Scale-invariance generalized logistic (GLO) model for estimating extreme design rainfalls in the context of climate change

EasyChair Preprint no. 325

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 presen 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 best models in practice. An illustrative application of the proposed model for evaluating the climate change impacts on the ERs in Ontario was also presented using rainfall projections under different climate change scenarios.

Keyphrases: climate change impact, design rainfall estimation, extreme rainfalls, generalized logistic distribution, IDF, scale invariance

@Booklet{EasyChair:325,
  author = {Truong-Huy Nguyen and Van-Thanh-Van Nguyen},
  title = {Scale-invariance generalized logistic (GLO) model for estimating extreme design rainfalls in the context of climate change},
  howpublished = {EasyChair Preprint no. 325},

  year = {EasyChair, 2018}}