Abstract: This paper presents a method to include damage at the initial stage of coastal flooding events definition and in return periods computation. The methodology is illustrated within a local study carried out in Biarritz Grande Plage, a meso-tidal, wave dominated beach located on the french basque coast in the south west of France. The work is based on two datasets covering the period 1949–2015: a first one, consisting of observation and synthetic data on wave characteristics and water level, and a second one, gathering storm dates and related damage intensities obtained through investigations in the press and in archives. A statistical analysis was first carried out to find the best combination of source variables explaining the reported damages for the identified storms. Maximal, mean and accumulated values were calculated over storm duration, considering source and aggregated variables based on the empirical run-up formula or the wave energy flux. Most rules combining a wave parameter and water level are found to provide satisfactory damage prediction as soon as maxima variables are considered. Rules based on mean variables are less accurate and those based on accumulated variable values are not relevant. The ability of the rules to be used as generic event definition rules is then tested by performing a retrospective analysis on the whole dataset, checking their efficiency in detecting historical storms (i.e., with damages) without finding too many false positives. Most of the rules formerly studied, except the ones using wave period only as wave parameter, were able to correctly perform this task. Coastal flood event return periods (RP) were then calculated by applying three of the best rules identified previously. The rule using non simultaneous maxima of wave energy flux and water level gives encouraging results for the RP values. Nevertheless, the discrepancy still observed among the different rules calls for further work in this direction.
Abstract: The evaluation of coastal damage caused by storms is not straightforward and different approaches can be applied. In this study, damage caused by extreme storms is evaluated at a regional scale based on news information published in regional newspapers. The data derived from the news are compared with hydrodynamic parameters to check the reliability of this methodology as a preliminary” fast approach” to evaluate storm damage and to identify hotspots along the coast. This methodology was applied to the two most extreme storms ever recorded along the Spanish Mediterranean coast, which occurred in January 2017 and January 2020, severely impacting the coast and causing significant community concerns. The news information from different media sources was processed and weighted to describe the resulting erosion, inundation, sand accumulation, and destruction of infrastructures. Moreover, an accuracy index for scoring the quality of the information was proposed. In spite of some limitations of the method, the resulting regional coastal hazard landscape of damage provides a rapid overview of the intensity and distribution of the damage and enables one to identify the location of potential hotspots for the analyzed extreme storm events. The results show that estimated damage intensity is better related to maximum wave energy than cumulative wave energy during a storm, and that beach characteristics should also be included for understanding the distribution of coastal damage.
Cites background from "Coastal flooding event definition b..."
...This information can help to better understand coastal impacts caused by storms, although a careful verification of their uncertainties and potential bias is required before its incorporation to a robust model for coastal risk assessment [8,12]....
Abstract: Over the last decades, the evaluation of hazards and risks associated with coastal flooding has become increasingly more important in order to protect population and assets. The general purpose of this research was to assess reliable coastal flooding hazard maps due to overflow and wave overtopping. This paper addresses the problem of defining credible joint statistics of significant wave heights Hs and water levels ζ, focusing on the selection of the sample pair that characterizes each sea storm, to evaluate the occurrence probability of extreme events. The pair is selected maximizing a spatial structure variable, i.e., a linear combination of Hs and Relaix, F., Zammit, P.S. Satellite cells are essential for skeletal muscle, specific to each point of the area at risk. The structure variable is defined by the sensitivity of the flooding process to Hs and ζ, as found by analyzing a set of inundation maps produced through a Simplified Shallow-Water numerical model (SSW). The proposed methodology is applied to a coastal stretch in the Venetian littoral (Italy), by means of a 30 year-long time series recorded at the “Acqua Alta” oceanographic research tower, located in the Northern Adriatic Sea in front of the Venetian lagoon. The critical combination of Hs and ζ forming the structure variable is presented in a map, and it can be related to the topography and the presence of mitigation measures. The return period associated with the two recent large storms that occurred in this area in 2018 and 2019 is also investigated. The proposed procedure gives credible occurrence probabilities for these events, whereas other approaches would consider them extremely unlikely.
Abstract: The NCEP and NCAR are cooperating in a project (denoted “reanalysis”) to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957–96. This eliminates perceived climate jumps associated with changes in the data assimilation system. The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided b...
TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually cataloging and modeling extreme value values in sequences.
Abstract: 1. Introduction.- 2. Basics of Statistical Modeling.- 3. Classical Extreme Value Theory and Models.- 4. Threshold Models.- 5. Extremes of Dependent Sequences.- 6. Extremes of Non-Stationary Sequences.- 7. A Point Process Characterization of Extremes.- 8. Multivariate Extremes.- 9. Further Topics.- Appendix A: Computational Aspects.- Index.
Abstract: Recent improvements in mapping of global population distribution makes it possible to estimate the number and distribution of people near coasts with greater accuracy than previously possible, and hence consider the potential exposure of these populations to coastal hazards. In this paper, we combine the updated Gridded Population of the World (GPW2) population distribution estimate for 1990 and lighted settlement imagery with a global digital elevation model (DEM) and a high resolution vector coastline. This produces bivariate distributions of population, lighted settlements and land area as functions of elevation and coastal proximity. The near-coastal population within 100 km of a shoreline and 100 m of sea level was estimated as 1.2 X 10(9) people with average densities nearly 3 times higher than the global average density. Within the near coastal-zone, the average population density diminishes more rapidly with elevation than with distance, while the opposite is true of lighted settlements. Lighted settlements are concentrated within 5 km of coastlines worldwide, whereas average population densities are higher at elevations below 20 m throughout the 100 km width of the near-coastal zone. Presently most of the near-coastal population live in relatively densely-populated rural areas and small to medium cities, rather than in large cities. A range of improvements are required to define a better baseline and scenarios for policy analysis. Improving the resolution of the underlying population data is a priority.