Automatic 2D mapping of flash floods: which possibilities and limits? An illustration based on the Cartino2D method

Efficient pluvial flood mapping methods are needed to produce realistic flood scenarios in very small upstream catchments. The Cartino2D method was developed to launch automatically 2D models based on the Telemac2D hydraulic software. The principle is quite simple, (1) create automatically the mesh with a topography based on Lidar, (2) manage automatically the boundary conditions, (3) run the model based on rainfall input data, and (4) postprocess the results. The extent of each 2D model generally varies between 2 to 10 km² with a maximum of 20km². The only manual work consists in checking or modifying the limits of hydrological catchments.

We began to use this method on the Toulon metropole (South of France) with 66 complementary computation domains covering about 180km² and using eight statistical rainfalls. We also tested and evaluated this method on twenty other case studies in different regions of France. In this presentation, we focus on two evaluations (flood of June 2010 in Draguignan and flood in 2014/2015 around Montpellier) conducted within the ANR PICS project.

In this project, we improved the method to automatically integrate radar rainfall and to compare the results with local knowledge, observed historical floods and local hydraulic studies.

Cartino2D offers interesting results in areas with natural, rural land-use or few urban developments. The density of the mesh (less than 3m in the thalwegs) and the Telemac2D model quality are sufficient to obtain a good accuracy in these areas.  

In urban areas, the method provides a first knowledge, but more complex input data are needed to improve the accuracy of results.

We try in this presentation to describe which databases should be created to improve the accuracy of such automatic computations. At the scale of urban areas with results around buildings, the databases need to be spatially well defined. We propose some standard of databases to be integrated in computations: for example, the main underground channels or culverts, the main aerial channels (particularly very small channels not recognized by Lidar), a spatial distribution of the Curve Number and of the Manning’s coefficient.

This kind of databases, which cannot be deduced automatically from Lidar data, appears as essential to improve the results of Cartino2D automatic process. This kind of knowledge exists locally, but up to now it is not integrated in homogeneous national or regional databases.

In the same way, we need also to have well-defined databases to compare automatic results with historical floods as flood marks, gauge stations.

Automatic 2D mapping of flash floods seems to be a realizable goal at a scale of a region or country with standard 2D hydraulic models. But the current main limits appear to be a lack of good input database management, which limits the current accuracy of mapping results.