Dépôt numérique

AI-driven morphoclimatic regional frequency modelling of sub-daily rainfall-extremes.

Magnini, Andrea ORCID logoORCID: https://orcid.org/0000-0002-0704-1759; Lombardi, Michele; Ouarda, Taha B. M. J. ORCID logoORCID: https://orcid.org/0000-0002-0969-063X et Castellarin, Attilio ORCID logoORCID: https://orcid.org/0000-0002-6111-0612 (2024). AI-driven morphoclimatic regional frequency modelling of sub-daily rainfall-extremes. Journal of Hydrology , vol. 631 . p. 130808. DOI: 10.1016/j.jhydrol.2024.130808.

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Common main limitations affect standard approaches to regional frequency analysis (RFA) of rainfall extremes. Our study focuses on three of them that are rather frequent: regional models address (a) a single duration, or (b) a single exceedance probability at a time, and/or (c) hold a small-to-medium homogeneous region only. We use unsupervised ensembles of artificial neural networks (ANNs) to set up four alternative RFA models of sub-daily rainfall extremes. These are fed with annual maximum series of rainfall depth of any length collected at 2238 raingauges in a large and climatically and morphologically heterogeneous region. Our models can predict parameters of a Gumbel distribution for any location within the study area and any duration in the 1–24 h range. Prediction is based on mean annual precipitation (MAP), or on twenty morphoclimatic covariates. Validation is performed over an independent set of 100 gauges, where locally fitted Gumbel distributions are used as reference. A common literature approach where Gumbel parameters are functions of MAP is used as benchmark. Our results show that multivariate ANNs remarkably improve the estimation of percentiles relative to the benchmark approach. Finally, we show that the very nature of the proposed ANN models makes them suitable for interpolating predicted sub-daily rainfall quantiles across time-aggregation intervals and space and can be adapted for considering more flexible target frequency distributions (e.g. 3-parameter models).

Type de document: Article
Mots-clés libres: neural networks; extreme rainfall; regional frequency analysis; italy; sub-daily time-interval
Centre: Centre Eau Terre Environnement
Date de dépôt: 09 juill. 2024 15:09
Dernière modification: 09 juill. 2024 15:09
URI: https://espace.inrs.ca/id/eprint/15498

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