Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

摘要

We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (iP/i) datasets for the period 2000–2016. Thirteen non-gauge-corrected iP/i datasets were evaluated using daily iP/i gauge observations from 76?086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized (?&??50?000?kmsup2/sup) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected iP/i datasets, the satellite- and reanalysis-based MSWEP-ng?V1.2 and?V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP?V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH?V1.0, GSMaP?V5/6, and TMPA 3B42RT?V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat?V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected iP/i datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP?V1.2 and?V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with iP/i estimates directly incorporating temporally coarser gauge data (CHIRPS?V2.0, GPCP-1DD?V1.2, TMPA 3B42?V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of iP/i dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based iP/i estimates.