Fire can result in hydro–geomorphic changes that are spatially variable and difficult to predict. In this research note we compile 294 infiltration measurements and 10 other soil, catchment runoff and erosion datasets from the eastern Victorian uplands in south-eastern Australia and argue that higher aridity (a function of the long-term mean precipitation and net radiation) is associated with lower post-fire infiltration capacities, increasing the chance of surface runoff and strongly increasing the chance of debris flows. Post-fire debris flows were only observed in the more arid locations within the Victorian uplands, and resulted in erosion rates more than two orders of magnitude greater than non-debris flow processes. We therefore argue that aridity is a high-order control on the magnitude of post-wildfire hydro–geomorphic processes. Aridity is a landscape-scale parameter that is mappable at a high resolution and therefore is a useful predictor of the spatial variability of the magnitude of post-fire hydro–geomorphic responses.
Post-wildfire runoff and erosion are major concerns in fire-prone landscapes around the world, but these hydrogeomorphic responses have been found to be highly variable and difficult to predict. Some variations have been observed to be associated with landscape aridity, which in turn can influence soil hydraulic properties. However, to date there has been no attempt to systematically evaluate the apparent relations between aridity and post-wildfire runoff. In this study, five sites in a wildfire burnt area were instrumented with rainfall-runoff plots across an aridity index (AI) gradient. Surface runoff and effective rainfall were measured over 10 months to allow investigation of short-(peak runoff) and longer-term (runoff ratio) runoff characteristics over the recovery period. The results show a systematic and strong relation between aridity and post-wildfire runoff. The average runoff ratio at the driest AI site (33.6%) was two orders of magnitude higher than at the wettest AI site (0.3%). Peak runoff also increased with AI, with up to a thousand-fold difference observed during one event between the driest and wettest sites. The relation between AI, peak 15-min runoff (Q 15 ) and peak 15-min rainfall intensity (I 15 ) (both in mm h -1 ) could be quantified by the equation: Q 15 = 0.1086I 15 × AI 2.691 (0.65
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.