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    <responseDate>2026-10-12T06:17:18Z</responseDate>
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    <identifier>10.57760/sciencedb.36642</identifier>
    <datestamp>2026-02-02T17:22:44Z</datestamp>
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  <dc:date>2026-02-02</dc:date>
  <dc:title>Global Monthly Terrestrial Water Balance and Surface Environment Dataset (2002&amp;ndash;2010, v1.0)</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.36642</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>This dataset is a coordinated, analysis-ready resource specifically designed for global terrestrial water cycle diagnostics and uncertainty attribution. It features a spatial resolution of 0.5&amp;deg; &amp;times; 0.5&amp;deg; and a monthly temporal resolution, integrating core water cycle fluxes (precipitation, evapotranspiration, runoff), terrestrial water storage change, and key land surface environmental covariates.Its core value lies in a dual-purpose framework: it not only provides the water balance closure residual (&amp;epsilon;) derived from the principle of mass conservation (P &amp;minus; ET &amp;minus; R &amp;minus; &amp;Delta;S = &amp;epsilon;), but also synchronizes these diagnostics with essential surface descriptors&amp;mdash;including topographic morphology (elevation, slope, and northness), vegetation dynamics (NDVI), land cover composition (fractional areas of seven major classes), and climate zones (K&amp;ouml;ppen&amp;ndash;Geiger classification). All variables are consistently regridded onto a unified 0.5&amp;deg; &amp;times; 0.5&amp;deg; grid using conservative remapping techniques, thereby eliminating spurious biases arising from spatial misalignment among original multi-source products. This integrated, harmonized structure offers a robust foundation for investigating the physical drivers of water balance non-closure through machine learning, statistical modeling, or process-based attribution.</dc:description>
  <dc:subject>Global water balance; Closure residual; Multi-source remote sensing; Data fusion; Land surface controls</dc:subject>
  <dc:creator>Zhao Jianghua</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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