Abstract
The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation on their performance in hydrological modeling is required to improve their performance. This study comprehensively evaluates the performance of eight widely used PPs in hydrological modeling by comparing with gauge-observed precipitation for a large number of catchments. These PPs include the Global Precipitation Climatology Centre (GPCC), Climate Hazards Group Infrared Precipitation with Station dataset (CHIRPS) V2.0, Climate Prediction Center Morphing Gauge Blended dataset (CMORPH BLD), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN CDR), Tropical Rainfall Measuring Mission multi-satellite Precipitation Analysis 3B42RT (TMPA 3B42RT), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2.0), European Center for Medium-range Weather Forecast Reanalysis 5 (ERA5) and WATCH Forcing Data methodology applied to ERA-Interim Data (WFDEI). Specifically, the evaluation is conducted over 1382 catchments in China, Europe and North America for the 1998-2015 period at a daily temporal scale. The reliabilities of PPs in hydrological modeling are evaluated with a calibrated hydrological model using rain gauge observations. The effectiveness of PPs-specific calibration and bias correction in hydrological modeling performances are also investigated for all PPs. The results show that: (1) compared with the rain gauge observations, GPCC provides the best performance overall, followed by MSWEP V2.0; (2) among the eight PPs, the ones incorporating daily gauge data (MSWEP V2.0 and CMORPH BLD) provide superior hydrological performance, followed by those incorporating 5-day (CHIRPS V2.0) and monthly (TMPA 3B42RT, WFDEI, and PERSIANN CDR) gauge data. MSWEP V2.0 and CMORPH BLD perform better than GPCC, underscoring the effectiveness of merging multiple satellite and reanalysis datasets; (3) regionally, all PPs exhibit better performances in temperate regions than in arid or topographically complex mountainous regions; and (4) PPs-specific calibration and bias correction both can improve the streamflow simulations for all eight PPs in terms of the Nash and Sutcliffe efficiency and the absolute bias. This study provides insights on the reliabilities of PPs in hydrological modeling and the approaches to improve their performance, which is expected to provide a reference for the applications of global precipitation datasets.
Highlights
Precipitation is closely related to atmospheric circulation and is a critical component of hydrological cycle [1,2,3]
Multi-Source Weighted-Ensemble Precipitation (MSWEP) V2.0 and CMORPH BLD perform better than Global Precipitation Climatology Centre (GPCC), underscoring the effectiveness of merging multiple satellite and reanalysis datasets; (3) regionally, all precipitation datasets (PPs) exhibit better performances in temperate regions than in arid or topographically complex mountainous regions; and (4) PPs-specific calibration and bias correction both can improve the streamflow simulations for all eight PPs in terms of the Nash and Sutcliffe efficiency and the absolute bias
2, a hydrological model was calibrated by each PPs, which was called PPs-specific calibration, and their performances were compared with the benchmark value; in step 3, the bias corrected-PPs” (BC-PPs) were used to drive the hydrological model based on the Reference Parametersets” (RP) in the calibration period and their performances were compared with the benchmark value
Summary
Precipitation is closely related to atmospheric circulation and is a critical component of hydrological cycle [1,2,3]. Accurate precipitation records are essential for meteorological and climatic analysis and the keys for successful water resource management [4,5]. Acquiring reliable and consistent precipitation series is Remote Sens. The advent of global precipitation datasets (PPs) including gauge-based, satellite-related, and reanalysis datasets, brings an unprecedented opportunity for precipitation estimation and hydrological application. These PPs differ in design objective, data sources, spatial resolution, spatial coverage, temporal resolution, temporal span, and latency. Evaluations have been carried out to understand the respective advantages and limitations of PPs [6,7]
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