Search for: All records

Storm surges are the most important driver of flooding in many coastal areas. Understanding the spatial extent of storm surge events has important financial and practical implications for flood risk management, reinsurance, infrastructure reliability and emergency response. In this paper, we apply a new tracking algorithm to a high-resolution surge hindcast (CODEC, 1980–2017) to characterize the spatial dependence and temporal evolution of extreme surge events along the coastline of the UK and Ireland. We quantify the severity of each spatial event based on its footprint extremity to select and rank the collection of events. Several surge footprint types are obtained based on the most impacted coastal stretch from each particular event, and these are linked to the driving storm tracks. Using the collection of the extreme surge events, we assess the spatial distribution and interannual variability of the duration, size, severity, and type. We find that the northeast coastline is most impacted by the longest and largest storm surge events, while the English Channel experiences the shortest and smallest storm surge events. The interannual variability indicates that the winter seasons of 1989-90 and 2013–14 were the most serious in terms of the number of events and their severity, based on the return period along the affected coastlines. The most extreme surge event and the highest number of events occurred in the winter season 1989–90, while the proportion of events with larger severities was higher during the winter season 2013–14. This new spatial analysis approach of surge extremes allows us to distinguish several categories of spatial footprints of events around the UK/Ireland coast and link these to distinct storm tracks. The spatial dependence structures detected can improve multivariate statistical methods which are crucial inputs to coastal flooding assessments. 

This paper describes a major update to the quasi-global, higher-frequency sea-level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset have been used in many published studies, across a wide range of oceanographic and coastal engineering-related investigations concerned with evaluating tides, storm surges, extreme sea levels, and other related processes. The third version of the dataset (released 2021), presented here, contains double the number of years of data, and nearly four times the number of records, compared to Version 2. The dataset consists of records obtained from multiple sources around the world. This paper describes the assembly of the dataset, its processing, and its format, and outlines potential future improvements 

Abstract. Flooding is of particular concern in low-lying coastal zones that are prone to flooding impacts from multiple drivers, such as oceanographic (storm surge and wave), fluvial (excessive river discharge), and/or pluvial (surface runoff). In this study, we analyse, for the first time, the compound flooding potential along the contiguous United States (CONUS) coastline from all flooding drivers, using observations and reanalysis data sets. We assess the overall dependence from observations by using Kendall's rank correlation coefficient (τ) and tail (extremal) dependence (χ). Geographically, we find the highest dependence between different drivers at locations in the Gulf of Mexico, southeastern, and southwestern coasts. Regarding different driver combinations, the highest dependence exists between surge–waves, followed by surge–precipitation, surge–discharge, waves–precipitation, and waves–discharge. We also perform a seasonal dependence analysis (tropical vs. extra-tropical season), where we find higher dependence between drivers during the tropical season along the Gulf and parts of the East Coast and stronger dependence during the extra-tropical season on the West Coast. Finally, we compare the dependence structure of different combinations of flooding drivers, using observations and reanalysis data, and use the Kullback–Leibler (KL) divergence to assess significance in the differences of the tail dependence structure. We find, for example, that models underestimate the tail dependence between surge–discharge on the East and West coasts and overestimate tail dependence between surge–precipitation on the East Coast, while they underestimate it on the West Coast. The comprehensive analysis presented here provides new insights on where the compound flooding potential is relatively higher, which variable combinations are most likely to lead to compounding effects, duringwhich time of the year (tropical versus extra-tropical season) compoundflooding is more likely to occur, and how well reanalysis data capture thedependence structure between the different flooding drivers. 

Abstract. In coastal regions, floods can arise through a combination of multipledrivers, including direct surface run-off, river discharge, storm surge, andwaves. In this study, we analyse compound flood potential in Europe andenvirons caused by these four main flooding sources using state-of-the-artdatabases with coherent forcing (i.e. ERA5). First, we analyse thesensitivity of the compound flooding potential to several factors: (1)sampling method, (2) time window to select the concurrent event of theconditioned driver, (3) dependence metrics, and (4) wave-driven sea leveldefinition. We observe higher correlation coefficients using annual maximathan peaks over threshold. Regarding the other factors, our results showsimilar spatial distributions of the compound flooding potential. Second, thedependence between the pairs of drivers using the Kendall rank correlationcoefficient and the joint occurrence are synthesized for coherent patterns ofcompound flooding potential using a clustering technique. This quantitativemulti-driver assessment not only distinguishes where overall compound floodingpotential is the highest, but also discriminates which driver combinations aremore likely to contribute to compound flooding. We identify that hotspots ofcompound flooding potential are located along the southern coast of the NorthAtlantic Ocean and the northern coast of the Mediterranean Sea. 

We demonstrate that long‐term tidally induced changes in extreme sea levels affect estimates of major flood hazard in a predictable way. Long‐term variations in tides due to the 4.4 and 18.6‐year cycles influence extreme sea levels at 380 global tide gauges out of a total of 581 analyzed. Results show coherent regions where the amplitudes of the modulations are particularly relevant in the 100‐year return sea level, reaching more than 20 cm in some regions (western Europe, north Australia, and Singapore). We identify locations that are currently in a positive phase of the modulation and therefore at a higher risk of flooding, as well as when (year) the next peak of the long‐term tidal modulations is expected to occur. The timing of the peak of the modulation is spatially coherent and influenced by the relative importance of each cycle (4.4 or 18.6‐year) over the total amplitude. An evaluation of four locations suggests that the potentially flooded area in a 100‐year event can vary up to ∼45% (in Boston) as a result of the long‐term tidal cycles; however, the flooded area varies due to local topography and tidal characteristics (6%–13%). We conclude that tidally modulated changes in extreme sea levels can alter the potentially inundated area in a 100‐year event and that the traditional, fixed 100‐year floodplain is inadequate for describing coastal flood risk, even without considering sea‐level rise.

 

We perform the first global analysis of the spatial footprints of storm surges, using observed and simulated storm surge data. Three different techniques are applied to quantify the spatial footprints: clustering analysis, percentage of co‐occurrence, and joint probability analysis. The capability of the simulated data to represent the observed storm surge footprints is demonstrated. Results lead to the identification of coastline stretches prone to be impacted simultaneously by the same storm surge events. The spatial footprint sizes differ around the globe, partially conditioned by the geography of the coastline, that is, more irregular coastlines consist of a larger number of different storm surge clusters with varying footprint sizes. For the northwestern Atlantic, spatial footprints of storm surges vary when specifically accounting for tropical cyclones, using storm track information in the storm surge simulations. Our results provide important new insights into the spatial footprints of storm surges at the global scale and will help to facilitate improvements in how coastal flood risk is identified, assessed, and managed, by taking these spatial features into account.