Dépôt numérique

Depth-based regional index-flood model.

Wazneh, Hussein, Chebana, Fateh et Ouarda, Taha B. M. J. (2013). Depth-based regional index-flood model. Water Resources Research , vol. 49 , nº 12. p. 7957-7972. DOI: 10.1002/2013WR013523.

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Regional flood frequency analysis aims to estimate flood risk at sites where little or no hydrological data are available. The index-flood model is one of the commonly employed models for this purpose. In this model, the predicted value depends on the growth curve and its regional parameters. The latter are estimated as weighted averages of the at-site parameters. Traditional approaches are mainly based on site record lengths or region size to define these weights. Hence, they are not representative of the hydrological similarity between sites within a region. In addition, they are not defined to reach optimality in terms of model performance. To overcome these limitations, the present paper aims to propose a new optimal iterative weighting scheme to the index-flood model. The proposed approach is based on a number of elements: a statistical depth function to introduce similarity between sites, a weight function to amplify and control the depth values, an iterative procedure to improve estimation accuracy, and an optimization algorithm to objectively automate the choice of the weight function. A data set from the Island of Sicily (Italy) is used to compare the proposed approach with traditional ones. On the basis of the L-moments and using cluster analysis techniques, the studied region is subdivided into three homogeneous subregions. The results indicate that the proposed approach performs significantly better than traditional ones both in terms of relative bias and relative root mean squares error. The proposed approach allows identification of cross correlation in the region and provides a significant performance improvement.

Type de document: Article
Mots-clés libres: cluster analysis technique; cross correlations; hydrological data; model performance; optimization algorithms; regional flood frequency analysis; statistical depth functions; traditional approaches
Centre: Centre Eau Terre Environnement
Date de dépôt: 13 déc. 2016 20:41
Dernière modification: 13 déc. 2016 20:41
URI: https://espace.inrs.ca/id/eprint/3527

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