Scale-Invariance Generalized Logistic (GLO) Model for Estimating Extreme Design Rainfalls in the Context of Climate Change

Abstract

Statistical models based on the scale-invariance (or scaling) concept has increasingly become an essential tool for modeling extreme rainfall processes over a wide range of time scales. In particular, in the context of climate change these scaling models can be used to describe the linkages between the distributions of sub-daily extreme rainfalls (ERs) and the distribution of daily ERs that is commonly provided by global or regional climate simulations. Furthermore, the Generalized Logistic distribution (GLO) has been recommended in UK for modeling of extreme hydrologic variables. Therefore, the main objective of the present study is to propose a scaling GLO model for modeling ER processes over different time scales. The feasibility and accuracy of this model were assessed using ER data from a network of 21 raingages located in Ontario, Canada. Results of this assessment based on different statistical criteria have indicated the comparable performance of the proposed scaling GLO model as compared to other popular models in practice. Furthermore, an illustrative application of the proposed model for evaluating the climate change impacts on the ERs in Ontario using the available NASA downscaled regional climate simulations has demonstrated the accuracy and robustness of the GLO model.

An ensemble approach for the analysis of extreme rainfall under climate change in Naples (Italy)

“…IDF curves for sub‐daily annual maximum precipitation indeed constitute an essential tool for the design of hydraulic structures, especially for urban drainage systems (Nguyen, Nguyen, & Nguyen, ), and for policy applications (Switzman et al, ). The state‐of‐the‐art provides for several examples of IDF updates due to climate change by means of novel statistical data analyses (Huang, Mirzaei, & Amin, ; Twfala, van Rensburg, Schall, Mosia, & Dlamini, ) or relying on climate models (Mirhosseini, Srivastava, & Stefanova, ; Rodriguez et al, ; Herath, Sarukkalige, & Nguyen, ; Simonovic, Schardong, Sandink, & Srivastav, ; DeGaetano & Castellano, ; Fadhel et al, ; Switzman et al, ; Forestieri et al,2018; Nguyen & Nguyen, ; Thanh & Dutto, ). In the latter case, problems are particularly posed by the failure of climate models to correctly simulate sub‐daily processes; this issue is usually solved relying on weather generators, disaggregating procedures, or regional models (Ciervo, Rianna, Mercogliano, & Papa, ; Forestieri et al, ; Switzman et al, ).…”