When nonparametric frequency analysis was performed on 183 stations from Ontario and Quebec, unimodal and multimodal maximum annual flood density functions were discovered. In order to determine generating mechanisms, a monthly partitioning of the annual maximum floods was undertaken. The timing of the floods revealed that the unimodal distributions reflected a single flood generating mechanism while the multi‐modal densities reflected two or more mechanisms. Based on the division of the flood series by mechanisms, nine homogeneous regions were delineated. L‐moment distributional homogeneity tests along with smaller standard errors for the regional equations supported the delineation.
Flood distributions can have unimodal or multimodal densities due to different flood generation mechanisms such as snowmelt and rainfall in the annual flood series. When applying nonparametric frequency analysis to annual flood data from the province of New Brunswick in Canada, unimodal, bimodal and heavy-tailed distribution shapes were found. By grouping basins with similarly-shaped densities on a geographical basis, homogeneous regions were delineated. Regional equations derived for a homogeneous region gave lower integral square errors than those of province-wide equations.
Délimitation de régions homogènes basée sur le mécanisme de la genèse de la crue annuelleRésumé Les distributions des crues peuvent avoir des densités unimodales ou multimodales par suite de différents mécanismes tels que la fonte des neiges et la pluie dans la série annuelle des crues. En applicant l'analyse de fréquence non-paramétrique aux crues de la province du Nouveau-Brunswick au Canada, des distributions unimodales, bimodales et avec grande queue ont été découvertes. En groupant des bassins avec des densités de même forme sur une base géographique, des régions homogènes ont été délimitées. Des équations régionales pour une région homogène ont donné des erreurs carrées intégrées plus petites que celles des équations pour la province entière.
Both L‐moment and nonparametric frequency analyses were performed on a series of annual maximum floods from New Brunswick, Canada. The L‐moment analysis concluded that the data were generated from a unimodal Generalized Extreme Value (GEV) distribution. However, the nonparametric frequency analysis indicated that a majority of stations followed nonunimodal mixed distributions since peak flows occur during different seasons and are the result of different generating mechanisms. The coupling of L‐moment and nonparametric analyses facilitates mixed distribution identification. Thus, the nonparametric method helps in identifying underlying probability distribution, especially when samples arise from mixed distributions.
A simulation study was undertaken to compare parametric L-moments and nonparametric approaches in flood frequency analysis. Data of various sample lengths were generated from a given generalized extreme value distribution and the quantiles estimated using the fixed-kernel nonparametric method and from a generalized extreme value distribution fitted by L-moments. From the resulting root-mean-square errors for various quantiles, it was concluded for unimodal distributions that nonparametric methods are preferable for large return period floods estimated from short (<30 years) samples while parametric methods are preferable in other circumstances. It was also pointed out that nonparametric methods are more suitable for mixed distributions. Key words: frequency analysis, L-moments, nonparametric methods, simulation.
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