SIOUX FALLS TOTAL MAXIMUM DAILY LOAD MODEL APPLICATION, DEVELOPMENT, CALIBRATION, AND VALIDATION

Transcription

1 SIOUX FALLS TOTAL MAXIMUM DAILY LOAD MODEL APPLICATION, DEVELOPMENT, CALIBRATION, AND VALIDATION Topical Report RSI-2272 prepared for City of Sioux Falls 1203 N. Western Avenue Sioux Falls, South Dakota April 2012

2 SIOUX FALLS TOTAL MAXIMUM DAILY LOAD MODEL APPLICATION, DEVELOPMENT, CALIBRATION, AND VALIDATION Topical Report RSI-2272 By Jason P. Lambert Jason T. Love Jared K. Oswald RESPEC P.O. Box 725 Rapid City, South Dakota prepared for City of Sioux Falls 1203 N. Western Avenue Sioux Falls, South Dakota April 2012

3 TABLE OF CONTENTS 1.0 INTRODUCTION HISTORICAL WATER-QUALITY DATA ADDITIONAL WATER-QUALITY MONITORING HYDROLOGIC MODEL DEVELOPMENT Time-Series Data Static Datasets Watershed Delineation Watershed Characterization HYDROLOGIC MODEL CALIBRATION AND RESULTS POLLUTANT MODEL DEVELOPMENT POLLUTANT MODEL CALIBRATION METHODS AND RESULTS REFERENCES APPENDIX A. BACTERIAL CALIBRATION FIGURES... A-1 APPENDIX B. TOTAL SUSPENDED SOLIDS CALIBRATION FIGURES... B-1 i

4 LIST OF TABLES TABLE PAGE 1-1 Summary of E. coli Data Analyzed per Data Station Summary of Total Suspended Solids Data Analyzed per Data Station Stormwater Monitoring Stations in the City of Sioux Falls Example FTABLE From Construction Specification Land Use/Cover Category Aggregation and Percent Impervious Aggregated Model Land Use Categories for the Sioux Falls Total Maximum Daily Load Assessment General Performance Criteria for Model Calibration and Validation Weight-of-Evidence Statistics for Full-Year Hydrology Calibration Weight-of-Evidence Statistics for Recreation Season Hydrology Calibration Minimum and Maximum Values Used for the Surface Runoff Associated With the HSPF Model ii

5 LIST OF FIGURES FIGURE PAGE 1-1 Big Sioux River Historical Water-Quality Monitoring Sites RESPEC 2009 Monitoring Locations on the Big Sioux River and Tributaries Stormwater Monitoring Sites in the City of Sioux Falls E. coli Boxplots for 2009 Monitoring Sites on the Big Sioux River and Its Tributaries E. coli Boxplots for 2009 Urban Stormwater Monitoring Sites Total Suspended Solids Boxplots for 2009 Base Flow and Storm Flow Combined From the Big Sioux River and Its Tributaries Total Suspended Solids Boxplots for 2009 Urban Stormwater Monitoring Sites Meteorological Stations for the Sioux Falls Total Maximum Daily Load Assessment Long-Term U.S. Geological Survey Streamflow Gauges on the Big Sioux River and Skunk Creek Plan View of U.S. Army Corps of Engineers HEC-RAS Model for the City of Sioux Falls Existing Detention Structures in the City of Sioux Falls Example Construction Specification for an Existing Detention Structure in Sioux Falls Storm Sewer System in the City of Sioux Falls National Land Cover Data for the Sioux Falls Total Maximum Daily Load Assessment Project Area Land Use Data for the City of Sioux Falls Delineation Points for the Sioux Falls Total Maximum Daily Load Assessment Project Final Storm Basin Delineation for the Sioux Falls Total Maximum Daily Load Assessment Meteorological Stations and Hydrozones for the Sioux Falls Total Maximum Daily Load Assessment National Land Cover Data and Sioux Falls Land Use Aggregated Into Model Categories Schematic of Sioux Falls Total Maximum Daily Load Assessment Project Area R and R 2 Performance Criteria for Model Calibration and Validation Flow Duration Curve at USGS During the Recreation Season Flow Duration Curve at USGS During the Recreation Season Flow Duration Curve at USGS During the Recreation Season iii

6 LIST OF FIGURES (Continued) FIGURE PAGE 1-25 Time-Series Plot of RESPEC Monitoring Site SKC030 During the 2009 Recreation Season Time-Series Plot of RESPEC Monitoring Site at BSR060 During the 2009 Recreation Season Time-Series Plot of RESPEC Monitoring Site DIV010 During the 2009 Recreation Season Time-Series Plot of RESPEC Monitoring Site at BSR080 During the 2009 Recreation Season Time-Series Plot of RESPEC Monitoring Site at BSR110 During the 2009 Recreation Season HSPF Reach Locations of Selected Pathogenic Model Calibration Points Total Suspended Solids Duration Curve at Outlet of Reach 70 Upstream of the City of Sioux Falls Total Suspended Solids Time Series at Outlet of HSPF Reach 70 Upstream of the City of Sioux Falls A-1 Bacterial Time Series at Outlet of Reach 70 Upstream of the City of Sioux Falls... A-2 A-2 Bacterial Time Series at Outlet of Reach 165 Along Skunk Creek... A-2 A-3 Bacterial Time Series at Outlet of Reach 190 (BSR060) in the City of Sioux Falls... A-3 A-4 Bacterial Time Series at Outlet of Reach 340 (BSR080) Downstream of the Diversion Confluence With the Big Sioux River in the City of Sioux Falls... A-3 A-5 Bacterial Time Series at Outlet of Reach 414 Along Slip-up Creek... A-4 A-6 Bacterial Time Series at Outlet of Reach 430 (System Outlet) Near Brandon... A-4 B-1 Total Suspended Solids at Reach B-2 B-2 Total Suspended Solids at Reach B-2 B-3 Total Suspended Solids at Reach B-3 B-4 Total Suspended Solids at Reach B-3 B-5 Total Suspended Solids at Reach B-4 B-6 Total Suspended Solids at Reach B-4 iv

7 1.0 INTRODUCTION The Big Sioux River is a natural, permanent, stable river with several intermittent tributaries that only flow during snowmelt and rainfall events. Discharge in the river can be significantly impacted by wet or dry periods as well as stormwater runoff. Skunk Creek contributes a significant flow volume, at times nearly the entire flow volume, to the Big Sioux River through Sioux Falls. During the recreation season (May September), much of the Big Sioux River flow upstream of the city of Sioux Falls is diverted through the diversion canal instead of flowing down the oxbow through the city with the flow from Skunk Creek. The majority of the Big Sioux River flow through the city during the recreation season is from urban runoff and flow from Skunk Creek. To assess the historical, current, and future health of the Big Sioux River, a comprehensive assessment approach was taken. Historical data (meteorological time series, streamflow, land use, and water quality) were gathered from agencies, including the U.S. Geological Survey (USGS), East Dakota Water Development District (EDWDD), South Dakota Department of Environment and Natural Resources (SD DENR), the city of Sioux Falls, and High Plains Regional Climate Center (HPRCC). Bacteria and sediments are pollutants that are commonly associated with rainfall events. To analyze the behavior of these constituents, a comprehensive monitoring plan was designed and implemented to characterize water quality during events and base flows, which can then be used for model calibration and validation. A hydrologic model was developed and calibrated based on historical and recent data collected as part of this project spanning from October 1, 2005, through September 30, The calibrated model was then used as a tool to simulate and predict impacts on water quality throughout the system. The monitoring plan was implemented throughout the project area in 2009 with the support of the Sioux Falls Water Reclamation and city health laboratories. Monitoring focused on sampling from stormwater outfalls, on three key tributaries (Skunk Creek, Slip-up Creek, and Silver Creek) on the diversion canal that sends flow around the Sioux Falls area, and at multiple sites along the Big Sioux River. This additional monitoring increased the understanding of the impact that the city of Sioux Falls stormwater has on the Big Sioux River compared to that not originating from this area. The hydrologic model chosen for the Sioux Falls Total Maximum Daily Load (TMDL) Assessment was the Hydrologic Simulation Program Fortran (HSPF) watershed modeling system. This watershed modeling package is a comprehensive package for simulating watershed hydrology and water quality for both conventional and toxic organic pollutants. HSPF can simulate the hydrologic and associated water-quality processes on pervious and impervious land surfaces and in streams and well-mixed impoundments. HSPF incorporates the watershed-scale Agricultural Runoff Management (ARM) and nonpoint source (NPS) models into a basin-scale analysis framework that includes fate and transport in one-dimensional stream channels. It is the only comprehensive model of watershed hydrology and water quality that allows the integrated simulation of land and soil contaminant runoff processes with in-stream hydraulic and 1

8 sediment/chemical interactions. The result of this simulation is a time history of the runoff flow rate, sediment load, and nutrient and pesticide concentrations, along with a time history of water quantity and water quality at any point in a watershed [Bicknell et al., 2001]. The model contains hundreds of process algorithms developed from theory, laboratory experiments, and empirical relations from instrumented watersheds. A large number of model parameters can be specified, although default values are provided where reasonable values are available. Option flags allow bypassing of whole sections of the program where data are not available [Bicknell et al., 2001]. HSPF uses continuous rainfall and other meteorological records to compute streamflow hydrographs and pollutographs. The model simulates interception of soil moisture, surface runoff, interflow, base flow, snowpack depth and water content, snowmelt, evapotranspiration, groundwater recharge, dissolved oxygen, biochemical oxygen demand, temperature, pesticides, conservatives, fecal coliforms, sediment (sand, silt, and clay) detachment and transport, sediment routing by particle size, channel routing, reservoir routing, constituent routing, ph, ammonia, nitrite/nitrate, organic nitrogen, orthophosphate, organic phosphorous, phytoplankton, and zooplankton. The in-stream model assumes the receiving waterbody is well mixed with width and depth and is limited to well-mixed rivers and reservoirs [Bicknell et al., 2001]. HSPF can simulate one or many pervious or impervious unit areas discharging to one or many river reaches or reservoirs. Frequency and duration analysis can be done for any time series. Any time step from 1 minute to 1 day that divides equally into 1 day can be used. Any period from a few minutes to hundreds of years may be simulated. HSPF is generally used to assess the effects of land-use change, reservoir operations, point or nonpoint source treatment alternatives, and flow diversions. Programs available separately support data preprocessing and postprocessing for statistical and graphical analysis of data saved to the Watershed Data Management (WDM) file. Output is either printed tables at any time step, a flat file, or the WDM file. Hundreds of computed time series may be selected for the output files [Bicknell et al., 2001]. 1.1 HISTORICAL WATER-QUALITY DATA Water-quality data from multiple sites in the Sioux Falls TMDL Assessment project area dating back to 2000 were compiled, although only data from were used in the modeling effort because they provided a sufficient time period for model calibration and validation, which better reflected recent watershed conditions. This research used a wealth of data, including the following stations located on the mainstem of the Big Sioux River: Minnehaha County line to below Baltic (BSR010), at the I-90 Bridge upstream of Sioux Falls (BSR020), at Silver Creek (BSR030), at the I-229 bridge (BSR050), near South Western Avenue Bridge at Sioux Falls (BSR060), before the diversion return (BSR070), at North Cliff at Sioux Falls (BSR080), at Bahnson Avenue Bridge (BSR090), at the bridge downstream of Slip-Up Creek (BSR100), and near Brandon (BSR110). Figure 1-1 shows the locations of the mainstem of the Big Sioux River. 2

9 RSI Figure 1-1. Big Sioux River Historical Water-Quality Monitoring Sites. 3

10 Historical bacterial data collected from May 1 to September 30 (recreation season) from each station were used to calculate the percent exceedance of the daily maximum E. coli bacteria criterion of 235 colony-forming units per 100 milliliters (cfu/100 ml) for immersion recreation and to find E. coli concentration ranges outlined in Table 1-1. A regression relationship was developed to convert the historical fecal coliform data to E. coli before generating a complete time series at each location. Exceedances of the water-quality standard in the watershed study area ranged from 38 percent at BSR090to 70 percent at BSR080. Table 1-1. Summary of E. coli Data Analyzed per Data Station Station I.D. TMDL Reach Time Period Number of Samples Exceeding Criterion Total Number of Samples Percent Exceedance Concentration Range (cfu/100 ml) BSR010 SD-BS-R- BIG_SIOUX_8 05/24/00 07/22/ ,499 BSR020 SD-BS-R- BIG_SIOUX_8 03/07/00 10/27/ ,790 BSR030 SD-BS-R- BIG_SIOUX_8 07/11/00 10/09/ ,370 BSR050 SD-BS-R- BIG_SIOUX_10 06/12/00 05/05/ ,635 BSR060 SD-BS-R- BIG_SIOUX_10 03/24/09 10/27/ ,033 BSR070 SD-BS-R- BIG_SIOUX_10 05/31/00 10/27/ ,136 BSR080 SD-BS-R- BIG_SIOUX_11 07/11/00 10/27/ ,286 BSR090 SD-BS-R- BIG_SIOUX_11 01/05/00 07/21/ ,931 BSR100 SD-BS-R- BIG_SIOUX_12 01/05/00 07/22/ ,227 BSR110 SD-BS-R- BIG_SIOUX_12 05/31/00 10/27/ ,724 Historical total suspended solids (TSS) data collected throughout the year from each station were used to calculate the percent exceedance of the daily maximum TSS criterion of 158 mg/l for maintenance of warm-water semipermanent fish life and to determine TSS concentration ranges as identified in Table 1-2. Exceedances of the water-quality standard in the watershed study area were consistently low and ranged from 1 percent near Dell Rapids to 11 percent near the intersection of Western Avenue and I

11 Table 1-2. Summary of Total Suspended Solids Data Analyzed per Data Station Station I.D. TMDL Reach Time Period Number of Samples Exceeding Criterion Total Number of Samples Percent Exceedance Concentration Range (mg/l) BSR010 SD-BS-R- BIG_SIOUX_8 1/25/00 7/22/ BSR020 SD-BS-R- BIG_SIOUX_8 1/31/00 10/27/ BSR060 SD-BS-R- BIG_SIOUX_10 3/3/09 10/27/ BSR070 SD-BS-R- BIG_SIOUX_10 1/31/00 10/27/ BSR080 SD-BS-R- BIG_SIOUX_11 3/3/09 10/27/ BSR090 SD-BS-R- BIG_SIOUX_11 1/21/00 7/21/ BSR100 SD-BS-R- BIG_SIOUX_12 1/31/00 7/22/ BSR110 SD-BS-R- BIG_SIOUX_12 1/31/00 10/27/ ADDITIONAL WATER-QUALITY MONITORING The monitoring plan for the Sioux Falls TMDL Assessment contained four key components: (1) increasing the sampling on the Big Sioux River, including both additional water-quality sampling and increased monitoring of river discharge; (2) sampling on the three key tributaries (Skunk Creek, Silver Creek, and Slip-up Creek); (3) understanding the flow diverted around the Sioux Falls area through the diversion canal; and (4) sampling stormwater runoff in the storm drainage network in Sioux Falls [Love, 2008]. The sampling period was from April to September Monitoring locations on the Big Sioux River and its tributaries around the city of Sioux Falls are shown in Figure 1-2. Long-term USGS streamflow gauging stations were selected as monitoring sites (SKC030, BSR060, and BSR080) to coincide with historical water-quality data acquisition. Other sites, such as SVC010 and SUC020 (Silver Creek and Slip-up Creek, respectively), used stage/discharge relationships to obtain flow measurements in addition to water-quality sampling. Urban stormwater monitoring sites in the storm drainage network of Sioux Falls are listed in Table 1-3 and shown in Figure 1-3. ISCO automatic samplers were installed at locations throughout the city of Sioux Falls to concurrently collect a minimum of six storm events during the sampling period. The ISCOs were equipped with cell-phone modems to allow remote triggering during runoff events. Along with the ISCO samplers, a grab sample for each event at 5

12 RSI Figure 1-2. RESPEC 2009 Monitoring Locations on the Big Sioux River and Tributaries. 6

13 each site was collected to compare with the ISCO sample as a quality assurance/quality control (QA/QC) measure. Stage and discharge data from the event were used to composite samples based on flow volume to create a single sample. Results from the composite sample analysis yielded the event mean concentration (EMC). Samples were analyzed for TSS, fecal coliform bacteria, and E. coli. In addition to event sampling, weekly grab samples were collected at ISCO sites to characterize the base flow conditions of the system. Table 1-3. Stormwater Monitoring Stations in the City of Sioux Falls Station I.D. Name STW010 STW030 STW040 STW049 STW050 STW100 STW110 STW140 STW150 STW160 STW170 Storm Drain Near the Zoo Urban Stream Channel Near S. Dunham Circle Stormwater Channel Near W. Silver Valley Drive Storm Drain Near PetSmart upstream of Detention Structure Storm Drain Near PetSmart Urban Stream Channel Near 57th Street Storm Drain in Yankton Trail Park Storm Drain Along Beadle Greenway Stormwater Channel Near E. Benson Road Storm Drain Near John Morrell Urban Stream Channel Near E. Rice Street Boxplots showing the range of E. coli concentrations (expressed as cfu/100 ml) were constructed using stormwater data from the 2009 monitoring effort. Figure 1-4 is a boxplot from sites on the Big Sioux River and its tributaries, and Figure 1-5 shows a boxplot from urban stormwater sites. Boxplots for sites on the Big Sioux River and its tributaries show exceedances of the E. coli daily maximum criterion (235 cfu/100 ml) ranging from 36 percent upstream of the city to 82 percent on Slip-up Creek. Boxplots for urban stormwater sites show exceedances of the E. coli daily maximum criterion ranging from 78 percent on the eastern side of Sioux Falls to 100 percent at multiple places throughout the city. It should be noted that the percent exceedances for the stormwater monitoring sites are for reference only because there is no water-quality standard for stormwater drains. Figures 1-6 and 1-7 are boxplots showing the range of TSS concentrations (expressed in mg/l) constructed using data from sites on the Big Sioux River and its tributaries and urban stormwater sites, respectively. This data is also from the 2009 monitoring effort. Boxplots for sites on the Big Sioux River and its tributaries show exceedances of the TSS daily maximum 7

14 RSI Figure 1-3. Stormwater Monitoring Sites in the City of Sioux Falls. 8

15 RSI Figure 1-4. E. coli Boxplots for 2009 Monitoring Sites on the Big Sioux River and Its Tributaries. RSI Figure 1-5. E. coli Boxplots for 2009 Urban Stormwater Monitoring Sites. 9

16 RSI Figure 1-6. Total Suspended Solids Boxplots for 2009 Base Flow and Storm Flow Combined From the Big Sioux River and Its Tributaries. RSI Figure 1-7. Total Suspended Solids Boxplots for 2009 Urban Stormwater Monitoring Sites. 10

17 criterion (158 mg/l) ranging from 3 percent near Dell Rapids (BSR020) to 23 percent before the diversion return (BSR070). Boxplots for urban stormwater sites show exceedances of the TSS daily maximum criterion ranging from 0 percent at a majority of the sites to 47 percent at STW150 which is located in a concrete channel that drains the parking lots in an industrial area in northern Sioux Falls. It should be noted once again that the percent exceedances for the stormwater monitoring sites are for reference only because there is no water-quality standard for stormwater drains. 1.3 HYDROLOGIC MODEL DEVELOPMENT The quality and consistency of input data are paramount in the accuracy of any model. Data input into the hydrologic model can be divided into two categories: time-series data and static data. Time-series data are those associated with discrete points in time and are usually collected on a predetermined frequency or event basis. Examples of time-series data include stream discharge measured regularly (e.g., daily or every 15 minutes) or precipitation data, such as rainfall measured during a storm event. Static data are those that either do not change or are associated with time periods where the start and end date are approximated or undefined because significant change in data occurs slowly. Examples of static datasets are land use and elevation. The simulation time period chosen was October 1, 2005 September 30, This period spans an adequate balance of wet and dry climatic periods, which is desirable when calibrating a hydrologic model in a region with variable meteorological and soil conditions. This time period also reflects a representative land use in and around the city of Sioux Falls, which has changed dramatically in the last 10 years. Data collected during the 2009 monitoring effort were used in validating model performance Time-Series Data Several meteorological time series are required by HSPF to effectively simulate watershedlevel hydrologic processes. Precipitation and potential evapotranspiration (ET) were needed to calculate the water balance. Air temperature, wind speed, solar radiation, dewpoint temperature, and cloud cover were needed to calculate snow processes using an energy balance method [Bicknell et al., 2001]. The energy balance method was chosen over the index method (based on temperature alone) to better represent the snow accumulation and snowmelt processes. Most of the meteorological data required by HSPF are available through the U.S. Environmental Protection Agency and the Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) system. This system uses data developed by the National Climatic Data Center (NCDC). Meteorological data from two stations, shown in Figure 1-8, were used as input time series for the hydrologic model. Additional time-series data at surrounding stations were not available during the chosen simulation period. 11

18 RSI Figure 1-8. Meteorological Stations for the Sioux Falls Total Maximum Daily Load Assessment. 12

19 Potential ET values for both stations were compared to expected values from the National Oceanic and Atmospheric Administration (NOAA) Evaporation Atlas [Farnsworth et al., 1982]. The NOAA atlas estimates an annual total free water surface evaporation of around 42 inches for the Big Sioux River Watershed study area. Potential ET time series were also obtained from the South Dakota State University (SDSU) Climatology Center and the High Plains Regional Climate Center (HPRCC). These time series only recorded data from May through September of each year. An aggregate time series for the stations in Sioux Falls and Dell Rapids were generated using the SDSU, HPRCC, and NCDC time-series datasets. Potential ET data developed by NCDC uses the Hamon equation, which is based on air temperature and tends to underestimate potential ET. The winter months (October through April) were filled using the daily data from NCDC. Although the NCDC data predict lower potential ET values, they had little effect on the model during the winter months. Historical streamflow data are necessary to calibrate the hydrologic model as well as to provide time series as boundary conditions. The USGS monitors long-term streamflow on the Big Sioux River at USGS Big Sioux River near Dell Rapids, USGS Big Sioux River at Sioux Falls, USGS Big Sioux River at Cliff Avenue at Sioux Falls, and on Skunk Creek at USGS Skunk Creek at Sioux Falls. These streamflow gauges are shown in Figure 1-9. Each of these stations provided an adequate streamflow dataset during the simulation period. Two additional streamflow gauges were used to calibrate the hydrologic model. These gauges at BSR020 (at the I-90 Bridge upstream of Sioux Falls) and BSR110 (near Brandon) did not contain continuous flow data. Flow data trends from the 2009 monitoring effort at these sites, which are shown in Figure 1-2, were used with the existing data to develop a continuous streamflow time series that could be used in the calibration process. One complicating factor involved with the hydrology calibration in the Sioux Falls TMDL Assessment project area was to understand how flows were being routed using the diversion structure just below BSR020. Since there are no designated flow-measurement structures or USGS monitoring points exist on the diversion canal, alternate means to estimate flow were employed. The city of Sioux Falls operates the gates at the diversion to divert flow around the city and control potential flooding in the oxbow of the Big Sioux River (TMDL Reach 10). Each time a gate position is changed, it is recorded. Once the measurements of the individual gates were gathered, the gate position data were input into a submerged orifice equation with water level data to determine the flow that was spilling into TMDL Reach 10. Often, a modeled watershed is only a portion of the greater basin in which it lies and may not include the headwaters. For this reason, a boundary condition (i.e., representation of a particular constituent beyond the model domain) is used to account for constituents (e.g., streamflow, sediment load) whose origin is not within the model domain. Two streamflow boundary conditions were required for the development of the hydrologic model: on the Big Sioux River upstream of Dell Rapids and on Skunk Creek west of Sioux Falls. The streamflow 13

20 RSI Figure 1-9. Long-Term U.S. Geological Survey Streamflow Gauges on the Big Sioux River and Skunk Creek. 14

21 boundary condition time series for the Big Sioux River entering the model domain upstream of Dell Rapids was generated through an initial model simulation process. Initially, the complete streamflow time series of USGS near Dell Rapids was used as the boundary condition, and hydrologic parameters for land and stream segments (land cover, infiltration, and depression storage) upstream of Dell Rapids were assigned values consistent with those applied throughout the northern extent of the watershed (upstream of the city of Sioux Falls). After calibrating the hydrologic parameters in the northern extent of the watershed, the flow contribution (a time series) from the five storm basins above USGS near Dell Rapids was subtracted from the streamflow time series of that gauge, and the resulting streamflow time series became the new boundary condition for the Big Sioux River. Upon performing another model simulation, runoff from the five storm basins between Dell Rapids and the upstream model boundary had very little effect on model performance at USGS near Dell Rapids. The streamflow boundary condition time series for Skunk Creek was generated using the same method as the Big Sioux River streamflow boundary condition. Once the system was calibrated, the contributing runoff from storm basins west of USGS Skunk Creek at Sioux Falls was subtracted from the streamflow time series of the gauge, resulting in a new time series representing the streamflow boundary condition for Skunk Creek. After reviewing model performance from subsequent model simulations, minor hydrologic parameter adjustments were made to account for the change in flow allocation. Time series were also obtained for the city of Sioux Falls drinking water demand and wastewater treatment plant (WWTP) discharge, John Morrell & Co. discharge, and the city of Brandon WWTP discharge Static Datasets Model input data that do not (or cannot) change in a simulation run are considered static. Static datasets used as part of this research include cross-sectional data of the Big Sioux River through Sioux Falls, sedimentation basin designs, and land use. It should be noted that the cross-sectional geometry of reach segments does not change in a model simulation run, although sediment scour and deposition might occur. Sediment flux is accounted for internally by the sediment module, which makes a time series available for export. Therefore, the geometry of a reach segment is considered static throughout a model simulation run. Various hydraulic datasets were obtained for input to the model. The U.S. Army Corps of Engineers (USACE) used the Hydrologic Engineering Center s River Analysis System (HEC-RAS) to perform a flood study through the city of Sioux Falls. HEC-RAS is a hydraulic model that requires detailed cross-sectional geometry throughout the model system. The HEC-RAS model (plan view shown in Figure 1-10) was obtained from the city of Sioux Falls and used to export hydraulic characteristics of reaches throughout the city. The green lines in Figure 1-10 represent cross sections for which there are station and elevation data. The model was used to 15

22 RSI B i g S i oux Above Silver Ck * si lver o ut Below Silver Silver Creek Ck * JnBgSiux&Div Above Weir silver in * Below Weir Ph II upstream D i ver s i on Chan ku S n Skunk Creek k C r ee k PhII dwn & Sec Phase 1B Junc_22_&_1B B i g S ioux x B i g S i ou Figure Plan View of U.S. Army Corps of Engineers HEC-RAS Model for the City of Sioux Falls. 16

23 predict stage, surface area, volume, and resonance time for each reach from 25 flow profiles (i.e., 25 flow rates through the channel system) ranging from 10 cubic feet per second (cfs) to 10,200 cfs. These characteristics constitute the hydraulic behavior of a reach and are specified in the FTABLE block of the HSPF user control input (UCI) file. FTABLE information for reaches upstream and downstream of the city of Sioux Falls was obtained from digital elevation data and cross sections provided by the USGS. The city of Sioux Falls provided detailed plans for most of the existing detention structures, shown in Figure 1-11, in addition to locations of planned structures. HSPF simulates reaches and reservoirs as open or closed channels and completely mixed lakes, respectively. Surface and subsurface flow from a land segment enter the reach or reservoir and are routed through a unidirectional channel. The free-flowing reach or mixed reservoir is called a RCHRES element in the model [Bicknell et al., 2001]. The detailed plans of existing detention structures (see Figure 1-12 for an example) were used to develop FTABLEs (Table 1-4) representing the hydraulic attributes of the detention structures in a manner similar to that used to determine the hydraulic attributes of a river segment. Figure 1-13 is layout of the storm sewer system (sewershed) obtained from the city of Sioux Falls. Sizing and material specifications (e.g., pipe shape, diameter, and type), invert elevations, and design notes were included with the spatial layout. Storm basins were delineated within the city limits based on the sewershed. Sewer laterals in each storm basin were rendered into a single sewer reach that combined the hydraulic properties of all sewer segments. To represent realistic timing of runoff from each storm basin surface to the outfall, the length of the single sewer reach was characterized as the length from the outfall point to the centroid of the runoff collection area (i.e., the centroid of sewer pipe density in each storm basin). Land use and cover datasets were obtained from the 2001 National Land Cover Data (NLCD), and the city of Sioux Falls are shown in Figure 1-14 and Figure 1-15, respectively. The dataset provided by the city included much greater detail and specificity than the 2001 NLCD categories for the same region. For display purposes, description details of the city land use dataset in Figure 1-15 were omitted from the legend. Impervious areas were determined based on a 2001 impervious surface coverage map obtained from the USGS National Map Seamless Server. These impervious surface values (expressed as percentages and shown in Table 1-5) were averaged over each similar land use/cover category and then reduced slightly, based on modeling experience, to represent an effective impervious value for that unique land use/cover. HSPF refers to pervious land segments (PERLNDs) as subdivisions of the simulated watershed that have similar hydrologic characteristics. Impervious land segments (IMPLNDs) use the same concept as PERLNDs but do not allow infiltration and subsurface processes [Bicknell et al., 2001]. IMPLND and PERLND percentages, shown in Table 1-5, are proportions of their respective land use/cover based on the impervious surface coverage and are later aggregated into more general model categories. The NLCD and city datasets were combined to 17

24 RSI Figure Existing Detention Structures in the City of Sioux Falls. 18

25 RSI Figure Example Construction Specification for an Existing Detention Structure in Sioux Falls. Table 1-4. Example FTABLE From Construction Specification Depth (ft) Surface Area (acre) Volume (acre per foot) Quantity (cubic feet) Resolution Time (minutes) ,

26 RSI Figure Storm Sewer System in the City of Sioux Falls. 20

27 RSI Figure National Land Cover Data for the Sioux Falls Total Maximum Daily Load Assessment Project Area. 21

28 RSI Figure Land Use Data for the City of Sioux Falls. 22

29 Table 1-5. Land Use/Cover Category Aggregation and Percent Impervious (Page 1 of 4) Land Use I.D. Description Area (acre) Percent of Total Area (%) USGS Seamless Percent Impervious (mean %) Model Value (% IMPLND) Model Value (% PERLND) City of Sioux Falls 1 Private roads Railroad rights-of-way Platted public rights-of-way Single-family residences 8, Two-family residences Manufactured housing Townhouses Apartments 1 to 3 stories Apartments 4 or more stories Banks and financial institutions Government offices Other offices Public parking ramps and lots Public service facilities Neighborhood and public utilities Leased private parking lots Churches Nursing homes

30 Table 1-5. Land Use/Cover Category Aggregation and Percent Impervious (Page 2 of 4) Land Use I.D. Description Area (acre) Percent of Total Area (%) USGS Seamless Percent Impervious (mean %) Model Value (% IMPLND) Model Value (% PERLND) 42 Assisted living and group homes Health clubs and activity centers Colleges, universities, and adult ed. facilities Hospitals/funeral homes Primary, secondary, and special education facilities Day care centers Assembly areas, arenas, stadiums Cultural activities, libraries, museums Low intensity Moderate intensity High intensity Industrial Mining also future reclamation areas 1, Airports Assembly, processing, and manufacturing facilities Warehousing, distribution, and wholesale facilities ,

31 Table 1-5. Land Use/Cover Category Aggregation and Percent Impervious (Page 3 of 4) Land Use I.D. Description Area (acre) Percent of Total Area (%) USGS Seamless Percent Impervious (mean %) Model Value (% IMPLND) Model Value (% PERLND) 65 Process plants and feedlots Salvage yards and junk yards Mini-storage facilities Large public or private utilities Military facilities Public parks 1, Public golf courses Private golf courses Private campgrounds Cemeteries Natural drainage facilities or dikes Public nature/conservation areas Bodies of water Private common spaces or nature areas NLCD 110 Open water Developed, open space 8, Developed, low intensity 3, Developed, medium intensity 2,

32 Table 1-5. Land Use/Cover Category Aggregation and Percent Impervious (Page 4 of 4) Land Use I.D. Description Area (acre) Percent of Total Area (%) USGS Seamless Percent Impervious (mean %) Model Value (% IMPLND) Model Value (% PERLND) 240 Developed, high intensity Barren land (rock/sand/clay) Deciduous forest 2, Evergreen forest Shrub/scrub Grassland/herbaceous 4, Pasture/hay 26, Cultivated crops 64, Woody wetlands Emergent herbaceous wetlands 1,

33 form one uniform land use and cover dataset. To achieve this, priority was given to the city land use (i.e., where overlap between NLCD and city land use occurred, city land use was preserved) and everywhere else was filled with NLCD Watershed Delineation To increase resolution and separate climatic and physiographic characteristics in the model, a watershed is often delineated into smaller, more manageable units to allow the model to produce results at more points of interest and to provide a more comprehensive and accurate calibration. Delineation points as part of the Sioux Falls TMDL Assessment shown in Figure 1-16 include streamflow gauges, water-quality monitoring sites, tributary confluences, boundary condition points, hydraulic structures, and the upper and lower boundaries of impaired reaches. The watershed delineation occurred in multiple steps. First, the entire basin was delineated with ArcHydro in ArcGIS 9.3 using an outlet point near Brandon, South Dakota, and the inlet point at the Minnehaha/Moody County line in South Dakota. ArcHydro is a sophisticated tool that uses a digital elevation model and the USGS National Hydrography Dataset flow lines to delineate a land surface based on flow accumulation. Next, the sewershed provided by the city of Sioux Falls was modified. Storm basins in this coverage were aggregated based on common outfall points and the proximity of those outfall points along the Big Sioux River through the city. For example, storm basins that flowed directly into sewers of another storm basin before entering the Big Sioux River were merged with the receiving storm basin. Storm sewer outfall points in very close proximity to monitoring sites and streamflow gauges were rigorously analyzed to ensure that the model accurately represented field conditions. Finally, the city sewershed delineation was combined with the ArcHydro delineation. Similar to land use/cover, priority was given to the sewershed, and all other areas in the watershed used the ArcHydro delineation. The final storm basin delineation is shown in Figure Watershed Characterization The Sioux Falls TMDL Assessment project area was characterized to account for variation in hydrologic and water-quality processes. Meteorological data and land use data were the two key attributes selected to represent the variations in these processes. Soil properties have a large influence on the infiltration of water to the subsurface, and vegetation affects water loss through transpiration and interception. Since these properties do not vary significantly within unique land use categories, land use is an effective attribute to represent their characteristics throughout the watershed. Of all meteorological data, precipitation data can possess the greatest spatial variability. It is highly desirable to account for this variability when modeling constituents such as bacteria and sediments that are functions of rainfall and runoff processes. Precipitation isohyetal contours [South Dakota State University, 2008] were used to determine the appropriate monitoring station for meteorological data for each storm basin. Figure 1-18 shows the hydrozone 27

34 RSI Figure Delineation Points for the Sioux Falls Total Maximum Daily Load Assessment Project. 28

35 RSI Figure Final Storm Basin Delineation for the Sioux Falls Total Maximum Daily Load Assessment. 29

36 RSI Figure Meteorological Stations and Hydrozones for the Sioux Falls Total Maximum Daily Load Assessment. 30

37 characterization of the watershed. The northern hydrozone uses meteorological data from the Dell Rapids station and data from the Sioux Falls station is applied to the southern hydrozone. Land use categories from the combined 2001 NLCD and city land use coverage were aggregated and distilled into seven model land use categories. Table 1-6 gives the aggregated model land use categories and the acres associated with each land use category. Table 1-6. Aggregated Model Land Use Categories for the Sioux Falls Total Maximum Daily Load Assessment Model Land Use Category Area (acre) Percent of Total Area (%) Residential 14, Commercial 7, Industrial 4, Park 4, Range and Pasture Lands 39, Forest 3, Cropland 64, Sum 137, A unique numbering system was developed for the entire watershed. HSPF requires a numbering scheme to identify a pervious land segment (PERLND), impervious land segment (IMPLND), and reach and reservoir (RCHRES) in the model. A unique numbering system also aids as a visual distinction between each PERLND, IMPLND, and RCHRES when calibrating or working with the model UCI. An identity (ID) number can consist of any integer from 1 to 999. As an example, if there were three attributes that characterized the watershed, then the ones place, tens place, and hundreds place would each represent a separate attribute. The Sioux Falls TMDL Assessment has three attributes that characterize the watershed: hydrozone, land use, and the reach receiving runoff. Because of the number of reaches in the project area, using the ones place, tens place, and hundreds place of a three-digit identification number was not possible. PERLNDs were assigned a ones place number from 1 to 7, indicating land use type (e.g., 5 = range and pasture lands). Similarly, IMPLNDs were assigned either 8 or 9, indicating Effective Impervious Area (EIA) residential or EIA commercial/industrial, respectively, from the impervious percentage of PERLND categories 1, 2, and 3. Hydrozones were used to create a unique numbering system in the model. Land segments receiving meteorological data from the station at Dell Rapids were assigned a hundreds place number 1, and land segments receiving meteorological data from the station at Sioux Falls were assigned a 31

38 hundreds place number 2. Land use category 106, for example, indicates forest receiving meteorological data from Dell Rapids. Figure 1-19 shows the land use and land cover data integrated into PERLND model categories. In general, the RCHRES ID numbers increase from upstream to downstream. Reaches representing the Big Sioux River have a ones place value of 0. The first reach of the system (at the upstream boundary condition) is 10, and the most downstream reach representing the Big Sioux River is 430 near Brandon. RCHRES areas that represent detention structures were assigned a hundreds place digit of 5 and then assigned a unique value of 1 to 14, from upstream to downstream (e.g., 507 is the Swift Park structure). A reach that is connected to another reach has an identification number smaller than the receiving reach yet incorporates the hundreds and tens place digits of the identification number of the Big Sioux River reach that it contributes to. For example, Detention Structure 508 is connected to Sewer Reach 241, which flows into Detention Structure 509; 509, and Sewer Reach 242 both flow into Sewer Reach 243, which then outfalls into Big Sioux River Reach 240. The diversion canal bypassing the city presents a slight deviation from this procedure (Figure 1-20). Throughout the watershed, each storm basin identification is identical to that of the RCHRES that routes its runoff. 1.4 HYDROLOGIC MODEL CALIBRATION AND RESULTS Hydrologic calibration is an iterative process intended to match simulated flow to observed flow by methodically adjusting model parameters. Calibration is required for parameters that cannot be determined by the physical characteristics of the watershed [Donigian, 2002]. It is common practice to divide the selected simulation period into calibration and validation periods. However, the period with the most available data (October 1, 2005 September 30, 2009) was relatively short. Thus a conventional validation period was not used. As a result, model output was calibrated using the entire data record appropriate for a particular calibration point. For example, USGS streamflow gauges with data for the entire simulation period were calibrated over that period of record, and 2009 monitoring stations were calibrated over the 2009 recreation season. Hence, the calibration process for this model consisted of model simulation, output performance qualification against observed data, and modification of model parameters until the best comparison results were obtained. This process also allowed individual years, whether wet or dry, to be assessed for bias as part of the calibration process which, along with evaluating spatial performance, is a form of (and served as) validation. The performance qualification process applied a hierarchical weight-of-evidence approach recommended by experienced modelers to evaluate the performance of the model. The weight-ofevidence approach relates measured endpoints to assessment endpoints using different types of data to evaluate model performance. The hydrologic model parameters were adjusted according to the error statistics and graphical comparisons of simulated flow (assessment endpoint) to observed flow (measured endpoint) produced in the weight of evidence evaluation. As an 32

39 RSI Figure National Land Cover Data and Sioux Falls Land Use Aggregated Into Model Categories. 33

40 RSI Figure Schematic of Sioux Falls Total Maximum Daily Load Assessment Project Area (not to scale). 34

41 example, USGS streamflow gauges were one set of delineation points used to create reach segments throughout the watershed. Conceptually, these gauges do not lie within any reach segment. Rather, the streamflow gauges are at the end of one reach and the beginning of another. Observed streamflow at a USGS gauge was compared to the simulated flow at the end of the reach where the gauge is located. A variety of graphical comparisons and statistical tests were used to assess the performance of the hydrologic model for the Sioux Falls TMDL Assessment. These procedures included flow duration curves, time-series plots, error statistics, and correlation coefficients. The graphs and statistics were placed in three categories to provide a systematic approach for calibration: comprehensive, water balance, and storm event statistics and graphs. The comprehensive category included flow duration curves, correlation coefficients (R), and coefficients of determination (R 2 ). The water balance category involved mean total runoff volume percent errors and average annual and monthly runoff errors and graphs. Event statistics consisted of storm runoff plots and hydrograph statistics. This approach provided increasingly tighter focus on a temporal scale (i.e., annually, seasonally, and monthly) for calibrating hydrology. A flow duration curve is a graph showing flow versus the percent of time that flow is exceeded. This plot represents the hydrologic response of a watershed from base flow to peak flow. Annual and monthly water balance statistics are useful for assessing the long-term and seasonal accuracy of the model. Capturing seasonal variability is important to represent because of the hydrologic influence of snow accumulation and snowmelt processes. Storm event plots and statistics were a key component of the hydrologic calibration, particularly in and around the urban area of Sioux Falls. Hydrologic response to storm events in the urban and developed areas is much faster and can generate much greater runoff compared to the flatter agricultural areas. Thus it was critically important to accurately represent hydrologic effect of the presence of the city of Sioux Falls. Calibration parameters were adjusted to improve the performance of the model until the desired performance criteria were met or no apparent improvement from parameter refinement was noted. Graphical plots were visually evaluated to objectively assess the model performance. Percent error statistics were evaluated using the hydrology criteria in Table 1-7. The correlation coefficient (R) and coefficient of determination (R 2 ) were compared with the criteria in Figure 1-21 to evaluate the performance of the hydrologic model. Calibration results are presented where simulated predictions were compared with observed data (i.e., at the streamflow gauge location). The three Big Sioux River USGS streamflow gauges were used as calibration gauges in the comprehensive and water balance categories. Stormwater monitoring sites were used as calibration gauges in the storm event category. Flow duration curves, time-series plots, and a weight-of-evidence table summarize model performance in the comprehensive and water balance categories, and time-series plots also indicate model performance in the storm event category. 35

42 Table 1-7. General Performance Criteria for Model Calibration and Validation [Donigian, 2002] % Difference Between Simulated and Recorded Values Very Good Good Fair Poor Hydrology/Flow < >25 Sediment < >45 Water-Quality/Nutrients < >35 RSI Figure R and R 2 Performance Criteria for Model Calibration and Validation [Donigian, 2002]. Weight-of-evidence results for the Big Sioux River gauges are provided for both the full year and the recreation season. Recreation season performance was explicitly evaluated to understand model accuracy when bacterial water-quality standards apply. Weight-of-evidence statistics for the full year and the recreation season are presented in Tables 1-8 and 1-9, respectively. Model performance of 1.00 at USGS indicates that simulated flow is identical to observed flow. Because USGS is physically close to the boundary condition flow input, it makes sense that streamflow at this location closely resembles the boundary condition flow. The difference in model performance seen between Tables 1-8 and 1-9 is a result of ice formation at the gauge station during winter months. Ice effects are difficult to measure and predict, and when the accuracy of observed data decreases because of these effects, the weight-of-evidence statistics become misleading. For this reason, the flow duration curves in Figures 1-22 through 1-24 and the time-series plots of 2009 monitoring data in Figures 1-25 through 1-29 show a comparison of data for the recreation season only. Overall, performance of the hydrologic model is very good for the recreational season and good for the full year. This calibration will provide a good estimate of flow throughout the watershed and supports the objectives of the TMDL project. 36

43 Table 1-8. Weight-of-Evidence Statistics for Full-Year Hydrology Calibration Daily, R Monthly, R Table 1-9. Weight-of-Evidence Statistics for Recreation Season Hydrology Calibration Daily, R Monthly, R POLLUTANT MODEL DEVELOPMENT As part of the Sioux Falls TMDL Assessment, pathogen and sediment loading from nonpoint source runoff was proposed to be simulated using event mean concentrations (EMCs) in the project area and are shown by land use in Table EMCs use discharge data from a storm event to composite samples based on flow volume to create a single sample. Results from the composite sample analysis yield the EMC. HSPF is capable of simulating the fate and transport of pathogens and sediment in a variety of ways. Furthermore, unlike other watershed-scale models, HSPF allows for the inclusion of subsurface concentrations in addition to surface concentrations where appropriate (e.g., tile drains). In terms of water-quality modeling, the use of EMCs allows a more streamlined conceptual framework and could potentially reduce compounded uncertainty from the inclusion of other models. An EMC inherently accounts for the physical, biological, and chemical processes that occur over and in land segments, thus eliminating the need for another model to simulate those processes and predict a concentration or load to receiving waterbodies. The EMCs used in the pollutant model were developed from data collected during the 2009 Stormwater Monitoring Plan throughout the city of Sioux Falls. ISCO samplers were rotated among proposed sites that had been identified as having relatively uniform land uses (e.g., residential, commercial, industrial, recreational). This method allowed land use-specific EMCs to be developed and applied in the model. Stage and discharge data from each event were used to composite samples based on flow volume, resulting in a single sample for each event. Results from the composite sample analysis yielded each land use-specific EMC for sediments and bacteria [Love, 2008]. EMCs were applied to the surface outflow for bacteria and sediment and interflow outflow for sediment of PERLND areas and only to the surface outflow of IMPLNDs. Fate and transport processes in the RCHRES areas were then simulated throughout the system. 37

44 RSI Figure Flow Duration Curve at USGS During the Recreation Season. RSI Figure Flow Duration Curve at USGS During the Recreation Season. 38

45 RSI Figure Flow Duration Curve at USGS During the Recreation Season. RSI Figure Time-Series Plot of RESPEC Monitoring Site SKC030 During the 2009 Recreation Season. 39

46 RSI Figure Time-Series Plot of RESPEC Monitoring Site at BSR060 During the 2009 Recreation Season. RSI Figure Time-Series Plot of RESPEC Monitoring Site DIV010 During the 2009 Recreation Season. 40

47 RSI Figure Time-Series Plot of RESPEC Monitoring Site at BSR080 During the 2009 Recreation Season. RSI Figure Time-Series Plot of RESPEC Monitoring Site at BSR110 During the 2009 Recreation Season. 41

48 Table Minimum and Maximum Values Used for the Surface Runoff Associated With the HSPF Model Model Land Use E. coli EMCs (cfu/100ml) Minimum Maximum Commercial Industrial Residential Crop/Range 50 3,470 Boundary condition time series were obtained for the city of Sioux Falls WWTP, the city of Brandon WWTP, and John Morrell & Co. The city of Sioux Falls and John Morrell & Co. time series were provided as average daily load per month. The city of Brandon WWTP time series was provided as daily load. These boundary conditions were represented as point sources to specific reaches in the model. Continuous bacterial and sediment time series for the Big Sioux River upstream of Dell Rapids and for Skunk Creek were developed using Load Estimator (LOADEST), a Fortran program developed by the USGS for estimating constituent loads in streams and rivers [U.S. Geological Survey, 2009]. Using a time series of streamflow, additional variables, and constituent concentration, LOADEST develops a regression model for the estimation of constituent load. The formulated regression model is then used to estimate loads over a userspecified time interval. The calibration and estimation procedures in LOADEST are based on three statistical estimation methods. The first two methods, Adjusted Maximum Likelihood Estimation and Maximum Likelihood Estimation, are appropriate when the calibration model errors (residuals) are normally distributed. The third method, Least Absolute Deviation, is an alternative to Maximum Likelihood Estimation when the residuals are not normally distributed [U.S. Geological Survey, 2009]. LOADEST has nine built-in equations used to develop the regression model. Users have an option to select a specific equation or allow a set of statistical criteria (Akaike Information Criterion and Schwarz Posterior Probability Criteria) to select the best equation. Bacterial data for the Big Sioux River near Dell Rapids were available for May 10, 2006 July 22, 2009, and data along Skunk Creek were available for October 25, 2005 October 27, These intermittent datasets were reported in terms of fecal coliform. Using samples collected during the 2009 monitoring effort, a regression relationship was developed to convert the historical fecal coliform data to E. coli before generating a complete time series at each location. Samples were analyzed for fecal coliform and E. coli at each site. Using the available paired streamflow and E. coli data (translated from historical fecal coliform data) 42

49 along with the full streamflow time series developed for each boundary condition, LOADEST generated full bacterial time series for the Big Sioux River and Skunk Creek with R 2 values of 0.29 and 0.45, respectively. LOADEST was also used to generate a full TSS time series. For Skunk Creek, continuous flow data was only available from 2008 on, so daily average daily flow was used with instantaneous TSS data where instantaneous flow data were not available. Continuous TSS data were matched to the current hour (i.e., 3:25 = 3:00) and paired with the average hourly flow or the average daily flow. This data was entered into the calibration file and run through LOADEST to acquire loads in tons/day. For the Big Sioux River at Dell Rapids, continuous turbidity data with some large gaps (month-long) were available for the modeling time period. Because of the data gaps, the turbidity data could not be used to calculate loads without LOADEST. When actual TSS data were available, it was entered in the LOADEST calibration file, and when TSS data were not available, it was calculated using a regression equation between TSS and turbidity and entered into the LOADEST calibration file. TSS values, both actual and calculated, were paired with average daily flow in the LOADEST calibration file. This full dataset rendered R 2 values of 0.87 and 0.80 for the boundary conditions on the Big Sioux River at Dell Rapids and Skunk Creek, respectively. 1.6 POLLUTANT MODEL CALIBRATION METHODS AND RESULTS The calibration of the pollutant model was very similar to the calibration of the hydrologic model. The pollutant model is an iterative process intended to match simulated bacterial and TSS concentrations and loads to observed concentrations and loads by methodically adjusting model parameters. Here again, the entire simulation period, October 1, 2005 September 30, 2009, was used in the calibration of the model. Similar to the hydrologic calibration, graphical comparisons of simulated and observed data were made using bacterial and sediment load duration curves and time-series plots. A weightof-evidence statistical approach was not used because the method of load introduction to the system, EMCs, would have resulted in misleading statistics. Bacterial and sediment loads were represented as the average concentration for a single event, which reduced the influence of peak concentrations (i.e., first-flush phenomenon) throughout the event. Thus this behavior of the data was not preserved; however, for the goals of the Sioux Falls TMDL Assessment, the total mass of sediments and bacteria was modeled. As with hydrology, the graphs represent a comprehensive and storm event approach toward calibration of the pollutant model. Calibration parameters were adjusted to improve the performance of the model until the desired performance criteria were met or no apparent improvement from parameter refinement was noted. Graphical plots were visually evaluated to objectively assess the model performance. 43

50 Three parameters were available for refinement during the pathogen calibration: bacterial decay (FSTDEC) and temperature correction coefficient (THFST) allowed calibration of instream processes, and the EMC of each PERLND allowed the calibration of load generation. FSTDEC is a first-order decay rate for general water-quality constituents, and THFST is the water temperature correction coefficient for the first-order decay of the water-quality constituent in HSPF. Temperature correction values have been found to be relatively constant throughout bacterial environments [Benham, 2006]. The HSPF default value of 1.07 was used initially and refinement was based on modeler experience. Decay of bacteria has been found to vary considerably depending on the physical location of the bacteria. For this reason, a range of first-order decay values was obtained from studies with similar characteristics of land uses and stream hydrology. These values, ranging from 0.5 per day to 1.5 per day, were adjusted until simulated results on the bacterial duration curves and time-series plots showed the same trend as observed data. After EMC values are adjusted to provide the expected initial input into the stream channel, calibration continues with adjustment of parameters governing the processes of deposition, scour, and transport of sediments in the stream. Calibration is performed on a reach-by-reach basis from upstream to downstream because of the influence of upstream parameter adjustments on downstream reaches. Bed behavior and sediment budgets are analyzed at each reach to ensure that results are consistent with field observations, historical reports, and expected behavior from past experience. Because observed data were not available at each reach, calibration was focused at locations where TSS concentration data were available. During in-stream sediment transport calibration it was not uncommon to adjust EMCs to create the desired concentrations and loadings based on observed data. The primary parameters involved in calibration of in-stream sediment transport and bed behavior include critical shear stresses for deposition and scour for cohesive soils (silt and clay) and the coefficient and exponent in the sand transport power function. TAUCD and TAUCS are the critical deposition and scour shear stress parameters, respectively, applied to both silt and clay processes. They are initially estimated as the 25th percentile of the bed shear stress for TAUCD and 75th percentile for TAUCS. Sediment is being transported when the bed shear stress is higher than TAUCD and settles and deposits when the bed shear stress is below TAUCD. Sediment is being scoured from the bed when the shear stress is greater than TAUCD. The M parameter for silt and clay determines the intensity of scour when scour is occurring. Bed shear stresses are calculated from hydrology and reach geometry in the model. KSAND and EXPSAND are the coefficient and exponent, respectively, of the sand transport power function. As mentioned, ISCO samplers were rotated among sites that had been identified as having relatively uniform land uses (e.g., residential, commercial, industrial, recreational), allowing land use-specific EMCs to be developed and applied within the model. Monitoring sites STW030, STW050, STW150, and SUC020 were used to develop EMCs for residential, commercial, industrial, and agricultural (rangeland, pasture, and cropland) land uses, respectively. Initially, each residential PERLND, for example, used the same EMC value. 44

51 Throughout the calibration process, site-specific EMCs were developed for multiple sites in the city because of differing conditions relative to the storm basin where the land use-specific EMCs were developed. Figure 1-30 shows the six primary calibration locations used to assess the effectiveness of the pollutant model calibration: Reach 70 (above the city, upstream of the diversion split), Reach 165 (along Skunk Creek), Reach 190 (BSR060, in oxbow near South Western Avenue), Reach 340 (BSR080, downstream of diversion return at North Cliff Avenue), Reach 414 (the outlet of Slip-up Creek), and Reach 430 (the system outlet near Brandon). RSI Diversion Canal Figure HSPF Reach Locations of Selected Pathogenic Model Calibration Points. The progress of the calibration is assessed visually by comparing observed to modeled (simulated) values represented on duration curves and time-series plots. Figure 1-31 is an example TSS duration curve for HSPF Reach 70. The y-axis represents TSS concentrations in mg/l and the x-axis displays the percentage of time TSS concentrations are equaled or exceeded. The green static line on the graph represents the acute water-quality standard of 158 mg/l and is merely used for reference. The blue line with the blue circles represents the observed data collected in the field, and the solid pink line represents the continuous data points simulated by the model on an hourly time step. The dashed red line with the red squares represents the data simulated when a field water-quality sample was collected, which provides a more direct comparison of the observed to the simulated data. The figure represents that the 45

52 RSI Figure Total Suspended Solids Duration Curve at Outlet of Reach 70 Upstream of the City of Sioux Falls. RSI Figure Total Suspended Solids Time Series at Outlet of HSPF Reach 70 Upstream of the City of Sioux Falls. 46