{
  "abstract": "Seasonal hydropower forecasts can inform power-system resource adequacy, but uncertainty propagates through climate, hydrologic, routing, water-management, and generation components.We develop a regional planning prototype linking North American Multi-Model Ensemble seasonal forecasts, Fourier Neural Operator (FNO) downscaling, an uncertainty-aware long short-term memory runoff model based on Uncountable Mixtures of Asymmetric Laplacians (UMAL-LSTM), MOSART-WM routing and water management, and plant-level autoregressive generation estimates. During 2011–2020, FNO reduced monthly spatially averaged mean absolute error relative to the best non-FNO baseline by 13.0% for precipitation, 16.3% for temperature, and 6.2% for wind speed, although residual biases and complex-terrain errors remained. Runoff evaluation showed broad skill relative to the GRFR reanalysis target; independent streamflow and controlled-forcing checks indicated substantial spatial heterogeneity and contributions from both meteorological downscaling and hydrologic-model limitations. Regional hydropower-generation manuscript CRPSS was approximately 0.25–0.33 at a one-month lead. Skill declined with lead and varied by region and season; by month four, several snow- and melt-season regional CRPSS values approached or fell below zero. The framework provides probabilistic hydropower indicators for regional resource-adequacy planning, but it is not a plant-operations model because it does not explicitly represent plant-specific submonthly constraints, flood-control optimization, turbine release versus spill, dynamic hydraulic head, market dispatch, or changing operating priorities",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Goutam Konapala"
    },
    {
      "name": "Cameron Bracken"
    },
    {
      "name": "Nathalie Voisin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/enkg8c0tmp9w3.bib",
  "cited_by": 0,
  "doi": "10.31223/x50b98",
  "fulltext_url": null,
  "id": "enkg8c0tmp9w3",
  "issns": [],
  "landing_url": "https://eartharxiv.org/repository/view/15843/",
  "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/legalcode",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Engineering"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://eartharxiv.org/repository/object/15843/download/27431/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/enkg8c0tmp9w3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:52Z",
      "native_id": "15843",
      "source": "eartharxiv",
      "url": "https://eartharxiv.org/repository/view/15843/"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "source:eartharxiv",
      "subfield": "general"
    }
  ],
  "title": "Seasonal hydropower forecast framework using a combination of deep learning and physics-based methods",
  "title_text": "Seasonal hydropower forecast framework using a combination of deep learning and physics-based methods",
  "type": "preprint",
  "updated_at": "2026-10-31T12:20:56Z",
  "url": "https://research.draconitic.com/paper/enkg8c0tmp9w3",
  "venue": "EarthArXiv",
  "versions": []
}