Abstract
Compound floods due to intense rainfall and storm surges in coastal areas have shown an increasing trend in some parts of the world, and many studies suggested a strong link with climate change. Yet, such link has not been fully explored and quantitively assessed. In this paper, we demonstrate the development and application of a nonstationary framework to determining different compound scenarios, where individual drivers and their interactions have altered under climate change. The framework has been applied to one of the most flood-prone areas: the Ho Chi Minh City of Vietnam, to help analyze the present and future compound flood risks in both the dry and wet seasons driven by the joint effect from heavy inland rainfall and high skew surge. Over the period of 1980–2017, the two drivers are found to be significantly correlated in March and April, corresponding to the transition from dry-to-wet seasons. We also find that the commonly-used traditional multivariate statistical models underestimate the flood magnitudes for both the current (represented by 2020) and future (represented by 2050) scenarios, when compared with the results produced by the nonstationary methods. In addition, the results reveal that the dry season is expected to receive more floods triggered by the increased intensity and frequency of rainfall extremes, with the magnitude reaching a similar level to that of the wet season. This is in line with the climate projections under RCP4.5 and 8.5 scenarios although the duration of dry spells is expected to increase and the total annual rainfall to decrease in Vietnam. The simulated flood inundations indicate remarkable increases in flood magnitude and extension, especially at the locations identified as low risk by the stationary models.
| Original language | English |
|---|---|
| Article number | 104330 |
| Pages (from-to) | 1-13 |
| Number of pages | 13 |
| Journal | Coastal Engineering |
| Volume | 183 |
| Early online date | 8 May 2023 |
| DOIs | |
| Publication status | Published - Aug 2023 |
Bibliographical note
Funding Information:This work was jointly supported by the Academy of Medical Sciences , Grant Ref: GCRFNGR4_1165 ; Swansea University and HEFCW GCRF Grant Ref: RIG1032-114 ; Dutch Research Council (NWO) (VIDI grant No. 016.161.324 ; and grant No. ALWOP.164 ). The authors would like to thank the Southern Regional Hydro-meteorological Center for providing the time series at the rainfall and sea level gauges via the Center of Water Management and Climate Change in HCMC, Vietnam. And the data used to produce the results presented in this study (the time series of rainfall, daily high tide, skew surge) are available on GitHub ( https://doi.org/10.5281/zenodo.5763288 ), which has been published in https://doi.org/10.1029/2021WR030002 . The ERA5 data used to analyze the underlying reasons for the correlation in all months are available at https://doi.org/10.1002/qj.3803 . The analysis of the data and all simulations are available on GitHub at https://github.com/wanghan924/HMCM_compound-flood ( https://doi.org/10.5281/zenodo.7310411 ).
Funding Information:
This work was jointly supported by the Academy of Medical Sciences, Grant Ref: GCRFNGR4_1165; Swansea University and HEFCW GCRF Grant Ref: RIG1032-114; Dutch Research Council (NWO) (VIDI grant No. 016.161.324; and grant No. ALWOP.164). The authors would like to thank the Southern Regional Hydro-meteorological Center for providing the time series at the rainfall and sea level gauges via the Center of Water Management and Climate Change in HCMC, Vietnam. And the data used to produce the results presented in this study (the time series of rainfall, daily high tide, skew surge) are available on GitHub (https://doi.org/10.5281/zenodo.5763288), which has been published in https://doi.org/10.1029/2021WR030002. The ERA5 data used to analyze the underlying reasons for the correlation in all months are available at https://doi.org/10.1002/qj.3803. The analysis of the data and all simulations are available on GitHub at https://github.com/wanghan924/HMCM_compound-flood (https://doi.org/10.5281/zenodo.7310411).
Publisher Copyright:
© 2023 The Authors
Funding
This work was jointly supported by the Academy of Medical Sciences , Grant Ref: GCRFNGR4_1165 ; Swansea University and HEFCW GCRF Grant Ref: RIG1032-114 ; Dutch Research Council (NWO) (VIDI grant No. 016.161.324 ; and grant No. ALWOP.164 ). The authors would like to thank the Southern Regional Hydro-meteorological Center for providing the time series at the rainfall and sea level gauges via the Center of Water Management and Climate Change in HCMC, Vietnam. And the data used to produce the results presented in this study (the time series of rainfall, daily high tide, skew surge) are available on GitHub ( https://doi.org/10.5281/zenodo.5763288 ), which has been published in https://doi.org/10.1029/2021WR030002 . The ERA5 data used to analyze the underlying reasons for the correlation in all months are available at https://doi.org/10.1002/qj.3803 . The analysis of the data and all simulations are available on GitHub at https://github.com/wanghan924/HMCM_compound-flood ( https://doi.org/10.5281/zenodo.7310411 ). This work was jointly supported by the Academy of Medical Sciences, Grant Ref: GCRFNGR4_1165; Swansea University and HEFCW GCRF Grant Ref: RIG1032-114; Dutch Research Council (NWO) (VIDI grant No. 016.161.324; and grant No. ALWOP.164). The authors would like to thank the Southern Regional Hydro-meteorological Center for providing the time series at the rainfall and sea level gauges via the Center of Water Management and Climate Change in HCMC, Vietnam. And the data used to produce the results presented in this study (the time series of rainfall, daily high tide, skew surge) are available on GitHub (https://doi.org/10.5281/zenodo.5763288), which has been published in https://doi.org/10.1029/2021WR030002. The ERA5 data used to analyze the underlying reasons for the correlation in all months are available at https://doi.org/10.1002/qj.3803. The analysis of the data and all simulations are available on GitHub at https://github.com/wanghan924/HMCM_compound-flood (https://doi.org/10.5281/zenodo.7310411).
Keywords
- Climate change
- Coastal compound flood
- Quantification framework
- Rainfall extremes
- Storm surge
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