Flood twin experiment for estimating the potential of satellite observations in shallow-water simulations

With more than one billion people exposed to floods throughout the world, this natural hazard is the most common and devastating one, resulting in loss of lives and damaging personal properties or sensitive infrastructures. Numerical models have become essential to forecast and to mitigate their consequences, but they remain uncertain mainly due to the lack of high-resolution data and the inherent uncertainties related to the simplified representation of natural phenomena.

The growing availability of satellite observations distributed in time and space is a valuable source of information for improving flood modelling. Additional data like water level or flood extent can be extracted and used to calibrate numerical models.

This study proposes to analyse the potential of remote sensing data as a complement to in-situ observations (from hydrometric stations) in the calibration process of shallow-water flood numerical models. A two-dimensional twin experiment of an extreme flood event overflowing into the floodplains is carried out on a 50 km reach on the Garonne River in France between Tonneins and La Réole. The roughness coefficients are computed as solutions to an inverse problem mixing both in-situ (pointwise and high-frequency) and satellite observations (spatially distributed but low-frequency) data. Data assimilation combining uncertain model simulations and observations has proven efficient for improving hydraulic models. However, an open question is the choice of the best information to assimilate (water level or/and flood extent maps) into the hydraulic models. We study this problem by testing different assimilation configurations. The satellite observations are not considered perfect, so the numerical solutions are compared with different noise levels.