|Nayak, Anurag - USU|
|Chandler, David - KSU|
Submitted to: Trans American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: December 20, 2006
Publication Date: December 20, 2006
Citation: Nayak, A., Marks, D., Chandler, D., and Winstral, A. 2006. Generating distributed forcing fields for spatial hydrologic modeling. EOS Transactions of the American Geophysical Union, 87(52) Fall Meeting Supplement, Abs C21B-1171. Technical Abstract: Spatial hydrologic modeling requires the development of distributed forcing fields of weather and precipitation. This is particularly difficult in mountainous regions of the western US, where measurement sites are limited and the landscape is dominated by complex terrain and variations in vegetation cover. The Reynolds Creek Experimental Watershed (RCEW), in southwestern Idaho offers a unique opportunity to evaluate the sensitivity of interpolation techniques to the number and location of measurement sites. The RCEW, a 239 km2 hydro-climatic observatory operated by the USDA Agricultural Research Service since the early 1960’s, contains 36 hydro-climatic measurement sites for monitoring the range of weather, snow and precipitation conditions across this complex mountain watershed. The MicroMet weather distribution utility, a process and topographically based weather interpolation tool (Liston and Elder, 2006), is used to generate surfaces of temperature, humidity, wind and precipitation over the snow-dominated 55 km2 (elevation range1398-2244m) Tollgate sub-catchment of RCEW. Nineteen meteorological stations were used to simulate the distribution of weather and precipitation for a series of storms during the 2004 water year. Measured and simulated values were compared to evaluate the accuracy of the model, and a jackknife approach was used to evaluate its sensitivity to data from particular stations. To evaluate the effect of elevation and storm track, different combinations of stations were selected, and to evaluate topographic exposure and vegetation shelter stations were divided into groups based on wind exposure. Results show that, even using a sophisticated weather distribution utility like MicroMet, the interpolation is very sensitive to station location and wind exposure. A certain amount of smoothing occurs even when using all 19 stations, but significant differences occur if only protected sites (similar to NRCS Snotel sites), or only wind-exposed sites are used. This research shows that citing hydro-meteorological monitoring stations is critical to improved hydrologic modeling in mountainous regions.