Modeling post-fire water erosion mitigation strategies
- 1Politecnico di Milano, Piazza L. Da Vinci, 32, Milano, 20133 Italy
- 2Centro euroMediterraneo sui Cambiamenti Climatici, Lecce, Italy
Abstract. Severe wildfires are often followed by significant increase in runoff and erosion, due to vegetation damage and changes in physical and chemical soil properties. Peak flows and sediment yields can increase up to two orders of magnitude, becoming dangerous for human lives and the ecosystem, especially in the wildland–urban interface. Watershed post-fire rehabilitation measures are usually used to mitigate the effects of fire on runoff and erosion, by protecting soil from splash and shear stress detachment and enhancing its infiltration capacity. Modeling post-fire erosion and erosion mitigation strategies can be useful in selecting the effectiveness of a rehabilitation method. In this paper a distributed model based on the Revised Universal Soil Loss Equation (RUSLE), properly parameterized for a Mediterranean basin located in Sardinia, is used to determine soil losses for six different scenarios describing both natural and post-fire basin condition, the last also accounting for the single and combined effect of different erosion mitigation measures. Fire effect on vegetation and soil properties have been mimed by changing soil drainage capacity and organic matter content, and RUSLE factors related to soil cover and protection measures.
Model results, validated using measured data on erosion rates from the literature and in situ field campaigns, show the effect of the analyzed rehabilitation treatments in reducing the amount of soil losses with the peculiar characteristics of the spatial distribution of such changes. In particular, the mulching treatment substantially decreases erosion both in its mean value (−75%) and in the spatially distribution of the erosion levels over the burned area . On the contrary, the breaking up of the hydrophobic layer decreases post-fire mean soil losses of about the 14%, although it strongly influences the spatial distribution of the erosion levels.