Submitted to: Proceedings of the Watershed Technology Conference and Workshop, Improving Water Quality and the Environment
Publication Type: Proceedings
Publication Acceptance Date: 12/22/2009
Publication Date: 2/21/2010
Citation: Sexton, A.M., Sadeghi, A.M., Shirmohammadi, A., McCarty, G.W., Hively, W.D. 2010. Modeling cover crop effectiveness on Maryland's Eastern Shore. In: Proceedings of the Watershed Technology, Improving Water Quality and Environment, February 21-24, 2010, San Jose, Costa Rica. 2010 CDROM. Interpretive Summary:
Technical Abstract: The value of watershed-scale, hydrologic/water quality models to ecosystem management is increasingly evident as more programs adopt these tools to evaluate the effectiveness of different management scenarios and their impact on the environment. Quality of precipitation data is critical for appropriate application of watershed models. In small watersheds, where no dense rain gauge network is available, modelers are faced with a dilemma to choose between different data sets. In this study, we used the German Branch (GB) watershed (~50 km2), which is included in the USDA-Conservation Effects Assessment Project (CEAP), to examine the implications of using surface rain gauge and Next Generation Radar (NEXRAD) precipitation data sets on the performance of the Soil and Water Assessment Tool (SWAT). The GB watershed is located in the Costal Plain of Maryland on the Eastern Shore of the Chesapeake Bay. Stream flow estimation results using surface rain gauge data revealed the importance of using rain gauges within the same direction as the storm pattern with respect to the watershed. In the absence of a spatially representative network of rain gauges within the watershed, NEXRAD data produced good estimates of stream flow at the outlet of the watershed. Three NEXRAD datasets, including 1) non-corrected (NC), 2) bias-corrected (BC), and 3) Inverse Distanced Weighted (IDW) corrected NEXRAD data, were produced. Nash-Sutcliffe Efficiency coefficients for daily stream flow simulation using these three NEXRAD data ranged from 0.46 to 0.58 during calibration, and 0.68 to 0.76 during validation. Overall, correcting NEXRAD with rain gauge data is promising to produce better hydrologic modeling results. Given the multiple precipitation datasets and corresponding simulations, we explored the combination of the multiple simulations using Bayesian Model Averaging. The results show that this Bayesian scheme can produce better deterministic prediction than any single simulation and provide reasonable uncertainty estimation. The optimal water balance obtained in this study is an essential precursor to acquiring realistic estimates of sediment and nutrient loads in future GB modeling efforts. The results presented in this study are expected to provide insights into selecting precipitation data for watershed modeling in small Coastal Plain catchments.