Regional flood frequency analysis II: Multivariate classification of drainage basins in Britain

Abstract

The homogeneity statistic developed in the previous paper (Wiltshire, 1986) is applied here to geographical regions and to clusters of basins formed in a flow-statistic dataspace. Clusters are seen to offer several advantages over geographical regions and they are interpreted in terms of basin characteristics through the use of a multivariate linear discriminant analysis. The discriminant scores of each basin based on basin characteristics can be used to assess the performance of the original independent classification based on flow statistics. An ungauged basin for which a design flood is required can have a fractional membership of more than one cluster, and the desired quantile can then be estimated as a weighted average of quantile estimates from each cluster. Experiments with hypothetical ungauged basins show that the allocation of basins to clusters is in accord with physical reasoning. (Wiltshire, 1986) sont appliquées ici aux régions géographiques et aux groupes de bassins considérés dans un ensemble spatial de données statistiques d'écoulement. On voit que ces groupes pré-sentent plusieurs avantages sur la classification par régions géographiques et on interprète leur caractéristi-ques en fonction des caractéristiques des bassins au moyen de l'utilisation de l'analyse linéaire a multivariates discriminantes. Les résultats discriminants de chaque bassin basés sur ses caractéristiques physiques peuvent être utilisés pour évaluer le caractère plus ou moins performant de la classification indépendante originelle basée sur les statistiques d'écoulement. Un bassin versant non-observe pour lequel on a besoin de l'estimation de la crue du projet peut avoir une participation fractionnaire à plus d'un groupe et le quantile cherché peut être donc évalué comme la moyenne pondérée des évaluations des évalué comme la moyenne pondérée des évaluations des quantiles de chaque groupe. Des essais effectués avec des bassins non-observés hypothétiques montrent que la répartition des bassins parmi les groupes est en accord avec le raisonnement physique. o^t-336 S. E. Wiltshire Analyse

A region of influence approach to predicting flow duration curves within ungauged catchments

“…In the context of flood estimation, this enables uncorrelated data to be pooled from similar catchments, whilst for low flow estimation the residual variation within a region that has to be explained using a subsequent, multivariate statistical model is normally minimised. A region may be defined by geography, (Institute of Hydrology 1975, 1980, by stream flow characteristics (Wiltshire, 1986;Hughes, 1987;Burn and Boorman, 1993) or by the physical and climatic characteristics of catchments (Acreman and Sinclair, 1986).…”

Regional flood frequency analysis using kernel‐based fuzzy clustering approach

“…Traditionally hard clusters were formed for RFFA by hydrologists using algorithms such as partitional [e.g., Wiltshire, 1986;Burn, 1989;Bhaskar and O'Connor, 1989;Burn and Goel, 2000], hierarchical [e.g., Mosley, 1981;Tasker, 1982;Nathan and McMahon, 1990;Burn et al, 1997], hybrid of partitional and hierarchical clustering [Hosking and Wallis, 1997;Rao and Srinivas, 2006a], and self-organizing feature maps (SOFMs) [e.g., Hall and Minns, 1999;Hall et al, 2002;Jingyi and Hall, 2004]. In a few studies [e.g., Bargaoui et al, 1998;Hall and Minns, 1999;Jingyi and Hall, 2004;Rao and Srinivas, 2006b], fuzzy clusters were formed.…”

Regional flood frequency analysis I: Homogeneity statistics

“…The test is distribution-specific, but this is not seen as a disadvantage since a choice of regional flood distribution is ultimately necessary and is generally made independently of the choice of regions. The following paper (Wiltshire, 1986b) describes the application of this test to regional data sets and to regions formed by clustering basins within a flood statistic dataspace.…”