Assessing the skill of satellite-based precipitation estimates in hydrologic applications

An important application of global precipitation measurement rainfall products is providing forcing inputs for hydrologic applications, and the goal of this study is to assess how skillful they are for such applications. To do that, we force a land surface model with both satellite estimates and ground-based measurements and test how well they can predict hydrologic states and fluxes useful for water resource applications, i.e., soil moisture, evapotranspiration, and river streamflow. A number of satellite-based precipitation products ranging from retrievals based only on microwave measurements, combined microwave + infrared estimates, to gauge-corrected products are tested over the entire continental United States region. As a reference to the satellite retrievals, estimates from global and regional weather model reanalyses (the analysis fields from these models) are tested as well. It is found that the microwave + infrared combined estimates can match the skill of the coarse resolution European Center for Medium-Range Weather Forecasts global reanalysis but not the regional National Center for Environmental Predictions reanalysis. Gauge corrections to satellite products significantly enhance their skill by greatly reducing the bias in hydrologic predictions, especially over mountainous areas. Rainfall errors are shown to have strong impact on river streamflow predictions and column total soil moisture and relatively weak impact on near surface moisture and evapotranspiration. River streamflow experiments also suggest that satellite rainfall errors are highly correlated in space within the range of one storm system and thus do not reduce in magnitude with spatial scale (basin size).