{
  "abstract": "Groundwater is one of the most valuable natural resources, yet it is increasingly under pressure from climate change and human activities. Shallow groundwater depth (GWD) is a key indicator for evaluating groundwater availability and flood hazards, yet the measurements are often too sparse to capture its variability both in time and space. In this study, we evaluated hybrid data‐driven machine learning (ML) models that combine the strengths of existing hydrological simulations with the predictive power of ML methods for improved GWD estimation in Denmark. The hybrid model reduced mean bias of hydrological simulations from 1.72 to 0.22 m and decreased the Root Mean Square Error by about 1.64 m at national scale. Furthermore, we demonstrated that increasing the availability of GWD observations over time and space enhances model performance. We also illustrated the flexibility and effectiveness of the hybrid approach for GWD estimation across scales, and results showed that the hybrid model developed with coarse spatial resolutions can be easily used for high‐resolution GWD estimation without significant decrease of accuracy. This study highlights the potential of hybrid modeling as a practical solution for improving groundwater quantification accuracy and shows avenues for higher‐resolution estimates.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Hydrology Geological Survey of Denmark and Greenland Copenhagen Denmark",
    "Forschungszentrum Jülich Institute of Bio‐ and Geosciences Agrosphere (IBG‐3) Jülich Germany",
    "Princeton University High Meadows Environmental Institute Princeton NJ USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jun Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Raphael J. M. Schneider"
    },
    {
      "affs": [
        0
      ],
      "name": "Lars Troldborg"
    },
    {
      "affs": [
        1
      ],
      "name": "Yueling Ma"
    },
    {
      "affs": [
        2
      ],
      "name": "Reed Maxwell"
    },
    {
      "affs": [
        0
      ],
      "name": "Julian Koch"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/82mdr7bk25gbk.bib",
  "cited_by": 0,
  "doi": "10.1029/2026wr043414",
  "fulltext_url": "https://doi.org/10.1029/2026WR043414",
  "id": "82mdr7bk25gbk",
  "issns": [
    "1944-7973",
    "0043-1397"
  ],
  "landing_url": "https://doi.org/10.1029/2026wr043414",
  "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": [
    [
      "lcc",
      "GE1-350"
    ],
    [
      "openalex",
      "2305|Hydrological Forecasting Using AI"
    ]
  ],
  "openalex": "W7223095662",
  "pdf_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2026WR043414",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/82mdr7bk25gbk.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:22:17Z",
      "native_id": "1c00f98c01d441ae87ca255ff108a478",
      "source": "doaj",
      "url": "https://doi.org/10.1029/2026wr043414"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Hydrological Forecasting Using AI",
      "subfield": "environmental-science"
    }
  ],
  "title": "A National Scale Hybrid Machine Learning Model for Enhanced Groundwater Depth Estimation",
  "title_text": "A National Scale Hybrid Machine Learning Model for Enhanced Groundwater Depth Estimation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:22:17Z",
  "url": "https://research.draconitic.com/paper/82mdr7bk25gbk",
  "venue": "Water Resources Research",
  "versions": []
}