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  1. 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
    Free, publicly-accessible full text available September 8, 2023
  2. 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 tailmore »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.« less
  3. 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.