Long-Term Impact of Soil and Water Conservation Measures on Soil Erosion in a Tunisian Semi-Arid Watershed

Understanding and long-term assessment of the efficacy of soil and water conservation (SWC) techniques is essential for sustainable watershed management. There have been few studies measuring the long-term impact of SWC on soil erosion at the catchment scale due to a lack of historical data. This research aims to measure and analyze the impact of SWC interventions on sediment yield using the Soil and Water Assessment Tool (SWAT) in the Wadi Rmel watershed between 2000 and 2020. The study approach has simultaneously three main aspects: (i) long-term and temporally dynamic, (ii) large-scale landscape distribution, and (iii) empirical evidence of impacts induced by terracing measures. Historical data on SWC in 2000, 2005, 2010, 2015, and 2020 were used for running the model. The monthly calibration (2001&ndash:2014) using the SUFI2 algorithm was implemented with 22 input parameters and displayed a good model performance, with a Nash&ndash:Sutcliffe efficiency (NSE) of 0.61 for daily runoff and a coefficient of determination (R2) of 0.66. A satisfactory result was obtained for the monthly validation process (2015&ndash:2020) with NSE value equal to 0.83 and R2 value of 0.85. The result showed the increase in terracing areas led to a cumulative decrease in watershed sediment yield in long-term, i.e., the reduced erosion per one hectare of the expanded terracing area after 10&ndash:20 years were about 9&ndash:33 times higher than those in the first 5 years. This finding suggests that maximal benefits of SWC should be expected in the long-term, i.e., beyond a decade. Additionally, the study revealed variations in sediment yield contribution among sub-basins, with the southwestern region being the degradation hotspot areas (having erosion exceeding tolerable thresholds) needing prioritization for erosion controls. These findings enable stakeholders to plan effective management in semi-arid wheat-based agricultural areas with scarce data.