This paper presents an application of a cellular automaton-based run-off model (RUICELLS) to a series of small dry valleys in the Seine-Maritime department, northern France, to better assess their susceptibility to flash flood. These muddy floods shortly follow high rainfall (50–100 mm in less than 6 h) and occur in very small areas (<20 km²). A surge generally rushes down through the main valley just a few minutes after rains have peaked. Previous events (n = 69, in the period 1983–2005) have occasionally threatened human lives and have caused significant damage to property and infrastructure. Nonetheless, given the variation among the valleys and the infrequency of events, these floods have not been numerous enough to permit a statistical analysis. Instead, we numerically simulate the possible future flash floods using RUICELLS, a cellular automaton model driven by a set of three deterministic hydrological rules. Simulations have been conducted for 148 basins, each subject to 16 different rainfall scenarios (2.368 simulations in total) to (1) estimate the peak flow discharges (Q), the specific peak flows (Q _s ), and the lag times (T) of the flash floods and (2) detect the critical rainfall intensities that would trigger warnings and increased vigilance. Our simulations indicate that the number of basins susceptible to flash flooding greatly increases with the higher rainfall intensity, the distribution of sensitive crops (sugar beet, corn, maize, and flax) and the basin morphology. Several small basins could also induce by convergence a bigger flood in the downstream humid valleys. The location of the highest simulated discharges is aligned with observed events, and this comparison provides an evaluation of the modelling performance and of the credibility of the results.