Temporal Downscaling of Daily Gauged Precipitation by Application of a Satellite Product for Flood Simulation in a Poorly Gauged Basin and Its Evaluation with Multiple Regression Analysis

Abstract

Abstract The study demonstrates that the temporal downscaling of rain gauge–measured precipitation with satellite-based precipitation estimates enhances the accuracy of hydrological simulations, especially for flood duration. Multiple regression analysis was examined to predict which hydrometeorological parameters have a significant influence on accuracy. The approach was examined at the Hương River basin in Vietnam (1520 km2), which is a mountainous region subject to heavy rainfall. The multisensor algorithm Global Satellite Mapping of Precipitation, version Moving Vector with Kalman (MVK; GSMaP_MVK; Psat), was employed to downscale the daily gauge precipitation measurements into 6-h time steps. Discharge in the rainy season in 2006–09 was simulated by a distributed hydrological model with 6-h time steps with four precipitation datasets: 6-h gauge (Pcontrol), daily uniform gauge (Puni), Psat, and the downscaled satellite product (Pds). Flood simulation with Pds performed better than that with Puni in 14 out of the 18 flood events, being close to the results with Pcontrol (median Nash–Sutcliffe efficiencies of 0.776, 0.261, and 0.710, respectively). Multiple regression analysis showed that the effectiveness of the downscaling method was significantly related to the bias and random errors of the satellite product. In conclusion, satellite-based precipitation measurements have potential for temporal downscaling of discharge simulations from daily to subdaily resolutions in moderate-sized watersheds that lack subdaily rainfall records, and the degree of simulation improvement can be estimated by statistical analysis. The suggested method can be broadly applied to watersheds where the daily precipitation is measured and when satellite-based precipitation measurements are available.