Flood occurrence and impact models for socioeconomic applications over Canada and the United States.

Grenier, Manuel ORCID: https://orcid.org/0000-0003-1284-4297; Boudreault, Mathieu ORCID: https://orcid.org/0000-0001-9316-6867; Carozza, David A. ORCID: https://orcid.org/0000-0001-7343-9442; Boudreault, Jérémie ORCID: https://orcid.org/0000-0002-3086-2635 et Raymond, Sébastien (2024). Flood occurrence and impact models for socioeconomic applications over Canada and the United States. Natural Hazards and Earth System Sciences , vol. 24 , nº 7. pp. 2577-2595. DOI: 10.5194/nhess-24-2577-2024.

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Résumé

Large-scale socioeconomic studies of the impacts of floods are difficult and costly for countries such as Canada and the United States due to the large number of rivers and size of watersheds. Such studies are however very important for analyzing spatial patterns and temporal trends to inform large-scale flood risk management decisions and policies. In this paper, we present different flood occurrence and impact models based upon statistical and machine learning methods of over 31 000 watersheds spread across Canada and the US. The models can be quickly calibrated and thereby easily run predictions over thousands of scenarios in a matter of minutes. As applications of the models, we present the geographical distribution of the modelled average annual number of people displaced due to flooding in Canada and the US, as well as various scenario analyses. We find for example that an increase of 10 % in average precipitation yields an increase in the displaced population of 18 % in Canada and 14 % in the US. The model can therefore be used by a broad range of end users ranging from climate scientists to economists who seek to translate climate and socioeconomic scenarios into flood probabilities and impacts measured in terms of the displaced population.

Type de document:	Article
Mots-clés libres:	statistical learning; probability measures; machine learning; watersheds; floods
Centre:	Centre Eau Terre Environnement
Date de dépôt:	14 janv. 2025 16:50
Dernière modification:	14 janv. 2025 16:50
URI:	https://espace.inrs.ca/id/eprint/16194

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