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Using Multiple Watershed Models to Predict Water,Nitrogen, and Phosphorus Discharges to the Patuxent Estuary1
Authors:Kathleen MB Boomer  Donald E Weller  Thomas E Jordan  Lewis Linker  Zhi‐Jun Liu  James Reilly  Gary Shenk  Alexey A Voinov
Institution:1. Respectively, Ecologist (Boomer);2. Senior Ecologists (Weller, Jordan), Smithsonian Environmental Research Center, 647 Contees Wharf Road, Edgewater, Maryland 21037‐0028;3. Modeling Coordinator (Linker);4. Associate Professor (Liu), Department of Geography, University of North Carolina, Greensboro, North Carolina 27402‐6170;5. Planner (Reilly), Maryland Department of Planning, Baltimore, Maryland 21201 [Reilly now at Reilly Consulting, Lafayette Hill, Pennsylvania 19444];6. Associate Professor (Voinov), The Gund Institute for Ecological Economics, University of Vermont, Burlington, Vermont 05405 [Voinov now at International Institute for Geo‐information Science and Earth Observation, Enschede, The Netherlands
Abstract:Boomer, Kathleen M.B., Donald E. Weller, Thomas E. Jordan, Lewis Linker, Zhi‐Jun Liu, James Reilly, Gary Shenk, and Alexey A. Voinov, 2012. Using Multiple Watershed Models to Predict Water, Nitrogen, and Phosphorus Discharges to the Patuxent Estuary. Journal of the American Water Resources Association (JAWRA) 1‐25. DOI: 10.1111/j.1752‐1688.2012.00689.x Abstract: We analyzed an ensemble of watershed models that predict flow, nitrogen, and phosphorus discharges. The models differed in scope and complexity and used different input data, but all had been applied to evaluate human impacts on discharges to the Patuxent River or to the Chesapeake Bay. We compared predictions to observations of average annual, annual time series, and monthly discharge leaving three basins. No model consistently matched observed discharges better than the others, and predictions differed as much as 150% for every basin. Models that agreed best with the observations in one basin often were among the worst models for another material or basin. Combining model predictions into a model average improved overall reliability in matching observations, and the range of predictions helped describe uncertainty. The model average was not the closest to the observed discharge for every material, basin, and time frame, but the model average had the highest Nash–Sutcliffe performance across all combinations. Consistently poor performance in predicting phosphorus loads suggests that none of the models capture major controls. Differences among model predictions came from differences in model structures, input data, and the time period considered, and also to errors in the observed discharge. Ensemble watershed modeling helped identify research needs and quantify the uncertainties that should be considered when using the models in management decisions.
Keywords:watersheds  watershed management  nonpoint source pollution  simulation  hydrological modeling  ensemble modeling  model comparison  model average  land use  model structure  model performance
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