François Paris

Scalable Interactive Platform for Geographic Evaluation of Sea-Level Rise Impact Combining High-Performance Computing and WebGIS Client

As the climate is changing, more applied information on resulting impacts are required to inform adaptation planning. Over the last decade, the amount of information relevant to climate change impact assessment has grown drastically. This can particularly be illustrated in coastal areas, threatened by sea-level rise due to climate change, where a key recent development has been the delivery of precise and accurate topography obtained by Light Detection and Ranging (Li-DAR) at regional and national scales, i.e., respectively, large and small scales. However, using such large, complex, and heterogeneous coastal data sets in a contextual manner is far from straightforward. It is the reason why these developments have not led to easier assessment of coastal climate change impacts so far. In this chapter, we address this interoperability challenge by developing and describing a prototype of Web service combining Li-DAR, tidal, and sea-level rise data to quickly communicate spatial information on the exposure to future coastal flooding along the French coastal zones. We discuss several issues related to data architecture, on-the-fly (geo)-processing capabilities, management of asynchronous workflows, and data diffusion strategies in the context of international standards such as Infrastructure for Spatial Information in Europe (INSPIRE). We believe that our flexible architecture mainly reusing off-the-shelf components is able to improve both complex scenarios’ analysis for experts and dissemination of these future coastal changes to the general public.

Casting light on forcing and breaching scenarios that lead to marine inundation: Combining numerical simulations with a random-forest classification approach

Abstract Identifying the offshore forcing and breaching conditions that lead to marine inundation is of high importance for risk management. This task cannot be conducted by using a numerical hydrodynamic model due to its high computation time cost (of several minutes or even hours). In the present study, we show how the random forest (RF) classification technique can approximate the numerical model to explore these critical conditions. We focus on the Boucholeurs site, which is located on the French Atlantic coast and exposed to overflow processes. An iterative strategy is developed for selecting the numerical simulations (a total of 200) to train the RF model. The sensitivity to the input parameters is studied using permutation-based importance measures and extended versions of the partial dependence plots. The results highlight the key interplay among the high-tide level, the surge peak and the phase difference, and the complex role of the breaching location.