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Spatial regression models for extreme precipitation in Belgium
Author(s) -
Van de Vyver H.
Publication year - 2012
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2011wr011707
Subject(s) - generalized extreme value distribution , extreme value theory , precipitation , consistency (knowledge bases) , statistics , environmental science , regression , independence (probability theory) , maxima , regression analysis , covariate , mathematics , climatology , spatial dependence , econometrics , meteorology , geography , geology , art , geometry , performance art , art history
Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the T year return level. Extreme precipitation depths in Belgium are analyzed for accumulation durations ranging from 10 min to 30 days. Spatial generalized extreme value (GEV) models are presented by considering multisite data and relating GEV parameters to geographical/climatological covariates through a common regression relationship. Methods of combining data from several sites are in common use, and in such cases, there is likely to be nonnegligible intersite dependence. However, parameter estimation in GEV models is generally done with the maximum likelihood estimation method (MLE) that assumes independence. Estimates of uncertainty are adjusted for spatial dependence using methodologies proposed earlier. Consistency of GEV distributions for various durations is obtained by fitting a smooth function to the preliminary estimations of the shape parameter. Model quality has been assessed by various statistical tests and indicates the relevance of our approach. In addition, a methodology is applied to account for the fact that measurements have been made in fixed intervals (usually 09:00 UTC–09:00 UTC). The distribution of the annual sliding 24 h maxima was specified through extremal indices of a more than 110 year time series of 24 h aggregated 10 min rainfall and daily rainfall. Finally, the selected models are used for producing maps of precipitation return levels.