ABSTRACT Reliable estimation of probable maximum precipitation (PMP) is critical to ensure the safety and resilience of communities. The aim of this study is to improve the estimation of 24-h PMP using ground-based and remotely sensed data, particularly over data-scarce regions. Gumbel copula, as a bivariate extreme value distribution based on a moisture maximization method, was applied to estimate PMP. The framework allows us to examine the simultaneous occurrence of extreme precipitable water vapour (PW) and precipitation efficiency (PE) and determines extreme PW values using a regional remote sensing algorithm. This novel framework was compared with conventional methods including the Hershfield and moisture maximization approaches, which do not consider the dependencies between extreme PW and PE. The results demonstrate the importance of considering the dependence structure between extreme PW and PE in the estimation of PMP and the applicability of remotely sensed data, especially for data-scarce regions.

中文翻译：

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基于遥感观测的伊朗西部可能的最大降水估计：比较确定性和概率方法

摘要 对可能的最大降水量 (PMP) 的可靠估计对于确保社区的安全和恢复力至关重要。本研究的目的是使用地基和遥感数据改进 24 小时 PMP 的估计，特别是在数据稀缺地区。Gumbel copula 是一种基于水分最大化方法的二元极值分布，用于估计 PMP。该框架允许我们检查极端可降水水汽 (PW) 和降水效率 (PE) 的同时发生，并使用区域遥感算法确定极端 PW 值。这种新颖的框架与传统方法进行了比较，包括 Hershfield 和水分最大化方法，这些方法不考虑极端 PW 和 PE 之间的依赖关系。