A spatio-temporal statistical downscaling approach to deriving extreme rainfall IDF relations at ungauged sites in the context of climate change

EasyChair Preprint 321

Abstract

This paper proposes an efficient spatio-temporal statistical downscaling approach for estimating IDF relations at an ungauged site using daily rainfalls downscaled from global climate model (GCM) outputs. More specifically, the proposed approach involves two steps: (1) a spatial downscaling using scaling factors to transfer the daily downscaled GCM extreme rainfall projections at a regional scale to a given ungauged site and (2) a temporal downscaling using the scale-invariance GEV model to derive the distribution of sub-daily extreme rainfalls from downscaled daily rainfalls at the same location. The feasibility and accuracy of the proposed approach were evaluated based on the climate simulation outputs from 21 GCMs that have been downscaled by NASA to a regional 25-km scale for two different RCP 4.5 and 8.5 scenarios and the observed extreme rainfall data available from a network of 15 raingauges located in Ontario, Canada. The jackknife technique was used to represent the ungauged site conditions. Results based on different statistical criteria have indicated the feasibility and accuracy of the proposed approach.

Keyphrases: GEV distribution, IDF curves, climate change impact, extreme rainfalls, scale invariance, ungauged sites

@booklet{EasyChair:321,
  author    = {Truong-Huy Nguyen and Van-Thanh-Van Nguyen and Hoang-Lam Nguyen},
  title     = {A spatio-temporal statistical downscaling approach to deriving extreme rainfall IDF relations at ungauged sites in the context of climate change},
  howpublished = {EasyChair Preprint 321},
  year      = {EasyChair, 2018}}