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A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam

A. Couasnon Orcid Logo, P. Scussolini Orcid Logo, T. V. T. Tran Orcid Logo, D. Eilander Orcid Logo, S. Muis Orcid Logo, Han Wang, J. Keesom, J. Dullaart Orcid Logo, Yunqing Xuan Orcid Logo, H. Q. Nguyen Orcid Logo, H. C. Winsemius Orcid Logo, P. J. Ward Orcid Logo

Water Resources Research, Volume: 58, Issue: 2

Swansea University Authors: Han Wang, Yunqing Xuan Orcid Logo

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DOI (Published version): 10.1029/2021wr030002

Abstract

State-of-the-art flood hazard maps in coastal cities are often obtained from simulating coastal or pluvial events separately. This method does not account for the seasonality of flood drivers and their mutual dependence. In this article, we include the impact of these two factors in a computationall...

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Published in: Water Resources Research
ISSN: 0043-1397 1944-7973
Published: American Geophysical Union (AGU) 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa59167
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spelling 2022-10-31T19:53:02.9549978 v2 59167 2022-01-13 A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam a718b31e3f1749890106d990af40fb3c Han Wang Han Wang true false 3ece84458da360ff84fa95aa1c0c912b 0000-0003-2736-8625 Yunqing Xuan Yunqing Xuan true false 2022-01-13 FGSEN State-of-the-art flood hazard maps in coastal cities are often obtained from simulating coastal or pluvial events separately. This method does not account for the seasonality of flood drivers and their mutual dependence. In this article, we include the impact of these two factors in a computationally efficient probabilistic framework for flood risk calculation, using Ho Chi Minh City (HCMC) as a case study. HCMC can be flooded sub-annually by high tide, rainfall and storm surge events or a combination thereof during the monsoon or tropical cyclones. Using long gauge observations, we stochastically model 10,000 years of rainfall and sea level events based on their monthly distributions, dependence structure and co-occurrence rate. The impact from each stochastic event is then obtained from a damage function built from selected rainfall and sea level combinations, leading to an expected annual damage (EAD) of $1.02B (95th annual damage percentile of $2.15B). We find no dependence for most months and large differences in expected damage across months ($36M-166M) driven by the seasonality of rainfall and sea levels. Excluding monthly variability leads to a serious underestimation of the EAD by 72% to 83%. This is because high-probability flood events, which can happen multiple times during the year and are properly captured by our framework, contribute the most to the EAD. This application illustrates the potential of our framework and advocates for the inclusion of flood drivers’ dynamics in coastal risk assessments. Journal Article Water Resources Research 58 2 American Geophysical Union (AGU) 0043-1397 1944-7973 compound flood; flood risk; skew surge; seasonality; ho chi minh city; dependence 1 2 2022 2022-02-01 10.1029/2021wr030002 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University Dutch Research Council (NWO) (VIDI; grant no. 016.161.324; and grant no. ALWOP.164); Vietnam National University –Ho Chi Minh City under grant C2018-48-01 2022-10-31T19:53:02.9549978 2022-01-13T07:42:13.2295436 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering A. Couasnon 0000-0001-9372-841x 1 P. Scussolini 0000-0001-6208-2169 2 T. V. T. Tran 0000-0003-1187-3520 3 D. Eilander 0000-0002-0951-8418 4 S. Muis 0000-0002-8145-0171 5 Han Wang 6 J. Keesom 7 J. Dullaart 0000-0001-9604-3328 8 Yunqing Xuan 0000-0003-2736-8625 9 H. Q. Nguyen 0000-0001-7685-8191 10 H. C. Winsemius 0000-0001-5471-172x 11 P. J. Ward 0000-0001-7702-7859 12 59167__22577__21f4511948eb428393e6306cf7afe484.pdf 59167.pdf 2022-03-11T15:33:30.5817388 Output 3441237 application/pdf Version of Record true © 2022. The Authors. This is an open access article under the terms of the Creative Commons Attribution License true eng http://creativecommons.org/licenses/by/4.0/
title A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam
spellingShingle A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam
Han Wang
Yunqing Xuan
title_short A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam
title_full A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam
title_fullStr A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam
title_full_unstemmed A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam
title_sort A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam
author_id_str_mv a718b31e3f1749890106d990af40fb3c
3ece84458da360ff84fa95aa1c0c912b
author_id_fullname_str_mv a718b31e3f1749890106d990af40fb3c_***_Han Wang
3ece84458da360ff84fa95aa1c0c912b_***_Yunqing Xuan
author Han Wang
Yunqing Xuan
author2 A. Couasnon
P. Scussolini
T. V. T. Tran
D. Eilander
S. Muis
Han Wang
J. Keesom
J. Dullaart
Yunqing Xuan
H. Q. Nguyen
H. C. Winsemius
P. J. Ward
format Journal article
container_title Water Resources Research
container_volume 58
container_issue 2
publishDate 2022
institution Swansea University
issn 0043-1397
1944-7973
doi_str_mv 10.1029/2021wr030002
publisher American Geophysical Union (AGU)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description State-of-the-art flood hazard maps in coastal cities are often obtained from simulating coastal or pluvial events separately. This method does not account for the seasonality of flood drivers and their mutual dependence. In this article, we include the impact of these two factors in a computationally efficient probabilistic framework for flood risk calculation, using Ho Chi Minh City (HCMC) as a case study. HCMC can be flooded sub-annually by high tide, rainfall and storm surge events or a combination thereof during the monsoon or tropical cyclones. Using long gauge observations, we stochastically model 10,000 years of rainfall and sea level events based on their monthly distributions, dependence structure and co-occurrence rate. The impact from each stochastic event is then obtained from a damage function built from selected rainfall and sea level combinations, leading to an expected annual damage (EAD) of $1.02B (95th annual damage percentile of $2.15B). We find no dependence for most months and large differences in expected damage across months ($36M-166M) driven by the seasonality of rainfall and sea levels. Excluding monthly variability leads to a serious underestimation of the EAD by 72% to 83%. This is because high-probability flood events, which can happen multiple times during the year and are properly captured by our framework, contribute the most to the EAD. This application illustrates the potential of our framework and advocates for the inclusion of flood drivers’ dynamics in coastal risk assessments.
published_date 2022-02-01T04:16:16Z
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score 11.037319

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