Articles | Volume 21, issue 12
https://doi.org/10.5194/hess-21-6201-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-21-6201-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling
Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
Noemi Vergopolan
Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
Vincenzo Levizzani
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Bologna, Italy
Albert I. J. M. van Dijk
Fenner School of Environment & Society, The Australian National University, Canberra, Australia
Graham P. Weedon
Met Office, Joint Centre for Hydro-Meteorological Research, Wallingford, UK
Luca Brocca
Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
Florian Pappenberger
European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, UK
George J. Huffman
Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Eric F. Wood
Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
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Short summary
This study represents the most comprehensive global-scale precipitation dataset evaluation to date. We evaluated 13 uncorrected precipitation datasets using precipitation observations from 76 086 gauges, and 9 gauge-corrected ones using hydrological modeling for 9053 catchments. Our results highlight large differences in estimation accuracy, and hence, the importance of precipitation dataset selection in both research and operational applications.
This study represents the most comprehensive global-scale precipitation dataset evaluation to...