Research Article
Extreme Value Distributions in Hydrological Analysis in the Mekong Delta: A Case Study in Ca Mau and An Giang Provinces, Vietnam
@ARTICLE{10.4108/eai.13-6-2019.159122, author={Dang Kien Cuong and Duong Ton Dam and Duong Ton Thai Duong and Nguyen Kim Loi and Nguyen-Son Vo and Ayse Kortun}, title={Extreme Value Distributions in Hydrological Analysis in the Mekong Delta: A Case Study in Ca Mau and An Giang Provinces, Vietnam}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={6}, number={19}, publisher={EAI}, journal_a={INIS}, year={2019}, month={6}, keywords={Extreme value distribution, Gumbel distribution, max-domain of attraction, maximum likelihood estimation, Newton -- Raphson method, Mekong Delta}, doi={10.4108/eai.13-6-2019.159122} }- Dang Kien Cuong
Duong Ton Dam
Duong Ton Thai Duong
Nguyen Kim Loi
Nguyen-Son Vo
Ayse Kortun
Year: 2019
Extreme Value Distributions in Hydrological Analysis in the Mekong Delta: A Case Study in Ca Mau and An Giang Provinces, Vietnam
INIS
EAI
DOI: 10.4108/eai.13-6-2019.159122
Abstract
Climate change poses a critical risk to the sustainable development of many regions in Vietnam, especially in the Mekong River. In this paper, we show the specific extreme value distributions of rainfall, flow, and crest of salinity based on the hydrological data from 1975 to 2017 in An Giang and Ca Mau provinces in the Mekong Delta. We also derive a theoretical model and validate its accuracy compared to the empirical data over the years. The results demonstrate that the extremely high flows increase in both magnitude and frequency, while the extremely low ones are projected to occur less often under the climate change. The results can further help the local governments reduce the risk of lack water in dry season, control the salinization, and avoid the threat of flooding in the downstream of the Mekong Delta.
Copyright © 2019 Dang Kien Cuong et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
