{
  "abstract": "Water within the root zone regulates water, energy, and carbon exchanges at the land‐atmosphere interface, yet the dynamically active portion of subsurface water that ecosystems mobilize for evapotranspiration (ET) remains unclear, because it represents only part of the total subsurface storage. To address this gap, we develop a catchment‐scale, ecosystem‐oriented hybrid machine learning model that integrates a parsimonious ecohydrological structure with data‐driven flexibility to infer time‐varying active root zone storage (a S rz ) as a latent state variable. Without prescribing rooting depth or soil layers, the model learns adaptive water partitioning from multi‐source observations of ET, runoff, snow, and terrestrial water storage, enabling diagnosis of both dynamic a S rz and its realized capacity. Applied across hundreds of U.S. catchments over the period 2001–2020, the framework reveals coherent spatial and hydroclimatic patterns, with a S rz peaking in transitional regions where atmospheric demand and water supply are balanced. The realized capacity reflects how belowground water access varies with long‐term hydroclimatic and substrate constraints. We further show that defining storage as a realized quantity through system dynamics improves stability and interpretability under varying observational and structural conditions. Overall, this study demonstrates how hybrid modeling can be used to diagnose ecosystem‐accessible water storage as a latent state, providing a physically consistent and observation‐informed pathway to quantify belowground processes that remain inaccessible to direct measurement and control key land‐atmosphere interactions.",
  "abstract_shown": true,
  "affiliations": [
    "Biogeochemical Integration Max Planck Institute for Biogeochemistry Jena Germany",
    "Department of Geography Friedrich Schiller University Jena Jena Germany",
    "ELLIS Unit Jena Jena Germany",
    "Max-Planck-Institut fur Biogeochemie",
    "Friedrich-Schiller-Universitat Jena",
    "European Commission Joint Research Centre (JRC) Ispra Italy",
    "European Commission, Joint Research Center (JRC)",
    "School of Geography and the Environment University of Oxford Oxford UK",
    "University of Oxford",
    "School of Environment, Beijing Normal University",
    "UFZ‐Helmholtz Centre for Environmental Research Leipzig Germany",
    "Helmholtz-Zentrum fur Umweltforschung UFZ",
    "School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China",
    "Southern University of Science and Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Georgios Blougouras",
      "orcid": "0009-0006-0746-8214"
    },
    {
      "affs": [
        0,
        2,
        3
      ],
      "name": "Shijie Jiang",
      "orcid": "0000-0002-2808-9559"
    },
    {
      "affs": [
        1,
        2,
        4
      ],
      "name": "Alexander Brenning",
      "orcid": "0000-0001-6640-679X"
    },
    {
      "affs": [
        5,
        6
      ],
      "name": "Mirco Migliavacca",
      "orcid": "0000-0003-3546-8407"
    },
    {
      "affs": [
        7,
        8
      ],
      "name": "Louise J. Slater",
      "orcid": "0000-0001-9416-488X"
    },
    {
      "affs": [
        0,
        2,
        9
      ],
      "name": "Jialiang Zhou",
      "orcid": "0000-0002-6791-4857"
    },
    {
      "affs": [
        10,
        11
      ],
      "name": "Rohini Kumar",
      "orcid": "0000-0002-4396-2037"
    },
    {
      "affs": [
        0,
        2,
        3
      ],
      "name": "Lu Tian",
      "orcid": "0000-0001-8403-8441"
    },
    {
      "affs": [
        0,
        2,
        12,
        13
      ],
      "name": "Chao Wang",
      "orcid": "0000-0002-4413-310X"
    },
    {
      "affs": [
        0,
        2,
        3
      ],
      "name": "Markus Reichstein",
      "orcid": "0000-0001-5736-1112"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7bxjeef3vw281.bib",
  "cited_by": 5,
  "doi": "10.1029/2025wr042803",
  "fulltext_url": null,
  "id": "7bxjeef3vw281",
  "issns": [
    "0043-1397",
    "1944-7973"
  ],
  "landing_url": "https://doi.org/10.1029/2025wr042803",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2305|Soil Moisture and Remote Sensing"
    ]
  ],
  "openalex": "W4416087034",
  "pdf_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2025WR042803",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/7bxjeef3vw281.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T13:20:55Z",
      "native_id": "W4416087034",
      "source": "openalex",
      "url": "https://doi.org/10.1029/2025wr042803"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Soil Moisture and Remote Sensing",
      "subfield": "environmental-science"
    }
  ],
  "title": "Spatiotemporal Dynamics of Active Root Zone Storage Revealed From Hybrid Machine Learning",
  "title_text": "Spatiotemporal Dynamics of Active Root Zone Storage Revealed From Hybrid Machine Learning",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:55Z",
  "url": "https://research.draconitic.com/paper/7bxjeef3vw281",
  "venue": "Water Resources Research",
  "versions": []
}