Towards integrated short-term Rain-on-Grid modeling and long-term RUSLE estimates for improved erosion susceptibility assessment in the Oltrepò Pavese hills of Northern Italy

This study focuses on three catchments located in the Oltrepò Pavese area of Northern Italy, a hilly landscape characterized by steep slopes, clay-marl lithologies, and extensive agricultural and viticultural land use. The region is prone to rainfall-induced soil erosion, making it an ideal setting to compare erosion modeling approaches. We evaluate the complementary use of the empirical Revised Universal Soil Loss Equation (RUSLE), which provides long-term average erosion estimates, and a two-dimensional Rain-on-Grid (RoG) hydrodynamic model, which simulates event-based runoff dynamics and sediment transport capacity. These models were applied using harmonized inputs to assess their spatial agreement in identifying erosion-prone areas and compared with erosion inventory zones mapped after a major storm in 2009. Despite operating at different temporal scales, the two approaches showed over 50% spatial overlap in detecting erosion-prone areas, particularly on steep slopes with concentrated runoff. RoG reproduced the mapped 2009 erosion zones more effectively than RUSLE, highlighting the dominant role of short-duration, high-intensity runoff-driven processes, while RUSLE better captured land-cover effects. Because validation is based on a single event with a different return period and includes processes not simulated by RoG, results should be interpreted as spatial-pattern agreement rather than quantitative predictive performance. The integrated use of both models offers a practical and transferable framework for data-scarce catchments. • RUSLE and RoG-based approaches showed significant overlap in erosion-prone areas. • RUSLE provides long-term trends, while RoG-based approach captures event-based dynamics. • RoG-based approach improves erosion assessment in data-scarce regions. • Integrating both approaches enhances erosion-prone hotspots prediction.