A Spatio-Temporal Statistical Downscaling Approach to Deriving Extreme Rainfall IDF Relations at Ungauged Sites in the Context of Climate Change

8 pages•Published: September 20, 2018

Truong-Huy Nguyen, Van-Thanh-Van Nguyen and Hoang-Lam Nguyen

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: climate change impact, extreme rainfalls, gev distribution, idf curves, scale invariance, ungauged sites

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 1539-1546.

Links:	https://easychair.org/publications/paper/fTw5
	https://doi.org/10.29007/nfk8

BibTeX entry

@inproceedings{HIC2018:Spatio_Temporal_Statistical_Downscaling,
  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},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/fTw5},
  doi       = {10.29007/nfk8},
  pages     = {1539-1546},
  year      = {2018}}

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