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An effective modelling approach to support probabilistic flood forecasting in coastal cities-Case study : Can Tho, Mekong Delta, Vietnam. / Ngo, Hieu; Pathirana, Assela; Zevenbergen, Chris; Ranasinghe, Roshanka.

In: Journal of Marine Science and Engineering, Vol. 6, No. 2, 55, 11.05.2018.

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Ngo, Hieu ; Pathirana, Assela ; Zevenbergen, Chris ; Ranasinghe, Roshanka. / An effective modelling approach to support probabilistic flood forecasting in coastal cities-Case study : Can Tho, Mekong Delta, Vietnam. In: Journal of Marine Science and Engineering. 2018 ; Vol. 6, No. 2.

BibTeX

@article{e08e8fd7e7724b3cb6ac40bc3c4d375f,
title = "An effective modelling approach to support probabilistic flood forecasting in coastal cities-Case study: Can Tho, Mekong Delta, Vietnam",
abstract = "Probabilistic flood forecasting requires flood models that are simple and fast. Many of the modelling applications in the literature tend to be complex and slow, making them unsuitable for probabilistic applications which require thousands of individual simulations. This article focusses on the development of such a modelling approach to support probabilistic assessment of flood hazards, while accounting for forcing and system uncertainty. Here, we demonstrate the feasibility of using the open-source SWMM (Storm Water Management Model), focussing on Can Tho city, Mekong Delta, Vietnam. SWMM is a dynamic rainfall-runoffsimulation model which is generally used for single event or long-term (continuous) simulation of runoffquantity and quality and its application for probabilistic riverflow modelling is atypical. In this study, a detailed SWMM model of the entire Mekong Delta was built based on an existing ISIS model containing 575 nodes and 592 links of the same study area. The detailed SWMM model was then systematically reduced by strategically removing nodes and links to eventually arrive at a level of detail that provides sufficiently accurate predictions of water levels for Can Tho for the purpose of simulating urban flooding, which is the target diagnostic of this study. After a comprehensive assessment (based on trials with the varying levels of complexity), a much reduced SWMM model comprising 37 nodes and 40 links was determined to be able to provide a sufficiently accurate result while being fast enough to support probabilistic future flood forecasting and, further, to support flood risk reduction management.",
keywords = "Can Tho city, Coastal cities, Mekong Delta, Simplified model, SWMM",
author = "Hieu Ngo and Assela Pathirana and Chris Zevenbergen and Roshanka Ranasinghe",
year = "2018",
month = "5",
day = "11",
doi = "10.3390/jmse6020055",
language = "English",
volume = "6",
journal = "Journal of Marine Science and Engineering",
issn = "2077-1312",
publisher = "MDPI",
number = "2",

}

RIS

TY - JOUR

T1 - An effective modelling approach to support probabilistic flood forecasting in coastal cities-Case study

T2 - Journal of Marine Science and Engineering

AU - Ngo, Hieu

AU - Pathirana, Assela

AU - Zevenbergen, Chris

AU - Ranasinghe, Roshanka

PY - 2018/5/11

Y1 - 2018/5/11

N2 - Probabilistic flood forecasting requires flood models that are simple and fast. Many of the modelling applications in the literature tend to be complex and slow, making them unsuitable for probabilistic applications which require thousands of individual simulations. This article focusses on the development of such a modelling approach to support probabilistic assessment of flood hazards, while accounting for forcing and system uncertainty. Here, we demonstrate the feasibility of using the open-source SWMM (Storm Water Management Model), focussing on Can Tho city, Mekong Delta, Vietnam. SWMM is a dynamic rainfall-runoffsimulation model which is generally used for single event or long-term (continuous) simulation of runoffquantity and quality and its application for probabilistic riverflow modelling is atypical. In this study, a detailed SWMM model of the entire Mekong Delta was built based on an existing ISIS model containing 575 nodes and 592 links of the same study area. The detailed SWMM model was then systematically reduced by strategically removing nodes and links to eventually arrive at a level of detail that provides sufficiently accurate predictions of water levels for Can Tho for the purpose of simulating urban flooding, which is the target diagnostic of this study. After a comprehensive assessment (based on trials with the varying levels of complexity), a much reduced SWMM model comprising 37 nodes and 40 links was determined to be able to provide a sufficiently accurate result while being fast enough to support probabilistic future flood forecasting and, further, to support flood risk reduction management.

AB - Probabilistic flood forecasting requires flood models that are simple and fast. Many of the modelling applications in the literature tend to be complex and slow, making them unsuitable for probabilistic applications which require thousands of individual simulations. This article focusses on the development of such a modelling approach to support probabilistic assessment of flood hazards, while accounting for forcing and system uncertainty. Here, we demonstrate the feasibility of using the open-source SWMM (Storm Water Management Model), focussing on Can Tho city, Mekong Delta, Vietnam. SWMM is a dynamic rainfall-runoffsimulation model which is generally used for single event or long-term (continuous) simulation of runoffquantity and quality and its application for probabilistic riverflow modelling is atypical. In this study, a detailed SWMM model of the entire Mekong Delta was built based on an existing ISIS model containing 575 nodes and 592 links of the same study area. The detailed SWMM model was then systematically reduced by strategically removing nodes and links to eventually arrive at a level of detail that provides sufficiently accurate predictions of water levels for Can Tho for the purpose of simulating urban flooding, which is the target diagnostic of this study. After a comprehensive assessment (based on trials with the varying levels of complexity), a much reduced SWMM model comprising 37 nodes and 40 links was determined to be able to provide a sufficiently accurate result while being fast enough to support probabilistic future flood forecasting and, further, to support flood risk reduction management.

KW - Can Tho city

KW - Coastal cities

KW - Mekong Delta

KW - Simplified model

KW - SWMM

UR - http://www.scopus.com/inward/record.url?scp=85047525227&partnerID=8YFLogxK

UR - http://resolver.tudelft.nl/uuid:e08e8fd7-e772-4b3c-b6ac-40bc3c4d375f

U2 - 10.3390/jmse6020055

DO - 10.3390/jmse6020055

M3 - Article

VL - 6

JO - Journal of Marine Science and Engineering

JF - Journal of Marine Science and Engineering

SN - 2077-1312

IS - 2

M1 - 55

ER -

ID: 45345490