Global-Scale Evaluation of 22 Precipitation Datasets Using Gauge Observations and Hydrological Modeling

Part of the Advances in Global Change Research book series (AGLO, volume 69)


We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76,086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the conceptual model HBV against streamflow records for each of 9053 small to medium-sized (<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 P 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 P 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 P 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 P 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 P estimates.


Precipitation Rainfall Evaluation Validation Raingauges Streamflow Satellite products Reanalysis Radar 



We gratefully acknowledge the P dataset developers for producing and making available their datasets. The Water Center for Arid and Semi-Arid Zones in Latin America and the Caribbean (CAZALAC) and the Centro de Ciencia del Clima y la Resiliencia (CR)2 (FONDAP 15110009) are thanked for sharing the Mexican and Chilean gauge data, respectively. We also acknowledge the gauge data providers in the Latin American Climate Assessment & Dataset (LACA&D) project: IDEAM (Colombia), INAMEH (Venezuela), INAMHI (Ecuador), SENAMHI (Peru), SENAMHI (Bolivia), and DMC (Chile). We further wish to thank Ali Alijanian, Koen Verbist, and Piyush Jain for providing additional gauge data. The Global Runoff Data Centre (GRDC) and the United States Geological Survey (USGS) are gratefully acknowledged for providing the majority of the observed Q data. We thank Mauricio Zambrano Bigiarini, Pete Peterson, Hamed Ashouri, Tomoo Ushio, Louise Slater, and three anonymous reviewers for thoughtful comments and suggestions which helped improve the quality of the paper. Graham P. Weedon was supported by the Joint DECC and Defra Integrated Climate Program – DECC/Defra (GA01101). Vincenzo Levizzani wishes to acknowledge funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 603608, “Global Earth Observation for integrated water resource assessment”: eartH2Observe, and from the “Progetto di Interesse NextData” of the Italian Ministry of Education, University, and Research (MIUR). The work was supported through IPA support for the first author from the US Army Corps of Engineers’ International Center for Integrated Water Resources Management (ICIWaRM), under the auspices of UNESCO.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringPrinceton UniversityPrincetonUSA
  2. 2.National Research Council of ItalyInstitute of Atmospheric Sciences and Climate (CNR-ISAC)BolognaItaly
  3. 3.Fenner School of Environment & SocietyThe Australian National UniversityCanberraAustralia
  4. 4.Met Office, Joint Centre for Hydro-Meteorological ResearchWallingfordUK
  5. 5.Research Institute for Geo-Hydrological Protection (IRPI), National Research Council (CNR)PerugiaItaly
  6. 6.European Centre for Medium-range Weather ForecastsReadingUK
  7. 7.NASA, Goddard Space Flight CenterGreenbeltUSA

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