Abstract
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.
Highlights
Accurate mean areal precipitation (MAP) estimates are essential forcing data for hydrologic models [1,2]
When the observed precipitation from all eleven monitoring sites were averaged, the results showed that the average annual total precipitation ranged from 743 mm during water year (WY) 2012 to 1543 mm during WY 2010 (Table 4)
The observed precipitation at Sanborn Field and South Farm was within 6 mm of the all-site mean, but the precipitation at Jefferson Farm was 67 mm less than the seven-year mean
Summary
Accurate mean areal precipitation (MAP) estimates are essential forcing data for hydrologic models [1,2]. Quantifying MAP can be confounding, when metrological conditions, topography, and/or land use influence the spatiotemporal variability of precipitation [3]. There are multiple techniques that have been used to estimate MAP including (but not limited to) nearest neighbor (or proximate gauge), direct weighted average, surface-fitting, and remote sensing (e.g., radar and satellite-based) methods. Selecting the most suitable method to estimate MAP can be daunting, in part, because there are so many methods to choose from, each with distinct strengths and weaknesses. Studies have shown a correlation between gauge density and the accuracy of MAP estimates [4]. In regions without observed data, MAP can be simulated using statistical weather generators.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
