The disaggregation of coarse precipitation products is desirable for spatially distributed hydrological applications where high-resolution rainfall fields are required. In this study, we generate an ensemble of satellite-based precipitation estimates using a spatiotemporal disaggregation framework for use in a spatially explicit hydrologic model. The framework effectively reproduced small, localized storm events using coarse precipitation estimates while simultaneously including known error characteristics into the precipitation fields. Ensemble precipitation inputs were propagated through a distributed hydrologic model to evaluate the impact on simulated hydrologic states and fluxes, such as streamflow, soil moisture content, and evapotranspiration. The disaggregation framework adds utility to the coarse satellite precipitation measurements by inducing a realistic hydrologic response in a manner similar to high-resolution ground-based radar observations. Errors introduced in the precipitation ensembles, namely, the overestimation of high-intensity, short-duration storm events, were tracked through the hydrological system, and physical mechanisms for the soil moisture and streamflow uncertainty are discussed. It was found that increases in streamflow magnitude and decreases in catchment area resulted in increased ensemble streamflow dispersion. In addition, precipitation uncertainty affected soil moisture predictions with more pronounced ensemble dispersion near the surface. Finally, spatial ensemble statistics were used to assess the precipitation and topographic control over soil moisture statistics in the study catchment.
ASJC Scopus subject areas
- Water Science and Technology