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

• Main Authors: H. E. Beck, N. Vergopolan, M. Pan, V. Levizzani, A. I. J. M. van Dijk, G. P. Weedon, L. Brocca, F. Pappenberger, G. J. Huffman, E. F. Wood
• Format: Article
• Language: English
• Published: Copernicus Publications 2017-12-01
• Series: Hydrology and Earth System Sciences
• Online Access: https://www.hydrol-earth-syst-sci.net/21/6201/2017/hess-21-6201-2017.pdf

We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (<i>P</i>) datasets for the period 2000–2016. Thirteen non-gauge-corrected <i>P</i> datasets were evaluated using daily <i>P</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 ( &lt;  50 000 km²) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected <i>P</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 <i>P</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 <i>P</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 <i>P</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 <i>P</i> estimates.