考虑海平面上升影响的极值水位计算

本研究基于非平稳序列极值理论,定量分析极端水位事件年超越概率受海平面上升的影响;以工程设计使用年限内极端水位发生概率作为控制条件,构建考虑海平面上升的极值水位计算方法;结合平均海平面的长期变化过程,推算海平面上升下的极值水位。基于全球10个验潮站历史水位观测资料,验证历史平均海平面长期变化与高、低水位耿贝尔分布位置参数变化的一致性以及构建方法的合理性。结合政府间气候变化专门委员会对海平面上升的预测,推算和对比分析不同海平面上升情景下的极值水位,并评估相应极值水位在当前极值分布中的重现期。     

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

气候变化背景下海平面上升、强台风和风暴潮对我国东南沿海地区的洪涝灾害影响日益严重,为应对气候变化的影响,本文以位于我国东南沿海的厦门地区为例,应用多种海洋大气观测资料和数理统计及模拟方法,分析了历史上9914号和1614号两次台风对厦门海域极端海面高度(极值水位)的影响,预估了未来海平面上升情景下厦门海域极值水位的变化及其危险性。结果表明:(1) 9914号台风期间,天文大潮、风暴增水和强降水的同时出现造成了厦门沿海地区超警戒极值水位(732 cm)的出现;(2) 风(向岸强风)、雨(强降水)、浪(巨浪)、潮(高潮位)、流(急流)等多致灾因子的共同作用是厦门沿海地区发生严重灾情的重要原因;(3) 在温室气体中等和高排放(RCP4.5和RCP8.5)情景下,到2050年(2100年),当前百年一遇的极值水位将分别变为30年(2年)一遇(RCP4.5)和25年(低于1年)一遇(RCP8.5)的频繁极端事件。这表明未来厦门沿海极值水位的危险性将显著上升,应采取充分的适应措施降低洪涝灾害风险。     

海平面变化研究进展

气候变化在世界范围内产生重要影响,其中之一即海平面变化。观测记录表明全球平均海平面在21世纪以来呈现加速上升趋势,海平面上升引发一系列的海岸带灾害,影响沿海地区的社会经济发展。本文回顾了海平面变化研究的前沿动态,介绍了海平面观测的不同手段及其特点,分别从全球、区域和中国几个空间尺度阐述了海平面变化事实的最新研究进展及我们的研究成果,给出了中国沿海全海域及各海区海平面的长期变化趋势,并在此基础上对未来海平面的变化幅度进行了预测,给出了全球和中国近海及沿海未来不同时期的海平面上升预测值,对沿海地区科学应对气候变化及海平面上升影响有一定的参考意义。     

中国近海海平面变化半经验预测方法研究

由于用数值模式预测未来海平面变化存在很大的不确定性,而统计预测方法又通常不考虑相关物理过程,为此Rahmstorf通过建立海平面变化与全球气温变化的相关模型,提出了一个可行的半经验方法预测全球海平面.本文将Rahmstoff模型应用于中国近海,初步建立了一个在气候变暖背景下中国近海海平面长期变化的预测方法,预测结果表明...     

全球变暖背景下基于Non-Boussinesq POP模式对海平面高度的模拟和预估

用Non-Boussinesq POP模式和1986—2005年SODA再分析资料的海表面温度、盐度和风应力,模拟了1986—2005年间的全球海平面变化,并根据RCP4.5和RCP8.5两种代表性浓度排放情景下未来气候变化趋势的预测,对未来一个世纪的海平面变化进行预估,在仅考虑热膨胀的前提下,得到了如下结论:(1)在过去20 a间,全球平均海平面高度上升了56 mm,上升较大的海域主要为西北太平洋、南太平洋中部和南大西洋;(2)到2100年,RCP4.5情景下全球平均海平面上升0.36 m,RCP8.5情景下全球平均海平面上升0.43 m;(3)未来海平面变化较大的海域包括西北太平洋、西南太平洋、西南大西洋和印度洋,南大洋、北大西洋和赤道太平洋海平面变化相对较小。     

对我国两种海堤规范设计波浪标准的风险分析

面对全球气候变化,海平面上升的趋势,极端气候致灾因素诱发的灾害已成为影响各大洋沿岸及亚洲各国人民生命财产安全和经济发展的重大问题。就我国而言,台风、暴潮、巨浪、暴雨灾害已直接影响到沿海和内陆各省人民的生命财产和可持续发展。介绍极值预测理论及其在国内外海岸工程中的应用,并对比我国水利部海堤工程设计规范建议的不同波高概率预测理论和方法作为设计标准进行了风险评估,以期工程设计达到防灾减灾的目的。     

东中国海海平面高度的时空变化特征

基于AVISO卫星高度计数据,采用小波分析和EOF分析方法,对1993—2015年东中国海海平面高度的时空变化进行分析,结合海水温度比容效应和ENSO过程探讨了海平面高度变化成因。东中国海海平面高度具有明显的季节变化,冬春季较低,夏秋季较高,且SSH极值滞后海水温度极值月份一个月出现。东中国海海平面高度整体为北低南高,由于海区水深和水文动力过程的影响,不同季节SSH空间分布区域性强。在23年间,海平面高度平均线性上升速率为2.82mm/a,具有1a、2.2a的特征变化周期。EOF分析三个主要模态依次反映了海水温度比容效应的季节变化、水动力过程的季节变化和ENSO事件的年际变化对东中国海海平面高度的影响。     

不同气候情景下中国滨海城市海岸极值水位重现期预估

气候变化背景下,海平面上升叠加台风—风暴潮、天文大潮等产生的海岸极值水位事件趋多增强,对我国滨海城市社会经济可持发展构成了严重威胁。为认识未来我国滨海城市海岸极值水位危害性（强度和频率）的变化,本文首先采用第五次国际耦合模式比较计划（CMIP5）数据,分析了不同气候情景下（RCP2.6, 4.5, 8.5,简称为RCPs）下,未来不同年代（2030年、2050年和2100年）我国滨海城市沿岸海平面变化幅度;其次,基于沿海验潮站的历史观测资料和文献数据,分析了未来热带气旋强度变化对海岸极值水位的影响;最后,利用皮尔逊Ⅲ型（P-Ⅲ）水文概率曲线方法,预估了不同气候（RCPs）情景下未来不同年代（2030年、2050年和2100年）我国9个滨海城市海岸极值水位重现期的变化。结果表明：（1）在不同气候情景下,我国滨海城市沿海平均海平面均呈现上升趋势,其中,到21世纪末,长三角地区沿海海平面上升幅度最大,上升速度比全国平均高出约30%;（2）热带气旋的强度与台风—风暴潮的增水幅度存在正相关关系。预计到21世纪末,热带气旋的整体强度很可能将增强,热带气旋引发的台风—风暴潮的增水幅度较当前很可能有明显提高。（3）未来我国滨海城市沿海极值水位将有显著增高的趋势,当前极值水位的重现期将明显缩短。到21世纪末,我国滨海城市当前百年一遇的极值水位,重现期几乎都将缩短至20年一遇以下,其中,大连、青岛、上海和厦门等城市海岸极值水位重现期很可能缩短为（或低于）1年一遇。本文虽在一定程度上反映了不同气候情景下海岸洪水危害性的变化,但对于未来热带气旋的变化及其影响的研究尚有待进一步深入。     

气候变化背景下海南东寨港红树林生态系统的脆弱性评估

鉴于红树林生态系统对气候变化背景下海平面上升和极端台风事件有高度敏感性,应用1980—2018年的海洋大气观测资料和实地调查数据,分析了海口东寨港地区的海平面、温度和台风最大风速的变化特征,并基于IPCC气候变化综合风险的理论框架,构建了"暴露度-敏感性-适应性"的脆弱性评价指标体系和估算方法,评估了海平面上升和台风事件背景下东寨港红树林生态系统的脆弱性主要特征。结果显示:①东寨港红树林生态系统的致灾影响因子主要为该地区沿海海平面的快速上升,其上升速率可达4. 6 mm/a,远高于全球和中国沿海平均值;其次为1993年之前和2006年之后,在海口地区250 km范围内出现的热带气旋或台风事件;②东寨港红树林脆弱性指数的平均值为0. 31,属于中度脆弱等级,其中三江片区的红树林脆弱性相对最高,演丰片区最低。构建的评价指标体系可较好地反映出海平面快速上升和热带气旋或台风影响下红树林生态系统的脆弱性特征。     

中国沿岩现代海平面变化及未来趋势分析

本文用线性回归分析方法，分１９８５年以前和１９９２年以前两个时段，对我国沿岩２５个验潮站近百年来的海平面资料进行了系统分析，计算了两个时段相对海平面变化的年速率和平均海面高度，论述了海平面变化的主要控制因素，并对未来海平面变化趋势进行了预测。计算结果表明，近百年来我国沿岸相对海平面在总体上不但持续上升，而且近年来上升速率普遍加快；根据海平面变化的主要控制因素变化趋向，预计到下世纪中叶前后，全球性海     

Changing extreme sea levels along European coasts

Extreme sea levels at European coasts and their changes over the twentieth and twenty-first centuries are considered, including a method to analyze extreme sea levels and to assess their changes in a consistent way at different sites. The approach is based on using a combination of statistical tools and dynamical modelling as well as observational data and scenarios for potential future developments. The analysis is made for both time series of extreme sea levels and individually for the different components contributing to the extremes comprising (i) mean sea level changes, (ii) wind waves and storm surges and (iii), for relevant places, river flows. It is found that while regionally results vary in detail, some general inferences can be obtained. In particular it is found, that extreme sea levels show pronounced short-term and long-term variability partly associated with seasonal and nodal tidal cycles. Long-term trends are mostly associated with corresponding mean sea level changes while changes in wave and storm surge climate mostly contribute to inter-annual and decadal variability, but do not show substantial long-term trends. It is expected that this situation will continue for the upcoming decades and that long-term variability dominates over long-term trends at least for the coming decades.     

Non-stationary extreme value models to account for trends and shifts in the extreme wave climate due to climate change

The extreme values of wave climate data are of great interest in a number of different ocean engineering applications, including the design and operation of ships and offshore structures, marine energy generation, aquaculture and coastal installations. Typically, the return values of certain met-ocean parameters such as significant wave height are of particular importance. There exist many methods for estimating such return values, including the initial distribution approach, the block maxima approach and the peaks-over threshold approach. In a climate change perspective, projections of such return values to a future climate are of great importance for risk management and adaptation purposes. However, many approaches to extreme value modelling assume stationary conditions and it is not straightforward how to include non-stationarity of the extremes due to for example climate change. In this paper, various non-stationary GEV-models for significant wave height are developed that account for trends and shifts in the extreme wave climate due to climate change. These models are fitted to block maxima in a particular set of wave data obtained for a historical control period and two future projections for a future period corresponding to different emission scenarios. These models are used to investigate whether there are trends in the data within each period that influence the extreme value analysis and need to be taken into account. Moreover, it will be investigated whether there are significant inter-period shifts or trends in the extreme wave climate from the historical period to the future periods. The results from this study suggest that the intra-period trends are not statistically significant and that it might be reasonable to ignore these in extreme value analyses within each period. However, when it comes to comparing the different data sets, i.e. the historical period and the future projections, statistical significant inter-period changes are detected. Hence, the accumulated effect of a climatic trend may not be negligible over longer time periods. Interestingly enough, such statistically significant shifts are not detected if stationary extreme value models are fitted to each period separately. Therefore, the non-stationary extreme value models with inter-period shifts in the parameters are proposed as an alternative for extreme value modelling in a climate change perspective, in situations where historical data and future projections are available.     

Regional projection of future extreme wave heights around Korean Peninsula

In this study, future changes in regional extreme wave heights around the Korean Peninsula are projected by using the results of an atmosphere general circulation model and a third-generation wave model. The direct use of the model output at each grid point is not appropriate even though high resolution of 20 km is used for the models. Therefore, the model output is grouped into six regions around the Korean Peninsula. The grouping approach is reasonable in assessing climate change effects with alleviated model uncertainty. The extreme wave heights are simulated for two climate periods of 1979–2003 (present climate) and 2075–2099 (future climate). The model results are validated by comparing the simulated wave heights for the present climate with observed and hindcasted wave data. The extreme wave heights for the future climate are then projected for different seasons and in different regions. The 50-year return wave height in summer is projected to increase in most regions, especially in the high-latitude Yellow Sea and the East Sea, while the wave height in winter is projected to decrease in all the regions, especially in the East Sea.     

Efficient estimation of extreme response of drag-dominated offshore structures by Monte Carlo simulation

The paper describes a novel approach to the problem of estimating the extreme response statistics of a drag-dominated offshore structure exhibiting a pronounced dynamic behaviour when subjected to harsh weather conditions. It is shown that the key quantity for extreme response prediction is the mean upcrossing rate function, which can be simply extracted from simulated response time histories. A commonly adopted practice for obtaining adequate extremes for design purposes requires the execution of 20 or more 3-h time domain analyses for several extreme sea states. For early phase considerations, it would be convenient if extremes of a reasonable accuracy could be obtained based on shorter and fewer simulations. The aim of the work reported in the present paper has therefore been to develop specific methods which make it possible to extract the necessary information about the extreme response from relatively short time histories.The method proposed in this paper opens up the possibility to predict simply and efficiently both short-term and long-term extreme response statistics. The results presented are based on extensive simulation results for the Kvitebjørn jacket structure, in operation on the Norwegian Continental Shelf. Specifically, deck response time histories for different sea states simulated from an MDOF model were used as the basis for our analyses.     

Sea Level Change Analysis and Models Identification Based on Short Tidal Gauge Measurements in Alexandria,Egypt

Sea level change analysis and models identification are important factors used for coastal engineering applications. Moreover, sea level change modeling is used widely to evaluate and study shoreline and climate changes. This study intends to analyze and model Alexandria, Egypt sea level change by investigating yearly tide gauge data collected in a short duration (2008–2011). The time-frequency method was used to evaluate the meteorological noise frequencies. Two models were used to predict the time series data: Neural Network Autoregressive Moving Average (NNARMA) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The time-frequency analysis and models identification results showed that no extreme events were detected for Alexandria point during the monitoring period. Therefore, the NNARMA and ANFIS models can be used to identify the sea level change. The estimates of the models were compared with the three different statistics, determination coefficient, root mean square errors, and auto-correlation function. Comparison of these results revealed that the NNARMA model performs better than the ANFIS model for the study area.     

The impact of sea level rise and climate change on inshore wave climate: A case study for East Anglia (UK)

In coastal areas, offshore wave propagation towards the shore is influenced by water depth variations, due to sea bed bathymetry, tides and surges. Considering implications of climate change both on atmospheric forcing and sea level rise, a simple methodology involving numerical modelling is implemented to compute inshore waves from 1960 to 2099. Simulations take into account five scenarios of linear sea level rise and one climatic scenario for storm surges and offshore waves. The methodology is applied to the East Anglia coast (UK). Extreme event analysis is performed to estimate climate change implication on inshore waves and the occurrence of extreme events. It is shown, for this coastal region, that wave statistics are sensitive to the trend in sea level rise, and that the climate change scenario leads to a significant increase of extreme wave heights in the northern part of the domain. For nearshore points, the increase of the mean sea level alters not only extreme wave heights but also the frequency of occurrence of extreme wave conditions.