)I = 296 i 2 Sample: 201996, sanidine from biotite rhyolite lava, J = 0.007278, sample wt = 121.7 mg Latitude: 31 ”56’19”N. Longitude: 109°19’15”W. 850 .16089 .08517 1.889 ----- 20.4 .7 24.63 i . 950 .28121 .13080 2.150 ----- 52.1 1.1 28.01 i 1050 .70405 .33878 2.078 ----- 75.6 3.0 27.08 i . 1100 .86402 .42025 2.056 ----- 91.1 3.7 26.79 :t . 1150 1.0600 .5218 2.031 —--- 93.6 4.6 26.47 i: . 1200 1.8335 .8949 2.049 ----- 96.4 7.8 26.70 i . 1250 2.5035 1.2220 2.049 —--—— 96.3 10.7 26.70 i . 1300 2.7035 1.3184 2.051 ----- 97.2 11.5 26.72 i . 1350 2.37204 1.1574 2.050 ————— 97.0 10.1 26.71 i . 1400 2.9481 1.4344 2.055 ————— 96.1 12.5 26.78 :1: . 1450 3.0153 1.4667 2.056 —--- 96.8 12.8 26.79 i . 1500 3.4135 1.6531 2.065 ----- 97.6 14.5 26.91 i . 1550 1.3569 .65337 2.077 ————— 94.7 5.7 27.06 i 1650 .29582 .13464 2.197 ----- 73.2 1.2 28.62 i . Total gas age = 26.80 :I: 0.06 Ma Plateau age (steps 6-11) = 26.74 i 0.05 Ma (for 65.6 percent of the gas produced during heating) 47 48 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona Table 3. 40Ar/agAr data for volcanic rocks of the central Chiricahua Mountains.”—Continued Temp 40ArR2/ 39ArK2/ 40ArR/39ArK2/ 39A r/r37A 2 ’3/ %40ArR %39Ar Apparent age4’5/ (° C) (Ma atzlo) Rocks associated with the Turkey Creek caldera (continued) Sample: 201996, b10111: from biotite rhyolite lava, J = 0.007148, sample wt = 74.4 mg Latitude: 31 ° 56’19 ”N. Longitude: 1‘09 °19’15 ”W. 800 .0684 .0265 2.587 -— 26.1 .4 33. 32 900 .0999 .0340 2.935 ————— 42.9 .6 37.4 31.0 950 .0328 .0167 1.967 ----- 13.8 .3 25. 3: 3 1000 .0546 .0207 2.640 ----- 20.9 .3 33.7 3 .6 1050 .0946 .04424 2.139 ----- 62.2 .7 27. 3 2 1100 .27051 .12193 2.219 ----- 84.3 2.0 28.383 .10 1150 .42437 .19741 2.150 ---- 89.7 3.3 27.513 .13 1200 .60224 .28319 2.127 ----- 92.3 47 27.223 .09 1250 .95712 .45167 2.119 ----- 94.0 7.5 27.123 .08 1300 1.5605 .74214 2.103 ----- 93.7 12.3 26.913: .06 1350 1. 8422 .87078 2.116 13.6 91.3 14.5 27.083 .04 1400 2.9339 1.3834 2.121 83.3 87.8 23.0 27.143 .04 1450 2.2666 1.0724 2.114 33.1 85.9 17.8 27.053: .04 1550 1.5975 .75188 2.125 ----- 91.4 12.5 27.193 .10 Total gas age‘ - 27.24 3: 0.10 Ma Plateau age (steps 11— —=14) 27.11 i 0.06 Ma (for 67. 8 percent of the gas produced during heating) Isochron age= 26. 71 3: 0.02 Ma; (4°Ar/36Ar)* * 366 i 15 Sample: 201538, sanidine from rhyolite moat lava—unit 1,1=0. 007168 sample wt= 51. 6 mg Latitude. 31° 52’24”N. Longitude. 109° 17’ 00”W. 500 .33103 .15699 2.109 7.95 9.6 2. 2 27.1 3: .6 600 .46422 .22179 2.093 13.5 40.6 3 1 26.9 3 .3 700 1.2383 .58556 2.115 19.1 80.1 8.2 27.143 .14 800 1.7972 .85737 2.096 23.3 90.2 12.1 26.90 3 .12 850 1.8730 .88533 2.116 23.0 90.8 12.4 27.15 3: .16 900 1.2565 .60105 2.090 23.7 90.8 8.5 26.83 3: .09 950 2.0174 .97148 2.077 21.6 92.1 13. 7 26.66 3 .10 1000 2.6956 1.2888 2.091 23.7 95.0 18.1 26.84 3 .10 1050 2.4154 1. 1484 2.103 23.3 94.0 16.1 26. 99 3 .09 1150 .73347 .34740 2.111 23.6 62.6 4. 9 27.10 3 .12 1300 .1081.0477 2. 266 20. 9 25.0 .7 29. 3 2 No plateau, total gas age* — 26.94 i 0.13 Ma; weighted mean age (steps 1-10)* 26.93 3 0.17 Ma Isochron age = 26.89 3: 0.05 Ma; (4°Ar/35A1')I* 298 3 1 Sample. 201580 sanidine from rhyolite moat lava— unit 1 1* 0.007143, sample wt= 57. 3 mg Latitude. 31° 55’20”N. Longitude. 109° 17’ 39”W. 500 .27438 .11329 2. 422 7.96 13.6 1.7 30.94 3 .47 600 .47682 .23254 2.050 6.49 52.9 3.4 26.233 .22 700 1.2090 .58283 2.074 8.11 90.6 8.6 26.53 3 .08 800 1.5634 .74834 2.089 11.0 94.3 11.1 26.723 .15 850 1.8546 .88798 2.089 14.0 95.8 13.2 26.713 .09 900 1.5144 .73336 2.065 15.8 94.9 10.9 26.41 3 .23 950 1.5239 .73312 2.079 15.2 94.2 10.9 26.593 .12 1000 2.9275 1.40960 2.077 16.2 96.5 20.9 26.57 3 .08 1050 2.0132 .96080 2.095 16.3 95.1 14.2 26. 80 3 .09 1150 .63156 .30099 2.098 8.83 83.8 4.5 26. 84 3.18 1250 .0872 .0432 2.02 5.28 40.7 .6 26.3 1 Total gas age = 26.69 3 0.13 Ma Plateau age (steps 3-10) : 26.64 i 0.13 Ma (for 94.2 percent of the gas produced during heating) Post-Turkey Creek caldera rocks Sample: DY92- 54, sanidine from rhyolite lava, J= 0.007237, sample wt= 74. 5 mg Latitude. 31°42’10”N. Longitude. 109° 29’ 50”W. 850 0134 .0056 2. 40 -—- 14. 7 .1 31. 3 6 950 .13173 .06109 2.156 ---- 89.0 .9 28. 3 2 1050 .57428 .27719 2.072 ----- 74.8 4.2 26.853 .18 1150 1.0428 .51297 2.033 ----- 97.6 7.8 26.353 .05 1200 1.0567 .52834 2.000 ----- 96.8 8.0 25.923 .07 1250 1.1476 .56920 2.016 ----- 97.7 8.6 26.133 .18 1300 1.6901 .83390 2.027 —- 98.5 12.7 26.273 .09 1350 1.5556 .76632 2.030 -—- 99.0 11.6 26.313 .04 1400 1.3902 .68581 2.027 -——-— 988 10.4 26.273: .10 1450 1.2786 .62862 2.034 ----- 98.9 9.6 26363 .10 1500 1.4064 .68959 2.039 ----- 98.9 10.5 26.433: .06 1550 1.4905 .73136 2.038 ----- 98.2 11.1 26.413 .06 1650 .60600 .29152 2.079 --—-- 95.7 4.4 26.943 .06 Total’gas age = 26.36 3 0.10 Ma Plateau age (steps 7-12) = 26.35 i 0.08 Ma (for 65.9 percent of the gas produced during heating) Geochronology 49 Table 3. 40Ar/39Ar data for volcanic rocks of the central Chiricahua Mountains.”—Continued Temp 40ArR2/ 39ArK2/ 40ArR/39ArK2/ 39Ar/37Ar2’3/ %40ArR u/L139Ar Apparent age4’5/ (° C) (Ma attic) Post-Turkey Creek caldera rocks (continued) Sample: 202151, sanidine from rhyolite lava of Dobson Peak, J = 0.00702, sample wt = 66.7 mg Latitude: 31 °46’36”N. Longitude: 109 °13’51”W. 850 .0016 .0037 .42 -—— 2.8 .1 5. a: 8 950 .28556 .14540 1.964 ----- 82.7 2.2 24.702t .36 1050 .87253 .42507 2.053 >-— 77.4 6.4 25.81i .18 1150 1.1464 .55507 2.065 -—- 97.0 8.4 25.971 .06 1200 1.1399 .54601 2.088 ----- 97.7 8.3 26.253: .11 1250 1.2690 .61365 2.068 ----- 97.2 9.3 26.003: .10 1300 1.4994 .71792 2.089 --- 98.3 10.9 26.2621: .10 1350 1.5883 .76260 2.083 —-- 98.8 11.6 26.193: .04 1400 1.6971 .81162 2.091 —-—- 99.1 12.3 26.29i .04 1450 1.4332 .68728 2.085 ----- 98.5 10.4 26.223: .04 1500 1.1885 .56667 2.097 ----- 98.4 8.6 26.37i .06 1600 1.5862 75997 2.087 ----- 95.0 11.5 26.24i .05 Totalgas age=26.13:l:0:10 Ma . _ Plateau age (steps 5-10) = 26.20 :I: 0.07 Ma (for 62.8 percent of the gas produced during heating) Sample: P5, sanidine from rhyolite of Packsaddle Mountain, J = 0.006809, sample wt = 95 mg Latitude: 32°36’45"N. Longitude: 109°25’20”W. 850 .00712 .00282 2.524 5479.74 17.5 0.0 30.74 i 19.84 850 .0071 .0028 2.52 --- 17.5 0.0 31. d: 20 950 .18435 .10729 1.718 --- 62.8 1.4 20.98 :1: .22 1050 .56527 .29758 1.900 ----- 69.4 4.0 23.18 i .07 1150 1.1616 .61307 1.895 ----- 95.0 8.3 23.13 :t .09 1200 .77133 .40730 1.894 ----- 95.1 5.5 23.11i .09 1250 .91981 .48465 1.898 ----- 95.6 6.5 23.16 i .11 1300 .72999 .37911 1.926 ----- 97.2 5.1 23.50 i .10 1350 .80739 .42362 1.906 ---- 97.2 5.7 23.26 i .07 1400 .87471 .45882 1.906 -—-— 97.3 6.2 23.27 3: .06 1450 1.1131 .58198 1.913 --- 97.5 7.8 23.34 i .10 1500 1.7836 .94134 1.895 —-— 96.2 12.7 23.13 at .05 1550 4.6120 2.42217 1.904 ----- 96.9 32.6 23.24 i .04 1600 .48109 .25010 1.924 -—- 91.1 3.4 23.47 i: .06 1650 .12014 .05782 2.08 ---- 87.6 .8 25.3 i .7 Total gas age = 2322 £0.08 Ma ‘ Plateau age (steps 8-12) = 23.23 i 0.06 Ma (for 65.0 percent of the gas produced during heating) U Mineral separates were prepared after crushing, grinding, and sieving by magnetic separator, mica-table, and heavy liquid methods; grains ranged in size between 60 and 120 mesh (250—125 urn). Individual samples ranged in mass from 36 to 416 mg. For irradiation an aluminum canister was loaded with six quartz vials each of which was loaded with samples and standards. Standards were placed between every two unknowns as well as at the top and bottom of each vial. Each standard was degassed to release argon in a single 20-minute long heating step at 1,250°C. Each sample was degassed stepwise in a series of 11 to 16 individual temperature steps for 20 minutes each. All analyses were done in the Argon Laboratory, US. Geological Survey, Denver, Colo. Decay constants are those of Steiger and Jager (1977). The standard for these experiments is hornblende MMhb-l with percent K=1.555, 40Ark =1 .624x10'9 mole/ g, and K—Ar age = 520.4 Ma (Samson and Alexander, 1987). ” Abundances of “Radiogenic 4"Ar” and “K-derived 3S'Ar” are reported in volts measured on a Mass Analyser Products 215 rare—gas mass spectrometer using the Faraday cup. Conversron to moles can be made using a sensitivity of 9.7367(10'13 moles argon per volt of signal. Detection limit at the time of this experiment was 2x10‘17 moles argon. Corrections were made for volume, mass fractionation, trap current, radioactive decay of 37Ar and 39Ar, and interfering A1 isotopes. Mass discrimination in our mass spectrometer is routinely determined by measuring the 40Ar ”Ar ratio of atmospheric argon; our measured value is generally 298.9 the accepted value is 295.5. The calculated discrimination is applied to all samples and standards equally. Production ratios measured on 3pure K1804 and Can salts irradiated with the samples were used to correct for irradiation-produced 40Ar (from K) and Ar (from Ca). Corrections for Cl—derived 36Ar were determined using the method of Roddick (1983). Production ratios determined for samples irradiated in the 1995 TRIGA reactor experiment are: (“Ar /’9Ar)K= 7 .92x1 03, (“Ar /”Ar)K = 1.309x10'2,(37Ar/39Ar)K =1.8X10“, (”Ar /37Ar)Ca = 2.68X10“, (”Ar /37Ar)Cu = 6.85x10“, (”Ar /37Ar)Ca = 4.4x10'5. Production ratios were not directly determined for samples irradiated in 1988', ratios sug ested bv Dalrymple and others (1981) were used. Analytical data for “Radiogenic 40Ar” and “ erived 39Ar” are calculated to five places; “4°ArRF9ArK” is calculated to three decimal places. “Radiogenic 40Ar”, “K—derived 39Ar”, and ‘qurRF9ArK” are rounded to significant figures using associated analytical precisions. Apparent ages and associated errors were calculated from unrounded data and then rounded using associated errors. ----- . Ar below detection; no ratio calculated. 3’To calculate apparent K/Ca ratios, divide 39Ar/37Ar value by 2. 4’ Uncertainties 1n calculations for the apparent date of individual steps for a sample were calculated using the equations ofDalrymple and others (1981). 5’ The reproducibility of split gas fractions from selected monitors was used to calculate im recision in J values This imprecision is generally 0.1%, 1 sigma. J values for each sample were interpolated from a jacent standards. The uncertainty in each apparent age includes the uncertainty in the J value. 50 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona tuff by the latite of Darnell Peak (Bryan, 1988), as described previously, for which our determined age is 26.581008 Ma. The age of the upper member of the tuff of Horseshoe Canyon has not been determined, although its age is bracketed between the ages of the latite of Darnell Peak (27.6 Ma), which intrudes the tuff, and that of the overlying Rhyolite Canyon Tuff (26.9 Ma). Rocks Associated with the Turkey Creek Caldera Several previous attempts have been made to accurately establish the age of the Rhyolite Canyon Tuff. Drewes (1982) summarized earlier K-Ar age determinations reported by Marjaniemi (1969) and Marvin and others (1978) for parts of the Rhyolite Canyon Tuff. These ages range from about 24.7 to 25.6 Ma, and are all young relative to new 40Ar/39Ar data presented herein. McIntosh and Bryan (2000) reported three 40Ar/39Ar ages for the Rhyolite Canyon Tuff and have estab- lished their preferred age as 26.8 Ma. We determined the ages of all three of the volumetrically significant members of out— flow facies Rhyolite Canyon Tuff using the 40Ar/39Ar method. Sample 201769 (fig. 1, locality B) represents the lower member of the Rhyolite Canyon Tuff. Two splits of this sanidine separate, as well as of the sanidine separate that represents the upper member (201765; fig. 1, locality D), were analyzed several years apart in order to evaluate the reproducibility of ages determined during the several years that geochronologic investigations were conducted. Both age spectra for sample 201769 are simple (fig. 115 and T) and give statistically indistinguishable plateau ages of 26.971009 and 26.931012 Ma, respectively. An isochron age for one yields a statistically identical age of 27.091004 Ma with a slightly elevated (‘40Ar/36Ar)I of 30912. The average of the two sani— dine ages, 26.9510.07 Ma, is our preferred age for the lower member of the Rhyolite Canyon Tuff. Sample DY91-36 (fig. 1, locality Q) represents the middle member of the Rhyolite Canyon Tuff. The age spec- trum for sanidine from this sample is simple (fig. 11U) and gives a plateau age of 27.031011 Ma. Sample 201765 (fig. 1, locality D) represents the upper member of the Rhyolite Canyon Tuff; as just indicated, two splits of this separate were analyzed. The age spectrum for one of these splits (fig. 11V) shows some evidence of excess argon, particularly in the low— temperature steps, is broadly U-shaped, and does not include a plateau age; the isochron age for this split is 26.981004 Ma with (40Ar/36Ar)l = 30912, which reflects the presence of minor excess argon. The spectrum for the other split (fig. 11W) is simple and gives a plateau age of 26.941012 Ma. The ages for the two splits are statistically indistinguishable, and their average, 26.961006 Ma, is our preferred age for the upper member of the Rhyolite Canyon Tuff. Data for two splits each of samples 201765 and 201769 indicate that variation of analytical data acquired over several years has not adversely impacted analytical precision or accuracy. The five analyses of sanidine from Rhyolite Canyon Tuff indicate that, within analytical uncertainty, each of the principal ash-flow tuffs that constitute this unit is the same age and that they must have been erupted in rapid succession. The absence of erosional breaks or other significant discontinuities between the mem- bers further indicates rapid eruption and emplacement of these pyroclastic flows. Samples 201587 and P3 (fig. 1, localities J and H, respectively) represent dacite porphyry that was emplaced, after eruption of the Rhyolite Canyon Tuff and caldera col- lapse, as a resurgent intrusion in the core of the Turkey Creek caldera (201587) and as lava flows in the caldera’s evolving moat (P3). The age spectrum for sanidine for sample 201587 is slightly disturbed (fig. 11X) and does not yield a plateau. The weighted average age for this sample is 26.841017 Ma and the isochron age is 26.901004 Ma with (“OAr/36Ar)I = 29612. The age spectrum for sanidine from sample P3 is also disturbed (fig. 11 Y), shows some evidence of excess argon, and does not yield a plateau. The isochron age for this sample is 27.441015 Ma with an elevated (“Ar/“A01 = 30312. The characteristic saddle-shaped spectrum results from the presence of excess argon. Others (Lanphere and Dalrymple, 1976) have interpreted the age of the lowest age step defin- ing the saddle to best represent a maximum age estimate. In this case, the lowest age step in the saddle is 26.971013 Ma, which is indistinguishable from the age of sanidine from sample 201587. Given this assessment of the 40Ar/39Ar data for samples of the dacite porphyry, our best estimate of its age is 26.901004 Ma, which is the isochron age for sample 201587. These age data indicate that dacite porphyry intru- sion and extrusion followed Rhyolite Canyon Tuff eruption and caldera collapse within less than 100,000 years, which is consistent with dacite porphyry lava flows being interbedded with lava-flow-like phase intracaldera Rhyolite Canyon Tuff in exposures north and west of John Long Canyon (fig. 1) (du Bray and others, 1997). Sample 201996 (fig. 1, locality F) represents the biotite rhyolite lava, the first of the rhyolite lavas erupted into the moat of the Turkey Creek caldera; both biotite and sanidine separates were analyzed. The age spectrum for biotite from this sample is relatively simple (fig. 11Z), although it shows some evidence of excess argon in the low-temperature steps. It gives a plateau age of 27.111006 Ma and an isochron age of 26.711002 Ma with a significantly enhanced (4"Ar/36Ar)I of 366115. The plateau age is implausible given the well-defined 26.9 Ma age for Rhyolite Canyon Tuff on which this rhyolite was deposited; the isochron age is more geologically reason- able. The age spectrum for sanidine from this sample is rela— tively simple (fig. llAA), although it also shows some slight evidence of excess argon; it gives a plateau age of 26.741005 Ma. This age is consistent with the 26.9 Ma age for Rhyolite Canyon Tuff and 26.9 Ma dacite porphyry on which the rhyo- lite was deposited. Samples 201538 and 201580 (fig. 1, localities K and G) represent unit 1 rhyolite lava (Tmr’l) in the moat of the Turkey Creek caldera; a sanidine separate from each of these samples was analyzed. The age spectrum for sanidine from sample 201538 is relatively simple (fig. 1138) but is disturbed and does not include a plateau; it gives a weighted-mean age of 269310.17 Ma and an isochron age of 26891005 Ma with (“Ar/“ADI of 29811. This apparent age is slightly too old to be consistent with the 26.7 Ma age of the biotite rhyolite on which unit 1 rhyolite was deposited. The age spectrum for sanidine from sample 201580 is relatively simple (fig. llCC), although it shows some evidence of excess argon in the low-temperature steps; it gives a plateau age of 266410.13 Ma. This age seems slightly too young given that this sample is from the same unit as sample 201538 (26.9 Ma). Averag— ing the isochron age for sample 201538 and the plateau age for sample 201580 yields a preferred age of 26.771012 Ma for unit 1 lava. Given the analytical uncertainties associated with the ages determined for the biotite rhyolite (201967) and the unit 1 lava (201538 and 201580), all of these ages are statistically indistinguishable. Perhaps the best reconciliation of the 40Ar/39Ar data for the rhyolite moat sequence would result in an age estimate of 26.7 Ma for the biotite rhyolite and unit 1 lava. Because units 2 and 3 lavas—the final rhyolite lavas erupted into the moat of the Turkey Creek caldera—are so crystal poor, no mineral separates could be prepared and their ages were not determined. Consequently, the full duration of volcanism associated with the caldera cannot be closely defined. However, the lack of significant erosional/ depositional break indicates that these moat lavas were erupted in a relatively short time interval, perhaps as little as 10,000 years. Post-Turkey Creek Caldera Rocks Sample 202151 (fig. 1, locality N) represents the rhyolite lava of Dobson Peak, which is the youngest volumetrically significant volcanic unit in the central Chiricahua Mountains. The age spectrum for sanidine from this sample is relatively simple (fig. llDD) and gives a plateau age of 262010.07 Ma. Because this unit overlies unit 1 lava, the youngest rocks asso— ciated with Turkey Creek caldera volcanism must be at least this old. Consequently, the full eruptive cycle associated with the Turkey Creek caldera lasted no more than 700,000 years, from 26.9 to no less than 26.2 Ma, and all middle Tertiary vol— canic activity in the central Chiricahua Mountains apparently ended about 26.2 Ma. Concluding Remarks Geochemical and petrographic data presented here show that volcanic units of the central Chiricahua Mountains are individually distinctive. These data can be used to corroborate stratigraphic identifications made with other data; they can also be used to identify units when either available data are insufficient or stratigraphic context is absent or ambiguous. Diagnostic age, petrographic, and geochemical features for Concluding Remarks 51 middle Tertiary volcanic rocks of the region are summarized in table 4. In many cases, macroscopically observable features of the area’s volcanic rocks, including contained phenocrysts (mineral, size, and abundance), textures (degree of welding or flow features), and (or) lithic or pumice content (size, number, and composition), are sufficient to enable rapid, field-based stratigraphic identification. When these features are insuf— ficient, other simple techniques, such as transmitted light microscopy, can in many cases be used to rapidly eliminate uncertainties. When more detailed data are required to deter- mine or confirm stratigraphic identity, geochemical, geochro- nologic, or paleomagnetic data may be necessary. Of these, geochemical data, especially trace-element data obtained by energy-dispersive X—ray fluorescence analysis of rock pow- ders, are probably the least expensive and most rapidly obtain— able type of data for establishing stratigraphic identity. Trace- element data demonstrate that almost all the stratigraphic units of this area have characteristic geochemical features diagnostic of their stratigraphic identity. Combining geochemical data with macroscopically observable rock features results in virtually certain identification of the study area rocks, which enhances correlation of isolated or ambiguous occurrences of these rocks throughout the Boot Heel volcanic field. The preponderant volume of volcanic rock preserved in the range is ash-flow tuff, although a significant volume of lava is present as well. The overwhelmingly dominant source of volcanic rocks preserved in the area is the Turkey Creek caldera. Between 500 and 1,000 km3 of Rhyolite Canyon Tuff is present, and approximately 60 km3 of caldera-related rhyolite lava is preserved in the caldera’s moat. Middle Ter- tiary volcanic activity is dominated by eruption of subalkaline rhyolite, most of which is high silica and weakly peralumi- nous. One of the most diagnostic compositional features of these rocks is their elevated K20 abundances. All of the area’s volcanic rocks are members of either the high-potassium calc- alkaline or the shoshonitic series. Volcanic rock within-strati- graphic—unit compositions are relatively homogeneous. The principal exception to this is among the voluminous outflow facies ash-flow tuffs, in which there is considerable composi- tional inhomogeneity. Sections of the Rhyolite Canyon Tuff and the tuff of Horseshoe Canyon are relatively strongly zoned in ways that appear to reflect progressive, top-down tapping of normally zoned magma reservoirs. Most of the observed zonation is consistent with crystal-liquid fractionation of the phenocryst phases that are characteristic of each unit. Compo- sitional variation within lavas is relatively minor. Compositional variation among eruptive products of the Turkey Creek caldera reflects a contrast between magmatic processes in the reservoirs that produced the normally zoned Rhyolite Canyon Tuff and those that subsequently produced the reversely zoned moat rhyolite sequence. As described in previous sections, geochemical zonation preserved within the principal outflow facies members of the Rhyolite Canyon Tuff is consistent with systematic magma extraction from a normally zoned reservoir. Considering the geochemical characteristics of these rocks, the phenocrysts they contain, 52 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona Table 4. Diagnostic age, petrographic, and geochemical features of middle Tertiary volcanic rocks of the central Chiricahua M o u nta i n s . [ND, not determined] Unit Unit Age, Petrographic Geochemical symbol in Ma characteristics characteristics Pre-Turkey Creek caldera rocks hitermediatc-compo- Tim ND Dark greenish gray. Plagioclase, Dacite with high FeO*, MgO, CaO, TiOZ, sition lava flows. hornblende and pyroxene pheno- P 05, Sr, Ba, Eu, Co, Ni, Cr, and low crysts. Variable crystal content. Rb, Nb, and Ta. Rhyolite tuff of High Th1 34.16i0.17 Quartz and sanidine Xi’henocrysts; Unusually low Zr and N310 relative to Lonesome Canyon. 5 ercent crystals. umice and other study area rhyolite tuffs. 1i 'c poor. Lower member of the Tjg 33.81i0.08 Biotite, feldspar, and quartz Higher FeO*, TiOz, and Ba and lower Nb rhyolite of Joe Glenn phenocrysts; 20-40 percent abundances than other study area rhyo- Ranch. crystal. 11te tuffs. Rhyolite lava of Krentz Tkr ND Aphyric. Massive to flow banded Low Nb abundances relative to other Ranch. aphyric rhyolite lavas. Rhyolite lava of Cave Tc 28.10i0.l2 Aphyric. Massive to flow banded Low Nb abundances relative to other Creek. aphyric rhyolite lavas. Rhyolite of Erickson Tfre 27.89i0.09 Biotite and feldspar phenocrysts; Low-silica rhyolite with high FeO*, CaO, Ridge. 4-10 percent crystals. Sr, Ba and u and low Rb, Ta, Th, and U abundances. Tuff of Horseshoe Canyon The] 27.62fir0.10 Biotite, quartz, and sanidine High K 0, Ba, and Eu and ‘low Zr, Nb, lower member. phenocrysts; 20-35 percent and Th abundances relative to other crystals. study area rhyolite tuffs. Tuft‘ of Horseshoe Canyon T hcu ND Biotite, quartz, and sanidine High A120 , FeO*, TiOZ, P205, Sr, Zr, Ba, upper member. phenocrysts; 10-20 percent Co, Ni, r, Sc, Hf,. and Eu and low crystals. SiOz, Ta, U, and Th abundances rela- tive to other study area rhyolite tuffs. Jesse James Canyon Tjj 27.56i0.04 Biotite, sanidine and quartz Low Fe0*, Zr, Ta, Th, and Hfabundances Tuff. phenocrysts; 10 percent crystals. relative to other rh olite tufls of the study area. Low 21 undances of K20, Rb, Ba, and Eu distinguish it from Thcl. Rocks associated with the Turkey Creek caldera Rhyolite Canyon Tuff Trcb ND Sanidine and quartz phenocrysts; High Zr, Nb, Ta, U, Th, and Hfabundances basal member. 7-35 percent crystals. relative to all study area tuffs. High SiOz, Rb, Nb, and Ta and low TiOz, Zr, Ba, and La abundances relative to Trcu, Trci, and Trcf. High K20 and Rb abundances relative to Trcl and Trcm. Rhyolite Can on Tuff Trcl 26.95i0.07 Sanidine and quartz phenocrysts; High Zr, Nb, Ta, U, Th, and Hf abundances lower mem er. 7-35 percent crystals. relative to all study area tuffs. High SiOZ, Rb, Nb, and Ta and low TiOz, Zr, Ba, and La abundances relative to Trcu, Trci, and Trcf. Rhyolite Canyon Tuff Trcm 27.031011 Sanidine and quartz phenocrysts; High Zr, Nb, Ta, U, Th, and Hfabundances middle member. 785 percent crystals. relative to all study area tuffs. High SiOz, Rb, Nb, and Ta and low TiOZ, Zr, Ba, and and La abundances relative to Trcu, Trcl, and Trcf. Rhyolite Can on Tuff Trcu 26.96i0.06 Sanidine and quartz phenocrysts, High Zr, Nb, Ta, U, Th, and Hfabundances upper mem er. 7-35 percent crystals. relative to all study area tufi‘s. Rhyolite Canyon Tuff Trci ND Sanidine and quartz phenocrysts; High Zr, Nb, Ta, U, Th, and Hf abundances intracaldera facies. 7-35 percent crystals. Red—brown relative to all study area tuffs. color. Abundant lithic fragments. Rhyolite Canyon Tuff Trcf ND Sanidine and quartz phenocrysts; High Zr, Nb, Ta, U, Th, and Hfabundances lava-flow-li e phase. 7-35 percent c stals. Sanidine relative to all study area tuffs. High K20 phenocrysts 0. to 1 cm long. and Rh abundances relative to Trcu and Lacks pumice and lithic fragments. Trci. Dacite porphyry pol 26.97zt0.13 Feldspar phenocrysts 5 mm to High Fe0*, MgO, CaO, TiOz, P205, Ba, Co, lava. 3 cm long. Granophyric to Cr, Ni, Sc, and Eu abundances. glassy groundmass. Concluding Remarks 53 Table 4. Diagnostic age, petrographic, and geochemical features of middle Tertiary volcanic rocks of the central Chiricahua Mountains—Continued [ND, not determined] Unit Unit Age, Petrographic Geochemical symbol in Ma characteristics characteristics Rocks associated with the Turkey Creek caldera (continued) ' Td ' 26.90i0.04 Felds ar henoc sts 5 mm to High FeO*, MgO, CaO, TiOZ, P205, Ba, Co, Dagglelgglrlphyry pl 3 01% loliig. Grgiiophyric Cr, Ni, Sc, and Eu abundances. groundmass. ' T ND S 'd' tals 2 mm to 1 cm Hi h Na 0, Y, Zr, and Nb and low .Sr and Tliirgyte porphyry tp Elgiiglllc crys Bga abuiidances relative to the dacite porphyry. B' t't h It 1 T rb 2674:2005 Biotite henoc sts; 5 to 20 er- Low SiQ , Rb, Nb, Ta, U, Th, and Hf, 10 l c r yo 1 e ava [[1 cent (‘li'ystalsry p and higli FeO*, MgO, CaO, T102 , Sr, Ba, Co, Sc, and Eu abundances. Rb-Sr ratio >1 distinct from that of Tfre. U 't 1 1 Tmrl 267720.12 A h ric. Flow banded Higher Nb abundances than Tc and n1 ava P y Tkr, lower Rb and Nb than Trd . Lower 8102 Nb, Ta, Th and hig er CaO, Ba, and Eu abundances than Tmr2 and Tmr3. Unit 2 lava. Tmr2 ND Aphyric. Flow banded Higher Nb abundances than To and Tkr, lower Rb and Nb than Trdp. Unit 3 lava Tmr3 ND Aphyric. Flow banded. Higher Nb abundances than To and Spherulitic. Tkr, IOWer Rb and Nb than Trdp. Post-Turkey Creek caldera rocks Pyroclastic deposits Ts ND Quartz, sanidine, albite, and bio- No diagnostic geochemical features. and lava flows of tite phenocrysts; >20 ercent Swede Peak. crystals. Abundant 1i ic fragments. Rhyolite lava of 26.20i0.07 Nearly aphyric Higher Rb and Nb abundances than Dobson Peak. Trdp other study area rhyohte lavas. pertinent mineral-melt distribution coefficients, and crystal- liquid fractionation processes, the source reservoir likely had an initial composition similar to that of the geochemically least evolved (last erupted) part of the outflow sequence, and developed more evolved (first erupted) parts. Gradients within the reservoir apparently resulted from crystallization and fractionation of small amounts of clinopyroxene, sanidine, and zircon, the principal phenocrysts characteristic of the reservoir’s least evolved part. In contrast, sequential evolu- tion of the moat rhyolite lavas from initial, relatively primitive rhyolite to final, highly evolved rhyolite appears to have been principally controlled by variable contamination of a homo- geneous reservoir by relatively primitive lithologic contami- nants derived from the reservoir’s roof and walls. In addition, evolution of the first erupted rhyolite (the biotite rhyolite) to the subsequent batch of rhyolite (unit 1 lava) requires fraction- ation of biotite and sanidine, the principal phenocrysts in the biotite rhyolite, to yield a composition like that of unit 1 lava. Subsequently erupted, more evolved, moat rhyolite lava (units 2 and 3 lavas) represents a composition similar to that of unit 1 lava that contains progressively less contamination derived from intermediate-composition lava flows and dacite porphyry that enclosed the source reservoir. New 40Ar/“Ar analyses provide accurate and precise absolute ages for many volcanic rock units whose ages were previously poorly known or unknown. These new ages are useful in stratigraphic correlation both within and beyond the central Chiricahua Mountains. For example, the new age data suggest that the Jesse James Canyon Tuff and the lower member of the tuff of Horseshoe Canyon are part of the same stratigraphic unit. Geochemical data are entirely consistent with this interpretation. The precision and small uncertainties associated with the new ages also help constrain the timing and nature of the volcanologic events that controlled middle Tertiary geologic evolution in this part of the Boot Heel volcanic field. Voluminous volcanic deposits, largely composed of regionally distributed ash-flow tuffs erupted from various caldera sources, were erupted between 34.2 and 26.2 Ma. A distinctive eruptive hiatus between 33.3 and 28.1 Ma indicates that magmatic activity in this region was not continuous but was confined to at least two discrete pulses separated by an approximately 5.2 m.y. hiatus. McIntosh 54 Middle Tertiary Volcanic Rocks, Central Chiricahua Mountains, Arizona and others (1992) defined a hiatus of 3.2 m.y., between 28.9 and 32.1 Ma, within volcanic rocks of the large Mogollon- Datil volcanic field, immediately north of the Boot Heel field. McIntosh and Bryan (2000) defined a hiatus of 5.1 m.y., between 27.6 and 32.7 Ma, for the Boot Heel volcanic field itself; they also indicated that the geographic evolution of the Boot Heel field was time transgressive, with magmatism sweeping from east to west across the field. Magmatism associated with the Turkey Creek caldera, the largest and best preserved volcanic system in the central Chiricahua Mountains, as well as the westernmost and youngest major component of the Boot Heel field, seems to have been confined to a narrow time window. The oldest rocks associated with this system, Rhyolite Canyon Tuff, were erupted 26.9 Ma, whereas the youngest, moat rhyolite lavas, have ages between 26.7 and 26.8 Ma. Consequently, all eruptions from the Turkey Creek caldera occurred in as little as 200,000 years. Volumetrically significant middle Tertiary volcanic activity seems to have ended about 26.2 Ma with eruption of the rhyolite lava of Dobson Peak, the stratigraphically youngest volcanic unit preserved in the study area. The age and composition of these volcanic rocks help to define the large-scale tectonic processes that were active along the western margin of North America and that controlled the geologic evolution of the Boot Heel volcanic field during middle Tertiary time. As suggested by du Bray and Pallister (1991) and substantiated herein, the geochemical characteris- tics of the area’s volcanic rocks indicate genesis that changes from a subduction or arc-related tectonic regime to a within- plate, extensional regime. The transitional geochemistry of these rocks, as well as the periodicity and east-to-west sweep of magmatism within the Boot Heel volcanic field during its 35 to 27 Ma history, may reflect a sequence of events that includes (1) subduction-related magmatism between 35 and 33 Ma, (2) amagmatism related to rapid, low-angle subduc— tion (Coney and Reynolds, 1977) between 33 and 28 Ma, (3) renewed magmatism and the beginning of extensional tecton- ics between about 28 and 27 Ma when subduction diminished or ceased and the downgoing Farallon plate began to founder and became more steeply inclined, and (4) magmatic re-initia- tion in response to restoration of an asthenospheric mantle wedge beneath this region (Coney and Reynolds, 1977; Armstrong and Ward, 1991). 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Manuscript approved for publication October 17, 2003 Published in the Central Region, Denver, Colorado Graphics by authors and Springfield and Springfield Photocomposition by Gayle M, Dumonceaux Edited by LM Carter References Cited 57 ‘1? U.S. GOVERNMENT PRINTING OFFICE: 2004 — 673-053 / 30127 Region N0. 8 * 125 earsnf * scwhce 7 f0; Amenca 187972004 * ISBN D-Ln7—ntr5'fi-7 In II III Illllll |I 9'780607 955590 ® Printed on recycled paper