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.

Fire regimes are important components of environmental dynamics, but our understanding of them is limited. Despite recent advances in the methodologies used to remotely sense and map fires and burned areas and new case studies that shed light on local fire use and management practices, the scientific community still has much to learn about anthropogenic fire regimes. We identify two areas for improvement: first, the fine-scale heterogeneity of fire dynamics for specific regions is often masked by global-scale approaches, and second, barriers between the disciplines focusing on fire impacts hamper the development of knowledge of the human dimensions of fire regimes. To address the “blind spot” that these limitations create, we present a simple dynamic model of fire ignition in savanna systems. The aim is to connect the local and global scales of fire regimes by focusing on human fire management (anthropogenic fire). Our dynamical model is based on a study area in Western Burkina Faso and integrates biophysical elements (climate and soil data), land cover, and fire management scenarios based on field surveys. The simulation results offer contrasting views of the impact of local fire management practices on regional fire regimes observed in savannas. Fire density and frequency are local variables that clearly change the fire regimes despite a complex and constrained biophysical system. This experience, drawing from fieldwork and modelling, may be a way to integrate some key aspects of anthropogenic fire research in savanna systems.

Land use and landscape structure play an important role in the functioning of flash floods on the arable plateaus of northern France. Landscape structures have changed considerably over the last decades with an increase in runoff-producing surfaces and an enlargement of the plots. This evolution results in an increase in runoff volumes accompanied by an increasingly easy diffusion of runoff on the slopes. There is therefore potentially an increasingly frequent and important spatial connectivity from the plots to the thalwegs, which can lead to the formation of flash floods. This study proposes to model this diachronic evolution of connectivity in a small agricultural basin of Seine Maritime using a multiagent system (MAS) and to develop synthetic indicators characterising these spatial links in the flow processes. The model outputs show that spatial connectivity has been steadily increasing over the past 70 years due to the enlargement of the parcel grid and the growth of runoff surfaces. For example, for the same 20 mm/h rainfall, the connectivity indicator increases from 40.99% (in 1947) to 78.33% (in 2015). This observation is observed for all levels of rainfall intensity, including the lowest. This modelling, carried out for a 116 ha basin in arable farming, can be transposed to all small agricultural basins.