Abstract
Gully head erosion is a significant process in semi-arid regions contributing to land degradation and hillslope dynamics. Predicting when and where gully heads will expand under different seasonal scenarios is an essential step to take the appropriate mitigation measures. The LANDPLANER model was applied in this study area in order to analyze and investigate specific issues related to the triggering of gully head processes. The model is mainly designed to describe the dynamic response of slopes (or basins) under changing scenarios, including meteorological factors, vegetation or land use, and slope morphology. This model enabled us to predict the occurrence of gully heads under different seasonal conditions defined by Curve Number and rainfall scenarios. The LANDPLANER erosion modeling schema is twofold and integrates two different approaches. The first is empirical and exploits an erosion topographic threshold equation, while the second is aimed to define an erosion index calculated starting from the results of the hydrological model. These two indices predict where erosion processes can occur. To analyze the impact of land use changes on slope response to rainfall events, we simulated seasonal land use changes scenarios, modifying the Curve Number values associated to each specific land use classes. The twofold strategy was also adopted to validate the model, namely testing the model with synthetic dataset, and applying the model to real cases. The results indicated that the density of gully heads is more frequent in the South facing slopes with low elevation (around 200–300 m), steeper slope (within 20–40°), and higher average accumulation value (>100 m). Higher values of topographic threshold and erosion index (region 1: 13.1, region 2: 8.9) are obtained for the cold season in winter, where rainfall is intense and vegetation cover is lowest in the rangelands. Given to both observing receiver operating characteristic curve, the best and worst performances of gully head occurrence belong to the third Curve Number scenario (the lowest vegetation cover in terms of rangelands in winter) and the second Curve Number scenario (the lowest vegetation covers in terms of agricultural lands in summer) in turn. Further, the LANDPLANNER model effectively characterizes the status and trend of gully head formation in the study area.
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