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Modelling extreme flood heights in the lower Limpopo River basin of Mozambique using a time-heterogeneous generalised Pareto distribution

机译:使用时间异质广义帕累托分布模型对莫桑比克林波波河下游盆地的极端洪水高度进行建模

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In this paper we fit a time-heterogeneous generalised Pareto distribution (GPD) to the flood heights in the lower Limpopo River basin of Mozambique (LLRB). The maximum likelihood method is used for parameter estimation of the nonstationary GPD. We take an in-depth review of the merits of peaks-over-threshold and block maxima. We also show the relationship between generalised extreme value (GEV) distribution and GPD in a mathematical proof and discuss the link between the mathematical proof and the findings. Nonstationary time-dependent GPD models with a trend in the scale parameter are considered in this study. The results show overwhelming evidence in support of the existence of a linear trend in the scale parameter of the GPD models at all the three sites in the LLRB. The time-heterogeneous GPD models developed in this study were found to be statistically worthwhile and provide an improvement in fit over the time-homogeneous GPD models based on the goodness-of-fit tests. This study shows the importance of extending the time-homogeneous GPD models to incorporate climate change factors such as trend in the LLRB. The models developed in this study are expected to be more reliable than their stationary counterparts for planning and decision making processes in Mozambique.
机译:在本文中,我们将时间非均一的广义帕累托分布(GPD)拟合到莫桑比克林波波河下游盆地(LLRB)的洪水高度。最大似然法用于非平稳GPD的参数估计。我们对峰值超过阈值和阻止最大值的优点进行了深入的审查。我们还在数学证明中显示了广义极值(GEV)分布与GPD之间的关系,并讨论了数学证明与发现之间的联系。在这项研究中考虑了比例参数趋势随时间变化的非平稳GPD模型。结果表明,压倒一切的证据支持在LLRB的所有三个地点的GPD模型的比例参数中存在线性趋势。发现在这项研究中开发的时间异质GPD模型具有统计学上的价值,并且比基于拟合优度检验的时间异质GPD模型具有更好的拟合度。这项研究表明,扩展时间均一的GPD模型以纳入气候变化因素(例如LLRB趋势)的重要性。对于莫桑比克的计划和决策过程,预计本研究中开发的模型比其固定模型更可靠。

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