Estimating Soil Erosion Utilising Geospatial Method and Revised Universal Soil Loss Equation (RUSLE) of Abu Ghraibat Watershed, Eastern Misan Governorate, Iraq

Estimating Soil Erosion Utilising Geospatial Method and Revised Universal Soil Loss Equation (RUSLE) of Abu Ghraibat Watershed, Eastern Misan Governorate, Iraq | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Estimating Soil Erosion Utilising Geospatial Method and Revised Universal Soil Loss Equation (RUSLE) of Abu Ghraibat Watershed, Eastern Misan Governorate, Iraq Bashar F. Maaroof, Hashim H. Kareem, Jaffar H. Al-Zubaydi, Nadhir Al-Ansari, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7137911/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract This study examined the synergistic and independent effects of soil properties, vegetation cover, conservation practices, and slope on the spatial distribution characteristics of soil erosion in the Abu-Ghraibat watershed in 2024. Soil samples have been collected and analysed in the laboratory, alongside high-resolution satellite photos, meteorological data, and information obtained from a digital elevation model (DEM). The findings indicate that soil erosion in the Abu-Ghraibat watershed in 2024 was minimal, with a progressively increasing severity from north to south. In the studied area, grassland accounts for over 50% of soil erosion, with regions exhibiting vegetation coverage of > 30% being the primary contributors to this erosion, all of which are influenced by slope. Moreover, the enhancement of vegetation in the lower strata of the basin and grasslands, especially on slopes ranging from 10° to 45°, along with the conversion of sloping woodlands and grasslands into terraces, has proven to be an effective strategy for mitigating soil erosion in the Abu-Ghraibat watershed. The present study has demonstrated that the RUSLEGIS integrated model may serve as an effective instrument for quantitatively and spatially mapping soil erosion at the watershed level on the Abu-Ghraibat, while considering the provision of landscape services. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Geohazards soil degradation GIS RUSLE Abu Ghraibat Watershed Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1. Introduction Soil is a natural resource, and anthropogenic and environmental factors have led to its degradation and reduced productivity (Mallick et al., 2025 ). Soil degradation is a critical environmental issue primarily linked to socio-economic aspects. All scientific evidence suggests that soil degradation is predominantly caused by human mismanagement of land, while the impact of natural processes (such as climate, geology, and environmental factors) on soil productivity degradation is minimal compared to the effects of human activities (Mathewos et al., 2024 ). The loss of soil's ability to provide essential landscape services, including habitats, fertile agricultural soils, and clean water, is one of the most significant consequences of soil degradation. The total area of land affected by soil degradation due to human activities is estimated at 2 billion hectares. Consequently, the land areas impacted by soil degradation from erosion are estimated at 1,100 million hectares due to water erosion and 550 million hectares due to wind erosion (Getahun et al., 2024 ). Soil erosion in Iraq has a profound impact on the agricultural sector, siltation in reservoirs, soil degradation, and other aspects of the country. Additionally, it is essential to acknowledge incorrect government policies that neglect necessary intervention measures to conserve water and soil, alongside issues such as population growth, deforestation, and land cover loss (Djoukbala et al., 2024 ). The International Union for Conservation of Nature defines soil degradation as "the deterioration of the natural potential of any soil form that affects the integrity of ecosystems, including a reduction in their sustainable ecological productivity" (Biswas & Pani, 2015 ). Concerns about soil erosion have recently emerged in the eastern regions of the Misan Governorate, where roughly 57% of the area is experiencing moderate to severe soil erosion. This situation is alarming, as it could worsen soil erosion, negatively impacting food security and agricultural productivity. As a type of land degradation, understanding the current rate of soil erosion is crucial, especially in areas where mining and agriculture are prevalent. The present study aims to determine the extent, distribution, and type of soil erosion, as well as the primary factors contributing to it. This research can help land use managers make more informed decisions about land management. It may also promote further investigation into developing practical solutions to mitigate soil erosion in this region. The Chinese Soil Loss Equation (CSLE), the Universal Soil Loss Equation (USLE), the Revised Universal Soil Loss Equation (RUSLE), the Soil and Water Assessment Tool (SWAT), and other models are now considered well-established empirical tools for assessing soil erosion. (Ahmad Bhat et al., 2017 ; Kadam et al., 2018 ). Several recent studies have examined the issue of soil erosion in various locations worldwide.(Yuan et al., 2024 ) analyzed soil erosion dynamics by combining terrain characteristics with socioeconomic factors that affect the Loess Plateau.(Dash & Maity, 2023 ) considered the impact of climate change on soil erosion, employing rainfall criteria and changes in land use and land cover (LULC).(Saha et al., 2022 ) evaluated the annual rates and spatial distribution of soil erosion in the Jamuna Basin using the Revised Universal Soil Loss Equation (RUSLE) model in Bangladesh. The RUSLE model is widely recognized for soil erosion assessment and is an excellent tool for monitoring erosion suitability, yielding highly reliable results. Furthermore, the RUSLE model was adapted for the Abu Ghraibat catchment in southeastern Iraq, resulting in a model that facilitates quantitative assessment of rainfall, vegetation, and soil erosion with remarkable accuracy. 2. Materials and Methods 2.1. Study Site : The Abu Ghraibat watershed is situated in the eastern regions of the Misan Governorate in southern Iraq, within the Al-Jazeera Eastern Region (Maaroof, 2025 ). Consequently, the study area lies within the Mesopotamian plain, which is characterised by its rich sediments resulting from the floods of the Tigris and Euphrates rivers (Maaroof et al., 2023 ). Geographically, the Abu Ghraibat watershed is bordered by Iranian territory to the north and northeast, to the east by the Al-Shakak watershed, to the south by the Al-Sanaf Marsh, and to the west by the Al-Teeb River (Maaroof & Kareem, 2022 ). Astronomically, the Abu Ghraibat watershed is positioned between longitudes (47°09′18.773″E − 47°27′57.589″E) east and latitudes (32°05′50.956″N − 32°29′39.368″N) north (Fig. 1 ). Its area covers 554.751 km² and its perimeter is 142.480 km. The total length of the Abu Ghraibat watershed is 45.295 km, extending from its sources in the northern regions near the Iraqi-Iranian border to its outlet in the Al-Sanaf Marsh in the south. The watershed slopes from the northeast towards the south and southwest, and is currently arid (B. F. Maaroof & Kareem, 2023 ). Water flows into it following rainfall in irregular torrents, shaping its hydro-geomorphological characteristics (Maaroof, 2022 b). The highest point in the watershed is 220 m (a.s.l.), while the lowest point is 10 m (a.s.l.) (Figs. 2 and 3 ). The Abu Ghraibat watershed is divided into three sub-watersheds: 1. Abu Ghraibat sub-watershed 1 (ASW1) : This sub-watershed is situated in the eastern part of the study area. It covers an area of 399.893 km², has a perimeter of 117.093 km, and is 38.671 km long (Table 1 and Fig. 4 Table 1 The area, perimeter, and length of Abu Ghraibat sub-watersheds. Sub-watersheds Area (km 2 ) Perimeter (km) Length (km) Abu Ghraibat sub-watershed (1) (ASW1) 399.893 117.093 38.671 Abu Ghraibat sub-watershed (2) (ASW2) 149.128 83.831 31.054 Abu Ghraibat sub-watershed (3) (ASW3) 4.433 13.834 5.023 2. Abu Ghraibat sub-watershed 2 (ASW2) : This sub-watershed is located in the western part of the study area. It spans an area of 149.128 km², has a perimeter of 83.831 km, and is 31.054 km long (Table 1 and Fig. 4 ). 3. Abu Ghraibat sub-watershed 3 (ASW3) : This sub-watershed is located in the southern part of the study area. It encompasses an area of 4.433 km², has a perimeter of 13.834 km, and is 5.023 km long (Table 1 and Fig. 4 ). Geologically, the Abu Ghraibat watershed is situated in the southeastern region of the Mesopotamian Plain sedimentary basin (Maaroof, 2025 ). This basin receives significant sediment yearly from the Tigris and Euphrates rivers (Maaroof et al., 2021 ; Maaroof & Kareem, 2020 ). Various geological formations are distributed throughout the study area. To the north, northeast, and west, one can find the Bai Hassan formations, where rock deposits and conglomerates formed by erosion are present. In the northern section of the study area, this formation appears exposed and somewhat thick (Fig. 5 a). The stratigraphic column of this formation indicates that layers of clay, typically flat and reaching a thickness of 580 m, constitute the upper section. At the same time, the lower part comprises layers of lime. Sheet run-off deposits are scattered across the center of the study area (Ali Al-Zubaydi & Al-Turaihi, 2024 ; Sissakian et al., 2020 ). Although these deposits originated early in the Pleistocene epoch, their surface layers date back to the Holocene era. These deposits arise from alluvial fans laid down in the northern and northeastern areas of the Al-Jazira Eastern Region, situated north of the study area (Sabah Y. Yacoub, 2010 ). Aeolian deposits cover an area of 65.71 km² and a longitudinal strip extending up to 13.267 km adjacent to the Bai Hassan Formation. These deposits consist of silt and fine sand, typically reaching heights of 5 meters. Wind action has transported fine sediments from the nearby floodplain areas, forming these deposits (Yacoub, 2011 ). The study area is geomorphologically divided into six topographic regions, each formed during a distinct geological period. One of these regions is the Homoclinal Structure region, formed by regressive erosion processes in areas of rock weakness, such as the edges of faults and the steeply inclined layers in anticlines (Fig. 5 b). The hill region extends to the south of the area above and is relatively higher than the surrounding lands. It is characterized by a group of semi-pyramidal or dome-shaped hills, with an average height not exceeding 110 m (a.s.l.) and a moderate slope that helps retain some of the local soils where plants grow during the wet seasons (Yacoub, 2011 ). Rocky ridges and cliffs with steep sides characterise the Anticlinal and Synclinal Ridges region. Tectonic activation processes directly influence this area, resulting in fault ridges that stretch longitudinally throughout the region. To the south of these regions lies the Flood Plains region, consisting of flat lands beside river channels, made up of sandy, silty, and clayey debris deposited by rivers deposit during flood events (Mustafa, 2011 ). Climatically, the study area is characterized by significant seasonal temperature variations (Al-Hasani et al., 2024 ; Maaroof, 2024 ). Summer temperatures reach remarkable highs of 32.3, 36.5, and 38.3°C in June, July, and August, respectively. In winter (December, January, and February), temperatures drop sharply to 13.9, 12.2, and 14.8°C, respectively. Rainfall occurs from November to May (Maaroof et al., 2023 ), averaging 9.1 mm. November experiences the highest rainfall, at 36.6 mm. The annual average wind speed is three m/s; in summer, it can reach 5.1, 5.2, and 4.4 m/s, respectively (Al-Hasani, et al., 2024). Figure 6 illustrates that the average wind speed falls to 2.6, 2.7, and 3.3 m/s during winter. 2.2. Data Sources and Processing : The soil erosion of the Abu Ghraibat watershed was assessed using geoinformatics applications and the Revised Universal Soil Loss Equation (RUSLE). The geospatial data was sourced from the US Department of Defence's Digital Elevation Model (DEM) type (SRTM). Along with topographic maps at a scale of 1:100,000 provided by the General Authority for Iraqi Survey, and geological and hydrological maps at a scale of 1:250,000 from the Iraqi Geological Survey, Landsat ETM + 8 satellite imagery for the year 2023, with a spatial resolution of 15 m, was employed. This data was imported into a Geographic Information System (GIS) using ArcGIS V.10.8 and integrated into a topological model as raster layers (B. Maaroof et al., 2025 ). In addition to delineating the river drainage network at all orders, the primary and sub-watersheds were identified and produced as vector layers (Maaroof, 2022 a). Various software tools for geographic analysis, including ArcGIS Earth 1.16, Surfer 10, Global Mapper 11, and Google Earth Pro 7.1, were also employed. A significant milestone was achieved with the availability of an integrated geospatial database for the study area. This database enabled us to explore the geomorphological aspects of soil erosion within the Abu Ghraibat watershed. We meticulously identified and evaluated environmental factors such as rainfall, terrain, vegetation cover, and soil, and assessed their impact on soil erosion in the study area, all within the framework of geospatial analysis (Fig. 7 ). The dimensions of soil erosion within the basin, driven by natural processes and forces, were quantified with precision, and the geographic scope of the phenomenon under investigation was clarified through the use of cartographic methods. The RUSLE model, the most widely used model for predicting soil loss globally, stands out for its simplicity and compatibility with Geographic Information Systems (GIS). Despite being an empirical model, it not only forecasts erosion rates in ungauged watersheds by utilizing information about the watershed's characteristics and the hydroclimatic conditions in the area, but also illustrates the spatial heterogeneity of soil erosion. This practical and cost-effective approach is beneficial in larger areas. By introducing improved methods for calculating soil erosion factors, RUSLE has been extensively utilized to predict average annual soil loss in the Abu Ghraibat watershed. In raster data format, this equation relies on five input factors: soil erodibility, slope length and steepness, cover management, rainfall erosivity, and support practice (Getu et al., 2022 ). Other input variables influence these variables and change over time and space (Prasannakumar et al., 2012 ). Therefore, the RUSLE was employed to estimate soil erosion within each pixel. The expression for the RUSLE method is: A = R * K * LS * C * P …………. (1) Where A represents the average erosion (ton h-1 y-1), R denotes the rainfall erosivity (MJ mm ha-1 y-1), and K signifies the soil erodibility (ton ha-1 MJ mm). Additionally, LS, C, and P refer to slope length, land cover management, and conservation measures, respectively, and these are dimensionless (Prasannakumar et al., 2012 ). 2.2.1. Rainfall erosivity factor (R) : The impact of rainfall on topsoil is assessed by rainfall erosivity (R). When raindrops collide with topsoil, they convert kinetic energy into potential energy, creating conditions that encourage soil erosion. As a result, an increase in rainfall intensity corresponds with a rise in rainfall erosivity. This understanding of the relationship between rainfall and soil erosion is crucial in our study (Getu et al., 2022 ). The formula for calculating rainfall erosivity is: R = 79 + 0.363 * P a …………… (2) Where: P a is the average annual rainfall. 2.2.2. Soil erodibility factor (K) : The K-factor is a quantitative value derived from experiments that indicate the soil's sensitivity to erosion. It fully expresses the capacity of soil erosion caused by a lack of resistance to runoff and rainfall. In this study, the K-factor was determined using the method proposed by RUSLE, which involves calculating it with the average geometric diameter of soil particles (Chuenchum et al., 2020 ). The precise formula for the calculation is as follows: K = [2.1 * 10 − 4 (12 – OM) M 1.14 + 3.25 (S – 2) + 2.5 (P – 3)] / 100 …………… (3) Where: OM = Percentage soil organic matter content, M = (% Silt + % Very Fine Sand) * (100 - % Clay), S = Soil structural code, P = Soil profile permeability rating was obtained using a combination of field observation, and default values were considered for S and P. 2.2.3. Slope length factor (LS) : The LS factor summarises how topography influences soil erosion and significantly impacts soil loss. The gradient of the local slope affects flow velocity and the erosion rate. The slope length indicates the distance between the start and end of the inter-rill processes (Schmidt et al., 2019 ). The following formula was employed to calculate the LS factor: LS = (X / 22.13) m (0.065 + 0.045 S + 0.0065 S 2 ) …………… (4) Where: S = Slope (%) calculated directly from the DEM, X = Value obtained by multiplying the flow accumulation by the cell value, M = Value that varied from 0.2 to 0.5 depending on the slope. 0.5 for slopes exceeding 5%, 0.4 for slopes 3–5%, 0.3 for 1–3%, and 0.2 for slopes < 1.0%. 2.2.4. Cover management factor (C) : The cover management factor C represents the influence of crops and other management practices on erosion rates. Vegetation cover is the second most vital factor in mitigating the risk of soil erosion following terrain. Its value ranges from 0 (water bodies) to 1 (barren land), reflecting the absence of vegetation, root biomass, or other surface covers that prevent soil erosion. Ground cover absorbs rainfall, enhancing infiltration and diminishing rainfall energy (Shekar & Mathew, 2024 ). The Normalised Difference Vegetation Index (NDVI) was used to determine the cover management factor (C). The following formula was applied to calculate the LS factor: C = 0.431 − 0.805 ∗ NDVI …………… (5) Where: NDVI = Near-infrared (NIR) – R/ Near-infrared (NIR) + red (R), NIR = Near-infrared band, and R is red band. 2.2.5. Conservation practices factor (P) : The factor of support practices P represents the outcomes of implementing water and soil conservation measures, which reduce the quantity and rate of runoff and the amount of soil loss. A value of 0 signifies no soil erosion in the region, whereas a value of 1 indicates that no conservation measures have been applied (Joshi et al., 2023 ). 3. Results 3.1. Soil characteristics : Table 2 illustrates nine soil characteristics that directly influence soil erodibility and soil erosivity, determining the output of the RUSLE model results. By taking a look at the table, we can notice that the studied soil locations were poor in organic matter, which ranged between 0.1 and 1.5 g.kg, this values actually was lower in the north section in subwatershed (1) (ASW1) and increases gradually towards south section subwatershed (3) (ASW3) which was the higher content in organic matter, the results also showed that the soil structural code (SSC) ranged between (2–3) were the higher values concentrated in the higher elevation sites in SW1 while the lower values were recorded in the low elevation sites in SW3, however, wonderful sand ranged between 8–14 gm/kg were the lower quantities were concentrated in the upper positions while the higher values recorded in the lower positions of soil samples, by taking a look on the particle size distribution we can noticed that the percentage soil particles were ranges between (29–55), (32–40), and (5–39) for sand, silt and clay respectively, which produced three groups of soil textures namely (sandy loam, loam and clay loam) as shown in table (2). Table 2 Soil properties were used to estimate RUSLE model parameters. No. Sub watershed O.M SSC SPC VFS Sand Silt Clay Texture Slope 1 ASW1 0.2 3 4 8 55 40 5 SANDY LOAM 70 2 ASW1 0.1 3 4 8 53 45 2 SANDY LOAM 60 3 ASW1 0.4 3 4 9 52 44 4 SANDY LOAM 50 4 ASW1 0.3 3 4 10 55 43 2 SANDY LOAM 50 5 ASW1 0.4 3 4 11 55 42 3 SANDY LOAM 50 6 ASW1 0.5 3 4 12 53 42 5 SANDY LOAM 40 7 ASW1 0.4 3 4 13 54 42 4 SANDY LOAM 40 8 ASW1 0.4 3 4 13 53 45 2 SANDY LOAM 40 9 ASW1 0.4 3 4 14 51 42 7 LOAM 40 10 ASW1 0.4 3 4 14 51 43 6 SANDY LOAM 40 11 ASW1 0.4 3 4 13 50 43 7 LOAM 40 12 ASW1 0.5 3 4 14 51 42 7 LOAM 38 13 ASW2 0.5 2 4 14 52 40 8 LOAM 38 14 ASW2 0.6 3 4 15 50 41 9 LOAM 38 15 ASW2 0.7 3 4 15 49 41 10 LOAM 30 16 ASW1 0.7 2 4 16 49 43 8 LOAM 30 17 ASW1 0.7 3 4 16 48 41 11 LOAM 30 18 ASW1 0.6 3 4 16 47 40 13 LOAM 30 19 ASW2 0.8 3 5 16 45 40 15 LOAM 25 20 ASW1 0.8 3 5 16 43 43 14 LOAM 25 21 ASW1 0.9 3 5 15 45 43 12 LOAM 20 22 ASW2 0.9 3 5 15 46 42 12 LOAM 20 23 ASW1 0.8 2 5 15 44 41 15 LOAM 20 24 ASW1 0.8 3 5 15 43 43 14 LOAM 20 25 ASW2 0.9 3 5 16 40 44 16 LOAM 20 26 ASW1 0.9 3 5 16 39 43 18 LOAM 20 27 ASW1 1.0 3 5 16 38 44 18 LOAM 20 28 ASW2 1.2 3 6 15 32 35 33 CLAY LOAM 14 29 ASW1 1.4 3 6 15 31 34 35 CLAY LOAM 12 30 ASW3 1.5 3 6 14 29 32 39 CLAY LOAM 10 3.2. Factors of RUSLE: 3.2.1. Rainfall-runoff erosivity (R-factor) : The computed rainfall-runoff erosivity (R-factor) values vary from 323.4935 at SW1 station to 10.70138 MJ mm ha⁻¹ hr⁻¹ yr⁻¹ at the lower site in SW3 station (Table 2 ). The maximum rainfall erosivity value is recorded in the northern region of the Abu Ghraibat watershed, attributed to the elevated terrain, which results in larger drop sizes, comparatively greater precipitation, and steep gradients. The rainfall erosivity progressively diminishes from the watershed's northern to the southern region. The south region of the watershed necessitates soil protection owing to elevated rainfall levels in contrast to the northern region. The soils in the research area can be classified into three textural groups according to the relative proportions of sand, silt, and clay (Table 2 ). 3.2.2. Soil erodibility (K-factor) : The determined soil erodibility (K-factor) values varied from 0.058767 to 0.10858 MJ mm h⁻¹ ha⁻¹ yr⁻¹. 3.2.3. The topographic factor (L.S) : The topographic factor (L.S) denotes the impact of slope length and steepness on the erosion process. The LS factor was computed using flow accumulation and slope percentage as inputs. The results indicate that the topographic factor value escalates from 0.108 to 0.127 as flow accumulation and slope rise. 3.2.4. Crop Management Factor (C) : The crop management factor (C) ranged between 0.074 and 0.326, with lower values concentrated in the lower regions of subwatershed 3. In comparison, the higher values were recorded in the upper areas of the subwatershed. 5. Computed spatial and temporal average soil loss per unit area (A) : Table 1 shows that the computed spatial and temporal average soil loss per unit area (A) values ranged from 11 to 823 t/yr. The higher quantity of soil loss was recorded in the upper regions of the Abu-Ghraibat watershed. In contrast, the lower quantities of soil loss were recorded in the lower areas of the watershed. Table 3 RUSLE model estimated parameters. No. Sub watershed R K L.S C P A 1 ASW1 323.49 0.06 0.13 0.326 1 823 2 ASW1 238.41 0.06 0.14 0.326 1 653 3 ASW1 166.32 0.06 0.14 0.323 1 451 4 ASW1 166.32 0.06 0.14 0.309 1 432 5 ASW1 166.32 0.06 0.14 0.309 1 432 6 ASW1 107.21 0.06 0.14 0.295 1 266 7 ASW1 107.21 0.06 0.14 0.271 1 244 8 ASW1 107.21 0.06 0.15 0.260 1 251 9 ASW1 107.21 0.06 0.14 0.257 1 231 10 ASW1 107.21 0.06 0.15 0.253 1 244 11 ASW1 107.21 0.06 0.14 0.236 1 213 12 ASW1 96.95 0.06 0.14 0.226 1 184 13 ASW2 96.95 0.06 0.14 0.222 1 181 14 ASW2 96.95 0.06 0.14 0.198 1 161 15 ASW2 61.08 0.06 0.14 0.187 1 96 16 ASW1 61.08 0.06 0.15 0.184 1 101 17 ASW1 61.08 0.06 0.14 0.180 1 92 18 ASW1 61.08 0.06 0.14 0.167 1 86 19 ASW2 42.88 0.08 0.14 0.156 1 75 20 ASW1 42.88 0.08 0.15 0.149 1 77 21 ASW1 27.92 0.08 0.14 0.146 1 46 22 ASW2 27.92 0.08 0.14 0.121 1 38 23 ASW1 27.92 0.08 0.14 0.114 1 36 24 ASW1 27.92 0.08 0.14 0.111 1 35 25 ASW2 27.92 0.08 0.15 0.097 1 32 26 ASW1 27.92 0.08 0.14 0.087 1 27 27 ASW1 27.92 0.08 0.15 0.763 1 256 28 ASW2 14.23 0.11 0.12 0.076 1 14 29 ASW1 10.70 0.11 0.12 0.076 1 11 30 ASW3 10.49 0.11 0.11 0.074 1 11 4. Discussion 4. 1. Soil properties used in estimation of RUSLE model parameters: 4.1.1. Organic Matter (OM): Variation in the organic matter content is apparent, as shown in Figure 1, where its deficiency can be recognised. The soil in the watershed is impoverished in terms of organic matter due to the lower vegetation cover, which has already been affected by water scarcity in the area. Organic matter is a crucial factor that enhances soil structure by forming peds, which connect and prevent soil particles from dispersing and becoming easily eroded. Many researchers have explained how soil organic matter can enhance soil structure and reduce soil erosion hazards (Zhang et al., 2024). The organic matter content is classified into seven categories, ranging from 0.029 to 0.313 for the first category and from 1.470 to 1.795 for the seventh category; the variation in organic matter content is reflected in the RUSLE parameters and erosion intensity. 4.1.2. Soil Structure Code (SSC): The soil structure code has a strong relationship with organic matter; the higher the number, the better the soil structure. Therefore, the upper sample locations are characterised by similarities in soil structure based on the porosity of organic matter (Wiltshire et al., 2024). The low clay content effectively influences soil structure, as shown in Table 2. The values of the soil structure code ranged from 2 to 3, with no discernible harmonic trend. Figure 2 illustrates the distribution of the soil structure code. 4.1.3. Soil Permeability Code (SPC): The Soil Permeability Code (SPC) indicates the ability of water to penetrate soil layers. This process prevents water runoff, which is considered the most significant factor promoting erosion (Naipal et al., 2015). The SPC values ranged from 4 to 6, with the higher values concentrated in the lower zones. In contrast, as expected based on the particle size distribution, lower values are found in the upper zones, where fine particles are predominant, and in the lower zones, where coarse particles are more prevalent, as shown in Figure 3. 4.1.4. Very fine sand (VFS): The quantity of very fine sand varies according to the location of the samples, with lower values concentrated in the upper locations and higher values found in the lower locations. This is expected due to the movement of fine particles from higher to lower areas, caused by gravity and facilitated by wind or water (Terefe et al., 2024). Figure 4 illustrates the distribution of very fine sand, categorising this material into seven distinct groups. 4.1.5. Particle size distribution: The percentages of soil fractions varied significantly according to the location of the samples, with the coarse fractions concentrated in the upper locations and the fine particles accumulating in the lower locations (Olika et al., 2023). The trend coincided harmoniously with the slope gradient, which determined the sedimentation aspect of soil fractions (Sand, Silt, and Clay) (Figures 11 and 12). 4.2. Factors of RUSLE: 4.2.1. Rainfall-runoff erosivity (R-factor): Numerous studies demonstrated that the soil erosion rate in the catchment is highly responsive to rainfall (Biswas & Pani, 2015; Brychta et al., 2022). Daily rainfall serves as a superior indicator of fluctuations in soil erosion rates, effectively characterising the seasonal distribution of sediment output (Prasannakumar et al., 2012). The benefits of utilising yearly rainfall encompass its accessibility, simplicity of calculation, and enhanced regional uniformity of the exponent (Dai et al., 2024). Consequently, in this analysis, the average yearly rainfall (calculated by dividing total rain by the number of rainy days) was utilised for the R factor computation (Eq. 2). The computed rainfall-runoff erosivity (R-factor) values vary from 323.4935 at SW1 to 10.70138 MJ mm ha⁻¹ hr⁻¹ yr⁻¹ at the lower site in SW3 (Table 2). The maximum rainfall erosivity value is recorded in the northern region of the Abu Ghraibat watershed, attributed to elevated terrain resulting in larger drop sizes, comparatively greater precipitation, and steep gradients. The rainfall erosivity progressively diminishes from the watershed's northern to the southern region. The southern region of the watershed necessitates soil protection owing to elevated rainfall levels, in contrast to the northern region 4.2.2. Soil erodibility (K-factor): The Soil Erodibility factor (K) denotes the vulnerability of soil or surface material to erosion, the transport capacity of sediment, and the volume and velocity of runoff resulting from specific rainfall input, as assessed under standardised conditions (Sinshaw et al., 2021). The standard condition is a unit plot measuring 22.6 meters long, featuring a 9% gradient, kept in continuous fallow and cultivated up-and-down along the hill slope (Kim, 2006). The soil erodibility factor K was assessed based on soil textures. The K factor indicates the soil or surface material's ability to resist erosion, the ease of sediment movement, and the volume and rate of runoff resulting from a specific rainfall input, as determined under typical conditions (Talebi & Karimi, 2024). The K factor is influenced by particle size distribution, organic matter composition, structure, and permeability (George et al., 2021). K values indicate the soil erosion rate per rainfall unit, represented by the Runoff Erosivity (R) index. The soil erodibility factors (K) presented in Eq. (3) are most accurately derived from direct measurements conducted on natural runoff plots. A nomograph is typically used to determine the K factor for soil, depending on its texture, percentage of silt plus extremely fine sand, percentage of sand, percentage of organic matter, soil structure, and permeability (Saha et al., 2022). 4.2.3. The topographic factor (L.S): The LS factor indicates a specific location's susceptibility to topographic erosion (Joshi et al., 2023). This study confirmed that the LS factor is a primary and sensitive determinant of soil erosion, with the Abu-Ghraibat Watershed, located in the north, identified as the principal physiographic unit. The steepness of a slope quantifies its influence on the rate of soil erosion. The gradient of the terrain has a significantly greater influence on soil erosion than the slope length. Table 2 indicates that roughly 30% of the territory has a very low slope, whereas moderately steep and very steep slopes characterise 60%. The northern, eastern, and northwestern regions of Abu-Ghraibat exhibit minimal vulnerability to soil erosion, characterised by LS factor values below 0.16 (Map 8). The topographic factor (L.S) denotes the impact of slope length and steepness on the erosion process. The LS factor was computed using flow accumulation and slope percentage as inputs. The results indicate that the topographic factor value escalates from 0.108 to 0.127 as flow accumulation and slope rise. 4.2.4. Crop Management Factor (C): The C factor denotes the land's condition regarding vegetation density. Elevated C factor values indicate a higher likelihood of soil erosion, as they correspond to areas with minimal vegetation cover. The spatial distribution of the C factor in the study area ranged between 0.074 and 0.326. The comparative impact of management decisions can be directly associated with variations in the C factor, which ranged from approximately 0 for well-vegetated land to 1.000 for desolate or bare areas. Approximately 60% of the land exhibits diminished green cover, rendering it more susceptible to soil erosion. Conversely, 40% of the entire region (554.751 km²) is moderately to highly susceptible to soil erosion. Crop management factor (C) ranged between 0.074 and 0.326, with the lower values concentrated in the lower regions of the subwatershed3 and the higher values recorded in the upper areas of the subwatershed1. 4.2.5. Conservation practices factor (P): The P factor is the ratio of soil erosion linked to certain support practices compared to the corresponding loss due to upslope management (Prasannakumar et al., 2012)The P factor denotes the influence of particular soil management practices, including contour cultivation, strip cropping, terrace cultivation, and subsurface drainage. The research region's land use and cover were uniform across all areas of the Abu-Ghraibat watershed, as this was subjected to the same practices and land use. 4.2.6. Computed spatial average soil loss and temporal average soil loss per unit of area (A): Table 1 showed that the Computed spatial average soil loss and temporal average soil loss per unit of area (A) values ranged between 11 and 823 t/yr, with the higher quantity of soil loss recorded in the upper regions of the Abu-Ghraibat watershed and the lower amount recorded in the lower areas. The soil erosion modulus increases markedly with the slope, initially rising before subsequently declining, as previously reported by (Getahun et al., 2024; Naipal et al., 2015). Nevertheless, after the slope approached the threshold, soil erosion diminished. Specifically, we observed that erosion diminished when the slope reached 35 °, aligning with the findings of Liu et al. (2019b). As the slope increases, the intensity of soil erosion progressively escalates, with the average erosion intensity in areas of mild slope (≤ 25◦) being lower than that of the entire gully, underscoring the significant impact of topography on soil erosion. The slope gradients of 15° to 45° contribute to 85% of erosion, which is the primary cause of soil erosion in the southern sites of the Abu-Ghraibat watershed. 5. Conclusions This study calculated soil erosion in the Abu-Ghraibat watershed using the Revised Universal Soil Loss Equation (RUSLE), Geographic Information Systems (GIS), and Remote Sensing models, revealing that it resulted from three interconnected processes. In climate change, identifying vegetation coverage and slope thresholds for various land-use/land-cover classes is crucial for effectively planning the spatial distribution of vegetation restoration and soil properties (organic matter, soil structure, soil permeability, and particle size distribution). Nonetheless, these thresholds may be influenced by geographical (local, watershed, and regional) and temporal scales, which impact the efficacy of soil erosion control —a vital landscape function. While it is accurate that soil erosion can affect land use, it is also established that no area experiences erosion if it possesses sufficient vegetation cover. Identifying and distributing susceptible lands, categorized by varying degrees and intensities of degradation, should guide managers and policymakers in enhancing environmental, social, and economic conditions to substantially mitigate the risk of land degradation. Given the complexities of soil degradation, achieving the Land Degradation Neutrality goal by 2040 necessitates collaboration among scientists, governments, and managers. They must identify the primary factors contributing to soil degradation and erosion to promote effective governance for soil sustainability. Consequently, effective land degradation neutrality strategies must enhance the preservation of the quality and quantity of soil that underpins landscape services, including food and materials, as well as the frequently neglected regulating and supporting services essential for provisioning these services. Declarations Acknowledgements: The authors thank the Department of Applied Geology at the University of Babylon and the Department of Civil Engineering and Built Environment at Liverpool John Moores University for their scientific support during this study. Ethics approval and consent to participate: All authors agreed to participate following scientific ethics. Consent for publication: All authors approved this copy of the paper for publication. Availability of data and material: Data can be asked by request from the corresponding author. Conflict of interests: The authors declare that there are no conflicts of interest concerning the publication of this manuscript. Funding: This research did not receive any funding. Author contributions: Bashar F. Maaroof: Project administration, conceptualization, data curation, formal analysis, investigation, methodology, supervision, validation, visualization, software, writing – original draft. Hashim H. Kareem: Supervise, visualize, methodology, resources, validate, write, review, and edit. Jaffar H. Al-Zubaydi: Supervision, data curation, formal analysis, validation, visualization, methodology, writing – review and editing. Nadhir Al-Ansari: Supervision, data curation, formal analysis, methodology, software, writing, review, and editing. Mohamed Alkhuzamy Aziz: Supervision, data curation, formal analysis, methodology, software, writing, review, and editing. Dhia Alden A. AL-Quraishy: Visualization, data curation, formal analysis, methodology, software, writing, review, and editing. Ban AL-Hasani: Formal analysis, methodology, validation. Mawada Abdellatif: Formal analysis, methodology, validation. Iacopo Carnacina: Formal analysis, methodology, validation. Rayan G. Thannoun: Data curation, formal analysis, methodology, validation. Manal Sh. Al-Kubaisi: Formal analysis, methodology, validation. Sama Al-Maarofi: Formal analysis, methodology, validation. 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Al-Maarofi","email":"","orcid":"","institution":"Lakehead University","correspondingAuthor":false,"prefix":"","firstName":"Sama","middleName":"S.","lastName":"Al-Maarofi","suffix":""}],"badges":[],"createdAt":"2025-07-16 08:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7137911/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7137911/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-33403-x","type":"published","date":"2025-12-24T15:58:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87602869,"identity":"b128e7d7-2b55-46ce-8a19-6265c80aefa0","added_by":"auto","created_at":"2025-07-25 17:11:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1224487,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/7232ae41a132153a953fefb4.png"},{"id":87602037,"identity":"501ff99a-7371-47fe-85d1-e30e1b35f386","added_by":"auto","created_at":"2025-07-25 17:03:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1002216,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/2c147930a3d58f84c746877f.png"},{"id":87602038,"identity":"9dbff5a7-b018-470c-964b-1c97bacc4403","added_by":"auto","created_at":"2025-07-25 17:03:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":384428,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/222424cadad5d1d908d652e7.png"},{"id":87603031,"identity":"fc00dc0b-734e-4e9d-842b-c200be4c98b8","added_by":"auto","created_at":"2025-07-25 17:19:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":932878,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/6d13799472c581c816908f68.png"},{"id":87602868,"identity":"d97bc900-aab0-4378-af0e-c03131dc6d93","added_by":"auto","created_at":"2025-07-25 17:11:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":952939,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/a95e340cb1902041158101dd.png"},{"id":87602040,"identity":"d35534d9-b525-46e7-956d-385a81ab72ff","added_by":"auto","created_at":"2025-07-25 17:03:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":87635,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/2f4666b8d779ca6f6c4e3046.png"},{"id":87602045,"identity":"105202ad-1816-4fa6-b16f-56014f7ed743","added_by":"auto","created_at":"2025-07-25 17:03:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":105388,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/de11904a2ec55c82cccb49fc.png"},{"id":87602877,"identity":"eebe737b-7b80-4017-8041-499254335096","added_by":"auto","created_at":"2025-07-25 17:11:28","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":833384,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/a62abb1588527fa82cd072c1.png"},{"id":87602053,"identity":"6b473c09-b8b0-4f42-8cb0-970ff4321884","added_by":"auto","created_at":"2025-07-25 17:03:28","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":877590,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/e8d253e6c9bed3a740dc5fd5.png"},{"id":87602046,"identity":"439ea1e0-bd51-4001-9abc-61108b88999e","added_by":"auto","created_at":"2025-07-25 17:03:28","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":725251,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/a51caeb090c99b3ec2a1fd02.png"},{"id":87603663,"identity":"c2a52a45-02f7-4689-ae71-5d0eae7de5ad","added_by":"auto","created_at":"2025-07-25 17:27:28","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":735081,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/107566d8609daa83605e82c9.png"},{"id":87602878,"identity":"6e19a7a2-9d13-4021-aa67-e2f4974a4b04","added_by":"auto","created_at":"2025-07-25 17:11:28","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":490185,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/93bd6b62422e9bac56c7a5cc.png"},{"id":87602890,"identity":"86d2d851-b913-4dc4-b71f-002d4baf3188","added_by":"auto","created_at":"2025-07-25 17:11:29","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":864926,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/2ca963b4e5d09c7c6c77fab2.png"},{"id":87602055,"identity":"4c3b61fb-6bc3-43ad-817a-cf1c702fa49c","added_by":"auto","created_at":"2025-07-25 17:03:28","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":871102,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/98b11c2c2d4db8e4a3c942bb.png"},{"id":87602058,"identity":"e40470fd-15b2-4639-bb92-b2b9cd10dfb6","added_by":"auto","created_at":"2025-07-25 17:03:28","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":877986,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/370018f694dc54033d5f360a.png"},{"id":99172427,"identity":"d251717a-c7c8-44f9-8121-e60b446ca603","added_by":"auto","created_at":"2025-12-29 16:09:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12007777,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7137911/v1/bd22bb09-5aed-4447-ac28-f53be3340160.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating Soil Erosion Utilising Geospatial Method and Revised Universal Soil Loss Equation (RUSLE) of Abu Ghraibat Watershed, Eastern Misan Governorate, Iraq","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSoil is a natural resource, and anthropogenic and environmental factors have led to its degradation and reduced productivity (Mallick et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Soil degradation is a critical environmental issue primarily linked to socio-economic aspects. All scientific evidence suggests that soil degradation is predominantly caused by human mismanagement of land, while the impact of natural processes (such as climate, geology, and environmental factors) on soil productivity degradation is minimal compared to the effects of human activities (Mathewos et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The loss of soil's ability to provide essential landscape services, including habitats, fertile agricultural soils, and clean water, is one of the most significant consequences of soil degradation. The total area of land affected by soil degradation due to human activities is estimated at 2\u0026nbsp;billion hectares. Consequently, the land areas impacted by soil degradation from erosion are estimated at 1,100\u0026nbsp;million hectares due to water erosion and 550\u0026nbsp;million hectares due to wind erosion (Getahun et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Soil erosion in Iraq has a profound impact on the agricultural sector, siltation in reservoirs, soil degradation, and other aspects of the country. Additionally, it is essential to acknowledge incorrect government policies that neglect necessary intervention measures to conserve water and soil, alongside issues such as population growth, deforestation, and land cover loss (Djoukbala et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The International Union for Conservation of Nature defines soil degradation as \"the deterioration of the natural potential of any soil form that affects the integrity of ecosystems, including a reduction in their sustainable ecological productivity\" (Biswas \u0026amp; Pani, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConcerns about soil erosion have recently emerged in the eastern regions of the Misan Governorate, where roughly 57% of the area is experiencing moderate to severe soil erosion. This situation is alarming, as it could worsen soil erosion, negatively impacting food security and agricultural productivity. As a type of land degradation, understanding the current rate of soil erosion is crucial, especially in areas where mining and agriculture are prevalent. The present study aims to determine the extent, distribution, and type of soil erosion, as well as the primary factors contributing to it. This research can help land use managers make more informed decisions about land management. It may also promote further investigation into developing practical solutions to mitigate soil erosion in this region. The Chinese Soil Loss Equation (CSLE), the Universal Soil Loss Equation (USLE), the Revised Universal Soil Loss Equation (RUSLE), the Soil and Water Assessment Tool (SWAT), and other models are now considered well-established empirical tools for assessing soil erosion. (Ahmad Bhat et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kadam et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral recent studies have examined the issue of soil erosion in various locations worldwide.(Yuan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) analyzed soil erosion dynamics by combining terrain characteristics with socioeconomic factors that affect the Loess Plateau.(Dash \u0026amp; Maity, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) considered the impact of climate change on soil erosion, employing rainfall criteria and changes in land use and land cover (LULC).(Saha et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) evaluated the annual rates and spatial distribution of soil erosion in the Jamuna Basin using the Revised Universal Soil Loss Equation (RUSLE) model in Bangladesh. The RUSLE model is widely recognized for soil erosion assessment and is an excellent tool for monitoring erosion suitability, yielding highly reliable results. Furthermore, the RUSLE model was adapted for the Abu Ghraibat catchment in southeastern Iraq, resulting in a model that facilitates quantitative assessment of rainfall, vegetation, and soil erosion with remarkable accuracy.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1.\u0026nbsp;\u003c/strong\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e\u003cstrong\u003eStudy Site\u003c/strong\u003e\u003c/span\u003e\u003cstrong\u003e:\u003c/strong\u003e The Abu Ghraibat watershed is situated in the eastern regions of the Misan Governorate in southern Iraq, within the Al-Jazeera Eastern Region (Maaroof, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Consequently, the study area lies within the Mesopotamian plain, which is characterised by its rich sediments resulting from the floods of the Tigris and Euphrates rivers (Maaroof et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Geographically, the Abu Ghraibat watershed is bordered by Iranian territory to the north and northeast, to the east by the Al-Shakak watershed, to the south by the Al-Sanaf Marsh, and to the west by the Al-Teeb River (Maaroof \u0026amp; Kareem, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Astronomically, the Abu Ghraibat watershed is positioned between longitudes (47\u0026deg;09\u0026prime;18.773\u0026Prime;E \u0026minus;\u0026thinsp;47\u0026deg;27\u0026prime;57.589\u0026Prime;E) east and latitudes (32\u0026deg;05\u0026prime;50.956\u0026Prime;N \u0026minus;\u0026thinsp;32\u0026deg;29\u0026prime;39.368\u0026Prime;N) north (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Its area covers 554.751 km\u0026sup2; and its perimeter is 142.480 km. The total length of the Abu Ghraibat watershed is 45.295 km, extending from its sources in the northern regions near the Iraqi-Iranian border to its outlet in the Al-Sanaf Marsh in the south. The watershed slopes from the northeast towards the south and southwest, and is currently arid (B. F. Maaroof \u0026amp; Kareem, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Water flows into it following rainfall in irregular torrents, shaping its hydro-geomorphological characteristics (Maaroof, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003eb). The highest point in the watershed is 220 m (a.s.l.), while the lowest point is 10 m (a.s.l.) (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe Abu Ghraibat watershed is divided into three sub-watersheds:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Abu Ghraibat sub-watershed 1 (ASW1)\u003c/strong\u003e: This sub-watershed is situated in the eastern part of the study area. It covers an area of 399.893 km\u0026sup2;, has a perimeter of 117.093 km, and is 38.671 km long (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe area, perimeter, and length of Abu Ghraibat sub-watersheds.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSub-watersheds\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePerimeter (km)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLength (km)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbu Ghraibat sub-watershed (1) (ASW1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e399.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.671\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbu Ghraibat sub-watershed (2) (ASW2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbu Ghraibat sub-watershed (3) (ASW3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Abu Ghraibat sub-watershed 2 (ASW2)\u003c/strong\u003e: This sub-watershed is located in the western part of the study area. It spans an area of 149.128 km\u0026sup2;, has a perimeter of 83.831 km, and is 31.054 km long (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3. Abu Ghraibat sub-watershed 3 (ASW3)\u003c/strong\u003e: This sub-watershed is located in the southern part of the study area. It encompasses an area of 4.433 km\u0026sup2;, has a perimeter of 13.834 km, and is 5.023 km long (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eGeologically, the Abu Ghraibat watershed is situated in the southeastern region of the Mesopotamian Plain sedimentary basin (Maaroof, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). This basin receives significant sediment yearly from the Tigris and Euphrates rivers (Maaroof et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maaroof \u0026amp; Kareem, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Various geological formations are distributed throughout the study area. To the north, northeast, and west, one can find the Bai Hassan formations, where rock deposits and conglomerates formed by erosion are present. In the northern section of the study area, this formation appears exposed and somewhat thick (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). The stratigraphic column of this formation indicates that layers of clay, typically flat and reaching a thickness of 580 m, constitute the upper section. At the same time, the lower part comprises layers of lime. Sheet run-off deposits are scattered across the center of the study area (Ali Al-Zubaydi \u0026amp; Al-Turaihi, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sissakian et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although these deposits originated early in the Pleistocene epoch, their surface layers date back to the Holocene era. These deposits arise from alluvial fans laid down in the northern and northeastern areas of the Al-Jazira Eastern Region, situated north of the study area (Sabah Y. Yacoub, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Aeolian deposits cover an area of 65.71 km\u0026sup2; and a longitudinal strip extending up to 13.267 km adjacent to the Bai Hassan Formation. These deposits consist of silt and fine sand, typically reaching heights of 5 meters. Wind action has transported fine sediments from the nearby floodplain areas, forming these deposits (Yacoub, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe study area is geomorphologically divided into six topographic regions, each formed during a distinct geological period. One of these regions is the Homoclinal Structure region, formed by regressive erosion processes in areas of rock weakness, such as the edges of faults and the steeply inclined layers in anticlines (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb). The hill region extends to the south of the area above and is relatively higher than the surrounding lands. It is characterized by a group of semi-pyramidal or dome-shaped hills, with an average height not exceeding 110 m (a.s.l.) and a moderate slope that helps retain some of the local soils where plants grow during the wet seasons (Yacoub, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). Rocky ridges and cliffs with steep sides characterise the Anticlinal and Synclinal Ridges region. Tectonic activation processes directly influence this area, resulting in fault ridges that stretch longitudinally throughout the region. To the south of these regions lies the Flood Plains region, consisting of flat lands beside river channels, made up of sandy, silty, and clayey debris deposited by rivers deposit during flood events (Mustafa, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eClimatically, the study area is characterized by significant seasonal temperature variations (Al-Hasani et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Maaroof, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Summer temperatures reach remarkable highs of 32.3, 36.5, and 38.3\u0026deg;C in June, July, and August, respectively. In winter (December, January, and February), temperatures drop sharply to 13.9, 12.2, and 14.8\u0026deg;C, respectively. Rainfall occurs from November to May (Maaroof et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), averaging 9.1 mm. November experiences the highest rainfall, at 36.6 mm. The annual average wind speed is three m/s; in summer, it can reach 5.1, 5.2, and 4.4 m/s, respectively (Al-Hasani, et al., 2024). Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates that the average wind speed falls to 2.6, 2.7, and 3.3 m/s during winter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.\u003c/strong\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eData Sources and Processing\u003c/span\u003e: The soil erosion of the Abu Ghraibat watershed was assessed using geoinformatics applications and the Revised Universal Soil Loss Equation (RUSLE). The geospatial data was sourced from the US Department of Defence\u0026apos;s Digital Elevation Model (DEM) type (SRTM). Along with topographic maps at a scale of 1:100,000 provided by the General Authority for Iraqi Survey, and geological and hydrological maps at a scale of 1:250,000 from the Iraqi Geological Survey, Landsat ETM\u0026thinsp;+\u0026thinsp;8 satellite imagery for the year 2023, with a spatial resolution of 15 m, was employed. This data was imported into a Geographic Information System (GIS) using ArcGIS V.10.8 and integrated into a topological model as raster layers (B. Maaroof et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition to delineating the river drainage network at all orders, the primary and sub-watersheds were identified and produced as vector layers (Maaroof, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003ea). Various software tools for geographic analysis, including ArcGIS Earth 1.16, Surfer 10, Global Mapper 11, and Google Earth Pro 7.1, were also employed.\u003c/p\u003e\n\u003cp\u003eA significant milestone was achieved with the availability of an integrated geospatial database for the study area. This database enabled us to explore the geomorphological aspects of soil erosion within the Abu Ghraibat watershed. We meticulously identified and evaluated environmental factors such as rainfall, terrain, vegetation cover, and soil, and assessed their impact on soil erosion in the study area, all within the framework of geospatial analysis (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The dimensions of soil erosion within the basin, driven by natural processes and forces, were quantified with precision, and the geographic scope of the phenomenon under investigation was clarified through the use of cartographic methods.\u003c/p\u003e\n\u003cp\u003eThe RUSLE model, the most widely used model for predicting soil loss globally, stands out for its simplicity and compatibility with Geographic Information Systems (GIS). Despite being an empirical model, it not only forecasts erosion rates in ungauged watersheds by utilizing information about the watershed\u0026apos;s characteristics and the hydroclimatic conditions in the area, but also illustrates the spatial heterogeneity of soil erosion. This practical and cost-effective approach is beneficial in larger areas. By introducing improved methods for calculating soil erosion factors, RUSLE has been extensively utilized to predict average annual soil loss in the Abu Ghraibat watershed. In raster data format, this equation relies on five input factors: soil erodibility, slope length and steepness, cover management, rainfall erosivity, and support practice (Getu et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Other input variables influence these variables and change over time and space (Prasannakumar et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, the RUSLE was employed to estimate soil erosion within each pixel. The expression for the RUSLE method is:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA\u0026thinsp;=\u0026thinsp;R * K * LS * C * P\u003c/em\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (1)\u003c/p\u003e\n\u003cp\u003eWhere A represents the average erosion (ton h-1 y-1), R denotes the rainfall erosivity (MJ mm ha-1 y-1), and K signifies the soil erodibility (ton ha-1 MJ mm). Additionally, LS, C, and P refer to slope length, land cover management, and conservation measures, respectively, and these are dimensionless (Prasannakumar et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1. Rainfall erosivity factor (R)\u003c/strong\u003e: The impact of rainfall on topsoil is assessed by rainfall erosivity (R). When raindrops collide with topsoil, they convert kinetic energy into potential energy, creating conditions that encourage soil erosion. As a result, an increase in rainfall intensity corresponds with a rise in rainfall erosivity. This understanding of the relationship between rainfall and soil erosion is crucial in our study (Getu et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The formula for calculating rainfall erosivity is:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eR\u0026thinsp;=\u0026thinsp;79\u0026thinsp;+\u0026thinsp;0.363 * P\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (2)\u003c/p\u003e\n\u003cp\u003eWhere: P\u003csub\u003ea\u003c/sub\u003e is the average annual rainfall.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2. Soil erodibility factor (K)\u003c/strong\u003e: The K-factor is a quantitative value derived from experiments that indicate the soil\u0026apos;s sensitivity to erosion. It fully expresses the capacity of soil erosion caused by a lack of resistance to runoff and rainfall. In this study, the K-factor was determined using the method proposed by RUSLE, which involves calculating it with the average geometric diameter of soil particles (Chuenchum et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The precise formula for the calculation is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eK = [2.1 * 10\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;4\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e(12 \u0026ndash; OM) M\u003c/em\u003e \u003csup\u003e\u003cem\u003e1.14\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e+ 3.25 (S \u0026ndash; 2)\u0026thinsp;+\u0026thinsp;2.5 (P \u0026ndash; 3)] / 100\u003c/em\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (3)\u003c/p\u003e\n\u003cp\u003eWhere: OM\u0026thinsp;=\u0026thinsp;Percentage soil organic matter content, M = (% Silt + % Very Fine Sand) * (100 - % Clay), S\u0026thinsp;=\u0026thinsp;Soil structural code, P\u0026thinsp;=\u0026thinsp;Soil profile permeability rating was obtained using a combination of field observation, and default values were considered for S and P.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3. Slope length factor (LS)\u003c/strong\u003e: The LS factor summarises how topography influences soil erosion and significantly impacts soil loss. The gradient of the local slope affects flow velocity and the erosion rate. The slope length indicates the distance between the start and end of the inter-rill processes (Schmidt et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The following formula was employed to calculate the LS factor:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLS = (X / 22.13)\u003c/em\u003e \u003csup\u003e\u003cem\u003em\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e(0.065\u0026thinsp;+\u0026thinsp;0.045 S\u0026thinsp;+\u0026thinsp;0.0065 S\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (4)\u003c/p\u003e\n\u003cp\u003eWhere: S\u0026thinsp;=\u0026thinsp;Slope (%) calculated directly from the DEM, X\u0026thinsp;=\u0026thinsp;Value obtained by multiplying the flow accumulation by the cell value, M\u0026thinsp;=\u0026thinsp;Value that varied from 0.2 to 0.5 depending on the slope. 0.5 for slopes exceeding 5%, 0.4 for slopes 3\u0026ndash;5%, 0.3 for 1\u0026ndash;3%, and 0.2 for slopes\u0026thinsp;\u0026lt;\u0026thinsp;1.0%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.4. Cover management factor (C)\u003c/strong\u003e: The cover management factor C represents the influence of crops and other management practices on erosion rates. Vegetation cover is the second most vital factor in mitigating the risk of soil erosion following terrain. Its value ranges from 0 (water bodies) to 1 (barren land), reflecting the absence of vegetation, root biomass, or other surface covers that prevent soil erosion. Ground cover absorbs rainfall, enhancing infiltration and diminishing rainfall energy (Shekar \u0026amp; Mathew, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Normalised Difference Vegetation Index (NDVI) was used to determine the cover management factor (C). The following formula was applied to calculate the LS factor:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC\u0026thinsp;=\u0026thinsp;0.431\u0026thinsp;\u0026minus;\u0026thinsp;0.805 \u0026lowast; NDVI\u003c/em\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (5)\u003c/p\u003e\n\u003cp\u003eWhere: NDVI\u0026thinsp;=\u0026thinsp;Near-infrared (NIR) \u0026ndash; R/ Near-infrared (NIR)\u0026thinsp;+\u0026thinsp;red (R), NIR\u0026thinsp;=\u0026thinsp;Near-infrared band, and R is red band.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.5. Conservation practices factor (P)\u003c/strong\u003e: The factor of support practices P represents the outcomes of implementing water and soil conservation measures, which reduce the quantity and rate of runoff and the amount of soil loss. A value of 0 signifies no soil erosion in the region, whereas a value of 1 indicates that no conservation measures have been applied (Joshi et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Soil characteristics :\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates nine soil characteristics that directly influence soil erodibility and soil erosivity, determining the output of the RUSLE model results. By taking a look at the table, we can notice that the studied soil locations were poor in organic matter, which ranged between 0.1 and 1.5 g.kg, this values actually was lower in the north section in subwatershed (1) (ASW1) and increases gradually towards south section subwatershed (3) (ASW3) which was the higher content in organic matter, the results also showed that the soil structural code (SSC) ranged between (2\u0026ndash;3) were the higher values concentrated in the higher elevation sites in SW1 while the lower values were recorded in the low elevation sites in SW3, however, wonderful sand ranged between 8\u0026ndash;14 gm/kg were the lower quantities were concentrated in the upper positions while the higher values recorded in the lower positions of soil samples, by taking a look on the particle size distribution we can noticed that the percentage soil particles were ranges between (29\u0026ndash;55), (32\u0026ndash;40), and (5\u0026ndash;39) for sand, silt and clay respectively, which produced three groups of soil textures namely (sandy loam, loam and clay loam) as shown in table (2).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSoil properties were used to estimate RUSLE model parameters.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSub watershed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eO.M\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSSC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSPC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVFS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSilt\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTexture\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSANDY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSANDY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSANDY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSANDY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSANDY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSANDY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSANDY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSANDY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSANDY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLAY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLAY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLAY LOAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.2. Factors of RUSLE:\u003c/strong\u003e\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.1. Rainfall-runoff erosivity (R-factor)\u003c/strong\u003e: The computed rainfall-runoff erosivity (R-factor) values vary from 323.4935 at SW1 station to 10.70138 MJ mm ha⁻\u0026sup1; hr⁻\u0026sup1; yr⁻\u0026sup1; at the lower site in SW3 station (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The maximum rainfall erosivity value is recorded in the northern region of the Abu Ghraibat watershed, attributed to the elevated terrain, which results in larger drop sizes, comparatively greater precipitation, and steep gradients. The rainfall erosivity progressively diminishes from the watershed\u0026apos;s northern to the southern region. The south region of the watershed necessitates soil protection owing to elevated rainfall levels in contrast to the northern region. The soils in the research area can be classified into three textural groups according to the relative proportions of sand, silt, and clay (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.2. Soil erodibility (K-factor)\u003c/strong\u003e: The determined soil erodibility (K-factor) values varied from 0.058767 to 0.10858 MJ mm h⁻\u0026sup1; ha⁻\u0026sup1; yr⁻\u0026sup1;.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.3. The topographic factor (L.S)\u003c/strong\u003e: The topographic factor (L.S) denotes the impact of slope length and steepness on the erosion process. The LS factor was computed using flow accumulation and slope percentage as inputs. The results indicate that the topographic factor value escalates from 0.108 to 0.127 as flow accumulation and slope rise.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.4. Crop Management Factor (C)\u003c/strong\u003e: The crop management factor (C) ranged between 0.074 and 0.326, with lower values concentrated in the lower regions of subwatershed 3. In comparison, the higher values were recorded in the upper areas of the subwatershed.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e5. Computed spatial and temporal average soil loss per unit area (A)\u003c/strong\u003e: Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the computed spatial and temporal average soil loss per unit area (A) values ranged from 11 to 823 t/yr. The higher quantity of soil loss was recorded in the upper regions of the Abu-Ghraibat watershed. In contrast, the lower quantities of soil loss were recorded in the lower areas of the watershed.\u003c/p\u003e\n \u003c/span\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRUSLE model estimated parameters.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSub watershed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL.S\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e323.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e238.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.\u003c/strong\u003e\u003cstrong\u003e1. Soil properties used in estimation of RUSLE model parameters:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.1. Organic Matter (OM):\u0026nbsp;\u003c/strong\u003eVariation in the organic matter content is apparent, as shown in Figure 1, where its deficiency can be recognised. The soil in the watershed is impoverished in terms of organic matter due to the lower vegetation cover, which has already been affected by water scarcity in the area. Organic matter is a crucial factor that enhances soil structure by forming peds, which connect and prevent soil particles from dispersing and becoming easily eroded. Many researchers have explained how soil organic matter can enhance soil structure and reduce soil erosion hazards (Zhang et al., 2024). The organic matter content is classified into seven categories, ranging from 0.029 to 0.313 for the first category and from 1.470 to 1.795 for the seventh category; the variation in organic matter content is reflected in the RUSLE parameters and erosion intensity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.2. Soil Structure Code (SSC):\u003c/strong\u003e The soil structure code has a strong relationship with organic matter; the higher the number, the better the soil structure. Therefore, the upper sample locations are characterised by similarities in soil structure based on the porosity of organic matter (Wiltshire et al., 2024). The low clay content effectively influences soil structure, as shown in Table 2. The values of the soil structure code ranged from 2 to 3, with no discernible harmonic trend. Figure\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e2 illustrates the distribution of the soil structure code.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.3. Soil Permeability Code (SPC):\u0026nbsp;\u003c/strong\u003eThe Soil Permeability Code (SPC) indicates the ability of water\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eto penetrate soil layers. This process prevents water runoff, which is considered the most significant factor promoting erosion (Naipal et al., 2015). The SPC values ranged from 4 to 6, with the higher values concentrated in the lower zones. In contrast, as expected based on the particle size distribution, lower values are found in the upper zones, where fine particles are predominant, and in the lower zones, where coarse particles are more prevalent, as shown in Figure\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.4. Very fine sand (VFS):\u0026nbsp;\u003c/strong\u003eThe quantity of very fine sand varies according to the location of the samples, with lower values concentrated in the upper locations and higher values found in the lower locations. This is expected due to the movement of fine particles from higher to lower areas, caused by gravity and facilitated by wind or water (Terefe et al., 2024). Figure\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e4 illustrates the distribution of very fine sand, categorising this material into seven distinct groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.5. Particle size distribution:\u0026nbsp;\u003c/strong\u003eThe percentages of soil fractions varied significantly according to the location of the samples, with the coarse fractions concentrated in the upper locations and the fine particles accumulating in the lower locations (Olika et al., 2023). The trend coincided harmoniously with the slope gradient, which determined the sedimentation aspect of soil fractions (Sand, Silt, and Clay) (Figures 11 and 12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Factors of RUSLE:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.1. Rainfall-runoff erosivity (R-factor):\u0026nbsp;\u003c/strong\u003eNumerous studies demonstrated that the soil erosion rate in the catchment is highly responsive to rainfall (Biswas \u0026amp; Pani, 2015; Brychta et al., 2022). Daily rainfall serves as a superior indicator of fluctuations in soil erosion rates, effectively characterising the seasonal distribution of sediment output (Prasannakumar et al., 2012). The benefits of utilising yearly rainfall encompass its accessibility, simplicity of calculation, and enhanced regional uniformity of the exponent (Dai et al., 2024). Consequently, in this analysis, the average yearly rainfall (calculated by dividing total rain by the number of rainy days) was utilised for the R factor computation (Eq. 2). The computed rainfall-runoff erosivity (R-factor) values vary from 323.4935 at SW1 to 10.70138 MJ mm ha⁻\u0026sup1; hr⁻\u0026sup1; yr⁻\u0026sup1; at the lower site in SW3 (Table 2). The maximum rainfall erosivity value is recorded in the northern region of the Abu Ghraibat\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewatershed, attributed to elevated terrain resulting in larger drop sizes, comparatively greater precipitation, and steep gradients. The rainfall erosivity progressively diminishes from the watershed\u0026apos;s northern to the southern region. The southern region of the watershed necessitates soil protection owing to elevated rainfall levels, in contrast to the northern region\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.2. Soil erodibility (K-factor):\u0026nbsp;\u003c/strong\u003eThe Soil Erodibility factor (K) denotes the vulnerability of soil or surface material to erosion, the transport capacity of sediment, and the volume and velocity of runoff resulting from specific rainfall input, as assessed under standardised conditions (Sinshaw et al., 2021). The standard condition is a unit plot measuring 22.6 meters long, featuring a 9% gradient, kept in continuous fallow and cultivated up-and-down along the hill slope (Kim, 2006). The soil erodibility factor K was assessed based on soil textures. The K factor indicates the soil or surface material\u0026apos;s ability to resist erosion, the ease of sediment movement, and the volume and rate of runoff resulting from a specific rainfall input, as determined under typical conditions (Talebi \u0026amp; Karimi, 2024). The K factor is influenced by particle size distribution, organic matter composition, structure, and permeability (George et al., 2021). K values indicate the soil erosion rate per rainfall unit, represented by the Runoff \u0026nbsp;Erosivity (R) index. The soil erodibility factors (K) presented in Eq. (3) are most accurately derived from direct measurements conducted on natural runoff plots. A nomograph is typically used to determine the K factor for soil, depending on its texture, percentage of silt plus extremely fine sand, percentage of sand, percentage of organic matter, soil structure, and permeability (Saha et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.3. The topographic factor (L.S):\u0026nbsp;\u003c/strong\u003eThe LS factor indicates a specific location\u0026apos;s susceptibility to topographic erosion (Joshi et al., 2023). This study confirmed that the LS factor is a primary and sensitive determinant of soil erosion, with the Abu-Ghraibat Watershed, located in the north, identified as the principal physiographic unit. The steepness of a slope quantifies its influence on the rate of soil erosion. The gradient of the terrain has a significantly greater influence on soil erosion than the slope length. Table 2 indicates that roughly 30% of the territory has a very low slope, whereas moderately steep and very steep slopes characterise 60%. The northern, eastern, and northwestern regions of Abu-Ghraibat exhibit minimal vulnerability to soil erosion, characterised by LS factor values below 0.16 (Map 8). The topographic factor (L.S) denotes the impact of slope length and steepness on the erosion process. The LS factor was computed using flow accumulation and slope percentage as inputs. The results indicate that the topographic factor value escalates from 0.108 \u0026nbsp;to 0.127 as flow accumulation and slope rise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.4. Crop Management Factor (C):\u0026nbsp;\u003c/strong\u003eThe C factor denotes the land\u0026apos;s condition regarding vegetation density. Elevated C factor values indicate a higher likelihood of soil erosion, as they correspond to areas with minimal vegetation cover. The spatial distribution of the C factor in the study area ranged between 0.074 and 0.326. The comparative impact of management decisions can be directly associated with variations in the C factor, which ranged from approximately 0 for well-vegetated land to 1.000 for desolate or bare areas. Approximately 60% of the land exhibits diminished green cover, rendering it more susceptible to soil erosion. Conversely, 40% of the entire region (554.751 km\u0026sup2;) is moderately to highly susceptible to soil erosion. Crop management factor (C) ranged between 0.074 and 0.326, with the lower values concentrated in the lower regions of the subwatershed3 and the higher values recorded in the upper areas of the subwatershed1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.5. Conservation practices factor (P):\u0026nbsp;\u003c/strong\u003eThe P factor is the ratio of soil erosion linked to certain support practices compared to the corresponding loss due to upslope management (Prasannakumar et al., 2012)The P factor denotes the influence of particular soil management practices, including contour cultivation, strip cropping, terrace cultivation, and subsurface drainage. The research region\u0026apos;s land use and cover were uniform across all areas of the Abu-Ghraibat watershed, as this was subjected to the same practices and land use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.6. Computed spatial average soil loss and temporal average soil loss per unit of area (A):\u0026nbsp;\u003c/strong\u003eTable 1 showed that the Computed spatial average soil loss and temporal average soil loss per unit of area (A) values ranged between 11 and 823 t/yr, with the higher quantity of soil loss recorded in the upper regions of the Abu-Ghraibat watershed and the lower amount recorded in the lower areas. The soil erosion modulus increases markedly with the slope, initially rising before subsequently declining, as previously reported by (Getahun et al., 2024; Naipal et al., 2015). Nevertheless, after the slope approached the threshold, soil erosion diminished. Specifically, we observed that erosion diminished when the slope reached 35 \u0026deg;, aligning with the findings of Liu et al. (2019b). As the slope increases, the intensity of soil erosion progressively escalates, with the average erosion intensity in areas of mild slope (\u0026le; 25◦) being lower than that of the entire gully, underscoring the significant impact of topography on soil erosion. The slope gradients of 15\u0026deg; to 45\u0026deg; contribute to 85% of erosion, which is the primary cause of soil erosion in the southern sites of the Abu-Ghraibat watershed.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study calculated soil erosion in the Abu-Ghraibat watershed using the Revised Universal Soil Loss Equation (RUSLE), Geographic Information Systems (GIS), and Remote Sensing models, revealing that it resulted from three interconnected processes. In climate change, identifying vegetation coverage and slope thresholds for various land-use/land-cover classes is crucial for effectively planning the spatial distribution of vegetation restoration and soil properties (organic matter, soil structure, soil permeability, and particle size distribution). Nonetheless, these thresholds may be influenced by geographical (local, watershed, and regional) and temporal scales, which impact the efficacy of soil erosion control \u0026mdash;a vital landscape function. While it is accurate that soil erosion can affect land use, it is also established that no area experiences erosion if it possesses sufficient vegetation cover. Identifying and distributing susceptible lands, categorized by varying degrees and intensities of degradation, should guide managers and policymakers in enhancing environmental, social, and economic conditions to substantially mitigate the risk of land degradation. Given the complexities of soil degradation, achieving the Land Degradation Neutrality goal by 2040 necessitates collaboration among scientists, governments, and managers. They must identify the primary factors contributing to soil degradation and erosion to promote effective governance for soil sustainability. Consequently, effective land degradation neutrality strategies must enhance the preservation of the quality and quantity of soil that underpins landscape services, including food and materials, as well as the frequently neglected regulating and supporting services essential for provisioning these services.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Department of Applied Geology at the University of Babylon and the Department of Civil Engineering and Built Environment at Liverpool John Moores University for their scientific support during this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e All authors agreed to participate following scientific ethics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors approved this copy of the paper for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e Data can be asked by request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that there are no conflicts of interest concerning the publication of this manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBashar F. Maaroof:\u0026nbsp;\u003c/strong\u003eProject administration, conceptualization, data curation, formal analysis, investigation, methodology, supervision, validation, visualization, software, writing – original draft.\u003cstrong\u003e\u0026nbsp;Hashim H. Kareem:\u0026nbsp;\u003c/strong\u003eSupervise, visualize, methodology, resources, validate, write, review, and edit.\u003cstrong\u003e\u0026nbsp;Jaffar H. Al-Zubaydi:\u0026nbsp;\u003c/strong\u003eSupervision, data curation, formal analysis, validation, visualization, methodology, writing – review and editing.\u003cstrong\u003e\u0026nbsp;Nadhir Al-Ansari:\u0026nbsp;\u003c/strong\u003eSupervision, data curation, formal analysis, methodology, software, writing, review, and editing.\u003cstrong\u003e\u0026nbsp;Mohamed Alkhuzamy Aziz:\u0026nbsp;\u003c/strong\u003eSupervision, data curation, formal analysis, methodology, software, writing, review, and editing.\u003cstrong\u003e\u0026nbsp;Dhia Alden A. AL-Quraishy:\u0026nbsp;\u003c/strong\u003eVisualization, data curation, formal analysis, methodology, software, writing, review, and editing.\u003cstrong\u003e\u0026nbsp;Ban AL-Hasani:\u0026nbsp;\u003c/strong\u003eFormal analysis, methodology, validation. \u003cstrong\u003eMawada Abdellatif:\u0026nbsp;\u003c/strong\u003eFormal analysis, methodology, validation.\u003cstrong\u003e\u0026nbsp;Iacopo Carnacina:\u0026nbsp;\u003c/strong\u003eFormal analysis, methodology, validation.\u003cstrong\u003e\u0026nbsp;Rayan G. Thannoun:\u0026nbsp;\u003c/strong\u003eData curation, formal analysis, methodology, validation.\u003cstrong\u003e\u0026nbsp;Manal Sh. Al-Kubaisi:\u0026nbsp;\u003c/strong\u003eFormal analysis, methodology, validation.\u003cstrong\u003e\u0026nbsp;Sama Al-Maarofi:\u0026nbsp;\u003c/strong\u003eFormal analysis, methodology, validation.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhmad Bhat, S., Hamid, I., Din Dar, M. U., Srinagar, N., Bashir Ahmad Pandit, I., Khan, S., Rasool, D., \u0026amp; Ahmad Pandit, B. (2017). Soil erosion modeling using RUSLE \u0026amp; GIS on micro watershed of J\u0026amp;K. \u003cem\u003e~ 838 ~ Journal of Pharmacognosy and Phytochemistry\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(5).\u003c/li\u003e\n \u003cli\u003eAl-Hasani, B., Abdellatif, M., Carnacina, I., Harris, C., Al-Quraishi, A. M. F., \u0026amp; Maaroof, B. F. (2024). Assessing Climate Change Impacts on Rainfall-Runoff in Northern Iraq: A Case Study of Kirkuk Governorate, a Semi-Arid Region. In A. and B. B. Al-Quraishi Ayad and Negm (Ed.), \u003cem\u003eClimate Change and Environmental Degradation in the MENA Region\u003c/em\u003e (pp. 93\u0026ndash;111). Springer Nature Switzerland. https://doi.org/10.1007/698_2024_1154\u003c/li\u003e\n \u003cli\u003eAl-Hasani, B., Abdellatif, M., Carnacina, I., Harris, C., Al-Quraishi, A., Maaroof, B. F., \u0026amp; Zubaidi, S. L. (2024). 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Estimation of soil erosion using RUSLE and GIS techniques: a case study of Barakar River basin, Jharkhand, India. \u003cem\u003eModeling Earth Systems and Environment\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(4). https://doi.org/10.1007/s40808-015-0040-3\u003c/li\u003e\n \u003cli\u003eBrychta, J., Podhr\u0026aacute;zsk\u0026aacute;, J., \u0026amp; \u0026Scaron;ťastn\u0026aacute;, M. (2022). Review of methods of spatio-temporal evaluation of rainfall erosivity and their correct application. In \u003cem\u003eCatena\u003c/em\u003e (Vol. 217). Elsevier B.V. https://doi.org/10.1016/j.catena.2022.106454\u003c/li\u003e\n \u003cli\u003eChuenchum, P., Xu, M., \u0026amp; Tang, W. (2020). Predicted trends of soil erosion and sediment yield from future land use and climate change scenarios in the Lancang\u0026ndash;Mekong River by using the modified RUSLE model. \u003cem\u003eInternational Soil and Water Conservation Research\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(3), 213\u0026ndash;227. https://doi.org/10.1016/j.iswcr.2020.06.006\u003c/li\u003e\n \u003cli\u003eDai, E., Lu, R., \u0026amp; Yin, J. (2024). Identifying the effects of landscape pattern on soil conservation services on the Qinghai-Tibet Plateau. \u003cem\u003eGlobal Ecology and Conservation\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e. https://doi.org/10.1016/j.gecco.2024.e02850\u003c/li\u003e\n \u003cli\u003eDash, S. S., \u0026amp; Maity, R. (2023). 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Quantification of soil erosion and sediment yield using the RUSLE model in Boyo watershed, central Rift Valley Basin of Ethiopia.\u0026nbsp;\u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(10). https://doi.org/10.1016/j.heliyon.2024.e31246\u003c/li\u003e\n \u003cli\u003eMustafa, M. M. (2011). MINERAL RESOURCES AND INDUSTRIAL DEPOSITS IN THE MESOPOTAMIA PLAIN. In \u003cem\u003eIraqi Bull. Geol. Min. Special Issue\u003c/em\u003e (Issue 4).\u003c/li\u003e\n \u003cli\u003eNaipal, V., Reick, C., Pongratz, J., \u0026amp; Van Oost, K. (2015). Improving the global applicability of the RUSLE model - Adjustment of the topographical and rainfall erosivity factors. \u003cem\u003eGeoscientific Model Development\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(9), 2893\u0026ndash;2913. https://doi.org/10.5194/gmd-8-2893-2015\u003c/li\u003e\n \u003cli\u003eOlika, G., Fikadu, G., \u0026amp; Gedefa, B. (2023). GIS based soil loss assessment using RUSLE model: A case of Horo district, western Ethiopia. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2). https://doi.org/10.1016/j.heliyon.2023.e13313\u003c/li\u003e\n \u003cli\u003ePrasannakumar, V., Vijith, H., Abinod, S., \u0026amp; Geetha, N. (2012). Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. \u003cem\u003eGeoscience Frontiers\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(2), 209\u0026ndash;215. https://doi.org/10.1016/j.gsf.2011.11.003\u003c/li\u003e\n \u003cli\u003eSabah Y. Yacoub. (2010). Geomorphology of the Mesopotamian Plain: A Critical Review. \u003cem\u003eJournal of Earth Sciences and Geotechnical Engineering\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(4), 1\u0026ndash;25.\u003c/li\u003e\n \u003cli\u003eSaha, M., Sauda, S. S., Real, H. R. K., \u0026amp; Mahmud, M. (2022). Estimation of annual rate and spatial distribution of soil erosion in the Jamuna basin using RUSLE model: A geospatial approach. \u003cem\u003eEnvironmental Challenges\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e. https://doi.org/10.1016/j.envc.2022.100524\u003c/li\u003e\n \u003cli\u003eSchmidt, S., Tresch, S., \u0026amp; Meusburger, K. (2019). Modification of the RUSLE slope length and steepness factor (LS-factor) based on rainfall experiments at steep alpine grasslands. \u003cem\u003eMethodsX\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 219\u0026ndash;229. https://doi.org/10.1016/j.mex.2019.01.004\u003c/li\u003e\n \u003cli\u003eShekar, P. R., \u0026amp; Mathew, A. (2024). GIS-based assessment of soil erosion and sediment yield using the revised universal soil loss equation (RUSLE) model in the Murredu Watershed, Telangana, India. \u003cem\u003eHydroResearch\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 315\u0026ndash;325. https://doi.org/10.1016/j.hydres.2024.05.003\u003c/li\u003e\n \u003cli\u003eSinshaw, B. G., Belete, A. M., Mekonen, B. M., Wubetu, T. G., Anley, T. L., Alamneh, W. D., Atinkut, H. B., Gelaye, A. A., Bilkew, T., Tefera, A. K., Dessie, A. B., Fenta, H. M., Beyene, A. M., Bizuneh, B. B., Alem, H. T., Eshete, D. G., Atanaw, S. B., Tebkew, M. A., \u0026amp; Mossie Birhanu, M. (2021). Watershed-based soil erosion and sediment yield modeling in the Rib watershed of the Upper Blue Nile Basin, Ethiopia. \u003cem\u003eEnergy Nexus\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e. https://doi.org/10.1016/j.nexus.2021.100023\u003c/li\u003e\n \u003cli\u003eSissakian, V. K., Al-Ansari, N., Adamo, N., Kh Al-Azzawi, M., Abdullah, M., \u0026amp; Laue, J. (2020). Geomorphology of the Mesopotamian Plain: A Critical Review Geology of Iraq View project Mosul dam View project Geomorphology of the Mesopotamian Plain: A Critical Review. In \u003cem\u003eJournal of Earth Sciences and Geotechnical Engineering\u003c/em\u003e (Vol. 10, Issue 4). online) Scientific Press International Limited. https://www.researchgate.net/publication/339952803\u003c/li\u003e\n \u003cli\u003eTalebi, A., \u0026amp; Karimi, Z. (2024). Incorporation of management responses in the direction of soil erosion changes from the past to the future based on the RUSLE and DPSIR model. In \u003cem\u003eEnvironmental and Sustainability Indicators\u003c/em\u003e (Vol. 23). Elsevier B.V. https://doi.org/10.1016/j.indic.2024.100412\u003c/li\u003e\n \u003cli\u003eTerefe, B., Melese, T., Temesgen, F., Anagaw, A., Afework, A., \u0026amp; Mitikie, G. (2024). Comparative analysis of RUSLE and SWPT for sub-watershed conservation prioritization in the Ayu watershed, Abay basin, Ethiopia. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(15). https://doi.org/10.1016/j.heliyon.2024.e35132\u003c/li\u003e\n \u003cli\u003eWiltshire, C., Meersmans, J., Waine, T. W., Grabowski, R. C., Thornton, B., Addy, S., \u0026amp; Glendell, M. (2024). Evaluating erosion risk models in a Scottish catchment using organic carbon fingerprinting. \u003cem\u003eJournal of Soils and Sediments\u003c/em\u003e. https://doi.org/10.1007/s11368-024-03850-6\u003c/li\u003e\n \u003cli\u003eYacoub, S. Y. (2011). GEOMORPHOLOGY OF THE MESOPOTAMIA PLAIN. In \u003cem\u003eIraqi Bull. Geol. Min. Special Issue\u003c/em\u003e (Issue 4).\u003c/li\u003e\n \u003cli\u003eYuan, S., Xu, Q., Zhao, K., Zhou, Q., Wang, X., Zhang, X., Chen, W., \u0026amp; Ji, X. (2024). Dynamic analyses of soil erosion and improved potential combining topography and socio-economic factors on the Loess Plateau. \u003cem\u003eEcological Indicators\u003c/em\u003e, \u003cem\u003e160\u003c/em\u003e. https://doi.org/10.1016/j.ecolind.2024.111814\u003c/li\u003e\n \u003cli\u003eZhang, Y., Zhang, P., Liu, Z., Xing, G., Chen, Z., Chang, Y., \u0026amp; Wang, Q. (2024). Dynamic analysis of soil erosion in the affected area of the lower Yellow River based on RUSLE model. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1). https://doi.org/10.1016/j.heliyon.2023.e23819\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Geohazards, soil degradation, GIS, RUSLE, Abu Ghraibat Watershed","lastPublishedDoi":"10.21203/rs.3.rs-7137911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7137911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examined the synergistic and independent effects of soil properties, vegetation cover, conservation practices, and slope on the spatial distribution characteristics of soil erosion in the Abu-Ghraibat watershed in 2024. Soil samples have been collected and analysed in the laboratory, alongside high-resolution satellite photos, meteorological data, and information obtained from a digital elevation model (DEM). The findings indicate that soil erosion in the Abu-Ghraibat watershed in 2024 was minimal, with a progressively increasing severity from north to south. In the studied area, grassland accounts for over 50% of soil erosion, with regions exhibiting vegetation coverage of \u0026gt; 30% being the primary contributors to this erosion, all of which are influenced by slope. Moreover, the enhancement of vegetation in the lower strata of the basin and grasslands, especially on slopes ranging from 10° to 45°, along with the conversion of sloping woodlands and grasslands into terraces, has proven to be an effective strategy for mitigating soil erosion in the Abu-Ghraibat watershed. The present study has demonstrated that the RUSLEGIS integrated model may serve as an effective instrument for quantitatively and spatially mapping soil erosion at the watershed level on the Abu-Ghraibat, while considering the provision of landscape services.\u003c/p\u003e","manuscriptTitle":"Estimating Soil Erosion Utilising Geospatial Method and Revised Universal Soil Loss Equation (RUSLE) of Abu Ghraibat Watershed, Eastern Misan Governorate, Iraq","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 17:03:23","doi":"10.21203/rs.3.rs-7137911/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-20T07:37:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T10:00:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71733048659511609501941192509147726951","date":"2025-10-14T18:03:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159422679496392061331002616986474204745","date":"2025-10-12T08:46:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-07T05:30:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285962886439796923315535661338507641903","date":"2025-09-06T03:46:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121820744774463375170392537253112461311","date":"2025-08-27T06:17:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310077644200151332759011175845928821035","date":"2025-07-30T12:34:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T06:07:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-22T06:41:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-19T04:01:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-18T09:07:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-16T08:43:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"acabc1ff-90c5-4654-b16f-2f22b7afd133","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52023625,"name":"Earth and environmental sciences/Climate sciences"},{"id":52023626,"name":"Biological sciences/Ecology"},{"id":52023627,"name":"Earth and environmental sciences/Ecology"},{"id":52023628,"name":"Earth and environmental sciences/Environmental sciences"},{"id":52023629,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-12-29T16:03:35+00:00","versionOfRecord":{"articleIdentity":"rs-7137911","link":"https://doi.org/10.1038/s41598-025-33403-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-24 15:58:34","publishedOnDateReadable":"December 24th, 2025"},"versionCreatedAt":"2025-07-25 17:03:23","video":"","vorDoi":"10.1038/s41598-025-33403-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-33403-x","workflowStages":[]},"version":"v1","identity":"rs-7137911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7137911","identity":"rs-7137911","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}