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Importance of Crop Yield in Calibrating Watershed Water Quality Simulation Tools1
Authors:Sujithkumar Surendran Nair  Kevin W. King  Jonathan D. Witter  Brent L. Sohngen  Norman R. Fausey
Affiliation:1. Respectively, Post Doctoral Researcher (Nair), 321B Morgan Hall, University of Tennessee, Knoxville, Tennessee 37996;2. Agricultural Engineer (King and Fausey), USDA/ARS – Soil Drainage Research Unit, 590 Woody Hayes Drive, Columbus, Ohio 43210;3. Research Assistant Professor (Witter), Department of Food, Agricultural and Biological Engineering, 590 Woody Hayes Drive, Columbus, Ohio 43210;4. Professor (Sohngen), Department of Agricultural, Environmental and Development Economics, The Ohio State University, 2120 Fyffe Road, Columbus, Ohio 43210
Abstract:Surendran Nair, Sujithkumar, Kevin W. King, Jonathan D. Witter, Brent L. Sohngen, and Norman R. Fausey, 2011. Importance of Crop Yield in Calibrating Watershed Water Quality Simulation Tools. Journal of the American Water Resources Association (JAWRA) 47(6):1285–1297. DOI: 10.1111/j.1752‐1688.2011.00570.x Abstract: Watershed‐scale water‐quality simulation tools provide a convenient and economical means to evaluate the environmental impacts of conservation practices. However, confidence in the simulation tool’s ability to accurately represent and capture the inherent variability of a watershed is dependent upon high quality input data and subsequent calibration. A four‐stage iterative and rigorous calibration procedure is outlined and demonstrated for Soil Water Analysis Tool (SWAT) using data from Upper Big Walnut Creek (UBWC) watershed in central Ohio, USA. The four stages and the sequence of their application were: (1) parameter selection, (2) hydrology calibration, (3) crop yield calibration, and (4) nutrient loading calibration. Following the calibration, validation was completed on a 10 year period. Nash‐Sutcliffe efficiencies for streamflow over the validation period were 0.5 for daily, 0.86 for monthly, and 0.87 for annual. Prediction efficiencies for crop yields during the validation period were 0.69 for corn, 0.54 for soybeans, and 0.61 for wheat. Nitrogen loading prediction efficiency was 0.66. Compared to traditional calibration approaches (no crop yield calibration), the four‐stage approach (with crop yield calibration) produced improved prediction efficiencies, especially for nutrient balances.
Keywords:SWAT  modeling  nutrients  crop yield  biomass  runoff
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