Modeling susceptibility to water erosion in the Pampa biome, southern Brazil

Soil erosion is one of the main environmental problems worldwide, affecting ecosystem productivity, water quality, and landscape stability. This study analyzed susceptibility to water erosion and soil loss through surface processes using the Simulated Water Erosion (SIMWE) model in the Areal do Limeira watershed (BHAL), located in the Pampa biome, southern Brazil. The analysis integrated topographic data from the FABDEM digital elevation model (30 m resolution), land use and land cover (LULC) mapping from satellite imagery, and field measurements of soil infiltration and permeability. Surface runoff, sediment transport, and erosion–deposition patterns were simulated considering different LULC classes and topographic positions. The results showed that surface runoff was concentrated mainly along drainage channels and flatter sectors of the watershed, with the highest modeled values occurring in these areas. Sediment transport capacity, sediment flux, and sediment concentration followed a similar spatial pattern, with greater values near the main drainage channels and in sectors with concentrated flow. The erosion–deposition balance indicated a predominance of erosive processes, resulting in an estimated total soil loss of 27,632 kg for the watershed and 441 kg within the mapped gullies. Crop fields showed the highest erosion susceptibility, associated with sandy soils and low clay content. Although gullies occur in runoff concentration areas, the results indicate that surface processes are not the main mechanism responsible for gully initiation and overall evolution in the BHAL, where subsurface erosion appears to play a dominant role. From a practical perspective, the study identifies sectors more susceptible to runoff concentration, sediment transport, and sediment redistribution, providing useful information for monitoring and soil and water conservation planning in sandy and environmentally fragile areas of the Brazilian Pampa. More broadly, the results demonstrate the applicability of SIMWE for identifying areas vulnerable to surface erosion and sediment dynamics in data-scarce regions.