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Article

Mapping of Soil Erosion Vulnerability in Wadi Bin Abdullah, Saudi Arabia through RUSLE and Remote Sensing

by
Majed Alsaihani
1,* and
Raied Alharbi
2
1
Saudi Red Crescent Authority, P.O. Box 2947, Riyadh 11129, Saudi Arabia
2
Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
*
Author to whom correspondence should be addressed.
Water 2024, 16(18), 2663; https://doi.org/10.3390/w16182663
Submission received: 30 June 2024 / Revised: 28 July 2024 / Accepted: 10 September 2024 / Published: 19 September 2024
(This article belongs to the Section Water Erosion and Sediment Transport)
Browse Figures
Figure 1
<p>Geographic location of the Wadi Bin Abdullah watershed.</p> "> Figure 2
<p>Flowchart depicting the conceptual framework for assessing soil loss retention using the RUSLE Model.</p> "> Figure 3
<p>Illustrates the four maps of Wadi Bin Abdullah. (<b>A</b>) shows the LS factor, (<b>B</b>) shows the R factor, (<b>C</b>) is the K factor map, and (<b>D</b>) shows the land cover land use.</p> "> Figure 4
<p>Illustrates the Total soil loss of Wadi Bin Abdullah.</p> "> Figure 5
<p>Illustrates the soil loss classification.</p> ">
Versions Notes

Abstract

:
This study investigates soil loss in the Wadi Bin Abdullah watershed using the Revised Universal Soil Loss Equation (RUSLE) combined with advanced tools, such as remote sensing and the Geographic Information System (GIS). By leveraging the ALOS PALSAR Digital Elevation Model (DEM), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall data, and the Digital Soil Map of the World (DSMW), the research accurately evaluates soil loss loads. The methodology identifies significant variations in soil loss rates across the entire watershed, with values ranging from 1 to 1189 tons per hectare per year. The classification of soil loss into four stages—very low (0–15 t/ha/yr), low (15–45 t/ha/yr), moderate (45–75 t/ha/yr), and high (>75 t/ha/yr)—provides a nuanced perspective on soil loss dynamics. Notably, 20% of the basin exhibited a soil loss rate of 36 tons per hectare per year. These high rates of soil erosion are attributed to certain factors, such as steep slopes, sparse vegetation cover, and intense rainfall events. These results align with regional and global studies and highlight the impact of topography, land use, and soil properties on soil loss. Moreover, the research emphasizes the importance of integrating empirical soil loss models with modern technological approaches to identify soil loss-prone locations and precisely quantify soil loss rates. These findings provide valuable insights for developing environmental management strategies aimed at mitigating the impacts of soil loss, promoting sustainable land use practices, and supporting resource conservation efforts in arid and semi-arid regions.

1. Introduction

Soil loss is a growing concern globally, especially in arid regions, like the Kingdom of Saudi Arabia, where it significantly impacts land cover and land use. Understanding and managing soil loss is crucial as it directly affects the efficiency and longevity of water storage systems, sustainability, and resilience against extreme climate events [1]. Maqsoom et al. [2] highlight that soil loss caused by water or tillage erodes nutrient-rich topsoil, increasing risks to land productivity. It is estimated that 80% of the world’s soil loss, amounting to 20 billion tons annually, is deposited into the oceans [3]. Both natural processes and human activities, such as rapid population growth, deforestation, and overgrazing, drive soil loss, making it a major contributor to reservoir sedimentation [2].
Recent studies highlight significant advancements in understanding soil loss, emphasizing its severity and spatial distribution. Panagos et al. [3] provide a comprehensive assessment of soil loss by water loss across Europe, while Wuepper et al. [4] emphasize the critical role of soil in sustaining human life and highlight the severe threat that soil loss poses to food security and ecosystems. These studies discuss how losses diminish soil quality, reduce agricultural productivity, and disrupt ecosystem services, underscoring the urgent need for effective soil conservation measures. Additionally, Colman et al. [5] discuss the impacts of climate change and conservation measures on loss processes and future challenges for soil conservation.
Soil loss is a widespread phenomenon occurring globally, even in regions with gentle slopes [6]. It is driven by a combination of mechanical, biochemical, and microbiological processes. Tamene et al. [7] stress that soil loss is a critical environmental issue with significant consequences. These processes result in the loss of fertile topsoil, reduced soil water retention capacity, nutrient depletion, soil accumulation in dams, the disruption of aquatic ecosystems, water pollution, and an increased risk of downstream flooding [8]. The loss of arable land could exacerbate food price fluctuations and profoundly affect the livelihoods of millions, potentially pushing them into poverty [9]. However, effective soil management approaches hold promise for improving food access, sequestering carbon, and preserving ecosystem health [10].
Global soil loss has escalated significantly over the twentieth century and is expected to worsen in some regions due to severe climate changes [11]. The global mean soil loss is estimated to range between 12 and 15 tons per hectare per year [12], resulting in an annual depletion of approximately 0.90–0.95 mm of soil from terrestrial surfaces. Moreover, losses induced by wind and water lead to an estimated yearly loss of around 75 billion tons of soil [13].
Almouctar [14] underscores the challenges associated with soil loss, attributing its complexity to various factors, such as the slope, precipitation, land use, altitude, and vegetation. In Saudi Arabia, Bahrawi et al. [15] highlight soil loss as a major environmental concern, leading to adverse impacts both on-site and off-site, such as reduced land productivity. Azaiez et al. [16] conducted research using the Wischmeier equation to evaluate soil loss in the Mirabah basin in the city of Abha, indicating that the watershed is highly sensitive to water loss. Soil loss causes several impacts, including soil quality deterioration, decreased agricultural output, and silt accumulation in lakes and dams, reducing their water storage capacity [17].
Furthermore, soil loss leads to the loss of nutrient-rich topsoil, the degradation of downstream water quality, and a potential decrease in the water storage capacity of dams and reservoirs, compromising the effectiveness of hydraulic structures (Negese). Globally, reservoirs experience an annual decrease in storage capacity of around 0.5% to 1% due to silt accumulation. Consequently, soil loss has become a pressing challenge that requires solutions using both physical and social techniques [18].
Predicting future soil loss rates and deposition is essential, in addition to addressing the current state of soil loss. Therefore, local authorities and property owners should implement strategic measures to reduce soil loss and address both current challenges and future consequences of climate change on land utilization, vegetation cover, and ecosystem characteristics.
For over seventy years, researchers worldwide have been engaged in studying soil loss modeling and forecasting [15], resulting in the development of numerous models. Among the prominent approaches used for predicting soil losses are the Soil Losses Model for Mediterranean Regions (SEMMED) [6,15], the Water Losses Prediction Project (WEPP), and the Soil and Water Assessment Tool (SWAT) [6,14,19]. The Universal Soil Loss Equation (USLE) and its updated version, RUSLE, are frequently utilized to estimate soil loss and subsequent soil loss accumulation in a watershed [6,14,20,21]. The Revised Universal Soil Loss Equation (RUSLE) is widely used for estimating soil loss. The model integrates several factors, including rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practices (P). Studies utilizing RUSLE have provided valuable insights into soil loss dynamics and conservation planning [6,14,20].
Although existing methods, like the Revised Universal Soil Loss Equation (RUSLE), and Geographic Information System (GIS) with remote sensing tools provide valuable insights into soil loss, their effectiveness is limited by such factors as restricted geographical coverage, a lack of real-time monitoring capabilities, and their inability to seamlessly integrate with other tools for dam management.
The Wadi Bin Abdullah (Harad) watershed in southwestern Saudi Arabia is a critical area for evaluating soil loss due to its unique topographic and climatic characteristics. The watershed originates from the western mountainous regions of Yemen, reaching altitudes of up to 1850 m, and flows into the Red Sea coast within Saudi Arabia. The area covers 1235 km2 and features high relief and a complex drainage network, making it susceptible to loss. The climate is marked by high temperatures, minimal rainfall, and varying wind speeds, further exacerbating loss processes.
To the best of the authors’ knowledge, no prior research has explored soil loss and sedimentation in the study area using both the RUSLE model and alternative methodologies. This investigation employs rainfall data from CHIRPS to accurately predict annual precipitation and to calculate the R factor, essential for soil loss assessment. By integrating the RUSLE model with other sophisticated techniques, this research aims to improve the soil loss distribution using RS and GIS technology in arid regions.
The research emphasizes enhancing sedimentation monitoring by integrating remote sensing and GIS data with the RUSLE model to enable real-time observation. Accurately evaluating soil loss levels is essential due to the interconnectedness between water management, reservoir soil loss, and water users.
This research focuses on improving sedimentation monitoring by incorporating remote sensing (RS) and Geographic Information System (GIS) techniques alongside the RUSLE model. The primary challenge in the study area is soil loss. Additionally, the research aims to utilize GIS and RS to collect comprehensive data on changes in land use and calculate annual variations in soil loss.
Finally, the RUSLE model will be employed to improve the operational efficiency of surface dams by calculating soil loss load. Moreover, it is acknowledged that there are very few studies focused on determining soil loss in Saudi Arabia, making this research particularly valuable. The integration of the RUSLE model with high-resolution satellite data and CHIRPS rainfall data provides a more precise and scalable method for assessing soil loss. The methodology of this study is designed to be adaptable for use in various regions, especially those where traditional soil loss measurement methods are impractical. Significant contributions are made towards advancing soil conservation strategies and enhancing our understanding of soil loss dynamics in dryland environments. By incorporating remote sensing (RS) and GIS techniques, this study aims to provide valuable insights for decision-makers and policymakers, enabling them to identify areas heavily impacted by soil loss and to determine sites with varying levels of risk.

Study Area

The Wadi Bin Abdullah (Harad) watershed originates from the western mountainous regions of the Republic of Yemen, reaching altitudes of up to 1850 m (Jabal Qarah). It flows near the Al Muwassim village into the Red Sea coast within Saudi Arabia. The drainage area of Wadi Bin Abdullah covers 1235 km2, and is situated between latitudes north of 16°10′–16°45′ and longitudes east of 42°45′–43°40′ Figure 1. The upper highlands of the watershed span 1235 km2, ranging in elevations from 500 to 1850 m within Yemeni territory, while the lower watershed covers 100 km2 in the Harad coastal plain.
Using ArcGIS Pro tools, relief computed indicators reveal the mountainous characteristics of Wadi Bin Abdullah watershed, with a high relief range of 2316 m, obtained by the difference between the maximum and minimum elevations. Morphometric parameters demonstrate the longitudinal shape of the drainage area, with circularity, elongation, and form factors registering lower values. Conversely, the length/width ratio, compactness coefficient, and lemniscate ratio exhibit inverse variations to the shape morphometric indicators (Table 1).
The drainage network in the Bin Abdullah watershed achieves the seventh order of the Strahler method, with a total of 2999 streams and a combined stream length of 138.5 km. The hypsometry integral of this stream network is 0.58, indicating that erosion dynamics have completed 42% of the erosion cycle in the watershed. Water erosion processes within the total drainage area generate a drainage density of 2.06 km/km2 and a stream frequency of 2.43 streams/km2 (Table 2). Also, the watershed has a gradient (longest path) of 138.5, a circulatory ratio (Rc) of 0.17, and the basin morphometry properties are listed in Table 3 and Table 4.
The study area, located in the southwestern part of Saudi Arabia, experiences a climate marked by high temperatures throughout the year, with maximum temperatures reaching 34.1 degrees Celsius and minimum temperatures recorded at 25.1 degrees Celsius. Wind speeds range between 7.1 and 5.8 km/h, while average rainfall amounts to approximately 6.2 mm. Humidity levels fluctuate between 37.4% and 53.2% (Table 5).

2. Materials and Methods

2.1. Data Sources

The datasets utilized in this research to achieve the respective objectives include the Digital Elevation Model (DEM), Climate Data, Soil Data, and Land Cover and Land Use, as listed in Table 6.

2.2. Morphometric Analysis

Morphometric analysis, described as the systematic measurement and quantitative assessment of landforms [14], is a critical methodology that offers significant insights into the drainage characteristics of basins [31]. Additionally, morphometric analysis serves as an important marker of terrain structure and hydrological dynamics [32]. Furthermore, it provides essential information on the sediment loss from a watershed, soil characteristics, land surface processes, and erosion patterns [22]. The concept of morphometric analysis was first proposed by Horton to investigate the origin of river networks [33]. Within morphometric analysis, three primary categories of watershed characteristics are typically computed, as follows:
Linear Aspects: Basin length, stream order, stream length, mean stream length, bifurcation ratio, and mean bifurcation ratio.
Areal Aspects: Basin area, drainage density, basin shape, drainage texture, circulatory ratio, stream frequency, and elongation ratio.
Relief Aspects: Basin relief, relief ratio, ruggedness number, gradient ratio, basin slope, and relative relief.
The equations used to calculate these morphometric properties are listed in Table 7.
Lithology, elevation, and climate are the primary environmental factors affecting basin characteristics and the behavior of water systems at the watershed scale [34]. With advancements in remote sensing and spatial technology, analyzing different terrain and hydro-morphometric features of drainage basins has been greatly simplified [31]. This analysis has shown that detailed and updated information about drainage basins can be systematically produced [31]. Daniel and Getachew mentioned that there are equations to calculate morphometric parameters, which are presented in Table 7 [35,36].

2.3. RUSLE Model

The Revised Universal Soil Loss Equation (RUSLE) is a widely recognized and implemented empirical model for calculating average annual soil loss. Also known as the “RKLSPC” equation, it considers five main factors: (i) R-factor (rainfall erosivity), (ii) K-factor (soil erodibility), (iii) LS-factor (slope length and steepness), (iv) C-factor (cover management), and (v) P-factor (conservation practice).
The RUSLE model illustrates how terrain, soil properties, land utilization, and climate conditions affect rill and inter-rill soil erosion triggered by rainfall. Highly regarded as a versatile and effective tool, it is commonly employed by planners for conservation planning purposes at the property level. However, quantifying soil loss using the RUSLE model on erosion plots has limitations due to data reliability, representation, and expense. In large and complex environments, the model may fail to provide spatial soil loss distribution accurately. To overcome these limitations, the RUSLE model is often combined with remote sensing and Geographic Information System (GIS) methods [33,34,35,36,37]. This integration enhances the assessment of soil erosion and its spatial distribution with greater precision.
In this study, the RUSLE model was utilized within a GIS platform to estimate annual soil loss and demonstrate the spatial distribution of potential erosion risk. This approach allows for the incorporation of erosional factors, such as topography, climate, soil properties, and land management practices. The model’s applicability extends to various geographical regions worldwide, ensuring the comparability of results and methods at a global level. Figure 2 illustrates the flowchart depicting the conceptual framework for assessing sediment retention using the RUSLE Model.
As stated by Renard et al. [37] the Revised Universal Soil Loss Equation is formulated as follows:
A = R × K × LS × C
where
  • A represents the annual average soil loss (t ha−1 yr−1);
  • R is the rainfall erosivity factor (Mj mm ha−1 h−1 yr−1);
  • K is the soil erodibility factor (t h Mj−1 mm−1);
  • LS indicates the slope length and gradient factor (dimensionless);
  • C is the land management factor (dimensionless).

2.3.1. Rainfall Erosivity (R)

The rainfall erosivity factor (R) assesses the potential for erosion based on the kinetic energy of rainfall impact and runoff. It is derived from average annual precipitation data obtained from the National Center for Meteorology (NCM). An annual precipitation raster covering the entirety of Saudi Arabia was created using the inverse distance weighting (IDW) method.
The formula employed to determine the erosivity factor (R) for arid regions of the equation [38], was selected due to simplicity. The equation is as follows:
R = 4.17 ⋅ ∑ i = 1 12     P i 2 P − 152
where R represent a parameter, like runoff or relief ratio, and P represents precipitation, while L is often used to represent length or basin length represent intensity.
The estimation of the erosivity factor (R) is closely linked to annual rainfall data. Therefore, higher annual rainfall results in a higher erosivity factor (R). The inverse distance weighting (IDW) method is considered one of the most accurate techniques for determining the spatial distribution and calculating the yearly average precipitation. The spatial distribution of yearly rainfall can be obtained using the IDW method available in ArcMap.

2.3.2. Soil Erodibility Factor (K)

The soil erodibility factor (K) is a key parameter in soil erosion modeling, as it indicates the soil’s vulnerability to erosion by rainfall and runoff. It considers various soil attributes, such as texture, organic matter content, structure, and permeability, all of which influence the soil’s resistance to erosion [39]. Additionally, the formula used to calculate the soil erodibility factor (K) as mentioned by [40] is given by the following equation:
K = 0.1317 × (Fcsand × Fsi-cl × Forg × Fhisand)
F c s a n d = 0.2 + 0.3 exp ⁡ − 0.0256 S a n d 1 − s i l 100
F s i − c l a = s i l c l a + s i l
F o r g = 1 − 0.25   C C + e x p   ( 3.72 − 2.95   C )
F h i s a n d = 1 − 0.70   S N 1 S N 1 + e x p   ( − 5.51 + 22.9   S N 1 )
where
  • San, sil, and cla are % sand, silt, and clay, respectively.
  • C: organic carbon content.
  • SN1: sand content subtracted from 1 and divided by 100 [38].

2.3.3. Topographic Factor (LS)

The topographic factor (LS) is determined by two subfactors, namely the slope gradient (S) and slope length (L), both derived from the Digital Elevation Model (DEM). These factors play an essential role in soil erosion modeling as they influence surface runoff and erosion within a drainage basin [33,41].
The slope gradient (S) and slope length (L) parameters are obtained from the DEM, which provides information about the terrain. These parameters are essential for calculating the LS factor. To generate the LS factor map, flow accumulation and flow direction maps are utilized as inputs for LS factor calculation. These maps are typically created using GIS tools, such as Arc Hydro Tools [14], and the LS values can be estimated using the following equation:
L S = F A   c e l l   s i z e 23.13 0.4 S i n ( s l o p e   o f   D E M ) 0.01745 0.09 1.3 1.6

2.3.4. Land Cover Management Factor (C)

The land cover management factor (C) quantifies the cumulative effects of land degradation caused by various land cover types, including trees, crops, and other vegetation, with values ranging from 0.01 to 1 [42]. The C factor maps are based on the land use/land cover classification of the drainage basins. These maps provide information about the spread of different land cover types across the study area. The C factor values can be obtained through the GIS database.

2.3.5. Conservation Practice Factor (P)

The conservation practice factor (P) represents the ratio of soil loss in a field utilizing a specific conservation method to soil loss in a field without such practices [13].
The P factor value indicates the effectiveness of land management practices in reducing soil degradation within drainage basins [43] This factor can be obtained using land use as well as land cover data available in the GIS database.

3. Results

3.1. RUSLE Model Parameters

3.1.1. R Factor

A map depicting the R factor for Wadi Bin Abdullah was created using the inverse distance weighted (IDW) technique, based on the average annual rainfall of the stations. Figure 3 Map B illustrates the mean R factor, which ranges from 86 to 285 MJ mm/ha/h/year across the study area. Lower values are observed in the lower basin, while higher values are typical in higher basin areas. The results indicate a spatial variation, with higher R factor values in the upper region of the study area compared to the lower region.

3.1.2. K Factor

Figure 3 displays the spatial distribution of K values within the Bin Abdullah region, ranging from 0.24 to 0.28 t ha h/ha MJ mm. The K factor range of 0.05 to 0.34 characterizes the study area’s overall susceptibility to erosion as moderate. This indicates that under certain conditions, soil erosion could occur, highlighting a moderate vulnerability to erosion. While erosion risk may vary across different parts of the study area, the soil is generally moderately susceptible to erosion. Table 8 provides detailed insights into soil characteristics, including type, texture, and corresponding K values within the Wadi Bin Abdullah watershed. The delineation reveals two primary soil classes: one characterized by 26% sand, 63% silt, 11% clay, and 1.15% organic carbon, while the other exhibits proportions of 73% sand, 46% silt, 17% clay, and 3.14% organic carbon. These distinctions offer a nuanced understanding of soil erodibility dynamics, with higher K values indicating heightened vulnerability to erosive processes, contrasting with lower K values, which denote enhanced resistance.

3.1.3. Land Cover and Land Use

In Wadi Bin Abdullah, the vegetation cover and land use, as depicted in Figure 3, reveal distinctive characteristics that reflect both the natural environment and human activities in the region. The dominant feature, comprising over 98% of the area, is shrublands, indicating a predominantly arid landscape with sparse vegetation typical of arid and semi-arid regions, such as parts of Saudi Arabia.
Shrublands (98.28%): The overwhelming presence of shrublands suggests that the area is predominantly covered by low-growing woody vegetation, including bushes, shrubs, and small trees. These shrublands are well-adapted to arid conditions and play a vital role in soil stabilization, conserving water, and providing a habitat for wildlife.
Bare land (0.52%): Bare land areas, constituting a small percentage of the landscape, likely consist of vegetated or sparsely vegetated surfaces. These areas may include rocky terrain, exposed soils, or areas undergoing natural or anthropogenic disturbance.
Tree cover (0.47%): The presence of tree cover indicates the existence of larger trees within the landscape, adding diversity to the vegetation profile.
Crops (0.46%): Cultivated areas for crops, though limited in coverage, demonstrate agricultural activities within Wadi Bin Abdullah, showcasing human intervention in the landscape.
Built-up areas (0.26%): The presence of built-up areas signifies human habitation and infrastructure development within the region, indicating the presence of settlements and urbanization.
Water (0.1%): Water bodies, although minimal in coverage, may include natural features, such as seasonal streams, wadis, or small ponds, contributing to the hydrological diversity of the landscape.
Overall, the land cover and land use in Wadi Bin Abdullah strike a balance between natural vegetation, human settlement, and agricultural activities, all adapted to the arid conditions of the region. This blend of natural and anthropogenic elements reflects the complex interplay between human activities and the environment in arid landscapes.

3.1.4. Cumulative Soil Loss Retention within the Basin

The geographic distribution of the yearly sedimentation burden in Wadi Bin Abdullah, as depicted in Figure 4, was analyzed applying the parameters of the Revised Universal Soil Loss Equation (RUSLE), which divides soil loss into 10 distinct categories. These categories span a wide range, from the lowest recorded soil loss rate of 1 ton per hectare per year to the highest recorded rate of 1189 tons per hectare per year.
Figure 5 illustrates the classification of soil loss into the following four stages based on the rates of erosion determined by the RUSLE model:
Very low (0–15 t/ha/yr): Represents areas with minimal erosion rates, indicating relatively stable soil conditions.
Low (15–45 t/ha/yr): Covers regions experiencing light erosion, where soil loss is more noticeable but not severe.
Moderate (45–75 t/ha/yr): Includes areas with moderate erosion, indicating significant soil loss that may impact land use and management.
High (>75 t/ha/yr): Identifies areas with severe erosion, where soil loss is critical and requires immediate intervention.
This classification, derived from [44], is used to provide a clear understanding of the spatial variability in soil loss and to facilitate targeted erosion control measures. By categorizing soil loss into these distinct stages, we aim to offer a more nuanced perspective on soil loss dynamics within the study area.
Of significant importance is the observation that a substantial segment of the study area, constituting 20% of the basin, exhibited a soil loss rate of 36 tons per hectare per year. This rate, depicted in the figure using a purple color scheme, provides a vivid representation of the spatial variation in soil loss across the study area. It indicates areas of particularly high soil loss deposition, likely influenced by factors, such as topography, land utilization, and soil properties. This insight into soil loss dynamics is crucial for understanding erosion processes and informing management strategies aimed at sustainable land use and conservation efforts

4. Discussion

This study delineated the Wadi Bin Abdullah basin utilizing the ALOS PALSAR Digital Elevation Model (DEM). Rainfall data sourced from the CHIRPS satellite facilitated the calculating the R factor for the period from 2015 to 2019. The estimation of the K factor was achieved through the utilization of the Digital Soil Map of the World (DSMW) data. Simultaneously, land use and land cover (LULC) classification maps were meticulously developed using high-definition imagery from the Sentinel-2 satellite.
Soil erosion results from a combination of natural processes and anthropogenic activities. Comprehending the extent and causes of soil erosion is imperative for devising effective mitigation strategies. Over time, various models have been devised and advocated on a worldwide scale to evaluate soil degradation across different geospatial dimensions. Among these, the Revised Universal Soil Loss Equation (RUSLE) stands out as a widely embraced and dependable approach for quantifying soil loss and predicting soil erosion transport processes [2]. The RUSLE model is an internationally recognized and dependable approach for assessing soil loss and soil loss transport. It proficiently predicts soil degradation in arid areas with scarce data [41]. Erosion emerges as a prominent concern within diverse local and regional contexts, notably in vast arid and semi-arid regions, such as the Kingdom of Saudi Arabia (KSA), distinguished by prolonged periods of aridity [42]. Within the RUSLE model, rainfall erosivity, soil erodibility, and land utilization and land cover (LULC) factors play crucial roles in affecting soil loss estimation and shaping the dynamics of soil degradation processes [10]. Spatial maps delineating RUSLE parameters were meticulously crafted for this scholarly inquiry to ascertain soil loss retention rates.
We found in this study that the Wadi Bin Abdullah (Harad) watershed originates in the western mountains of the Republic of Yemen at altitudes of up to 1850 m (Jabal Qarah). Notably, a significant portion of the study area, comprising 20% of the basin, exhibited a soil loss rate of 36 tons per hectare per year. This result was corroborated by other studies conducted in the region. For example, soil loss in Wadi El Hayat within the Jazan region, assessed using the FAO and RUSLE methods, ranged from 3.7 to 40 t/ha/yr [42]. Similarly, the analysis in Wadi Yalamlam, southeast of Jeddah, using both RUSLE and remote sensing approaches, resulted in an annual soil loss of 40 tons per hectare [14].
A comparative analysis with global studies reveals regional variability in soil erosion patterns. For instance, a study in Wadi Baysh reported a soil erosion rate of 57 t/ha/yr [14]. Furthermore, the outcomes of that investigation reveal an average soil erosion rate of 45 t/ha/yr, as reported in previous studies [15,43], while the average soil erosion rate reported in this study was 50 t/ha/yr. This discrepancy may be influenced by various factors, such as cloud cover affecting satellite data. Despite these variations, the results are generally consistent with other regional studies given the similar climate and topography.
Several limitations of this study must be acknowledged. Firstly, the RUSLE model is empirical and relies on input parameters, such as rainfall erosivity, soil erodibility, and land utilization/land cover (LULC). The accuracy of RUSLE’s predictions depends heavily on the quality and precision of these inputs. Due to its empirical nature, the model’s predictions are inherently uncertain. The lack of model calibration and the absence of verification with bathymetry data may impact the reliability of the results.
While RUSLE is widely accepted, its outputs are sensitive to the accuracy of the input parameters. In this study, we did not calibrate the model or use bathymetry data for verification, which could affect the precision of the soil loss estimates. The generalized parameters, especially the soil erodibility (K factor), may not fully account for local variations in soil layers or land use types within the Wadi Bin Abdullah basin. This limitation may restrict the model’s applicability to other regions without necessary adjustments.
The study’s temporal scope, covering 2015 to 2020, and its reliance on satellite-derived data, introduce limitations related to data resolution and accuracy. Changes in soil properties, land cover, and climatic conditions over time could influence the precision of RUSLE’s predictions. Future research should incorporate higher-resolution data and extend the study period to enhance accuracy.
Furthermore, RUSLE operates under assumptions about soil erosion processes that may not fully capture extreme weather events or rapid land use changes. The reliance on model-based estimates rather than direct field measurements introduces additional uncertainties in soil loss calculations.
To address these limitations, future research should include ground-based measurements, extend the study period, and use higher-resolution data. Acknowledging these limitations is crucial for improving the accuracy and applicability of soil erosion assessments in future studies.

5. Conclusions

This research thoroughly investigated potential soil erosion areas in Wadi Bin Abdullah by integrating empirical soil degradation modeling (RUSLE) with remote sensing and GIS tools. The demarcation of the basin was facilitated by using ALOS PALSAR DEM as a fundamental input. Additionally, CHIRPS precipitation data spanning 2015 to 2020 were utilized to estimate the R factor annually, and DSMW and Sentinel-2 datasets were used to determine the K factor and classify LULC within Wadi Bin Abdullah.
The R factor map created using the IDW (inverse distance weighting) technique illustrated the spatial distribution of the R factor in Wadi Bin Abdullah, with average values fluctuating between 224 to 775 MJ mm/ha/h/year. The K values varied between 0.24 to 0.28 t ha h/ha MJ mm, suggesting a moderate vulnerability to erosion under certain circumstances.
Soil attributes, including type, texture, and matching K values along the watershed, were provided, emphasizing two main soil groups. The differences provided a detailed understanding of soil erodibility dynamics, where elevated K values denote increased vulnerability to erosion, whereas lower values signify enhanced resilience.
The land cover and land use patterns of Wadi Bin Abdullah showcased a blend of natural vegetation, human habitation, and agricultural operations tailored to suit the arid conditions. Shrublands dominated the terrain, with other features including barren ground, tree cover, crops, urban areas, and aquatic features revealing human interventions and environmental adaptations.
The spatial distribution of the yearly soil loss load was thoroughly examined using RUSLE parameters, resulting in the identification of 10 unique soil loss categories. Rates varied widely from 1 to 1198 tons per hectare per year. Notably, the average soil loss rate is 50 tons per hectare per year, indicating a significant variability in soil loss across the region.
These findings provide crucial insights into soil loss movement in Wadi Bin Abdullah, aiding in understanding erosion processes and guiding future plans for sustainable land use and conservation activities. Proactive environmental management, supported by dam infrastructure, aims to control soil loss and manage water resources, reducing the risk of substantial ecosystem deterioration caused by soil erosion.
While RUSLE is widely accepted, its outputs are sensitive to the accuracy of the input parameters. In this study, we did not calibrate the model or use bathymetry data for verification, which could affect the precision of the soil loss estimates. Additionally, the generalized parameters, especially the soil erodibility (K factor), may not fully account for local variations in soil layers or land use types within the Wadi Bin Abdullah basin. Future research should incorporate ground-based measurements and higher-resolution data to enhance accuracy.

Author Contributions

Conceptualization, M.A. and R.A.; methodology, M.A.; software, M.A. and R.A.; validation, M.A. and R.A.; formal analysis, M.A.; investigation, M.A. and R.A.; resources M.A. and R.A.; data curation, M.A. and R.A.; writing—original draft preparation, M.A. and R.A.; writing—review and editing, R.A.; visualization, M.A. and R.A.; supervision, R.A.; project administration, R.A.; funding acquisition, R.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Researchers Supporting Project number (RSP2024R310), King Saud University, Riyadh, Saudi Arabia.

Data Availability Statement

New data were not created through this work.

Acknowledgments

Authors sincerely grateful to the Researchers Supporting Project at King Saud University, under grant number RSP2024R310, for their invaluable support.

Conflicts of Interest

Majed Alsaihani is a doctoral student at King Saud University and is employed by the Saudi Red Crescent Authority. This work is part of his doctoral research. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Kumar, S.; Mishra, A.; Singh, U.K. Assessment of Land Cover Changes and Climate Variability Effects on Catchment Hydrology Using a Physically Distributed Model. Sustainability 2023, 15, 304. [Google Scholar] [CrossRef]
  2. Kandekar, V.U.; Pande, C.B.; Rajesh, J.; Atre, A.A.; Gorantiwar, S.D.; Kadam, S.A.; Gavit, B. Surface Water Dynamics Analysis Based on Sentinel Imagery and Google Earth Engine Platform: A Case Study of Jayakwadi Dam. Sustain. Water Resour. Manag. 2021, 7, 44. [Google Scholar] [CrossRef]
  3. Panagos, P.; Borrelli, P.; Matthews, F.; Liakos, L.; Bezak, N.; Diodato, N.; Ballabio, C. Global Rainfall Erosivity Projections for 2050 and 2070. J. Hydrol. 2022, 610, 127865. [Google Scholar] [CrossRef]
  4. Wuepper, D. Countries and the Global Rate of Soil Erosion. Nat. Sustain. 2020, 3, 51–55. [Google Scholar] [CrossRef]
  5. Colman, C.B.; Oliveira, P.T.S.; Almagro, A.; Soares-filho, B.S.; Rodrigues, D.B.B. Effects of Climate and Land-Cover Changes on Soil Erosion in Brazilian Pantanal. Sustainability 2019, 11, 7053. [Google Scholar] [CrossRef]
  6. Panagos, P.; Ballabio, C.; Poesen, J.; Lugato, E.; Scarpa, S.; Montanarella, L.; Borrelli, P. A Soil Erosion Indicator for Supporting Agricultural, Environmental and Climate Policies in the European Union. Remote Sens. 2020, 12, 1365. [Google Scholar] [CrossRef]
  7. Betrie, G.D.; Mohamed, Y.A.; Van Griensven, A.; Srinivasan, R. Sediment Management Modelling in the Blue Nile Basin Using SWAT Model. Hydrol. Earth Syst. Sci. 2011, 15, 807–818. [Google Scholar] [CrossRef]
  8. Pimentel, D. Soil Erosion: A Food and Environmental Threat. Environ. Dev. Sustain. 2006, 8, 119–137. [Google Scholar] [CrossRef]
  9. Waseem, M.; Iqbal, F.; Humayun, M.; Umais Latif, M.; Javed, T.; Kebede Leta, M. Spatial Assessment of Soil Erosion Risk Using RUSLE Embedded in GIS Environment: A Case Study of Jhelum River Watershed. Appl. Sci. 2023, 13, 3775. [Google Scholar] [CrossRef]
  10. Arabameri, A.; Yamani, M.; Pradhan, B.; Melesse, A.; Shirani, K.; Tien Bui, D. Novel Ensembles of COPRAS Multi-Criteria Decision-Making with Logistic Regression, Boosted Regression Tree, and Random Forest for Spatial Prediction of Gully Erosion Susceptibility. Sci. Total Environ. 2019, 688, 903–916. [Google Scholar] [CrossRef]
  11. Jing, T.; Fang, N.; Zeng, Y.; Huang, X.; Shi, Z. Catchment Properties Controlling Suspended Sediment Transport in Wind-Water Erosion Crisscross Region. J. Hydrol. Reg. Stud. 2022, 39, 100980. [Google Scholar] [CrossRef]
  12. Thapa, P. Spatial Estimation of Soil Erosion Using RUSLE Modeling: A Case Study of Dolakha District, Nepal. Environ. Syst. Res. 2020, 9, 15. [Google Scholar] [CrossRef]
  13. Senanayake, S.; Pradhan, B.; Huete, A.; Brennan, J. Assessing Soil Erosion Hazards Using Land-Use Change and Landslide Frequency Ratio Method: A Case Study of Sabaragamuwa Province, Sri Lanka. Remote Sens. 2020, 12, 1483. [Google Scholar] [CrossRef]
  14. Almouctar, M.A.S.; Wu, Y.; Zhao, F.; Dossou, J.F. Soil Erosion Assessment Using the Rusle Model and Geospatial Techniques (Remote Sensing and Gis) in South-Central Niger (Maradi Region). Water 2021, 13, 3511. [Google Scholar] [CrossRef]
  15. Singh, M.C.; Sur, K.; Al-Ansari, N.; Arya, P.K.; Verma, V.K.; Malik, A. GIS Integrated RUSLE Model-Based Soil Loss Estimation and Watershed Prioritization for Land and Water Conservation Aspects. Front. Environ. Sci. 2023, 11, 243. [Google Scholar] [CrossRef]
  16. Azaiez, N.; Alleoua, A.; Baazaoui, N.; Qhtani, N. Assessment of Soil Loss in the Mirabah Basin: An Overview of the Potential of Agricultural Terraces as Ancestral Practices (Saudi Arabia). Open J. Soil Sci. 2020, 10, 159–180. [Google Scholar] [CrossRef]
  17. Zandalinas, S.I.; Fritschi, F.B.; Mittler, R. Global Warming, Climate Change, and Environmental Pollution: Recipe for a Multifactorial Stress Combination Disaster. Trends Plant Sci. 2021, 26, 588–599. [Google Scholar] [CrossRef] [PubMed]
  18. Montgomery, D.R.; Matson, P.A. Soil Erosion and Agricultural Sustainability. Proc. Natl. Acad. Sci. USA 2007, 104, 13268–13272. [Google Scholar] [CrossRef]
  19. Igwe, P.U.; Onuigbo, A.A.; Chinedu, O.C.; Ezeaku, I.I.; Muoneke, M.M. Soil Erosion: A Review of Models and Applications. Int. J. Adv. Eng. Res. Sci. 2017, 4, 138–150. [Google Scholar] [CrossRef]
  20. Merritt, W.S.; Letcher, R.A.; Jakeman, A.J. A Review of Erosion and Sediment Transport Models. Environ. Model. Softw. 2003, 18, 761–799. [Google Scholar] [CrossRef]
  21. Islam Saikh, N.; Sarkar, D.; Saha, S.; Mondal, P. Application of Machine Learning Algorithms for Soil Erosion Susceptibility Estimation in Gumani River Basin, Eastern India. J. Geol. Soc. India 2024, 100, 320–334. [Google Scholar] [CrossRef]
  22. Horton, R. Drainage Basin Characteristics. Trans. Am. Geophys. Union 1932, 13, 350–361. [Google Scholar] [CrossRef]
  23. Langbein, W.B. Topographic Characteristics of Drainage Basins. U.S. Geol. Surv. Water-Supply Pap. 1947, 968-C, 125–157. [Google Scholar]
  24. Appolov, B.A. A Study of Rivers; Izdatel’stvo Moskovskogo Universiteta: Moscow, Russia, 1963. [Google Scholar]
  25. Schumm, S.A. Evolution of Drainage Systems and Slopes in Badlands at Perth Amboy, New Jersey. Bull. Geol. Soc. Am. 1956, 67, 597–646. [Google Scholar] [CrossRef]
  26. Strahler, N. Dimensional Analysis Applied to Fluvially Eroded Landforms. Geol. Soc. Am. Bull. 1958, 69, 279–300. [Google Scholar] [CrossRef]
  27. Pike, R.J.; Wilson, S.E. Elevation-Relief Ratio, Hypsometric Integral, and Geomorphic Area-Altitude Analysis. Geol. Soc. Am. Bull. 1971, 82, 1079–1084. [Google Scholar] [CrossRef]
  28. Miller, V.C. A Quantitative Geomorphic Study of Drainage Basin Characteristics in the Clinch Mountain Area, Virginia and Tennessee; Technical Report 3; Columbia University, Department of Geology: New York, NY, USA, 1953. [Google Scholar]
  29. Gravelius, H. Morphometry of Drainage Basins; Elsevier: Amsterdam, The Netherlands, 1914. [Google Scholar]
  30. Chorley, R.J.; Donald, M.; Pogrzelski, H. A New Standard for Estimating Drainage Basin Shape. Am. J. Sci. 1957, 255, 138–141. [Google Scholar] [CrossRef]
  31. Aparna, P.; Nigee, K.; Shimna, P.; Drissia, T.K. Quantitative Analysis of Geomorphology and Flow Pattern Analysis of Muvattupuzha River Basin Using Geographic Information System. Aquat. Procedia 2015, 4, 609–616. [Google Scholar] [CrossRef]
  32. Kabite, G.; Gessesse, B. Hydro-Geomorphological Characterization of Dhidhessa River Basin, Ethiopia. Int. Soil Water Conserv. Res. 2018, 6, 175–183. [Google Scholar] [CrossRef]
  33. Melton, M.A. Drainage Structure of Morphometric Properties of Systems and Their Controlling Agents. J. Geol. 1958, 66, 442–460. [Google Scholar] [CrossRef]
  34. Rastogi, R.A.; Sharma, T. Quantitative analysis of drainage basin characteristics. J. Soil Water Conserv. India 2022, 26, 18–25. [Google Scholar]
  35. Asfaw, D.; Workineh, G. International Soil and Water Conservation Research Quantitative Analysis of Morphometry on Ribb and Gumara Watersheds: Implications for Soil and Water Conservation. Int. Soil Water Conserv. Res. 2019, 7, 150–157. [Google Scholar] [CrossRef]
  36. Uddin, M.J.; Babu, M.A.H.; Islam, M.R.; Farzana, F.; Khatun, M.L.; Sazzad, S.M.F.; Islam, M.S. Estimation of Morphometric Parameters of Karatoya Sub-River Basin in Bangladesh Using GIS and Remote Sensing Techniques. Grassroots J. Nat. Resour. 2020, 3, 73–89. [Google Scholar] [CrossRef]
  37. Renard, K.G.; Foster, G.R.; Weesies, G.A.; Porter, J.P. RUSLE: Revised universal soil loss equation. J. Soil Water Conserv. 1991, 46, 30–33. [Google Scholar]
  38. Arnoldus, H.M.J. An Approximation of the Rainfall Factor in the Universal Soil Loss Equation. Assess. Eros. 1980, 6, 127–132. [Google Scholar]
  39. Erencin, Z. C-Factor Mapping Using Remote Sensing and GIS; A Case Study of Lom Sak/Lom Kao, Thailand; International Institute for Aerospace Survey and Earth Sciences (ITC): Upper Aise, The Netherlands, 2000. [Google Scholar]
  40. Kouli, M.; Soupios, P. Soil Erosion Prediction Using the Revised Universal Soil Loss Equation (RUSLE) in a GIS Framework, Chania, Northwestern Crete, Greece. Environ. Geol. 2008, 57, 483–497. [Google Scholar] [CrossRef]
  41. Li, P.; Tariq, A.; Li, Q.; Ghaffar, B.; Farhan, M.; Jamil, A.; Soufan, W.; El Sabagh, A.; Freeshah, M. Soil Erosion Assessment by RUSLE Model Using Remote Sensing and GIS in an Arid Zone. Int. J. Digit. Earth 2023, 16, 3105–3124. [Google Scholar] [CrossRef]
  42. Wischmeier, W.H.; Smith, D.D. Predicting Rainfall Erosion Losses: A Guide to Conservation Planning; Agricultural Handbook No. 537.; U.S. Department of Agriculture: Washington, DC, USA, 1978. [Google Scholar]
  43. Maqsoom, A.; Aslam, B.; Hassan, U.; Kazmi, Z.A.; Sodangi, M.; Tufail, R.F.; Farooq, D. Geospatial Assessment of Soil Erosion Intensity and Sediment Yield Using the Revised Universal Soil Loss Equation (RUSLE) Model. ISPRS Int. J. Geo-Inf. 2020, 9, 356. [Google Scholar] [CrossRef]
  44. Barbosa, W.C.d.S.; Guerra, A.J.T.; Valladares, G.S. Soil Erosion Modeling Using the Revised Universal Soil Loss Equation and a Geographic Information System in a Watershed in the Northeastern Brazilian Cerrado. Geosciences 2024, 14, 78. [Google Scholar] [CrossRef]
Water 16 02663 g001
Figure 1. Geographic location of the Wadi Bin Abdullah watershed.
Figure 1. Geographic location of the Wadi Bin Abdullah watershed.
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Figure 2. Flowchart depicting the conceptual framework for assessing soil loss retention using the RUSLE Model.
Figure 2. Flowchart depicting the conceptual framework for assessing soil loss retention using the RUSLE Model.
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Figure 3. Illustrates the four maps of Wadi Bin Abdullah. (A) shows the LS factor, (B) shows the R factor, (C) is the K factor map, and (D) shows the land cover land use.
Figure 3. Illustrates the four maps of Wadi Bin Abdullah. (A) shows the LS factor, (B) shows the R factor, (C) is the K factor map, and (D) shows the land cover land use.
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Figure 4. Illustrates the Total soil loss of Wadi Bin Abdullah.
Figure 4. Illustrates the Total soil loss of Wadi Bin Abdullah.
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Figure 5. Illustrates the soil loss classification.
Figure 5. Illustrates the soil loss classification.
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Table 1. Morphometric parameters references.
Table 1. Morphometric parameters references.
VariableSymbolSource
Basin reliefMean elevation H′ (m)(Horton, 1932) [22]
Minimum elevation h min (m)WMS output
Maximum elevation H max (m)WMS output
Mean basin slope I b (m/m)(Horton, 1932) [22]
Main channel slope I s (m/m)(Langbein, 1947) [23]
Mean slope of water divide I p (m/km)(Appolov, 1963) [24]
Basin perimeter P (km)(Schumm, 1956) [25]
Total basin relief Z-z (m)(Strahler, 1952) [26]
Hypsometric IntegralHi (m/m)(Pike et al., 1971) [27]
Basin shapeForm factor ratioFfHorton, 1932 [22]
Elongation ratioReSchumm, 1956 [25]
Circularity ratioRcMiller, 1953 [28]
Compactness coefficientCcGravelius, 1914 [29]
Lemniscate factorkChorley, 1957 [30]
Table 2. Morphometry of the stream network.
Table 2. Morphometry of the stream network.
Stream Order
(u)		Stream Number (Nu)Stream Length (km)
(Lu)		Mean stream Length (km)
(Lu′)
123411262.30.54
2520615.71.18
3105285.32.72
425193.07.72
5564.012.80
6259.629.80
7160.860.80
Total29992540.80.85
Table 3. Morphometry properties of the stream network.
Table 3. Morphometry properties of the stream network.
Morphometry IndicatorValue
Stream frequencyFs2.43
Drainage density Dd2.06
Drainage intensityFs/Dd1.18
Infiltration numberFs*Dd5
Overland flowFo1.03
Mean bifurcation ratio Rbm4.9
Weighted bifurcation ratio WRb4.3
Stream slope Ss1.89
Constant of channel MaintenanceCCM0.49
Table 4. Basin morphometry properties.
Table 4. Basin morphometry properties.
Morphometry IndicatorValue
Basin area (km2)1235
Basin length (km)84.8
Basin width (km)14.6
Perimeter length (km)304
Gradient (longest path)138.5
Circulatory ratio (Rc) ratio (Rc)0.17
Elongation ratio (Re)0.23
Form factor (Ff)0.17
Compactness coefficient (Cc)2.44
Length/width ratio5.82
Leminescate ratio1.46
Table 5. The climatic data for the study area (2015–2020).
Table 5. The climatic data for the study area (2015–2020).
MonthsAverage Temperature (°C)Relative Humidity
(%)		Wind Speed km/hRainfall (mm)
January25.153.267.6
February26.351.75.93.9
March28.647.164.3
April3142.35.94.3
May32.7405.83.6
June3437.462.2
July33.2446.310.8
August32.550.36.120.6
September33.142.16.32.09
October30.8386.92.5
November27.843.77.14.5
December25.749.66.79.2
Table 6. Data used.
Table 6. Data used.
DataSpatial ResolutionSource
Digital Elevation Model (DEM)30 mUSGS
Climate Data30 pixel sizeGlobal Rainfall Erosivity
Soil Data 30 pixel sizeHWSD Dataset
Land Cover Land Use10 mSentinal-2 Data
Table 7. Morphometric properties equations.
Table 7. Morphometric properties equations.
Basin reliefBasin area (km2)AHierarchical rank
Basin length (km)LbObtained from ArcGIS Pro
Basin width (km)WbObtained from ArcGIS Pro
Perimeter length (km)PObtained from ArcGIS Pro
Maximum elevation (m)HObtained from ArcGIS Pro
Minimum elevation (m)hObtained from ArcGIS Pro
Mean elevation (m)H’Obtained from ArcGIS Pro
Main stream length (km)LObtained from ArcGIS Pro
Total stream length (km)ΣLuObtained from ArcGIS Pro
Hypsometric integralHi (m/m)Hi (m/m)
Basin formCirculatory ratio (Rc)RcRc = 4π × A/P2
Elongation ratio (Re)ReRe = (2/Lb) × 2√(A/π)
Form factor (Ff)FfFf = A/Lb2
Compactness coefficient (Cc)CcCc = 0.2824 × p/√A
Length/width ratioLb/WbLb/Wb
Lemniscate ratiokK = Lb2/A
Table 8. Soil type, texture, and K Value.
Table 8. Soil type, texture, and K Value.
Soil UnitSand %Silt %Clay %Organic Carbon %Soil TypeK Factor
12663111.15Silty Loam0.24
13746173.14Loam0.28
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Alsaihani, M.; Alharbi, R. Mapping of Soil Erosion Vulnerability in Wadi Bin Abdullah, Saudi Arabia through RUSLE and Remote Sensing. Water 2024, 16, 2663. https://doi.org/10.3390/w16182663

AMA Style

Alsaihani M, Alharbi R. Mapping of Soil Erosion Vulnerability in Wadi Bin Abdullah, Saudi Arabia through RUSLE and Remote Sensing. Water. 2024; 16(18):2663. https://doi.org/10.3390/w16182663

Chicago/Turabian Style

Alsaihani, Majed, and Raied Alharbi. 2024. "Mapping of Soil Erosion Vulnerability in Wadi Bin Abdullah, Saudi Arabia through RUSLE and Remote Sensing" Water 16, no. 18: 2663. https://doi.org/10.3390/w16182663

APA Style

Alsaihani, M., & Alharbi, R. (2024). Mapping of Soil Erosion Vulnerability in Wadi Bin Abdullah, Saudi Arabia through RUSLE and Remote Sensing. Water, 16(18), 2663. https://doi.org/10.3390/w16182663

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