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      <source-app name="Horizon">Horizon</source-app>
      <rec-number>1</rec-number>
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      <ref-type name="Journal Article">17</ref-type>
      <work-type>ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES</work-type>
      <contributors>
        <authors>
          <author>
            <style face="normal" font="default" size="100%">Ollivier, C. C.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Carriere, S. D.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Heath, T.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Olioso, A.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Rabefitia, Z.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Rakoto, H.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Oudin, L.</style>
          </author>
          <author>
            <style face="bold" font="default" size="100%">Satgé, Frédéric</style>
          </author>
        </authors>
      </contributors>
      <titles>
        <title>Ensemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across Madagascar</title>
        <secondary-title>Journal of Hydrology : Regional Studies</secondary-title>
      </titles>
      <pages>101400 [20 p.]</pages>
      <keywords>
        <keyword>Precipitation products</keyword>
        <keyword>Remote sensing</keyword>
        <keyword>Ensemble approach</keyword>
        <keyword>Hydrology</keyword>
        <keyword>Madagascar</keyword>
        <keyword>MADAGASCAR</keyword>
      </keywords>
      <dates>
        <year>2023</year>
      </dates>
      <call-num>fdi:010090021</call-num>
      <language>ENG</language>
      <periodical>
        <full-title>Journal of Hydrology : Regional Studies</full-title>
      </periodical>
      <accession-num>ISI:001044653500001</accession-num>
      <electronic-resource-num>10.1016/j.ejrh.2023.101400</electronic-resource-num>
      <urls>
        <related-urls>
          <url>https://www.documentation.ird.fr/hor/fdi:010090021</url>
        </related-urls>
        <pdf-urls>
          <url>https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2023-09/010090021.pdf</url>
        </pdf-urls>
      </urls>
      <volume>47</volume>
      <remote-database-provider>Horizon (IRD)</remote-database-provider>
      <abstract>Study region: this study focuses on Madagascar. This island is characterized by a great diversity of climate, due to trade winds and the varying topography. This country is also undergoing extreme rainfall events such as droughts and cyclones. Study focus: the rain gauge network of Madagascar is limited (about 30 stations). Consequently, we consider relevant satellite-based precipitation datasets to fill gaps in ground-based datasets. We assessed the reliability of 21 satellite-based and reanalysis precipitation products (P-datasets) through a direct comparison with 24 rain gauge station measurements at the monthly time step, using four statistical indicators: Kling-Gupta Efficiency (KGE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Bias. Based on this first analysis, we produced a merged dataset based on a weighted average of the 21 products. New hydrological insights for the region: based on the KGE and the CC scores, WFDEI (WATCH Forcing Data methodology applied to ERA-Interim), CMORPH-BLD (Climate Prediction Center MORPHing satellite-gauge merged) and MSWEP (Multi-Source Weighted Ensemble Precipitation) are the most accurate for estimating rainfall at the national scale. Additionally, the results reveal a high discrepancy between bio-climatic regions. The merged dataset reveals higher performance than the other products in all situations. These results demonstrate the usefulness of a merging approach in an area with a deficit of rainfall data and a climatic and topographic diversity.</abstract>
      <custom6>062 ; 126</custom6>
      <custom1>UR228</custom1>
      <custom7>Madagascar</custom7>
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