ALBERT

Description: Skillful and reliable precipitation data is essential for seasonal hydrologic forecasting, and generation of hydrological data. Though output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States (U.S.) that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (NCEP R2 and ERA40) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10 km grid resolution; stochastically generated data from weather generator; bias-corrected dynamically downscaled and bias-corrected global reanalysis. The reanalysis products are considered as surrogates for large-scale observations. Our study indicates that over the 28 watersheds in the southeastern U.S., the simulated hydrological response to the bias-corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison to synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive is relatively more useful than other sources of meteorological data with comparable time period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.