An Efficient Modeling Approach for Probabilistic Assessments of Present-Day and Future Fluvial Flooding
Risk-informed flood risk management requires a comprehensive and quantitative risk assessment, which often demands multiple (thousands of) river and flood model simulations. Performing such a large number of model simulations is a challenge, especially for large, complex river systems (e.g., Mekong)...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-07-01
|
| Series: | Frontiers in Climate |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fclim.2022.798618/full |
| _version_ | 1817969855178997760 |
|---|---|
| author | Hieu Ngo Hieu Ngo Roshanka Ranasinghe Roshanka Ranasinghe Roshanka Ranasinghe Chris Zevenbergen Chris Zevenbergen Ebru Kirezci Dikman Maheng Dikman Maheng Dikman Maheng Mohanasundar Radhakrishnan Assela Pathirana Assela Pathirana Assela Pathirana |
| author_facet | Hieu Ngo Hieu Ngo Roshanka Ranasinghe Roshanka Ranasinghe Roshanka Ranasinghe Chris Zevenbergen Chris Zevenbergen Ebru Kirezci Dikman Maheng Dikman Maheng Dikman Maheng Mohanasundar Radhakrishnan Assela Pathirana Assela Pathirana Assela Pathirana |
| author_sort | Hieu Ngo |
| collection | DOAJ |
| description | Risk-informed flood risk management requires a comprehensive and quantitative risk assessment, which often demands multiple (thousands of) river and flood model simulations. Performing such a large number of model simulations is a challenge, especially for large, complex river systems (e.g., Mekong) due to the associated computational and resource demands. This article presents an efficient probabilistic modeling approach that combines a simplified 1D hydrodynamic model for the entire Mekong Delta with a detailed 1D/2D coupled model and demonstrates its application at Can Tho city in the Mekong Delta. Probabilistic flood-hazard maps, ranging from 0.5 to 100 year return period events, are obtained for the urban center of Can Tho city under different future scenarios taking into account the impact of climate change forcing (river flow, sea-level rise, storm surge) and land subsidence. Results obtained under present conditions show that more than 12% of the study area is inundated by the present-day 100 year return period of water level. Future projections show that, if the present rate of land subsidence continues, by 2050 (under both RCP 4.5 and RCP 8.5 climate scenarios), the 0.5 and 100 year return period flood extents will increase by around 15- and 8-fold, respectively, relative to the present-day flood extent. However, without land subsidence, the projected increases in the 0.5 and 100 year return period flood extents by 2050 (under RCP 4.5 and RCP 8.5) are limited to between a doubling to tripling of the present-day flood extent. Therefore, adaptation measures that can reduce the rate of land subsidence (e.g., limiting groundwater extraction), would substantially mitigate future flood hazards in the study area. A combination of restricted groundwater extraction and the construction of a new and more efficient urban drainage network would facilitate even further reductions in the flood hazard. The projected 15-fold increase in flood extent projected by 2050 for the twice per year (0.5 year return period) flood event implies that the “do nothing” management approach is not a feasible option for Can Tho. |
| first_indexed | 2024-04-13T20:26:30Z |
| format | Article |
| id | doaj.art-4c936159951a4149b8b24178f5049c13 |
| institution | Directory Open Access Journal |
| issn | 2624-9553 |
| language | English |
| last_indexed | 2024-04-13T20:26:30Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Climate |
| spelling | doaj.art-4c936159951a4149b8b24178f5049c132022-12-22T02:31:19ZengFrontiers Media S.A.Frontiers in Climate2624-95532022-07-01410.3389/fclim.2022.798618798618An Efficient Modeling Approach for Probabilistic Assessments of Present-Day and Future Fluvial FloodingHieu Ngo0Hieu Ngo1Roshanka Ranasinghe2Roshanka Ranasinghe3Roshanka Ranasinghe4Chris Zevenbergen5Chris Zevenbergen6Ebru Kirezci7Dikman Maheng8Dikman Maheng9Dikman Maheng10Mohanasundar Radhakrishnan11Assela Pathirana12Assela Pathirana13Assela Pathirana14Department of Coastal and Urban Risk and Risk Resilience, IHE Delft Institute for Water Education, Delft, NetherlandsDepartment of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, NetherlandsDepartment of Coastal and Urban Risk and Risk Resilience, IHE Delft Institute for Water Education, Delft, NetherlandsDepartment of Water Engineering and Management, University of Twente, Enschede, NetherlandsHarbour, Coastal and Offshore Engineering, Deltares, Delft, NetherlandsDepartment of Coastal and Urban Risk and Risk Resilience, IHE Delft Institute for Water Education, Delft, NetherlandsDepartment of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, NetherlandsDepartment of Infrastructure Engineering, University of Melbourne, Melbourne, VIC, AustraliaDepartment of Coastal and Urban Risk and Risk Resilience, IHE Delft Institute for Water Education, Delft, NetherlandsDepartment of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, NetherlandsDepartment of Environmental Engineering, Universitas Muhammadiyah Kendari, Kendari, IndonesiaAgile Lotus Advisory, Schiedam, NetherlandsDepartment of Coastal and Urban Risk and Risk Resilience, IHE Delft Institute for Water Education, Delft, NetherlandsUnited Nations Development Programme, Malé, MaldivesMinistry of Environment, The Government of the Maldives, Green Building, Malé, MaldivesRisk-informed flood risk management requires a comprehensive and quantitative risk assessment, which often demands multiple (thousands of) river and flood model simulations. Performing such a large number of model simulations is a challenge, especially for large, complex river systems (e.g., Mekong) due to the associated computational and resource demands. This article presents an efficient probabilistic modeling approach that combines a simplified 1D hydrodynamic model for the entire Mekong Delta with a detailed 1D/2D coupled model and demonstrates its application at Can Tho city in the Mekong Delta. Probabilistic flood-hazard maps, ranging from 0.5 to 100 year return period events, are obtained for the urban center of Can Tho city under different future scenarios taking into account the impact of climate change forcing (river flow, sea-level rise, storm surge) and land subsidence. Results obtained under present conditions show that more than 12% of the study area is inundated by the present-day 100 year return period of water level. Future projections show that, if the present rate of land subsidence continues, by 2050 (under both RCP 4.5 and RCP 8.5 climate scenarios), the 0.5 and 100 year return period flood extents will increase by around 15- and 8-fold, respectively, relative to the present-day flood extent. However, without land subsidence, the projected increases in the 0.5 and 100 year return period flood extents by 2050 (under RCP 4.5 and RCP 8.5) are limited to between a doubling to tripling of the present-day flood extent. Therefore, adaptation measures that can reduce the rate of land subsidence (e.g., limiting groundwater extraction), would substantially mitigate future flood hazards in the study area. A combination of restricted groundwater extraction and the construction of a new and more efficient urban drainage network would facilitate even further reductions in the flood hazard. The projected 15-fold increase in flood extent projected by 2050 for the twice per year (0.5 year return period) flood event implies that the “do nothing” management approach is not a feasible option for Can Tho.https://www.frontiersin.org/articles/10.3389/fclim.2022.798618/fullurban floodsurrogate model1D/2D modelingurban drainageMekong DeltaVietnam |
| spellingShingle | Hieu Ngo Hieu Ngo Roshanka Ranasinghe Roshanka Ranasinghe Roshanka Ranasinghe Chris Zevenbergen Chris Zevenbergen Ebru Kirezci Dikman Maheng Dikman Maheng Dikman Maheng Mohanasundar Radhakrishnan Assela Pathirana Assela Pathirana Assela Pathirana An Efficient Modeling Approach for Probabilistic Assessments of Present-Day and Future Fluvial Flooding Frontiers in Climate urban flood surrogate model 1D/2D modeling urban drainage Mekong Delta Vietnam |
| title | An Efficient Modeling Approach for Probabilistic Assessments of Present-Day and Future Fluvial Flooding |
| title_full | An Efficient Modeling Approach for Probabilistic Assessments of Present-Day and Future Fluvial Flooding |
| title_fullStr | An Efficient Modeling Approach for Probabilistic Assessments of Present-Day and Future Fluvial Flooding |
| title_full_unstemmed | An Efficient Modeling Approach for Probabilistic Assessments of Present-Day and Future Fluvial Flooding |
| title_short | An Efficient Modeling Approach for Probabilistic Assessments of Present-Day and Future Fluvial Flooding |
| title_sort | efficient modeling approach for probabilistic assessments of present day and future fluvial flooding |
| topic | urban flood surrogate model 1D/2D modeling urban drainage Mekong Delta Vietnam |
| url | https://www.frontiersin.org/articles/10.3389/fclim.2022.798618/full |
| work_keys_str_mv | AT hieungo anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT hieungo anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT roshankaranasinghe anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT roshankaranasinghe anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT roshankaranasinghe anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT chriszevenbergen anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT chriszevenbergen anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT ebrukirezci anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT dikmanmaheng anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT dikmanmaheng anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT dikmanmaheng anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT mohanasundarradhakrishnan anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT asselapathirana anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT asselapathirana anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT asselapathirana anefficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT hieungo efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT hieungo efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT roshankaranasinghe efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT roshankaranasinghe efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT roshankaranasinghe efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT chriszevenbergen efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT chriszevenbergen efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT ebrukirezci efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT dikmanmaheng efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT dikmanmaheng efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT dikmanmaheng efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT mohanasundarradhakrishnan efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT asselapathirana efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT asselapathirana efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding AT asselapathirana efficientmodelingapproachforprobabilisticassessmentsofpresentdayandfuturefluvialflooding |