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The consequences of flash floods can be dramatic in terms of casualties or economic losses. Jonkman (2005), in a global assessment of flood-related casualties, showed that flash floods lead to the highest mortality (number of fatalities divided by the number of affected people). For example, in the recent flash flood that occurred in the French Riviera around Cannes on 3 October 2015, 20 casualties and 650 billion euros of insured damage (source: http://www.ccr.fr/) were reported. Flash flood forecasting systems are critically needed to better organize crisis management and rescue operations.As mentioned in chapter 1.3.4 (Gaume et al. 2016), flash floods are characterized by a rapid increase of river water levels. They often affect small watersheds, generally ungauged. The spatial and temporal variability of rainfall, landscape characteristics and pre-event catchment wetness are important influential factors in flash flood generation, contributing to the large space-time variability of hydrological responses (Borga et al. 2011).Forecasting flash floods is therefore a complex task. It necessitates the monitoring of large areas, where each small watershed of a few square kilometres can possibly be affected. Real-time observation networks and models must run at small temporal and spatial scales, on the order of a few minutes and kilometres. Furthermore, discharge time series are not available for the majority of the possibly affected watersheds, posing a real challenge for model calibration and evaluation. In this context, radar based precipitation products and/or meteorological forecasts with a high resolution (typically 1 km2 grid size) are crucial (Creutin and Borga, 2003). Slight misplacements of the precipitation may for instance lead towarnings attributed to the wrong river network and to inappropriate flood management decisions.