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An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

by Sean Zeiger 1,* and Jason Hubbart 2,3
Department of Forestry, Water Resources Program, School of Natural Resources, University of Missouri, 203-T ABNR Building, Columbia, MO 65211, USA
Davis College, Schools of Agriculture and Food, and Natural Resources, West Virginia University, 3109 Agricultural Sciences Building, Morgantown, West Virginia 26506, USA
Institute of Water Security and Science, West Virginia University, Morgantown, WV, USA
Author to whom correspondence should be addressed.
Water 2017, 9(7), 459;
Received: 2 April 2017 / Revised: 17 June 2017 / Accepted: 21 June 2017 / Published: 24 June 2017
Accurate mean areal precipitation (MAP) estimates are essential input forcings for hydrologic models. However, the selection of the most accurate method to estimate MAP can be daunting because there are numerous methods to choose from (e.g., proximate gauge, direct weighted average, surface-fitting, and remotely sensed methods). Multiple methods (n = 19) were used to estimate MAP with precipitation data from 11 distributed monitoring sites, and 4 remotely sensed data sets. Each method was validated against the hydrologic model simulated stream flow using the Soil and Water Assessment Tool (SWAT). SWAT was validated using a split-site method and the observed stream flow data from five nested-scale gauging sites in a mixed-land-use watershed of the central USA. Cross-validation results showed the error associated with surface-fitting and remotely sensed methods ranging from −4.5 to −5.1%, and −9.8 to −14.7%, respectively. Split-site validation results showed the percent bias (PBIAS) values that ranged from −4.5 to −160%. Second order polynomial functions especially overestimated precipitation and subsequent stream flow simulations (PBIAS = −160) in the headwaters. The results indicated that using an inverse-distance weighted, linear polynomial interpolation or multiquadric function method to estimate MAP may improve SWAT model simulations. Collectively, the results highlight the importance of spatially distributed observed hydroclimate data for precipitation and subsequent steam flow estimations. The MAP methods demonstrated in the current work can be used to reduce hydrologic model uncertainty caused by watershed physiographic differences. View Full-Text
Keywords: SWAT; PRISM; TRMM; CHIRPS; MAP; mixed-land-use hydrology; precipitation SWAT; PRISM; TRMM; CHIRPS; MAP; mixed-land-use hydrology; precipitation
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Zeiger, S.; Hubbart, J. An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment. Water 2017, 9, 459.

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