nav emailalert searchbtn searchbox tablepage yinyongbenwen piczone journalimg journalInfo journalinfonormal searchdiv searchzone qikanlogo popupnotification paper paperNew
2026, 02, v.57 95-106
海平面上升下沿海极值海水位非平稳性特征演变分析
基金项目(Foundation): 国家自然科学基金项目(42001096)
邮箱(Email): jyfang@hznu.edu.cn;
DOI: 10.13928/j.cnki.wrahe.2026.02.007
摘要:

【目的】全球海平面上升破坏了沿海观测水位的平稳性假设,导致沿海极值水位重现期和重现水平发生巨变。因此,需要在非平稳性假设下分析海平面上升对极值水位的影响,为沿海地区海岸防护工程设计提供科学参考。【方法】以浙江温台沿海的验潮站为例,通过趋势分析,检验验证温州、瑞安、坎门三个验潮站观测数据的非平稳性;构建广义极值分布(GEV)和广义帕累托分布(GPD)非平稳模型,以时间协变量表征参数时变特征,利用贝叶斯框架下的马尔可夫链蒙特卡罗(MCMC)方法估计参数后验分布;通过赤池信息量准则(AIC)和贝叶斯信息量准则(BIC)评估模型性能,并结合有效重现水平(ERL)和预期等待时间(EWT)分析非平稳条件下的致灾风险。【结果】结果表明:浙江沿海的三个验潮站数据具有明显的趋势性,因此选用非平稳极值理论模型更为合理;GPD模型在拟合优度上优于GEV模型;非平稳条件下,百年一遇极值水位重现期显著缩短。【结论】GPD模型因对尾部高频极值的敏感性,更适合非平稳条件下的风险预测。模型阈值的选取会影响极值水位的预估结果。在海平面上升条件下,沿海地区面临的洪水灾害风险的频率和强度将显著增大,建议动态调整沿海工程防洪标准。研究成果为非平稳极值理论在气候变化的应用以及海防工程设计提供参考。

Abstract:

[Objective]The rising global sea level has undermined the stationary assumption in coastal water level observations, [Results]ing in significant alterations in the return period and return level of extreme water levels in coastal regions. Hence, it is imperative to assess the impact of sea level rise on extreme water levels under a non-stationary framework, offering scientific insights for coastal protection engineering design in these areas.[Methods]Tidal observation from the tide gauges in Wenzhou and Taizhou in Zhejiang Province was analyzed. Non-stationary assumption was validated via trend analysis. Non-stationary Generalized Extreme Value(GEV) and Generalized Pareto Distribution(GPD) models were constructed with time-dependent parameters, and Bayesian Markov Chain Monte Carlo(MCMC) sampling was applied for posterior parameter estimation. Model performance was evaluated using Akaike(AIC) and Bayesian(BIC) information criteria, while risk dynamics were quantified through effective return levels(ERL) and expected waiting time(EWT).[Results]Distinct trends were detected in all three stations, making the adoption of non-stationary extreme value theory models more appropriate. The GPD model outperformed GEV. Under non-stationary, the return period of the 100-year extreme water level is projected to reduce dramatically.[Conclusion]The GPD model is more suitable for non-stationary risk prediction due to its sensitivity to high-frequency extremes. The estimation result of extreme water levels are impacted by the selection of model thresholds. With the rise in sea level, the frequency and intensity of flood disaster risks in coastal areas will significantly increase, necessitating dynamic updates of coastal defense standards. The findings advances the method ological application of non-stationary extreme value theory in climate change adaptation and serves as a reference for coastal defense engineering design.

参考文献

[1] 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.FANG Jiayi,SHI Peijun.A review of coastal flood risk research under global climate change[J].Progress in Geography,2019,38(5):625-636.

[2] 李珠,杨默远,桑燕芳,等.密云水库流域降水径流非平稳特征识别及归因[J].南水北调与水利科技(中英文),2024,22(2):331-338.LI Z,YANG M Y,SANG Y F,et al.Detection and attribution of the non-stationary characteristics of precipitation-runoff processes in the Miyun Reservoir basin[J].South-to-North Water Transfers and Water Science & Technology,2024,22(2):331-338.

[3] 王易,姚蕊,孙鹏,等.基于Copula 函数的淮河流域非平稳气象干旱特征研究[J].水利水电技术(中英文),2024,55(11):26-38.WANG Yi,YAO Rui,SUN Peng,et al.Characterization of non-stationary meteorological drought in Huaihe River Basin based on Copula function[J].Water Resources and Hydropower Engineering,2024,55(11):26-38.

[4] ARNS A,DANGENDORF S,JENSEN J,et al.Sea-level rise induced amplification of coastal protection design heights[J].Scientific Reports,2017,7:40171.

[5] 石先武,谭骏,国志兴,等.风暴潮灾害风险评估研究综述[J].地球科学进展,2013,28(8):866-874.SHI Xianwu,TAN Jun,GUO Zhixing,et al.A review of risk assessment of storm surge disaster[J].Advances in Earth Science,2013,28(8):866-874.

[6] 陈子煜,刘德辅,王风清.中国南海极端海况概率预测及海洋工程防灾标准研究[J].中国海洋大学学报(自然科学版),2019,49(1):115-120.CHEN Ziyu,LIU Defu,WANG Fengqing.Study of probability prediction of extreme sea hazard and disaster prevention design criteria at South China Sea[J].Periodical of Ocean University of China,2019,49(1):115-120.

[7] MUIS S,VERLAAN M,WINSEMIUS H C,et al.A global reanalysis of storm surges and extreme sea levels[J].Nature communications,2016,7(1):1-12.

[8] 梁忠民,胡义明,王军.非一致性水文频率分析的研究进展[J].水科学进展,2011,22(6):864-871.LIANG Zhongming,HU Yiming,WANG Jun.Advances in hydrological frequency analysis of nonstationary time series[J].Advances in Water Science,2011,22(6):864-871.

[9] 郭生练,刘章君,熊立华.设计洪水计算方法研究进展与评价[J].水利学报,2016,47(3):302-314.GUO Shenglian,LIU Zhangjun,XIONG Lihua.Advances and assessment on design flood estimation methods[J].Journal of Hydraulic Engineering,2016,47(3):302-314.

[10] 杜涛,欧阳硕,李帅,等.考虑气象协变量的非一致性设计枯水流量研究[J].长江科学院院报,2018,35(11):26-31.DU Tao,OUYANG Shuo,LI Shuai,et al.Nonstationary design low-flow analysis in consideration of climate covariates[J].Journal of Yangtze River Scientific Research Institute,2018,35(11):26-31.

[11] 张月霞,王辉.台风风暴潮灾害风险评估研究综述[J].海洋预报,2016,33(2):81-88.ZHANG Yuexia,WANG Hui.Review of risk assessment of typhoon storm surge disaster[J].Marine Forecasts,2016,33(2):81-88.

[12] 汪杨骏,侯太平,张韧,等.基于动态极值理论和Copula函数的极端海平面高度预测建模[J].海洋工程,2016,34(4):62-70.WANG Yangjun,HOU Taiping,ZHANG Ren,et al.Prediction modeling of extreme sea level based on dynamic extreme value theory and Copula function[J].The Ocean Engineering,2016,34(4):62-70.

[13] 汪杨骏,张韧,钱龙霞,等.海平面上升引发的极端高水位的频率风险评估模型及其应用:以宁波为例[J].灾害学,2016,31(1):213-218.WANG Yangjun,ZHANG Ren,QIAN Longxia,et al.Model for probabilistic risk assessment in extreme high water level caused by rising sea level and its application:A case study in Ningbo[J].Journal of Catastrophology,2016,31(1):213-218.

[14] GHANBARI M,ARABI M,OBEYSEKERA J,et al.A coherent statistical model for coastal flood frequency analysis under nonstationary sea level conditions[J].Earth's Future,2019,7(2):162-177.

[15] 许炜宏,蔡榕硕.不同气候情景下中国滨海城市海岸极值水位重现期预估[J].海洋通报,2022,41(4):379-390.XU Weihong,CAI Rongshuo.Estimating the return period of extreme water level in coastal cities of China under different climate scenarios[J].Marine Science Bulletin,2022,41(4):379-390.

[16] 许炜宏,蔡榕硕.海平面上升、强台风和风暴潮对厦门海域极值水位的影响及危险性预估[J].海洋学报,2021,43(5):14-26.XU Weihong,CAI Rongshuo.Impacts of sea level rise,strong typhoon and storm surge on extreme sea level in coastal waters of Xiamen and hazards estimation[J].Haiyang Xuebao,2021,43(5):14-26.

[17] ROHMER J,THIEBLEMONT R,COZANNET G L.Revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysis[J].Weather and Climate Extremes,2021,33:100352.

[18] BALDAN D,CORACI E,CROSATO F,et al.Importance of non-stationary analysis for assessing extreme sea levels under sea level rise[J].Hazards Earth System Science,2022,22(11):3663-3677.

[19] FANG J Y,WAHL T,ZHANG Q,et al.Extreme sea levels along coastal China:Uncertainties and implications[J].Stochastic Environmental Research and Risk Assessment,2021,35(2):405-418.

[20] 谢冬梅,潘军宁,王红川,等.考虑海平面上升影响的极值水位计算[J].海洋学报,2023,45 (4):17-30.XIE Dongmei,PAN Junning,WANG Hongchuan,et al.Calculation of extreme water level with the effect of sea level rise[J].Haiyang Xuebao,2023,45(4):17-30.

[21] 庄圆,纪棋严,左军成,等.海平面上升对中国沿海地区极值水位重现期的影响[J].海洋科学进展,2021,39(1):20-29.ZHUANG Yuan,JI Qiyan,ZUO Juncheng,et al.Effects of sea-level rise on the recurrence periods of extreme water levels in coastal areas of China[J].Advances in Marine Science,2021,39(1):20-29.

[22] 李健,刘清容,连喜虎,等.警戒潮位核定中建立年极值水位序列的方法研究[J].海洋开发与管理,2017,34(6):77-80.LI Jian,LIU Qingrong,LIAN Xihu,et al.Precautions on the establishment of annual extreme water level series for approved warning water level[J].Ocean Development and Management,2017,34(6):77-80.

[23] 徐昕,殷成团,章卫胜,等.长江口徐六泾水文站极值水位研究[J].水运工程,2024(4):1-8.XU Xin,YIN Chengtuan,ZHANG Weisheng,et al.Extreme water level at Xuliujing hydrological station in the Yangtze Estuary[J].Port & Waterway Engineering,2024(4):1-8.

[24] GILLELAND E,RIBATET M,STEPHENSON A G.A software review for extreme value analysis[J].Extremes,2013,16(1):10-119.

[25] GILLELAND E,KATZ R W.Extremes 2.0:An extreme value analysis package in R[J].Journal of Statistical Software,2016,72(8):1-39.

[26] VILLARINI G,SERINALDI F,SMITH J A,et al.On the stationarity of annual flood peaks in the continental united states during the 20th century[J].Water Resource Research,2009,45(8):1-17.

[27] WIGLEY T M L.The effect of changing climate on the frequency of absolute extreme events[J].Climate Change,2009,97(1):67-76.

[28] SALAS J D,OBEYSEKERA J.Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events[J].Journal of Hydrologic Engineering,2013,19(3):554-568.

[29] MENTASCHI L,VOUSDOUKAS M,VOUKOUVALAS E,et al.The transformed-stationary approach:a generic and simplified methodology for non-stationary extreme value analysis[J].Hydrology Earth System Sciences,2016,20(9):3527-3547.

[30] YUE Z Z,XIONG L H,ZHA X N,et al.Impact of thresholds on nonstationary frequency analyses of peak over threshold extreme rainfall series in Pearl River Basin,China[J].Atmospheric Research,2022,276:106269.

[31] 孙志林,卢美,聂会,等.气候变化对浙江沿海风暴潮的影响[J].浙江大学学报(理学版),2014,41(1):90-94.SUN Zhilin,LU Mei,NIE Hui,et al.Impacts of climatological change on storm surge in Zhejiang coastal water[J].Journal of Zhejiang University (Science Edition),2014,41(1):90-94.

[32] HAIGH I D,MARCOS M,TALKE S A,et al.A major update to the global higher frequency sea level dataset[J].Geoscience Data Journal,2023,10(3):293-314.

[33] RAGNO E,AGHAKOUCHAK A,CHENG L Y,et al.A generalized framework for process-informed nonstationary extreme value analysis[J].Advances in Water Resources,2019,130:270-282.

[34] RADFAR S,GALIATSATOU P.Influence of non-stationarity and dependence of extreme wave parameters on the reliability assessment of coastal structures:A case study[J].Ocean Engineering,2023,273:113862.

[35] MACKAY E,JONATHAN P.Assessment of return value estimates from stationary and non-stationary extreme value models[J].Ocean Engineering,2020,207:107406.

[36] STEPHENSON A,TAWN J.Bayesian inference for extremes:accounting for the three extremal types[J].Extremes,2004,7:291-307.

[37] COLES S,POWELL E A.Bayesian methods in extreme value modelling:A review and new developments[J].International Statistical Review,1996,64:119-136.

[38] LI D Y,MARSHALL L,ZHOU Y,et al.Enhancing probabilistic hydrological predictions with mixture density networks:Accounting for heteroscedasticity and non-gaussianity[J],Journal of Hydrology,2024,641:131737.

[39] COOLEY D.Return periods and return levels under climate change[J].Extremes in a Changing Climate,2013,65:97-114.

[40] KATZ R W,PARLANGE M B,NAVEAU P.Statistics of extremes in hydrology[J].Advances in Water Resources,2002,25(8):1287-1304.

[41] COLES S G.An Introduction to Statistical Modeling of Extreme Values[M].London:Springer,2001.

[42] 高斌.长江上游极端降水非平稳拟合及致灾效应分析[J].水利水电技术(中英文),2025,56(2):1-14.GAO Bin.Non-stationary fitting of extreme precipitation in the upper reaches of the Yangtze River and analysis of its disaster causing effects[J].Water Resources and Hydropower Engineering,2025,56(2):1-14.

基本信息:

DOI:10.13928/j.cnki.wrahe.2026.02.007

中图分类号:P731.23

引用信息:

[1]杨思茹,杨洋央,方佳毅,等.海平面上升下沿海极值海水位非平稳性特征演变分析[J].水利水电技术(中英文),2026,57(02):95-106.DOI:10.13928/j.cnki.wrahe.2026.02.007.

基金信息:

国家自然科学基金项目(42001096)

引用

GB/T 7714-2015 格式引文
MLA格式引文
APA格式引文
检 索 高级检索