Probable maximum precipitation estimation over western Iran based on remote sensing observations: comparing deterministic and probabilistic approaches

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.

Automated summary

The study aimed to improve the estimation of 24-h PMP using Gumbel copula based on a moisture maximization method and regional remote sensing algorithm. Results show 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.