Soil erosion remains one of the most pressing global environmental challenges, with profound implications for agricultural productivity, food security, and ecosystem resilience (Rhodes, 2014; Sartori et al., 2024; Mi et al., 2025). Its effects are particularly pronounced in semi-arid regions, where the combined influence of climatic variability, unsustainable land management, and accelerated land-use change intensifies surface degradation (Khanchoul et al., 2020; Chakrabortty et al., 2025). The gradual removal of fertile topsoil diminishes agricultural yields, promotes sediment accumulation in reservoirs, and deteriorates water quality and aquatic ecosystems (Lizaga et al., 2025).

In Algeria, water-induced erosion is recognized as one of the principal drivers of land degradation, especially in the northern regions characterized by steep slopes, dense population, and a significantly degraded vegetation cover (Mahleb et al., 2022; Hadji et al., 2025; Taib et al., 2024; 2025). Numerous studies have highlighted not only the magnitude of this phenomenon but also its irregular and spatially discontinuous nature (Benaiche et al., 2024). Approximately 45% of Algeria’s arable land has already been affected by erosion processes. Nearly 50 million tons of suspended sediments are deposited annually along the country’s Mediterranean coastline (Probst, 1992; Morsli et al., 2006; Nehaï, and Guettouche, 2020). A survey conducted by the Ministry of Agriculture and Rural Development reported that around 15 million hectares in northern Algeria have experienced severe soil degradation due to hydrological erosion.

Conventional approaches for assessing erosion (such as field surveys, runoff plot experiments, and empirical or physically based models) have generated valuable understandings into erosion mechanisms and rates (Bueno-Hurtado and Seidou, 2024). However, these methods are often constrained by their labor-intensive nature, high cost, and limited spatial representativeness (Ketema and Dwarakish, 2021).

The emergence of Geographic Information System (GIS) and remote sensing (RS) has revolutionized erosion research by enabling continuous, large-scale spatial analysis using geo-environmental indicators such as rainfall patterns, vegetation cover, land use, and topography (Abdo, 2022; Hamed et al., 2024).

Several reserchers have significantly advanced the understanding of mass-wasting processes across varied geological and climatic contexts. Machine-learning approaches, including support vector machines (SVM), artificial neural networks (ANN), random forests (RF), and logistic regression, have been effectively applied to map susceptibility in hilly regions (Dahoua et al., 2017; Zerzour et al., 2020; Ahadi and Rosta, 2024; Abgrami et al., 2025), capturing complex spatial patterns of slope instability. Logistic regression and multi-criteria decision-making methods (AHP) have also been used to provide robust, site-specific assessments in Maragheh and Fuman counties (Cemiloglu et al., 2023; Hamedi et al., 2025). Complementing these modeling efforts, Yeganeh et al. (2025) developed a comprehensive landslide inventory for the Alborz Mountains, supporting hazard modeling and risk evaluation. Beyond landslides, advanced computational approaches have been successfully integrated with GIS to map rockfall susceptibility, as shown by Nanehkaran et al. (2022). Together, these studies provide technically rigorous, modern frameworks that enhance predictive accuracy and support effective hazard management across diverse environmental settings.

The availability of Digital Elevation Models (DEMs) and multi-temporal satellite imagery has further enhanced the capacity to identify erosion-prone areas, and monitor landscape evolution, (Huang et al., 2023). Despite these advances, several limitations persist. The spatial and vertical resolution of existing DEMs, and the lack of field-based validation data continue to reduce the reliability of erosion predictions, particularly in mountainous and data-scarce regions (Li et al., 2021). Moreover, while various models; including empirical approaches such as USLE, RUSLE, and MUSLE (Ghosal and Das Bhattacharya, 2020), as well as physically based ones like Unit Power-Based Soil Erosion and Deposition (USPED) have been widely applied across different environments (Prasad et al., 2024). Their performance often varies depending on local geomorphological, climatic, and land-use conditions (Wischmeier, 1959; Stocking and Elwell, 1973; Lo and MacKinlay, 1985). The RUSLE model is one of the most widely used tools for assessing soil erosion due to its flexibility and computational simplicity, making it suitable for studies with limited data and the absence of precise field measurements (Kouli et al., 2009). Numerous studies have shown that RUSLE’s ability to integrate with Geographic Information Systems (GIS) and remote sensing techniques provides high efficiency in producing accurate spatial erosion maps over large areas (Panagos et al., 2015). Compared to models such as WEPP and EUROSEM, RUSLE requires fewer input data and remains applicable in mountainous and semi-arid environments where detailed information is difficult to obtain (Renard and Freimund, 1994). However, as an empirical model, its accuracy may be affected by environmental and climatic variations unless its factors are locally calibrated (Prasannakumar et al., 2012). Despite these limitations, the balance between the model’s simplicity and reliability, along with the possibility of improvement through modern spatial data, makes RUSLE a suitable and effective choice for contemporary studies on erosion risk assessment and soil conservation planning (Borrelli et al., 2017; Rejani et al., 2022).

This study aims to quantify and map soil loss rates in the Upper Bousselam watershed (UBW) by applying the RUSLE Equation within a Geographic Information System (GIS) environment. This integrated approach combines multiple geo-environmental parameters such as rainfall erosivity (R), soil erodibility (K), slope gradient (LS), vegetation cover (C), and conservation practices (P); to produce spatially explicit soil loss maps. These maps identify erosion hotspots and provide insights into the relative influence of different controlling factors.

Recent research has increasingly highlighted the value of RUSLE-based modelling for understanding soil erosion processes in mountainous and semi-arid environments. For example, Prasannakumar et al. (2012) demonstrated how the integration of RUSLE with GIS can effectively capture the influence of slope gradients, vegetation cover and rainfall on erosion patterns in a forested watershed in Kerala, revealing clear erosion hotspots and underscoring the utility of high-resolution spatial mapping for land-use planning. In a similar context, Thomas et al. (2012) coupled RUSLE with a transport-limited sediment delivery model to evaluate both erosion and deposition dynamics in the Western Ghats, showing that only a fraction of the gross soil loss is ultimately exported and emphasizing the role of human activities in shaping sediment transport. More recently, Mi et al. (2025) combined the RUSLE–SDR approach with geographically weighted regression and path analysis to investigate erosion behaviour in Longnan City, illustrating how vegetation, slope and precipitation interact across different environmental thresholds to control erosion intensity. RUSLE applications in other mountain and semi-arid regions further confirm these trends. In the Gorgol basin, topography was shown to dominate erosion risk under current and projected climate conditions (Alioune et al., 2025), while studies in the Cheliff Basin of Algeria have demonstrated how land use, soil characteristics and aridity collectively influence soil-loss patterns (Zerouali et al., 2025). Comparable findings have been reported in the High Atlas Mountains of Morocco, where nearly 40% of the basin exhibits high to very high erosion susceptibility (Ait Haddou et al., 2024), and in the Ethiopian highlands, where long-term monitoring has linked erosion dynamics to changes in land cover (Guche et al., 2024).

Taken together, these studies highlight both the robustness of RUSLE for quantifying soil loss in complex terrain and the pronounced spatial and temporal variability that characterizes erosion in such settings, calling for context-specific model calibration. Building on this body of work, the present study applies RUSLE to (your study area), using updated climatic, soil and topographic datasets to generate a detailed erosion-risk assessment. This approach not only aligns with established methodological advances but also addresses a critical gap in the literature, namely, (insert novelty, e.g., “the combined influence of steep slopes and semi-arid Mediterranean conditions under ongoing land-use change”), which remains insufficiently explored in the region.

Recent studies have highlighted the importance of integrating the RUSLE model with advanced geospatial techniques, such as remote sensing and high-resolution digital elevation models, to improve the representation of soil erosion dynamics in semi-arid mountainous areas. Hateffard et al. (2021) reported that combining RUSLE with advanced topographic analysis allows for a more accurate understanding of the spatial variations in erosion intensity on steep slopes. Similarly, Gezici et al. (2025) demonstrated that integrating the model with future climate scenarios enhances the capacity to assess erosion development under climate change. In North Africa, Sahli et al. (2019) and Ait Haddou et al. (2024) showed the effectiveness of remote sensing in improving the accuracy of RUSLE factors in degraded mountainous environments.

By coupling the model with advanced geospatial techniques, this research contributes to a more detailed understanding of erosion dynamics in mountainous semi-arid regions. The findings offer a scientific foundation for designing targeted soil conservation and sustainable land-use strategies aimed at reducing erosion and safeguarding agricultural productivity in northern Algeria. Furthermore, the methodological framework proposed here demonstrates the broader applicability of combining empirical erosion modeling and remote sensing (RS) for data-limited environments, particularly across the MENA region.

The Soummam watershed ranks among the largest hydrological basins in Algeria, encompassing an area of approximately 9,125 km², which accounts for nearly 19% of the total surface of the broader Algiers–Hodna–Soummam (AHS) Mega-basin. The Soummam drainage network is composed of nine major tributaries, among which Wady Boussellam constitutes the main contributor. This river stretches for about 150 km and drains a surface area of 5,010 km². Its catchment is subdivided into four sub-basins: UBW (C/15-06, S/1,785 km²), Middle Boussellam (C/15-07, S/1,234 km²), El Main (C/15-08, S/930 km²), and Lower Boussellam (C/15-09, S/1,061 km²). In this classification, C represents the sub-basin code assigned by the Agence des Bassins Hydrographiques (ABH), and S denotes the corresponding surface area (Figure 1a).

This study focuses on the Upper Boussellam Watershed (UBW), which spans approximately 1,785 km², accounting for nearly 35% of the total Boussellam catchment area. Designated under the ABH code 15–06, the watershed extends longitudinally for about 65 km (Figure 1b). As illustrated by the DEM (Figure 1c), the basin predominantly consists of an extensive plain, bordered by several notable highlands: to the north by Jebel Medjounes, Jebel Aïssel, Jebel Megris, and Jebel Matrona; to the east by Kef Boudjemline, Jebel Merouane, Jebel Youssef, and Jebel Sekrine; and to the south by Jebel Hassane, Jebel Sattor, and Jebel Boutaleb.

The climate within the UBW varies from semi-arid in the central and southern regions to humid in the northern parts. Winters are generally mild and relatively wet, while summers are hot and dry. The rainy season typically extends from September to May, representing the transitional period between the humid and arid climatic phases. The precipitation shows pronounced year-to-year variability, with annual totals typically falling between 300 and 500 mm and averaging roughly 449 mm/year (Table 1). Mean annual temperature is around 16.65 °C, while seasonal values range from approximately −3 °C in winter to nearly 38 °C in summer (Table 2).

Moreover, the precipitation isohyet maps produced by the National Agency for Water Resources indicate that the basin lies within a zone of pronounced spatial and temporal rainfall variability in eastern Algeria. This climatic heterogeneity plays a key role in shaping both hydrological dynamics and erosion processes in the region, (Hamed et al., 2017; Hamad et al., 2021; Benmarce et al., 2023; Hamed et al., 2024; Ncibi et al., 2022; Ncibi et al., 2023).

From a hydrological standpoint, the sub-basin is drained by the Wady Boussellam, which forms the main watercourse and receives inflows from several tributaries, including Wady Malah (Aïn Arnat), Wady Malah (Ksar El Abtal), Wady Fermatou, Wady Guellal, Wady Tixter, and Wady Ftaïssa. Together, these streams contribute an estimated annual discharge of approximately 50 M hm³/year (Khemmal et al., 2025).

The topography of the basin is characterized by moderate to steep slopes, with elevations ranging between 900 and 1,600 m. The highest altitudes are found in the mountainous regions to the north and south, while the central portion of the basin; extending toward the Aïn Zada Dam; is distinguished by gentler relief and lower elevations around 900 m (Rouibah and Belabbas, 2022).

In terms of geology, the UBW is largely covered by Quaternary deposits, particularly in its northern sector, where Eocene formations with intermediate materials are also present, along with flysch sequences in the upstream zones (Hadji et al., 2016). The central area features Eocene parautochthonous, autochthonous, and allochthonous units, whereas Mio-Pliocene formations occur in the northwestern part. Quaternary accumulations are also found along the piedmont zones. In the southwestern sector, the substratum is mainly composed of Hodna parautochthonous and autochthonous units, interspersed with Triassic exotic outcrops (Figure 2) (Benmarce et al., 2024).

Extensive carbonate formations outcrop throughout the region, consisting primarily of limestone, marl, clay, sandstone, dolomite, and Quaternary crusts, often associated with tufa deposits (Mebarkia and Boufekane, 2020).

The results obtained from applying the RUSLE model to the UBW reveal complex, spatially heterogeneous erosion dynamics; shaped by the combined influence of rainfall, soil properties, topography, vegetation cover, and land management. Below I discuss how each RUSLE factor contributes to the observed patterns, compare with findings from recent studies, and reflect on regional implications of the results.

This study provides a comprehensive evaluation of soil erosion dynamics in the UBW through the integration of the RUSLE model with GIS and field-based analyses. By combining quantitative modeling with spatial data, the research quantified the influence of five key parameters: rainfall erosivity (R), soil erodibility (K), topographic factor (LS), vegetation cover (C), and conservation practices (P); which collectively determine the spatial variability of erosion across the UBW.

The results reveal a pronounced spatial heterogeneity in soil loss rates, reflecting the complex interplay between natural and anthropogenic factors. More than two-thirds of the watershed is characterized by low to moderate erosion potential, with estimated soil losses below 6 t/ha/year, primarily in gently sloping areas and zones with stable vegetation cover. In contrast, the northern and southwestern sectors exhibit the highest erosion intensities, exceeding 33 t/ha/year over approximately 9% of the total area. These critical zones correspond to steep topographic gradients, friable geological formations, and reduced vegetative protection, conditions that enhance the impact of intense rainfall events and accelerate surface runoff and sediment detachment.

The integrated RUSLE–GIS approach proved effective in capturing the spatial distribution and magnitude of soil erosion, offering a valuable decision-support framework for land and water management. The derived maps not only identify the most erosion-prone areas but also provide essential insights into the underlying processes driving land degradation. These findings underscore the importance of adopting targeted soil and water conservation strategies, including reforestation, contour farming, terracing, and slope stabilization, particularly in high-susceptible zones.

At last, this work highlights the urgent need for sustainable land-use planning and adaptive watershed management to mitigate soil loss and preserve ecosystem functions. Future research should aim to refine erosion modeling by integrating temporal datasets, sediment yield measurements, and hydrological simulations, thereby improving predictive accuracy and supporting long-term resilience of the UBW under changing climatic and land-use conditions.

The results of this study provide a scientific basis for prioritizing soil and water conservation interventions within the semi-arid environments of the MENA region. By identifying erosion-prone zones with spatial precision, the findings enable policymakers and local planners to implement targeted measures that optimize resource allocation and reduce land degradation risks. Integrating RUSLE–GIS assessments into regional land management programs would strengthen soil conservation planning, improve agricultural productivity, and enhance watershed resilience to climatic extremes. Furthermore, the methodological framework developed here can be replicated in other semi-arid regions facing similar geomorphological and hydrological challenges, thereby contributing to broader sustainable land management efforts.