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Title:   Author  item
                                            SEXTON, AISHA - University Of Marylanditem
                                            Sadeghi, Aliitem
                                            McCarty, Gregoryitem
                                            LANG, MEGAN - Us Forest Service (FS)item
                                            HIVELY, W - Us Geological Survey (USGS)item
                                            SHIRMOHAMMADI, ADEL - University Of Maryland
  Submitted to: BARC Poster Day
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 3/14/2011
                                    Publication  Date: 4/27/2011
                                    Citation: Sexton, A., Sadeghi, A.M., McCarty, G.W., Lang, M., Hively, W.D., Shirmohammadi, A. 2011. Examining issues with water quality model configuration [abstract]. Abs. 45, BARC Poster Day.Interpretive Summary:
                                Technical Abstract: Complex 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 issues incurred while building and calibrating a model of the German Branch watershed using the Soil and Water Assessment Tool (SWAT). The German Branch is a sub-watershed of the Choptank, a benchmark basin of USDA’s Conservation Effects Assessment Project (CEAP). 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 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. 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. Sediment rating curves provided good estimates of monthly sediment loads during model calibration period (NSE = 0.62), but strongly underestimated sediment loads during the validation period (NSE = -1.27).


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