{
  "abstract": "Accurate streamflow forecasts are essential for flood risk management and impact mitigation. Coupling hydrological models with machine learning techniques has gained increasing attention as a means to improve forecast skill, with post-processing emerging as a promising approach to correcting systematic model residuals. However, the interaction between machine-learning post-processing, data assimilation and calibration strategies remains insufficiently explored. In this study, we assess the contribution of machine-learning-based post-processing to hourly streamflow forecasts for 687 catchments in metropolitan France, generated using a GR-family hydrological model (GR5H-RI) under three forecasting approaches differing in calibration strategy and use of data assimilation. Random Forest and Multilayer Perceptron models are used to post-process forecasts at lead times of 3, 6, 12 and 24 h. Forecast performance is evaluated using both continuous skill metrics relative to persistence and threshold-based metrics for flood event detection. Results show that (1) post-processing consistently improves forecast skill at short lead times, especially for slower-response catchments, with the median persistence score increasing from 0.33 to 0.54 at a 3-h lead time in the highest-skill forecasting setup and performance improving in 89.7% of catchments; (2) the largest gains occur for forecasts without data assimilation, suggesting that post-processing can partially compensate for the lack of state updating but cannot fully replace it; and (3) neural network-based post-processing slightly outperforms tree-based models in continuous metrics, while differences are more limited for event detection. Overall, results highlight the complementary roles of data assimilation and machine-learning post-processing and demonstrate the potential of such frameworks for operational flood forecasting.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Gustavo Gabbardo dos Reis"
    },
    {
      "name": "Paul Astagneau"
    },
    {
      "name": "François Bourgin"
    },
    {
      "name": "Vazken Andréassian"
    },
    {
      "name": "Charles Perrin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/er0r18z3k08wn.bib",
  "cited_by": 0,
  "doi": "10.1016/j.jhydrol.2026.136402",
  "fulltext_url": null,
  "id": "er0r18z3k08wn",
  "issns": [
    "0022-1694",
    "1879-2707"
  ],
  "landing_url": "https://hal.inrae.fr/hal-05766794v1",
  "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": [
    [
      "hal",
      "0.sdu"
    ],
    [
      "hal",
      "1.sdu.stu"
    ],
    [
      "hal",
      "2.sdu.stu.hy"
    ],
    [
      "openalex",
      "2305|Hydrological Forecasting Using AI"
    ]
  ],
  "openalex": "W7213411828",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S002216942601499X/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/er0r18z3k08wn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:42Z",
      "native_id": "hal-05766794",
      "source": "hal",
      "url": "https://hal.inrae.fr/hal-05766794v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "hal:2.sdu.stu.hy",
      "subfield": "hydrology-soils"
    }
  ],
  "title": "Improving flood forecasts: the combined impact of data assimilation and machine learning post-processing",
  "title_text": "Improving flood forecasts: the combined impact of data assimilation and machine learning post-processing",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:42Z",
  "url": "https://research.draconitic.com/paper/er0r18z3k08wn",
  "venue": "Journal of Hydrology",
  "versions": []
}