Spatial variability in water-balance model performance in the conterminous United States

Abstract

A monthly water-balance (WB) model was tested in 44 river basins from diverse physiographic and climatic regions across the conterminous United States (U.S.). The WB model includes the concepts of climatic water supply and climatic water demand, seasonality in climatic water supply and demand, and soil-moisture storage. Exhaustive search techniques were employed to determine the optimal set of precipitation and temperature stations, and the optimal set of WB model parameters to use for each basin. It was found that the WB model worked best for basins with: (1) a mean elevation less than 450 meters or greater than 2000 meters, and/or (2) monthly runoff that is greater than 5 millimeters (mm) more than 80 percent of the time. In a separate analysis, a multiple linear regression (MLR) was computed using the adjusted R-square values obtained by comparing measured and estimated monthly runoff of the original 44 river basins as the dependent variable, and combinations of various independent variables [streamflow gauge latitude, longitude, and elevation; basin area, the long-term mean and standard deviation of annual precipitation; temperature and runoff; and low-flow statistics (i.e., the percentage of months with monthly runoff that is less than 5 mm)]. Results from the MLR study showed that the reliability of a WB model for application in a specific region can be estimated from mean basin elevation and the percentage of months with gauged runoff less than 5 mm. The MLR equations were subsequently used to estimate adjusted R-square values for 1,646 gauging stations across the conterminous U.S. Results of this study indicate that WB models can be used reliably to estimate monthly runoff in the eastern U.S., mountainous areas of the western U.S., and the Pacific Northwest. Applications of monthly WB models in the central U.S. can lead to uncertain estimates of runoff.