Day 4

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Paper title Proxy data of surface water floods in rural areas: application to the evaluation of the IRIP intense runoff mapping method based on satellite remote sensing and rainfall radar
  1. Arnaud Cerbelaud ONERA Speaker
  2. Gwendoline Blanchet CNES - Centre national d'études spatiales, France
  3. Laure Roupioz ONERA
  4. Pascal Breil INRAE, Lyon
  5. Xavier Briottet ONERA/DOTA, Université Fédérale de Toulouse
Form of presentation Poster
  • D1. Managing Risks
    • D1.01 Satellite EO for Geohazard Risks
Abstract text Along with fluvial floods (FFs), surface water floods (SWFs) caused by extreme overland flow are one of the main flood hazard occurring following heavy rainfall. Using physics-based distributed hydrological models, surface runoff can be simulated from precipitation inputs to investigate regions prone to soil erosion, mudflows or landslides. Geomatics approaches have also been developed to map susceptibility towards intense surface runoff without explicit hydrological modeling or event-based rainfall forcing. However, in order for these methods to be applicable for prevention purposes, they need to be comprehensively evaluated using proxy data of runoff-related impacts following a given event. Here, the IRIP geomatics mapping model, or “Indicator of Intense Pluvial Runoff”, is faced with rainfall radar measurements and damage maps derived from satellite imagery (Sentinel) and classification algorithms in rural areas. Six watersheds in the Aude and Alpes-Maritimes departments in the South of France were investigated during two extreme storms. The results of this study showed that the higher the IRIP susceptibility scores, the more likely SWFs were detected in plots by the EO-based detection algorithm. Proportion of damaged plots was found to be even greater when considering areas which experienced larger precipitation intensities. Land use and soil hydraulic conductivity were found to be the most relevant indicators for IRIP to define production areas responsible for downslope deteriorations. Multivariate logistic regression was also used to determine the relative weights of upstream and local topography, uphill production areas and rainfall intensity in explaining intense surface runoff occurrence. Modifications in IRIP's core framework were thus suggested to better represent SWF-prone areas. This work overall confirms the relevance of IRIP methodology and suggests improvements to implement better prevention strategies against flood-related hazards.