Modeling current and future run-off and soil erosion dynamics in Eastern Mediterranean ecosystems using the WEPP model

Abstract

Run-off (RF) and soil erosion (SE) have a negative impact on the environmental system, with both on-site and off-site effects. This research aimed to evaluate the performance of the Water Erosion Prediction Project (WEPP) model in predicting SE and RF across four locations representing three ecosystems: agricultural land (location-KS at 8 % slope, location-MN at 20 % slope), forest (location-AINF at 35 % slope), and burned forest (location-AINBF at 35 % slope). It also predicts the future responses of the studied ecosystems to SE and RF for the upcoming 20 years. The study was conducted in the eastern Mediterranean from 2019 to 2038, 2019 served as the reference year to compare WEPP model output with measured values at the studied locations. Model performance indicators showed the applicability of WEPP model in predicting SE (model efficiency (NSE) =0.67, coefficient of determination (R²) =0.97), RF (NSE=0.66, R²=0.78). Future projections revealed that the agricultural ecosystem exceeded others in terms of SE and RF. However, annual RF can be ranked as follows: KS (234.7mm±75.6)> MN (141.1mm±50.2)> AINF (145.4mm±47.4) > AINBF (100.3mm±49.2). Similarly, SE can be classified as: KS (2.38 kg/m² ±1.36)> AINF (0.45 kg/m² ±0.17) > MN (0.31 kg/m² ±0.11)> AINBF (0.24 kg/m² ±0.15). However, monthly results of SE and RF were more intense in the KS location and less pronounced in the rest of the ecosystems. An analysis of associated factors, namely, soil water content (θ, %), and soil water hydraulic conductivity (K_sw, mm/hr), revealed the lowest θ values occurred during the summer season (June to August), reaching 46.3 %, 50.3 %, 46.2 %, and 49.2 %, for KS, MN, AINF, and AINBF, respectively. Conversely, the highest K_sw was forecasted in summer, ranging from 3.3 to 6.3 mm/hr. This research promotes utilizing the WEEP model as a sustainable tool for ecosystem management in the eastern Mediterranean, aiding decision-makers in rehabilitation planning.