Publications scientifiques

Dike breaches occurs regularly during storm events. This phenomenon contributes to amplify considerably the impact of floods on coastal areas. It represents an important cost for repairing existing infrastructures in the vicinity of the sea-dike. Then, they must be upgraded to prevent breaches. In the present study, ANEMOC and REFMAR dataset were analysed, off Camargue coasts, to quantify the storm hazards in terms of wave height and sea level wind setup. Repartition laws were adjusted on dataset to build a 2D-copula which is used to estimate events return periods.

In this chapter, we present a second example of statistical estimation of extreme quantiles: millennial quantiles for storm surges at Brest (France), based on hourly sea-level measurements. We run sensitivity tests on parameter values, on the choice of analytic models for distributions, and on the statistical estimation methods chosen. Uncertainty in estimates and associated confidence intervals are also calculated and compared.

Submersion risks assessment requires different tools and methods from regional to coastal scales. The Shom’s strategy relies on numerical modelling and observational systems applied in a challenging multi-scale context. At regional scales, storm surge and wave models Hycom and Wavewatch III, bathymetric digital terrain models (DTMs) and observational tide/buoy networks used within the operational national storm surge service (Homonim project with Météo-France) are presented, as well as their applications in climatological 40-year hindasts.

Significant developments have been made in the observation systems and techniques of estimating sea level towards meeting the standard accuracy requirement of Global Climate Observation Systems (GCOS). This study undertakes a systematic review of the current advances in estimating sea level change in the context of the 4^th industrial revolution. Trends in the use of main observation systems such as tide gauges, satellite altimetry, and ancillary systems such as GNSS and Autonomous Surface Vehicles were explored.

Satellite altimetry and tide gauges are the two main techniques used to measure sea level. Due to the limitations of satellite altimetry, a high-quality unified sea level model from coast to open ocean has traditionally been difficult to achieve. This study proposes a fusion approach of altimetry and tide gauge data based on a deep belief network (DBN) method.

Sea-level rise due to anthropogenic climate change is projected not only to exacerbate extreme events such as cyclones and storms but also to cause more frequent chronic flooding occurring at high tides under calm weather conditions. Chronic flooding occasionally takes place today in the low-lying areas of the Petit Cul-de-sac marin (Guadeloupe, West Indies, French Antilles). This area includes critical industrial and harbor and major economic infrastructures for the islands.

Increasing our capacity to predict extreme storm surges is one of the key issues in terms of coastal flood risk prevention and adaptation. Dynamically forecasting storm surges is computationally expensive. Here, we focus on an alternative data-driven approach and set up a weather-type statistical downscaling for daily maximum storm surge (SS) prediction, using atmospheric hindcasts (CFSR and CFSv2) and 15 years of tidal gauge station measurements. We focus on predicting the storm surge at La Rochelle–La Pallice tidal gauge station.

Coastal facilities such as nuclear power plants (NPPs) have to be designed to withstand extreme weather conditions and must, in particular, be protected against coastal floods because it is the most important source of coastal lowland inundations. Indeed, considering the combination of tide and extreme storm surges (SSs) is a key issue in the evaluation of the risk associated with coastal flooding hazard. Most existing studies are generally based on the assumption that high tides and extreme SSs are independent.

Intertidal communities dominated by canopy-forming brown algae (Phaeophyceae) usually prove to be highly productive systems, based on short-term measurements. However, long-term metabolism (primary production and respiration) is sensitive to several factors acting on different time scales (e.g. tidal cycle, seasonality), making its assessment challenging. Here, we used mathematical modelling to investigate the metabolism of a Fucus serratus-dominated community on daily and annual time scales.