Generalized extreme value distribution fitted by LH moments for low-flow frequency analysis

Autor: GA Hewa, Quan J. Wang, Thomas A. McMahon, Murray C. Peel, Rory Nathan
Přispěvatelé: Hewa Alankarage, Gunawathie, Wang, Q, McMahon, Thomas, Nathan, R, Peel, M
Rok vydání: 2007
Předmět:
Zdroj: Water Resources Research. 43
ISSN: 0043-1397
DOI: 10.1029/2006wr004913
Popis: [1] This study introduces a method based on LH moments to use the generalized extreme value (GEV) distribution for low-flow frequency analysis and investigates the capability of the GEV distribution fitted by LH moments to effectively model the lower tail of the low-flow frequency curve, without explicitly censoring the data sample. The performance of the GEV/L moment (the GEV distribution fitted by L moments) and GEV/LH moment (the GEV distribution fitted by LH moments) methods are assessed by evaluating the bias, mean square error, and relative accuracy of quantile estimates through Monte Carlo simulations. It is shown that when both frequent low flows and extreme low flows can be adequately described by an assumed parent distribution, both GEV/L2 moment and GEV/L moment methods make equally accurate quantile estimates. However, when frequent low flows and extreme low flows do not follow a single trend, the bias of the GEV/L2 moment estimator is negligible, while the bias of the GEV/L moment estimator is significant at large annual recurrence intervals. On average, the GEV/L2 moment quantile estimator displays less bias than the GEV/L moment quantile estimator. Furthermore, as the annual recurrence interval increases, the relative accuracy of the GEV/L2 moment method consistently improves over the GEV/L moment method. The GEV/LH moment method is thus considered to be the more suitable method to model low flows.
Databáze: OpenAIRE
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