Scale dependencies of hydrologic models to spatial variability of precipitation

Abstract This study is focused on analyses of scale dependency of lumped hydrological models with different formulations of the infiltration processes. Three lumped hydrological models of differing complexity were used in the study: the SAC-SMA model, the Oregon State University (OSU) model, and the simple water balance (SWB) model. High-resolution (4×4 km) rainfall estimates from the next generation weather radar (NEXRAD) Stage III in the Arkansas-Red river basin were used in the study. These gridded precipitation estimates are a multi-sensor product which combines the spatial resolution of the radar data with the ground truth estimates of the gage data. Results were generated from each model using different resolutions of spatial averaging of hourly rainfall. Although all selected models were scale dependent, the level of dependency varied significantly with different formulations of the rainfall-runoff partitioning mechanism. Infiltration-excess type models were the most sensitive. Saturation-excess type models were less scale dependent. Probabilistic averaging of the point processes reduces scale dependency, however, its effectiveness varies depending on the scale and the spatial structure of rainfall.