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.
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