Modeling Metals Transport and Sediment/Water ...

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KEYWORDS: Metals, TMDLs, mining, modeling, watershed, water quality, restoration, WASP ...... AscI Corporation, Athens, Georgia. Banwart, S.A.; Malmstrom ...
MODELING METALS TMDLS IN A MINING-IMPACTED WATERSHED Brian S. Caruso ENTRIX, Inc. 531 Left Fork Rd. Boulder, Colorado 80302 ABSTRACT Metals transport modeling was performed to help develop metals total maximum daily loads (TMDLs) and evaluate the effects of restoration alternatives on metals transport in a mountain watershed in Montana impacted by hundreds of abandoned hardrock metal mines. These types of watersheds are widespread in Montana and many other areas of the western U.S. Impacts from abandoned hardrock, or metal mines include loadings of sediment, metals, and other pollutants causing impairment of multiple beneficial uses and exceedances of water quality standards. The U.S. Environmental Protection Agency (EPA) Water Quality Analysis Simulation Program (WASP) was used to model and evaluate TMDLs for several heavy metals in Tenmile Creek, a mountain stream supplying drinking water to the City of Helena, Montana. The model was calibrated for baseflow conditions, validated using data collected by EPA and the U.S. Geological Survey, and used to assess existing metals loadings and losses, including interactions between metals in water and bed sediment, uncertainty, water quality standard exceedances, TMDLs, potential source areas, and required reductions in loadings. During baseflow conditions, adits and point sources contribute significant metals loadings to Tenmile Creek. Shallow groundwater and bed sediment also contribute metals loadings in some key locations under these conditions. Adsorption and precipitation onto bed sediments play a primary role in losses from the water column in some areas. Modeling results indicate that some uncertainty exists in the metal partition coefficients associated with sediment, significance of precipitation reactions, and in the specific locations of unidentified sources and losses of metals. Exceedances of standards are widespread throughout the stream under both baseflow and higher flow conditions. TMDLs were evaluated for conservative conditions assuming no reactions in the stream, as well as using the model incorporating the full range of instream processes. In most cases, considerable reductions in loadings are required to achieve TMDLs and water quality standards. The modeling showed that reductions in loadings from point sources, mine waste near watercourses, and streambed sediment can help improve water quality, but alteration of the water supply scheme and increasing baseflow will also be needed to achieve target loadings and concentrations. KEYWORDS: Metals, TMDLs, mining, modeling, watershed, water quality, restoration, WASP INTRODUCTION Increased metals loadings to streams and impairment of water quality from hardrock metal mines is a common problem in mountainous watersheds in the western U.S. Impacts in mined watersheds include elevated dissolved and particulate metals concentrations; enhanced erosion, transport and deposition of exposed sediment, tailings and waste rock; and low pH (EPA, 1997; Caruso & Ward, 1998; Gurrieri, 1998; Malmqvist & Hoffsten, 1999). The significant spatial and temporal variability in hydrology and stream chemistry (Sullivan & Drever, 2001), and geochemical reactions and attenuation of metals in the hyporheic zone (Fuller & Judson, 2000) often complicate the evaluation of loadings, development of TMDLs, and the restoration of these impacted surface waters.

Metals transport modeling is often required in these watersheds as part of evaluation of loadings, development of TMDLs, and restoration planning and design using quantitative information (Nimick & Moore, 1991; Simons et al., 1995). This modeling can greatly aid in understanding contaminant movement and other watershed and receiving stream processes as well as water quality impacts. Modeling metals fate and transport can also greatly assist in assessing uncertainties, data gaps, and the effectiveness of restoration measures (Jenne & Zachara, 1987; Combest, 1991). However, there have been very few studies performed on the application of metals transport modeling to developing metals TMDLs or to evaluation of the effectiveness of restoration strategies in mining-impacted mountain watersheds. Although protocols have been developed by the U.S. Environmental Protection Agency (EPA) for developing sediment, nutrient, and pathogen TMDLs (EPA, 1999a, b, 2001), none have been formally developed for metals. Few models are available for modeling metals transport and the applicability of available models depends on the objectives, scope, and complexity of the modeling required (EPA, 1992; Sanden, 1991; Bouchard et al., 1995; Whitehead & Jeffrey, 1995; Banwart & Malmstrom, 2001; Runkel & Kimball, 2002). Models such as EPA’s Better Assessment Science Integrating Point and Nonpoint Sources (BASINS), Hydrologic Simulation Program Fortran (HSPF), and Enhanced Stream Water Quality Model (QUAL2E) are commonly used for watershed and receiving water quality assessment, but cannot be used for modeling metals transport. Some studies, therefore, have used equilibrium metals speciation models based on detailed pH and geochemical information, including MINTEQ (Pitt et al., 1998), WATEQ (Williams & Smith, 2000) and PHREEQC (Tonkin et al., 2002). Other studies have used stochastic approaches for concentrations or flows based on empirical data (Whitehead & Jeffrey, 1995; Caruso and Wangerud, 2002) or attempted estimating long-term contamination source strength, longevity, and possible future changes in discharge quality (Banwart & Malmstrom, 2000). However, these techniques do not model metals transport in streams directly, which is required for estimating TMDLs. Other have combined some of these models or geochemical data with simple hydrologic/flow models (Sanden, 1991; Broshears, 1996). However, these models are generally not widely available for use in developing TMDLs. The EPA Water Quality Analysis Simulation Program (WASP5) is one of the only models available that can simulate metals transport in receiving streams under steady-state or dynamic conditions. The Upper Tenmile Creek Watershed in Montana is a primary drinking water supply for the City of Helena, but has been severely impacted by hundreds of abandoned metal mines. Metals transport modeling was required to assist in development of metals TMDLs and restoration planning due to impairment of beneficial uses and water quality. The entire watershed has also been designated as a Superfund site. Metals of concern that require TMDLs in surface water include arsenic (As), cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) (CDM, 2001a). The goal of this study was to model metals fate and transport in the Upper Tenmile Creek mainstem under low-flow, steady-state conditions using WASP5 to help develop TMDLs and plan restoration measures. This modeling involved evaluating the effectiveness of restoration alternatives in achieving TMDLs and water quality standards. It was also used for the assessment of associated downstream water quality impacts; understanding of watershed and in-stream transport and fate mechanisms; and uncertainties associated with watershed processes, the model, and input/monitoring data. METHODS Site Description The Upper Tenmile Creek Watershed has an area of 51 km2 and ranges in elevation from 1,335 to 2,485 m at the Continental Divide. It is located primarily in Lewis and Clark County, Montana

(Figure 1). The area is characterized by a continental climate that is modified by Pacific Ocean air masses with warm, dry summers and cold, moist winters. The average annual precipitation is 624.8 mm (24.6 inches), as measured at the nearest weather station at Frohner Meadows. The watershed is in the Northern Rocky Mountain physiographic province, which is characterized by mountainous terrain with high and sharp relief developed from glaciation. This includes cirque basins and moraine deposits, terrace and floodplains, and other glacial features covering more than half of the basin. Large areas of Cretaceous and Tertiary igneous rocks and small areas of Cretaceous sedimentary rocks compose the bedrock. Silver-lead ore bodies, rich in galena and pyrite (FeS2), and disseminated gold ore resulted from mineralization during these two periods. There is a thin veneer of glacial deposits and alluvium in valleys and soils are typically well-draining sandy loams (CDM, 2001a). Ponderosa Pine and Douglas Fir forest are the predominant vegetation types, with some tundra grassland at the highest exposed elevations and willows, grasses, and sedges in lower riparian and wetland areas. From the headwaters to the Helena water treatment plant, the mainstem of the creek is approximately 17 km long. The small town of Rimini is located approximately in the middle of the watershed adjacent to the creek. Similar to many other areas in this part of Montana, the watershed is rich in metal ore deposits associated with the pyrite. Gold, lead, zinc, and copper were mined between 1870 and the 1920s. This resulted in hundreds of abandoned mines and tailings areas throughout the watershed. Severe impairment of water quality and beneficial uses, particularly of aquatic ecosystems, is caused by significant loadings of metals, sediment, and acidity from these areas (CDM, 2001a). WASP5 Model Development The WASP5 model was developed by EPA to simulate surface water quality and 3-dimensional fate and transport of solutes in either the steady-state or dynamic mode (Ambrose et al., 1993). This model was used to simulate the fate and transport of metals in the Upper Tenmile Creek Watershed and to help with developing TMDLs and evaluate the effectiveness of a number of restoration alternatives. TOXI5 is the subcomponent toxics model which is used to estimate metals concentrations in water column or benthic/stream bed compartments. It also simulates interactions between the two types of compartments. Metals fate and transport processes simulated in the model include loadings of point and nonpoint source water and constituents, including from tributaries, groundwater, and runoff; advection, dispersion and diffusion in stream segments; adsorption/desorption associated with sediment; precipitation/dissolution; and sediment transport and settling/scour of particulates (Figure 2). The Upper Tenmile Creek model was developed for steady-state, low-flow conditions based on flows measured along the mainstem during the June 2000 synoptic survey (Figure 3). The model was based on constant metals loadings from tributaries, point sources such as adits, and nonpoint sources. Equilibrium concentrations and loadings of As, Cd, Cu, Pb, and Zn, were calculated using the model, extending over a length of approximately 17 km to the water treatment plant (Figure 4). Fifty-eight segments (length of approximately 300 m each) were used for the water column layer. Six segments were used for the underlying benthic layer. The benthic layer lengths varied from approximately 1,200 to 5,500 m, depending on how many water column segments were above them. A synoptic water quality and flow sampling event was performed by EPA in the watershed under base- or low-flow conditions in June 2000 (flow at outlet was approximately 0.1 m3 s-1). Model calibration was performed using this data because it was the most complete data set available.

Figure 2. Schematic diagram of major metals fate and transport processes in WASP5.

Another survey under higher flow conditions was conducted by the U.S. Geological Survey (USGS) in June 1997 (flow of approximately 3 m3 s-1). Model validation was performed using this data set. During both sampling events considerable metals concentrations and loadings increases were found immediately downstream of the Suzie Load and Lee Mountain Mine area in the mainstem. User-specified headwater and tributary constant inflows were used to compute flows for each stream segment based on data from the two sampling events, including flows in the City of Helena water supply diversions (Figure 4). An apparent groundwater discharge to the stream near the Lee Mountain Mine that was observed during the June 2000 event was also included. Flow and channel measurements (average depths, widths, and velocities) were also made at two stations during the June 2000 event. These were used to define channel hydraulic parameters for the power equations in the model used to estimate the flow hydraulics. The hydraulics were used in the calculations of metals transport velocities and dispersion. Sediment loadings to the stream segments were calculated based on the available data outside of the model. Sediment transport through the mainstem was simulated based on flows and vertical net settling/scour rates estimated outside of WASP5. Vertical sediment transport rates are generally not significant under low-flow, steady-state conditions. However, they are important for modeling particulate metals fate and transport during dynamic storm and snowmelt events.

Figure 3. Flows in Upper Tenmile Creek mainstem for calibrated model (June 2000).

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The user-specified metal loadings were input as tributary flows and concentrations or point source mass loads (Table 1). Advection, diffusion and dispersion in two dimensions were used to model metals transport. A longitudinal dispersion coefficient of 1 m2 s-1 was initially used. Vertical dispersion/diffusion was used to model the transport of dissolved metals between the water and underlying benthic layers. Bed sediment metals concentrations measured during June 2000 were also used as initial values (Table 2). Exchange coefficients and mixing lengths specified by the user were used to model diffusion/dispersion. To simulate the distribution of metals between the dissolved and particulate (includes both sorbed and precipitated metals) phases, a "lumped" partition coefficient (Kd) was used. Because pH in Tenmile Creek only ranges between 5 and 8.9 (based on June 2000 data), this technique can provide reasonable results. However, in systems where the pH is lower or more variable, the lumped Kd does not provide results that are as useful. In these cases, separate, more detailed geochemical modeling of precipitation reactions should be considered. Spatially-variable partition coefficients were input by the user. Particulate metals transport was modeled based on fluvial sediment transport. Sorption/desorption of metals between the water column and underlying bed sediment was calculated using a dispersion/diffusion coefficient for exchange and partitioning between the dissolved and particulate fractions within the bed layer.

Figure 4. Schematic diagram of Upper Tenmile Creek mainstem WASP5 model with segmentation and inputs/outputs.

Model Calibration and Validation The June 2000 synoptic survey data was used for model calibration. Parameters included Kd values, vertical dispersion/diffusion coefficients, and unaccounted flows. Vertical sediment exchange (settling/scour) rates were assumed to be zero under low-flow, steady-state conditions. An unaccounted inflow to the mainstem upstream of Minnehaha Creek was indicated by the flow calibration. This was probably from groundwater discharge or a small tributary, and was later included in the model as part of the calibration. WASP5 User’s Manual Kd values were initially used (Ambrose et al., 1993). According to the manual and other references, these values can be a function of solids concentrations (EPA 1989, 1996). The higher the solids concentration, therefore, the higher the Kd. However, some studies and reviews of this effect have not supported this relationship (McKinley & Jenne, 1991). A relationship discussed in EPA (1996) was used with a solids concentration equal to 4 mg L-1 for water and ranging from 100,000 to 500,000 mg L-1 for the benthic layer. Calibration of water quality was achieved by manipulation of Kd values and dispersion/ diffusion coefficients within ranges in the literature (Ambrose et al., 1993) and of metals concentrations in unaccounted groundwater inflow. Values for Kd were also estimated based on laboratory adsorption/desorption batch test results for

Table 1. Upper Tenmile Creek flow, concentration, and loading model calibration inputs from June 2000. Segment Name 1 HW 2 Monitor 8 Ruby 12 Banner 16 Poison 18 Redwater Adit 19 City Diversion 20 Lee Mtn Spring Ck/Suzie1 21 22 Moores Ck 27 GW 28 Minnehaha 36 Moose Bear Gulch1 43 50 Walker

Distance (m) 152.4 457.2 2286 3505.2 4724 5334 5638.8 5943.6 6248.4 6553.2 8077.2 8382 10820.4 12954 15087.6 Totals

Flow (cms) 0.064 0.033 0.036 0.100 0.002 0.001 -0.237 0.010 0.001 0.001 0.025 0.045 0.000

As (mg/L) (kg/day) 0.0021 0.0117 0.0009 0.0026 0.0032 0.0100 0.0011 0.0095 0.0090 0.0013 0.1230 0.0057

0.000 0.081

0.0130 0.0110 0.0580 0.0450 0.0030 0.0008 0.0012

0.0117 0.2208 0.0057 0.0972 0.0117 0.0000 0.0001 0.0000 0.3880

Cd (mg/L) (kg/day) 0.00006 0.00032 0.00092 0.00263 0.00030 0.00093 0.00004 0.00033 0.03300 0.00485 0.03150 0.00147 0.00220 0.00100 0.00500 0.00150 0.00200 0.00050 0.00050

0.00198 0.00598 0.00049 0.00324 0.00778 0.00000 0.00003 0.00000 0.03001

Cu (mg/L) (kg/day) 0.0016 0.0089 0.0002 0.0006 0.0023 0.0072 0.0022 0.0190 0.3300 0.0485 0.0067 0.0003 0.0070 0.0091 0.0070 0.0070 0.0060 0.0005 0.0016

0.0063 0.0036 0.0007 0.0151 0.0233 0.0000 0.0000 0.0000 0.1335

Pb (mg/L) (kg/day) 0.00097 0.00539 0.00200 0.00572 0.00140 0.00435 0.00051 0.00441 0.08700 0.01278 0.00058 0.00003 0.01220 0.00025 0.00700 0.00000 0.00050 0.00010 0.00011

0.01096 0.00029 0.00068 0.00000 0.00194 0.00000 0.00003 0.00000 0.04658

Zn (mg/L) (kg/day) 0.0085 0.0472 0.0006 0.0017 0.0453 0.1409 0.0162 0.1400 4.4000 0.6463 7.2700 0.3392 0.3440 0.0850 0.6900 0.2000 0.2300 0.0044 0.0045

0.3091 0.7904 0.0674 0.4320 0.8942 0.0000 0.0003 0.0000 3.8086

All data obtained from WinWASP input (*.wif files) All values are reported above the analytical detection limits Suzie Load and Bear Gulch are input as loadings only with no contributing flow due to model limitations with regard to number of flow inputs

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Table 2. Upper Tenmile Creek initial WASP model benthic sediment metals concentrations (mg/kg). Segment # 59 60 61 62 63 64

As 1 68 2770 123 132 87

Cd 0.45 1.2 40 3 3 3

Cu 3.8 11 47 18 33 21

Pb 16 90 97 90 75 49

Zn 40 132 4047 325 299 355

equilibrium metals partitioning between bed sediment and porewater for different solids/water ratios at important locations in the creek (CDM, 2001b). These values were generally within an order of magnitude and in agreement with the calibrated model values. USGS data from the June 1997 synoptic survey during much higher flows (flows of approximately 3 m3 s-1) were used for model validation. Model Application The concentrations of metals in the mainstem from existing loadings were simulated based on the model calibration and validation, and compared to measured values as well as current water quality standards. Inputs of total metal loads to the Upper Tenmile Creek mainstem were measured as part of the June 2000 synoptic survey based on flow and concentration data. The outputs of total metal loads were estimated based on the measured flow and the modeled concentrations using the calibrated model. The differences between the measured inputs and output were also calculated to estimate the overall gain or loss of metals along the length of the mainstem. Flows vary with distance along the mainstem of Upper Tenmile Creek, and most metals water quality standards (except for As) also vary depending on the hardness. For comparison purposes, therefore, four TMDLs for each metal were calculated based on the (1) minimum, (2) maximum, and (3) mean total load within the mainstem using observed flow data and calculated standards for all mainstem

monitoring stations, and based on (4) total measured input flows in tributaries and other sources and the mean of the water quality standards along the mainstem. The existing minimum, maximum, and mean total load of each metal within the mainstem was also computed based on the measured flow and modeled concentrations at all mainstem monitoring stations. The reductions in loadings, and percent reductions in loadings, required to meet each TMDL were then estimated as the difference between the existing loads and the TMDLs. These procedures were used to compute the TMDLs for the chronic and acute aquatic life standards for Cd, Cu, and Pb. The chronic and acute standards are the same for Zn, so only one TMDL was required. The human health standard was only used for As because it is the most stringent standard. Eight restoration alternative scenarios were also modeled using steady-state, low-flow (June 2000 measured flows) conditions following calibration and validation: Alternative 1: Water treatment of adit discharges in the Rimini subarea achieving an 80% reduction in metals concentrations. These discharges include the Redwater, Suzie Load, and Lee Mountain adits. Alternative 2: Water treatment of adit discharges in the Rimini subarea achieving reduction in metals concentrations to State of Montana water quality standards. These discharges also include the Redwater, Suzie Load, and Lee Mountain adits. Alternative 3: Diversion, treatment, and consumption (no discharge back to Tenmile Creek) of adit discharges in the Rimini subarea through a community water system. These discharges also include the Redwater, Suzie Load, and Lee Mountain adits. Alternative 4: Bypass 0.028 cubic meters per second (cms; 1 cubic foot per second (cfs)) of water through the City of Helena's Rimini diversion into Tenmile Creek. Alternative 5(a): Combination of alternatives 1 and 4. Alternative 5(b): Combination of alternatives 2 and 4. Alternative 5(c): Combination of alternatives 3 and 4. Alternative 6: Bypass 0.084 cms (3 cfs) of water through the City of Helena's Rimini diversion into Tenmile Creek. For modeling restoration alternatives, model inputs were those used for calibration (except where specific inputs were changed based on the characteristics of each restoration alternative). Graphs of concentration vs. distance from the headwaters of the main stem were used to compare modeled and measured total and dissolved concentrations and State of Montana water quality criteria for each metal. The State criteria are for total recoverable metals. The acute and chronic criteria vary along the length of the creek based on the hardness measured during June 2000. Additional techniques were used to assess the effects of existing bed sediment metals concentrations on the water column in Tenmile Creek. These assumed that there were no other metals inputs to the mainstem (that all other sources were remediated). The WASP5 model was used for one method. The other two techniques were performed outside of the model. Method 1. For the first method, the model was used with all metals loadings to the creek equal to zero, existing bed metals concentrations measured during the June 2000 synoptic survey, and the average equilibrium partition coefficient for dissolved and particulate forms of each metal calculated from the laboratory desorption batch tests. Results of this method represented equilibrium conditions in the water column with sediment as the only source of metals. Method 2. Method 2 involved using the resulting equilibrium benthic porewater concentrations for each analyte based on the desorption batch test results. Results for the following four locations were

used: benthic segment 61 (near Rimini), benthic segment 62 (downstream from Moore Creek), Tenmile Creek below Suzie Adit, and Poison Creek near Red Mountain. Equilibrium low-flow conditions were assumed; i.e., the mass in the porewater resulting from desorption from the bed sediment was distributed throughout both the porewater and overlying water column. Additional assumptions included:    

bed sediment porosity = 0.25 bed sediment density = 2.5 kg/L bed sediment depth = 0.3 m (1 ft) water depth = 0.3 m (1 ft) under steady-state, low-flow conditions

Method 3. Method 3 used a probablistic approach to evaluate the time required to scour and flush contaminated bed sediments from Tenmile Creek past the water treatment facility based on high flow/flood events. A preliminary hydrologic analysis of flows in the creek was performed. High flows and associated return periods (probabilities) were estimated based on analysis of historic flows at the USGS gauging station in Tenmile Creek near Rimini (station 06062500). The 1-, 5-, and 10year return period flows were assessed (the 1-year flow is also known as the mean annual flood (MAF)). These flows have probabilities of occurring in any given year of greater than 95%, 20%, and 10%, and the calculated flows for these return periods are 286, 364, and 511 cfs, respectively. Graphs developed by Colby (ASCE, 1975) were used to estimate sediment discharge for the evaluation of sand (size range of 0.062 mm to 2 mm) at each site. These graphs are based on empirical results from a range of rivers in the U.S. This method is based on water velocity, median particle size (d50) for sand (0.41 mm was used), depth of flow, and water temperature (equations are currently not available for estimation of silt and clay discharge). The estimated flood discharge and the stream cross-sectional area were used to compute the velocities for each selected location. Existing data and hydraulic analysis based on Manning’s Equation (Chow, 1959) was used to estimate the parameters for this calculation. The following assumptions and input parameters for dimensions of the channel and contaminated sediment were used: Trapezoidal channel with 1:1 side slopes and 1.2 m (4 ft) bottom width Stream slope (S) = 0.0238 Roughness coefficient (n) = 0.05

Contaminated bed sediment depth = 0.3 m (1 ft), width = 1.22 m (4 ft), and length = 1.61 km (1 mile) (total volume of 595 m3) Bed sediment density = 2.5 kg/L

The Colby graphs were used in conjunction with the calculated velocity and assumed depth to estimate the sand sediment discharge for each flood flow. We then calculated the probability of removing a specific volume of sediment in any given year and the total amount of time needed to remove all of the assumed contaminated sediment. Contaminated bed sediment is predominantly fine-grained (sand-size particles). Because this method does not explicitly take into account the coarse-grained material and armoring of sands with gravels or cobbles in the creek that could reduce scouring and flushing of the finer-grained material, these estimates are probably conservative. They might overestimate the quantity of contaminated sediment flushed and underestimate the time required for such flushing. However, they do provide rough estimates of the quantities and timing of sediment flushing.

RESULTS AND DISCUSSION Good calibration results were achieved for all of the metal concentrations. Modeled total and dissolved metals concentrations were generally within 25% of values observed during June 2000. In most cases they were within 10%. The best calibrations were achieved for As, Cd (Figure 5), and Zn.

Figure 5. Comparison of measured (June 200) and modeled Cd concentrations vs. distance in calibrated model, with applicable standards.

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Pb and Cu results were not as close, although the reasons for this are not clear. Correlation coefficients for modeled and observed results along the mainstem ranged from 0.72 for dissolved Cu (all others were greater than or equal to 0.87) to 0.99 for total Cd (Table 3, Figure 6 for Cd). Standard errors ranged from 12% (total Cd) to 51% (dissolved Pb) of measured mean concentrations. The standard errors for dissolved and total Zn were relatively high (36% and 41%, respectively). Most of these errors, however, are a result of the relatively large errors in the modeled peak values at one station which is approximately 6200 m downstream from the headwaters. The simulated peak Table 3. Model efficiencies for metal concentrations (ug/L). R2 - Correlation Coefficient Standard Error Observed Mean Standard Error % of Mean

Dissolved As 0.90 4.04 13.97 29

Total As Dissolved Cd 0.87 0.98 6.38 0.19 17.56 1.27 36 15

Total Cd Dissolved Cu 0.99 0.72 0.17 0.98 1.41 4.36 12 22

Total Cu Dissolved Pb 0.90 0.94 0.70 0.76 5.02 1.51 14 51

Total Pb Dissolved Zn 0.93 0.91 0.98 92.79 3.13 257.31 31 36

Total Zn 0.90 99.86 245.29 41

Modeled Total Cd Concentrations (ug/L)

Figure 6. Measured vs. modeled concentrations for (a) total Cd and (b) dissolved Cd along the mainstem of Upper Tenmile Creek based on calibrated model.

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dissolved and total Zn values are displaced only 200 m upstream from the actual peaks at this station. In general, a good match was achieved for simulated and measured flows in the mainstem as part of validation. Additional water sources were indicated approximately 5,000 m downstream of the headwaters that are not accounted for in the model because flows were under-predicted by 10% in this area. Flows were also over-predicted by approximately 8% 17,000 m downstream of the headwaters, indicating some flow losses in this mainstem area. Good validation was achieved for most of the metals (see Figure 7 for Cd). In the Rimini area, however, the peak total Zn concentrations were significantly under-predicted. Peak total Cu values were also over-predicted here. Causes of these results could include uncertainties in some of the model inputs, including point source loading and tributary flow data. Additional, unaccounted for nonpoint source metals loadings associated with erosion and sediment transport during higher flows could be another key factor. These potential

Figure 7. Comparison of measured (June 2000) and modeled Cd concentrations vs. distance in Tenmile Creek mainstem based on validated model.

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Distance downstream (m)

loadings, however, were not explicitly modeled as part of the steady-state, low-flow study. The constructed model is believed to be a very useful tool for prediction of metals concentrations and loadings under these base-flow conditions. This validation information also plays a very important role in identifying and evaluating these unaccounted nonpoint source metals and sediment loadings. Results show that point sources, including adits (particularly at Redwater Adit, Suzie Load, and near Lee Mountain), contribute high metals loadings to the mainstem both during baseflow and higher flows. However, loadings can also be generated from shallow groundwater and bed sediment in some areas. Water quality criteria exceedances occur at many locations throughout the mainstem. Losses of dissolved metals from the water column can occur due to adsorption and precipitation onto bed sediments in some key areas. The details of monitoring results for other analytes, such as pH, redox conditions, and electrical conductivity, are not presented in this study. However, some typical and interesting patterns can be observed. High total metals loadings to the mainstem from point sources include mostly elevated dissolved metals associated with low pH and reducing conditions in groundwater. Mainstem total loadings and concentrations increase significantly as this water enters Upper Tenmile Creek. However, a large proportion of these metals come out of solution through adsorption and/or precipitation reactions as this water mixes with the mainstem water that has greater pH and oxidizing conditions. Therefore, most of the total metals loadings and concentrations downstream from here are composed of particulate metals with dissolved metals contributing a smaller proportion. As more metals are adsorbed or precipitated onto the bed sediments with distance downstream, the total metals concentrations decrease. Based on the development and application of the model, it was found that some level of uncertainty exists in the Kd values, the importance of precipitation reactions, and specific locations of unaccounted metals sources and losses.

Measured inputs for total loads in tributaries and other sources were greatest for Zn (approximately 3.8 kg/day) and least for Cd (0.03 kg/day). Comparison of these measured inputs to outputs at the mouth shows that there is a loss of mass of all metals along the length of the mainstem (Table 4). These vary from approximately 0.02 kg/day for Cd to 2.05 kg/day for Zn, and can be attributed to diversion of flow, loss to groundwater, or adsorption/precipitation to bed sediment. Results of modeling loadings from contaminated bed sediment and fluvial tailings show that these sources are generally not significant because most metals concentrations decrease with distance downstream from the major point source adit and tributary inputs. Modeled total loads for each metal vary considerably with distance along the mainstem (Figure 8). This is due in part to the varying acute and chronic aquatic life standards based on varying hardness along the mainstem, but is more influenced by significant changes in flow with inputs and losses, particularly decreases in flow at city diversions. The TMDLs based on the locations of minimum and maximum loadings and based on the mean loading in the mainstem also vary considerably. In all cases TMDLs estimated based on the minimum loading are greater than the current modeled loading in upstream locations and/or immediately downstream from the largest city diversion because the flows are very low. In these areas, therefore, a reduction of loads is not required. In contrast to this, TMDLs based on the maximum loading are less than the modeled loading immediately downstream from the main adit discharges, the main city diversion, or in other downstream locations. For the TMDLs based on the chronic aquatic life (all metals) or human health (As) standards (the most stringent standards), the required reductions for the maximum loading vary from 58% for Cu to 95% for Zn. If the mean total loads in the mainstem are used as the basis for developing TMDLs, reductions are required for Cu, Pb, and Zn (ranging from 2% for Cu to 35% Pb). However, reductions are not required for Cd and As based on the mean. Lastly, TMDLs based on the measured total inputs are all less than the input loadings, so reductions are required ranging from 68% for As to 91% for Zn. These estimates are relatively close to those based on the maximum total loadings in the mainstem. Therefore, it appears that these TMDL estimates can be used if conservative, but consistent, values are required to meet the most stringent aquatic life standards throughout the mainstem. Differences in estimated TMDLs based on chronic and acute aquatic life standards are relatively small for Cd and Cu, but are quite large for Pb (reductions are not required for Pb acute standards). These TMDLs are based on the baseflows measured during the June 2000 synoptic survey. Other flows, such as the critical mean annual low flow or 10-year low flows, could also be used to provide more conservative TMDL estimates or percent compliance under these conditions. In general, results of modeling restoration alternatives indicated that removal or reduction of point sources (including adits) can decrease most metals concentrations in the mainstem considerably. However, removal or stabilization of mine waste near watercourses and streambed sediment may also be needed to achieve water quality standards for some metals, such as Zn. Changes to the Helena water supply scheme and increasing baseflow in the mainstem will probably be necessary to meet all water quality criteria, particularly for Zn. Results of simulating the restoration alternatives are discussed below for each metal.

Table 4. Summary of modeling metals loads, TMDLs and required reductions.

Cd Mainstem1 Minimum= Maximum= Mean= Measured Inputs2 Output3 Gain or Loss4 Cu Mainstem Minimum= Maximum= Mean= Measured Inputs Output Gain or Loss Pb Mainstem Minimum= Maximum= Mean= Measured Inputs Output Gain or Loss As Mainstem Minimum= Maximum= Mean= Measured Inputs Output Gain or Loss Zn Mainstem Minimum= Maximum= Mean= Measured Inputs Output Gain or Loss

Total Load (kg/day)

TMDL (Chronic) (kg/day)

Reduction (kg/day)

Reduction TMDL (Acute) Reduction (%) (kg/day) (kg/day)

0.0003 0.0117 0.0057 0.0300 0.0086 -0.0214

0.0007 0.0175 0.0085 0.0078

-0.0117 0.0047 -0.0027 0.0222

-1329 76 -204 74

0.0009 0.0208 0.0109 0.0103

-0.0135 0.0039 -0.0051 0.0197

-1529 69 -261 66

0.0059 0.0818 0.0317 0.1335 0.0362 -0.0973

0.0025 0.0608 0.0298 0.0276

-0.0145 0.0244 0.0019 0.1059

-80 58 2 79

0.0034 0.0815 0.0408 0.0381

-0.0346 0.0081 -0.0091 0.0954

-139 42 -34 71

0.0053 0.0262 0.0112 0.0466 0.0083 -0.0382

0.0006 0.0130 0.0066 0.0063

-0.0046 0.0142 0.0046 0.0403

-56 94 35 86

0.0143 0.3328 0.1697 0.1623

-0.3245 -0.0050 -0.1585 -0.1157

-3894 -53 -1580 -248

-0.3092 0.0988 -0.0581 0.2620

-976 62 -307 68

-0.2987 1.4494 0.6475 3.4514

-336 95 14 91

TMDL (Human Health) (kg/day) 0.0116 0.2095 0.0880 0.3880 0.2095 -0.1785

0.0116 0.3479 0.1461 0.1260

TMDL (Chronic & Acute) (kg/day) 0.0470 1.9971 1.0334 3.8086 1.7622 -2.0464

0.0324 0.7876 0.3859 0.3572

1

Mainstem total loadings from calibrated model.

2

Measured inputs are total loadings form tributaries and other sources from June 2000 synoptic

3 4

Reduction (%)

survey measured flows and concentrations. Output is total loading at mouth from calibrated model. Gain or loss is output - measured input (positive is gain and negative is loss).

Arsenic. Results of the modeling indicate that all of the alternatives reduce total As concentrations relative to observed values along the mainstem downstream from the headwaters (Figure 9a). Because Alternative 5 achieved the human health standard of 18 g L-1 at all locations, Alternative 6 was not modeled. Alternative 2 alone has the smallest effect on concentrations; concentrations remain elevated above the standard in the Rimini subarea and downstream. Alternative 5 results in the greatest reduction in values, from >40 to 30 g L-1 here. Cadmium. All of the restoration alternatives reduce total Cd concentrations relative to measured values (Figure 9b). Alternatives 4 and 5 result in the greatest reductions. Concentrations decrease below the aquatic life chronic standard (varies along the length of the creek with hardness) with distance along the creek. Alternatives 1, 2, and 3 also decrease values considerably. However, values remain above the standard, up to >2 g L-1, in the Rimini area. Copper. All alternatives decrease total Cu values considerably. Alternatives 5 and 6 generally decrease concentrations the most (Figure 9c). However, all alternatives result in total Cu concentrations above the aquatic life chronic and/or acute standard near the Redwater Adit and Suzie Load (up to approximately 6 g L-1). Depending on the alternative, values vary further than 10,000 m downstream from the headwaters. All are below the chronic standard (95 20 10

1.5 1.35 1

Assumptions: Trapezoidal channel with 1:1 side slopes and 1.22 m (4 ft) bottom width Stream slope (S ) = 0.0238 Manning's n/Roughness coefficient (n) = 0.05 Contaminated bed sediment depth = 0.3 m (1 ft), width = 1.22 m (4 ft), and length = 1.61 km (1 mile) (total volume of 595 m3) Contaminated bed sediment is predominantly fine-grained, i.e., sand-size particles (methods are currently not available for evaluation of discharge of silt and clay-size particles) Bed sediment density = 2.5 kg/L

Results of the probabilistic approach to assess the time required to scour and flush contaminated bed sediments from Tenmile Creek past Rimini based on flood events indicate that a 1-year flood (MAF) could flush approximately 884 tonnes of sediment. This assumes that contaminated sediment is 1.61 km (1 mile) long, 0.3 m (1 ft) thick, and 1.22 m (4 ft wide) (Table 6). It would take approximately 1.5 days of a 1-year flood to flush all of the sediment. This analysis assumes that the peak flow of a 1-

year flood is constant over 1.5 days. It would take, on average, 1.5 one-year floods to remove this sediment if the peak flow occurs over a 24-hour period. Therefore, there is approximately a 99% probability of flushing 65% of the sediment in any given year (assuming no additional inputs of contaminated sediment). A 5-year flood could flush approximately 1,007 tonnes/per day of sediment. It would take approximately 1.35 days of a 5-year flood to flush all of the sediment. This assumes the peak flow is constant over 1.35 days. If the peak flow occurs over a 24-hour period, it would take, on average, 1.35 five-year floods to remove this sediment. There is approximately a 20% probability of flushing 73% of the sediment in any given year. A 10-year flood could flush approximately 1,291 tonnes/per day of sediment. It would take approximately 1 day of a 10-year flood to flush all of the sediment. Again, this assumes the peak flow is constant over 1 day. Therefore, there is approximately a 10% probability of flushing all of the sediment in any given year. These peak flood flows would not be constant over a 1-day period in actuality. Therefore, it would probably take longer than these estimates. CONCLUSIONS This study has illustrated the techniques and results for modeling metals transport, TMDLs, and the effectiveness of potential restoration alternatives in a mining-impacted mountain watershed using the EPA WASP5 model and the Upper Tenmile Creek, Montana as a case study. This model has proven to be an important tool for developing and evaluating TMDLs and planning effective restoration measures in this basin. The model development, calibration, validation, and application methods discussed in this study are all important for understanding processes within Tenmile Creek and for quantitative development of TMDLs and restoration scenarios. The WASP5 model and methodology used here can be used in other watersheds where metals are problems to assist in estimating TMDLs and understanding contaminant sources, transport, impacts, uncertainties, and effects of restoration measures. In general, the model confirmed metals concentrations in the mainstem measured during the synoptic surveys. The model helped to identify and evaluate flows and metals loadings that are unaccounted for, as well as uncertainties in the watershed, sampling results, and in the model itself. It was also used to help identify many of the important processes in the Tenmile Creek. Modeling results showed that TMDL estimates based on either mean total loads in the mainstem or on measured inputs to the creek can be used to provide conservative, but consistent, values required to meet the most stringent aquatic life standards throughout the mainstem. The model will probably be revised as needed in the future to improve the accuracy of results for TMDL development and design of restoration measures. A dynamic version of the model may be developed and implemented to evaluate sediment and associated metals loadings under high-flow conditions during snowmelt and storm runoff. However, the water and sediment load inputs must currently be estimated outside of the WASP model. This can be accomplished using separate models or other estimation techniques, such as the Rational Method for peak flows and the Revised Universal Soil Loss Equation (RUSLE) for erosion. Flows, loadings, and losses that are currently unaccounted for in the model could be evaluated in more detail using additional sampling, including sampling high-flow events to calibrate a dynamic model. A new WASP module called Metal Exposure and Transformation Assessment (META4; Martin & Medine, in preparation) will also soon be available for future applications. This module explicitly models precipitation/dissolution reactions, in addition to adsorption/desorption, which can be important metals fate and transport processes in mined watersheds. These reactions are often particularly important in areas with extreme and highly variable pH.

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