Exploring the Inclusion of Soil Management Practices in Erosion Models Towards the Improvement of Post-Fire Predictions

Abstract

Climate projections indicate that wildfires will be increasingly more recurrent, severe and extensive in the future. Wildfires can have a strong impact on forest soils, which can be further aggravated by inadequate pre-fire land management practices. Land management operations, like plowing, are applied on a recurrent basis, for cultural reasons, and can impact soils even decades after being implemented. Thus, the pre-fire land management background must be considered when predicting post-fire sediment losses in burnt areas, being decisive for a realistic assessment of soil erosion risk and consequently for an adequate implementation of emergency stabilization and/or rehabilitation measures.The aim of the present study was to integrate pre-fire land management practices into erosion models to improve post-fire sediment losses predictions at slope scale. To achieve this purpose, a multiple linear regression (MLR) and the revised-MMF model were applied to the Colmeal burnt area (Central Portugal), being adapted to reflect the effects of different management options, i.e., no plowing (U), downslope-plowing (DP) and contour-plowing (CP) in the erosive response after wildfire.The results showed that both model performances changed for each soil management practice, and with time since the wildfire. Despite such variability, we should highlight the positive results obtained with the revised-MMF model for the three monitoring years with contour-plowing. These results evidence that the best model performances are achieved when soil management was individualized and analyzed separately. In the same line, the MLR model performed better when management practices are integrated in the model predictions. This work allowed to evidence that impacts on topsoil, either by wildfires or by soil management operations, are important drivers of change in soil erosion and need to be integrated in models, to provide relevant information for a mitigation and/or restoration strategy of prone erosion risk areas.

Keywords: regression model, revised-MMF model, forest soil management, wildfires