From soil loss to sediment delivery: GeoAI-enhanced RUSLE modeling of erosion and sediment dynamics in a hyper-arid watershed

Abstract

In the delineated watershed containing Wadi Samnan near Az Zulfi, Saudi Arabia, soil erosion is a local management concern because sparse vegetation, erodible sandy surface materials, escarpment-influenced terrain, and episodic rainfall events can concentrate runoff and sediment movement along wadi channels and drainage corridors. These conditions make it difficult to identify erosion-prone zones using field observations alone, particularly in data-scarce hyper-arid environments. This study presents a GeoAI-enhanced RUSLE-based framework to model water-induced soil loss and sediment delivery dynamics in the study watershed. The Revised Universal Soil Loss Equation (RUSLE) was integrated with geospatial datasets, remote sensing products, digital elevation model-derived terrain attributes, and machine-learning-based spatial analysis to estimate the spatial distribution of water-induced erosion risk. Rainfall erosivity, soil erodibility, topographic influence, land-cover conditions, and conservation-practice factors were derived and mapped within a GIS environment. In addition, the Sediment Delivery Ratio (SDR) was incorporated to evaluate relative sediment-delivery potential and improve the interpretation of how estimated hillslope soil loss may be transferred into sediment yield within the drainage system. The results show that RUSLE-derived potential soil-loss estimates ranged from 0 to 319.5 t ha -1 yr -1 , with the slight erosion class covering 43.2% of the watershed and modeled severe-erosion hotspots covering 10.9%. High-risk areas were mainly concentrated along steep slopes, drainage corridors, sparsely vegetated surfaces, and erodible sandy soils. SDR results indicated low overall sediment connectivity, with approximately 85%–99% of eroded material likely retained locally within the watershed, depending on the spatially varying SDR values. The Random Forest internal consistency analysis explained 81.73% of the variance in RUSLE-derived soil-loss estimates, indicating that the input factors captured much of the modeled spatial variability; however, this result should not be interpreted as independent field validation. Overall, the integration of RUSLE, SDR, remote sensing, GIS, and GeoAI provides a useful framework for identifying erosion-prone zones, distinguishing soil-loss potential from sediment-delivery potential, prioritizing conservation interventions, and supporting sustainable watershed management in hyper-arid environments.