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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ESSDD</journal-id>
<journal-title-group>
<journal-title>Earth System Science Data Discussions</journal-title>
<abbrev-journal-title abbrev-type="publisher">ESSDD</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Earth Syst. Sci. Data Discuss.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1866-3591</issn>
<publisher><publisher-name></publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/essd-2025-556</article-id>
<title-group>
<article-title>Spatially Resolved Meteorological and Ancillary Data in Central Europe for Rainfall Streamflow Modeling</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Vischer</surname>
<given-names>Marc Aurel</given-names>
<ext-link>https://orcid.org/0009-0009-2183-709X</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Otero</surname>
<given-names>Noelia</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Ma</surname>
<given-names>Jackie</given-names>
<ext-link>https://orcid.org/0000-0002-2268-1690</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Fraunhofer Heinrich-Hertz Institute, Applied Machine Learning Group, 10587 Berlin, Germany</addr-line>
</aff>
<pub-date pub-type="epub">
<day>11</day>
<month>11</month>
<year>2025</year>
</pub-date>
<volume>2025</volume>
<fpage>1</fpage>
<lpage>11</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2025 Marc Aurel Vischer et al.</copyright-statement>
<copyright-year>2025</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://essd.copernicus.org/preprints/essd-2025-556/">This article is available from https://essd.copernicus.org/preprints/essd-2025-556/</self-uri>
<self-uri xlink:href="https://essd.copernicus.org/preprints/essd-2025-556/essd-2025-556.pdf">The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2025-556/essd-2025-556.pdf</self-uri>
<abstract>
<p>We present a gridded dataset for rainfall streamflow modeling that is fully spatially resolved and covers five complete river basins in central Europe: upper Danube, Elbe, Oder, Rhine, and Weser. We compiled meteorological forcings and a variety of ancillary information on soil, rock, land cover, and orography. The data is harmonized to a regular 9 km&amp;times;9 km grid, temporal resolution is daily from 1980 to 2024. We also provide code to further combine our dataset with publicly available river discharge data for end-to-end rainfall streamflow modeling. We have used this data to demonstrate how neural network-driven hydrological modeling can be taken beyond lumped catchments, and want to facilitate direct comparisons between different model types.</p>
</abstract>
<counts><page-count count="11"/></counts>
<funding-group>
<award-group id="gs1">
<funding-source>Bundesministerium für Wirtschaft und Klimaschutz</funding-source>
<award-id>01MK21009A</award-id>
</award-group>
<award-group id="gs2">
<funding-source>HORIZON EUROPE Framework Programme</funding-source>
<award-id>101121192</award-id>
</award-group>
</funding-group>
</article-meta>
</front>
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