Soil loss due to water erosion poses a significant threat to agricultural sustainability and food security, particularly across semi-arid Mediterranean regions. In Tunisia, this phenomenon is intensified by the interplay of soil properties and spatiotemporal dynamics of Land Use and Land Cover (LULC). Accurately assessing soil erosion through physically based, distributed, and continuous models is critical for effective land management. However, such assessments demand high-resolution, temporally dynamic input data, making manual parameterization both complex and time-consuming. This study aims to enhance the parameterization of the ANSWERS-2000 hydrological model by leveraging Google Earth Engine (GEE) for the automated extraction of key MODIS-derived input, including annual Land cover, phenological transition dates (green-up, maturity, senescence, and dormancy), land Surface Temperature, and solar radiation, over the M’richet El Anse catchment (1.65 km2, Siliana, Tunisia), spanning January 1, 2001, to August 31, 2002. GEE extracted inputs replaced traditionally estimated parameters for crop rotation scheduling and C-factor dynamics within ANSWERS-2000. Model performance was evaluated against observed sediment yield data from bathymetric surveys and hydrological records using statistical indicators, including the Nash-Sutcliffe Efficiency (NSE) and coefficient of determination (R²). Results show that GEE enhanced model achieved an R² of 0,81 and an NSE of 0.72 outperforming simulation based on traditionally estimated parameters. Spatial mapping of annual soil loss revealed a highly heterogeneous erosion pattern across the catchment, with negative values indicating sediment detachment zone and positive values indicating deposition areas. These findings demonstrate that integrating GEE based remote sensing data extraction into physically and continuous based erosion modelling significantly improves input accuracy and model predictive performance, offering a scalable and reproductible approach for water erosion assessment in data scarce semi-arid environments.
更多
原文链接
分享
引用
