{
  "abstract": "The High Plains Aquifer (HPA) is a critical water resource in the Central United States, yet its depletion remains a major concern. While the Gravity Recovery and Climate Experiment (GRACE) satellite mission provides large-scale estimates of liquid water equivalent thickness (LWET), its coarse spatial resolution (approx. 24 km) limits local inference. In contrast, well observations from the National Ground-Water Monitoring Network (NGWMN) offer valuable but spatially sparse measurements of depth-to-groundwater. In this paper, we develop a downscaling framework for the satellite data that integrates the two sources and covariates using a Bayesian hierarchical framework. Our model uses a latent Gaussian Markov Random Field (GMRF) that describes the groundwater storage at a high spatial resolution. To address computational issues, we use a basis representation approach specified via the Moran basis. We find that fine-scale covariates like irrigation intensity and precipitation help refine spatial predictions. We thus provide, to our knowledge, the first statistically-rigorous approach for downscaling groundwater information based on GRACE satellite data and NGWMN groundwater measurements. Our approach yields high-resolution estimates of groundwater variations across the HPA from 2002--2022. The resulting fine-scale inference provides valuable insights into groundwater dynamics, highlighting the effects of land use and local extraction patterns.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.05753",
  "author_total": 3,
  "authors": [
    {
      "name": "Anis Pakrashi"
    },
    {
      "name": "Murali Haran"
    },
    {
      "name": "Shan Zuidema"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dnzfmh9grx28q.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.05753",
  "fulltext_url": null,
  "id": "dnzfmh9grx28q",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.05753",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "1910|Geophysics and Gravity Measurements"
    ]
  ],
  "openalex": "W7212016809",
  "pdf_url": "https://arxiv.org/pdf/2609.05753",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dnzfmh9grx28q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:07Z",
      "native_id": "2609.05753",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.05753"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Estimating water levels in the High Plains Aquifer by synthesizing satellite data with groundwater well observations",
  "title_text": "Estimating water levels in the High Plains Aquifer by synthesizing satellite data with groundwater well observations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:07Z",
  "url": "https://research.draconitic.com/paper/dnzfmh9grx28q",
  "venue": "arXiv",
  "versions": []
}