Abstract
Studies and management of coastal hazards are of high concerns in our society, since they engage highly valuable economical and ecological stakes. Coastal hazards are generally responding to extreme environmental conditions. The study of these physical phenomena relies on the understanding of such environmental conditions, which are rarely (or even never) observed.In coastal areas, waves are the main source of energy. This energy is responsible of coastal hazards developed at different time-scales, like the submersion or the erosion.The presented work, taking place at the interface between Statistical Analysis, Geophysics and Computer Sciences, aiming at bringing forward tools and methods serving decision makers in charge of the management of such risks.In practice, the proposed solutions answer to the questionings with a consideration of the space dimension rather than only punctual aspects. This approach is more natural considering that environmental phenomena are generally spatial, as the sea-waves fields.The study of extreme realisations of such processes is based on the availability of a representative data set, both in time and space dimensions, allowing to extrapolating information beyond the actual observations. In particular for sea-waves fields, we use numerical simulation on high performance computational clusters (HPC) to product such a data set. The outcome of this work offers many application possibilities.Most notably, we propose from this data set two statistical methodologies, having respective goals of dealing with littoral hazards long-terms questionings (e.g., erosion) and event-scale questionings (e.g., submersion).The first one is based on the application of stochastic models so-called max-stable models, particularly adapted to the study of extreme values in a spatial context. Indeed, additionally to the marginal information, max-stable models allow to take into account the spatial dependence structures of the observed extreme processes. Our results show the interest of this method against the ones neglecting the spatial dependence of these phenomena for risk indices computation.The second approach is a semi-parametric method aiming at simulating extreme waves space-time processes. Those processes, interpreted as storms, are controlled and bi-variate uplifting of already observed extreme episodes. In other words, we create most severe storms than the one already observed. These processes simulated at a controlled intensity may feed littoral physical models in order to describe a very extreme event in both space and time dimensions. They allow helping decision-makers in the anticipation of hazards not yet observed.Finally and from the construction of these extreme scenarios, we introduce a pre-computing paradigm in the goal of providing the decision-makers with a real-time and accurate information in case of a sudden coastal crisis, without performing any physical simulation.This work fits into a growing industrial demand of modelling help. Most notably a need related to the chaining of numerical and statistical models. Consequently, the industrial dimension of this PhD.~is mostly dedicated to the design and development of a prototype modelling platform. This platform aims at systematically using HPC resources to run simulations and easing the chaining of models.Embracing solutions towards questionings related to the management of coastal hazard, this thesis demonstrates the benefits of a research work placed at the interface between several domains. This thesis answers such questionings by providing end-users with cutting-edge methods stemming from each of those domains.