Exploring the inclusion of soil management practices in erosion models towards the improvement of post-fire predictions

Abstract

Wildfires are recognized for having a strong impact on forest soils, a situation aggravated by inadequate pre-fire land management practices. Land management operations, such as plowing, are routinely carried out for cultural reasons and can impact soils for decades after their implementation. Therefore, it is crucial to take into account the pre-fire land management history when predicting post-fire sediment losses in burnt areas. This consideration is critical for a realistic assessment of soil erosion risk and, consequently, for effectively implementing emergency stabilization and/or rehabilitation measures.The aim of the study was to integrate pre-fire land management practices into erosion models, to enhance post-fire sediment losses predictions at slope scale. To accomplish this goal, both Multiple Linear Regression (MLR) and the revised-Morgan-Morgan-Finney model (revised-MMF) were applied in the Colmeal burnt area (Central Portugal). These models were adapted to account the impacts of different management options, specifically no plowing, downslope-plowing and contour-plowing, on the erosive response following a wildfire.The results revealed fluctuations in the performance of both models across different soil management, and over time since the wildfire. Despite the observed variability, it is important to highlight the positive outcomes achieved with the revised-MMF model over the three monitoring years where contour-plowing was applied. These results demonstrate that the best model performances are achieved when soil management is individualized and analyzed independently. Similarly, the MLR model exhibited improved performance when incorporating management practices into its predictions. This study confirms that disturbances on topsoil, whether caused by wildfires or soil management operations, play key roles in driving change in soil erosion. Hence, integrating these factors into models is essential for providing relevant information for the development of mitigation and/or restoration strategies in areas at high risk of erosion.