Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling

The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation their performance in modeling required to improve performance. This study comprehensively evaluates eight widely PPs by comparing with gauge-observed a large catchments. These include Global Precipitation Climatology Centre (GPCC), Climate Hazards Group Infrared Station dataset (CHIRPS) V2.0, Prediction Center Morphing Gauge Blended (CMORPH BLD), Estimation from Remotely Sensed Information using Artificial Neural Networks Data Record (PERSIANN CDR), Tropical Rainfall Measuring Mission multi-satellite Analysis 3B42RT (TMPA 3B42RT), Multi-Source Weighted-Ensemble (MSWEP V2.0), European Medium-range Weather Forecast Reanalysis 5 (ERA5) WATCH Forcing methodology applied ERA-Interim (WFDEI). Specifically, conducted over 1382 catchments China, Europe North America 1998-2015 period at daily temporal scale. reliabilities evaluated calibrated model rain gauge observations. effectiveness PPs-specific calibration bias correction performances also investigated all PPs. results show that: (1) compared observations, GPCC provides best overall, followed MSWEP V2.0; (2) among PPs, ones incorporating data V2.0 CMORPH BLD) provide superior performance, those 5-day (CHIRPS V2.0) monthly 3B42RT, WFDEI, PERSIANN CDR) data. BLD perform better than GPCC, underscoring merging multiple satellite reanalysis datasets; (3) regionally, exhibit temperate regions arid or topographically complex mountainous regions; (4) both can streamflow simulations terms Nash Sutcliffe efficiency absolute bias. insights approaches which expected reference applications datasets.