Abstract
Extreme sea levels (ESLs) due to typhoon-induced storm surge threaten the societal security of densely populated coastal China. Uncertainty in extreme value analysis (EVA) for ESL estimation has large implications for coastal communities’ adaptation to natural hazards. Here we evaluate uncertainties in ESL estimation and relevant driving factors based on hourly observations from 13 tide gauge stations and a complementary dataset derived from a hydrodynamic model. Results indicate significant uncertainties in ESL estimations stemming from using different EVA methods, which then propagate to the inundation assessment. Amplification factors due to sea-level rise (SLR) are highly sensitive to local relative SLR and the shape of the exceedance probability curve, which in turn depends on the selected EVA method. The hydrodynamic model hindcast indicates that high ESLs mainly occurred in eastern coastal China due to typhoon-induced storm surge. Larger uncertainties in the modelled ESLs are found for the coasts of the Yangtze River Delta, and particularly in the river mouth region. Future research and adaptation planning should prioritize these regions given expected future rising sea level, compound flood events, and human-induced factors (e.g. subsidence). This study provides theoretical and practical references for adaptation to ESL-related hazards along coastal China, with implications for coastal regions worldwide.
| Original language | English |
|---|---|
| Pages (from-to) | 405-418 |
| Number of pages | 14 |
| Journal | Stochastic Environmental Research and Risk Assessment |
| Volume | 35 |
| Issue number | 2 |
| Early online date | 8 Jan 2021 |
| DOIs | |
| Publication status | Published - Feb 2021 |
Bibliographical note
Funding Information:This work is funded by the National Key R & D Program of China (2017YFC1503001, 2016YFA0602404, 2018YFC1406104, 2017YFE0100700); National Natural Science Foundation of China (42001096); Shanghai Sailing Program (19YF1413700); China Postdoctoral Science Foundation (2019M651429); Special thanks to China Scholarship Council. T.W. acknowledges support by the National Science Foundation (under Grant ICER-1854896).
Funding Information:
This work is funded by the National Key R & D Program of China (2017YFC1503001, 2016YFA0602404, 2018YFC1406104, 2017YFE0100700); National Natural Science Foundation of China (42001096); Shanghai Sailing Program (19YF1413700); China Postdoctoral Science Foundation (2019M651429); Special thanks to China Scholarship Council. T.W. acknowledges support by the National Science Foundation (under Grant ICER-1854896).
Publisher Copyright:
© 2021, The Author(s).
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Funding
This work is funded by the National Key R & D Program of China (2017YFC1503001, 2016YFA0602404, 2018YFC1406104, 2017YFE0100700); National Natural Science Foundation of China (42001096); Shanghai Sailing Program (19YF1413700); China Postdoctoral Science Foundation (2019M651429); Special thanks to China Scholarship Council. T.W. acknowledges support by the National Science Foundation (under Grant ICER-1854896).
| Funders | Funder number |
|---|---|
| China Scholarship Council | |
| National Key Research and Development Program of China | 2016YFA0602404, 2017YFC1503001, 2018YFC1406104, 2017YFE0100700 |
| China Postdoctoral Science Foundation | 2019M651429 |
| National Science Foundation | 1854896, ICER-1854896 |
| Shanghai Sailing Program | 19YF1413700 |
| National Natural Science Foundation of China | 42001096 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
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SDG 14 Life Below Water
Keywords
- China
- Extreme sea levels
- Extreme value analysis
- Impacts
- Uncertainties
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