Threshold models for river flow extremes
Threshold models for river flow extremes
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DOI：
10.1002/env.2138
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发表时间：
2012-06
期刊：
影响因子：
1.7
通讯作者：
O. Grigg;J. Tawn
中科院分区：
文献类型：
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作者：
O. Grigg;J. Tawn
我们对来自英国五条具有不同水文特性的河流的极端河流流量数据进行建模。这些数据表现出显着且复杂的非平稳性，我们使用与土壤饱和度、河流潜流量和降雨量相对应的水文协变量的非线性函数对其进行建模。我们另外将季节视为协变量，尽管水文协变量直接解释了大部分季节效应。对此类数据进行建模的标准方法是固定阈值并使用广义帕累托分布对超出该阈值的情况进行建模。我们在非平稳情况下发现了这种方法的许多问题。为了克服这些问题，我们建议对阈值超出使用审查广义极值分布。数据分析说明了模型拟合的许多特征，特别是模型参数的稳定性和阈值选择的返回水平。版权所有 © 2012 约翰·威利父子有限公司
We model extreme river flow data from five UK rivers with distinct hydrological properties. The data exhibit significant and complex nonstationarity, which we model using a nonlinear function of hydrological covariates corresponding to soil saturation, latent flow of the river and rainfall. We additionally consider season as a covariate, although the hydrological covariates explain most of the seasonal effect directly. The standard approach to modelling data of this kind is to fix a threshold and to model exceedances of this threshold using the generalised Pareto distribution. We identify a number of problems with this approach in nonstationary cases. To overcome these issues, we propose the use of a censored generalised extreme value distribution for threshold exceedances. The data analysis illustrates a number of features of model fit and in particular the stability of the model parameters and return levels to threshold choice. Copyright © 2012 John Wiley & Sons, Ltd.