Accounting for Seasonality in Extreme Sea Level Estimation

2021 ◽  
Author(s):  
Eleanor D'Arcy
Keyword(s):  
Water Level ◽  
Sea Level ◽  
Flood Risk ◽  
Storm Surges ◽  
Value Theory ◽  
Return Level ◽  
Geophysical Research ◽  
Coastal Flood ◽  
Return Levels ◽  
The Impact

<p>Storm surges pose an increasing risk to coastline communities. These events, combined with high tide, can result in coastal flooding. To reduce the impact of storm surges, an accurate estimate of coastal flood risk is necessary. Specifically, estimates are required for the return level of sea levels (still water), which is the level with annual exceedance probability <em>p</em>. This estimate is used as an input to determine the height for a coastal defence, such as a sea wall. The return level estimation requires statistical analysis based on extreme value theory, as we need to know about the frequency of events that are more extreme than those previously observed.</p><p>Large storm surges exhibit seasonality, they are typically at their worst in the winter and least extreme in the summer. This seasonal pattern differs from that of the tide, whose seasonality is driven astronomically, resulting in tidal peaks at the spring and autumn equinoxes. Hence, the worst levels of these two components of still water level are likely to peak at different times in the year, and so statistical methods that treat them as independent variables are likely to over-estimate return levels.</p><p>We focus on the skew surge: the difference between the observed and predicted high water within a tidal cycle. Williams et al. (2016) show that tide and skew surge are independent conditional on the time of year. Batstone et al. (2013) used this property to derive estimates used for UK coastal flood defences. They used generalised Pareto distributions for the skew surge tail but did not account for the separate seasonality of tide and skew surge.</p><p>This work aims to model how the distribution of skew surges changes over a year and we combine our results with the known seasonality of tides to derive estimates of still water level return levels. We compare our results with the Batstone et al. (2013) approach at a few locations on the UK coastline.</p><p>References:</p><p>Batstone, C., Lawless, M., Tawn, J., Horsburgh, K., Blackman, D., McMillan, A., Worth, D., Laeger, S. and Hunt, T., 2013. A UK best-practice approach for extreme sea-level analysis along complex topographic coastlines. Ocean Engineering, 71, pp.28-39.</p><p>Williams, J., Horsburgh, K.J., Williams, J.A. and Proctor, R.N., 2016. Tide and skew surge independence: New insights for flood risk. Geophysical Research Letters, 43(12), pp.6410-6417.</p>

scholarly journals “Grey swan” storm surges pose a greater coastal flood hazard than climate change

2021 ◽  
Author(s):  
Kevin Horsburgh ◽  
Ivan D. Haigh ◽  
Jane Williams ◽  
Michela De Dominicis ◽  
Judith Wolf ◽  
...  
Keyword(s):  
Sea Level ◽  
Tide Gauge ◽  
Storm Surges ◽  
Natural Variability ◽  
Major Incident ◽  
Dynamical Response ◽  
Mean Sea Level ◽  
Coastal Flood ◽  
Extreme Storm ◽  
Observational Record

AbstractIn this paper, we show that over the next few decades, the natural variability of mid-latitude storm systems is likely to be a more important driver of coastal extreme sea levels than either mean sea level rise or climatically induced changes to storminess. Due to their episodic nature, the variability of local sea level response, and our short observational record, understanding the natural variability of storm surges is at least as important as understanding projected long-term mean sea level changes due to global warming. Using the December 2013 North Atlantic Storm Xaver as a baseline, we used a meteorological forecast modification tool to create “grey swan” events, whilst maintaining key physical properties of the storm system. Here we define “grey swan” to mean an event which is expected on the grounds of natural variability but is not within the observational record. For each of these synthesised storm events, we simulated storm tides and waves in the North Sea using hydrodynamic models that are routinely used in operational forecasting systems. The grey swan storms produced storm surges that were consistently higher than those experienced during the December 2013 event at all analysed tide gauge locations along the UK east coast. The additional storm surge elevations obtained in our simulations are comparable to high-end projected mean sea level rises for the year 2100 for the European coastline. Our results indicate strongly that mid-latitude storms, capable of generating more extreme storm surges and waves than ever observed, are likely due to natural variability. We confirmed previous observations that more extreme storm surges in semi-enclosed basins can be caused by slowing down the speed of movement of the storm, and we provide a novel explanation in terms of slower storm propagation allowing the dynamical response to approach equilibrium. We did not find any significant changes to maximum wave heights at the coast, with changes largely confined to deeper water. Many other regions of the world experience storm surges driven by mid-latitude weather systems. Our approach could therefore be adopted more widely to identify physically plausible, low probability, potentially catastrophic coastal flood events and to assist with major incident planning.

scholarly journals Global LiDAR land elevation data reveal greatest sea-level rise vulnerability in the tropics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
A. Hooijer ◽  
R. Vernimmen
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Flood Risk ◽  
Land Area ◽  
Area At Risk ◽  
Population Number ◽  
Coastal Flood ◽  
Elevation Data ◽  
Coastal Flood Risk ◽  
The Tropics

AbstractCoastal flood risk assessments require accurate land elevation data. Those to date existed only for limited parts of the world, which has resulted in high uncertainty in projections of land area at risk of sea-level rise (SLR). Here we have applied the first global elevation model derived from satellite LiDAR data. We find that of the worldwide land area less than 2 m above mean sea level, that is most vulnerable to SLR, 649,000 km2 or 62% is in the tropics. Even assuming a low-end relative SLR of 1 m by 2100 and a stable lowland population number and distribution, the 2020 population of 267 million on such land would increase to at least 410 million of which 72% in the tropics and 59% in tropical Asia alone. We conclude that the burden of current coastal flood risk and future SLR falls disproportionally on tropical regions, especially in Asia.

scholarly journals Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates

2018 ◽  
Author(s):  
Michalis I. Vousdoukas ◽  
Dimitrios Bouziotas ◽  
Alessio Giardino ◽  
Laurens M. Bouwer ◽  
Evangelos Voukouvalas ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Flood Risk ◽  
Large Scale ◽  
Epistemic Uncertainty ◽  
Coastal Protection ◽  
Flood Risk Assessment ◽  
Coastal Flood ◽  
Coastal Flood Risk ◽  
The Impact

Abstract. An upscaling of flood risk assessment frameworks beyond regional and national scales has taken place during recent years, with a number of large-scale models emerging as tools for hotspot identification, support for international policy-making and harmonization of climate change adaptation strategies. There is, however, limited insight on the scaling effects and structural limitations of flood risk models and, therefore, the underlying uncertainty. In light of this, we examine key sources of epistemic uncertainty in the Coastal Flood Risk (CFR) modelling chain: (i) the inclusion and interaction of different hydraulic components leading to extreme sea-level (ESL); (ii) inundation modelling; (iii) the underlying uncertainty in the Digital Elevation Model (DEM); (iv) flood defence information; (v) the assumptions behind the use of depth-damage functions that express vulnerability; and (vi) different climate change projections. The impact of these uncertainties to estimated Expected Annual Damage (EAD) for present and future climates is evaluated in a dual case study in Faro, Portugal and in the Iberian Peninsula. The ranking of the uncertainty factors varies among the different case studies, baseline CFR estimates, as well as their absolute/relative changes. We find that uncertainty from ESL contributions, and in particular the way waves are treated, can be higher than the uncertainty of the two greenhouse gas emission projections and six climate models that are used. Of comparable importance is the quality of information on coastal protection levels and DEM information. In the absence of large-extent datasets with sufficient resolution and accuracy the latter two factors are the main bottlenecks in terms of large-scale CFR assessment quality.

scholarly journals Analysis of Flood Risk of Urban Agglomeration Polders Using Multivariate Copula

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1470 ◽  
Author(s):  
Yuqin Gao ◽  
Dongdong Wang ◽  
Zhenxing Zhang ◽  
Zhenzhen Ma ◽  
Zichen Guo ◽  
...  
Keyword(s):  
Water Level ◽  
River Basin ◽  
Flood Risk ◽  
Eastern China ◽  
Urban Agglomeration ◽  
Dependence Structure ◽  
Flood Risks ◽  
The Impact ◽  
Flood Characteristics ◽  
Qinhuai River

Urban agglomeration polders (UAPs) are often used to control flooding in eastern China. The impacts of UAPs on individual flood events have been extensively examined, but how flood risks are influenced by UAPs is much less examined. This study aimed to explore a three-dimensional joint distribution of annual flood volume, peak flow and water level to examine UAPs’ impact on flood risks based on hydrological simulations. The dependence between pairwise hydrological characteristics are measured by rank correlation coefficients and graphs. An Archimedean Copula is applied to model the dependence structure. This approach is applied to the Qinhuai River Basin where UAPs are used proactively for flood control. The result shows that the Frank Copula can better represent the dependence structure in the Qinhuai River Basin. UAPs increase risks of individual flood characteristics and integrated risks. UAPs have a relatively greater impact on water level than the other two flood characteristics. It is noted that the impact on flood risk levels off for greater floods.

scholarly journals Determining Extreme Still Water Levels for Design and Planning Purposes Incorporating Sea Level Rise: Sydney, Australia

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 95
Author(s):  
Phil J. Watson
Keyword(s):  
Sea Level Rise ◽  
Water Level ◽  
Sea Level ◽  
Tide Gauge ◽  
Water Levels ◽  
Extreme Value Analysis ◽  
Tide Gauge Record ◽  
Value Analysis ◽  
Mean Sea Level ◽  
The Impact

This paper provides an Extreme Value Analysis (EVA) of the hourly water level record at Fort Denison dating back to 1915 to understand the statistical likelihood of the combination of high predicted tides and the more dynamic influences that can drive ocean water levels higher at the coast. The analysis is based on the Peaks-Over-Threshold (POT) method using a fitted Generalised Pareto Distribution (GPD) function to estimate extreme hourly heights above mean sea level. The analysis highlights the impact of the 1974 East Coast Low event and rarity of the associated measured water level above mean sea level at Sydney, with an estimated return period exceeding 1000 years. Extreme hourly predictions are integrated with future projections of sea level rise to provide estimates of relevant still water levels at 2050, 2070 and 2100 for a range of return periods (1 to 1000 years) for use in coastal zone management, design, and sea level rise adaptation planning along the NSW coastline. The analytical procedures described provide a step-by-step guide for practitioners on how to develop similar baseline information from any long tide gauge record and the associated limitations and key sensitivities that must be understood and appreciated in applying EVA.

scholarly journals Uncertainties in Coastal Flood Risk Assessments in Small Island Developing States

2019 ◽  
Author(s):  
Matteo U. Parodi ◽  
Alessio Giardino ◽  
Ap van Dongeren ◽  
Stuart G. Pearson ◽  
Jeremy D. Bricker ◽  
...  
Keyword(s):  
Flood Risk ◽  
Input Data ◽  
Flood Damage ◽  
Small Island ◽  
Small Island Developing States ◽  
Damage Functions ◽  
Current Time ◽  
Coastal Flood ◽  
Coastal Flood Risk ◽  
The Impact

Abstract. Considering the likely increase of coastal flooding in Small Island Developing States (SIDS), coastal managers at the local and global level have been developing initiatives aimed at implementing Disaster Risk Reduction (DRR) measures and adapting to climate change. Developing science-based adaptation policies requires accurate coastal flood risk (CFR) assessments, which are often subject to the scarcity of sufficiently accurate input data for insular states. We analysed the impact of uncertain inputs on coastal flood damage estimates, considering: (i) significant wave height, (ii) storm surge level and (iii) sea level rise (SLR) contributions to extreme sea levels, as well as the error-driven uncertainty in (iv) bathymetric and (v) topographic datasets, (vi) damage models and (vii) socioeconomic changes. The methodology was tested through a sensitivity analysis using an ensemble of hydrodynamic models (XBeach and SFINCS) coupled with an impact model (Delft-FIAT) for a case study at the islands of São Tomé and Príncipe. Model results indicate that for the current time horizon, depth damage functions (DDF) and digital elevation model (DEM) dominate the overall damage estimation uncertainty. We find that, when introducing climate and socioeconomic uncertainties to the analysis, SLR projections become the most relevant input for the year 2100 (followed by DEM and DDF). In general, the scarcity of reliable input data leads to considerable predictive error in CFR assessments in SIDS. The findings of this research can help to prioritise the allocation of limited resources towards the acquisitions of the most relevant input data for reliable impact estimation.

scholarly journals The Role of Mean Sea Level Annual Cycle on Extreme Water Levels Along European Coastline

2020 ◽  
Vol 12 (20) ◽  
pp. 3419
Author(s):  
Tomás Fernández-Montblanc ◽  
Jesús Gómez-Enri ◽  
Paolo Ciavola
Keyword(s):  
Water Level ◽  
Sea Level ◽  
North Sea ◽  
Extreme Events ◽  
Annual Cycle ◽  
Coastal Flooding ◽  
Water Levels ◽  
Total Water ◽  
Mean Sea Level ◽  
The Impact

The knowledge of extreme total water levels (ETWLs) and the derived impact, coastal flooding and erosion, is crucial to face the present and future challenges exacerbated in European densely populated coastal areas. Based on 24 years (1993–2016) of multimission radar altimetry, this paper investigates the contribution of each water level component: tide, surge and annual cycle of monthly mean sea level (MMSL) to the ETWLs. It focuses on the contribution of the annual variation of MMSL in the coastal flooding extreme events registered in a European database. In microtidal areas (Black, Baltic and Mediterranean Sea), the MMSL contribution is mostly larger than tide, and it can be at the same order of magnitude of the surge. In meso and macrotidal areas, the MMSL contribution is <20% of the total water level, but larger (>30%) in the North Sea. No correlation was observed between the average annual cycle of monthly mean sea level (AMMSL) and coastal flooding extreme events (CFEEs) along the European coastal line. Positive correlations of the component variance of MMSL with the relative frequency of CFEEs extend to the Central Mediterranean (r = 0.59), North Sea (r = 0.60) and Baltic Sea (r = 0.75). In the case of positive MMSL anomalies, the correlation expands to the Bay of Biscay and northern North Atlantic (at >90% of statistical significance). The understanding of the spatial and temporal patterns of a combination of all the components of the ETWLs shall improve the preparedness and coastal adaptation measures to reduce the impact of coastal flooding.

The Study of the Impact for Coastal Engineering on High Water Level Induced by Storm Surges in Bohai Bay

2011 ◽  
Vol 94-96 ◽  
pp. 810-814
Author(s):  
Jin Shan Zhang ◽  
Wei Sheng Zhang ◽  
Chen Cheng ◽  
Lin Yun Sun
Keyword(s):  
Water Level ◽  
Storm Surge ◽  
Large Scale ◽  
Storm Surges ◽  
High Water ◽  
Coastal Engineering ◽  
Cold Wave ◽  
Bohai Bay ◽  
Ocean Engineering ◽  
The Impact

Bohai Bay is an semi-closed bay, the storm surge disaster is very serious in past. Now more and more large ocean engineering are built here, To study changes of storm surge induced by the construction of large-scale coastal engineering in Bohai Bay in present, 2D numerical storm surge model is established with large - medium - small model nested approach. The three most typical storms surges: 9216, 9711 and by cold wave in October 2003 are simulated in the condition of before and after implementation of planning projects in Bohai Bay. Changes of storm surge water level due to implementation of artificial projects are analysis in this paper.

scholarly journals Return Levels Approach and Periods of Currency Crises

2018 ◽  
Vol 10 (3) ◽  
pp. 77
Author(s):  
Mouridi HAMIDOU ◽  
Joseph Mung'atu ◽  
George Orwa
Keyword(s):  
Currency Crises ◽  
Currency Crisis ◽  
Value Theory ◽  
Extreme Value ◽  
Return Level ◽  
Sample Mean ◽  
New Approach ◽  
Gev Distribution ◽  
Return Levels ◽  
Block Maxima

Dating and observing currency crisis periods lie at the heart of much international researchers. This is due to the lack of agreement in one research methodology. Until today, there does not exist a single theory or specific international policy regulation that can explain this phenomenon in global. To identify the periods of currency crisis, many methods have been brought out. Literature first employed a combination of sample mean and standard deviation. Some recent studies have attempted to use extreme value theory (EVT). Although these procedures have been more criticized in most of the literature. These drawbacks of existing approaches give rise to a new approach which is the main goal of this research. The main purpose of this study is to employ return levels technique to date currency crisis periods. The study will discuss only one method the block maxima approach. The stress losses i.e the generalized extreme value (GEV) distribution will be fitted to the annual block maxima to estimate the T-year return levels of extreme exchange market pressure index (EMPI). The parameters of the GEV distribution are estimated using the ML estimator method. Beside, a detailed procedure of the new approach is implemented. A comparison study between our identification approach and the existing conventional approach in the most literature is also conducted. We further illustrate the method by an empirical study on identifying periods of currency crisis of Kenya as case study. For practical implement the study focuses only on one single currency crisis model known as the alternative EMP index model for the intent of arbitrating the performance among various techniques. Results suggest that our new approach (RLDT) is performing better than the conventional method when the return period is considered big. Nonetheless, our technique appears to dominate the existing conventional approaches. This paper covers only a small area of this growing field of research. Hopefully, our investigations to contribute to these efforts by showing that return level dating technique derived from stress-losses model may have a place in the toolbox of economists looking for more accurate techniques in predicting currency crises.

Assessing the global risk of climate change to re/insurers using catastrophe models and hazard maps

2020 ◽  
Author(s):  
Sarah Jones ◽  
Emma Raven ◽  
Jane Toothill
Keyword(s):  
Climate Change ◽  
Sea Level ◽  
Flood Risk ◽  
River Flow ◽  
Climate Model ◽  
Flood Hazard ◽  
Insurance Industry ◽  
Global Climate Model ◽  
Hazard Maps ◽  
The Impact

&lt;p&gt;In 2018 worldwide natural catastrophe losses were estimated at around USD $155 billion, resulting in the fourth-highest insurance payout on sigma records, and in 2020 JBA Risk Management (JBA) estimate 2 billion people will be at risk to inland flooding. By 2100, under a 1.5&amp;#176;C warming scenario, the cost of coastal flooding alone as a result of sea level rise could reach USD $10.2 trillion per year, assuming no further adaptation. It is therefore imperative to understand the impact climate change may have on global flood risk and insured losses in the future.&lt;/p&gt;&lt;p&gt;The re/insurance industry has an important role to play in providing financial resilience in a changing climate. Although integrating climate science into financial business remains in its infancy, modelling companies like JBA are increasingly developing new data and services to help assess the potential impact of climate change on insurance exposure.&lt;/p&gt;&lt;p&gt;We will discuss several approaches to incorporating climate change projections with flood risk data using examples from research collaborations and commercial projects. Our case studies will include: (1) building a national-scale climate change flood model through the application of projected changes in river flow, rainfall and sea level to the stochastic event set in the model, and (2) using Global Climate Model data to adjust hydrological inputs driving 2D hydraulic models to develop climate change flood hazard maps.&lt;/p&gt;&lt;p&gt;These tools provide outputs to meet different needs, and results may sometimes invoke further questions. For example: how can an extreme climate scenario produce lower flood risk than a conservative one? Why may adjacent postcodes' flood risk differ? We will explore the challenges associated with interpreting these results and the potential implications for the re/insurance industry.&lt;/p&gt;

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