January 21, 2022 Dataset Open Access

Koppa, Akash; Rains, Dominik; Hulsman, Petra; Poyatos, Rafael; Miralles, Diego G.

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  <identifier identifierType="DOI">10.5281/zenodo.5886608</identifier>
  <creators>
    <creator>
      <creatorName>Koppa, Akash</creatorName>
      <givenName>Akash</givenName>
      <familyName>Koppa</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5671-0878</nameIdentifier>
      <affiliation>Hydro-Climate Extremes Lab (H-CEL), Ghent University</affiliation>
    </creator>
    <creator>
      <creatorName>Rains, Dominik</creatorName>
      <givenName>Dominik</givenName>
      <familyName>Rains</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0768-4209</nameIdentifier>
      <affiliation>Hydro-Climate Extremes Lab (H-CEL), Ghent University</affiliation>
    </creator>
    <creator>
      <creatorName>Hulsman, Petra</creatorName>
      <givenName>Petra</givenName>
      <familyName>Hulsman</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9764-3357</nameIdentifier>
      <affiliation>Hydro-Climate Extremes Lab (H-CEL), Ghent University</affiliation>
    </creator>
    <creator>
      <creatorName>Poyatos, Rafael</creatorName>
      <givenName>Rafael</givenName>
      <familyName>Poyatos</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0521-2523</nameIdentifier>
      <affiliation>CREAF, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain</affiliation>
    </creator>
    <creator>
      <creatorName>Miralles, Diego G.</creatorName>
      <givenName>Diego G.</givenName>
      <familyName>Miralles</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6186-5751</nameIdentifier>
      <affiliation>Hydro-Climate Extremes Lab (H-CEL), Ghent University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>Deep learning</subject>
    <subject>Hybrid Modeling</subject>
    <subject>Evaporation</subject>
    <subject>Evaporative Stress</subject>
    <subject>Transpiration</subject>
    <subject>GLEAM</subject>
    <subject>Machine Learning</subject>
    <subject>Earth System Modeling</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-01-21</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5886608</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5220752</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This repository contains the datasets used in the research article &amp;quot;A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation&amp;quot;.&lt;/p&gt;

&lt;p&gt;The repository contains the following files: 1) Input - contains all the processed input used for training the deep learning models and the datasets used for creating the figures in the article. 2) Output - contains the final deep learning models and the outputs (evaporation and transpiration stress factor) outputs from the hybrid model developed in the study.&lt;/p&gt;

&lt;p&gt;Formats: All scripts are in the programming language Python. The datasets are in HDF5 and NetCDF file formats.&lt;/p&gt;

&lt;p&gt;The codes related to the research article and deep learning model are available in the following repository: https://github.com/akashkoppa/StressNet&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/869550/">869550</awardNumber>
      <awardTitle>DOWN2EARTH: Translation of climate information into multilevel decision support for social adaptation, policy development, and resilience to water scarcity in the Horn of Africa Drylands</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/715254/">715254</awardNumber>
      <awardTitle>Do droughts self-propagate and self-intensify?</awardTitle>
    </fundingReference>
  </fundingReferences>
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