Risk of sea level rise in China under extreme scenarios of rapid ice sheet retreat

doi:10.12006/j.issn.1673-1719.2024.308

Abstract

Abstract:

The current global average sea level rise is experiencing an acceleration. This study assesses the risk of permanent inundation in coastal China caused by sea-level rise under the extreme scenario of rapid ice sheet retreat. The bath-tub approach is employed to simulate the inundation extent due to future sea-level rise. Under the SSP1-2.6 and SSP5-8.5 scenarios, the newly added static permanent inundation area along the coast of China increases from 0.32%-2.29% in 2100 to 1.14%-6.33% in 2150. Existing coastal defenses can effectively cope with the process of gradual permanent inundation, with major risks lying in high-tide flooding and backwater effects caused by the rise in baseline water levels, as well as the amplification effect on the frequency of extreme coastal floods. It is necessary to promptly assess the capabilities of coastal defense levels and take corresponding measures, emphasizing the impact of amplification effects from extreme compound disasters. In the Black Swan scenario of ice sheet instability, the range of sea-level rise far exceeds coastal defense capabilities, leading to widespread static inundation. By 2300, the inundation areas will account for 6.26%-18.89% of coastal regions. The destabilization of polar ice sheets can lead to extremely high rates of sea-level rise, leaving a relatively short window for coastal adaptation measures, such as enhanced defenses, and increasing the risk of systemic failures. Coastal regions need to prioritize emerging risks under the Gray Rhino and Black Swan scenarios of sea-level rise to meet the demands of climate change adaptation planning and risk management decisions.

Key words: Sea level rise, Ice sheet instability, Black Swan, Grey Rhino, Emerging risks

FANG Jia-Yi, ZHANG Tong, XIAO Cun-De. Risk of sea level rise in China under extreme scenarios of rapid ice sheet retreat[J]. Climate Change Research, 2025, 21(5): 602-612.

Figures/Tables 5

Table 1 Global mean sea level change projections (relative to 1995-2014)

Fig. 1 Spatial distribution of sea level rise inundation simulation under extreme scenarios of rapid ice sheet retreat. (a) Coastal lowlands with DEM < 10 m, (b) Gray Rhino scenario, (c) Black Swan scenario

Fig. 2 Sea level rise inundation extent statistics under extreme scenarios of rapid ice sheet retreat

Table 2 Inundation statistics under extreme scenarios of rapid ice sheet retreat

Fig. 3 Extreme sea levels for the Lianyungang (a) and Xiamen (b) tide gauge stations under sea level rise scenarios and subsidence scenario. (Dashed grey lines for display purpose, sampling method is GEV-r1)

References 40

[1]	IPCC. Climate change 2021: the physical science basis[M]. Cambridge: Cambridge University Press, 2021: 1211-1362
[2]	方佳毅, 史培军. 全球气候变化背景下海岸洪水灾害风险评估研究进展与展望[J]. 地理科学进展, 2019, 38 (5): 625-636. doi: 10.18306/dlkxjz.2019.05.001
	Fang J Y, Shi P J. A review of coastal flood risk research under global climate change[J]. Progress in Geography, 2019, 38 (5): 625-636 (in Chinese) doi: 10.18306/dlkxjz.2019.05.001
[3]	方佳毅, 殷杰, 石先武, 等. 沿海地区复合洪水危险性研究进展[J]. 气候变化研究进展, 2021, 17 (3): 317-328.
	Fang J Y, Yin J, Shi X W, et al. A review of compound flood hazard research in coastal areas[J]. Climate Change Research, 2021, 17 (3): 317-328 (in Chinese)
[4]	李思达, 方佳毅, 周巍, 等. 高潮位洪水的致灾机制、风险评估与预报评述[J]. 地理科学进展, 2024, 43 (1): 190-202. doi: 10.18306/dlkxjz.2024.01.014
	Li S D, Fang J Y, Zhou W, et al. High tide flooding: drivers, risk assessment, and prediction[J]. Progress in Geography, 2024, 43 (1): 190-202 (in Chinese) doi: 10.18306/dlkxjz.2024.01.014
[5]	Dutton A, Carlson A E, Long A J, et al. Sea-level rise due to polar ice-sheet mass loss during past warm periods[J]. Science, 2015, 349 (6244): aaa4019
[6]	Oppenheimer M, Glavovic B C, Hinkel J, et al. Sea level rise and implications for low-lying islands, coasts and communities. IPCC special report on the ocean and cryosphere in a changing climate[M]. Cambridge: Cambridge University Press, 2019: 321-445
[7]	张通, 俞永强, 效存德, 等. IPCC AR6解读: 全球和区域海平面变化的监测和预估[J]. 气候变化研究进展, 2022, 18 (1): 12-18.
	Zhang T, Yu Y Q, Xiao C D, et al. Interpretation of IPCC AR6 report: monitoring and projections of global and regional sea level change[J]. Climate Change Research, 2022, 18 (1): 12-18 (in Chinese)
[8]	Garbe J, Albrecht T, Levermann A, et al. The hysteresis of the Antarctic ice sheet[J]. Nature, 2020, 585 (7826): 538-544
[9]	IPCC. The ocean and cryosphere in a changing climate: a special report of Working Groups I and II of the Intergovernmental Panel on Climate Change[M]. Cambridge: Cambridge University Press, 2019
[10]	Jeltsch-Thömmes A, Stocker T F, Joos F. Hysteresis of the Earth system under positive and negative CO₂ emissions[J]. Environmental Research Letters, 2020, 15 (12): 124026
[11]	Williams C R, Thodoroff P, Arthern R J, et al. Calculations of extreme sea level rise scenarios are strongly dependent on ice sheet model resolution[J]. Communications Earth & Environment, 2025, 6 (1): 60
[12]	Shepherd A, Ivins E, Rignot E, et al. Mass balance of the Antarctic ice sheet from 1992 to 2017[J]. Nature, 2018, 558 (7709): 219-222
[13]	潘家华, 张莹. 中国应对气候变化的战略进程与角色转型:从防范“黑天鹅”灾害到迎战“灰犀牛”风险[J]. 中国人口∙资源与环境, 2018, 28 (10): 1-8.
	Pan J H, Zhang Y. Evolution and transformation of China’s climate change strategy: from preventing ‘Black Swan’ disasters to reducing ‘Gray Rhino’ risks[J]. China Population, Resources and Environment, 2018, 28 (10): 1-8 (in Chinese)
[14]	Flage R, Aven T. Emerging risk: conceptual definition and a relation to Black Swan type of events[J]. Reliability Engineering & System Safety, 2015, 144: 61-67
[15]	温家洪, 袁穗萍, 李大力, 等. 海平面上升及其风险管理[J]. 地球科学进展, 2018, 33 (4): 350-360. doi: 10.11867/j.issn.1001-8166.2018.04.0350
	Wen J H, Yuan S P, Li D L, et al. Sea level rise and its risk management[J]. Advances in Earth Science, 2018, 33 (4): 350-360 (in Chinese) doi: 10.11867/j.issn.1001-8166.2018.04.0350
[16]	Scherer R P, Aldahan A, Tulaczyk S, et al. Pleistocene collapse of the west Antarctic ice sheet[J]. Science, 1998, 281 (5373): 82-85 pmid: 9651249
[17]	Dutton A, DeConto R M. Genetic insight on ice sheet history[J]. Science, 2023, 382 (6677): 1356-1357 doi: 10.1126/science.adm6957 pmid: 38127738
[18]	Kopp R E, Shwom R L, Wagner G, et al. Tipping elements and climate-economic shocks: pathways toward integrated assessment[J]. Earth’s Future, 2016, 4 (8): 346-372
[19]	Lenton T M, Rockstrom J, Gaffney O, et al. Climate tipping points: too risky to bet against[J]. Nature, 2019, 575 (7784): 592-595
[20]	Mitrovica J X, Tamisiea M E, Davis J L, et al. Recent mass balance of polar ice sheets inferred from patterns of global sea-level change[J]. Nature, 2001, 409 (6823): 1026-1029
[21]	Vousdoukas M I, Bouziotas D, Giardino A, et al. Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates[J]. Natural Hazards and Earth System Sciences, 2018, 18 (8): 2127-2142
[22]	Gesch D B. Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure[J]. Frontiers in Earth Science, 2018, 6: 230
[23]	Muis S, Verlaan M, Nicholls R J, et al. A comparison of two global datasets of extreme sea levels and resulting flood exposure[J]. Earth’s Future, 2017, 5: 379-392
[24]	Muis S, Apecechea M I, Dullaart J, et al. A high-resolution global dataset of extreme sea levels, tides, and storm surges, including future projections[J]. Frontiers in Marine Science, 2020, 7: 263
[25]	Fang J, Lincke D, Brown S, et al. Coastal flood risks in China through the 21st century: an application of DIVA[J]. Science of the Total Environment, 2020, 704: 135311
[26]	Vousdoukas M I, Voukouvalas E, Mentaschi L, et al. Developments in large-scale coastal flood hazard mapping[J]. Natural Hazards and Earth System Sciences, 2016, 16 (8): 1841-1853
[27]	Jin H, Yuan J, Kulp S, et al. Substantial reduction in population exposure to sea level changes along the Chinese mainland coast through emission mitigation[J]. Environmental Research Letters, 2024, 19 (11): 114044
[28]	Wu S, Feng A, Gao J, et al. Shortening the recurrence periods of extreme water levels under future sea-level rise[J]. Stochastic Environmental Research and Risk Assessment, 2017, 31 (10): 2573-2584
[29]	Fang J, Wahl T, Zhang Q, et al. Extreme sea levels along coastal China: uncertainties and implications[J]. Stochastic Environmental Research and Risk Assessment, 2021, 35: 405-418
[30]	詹雅婷, 朱叶飞, 王玉军, 等. 江苏沿海地面沉降的高分辨率时序InSAR监测与分析[J]. 测绘科学, 2022, 47 (7): 69-76.
	Zhan Y T, Zhu Y F, Wang Y J, et al. Land subsidence monitoring of Jiangsu coastal areas with high resolution time series InSAR[J]. Science of Surveying and Mapping, 2022, 47 (7): 69-76 (in Chinese)
[31]	范雪婷, 潘九宝. 连云港防波堤时序InSAR沉降监测研究[J]. 地理空间信息, 2021, 19 (10): 55-59, 150.
	Fan X T, Pan J B. Subsidence monitoring of Lianyungang breakwater based on time series InSAR[J]. Geospatial Information, 2021, 19 (10): 55-59, 150 (in Chinese)
[32]	郑楷源, 高超, 郑铣鑫, 等. 中国沿海地区相对海平面上升研究进展[J]. 宁波大学学报 (理工版), 2022, 35 (2): 113-120.
	Zheng K Y, Gao C, Zheng X X, et al. Research progresses in relative sea-level rise in China’s coastal regions[J]. Journal of Ningbo University (NESS), 2022, 35 (2): 113-120 (in Chinese)
[33]	Nicholls R J, Lincke D, Hinkel J, et al. A global analysis of subsidence, relative sea-level change and coastal flood exposure[J]. Nature Climate Change, 2021, 11 (4): 338-342
[34]	Tay C, Lindsey E O, Chin S T, et al. Sea-level rise from land subsidence in major coastal cities[J]. Nature Sustainability, 2022, 5 (12): 1049-1057
[35]	Ohenhen L O, Shirzaei M, Ojha C, et al. Disappearing cities on US coasts[J]. Nature, 2024, 627 (8002): 108-115
[36]	Fang J, Nicholls R J, Brown S, et al. Benefits of subsidence control for coastal flooding in China[J]. Nature Communications, 2022, 13 (1): 6946 doi: 10.1038/s41467-022-34525-w pmid: 36376281
[37]	蔡榕硕, 刘克修, 谭红建. 气候变化对中国海洋和海岸带的影响、风险与适应对策[J]. 中国人口∙资源与环境, 2020, 30 (9): 1-8.
	Cai R S, Liu K X, Tan H J. Impacts and risks of climate change on China’s coastal zones and seas and related adaptation[J]. China Population, Resources and Environment, 2020, 30 (9): 1-8 (in Chinese)
[38]	田展, 吴文娴, 刘俊国, 等. 深度不确定性下沿海洪水气候变化适应决策方法述评[J]. 科学通报, 2022, 67 (22): 2638-2650.
	Tian Z, Wu W X, Liu J G, et al. A review of decision-making methods for climate change adaptation under deep uncertainty: with a focus on flooding control in coastal cities[J]. Chinese Science Bulletin, 2022, 67 (22): 2638-2650 (in Chinese)
[39]	Hinkel J, Feyen L, Hemer M, et al. Uncertainty and bias in global to regional scale assessments of current and future coastal flood risk[J]. Earth’s Future, 2021, 9 (7): e2020EF001882
[40]	Adhikari S, Ivins E R, Larour E. ISSM-SESAW v1.0: mesh-based computation of gravitationally consistent sea-level and geodetic signatures caused by cryosphere and climate driven mass change[J]. Geoscientific Model Development, 2016, 9 (3): 1087-1109