Abstract. 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. While there are several approaches to analyze and characterize coastal flooding hazard with either extreme SSs or sea levels, only few studies propose and compare several approaches combining the tide density with the SS variable. Thus this study aims to develop a method for modeling dependence and coincidence of SSs and high tide. In this work, we have used existing methods for tide and SS combination and tried to improve the results by proposing a new alternative approach while showing the limitations and advantages of each method. Indeed, in order to estimate extreme sea levels, the classic joint probability method (JPM) is used by making use of a convolution between tide and the skew storm surge (SSS). Another statistical indirect analysis using the maximum instantaneous storm surge (MSS) is proposed in this paper as an alternative to the first method with the SSS variable. A direct frequency analysis using the extreme total sea level is also used as a reference method. The question we are trying to answer in this paper is then the coincidence and dependency essential for a combined tide and SS hazard analysis. The results brought to light a bias in the MSS-based procedure compared to the direct statistics on sea levels, and this bias is more important for high return periods. It was also concluded that an appropriate coincidence probability concept, considering the dependence structure between SSs, is needed for a better assessment of the risk using the MSS. The city of Le Havre in France was used as a case study. Overall, the example has shown that the return level (RL) estimates using the MSS variable are quite different from those obtained with the method using the SSSs, with acceptable uncertainty. Furthermore, the shape parameter is negative from all the methods with a much heavier tail when the SSS and the extreme sea levels (ESLs) are used as variables of interest.
Many coastal urban areas and many coastal facilities must be protected against pluvial and marine floods, as their location near the sea is necessary. As part of the development of a Probabilistic Flood Hazard Approach (PFHA), several flood phenomena have to be modelled at the same time (or with an offset time) to estimate the contribution of each one. Modelling the combination and the dependence of several flooding sources is a key issue in the context of a PFHA. As coastal zones in France are densely populated, marine flooding represents a natural hazard threatening the coastal populations and facilities in several areas along the shore. Indeed, marine flooding is the most important source of coastal lowlands inundations. It is mainly generated by storm action that makes sea level rise above the tide. Furthermore, when combined with rainfall, coastal flooding can be more consequent. While there are several approaches to analyse and characterize marine flooding haz ard with either extreme sea levels or intense rainfall, only few studies combine these two phenomena in a PFHA framework. Thus this study aims to develop a method for the analy sis of a combined action of rainfall and sea level. This analysis is performed on the city of Le Havre, a French urban city on the English Channel coast, as a case study. In this work, we have used deterministic materials for rainfall and sea level modelling and proposed a new approach for estimating the probabilities of flooding.
Abstract. 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 lowlands inundations. Indeed, considering the combination of tide and extreme storm surges (SSs) is a key issue in the evaluation of the risk associated to coastal flooding hazard. Tide and extreme SSs are considered as independent. While there are several approaches to analyze and characterize coastal flooding hazard with either extreme SSs or sea levels, only few studies propose and compare several approaches combining the tide density with the SS variable. Thus this study aims to develop a method for modelling dependence and coincidence of SSs and high tide. In this work, we have used existing methods for tide and SS combination and tried to improve the results by proposing a new alternative approach while showing the limitations and advantages of each method. The city of Le Havre in France was used as a case study. Overall, the example has shown that the return levels estimates using different combinations are quite different. It has also been suggested that the questions of coincidence and dependency are essential for a combined tide and SS hazard analysis.
Improving the reliability of automotive perceptive sensors in degraded weather conditions, including fog, is an important issue for road safety and the development of automated driving. Cerema has designed the PAVIN platform reproducing fog and rain conditions to evaluate optical automotive sensor performance under these conditions. In order to increase the variety of scenarios and technologies under test, the use of digital simulation becomes a major asset. The purpose of this paper is to revive the debate around the realism of the various models underlying the numerical methods. The simulation of the radiative transfer equation by Monte Carlo methods and by simplified noise models is examined. The results of this paper show some gaps in foggy scenes between the ray-tracing method, which is considered to be the most realistic, and simple models for contrast evaluation, which can have a particularly strong impact on obstacle detection algorithms.
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