Semi-automatic Maps for 2015 French Riviera Floods

Pons, Frédéric; Alquier, M.; Roux, Isabelle

doi:10.1007/978-981-10-7218-5_35

Cited by 2 publications

(1 citation statement)

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“…We propose to test our approach on a typical coastal watershed of the French Riviera, the Brague River catchment (68 km 2 ), on the flash flood that occurred during the rainfall event of the 3rd of October 2015. During this event, 650 billion of insured damage and 20 casualties were reported (source: http://basilisk.fr/, see also [10]. As a first step, we describe the watershed of the Brague River and the rainfall event of the 3rd of October 2015.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Inundation Mapping with a 2D Hydraulic Modelling Tool Based on Adaptive Grid Refinement: The Case of the October 2015 French Riviera Flood

Kirstetter

Bourgin²,

Brigode

et al. 2020

Advances in Hydroinformatics

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The vulnerability of the French Riviera territories to hydro-meteorological hazards has been highlighted by the flash flood of the 3rd of October 2015, which had catastrophic consequences. In the view of the speed and violence of these phenomena, the development of warning systems able to provide real-time flood mapping should make it possible to limit their impacts. In this context, the main objective of this work is to study the feasibility of using a 2D hydraulic modelling tool with adaptive grid refinement, the Basilisk calculation code http://basilisk.fr/, which is based on state-of-the-art finite volume methods. This code has been used to model the October 2015 flood event on one French Riviera coastal catchment, the Brague River catchment. Results obtained with Basilisk were compared with available information from post-event surveys, showing promising performances in terms of simulated peak discharges, inundation extent and water levels in the floodplain. These results highlight that forecasting inundation extents generated by flash flood events on small watersheds (less than 100 km 2) is possible at a moderate computational cost.

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Section: Introductionmentioning

confidence: 99%