{
  "abstract": "Groundwater is the primary source of water in Madhesh Province, Nepal, yet the province lacks a reliable forecasting framework. This study develops a provincial-scale groundwater forecasting system by integrating satellite-derived groundwater storage (GWS), machine learning, and CMIP6 climate forcing. A Long Short-Term Memory (LSTM) model was trained using two decades of GRACE–GLDAS anomalies, observed climate data, and land use–land cover (LULC) information, and was forced with bias-corrected MIROC6 projections under the SSP5-8.5 scenario. Results reveal a persistent historical GWS decline of approximately 50 mm over 20 years (2.5 mm yr⁻¹), driven primarily by rainfall variability and intensified by urban expansion and surface water loss. Two LSTM setups were tested: a climate-driven model using precipitation and temperature, which achieved higher predictive accuracy but underrepresented human influences, and a multi-parameter model incorporating LULC, soil moisture, groundwater-irrigated area, and domestic demand, which captured more realistic depletion dynamics despite slightly lower statistical performance. Both the climate-driven and multi-parameter LSTM models indicate a monsoon-dependent, highly seasonal recharge with weakening peaks and continued decline through 2045. Furthermore, scenario-based demand sensitivity showed that mean GWS increased from 635.20 mm to 639.82 mm under demand − 10%, but declined to 623.91 mm under total demand + 30%, showing a mean 95% confidence interval width of 9.24 mm and a mean model spread of 7.78 mm, indicating stable projection responses across model structures. The bias-corrected Machine Learning-driven GWS and predictor dataset provides a reproducible basis for groundwater assessments and model benchmarking in data scare regions. These findings provide actionable insights for groundwater management, supporting climate-resilient water resource planning, sustainable irrigation practices, and policy development in Madhesh Province and similar data-scarce alluvial regions.",
  "abstract_shown": true,
  "affiliations": [
    "Environmental Engineering Program, Department of Civil Engineering, Institute of Engineering, Tribhuvan University",
    "Department of Civil Engineering, Institute of Engineering, Tribhuvan University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Anjana Kumari Mahto"
    },
    {
      "affs": [
        1
      ],
      "name": "Vishan Dahal"
    },
    {
      "affs": [
        1
      ],
      "name": "Ram Krishna Regmi"
    },
    {
      "affs": [
        1
      ],
      "name": "Govinda Prasad Poudel"
    },
    {
      "affs": [
        1
      ],
      "name": "Ramesh Karki"
    },
    {
      "affs": [
        1
      ],
      "name": "Prakash Chandra Ghimire"
    },
    {
      "affs": [
        0
      ],
      "name": "Shukra Raj Paudel"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4yg16y68dje14.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-70415-7",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-70415-7",
  "id": "4yg16y68dje14",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-70415-7",
  "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",
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      "lcc",
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      "1910|Geophysics and Gravity Measurements"
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  "openalex": "W7211997133",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/4yg16y68dje14.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:59Z",
      "native_id": "c4be3ba6148b4a7cb07dede77098b740",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-70415-7"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1910|Geophysics and Gravity Measurements",
      "subfield": "oceanography"
    }
  ],
  "title": "Machine-learning projections of groundwater storage under climate, land-use, and human-driven changes in Madhesh Province, Nepal",
  "title_text": "Machine-learning projections of groundwater storage under climate, land-use, and human-driven changes in Madhesh Province, Nepal",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:59Z",
  "url": "https://research.draconitic.com/paper/4yg16y68dje14",
  "venue": "Scientific Reports",
  "versions": []
}