A space-time Neyman-Scott model of rainfall: Empirical analysis of extremes

被引:90
作者
Cowpertwait, PSP
Kilsby, CG
O'Connell, PE
机构
[1] Massey Univ, Inst Informat & Math Sci, Auckland, New Zealand
[2] Univ Newcastle Upon Tyne, Dept Civil Engn, Water Resource Syst Res Lab, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
关键词
time series; point process; Poisson cluster model; L moments; regional frequency analysis;
D O I
10.1029/2001WR000709
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
[1] A spatial-temporal model of rainfall, based on a Neyman-Scott stochastic point process, is fitted to hourly data taken from nine sites in the Arno Basin, Italy. The stochastic model is an extension of the temporal Neyman-Scott rectangular pulses model into two-dimensional space and introduces a further parameter into the model. In the model, storms arrive in a Poisson process, where each storm consists of discs representing rain cells, with centers distributed over an area according to a spatial Poisson process. The cells have a random radius, lifetime, and intensity, with the intensity remaining constant over the area of the disc and cell lifetime. A fitting procedure is proposed which couples the results obtained in two preceding papers: the second-order properties of the spatial-temporal model and the third moment function of the single site model [Cowpertwait, 1995, 1998]. The model is validated by comparing extreme historical hourly data and equivalent data simulated using the fitted spatial-temporal model. These comparisons are made using a regional frequency analysis, based on L moments, and log-log plots of the upper distribution tail. The results indicate that the model is able to preserve regional extremes and support the use of the model in hydrological applications.
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页数:14
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