香港复合洪水风险的空间无缝和时间连续性评估

IF 5.9 1区 地球科学 Q1 ENGINEERING, CIVIL Journal of Hydrology Pub Date : 2024-10-22 DOI:10.1016/j.jhydrol.2024.132217
Jiewen You , Shuo Wang , Boen Zhang
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引用次数: 0

摘要

极端风暴潮、海平面上升和暴雨的同时发生会导致复合洪灾。这些事件造成的影响往往比任何一个单独的洪水诱发因素造成的影响严重得多。然而,由于潮汐测量数据有限且稀少,妨碍了对沿海城市未测量地点进行精确的风险评估。我们的研究通过将集合机器学习与贝叶斯推理相结合,对香港从 1979 年到 2022 年的复合洪水风险进行了全面的时空分析,从而弥补了这一不足。我们在贝叶斯分层建模框架内开发了一种集合机器学习方法,以实现在没有潮汐测量站的地点估算极端风暴潮和平均海平面的时空连续性。结果表明,香港的最大风暴潮水平每年显著增加 3 毫米,平均海平面每十年显著上升 25 毫米。我们的分析还表明,日暴雨强度显著增加。此外,14.54%的极端风暴潮与暴雨同时发生,而 13.69%的暴雨事件与极端海平面同时发生。基于 copula 的联合分析显示,这些极端事件之间存在显著的正相关关系。我们的研究结果进一步显示,100 年一遇的暴雨事件的重现水平从单变量情况下的 126.36 毫米急剧上升到三变量情况下的 261.16 毫米,凸显了与复合洪水相关的风险升级。同样,对于风暴潮极值,三变量分析显示复合洪水事件的风险升高,在 100 年重现期,重现水位从 1.18 米(单变量情景)上升到 1.40 米(三变量情景)。这些时空地图和全面的复合洪水风险评估为应对沿海城市地区的多种灾害洪水风险提供了重要启示。
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Spatially seamless and temporally continuous assessment on compound flood risk in Hong Kong
Compound flooding results from the simultaneous occurrence of extreme storm surges, sea level rise, and heavy rainfall. These events often lead to impacts significantly more severe than those caused by any individual flood-inducing factor alone. However, the limited and sparse data from tidal gauges hampers precise risk assessment at ungauged sites in coastal cities. Our study addresses this gap by integrating ensemble machine learning with Bayesian inference, offering a comprehensive spatial–temporal analysis of compound flood risk from 1979 to 2022 in Hong Kong. We developed an ensemble machine learning approach within the Bayesian hierarchical modeling framework to achieve spatial–temporal continuity in the estimation of extreme storm surges and mean sea level at sites without tidal gauge stations. Results show a significant yearly increase in maximum storm surge levels by 3 mm and a significant rise in mean sea level of 25 mm per decade in Hong Kong. Our analysis also indicates a significant increase in daily heavy rainfall intensity. Furthermore, in 14.54 % of cases, extreme storm surges coincided with heavy rainfall, while 13.69 % of heavy rainfall events occurred alongside extreme sea level conditions. The copula-based joint analysis reveals significant positive correlations among these extreme events. Our findings further reveal that the return level for a 100-year heavy rainfall event increases dramatically from 126.36 mm in the univariate case to 261.16 mm in the trivariate scenario, underlining the escalated risk associated with compound flooding. Similarly, for storm surge extremes, trivariate analysis reveals elevated risk during compound flood events, with the return level rising from 1.18 m (univariate scenario) to 1.40 m (trivariate scenario) for a 100-year return period. These spatial–temporal maps and comprehensive compound flood risk assessments offer crucial insights for addressing the multi-hazard flood risk in coastal urban areas.
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来源期刊
Journal of Hydrology
Journal of Hydrology 地学-地球科学综合
CiteScore
11.00
自引率
12.50%
发文量
1309
审稿时长
7.5 months
期刊介绍: The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.
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