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Information regarding topographic, meteorologic, geologic, and geomorphic characteristics is increasingly available in spatially explicit digital formats. Of interest is whether enhanced spatial processing of newly available digital grids can lead to new estimators of watershed characteristics which may in turn, improve our ability to predict extreme hydrologic events. Regional hydrologic models of low-flow processes often produce estimators with unacceptably large errors. Using a continuous digital elevation model (DEM) of the conterminous United States, watershed boundaries were developed for the streamflow gauges of the USGS’s Hydro-Climatic Data Network. Using these watershed boundaries, many watershed characteristics were developed from digital grids, including: the original DEM, the USDA’s State Soil Geographic grids, and the Spatial Climate Analysis Service’s orographically weighted precipitation and temperature grids of varying spatial and temporal resolution. Digital processing of grids leads to improvements in estimation and reproducibility of spatial statistics over traditional manual processing approaches. Low-flow regional regression models were developed for regions across the conterminous United States. Inclusion of the new watershed characteristics led to improvements in regional regression models for all regions. The inclusion of hydrogeologic indices, in particular a new smoothed baseflow recession constant estimator, led to dramatic improvements in low-flow prediction.
Kroll et al. (Fri,) studied this question.