Exploring the factors influencing the hydrological response of soil after low and high-severity fires with post-fire mulching in Mediterranean forests

Despite ample literature, the influence of the individual soil properties and covers on the hydrological response of burned soils of forests has not clearly identified. A clear understanding of the surface runoff and erosion rates altered by wildfires and prescribed fires is beneficial to identify the most suitable postfire treatment. This study has carried out a combined analysis of the hydrological response of soil and its driving factors in burned forests of Central-Eastern Spain. The pine stands of these forests were subjected to both prescribed fire and wildfire, and, in the latter case, to post-fire treatment with mulching. Moreover, simple multi-regression models are proposed to predict runoff and erosion in the experimental conditions. In the case of the prescribed burning, the fire had a limited impact on runoff and erosion compared to the unburned areas, due to the limited changes in soil parameters. In contrast, the wildfire increased many-fold the runoff and erosion rates, but the mulching reduced the hydrological response of the burned soils, particularly for the first two-three rainfalls after the fire. The increase in runoff and erosion after the wildfire was associated to the removal of the vegetation cover, soil water repellency, and ash left by fire; the changes in water infiltration played a minor role on runoff and erosion. The multi-regression models developed for the prescribed fire were accurate to predict the postfire runoff coefficients. However, these models were less reliable for predictions of the mean erosion rates. The predictions of erosion after wildfire and mulching were excellent, while those of runoff were not satisfactory (except for the mean values). These results are useful to better understand the relations among the hydrological effects of fire on one side and the main soil properties and covers on the other side. Moreover, the proposed prediction models are useful to support the planning activities of forest managers and hydrologists towards a more effective conservation of forest soils.