1. Consideration of Drainage Ditches and Sediment Rating Curve on SWAT Model Performance A.M. Sadeghi *1, A.M. Sexton 1, G.W. McCarty 1, M.W. Lang 3,1, W.D. Hively 4,1, and A. Shirmohammadi 2 (1) Hydrology and Remote Sensing Laboratory (HRSL), USDA-ARS, Beltsville, MD (2) Fischell Dept. of Bioengineering, University of Maryland, College Park, MD (3) USFS-Forest Inventory and Analysis, Beltsville, MD (4) USGS Eastern Geographic Science Center, Reston, VA Introduction C omplex watershed–scale, water quality models require a considerable amount of data in order to be properly configured, especially in view of the scarcity of data in many regions due to temporal and economic constraints. In this study, we examined two different input data issues incurred while building and calibrating a model of the German Branch watershed (figure 1) using the watershed scale “Soil and Water Assessment Tool (SWAT).” Site Information The German Branch (~50 km 2 ) is a sub-watershed of the Choptank, a benchmark basin of USDA’s Conservation Effects Assessment Project (CEAP). CEAP is a multi-agency effort to quantify the environmental effects of conservation practices to improve their efficacy. In order to provide the most accurate model estimates of watershed response, consideration must be given to the unique features of the watershed as well as the quality of available input data. One set-up scenario addressed the issue of accounting for copious drainage ditches within the study area by comparing stream flow estimates derived using a conventional Digital Elevation Model (DEM) versus a DEM, hand-edited to include drainage ditches in the topography. The second set-up scenario examined the issue of estimating measured sediment loads for time-periods lacking data using sediment rating curves. The Approach Results and Discussions Conclusions The major land uses in the German Branch (GB) are agriculture (~61%) and forest (~33%), followed by developed land (~5%) and water (~1%). The agricultural landscape in the region is dominated by the poultry industry. Corn and soybean are grown to supply feed, and poultry litter is used to fertilize the crops. Calibration and Validation Analyses Figure 1. Location of the Choptank River watershed (in light blue) on Maryland’s Eastern Shore of the Chesapeake Bay, and map of delineated German Branch watershed including land uses and stream reaches. Site Description and Model Data Acquisition Calibration and Validation Analyses Figure 2. Sediment rating curve (power function) obtained using monthly sediment loads from July 1990 to September 1995. SWAT requires topographic, soils, and land use maps to operate. Light Detection and Ranging (LIDAR) DEMs (~3m, one conventional and one hand-edited to include drainage ditches in the topography) were provided by HRSL. Weather data and land management data are also required to run the model. A GIS program called NEXRAD_SWAT (Zhang and Srinivasan, 2009) was used to incorporate NEXRAD MPE data, providing distributed estimation of the average daily precipitation associated with each sub-basin. The stream flow component of GB SWAT (for the 2 DEMS) was calibrated with observed flow at the GB outlet using a daily time step from 2005 to 2006 with one year of spin-up (2004). Validation was conducted using the time period from 1/1/07 through 4/15/07. The sediment component was calibrated and validated on a monthly time scale in similar time frames. where x i,measured are measured and x i, simulated are simulated data; and n represents the number of observations. Calibration was carried out using Parameter Solutions (Parasol) (van Griensven and Meixner, 2007). It uses a global search algorithm to minimize the objective function(s). The objective functions include sum of squares of the residuals (SSQ) and SSQ after ranking. The equation for SSQ is as follows: Sediment Estimation Results indicated a slight improvement in model performance (a 4% increase in NSE value) to estimate flow during the calibration period and a significant decrease in model performance (15% in NSE value) during the validation period (Table 1). The decrease in model performance during the validation period is attributed to increased overestimation of flow. It seems that the inclusion of drainage ditches, increased the amount of flow simulated by the model, especially during a dry period which was the case for the 2007 months. Sediment rating curves provided good estimates of monthly sediment loads during model calibration period (NSE = 0.59) and validation period (NSE = 0.63) as shown in figures 3 and 4. Time series plots showed that the model followed the monthly trend fairly well. Figure 3. Time series and scattergram plot of sediment load during calibration period (December 2004 to January 2006) on the monthly time scale. DEM Scenario Mean (cms) StDev (cms) No. of Samples NSEr2r2 RMSE (cms) PBIAS (%) Calibration Period (2005 2006) NE Measured0.631.22730 Simulated0.450.897300.580.600.79-28.44 ED Measured0.631.22730 Simulated0.521.017300.600.610.78-17.98 Validation Period (1 Jan. to 15 April 2007) NE Measured1.242.08105 Simulated1.262.041050.730.751.071.14 ED Measured1.242.08105 Simulated1.362.321050.620.701.289.74 Table 1. Model performance measures for daily stream flow estimates using non-edited (NE) and hand-edited LIDAR DEMs. Insufficient sediment data were available for the GB watershed in the most recent years. Therefore, a sediment rating curve was developed using measured sediment loading data from July 1990 to September 1995. The resulting curve was a power function with an r 2 value of 0.63. Figure 4. Time series and scattergram plot of sediment load during validation period (February 2006 to March 2007) on the monthly time scale. Using an updated DEM, hand-edited to include extensive drainage ditches did not provide much of an improvement in model stream flow estimations. In fact, it caused an increased overestimation of flow during dry weather periods. Hence, this method of accounting for drainage ditches in areas such as GB should be used with caution. Although the method used to derive sediment loading data was quick and rudimentary, it provided a good calibration of the sediment component of SWAT in the GB considering the scarce amount of measured data available. This method, however is not expected to always give good results especially with more drastic changes in land use and climate changes.