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    <titleInfo>
      <title>Evaluation of 23 gridded precipitation datasets across West Africa</title>
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      <namePart type="family">Satgé</namePart>
      <namePart type="given">Frédéric</namePart>
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      <namePart type="family">Defrance</namePart>
      <namePart type="given">Dimitri</namePart>
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    <name type="personnal">
      <namePart type="family">Sultan</namePart>
      <namePart type="given">Benjamin</namePart>
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    <name type="personnal">
      <namePart type="family">Bonnet</namePart>
      <namePart type="given">Marie-Paule</namePart>
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      <namePart type="given">Nathalie</namePart>
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      <namePart type="given">Jean-Emmanuel</namePart>
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    <abstract>This study aims reporting on 23 gridded precipitation datasets (P-datasets) reliability across West Africa through direct comparisons with rain gauges measurement at the daily and monthly time scales over a 4 years period (2000-2003). All P-datasets reliability vary in space and time. The most efficient P-dataset in term of Kling-Gupta Efficiency (KGE) changes at the local scale and the P-dataset performance is sensitive to seasonal effects. Satellite-based P-datasets performed better during the wet than the dry season whereas the opposite is observed for reanalysis P-datasets. The best overall performance was obtained for MSWEP v.2.2 and CHIRPS v.2 for daily and monthly time-step, respectively. Part of the differences in P-dataset performance at daily and monthly time step comes from the time step used to proceed the gauges adjustment (Le day or month) and from a mismatch between gauge and satellite reporting times. In comparison to the others P-datasets, TMPA-Adj v.7 reliability is stable and reach the second highest KGE value at both daily and monthly time step. Reanalysis P-datasets (WFDEI, MERRA-2, JRA-55, ERA-Interim) present among the lowest statistical scores at the daily time step, which drastically increased at the monthly time step for WFDEI and MERRA-2. The non-adjusted P-datasets were the less efficient, but, their near-real time availability should be helpful for risk forecast studies (i.e. GSMaP-RT v.6). The results of this study give important elements to select the most adapted P-dataset for specific application across West Africa.</abstract>
    <targetAudience authority="marctarget">specialized</targetAudience>
    <subject>
      <topic>Precipitation datasets</topic>
      <topic>Reliability</topic>
      <topic>West Africa</topic>
    </subject>
    <subject authority="local">
      <geographic>AFRIQUE DE L'OUEST</geographic>
    </subject>
    <classification authority="local">062</classification>
    <relatedItem type="host">
      <titleInfo>
        <title>Journal of Hydrology</title>
      </titleInfo>
      <part>
        <detail type="volume">
          <number>581</number>
        </detail>
        <extent unit="pages">
          <list>124412 [19 ]</list>
        </extent>
      </part>
      <originInfo>
        <dateIssued>2020</dateIssued>
      </originInfo>
      <identifier type="issn">0022-1694</identifier>
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    <identifier type="uri">https://www.documentation.ird.fr/hor/fdi:010077958</identifier>
    <identifier type="doi">10.1016/j.jhydrol.2019.124412</identifier>
    <identifier type="issn">0022-1694</identifier>
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      <recordCreationDate encoding="w3cdtf">2020-05-18</recordCreationDate>
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