Extreme Sea Levels Research Articles

Estimation of Return Levels with Long Return Periods for Extreme Sea Levels by the Average Conditional Exceedance Rate Method

Estimation of so-called return levels for environmental extremes is of importance for risk assessment. A particular challenge is to find estimates corresponding to long return periods, as uncertainties in the form of confidence intervals became too wide for practical use when applying conventional methodology where large portions of data are not used. A recently proposed technique, the Average Conditional Exceedance Rate (ACER), makes effective use of all available data. For risk analysis related to nuclear infrastructure, usually located along a coastline, extreme sea levels are of concern. We demonstrate, for measurements of the sea level along the Swedish coast at locations close to nuclear power plants, that the methodology results in considerably shorter confidence intervals compared to conventional approaches.

Seasonal Shift of Storm Surges in the Yangtze Estuary, China

This study explores storm floods in the Yangtze Estuary to investigate how extreme sea levels and storm surges change in the context of global warming. Previous studies focused on the long-term variations in amplitude or frequency of storm surges, with limited research conducted on the timing of extreme storm surge events. Based on the methods of non-stationary extreme value theory, we explored the last 33-year tidal levels at Xuliujing Station and found that the annual extreme water level has exhibited a slight downward trend, which is directly attributed to the decrease in mean sea level resulting from reduced upstream river flow. The storm surge season of the Yangtze Estuary experienced a significant lag in the period 2005–2018, which is not restricted to the Yangtze Estuary but is rather a large-scale climate characteristic of a broad oceanic region. The reason for this shift is the sustained increase in the intensity of the western Pacific subtropical high in the last 15 years, leading to the appearance of low-pressure channels in the East China Sea in September and October and thus causing more typhoons to enter the East China Sea during the later period of the storm surge season.

Higher quantiles of sea levels rise faster in Baltic Sea Climate projections

Absolute sea levels in the Baltic Sea will rise under the influence of climate warming, similar to those in the world ocean. For extreme sea levels, there are indications that they will rise even faster than mean sea levels, but that topic is still controversially discussed and existing studies point into different directions. We analyzed a regional climate model ensemble for the Baltic Sea for future sea level changes. We find that the rate of change differs between high sea levels and the average: In the eastern part of the Baltic Sea, the 99th percentile of sea level was predicted to rise faster than the median. In the south-western part, the relation was opposite. Thus, our simulations predict a change not only in the sea level mean, but also in its distribution. This pattern was almost consistent between the individual ensemble members. We investigated the 99th percentile as a proxy for extreme sea levels, since their partially stochastic nature limits the predictive skill of our 20-member ensemble. Our findings imply that adapting coastal protection to mean sea level change only may be regionally insufficient.

Wave contributions to sea levels along the coast of northern South China Sea over 2002–2022

To better understand the wave contribution to coastal sea level variation in the northern South China Sea (SCS), the total sea level time series, which is estimated as the sum of waves, tides, atmospheric surges and altimetric sea levels, over the period of 2002–2022 are used in this study. It is found that tides and waves are two dominate contributors to coastal sea level changes at seasonal (45.7 % and 40.8 %) and interannual (58.6 % and 28.4 %) time scales. The sea level trend is dominated by the altimetric sea levels, but waves can also influence the trends in the Taiwan Strait and Beibu Gulf. The amount of sea level rise that doubles the frequency of the former 50-year return level is analyzed by using the Generalized Extreme Value Distribution (GEVD) model. The results show that the amount of sea level rise is closely related to the amplitude and variance of extreme sea levels. Because waves can significantly increase probability of occurrence of exceedingly large extreme sea levels, the amount of sea level rise (∼1 m) that doubles the coastal flooding frequency is much greater than that (∼10 cm) without the wave contribution. This result demonstrates the important role of waves in changing the coastal flooding frequency.

Stochastic simulation of storm surge extremes along the contiguous United States coastlines using the max-stable process

Extreme sea levels impact coastal society, property, and the environment. Various mitigation measures are engineered to reduce these impacts, which require extreme event probabilities typically estimated site-by-site. The site-by-site estimates usually have high uncertainty, are conditionally independent, and do not provide estimates for ungauged locations. In contrast, the max-stable process explicitly incorporates the spatial dependence structure and produces more realistic event probabilities and spatial surfaces. We leverage the max-stable process to compute extreme event probabilities at gridded locations (gauged and ungauged) and derive their spatial surfaces along the contiguous United States coastlines by pooling annual maximum (AM) surges from selected long-record tide gauges. We also generate synthetic AM surges at the grid locations using the predicted distribution parameters and reordering them in the rank space to integrate the spatiotemporal variability. The results will support coastal planners, engineers, and stakeholders to make the most precise and confident decisions for coastal flood risk reduction.

Technical note: Extending sea level time series for the analysis of extremes with statistical methods and neighbouring station data

Abstract. Extreme sea levels may cause damage and the disruption of activities in coastal areas. Thus, predicting extreme sea levels is essential for coastal management. Statistical inference of robust return level estimates critically depends on the length and quality of the observed time series. Here, we compare two different methods for extending a very short (∼ 10-year) time series of tide gauge measurements using a longer time series from a neighbouring tide gauge: linear regression and random forest machine learning. Both methods are applied to stations located in the Kattegat Basin between Denmark and Sweden. Reasonable results are obtained using both techniques, with the machine learning method providing a better reconstruction of the observed extremes. By generating a set of stochastic time series reflecting uncertainty estimates from the machine learning model and subsequently estimating the corresponding return levels using extreme value theory, the spread in the return levels is found to agree with results derived by more physically based methods.

A Comprehensive Probabilistic Flood Assessment Accounting for Hydrograph Variability of ESL Events

AbstractFlood characteristics caused by extreme sea level (ESL) events depend largely on the magnitude of peak water levels (WLs) and their temporal evolution. However, coastal flood risk is generally assessed based on only a limited number of potential peak WLs and a selection of past events or a design hydrograph. We address this gap and systematically estimate (a) spatial annual and (b) event‐based flood probabilities by comprehensively accounting for both a wide range of peak ESLs and their temporal evolution, herein referred to as hydrograph intensity. We simulate flooding at the German Baltic Sea coast with the hydrodynamic model Delft3D. We produce probabilistic flood maps, which detail flood exposed areas together with annual probability of flooding. Additionally, we show how the flood extent changes, when accounting for upper, median, and lower quantiles of hydrograph intensities. Our results demonstrate that the relevance of the intensity is site and ESL dependent. While flood extents of some ESLs of the upper and lower intensity bounds indicate no differences, others differ by up to 45%. Further, we consider two ESLs (2.24 and 2.55 m) and simulate 100 intensities for each. Compared to intensity quantiles, this results in flood extents of up to 60% difference. Hence, we find that quantiles of intensity do not cover the full range when addressing uncertainty due to hydrograph variability. We, therefore, recommend accounting for a wide range of hydrograph intensities in addition to using a wide range of ESL in future flood risk assessments.

Uncertainties and discrepancies in the representation of recent storm surges in a non-tidal semi-enclosed basin: a hindcast ensemble for the Baltic Sea

Abstract. Extreme sea level events, such as storm surges, pose a threat to coastlines around the globe. Many tide gauges have been measuring the sea level and recording these extreme events for decades, some for over a century. The data from these gauges often serve as the basis for evaluating the extreme sea level statistics, which are used to extrapolate sea levels that serve as design values for coastal protection. Hydrodynamic models often have difficulty in correctly reproducing extreme sea levels and, consequently, extreme sea level statistics and trends. In this study, we generate a 13-member hindcast ensemble for the non-tidal Baltic Sea from 1979 to 2018 using the coastal ocean model GETM (General Estuarine Transport Model). In order to cope with mean biases in maximum water levels in the simulations, we include both simulations with and those without wind-speed adjustments in the ensemble. We evaluate the uncertainties in the extreme value statistics and recent trends of annual maximum sea levels. Although the ensemble mean shows good agreement with observations regarding return levels and trends, we still find large variability and uncertainty within the ensemble (95 % confidence levels up to 60 cm for the 30-year return level). We argue that biases and uncertainties in the atmospheric reanalyses, e.g. variability in the representation of storms, translate directly into uncertainty within the ensemble. The translation of the variability of the 99th percentile wind speeds into the sea level elevation is in the order of the variability of the ensemble spread of the modelled maximum sea levels. Our results emphasise that 13 members are insufficient and that regionally large ensembles should be created to minimise uncertainties. This should improve the ability of the models to correctly reproduce the underlying extreme value statistics and thus provide robust estimates of climate change-induced changes in the future.

Bayesian extreme value analysis of extreme sea levels along the German Baltic coast using historical information

Abstract. Developed coastlines require considerable investments into coastal protection measures to mitigate the effects of flooding caused by extreme sea levels (ESLs). To maximize the effectiveness of these measures, accurate estimates of the underlying hazard are needed. These estimates are typically determined by performing extreme value analysis on a sample of events taken from tide-gauge observations. However, such records are often limited in duration, and the resulting estimates may be highly uncertain. Furthermore, short records make it difficult to assess whether exceptionally large events within the record are appropriate for analysis or should be disregarded as outliers. In this study, we explore how historical information can be used to address both of these issues for the case of the German Baltic coast. We apply a Bayesian Markov chain Monte Carlo approach to assess ESLs using both systematic tide-gauge observations and historical information at seven locations. Apart from the benefits provided by incorporating historical information in extreme value analysis, which include reduced estimate uncertainties and the reclassification of outliers into useful samples, we find that the current tide-gauge records in the region alone are insufficient for providing accurate estimates of ESLs for the planning of coastal protection. We find long-range dependence in the series of ESLs at the site of Travemünde, which suggests the presence of some long-term variability affecting events in the region. We show that ESL activity over the full period of systematic observation has been relatively low. Consequently, analyses which consider only these data are prone to underestimations.

CHANGING EXTREME SEA LEVELS ALONG THE COAST OF MALAYSIA

This study examined extreme sea level (ESL) variations along the Malaysian coast using hourly sea level data from 17 tide gauge stations. The maximum observed ESLs varied from 1.26 m at Bintulu to 2.92 m at Port Klang, with tides playing a significant role in ESLs, especially along the west coast of Peninsular Malaysia. The spatial variation of seasonal ESLs showed a higher maximum of non-tidal residuals (0.8-1.1 m) along the east coast of Peninsular Malaysia during the northeast monsoon. This can be mainly attributed to the influence of the monsoon wind. Moreover, significant increases in ESLs were observed at 14 stations, largely due to rises in mean sea level. The interannual variability of ESL could be associated with the El Niño-Southern Oscillation at most sites except the northeast coast of Peninsular Malaysia. Interestingly, the interannual variability of the non-tidal residuals could be linked to the monsoon at sites located on the west coast of Peninsular Malaysia and East Malaysia. These findings provide valuable insights to relevant authorities for coastal planning, especially regarding flood risk management and the formulation of effective mitigation strategies.

Dairy farming exposure and impacts from coastal flooding and sea level rise in Aotearoa-New Zealand

Globally, episodic coastal flooding is expected to have increasingly severe impacts on agriculture in low-lying coastal areas, as climate change continues to cause relative sea level rise (RSLR). This study analyses dairy farm exposure and impacts from extreme sea levels (ESL) and RSLR in Aotearoa-New Zealand (A-NZ). Spatio-temporal modelling is undertaken using: 1) a dairy production land layer; 2) a static coastal inundation model from 10, 100, 200 and 500-year annual recurrence interval (ARI) ESL events at 0–2 m RSLR (0.5 m increments); and 3) an Impact State (IS) scheme relating percentage of farmland inundated and depth of flooding at milking shed locations to categorise the severity of damage. Through this analysis we highlight the increasing exposure of the dairy industry to coastal flooding, with 472 farms potentially impacted (at or greater than IS 1) by a 10-year ARI event and 1276 farms exposed to a 500-year ARI event at current sea levels, rising to 1276 (10-year ARI) and 1344 farms (500-year ARI) with 2 m RSLR. RSLR is identified as the factor driving this increase in exposure and severity of impacts to dairying, with the increase in the number of farms impacted in a 500-year compared to 10-year event only 5 % greater when considered with 1 m of RSLR, compared to 37 % greater at 0 m RSLR. This has significant implications for A-NZ where the dairy industry is of high economic and social importance, demonstrating the need for industry targeted climate change adaptation and disaster risk reduction measures.

Effects of tidal wave modulation on coastal flooding along a tide-river context: Case of the seine estuary

Tidal waves modulated over a range of timescales, from inter-daily to inter-annual variations, are responsible for coastal flooding which is enhanced in tide-river environments. The research described here investigates the tidal dynamics interacting with the fluvial component in the Seine estuary (NE France), an excellent natural laboratory for its time-varying flow and the available water-level records provided by tide gauges along the estuary. The tidal signal has been analyzed together with the river discharge during 25 years (1990-2015) in the Seine Bay, including the estuary and the marine tides at Cherbourg, by the use of a series of spectral and physical approaches. The main results highlighted that the tidal modulation is basically induced by the time changes of the harmonic components of M2, S2, N2, and K2 with significant shifts in the middle of the estuary. The estuary takes a flood-dominant behavior when the deformation of tides increases in the river section during the period 1999-2002 with an interaction between the high variability of tidal range and the extreme river discharge. This opposite effect of the river and its resistance is enhanced under higher tidal flow velocity; which increases the water depth and reduces the hydraulic head and the tidal amplification factor in a low turbid estuary. This work carried out in the framework of the SWOT 3MC research program, gives some insights into the complex behavior of tidal propagation in the Seine estuary and its non-stationary dynamics close to the river effects in response to extreme sea levels leading to coastal flooding.

Small Island Developing States under threat by rising seas even in a 1.5 °C warming world

Small Island Developing States (SIDS) have long been recognized as some of the planet’s most vulnerable areas to climate change, notably to rising sea levels and coastal extremes. They have been crucial in raising ambitions to keep global warming below 1.5 °C and in advancing the difficult debate on loss and damage. Still, quantitative estimates of loss and damage for SIDS under different mitigation targets are lacking. Here we carry out an assessment of future flood risk from slow-onset sea-level rise and episodic sea-level extremes along the coastlines of SIDS worldwide. We show that by the end of this century, without adaptation, climate change would amplify present direct economic damages from coastal flooding by more than 14 times under high-emissions scenarios. Keeping global warming below 1.5 °C could avoid almost half of unmitigated damage, depending on the region. Achieving this climate target, however, would still not prevent several SIDS from suffering economic losses that correspond to considerable shares of their GDP, probably leading to forced migration from low-lying coastal zones. Our results underline that investments in adaptation and sustainable development in SIDS are urgently needed, as well as dedicated support to assisting developing countries in responding to loss and damage due to climate change.

Long-term climate change impacts on regional sterodynamic sea level statistics analyzed from the MPI-ESM large ensemble simulation

Statistics of regional sterodynamic sea level variability are analyzed in terms of probability density functions of a 100-member ensemble of monthly mean sea surface height (SSH) timeseries simulated with the low-resolution Max Planck Institute Grand Ensemble. To analyze the impact of climate change on sea level statistics, fields of SSH variability, skewness and excess kurtosis representing the historical period 1986–2005 are compared with similar fields from projections for the period 2081–2100 under moderate (RCP4.5) and strong (RCP8.5) climate forcing conditions. Larger deviations of the models SSH statistics from Gaussian are limited to the western and eastern tropical Pacific. Under future climate warming conditions, SSH variability of the western tropical Pacific appear more Gaussian in agreement with weaker zonal easterly wind stress pulses, suggesting a reduced El Niño Southern Oscillation activity in the western warm pool region. SSH variability changes show a complex amplitude pattern with some regions becoming less variable, e.g., off the eastern coast of the north American continent, while other regions become more variable, notably the Southern Ocean. A west (decrease)-east (increase) contrast in variability changes across the subtropical Atlantic under RCP8.5 forcing is related to changes in the gyre circulation and a declining Atlantic Meridional Overturning Circulation in response to external forcing changes. In addition to global mean sea-level rise of 16 cm for RCP4.5 and 24 cm for RCP8.5, we diagnose regional changes in the tails of the probability density functions, suggesting a potential increased in variability-related extreme sea level events under global warmer conditions.

Role of surface rainfall to the variability of extreme sea level along the eastern coast of Bangladesh

Bangladesh is highly vulnerable to the adverse impacts of extreme sea levels (ESLs) because of its geographical location with low-lying coast. In addition, high discharge of huge rain water to the nearby coast may influence the ESL. The effects of different meteorological forcings like atmospheric pressure, wind-induced wave, and surges to ESL have been investigated intensively around Bangladesh. However, the role of surface rainfall to the ESL along the coast of Bangladesh remains unknown. In this study, the role of surface rainfall to the ESL was investigated for Cox's Bazar area along the eastern coast of Bangladesh. The ESL events were selected by applying the threshold of 500 ​mm height. The variations in SLA during seven days in prior to the ESL was predicted by multivariate regression using selected climatic variables of rainfall, sea level pressure, and wind. It was revealed that the prediction of ESL considering the contribution of rainfall outperforms the predictions without rainfall. The significant contribution of rainfall for prediction of ESL at Cox's Bazar, reflecting the hilly landscape at Cox's Bazar where a clear response of high surface runoff is expected. The findings suggest that the rainfall have an important effect to the ESL change along the eastern coast of Bangladesh. Therefore, incorporating rainfall effect is essential for better prediction of the ESLs which helps coastal management and reduction of hazards.

Extreme Sea Level Variations in the Sea of Japan Caused by the Passage of Typhoons Maysak and Haishen in September 2020

Extreme Sea Level Variations in the Sea of Japan Caused by the Passage of Typhoons Maysak and Haishen in September 2020

Analysis of the blade aeroelastic effect on the floating offshore wind turbine wake in a focusing wave with a hybrid model

With the recent trend toward larger floating offshore wind turbines (FOWT), the influence of blade deformation becomes more obvious. Modelling this fully coupled fluid-structure interaction (FSI) system, especially at extreme sea level with large-scale motion of the entire system, is an essential challenge. A hybrid numerical model integrating the potential-viscous flow model (qaleFOAM), the unstable actuator line method (UALM), the Legendre spectral finite element model (BeamDyn), and the Lumped Mass Mooring Model (MoorDyn) is employed in the current research. This model is capable of effortlessly and accurately reflecting the blade aeroelastic effect on the dynamics of a FOWT system under the circumstances of a focused wave and uniform wind. The result reveals that the aeroelastic impacts have been identified to not only increase the fatigue damage on the turbine blades but also affect the wake field distribution. Based on the temporal and spatial distribution of the velocity fields, it is found that the coupled effects of the large wave elevation and the FOWT high-frequency motions will disturb the velocity in the wake region. The slight variation of the wind speed in the wake region will be exacerbated by about 0.21% to 18.6%, and it transforms the shape of the wake region when the blade aeroelastic effect participates in the coupled effect. This phenomenon may further affect the downstream FOWT system features through the upstream FOWT wake field.

Mangrove ecosystem properties regulate high water levels in a river delta

Abstract. Intertidal wetlands, such as mangroves in the tropics, are increasingly recognized for their role in nature-based mitigation of coastal flood risks. Yet it is still poorly understood how effective they are at attenuating the propagation of extreme sea levels through large (order of 100 km2) estuarine or deltaic systems, with complex geometry formed by networks of branching channels intertwined with mangrove and intertidal flat areas. Here, we present a delta-scale hydrodynamic modelling study, aiming to explicitly account for these complex landforms, for the case of the Guayas delta (Ecuador), the largest estuarine system on the Pacific coast of Latin America. Despite coping with data scarcity, our model accurately reproduces the observed propagation of high water levels during a spring tide. Further, based on a model sensitivity analysis, we show that high water levels are most sensitive to the mangrove platform elevation and degree of channelization but to a much lesser extent to vegetation-induced friction. Mangroves with a lower surface elevation, lower vegetation density, and higher degree of channelization all favour a more efficient flooding of the mangroves and therefore more effectively attenuate the high water levels in the deltaic channels. Our findings indicate that vast areas of channelized mangrove forests, rather than densely vegetated forests, are most effective for nature-based flood risk mitigation in a river delta.

Regional assessment of extreme sea levels and associated coastal flooding along the German Baltic Sea coast

Abstract. Among the Baltic Sea littoral states, Germany is anticipated to endure considerable damage as a result of increased coastal flooding due to sea-level rise (SLR). Consequently, there is a growing demand for flood risk assessments, particularly at regional scales, which will improve the understanding of the impacts of SLR and assist adaptation planning. Existing studies on coastal flooding along the German Baltic Sea coast either use state-of-the-art hydrodynamic models but cover only a small fraction of the study region or assess potential flood extents for the entire region but rely on global topographic data sources and apply the simplified bathtub approach. In addition, the validation of produced flood extents is often not provided. Here we apply a fully validated hydrodynamic modelling framework covering the German Baltic Sea coast that includes the height of natural and anthropogenic coastal protection structures in the study region. Using this modelling framework, we extrapolate spatially explicit 200-year return water levels, which align with the design standard of state embankments in the region, and simulate associated coastal flooding. Specifically, we explore (1) how flood extents may change until 2100 if dike heights are not upgraded, by applying two high-end SLR scenarios (1 and 1.5 m); (2) hotspots of coastal flooding; and (3) the use of SAR imagery for validating the simulated flood extents. Our results confirm that the German Baltic coast is exposed to coastal flooding, with flood extent varying between 217 and 1016 km2 for the 200-year event and a 200-year event with 1.5 m SLR, respectively. Most of the flooding occurs in the federal state of Mecklenburg-Western Pomerania, while extreme water levels are generally higher in Schleswig-Holstein. Our results emphasise the importance of current plans to update coastal protection schemes along the German Baltic Sea coast over the 21st century in order to prevent large-scale damage in the future.

Extreme Sea Level Variations in the Sea of Japan Caused by the Passage of Typhoons Maysak and Haishen in September 2020

This study investigates extreme sea level variations recorded by tide gauges in the Sea of Japan during the passages of typhoons Maysak and Haishen in September 2020. Specific focus is on storm surges, seiches and infragravity waves identified in the records using tidal and statistical analyses. At most locations, storm surges, formed through the combined effect of atmospheric pressure changes and strong winds, were the major contributors to the extreme sea level events. For the Russian and Japanese coasts, the first typhoon, Maysak, turned out to have the greatest impact, while for the southeastern coast of the Korean Peninsula, the largest sea level variations were caused by the second typhoon, Haishen. At all sites, the passing storms induced eigen oscillations in the respective bays and harbours. Pronounced high-frequency oscillations with periods of two to seven minutes, which appear to be associated with infragravity waves, were recorded, reaching a maximum range of 2 m at Preobrazheniye. The observed differences in component content and associated characteristics of the observed sea level variations arise from differences in the topographic features of the corresponding coastal sites. Statistical analysis of the atmospheric pressure and wind speed series taken from weather stations and from the ERA5 reanalysis shows that atmospheric pressure is reproduced by reanalysis with high accuracy, whereas wind speed at different stations has significant discrepancies related to differences in orographic features.