{
  "abstract": "Progressive groundwater depletion in arid regions represents one of the most critical water security challenges of the twenty-first century. Despite intensive agricultural abstraction in Egypt’s Wadi El-Assiuti since the 1990s, a continuous basin-scale assessment of groundwater storage dynamics has remained lacking. This study integrates GRACE/GRACE-FO Terrestrial Water Storage Anomalies (TWSA), GLDAS-2.2 soil moisture data, CHIRPS precipitation, and multi-temporal Sentinel-2/Landsat land-use datasets to derive monthly groundwater storage anomalies (GWSA) and cumulative variations over the period 2003–2024. Long-term trends were evaluated using the Mann–Kendall test and Sen’s slope estimator, while relationships were quantified using Pearson correlation analysis. GRACE-derived estimates were evaluated against available piezometric records. An XGBoost machine learning model was trained on the period 2003–2016, independently validated over 2017–2024, and subsequently applied for a 72-month forecasting horizon extending to 2030. Results indicate statistically significant declining trends in both GWSA ( p < 0.001) and cumulative groundwater storage, with an average depletion rate of approximately − 987,000 m ³ yr⁻ ¹ . Annual rainfall showed weak, non-significant correlation with groundwater storage ( p = 0.471) and no monotonic trend (Sen's slope = 2.08 × 10⁻ ⁷ mm month⁻ ¹ ), confirming climatic variability is not the primary depletion driver. In contrast, agricultural land expansion increased by approximately 273% between 2003 and 2024 and showed a strong negative correlation with groundwater storage (r = − 0.76, p < 0001), confirming irrigation-driven abstraction as the dominant control on aquifer depletion. The XGBoost model demonstrated high predictive performance (R ² > 0.92) and projected a continued depletion trajectory under current land-use conditions through 2030. Overall, the integrated framework indicates that anthropogenic land-use change is the dominant anthropogenic factor associated with groundwater decline in Wadi El-Assiuti. The approach is fully reproducible using open-access datasets and is readily transferable to data-scarce arid aquifer systems across the Middle East.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Mohammed Hagage"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8cqbkda82tbeg.bib",
  "cited_by": 2,
  "doi": "10.1038/s41598-026-70664-6",
  "fulltext_url": null,
  "id": "8cqbkda82tbeg",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-70664-6",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1910|Geophysics and Gravity Measurements"
    ]
  ],
  "openalex": "W7219566089",
  "pdf_url": "https://www.nature.com/articles/s41598-026-70664-6.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-04",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/8cqbkda82tbeg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.1038/s41598-026-70664-6",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-70664-6"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1910|Geophysics and Gravity Measurements",
      "subfield": "oceanography"
    }
  ],
  "title": "Monitoring and forecasting groundwater storage dynamics in arid Wadi systems using GRACE/GRACE-FO satellite observations and XGBoost machine learning: Wadi El-Assiuti, Egypt (2003–2030)",
  "title_text": "Monitoring and forecasting groundwater storage dynamics in arid Wadi systems using GRACE/GRACE-FO satellite observations and XGBoost machine learning: Wadi El-Assiuti, Egypt (2003–2030)",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/8cqbkda82tbeg",
  "venue": "Scientific Reports",
  "versions": []
}