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dc.contributor.authorBECK HYLKEen_GB
dc.contributor.authorVAN DIJK Albert I.j.m.en_GB
dc.contributor.authorLEVIZZANI Vincenzoen_GB
dc.contributor.authorSCHELLEKENS Jaapen_GB
dc.contributor.authorMIRALLES Diego G.en_GB
dc.contributor.authorMARTENS Brechten_GB
dc.contributor.authorDE ROO Arieen_GB
dc.date.accessioned2017-02-11T01:29:23Z-
dc.date.available2016-08-26en_GB
dc.date.available2017-02-11T01:29:23Z-
dc.date.created2016-08-18en_GB
dc.date.issued2017en_GB
dc.date.submitted2016-04-28en_GB
dc.identifier.citationHYDROLOGY AND EARTH SYSTEM SCIENCES DISCUSSIONS vol. 21 no. 1 p. 589-615en_GB
dc.identifier.issn1812-2108en_GB
dc.identifier.urihttp://www.hydrol-earth-syst-sci-discuss.net/hess-2016-236/en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC101464-
dc.description.abstractCurrent global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP), a global P dataset for the period 1979–2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of time scale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13,762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 versus 0.44–0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50,000 km2) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV against daily Q observations with P from each of the different datasets. For the 1058 sparsely-gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 versus 0.29–0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.en_GB
dc.description.sponsorshipJRC.D.2-Water and Marine Resourcesen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherCOPERNICUS GMBH ON BEHALF OF THE EUROPEAN GEOSCIENCES UNIONen_GB
dc.relation.ispartofseriesJRC101464en_GB
dc.titleMSWEP: 3-hourly 0.25° global gridded precipitation (1979–2014) by merging gauge, satellite, and reanalysis dataen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.5194/hess-2016-236en_GB
JRC Directorate:Sustainable Resources

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