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1. Introduction
African countries in the sub-Saharan region are particularly susceptible to severe soil erosion and nutrient loss, which affect crop productivity and environmental quality [1]. Everywhere in Africa, especially Ethiopia, there is a significant amount of soil erosion since higher places are more susceptible to it. Moreover, excessive grazing and agriculture have caused soil erosion in Ethiopia, where agricultural production is the foundation of the economy. As a result, soil degradation hinders production in rural agricultural areas and is a barrier to wealth for the majority of developing countries [2].
Soil erosion is the primary cause of poor environmental quality because it limits soil fertility and environmental sustainability [3–8]. Soil erosion is a critical factor in land degradation and is regarded as a significant environmental threat [9, 10]. It is also a major problem in ensuring crop productivity and environmental sustainability [11, 12]. Recently, this issue has emerged as a significant environmental challenge in various parts of the globe, especially in developing nations where agriculture serves as a primary source of livelihood [13, 14]. The cropland recorded soil erosion ranging from 22 to 100 ton ha−1 and declined crop production [15]. It is also a serious issue in the country resulting in the decline of soil fertility via the removal of organic matter and clay fractions [16–21]). The mean soil loss from cultivated land in the highlands of the country is 100 Mg ha−1 yr−1 [22] and 200–300 Mg ha−1 yr−1 [23]. Hence, soil fertility decline is a threat to increased crop productivity in the country [24–26].
Soil erosion has emerged as a critical concern across nearly all regions of the world [11, 27, 28]. While soil erosion is inherently a natural phenomenon, it has escalated into a major environmental challenge over the past century, primarily due to human activities [29]. This issue is anticipated to worsen throughout the 21st century [30]. Among the various forms of soil erosion, soil erosion is particularly alarming, as it disrupts soil texture, structure, and quality, while also threatening other vital natural resources, such as land and water [31, 32]. The immediate consequences include a reduction in soil productivity, while the broader impacts involve sediment deposition that can lead to flooding [33, 34].
In the country’s northern regions, the government has implemented various land resource conservation and management techniques. The techniques were crucial for boosting soil fertility [35], preserving soil nutrients against soil erosion [36], raising soil water contents [37], and restoring land productivity [35]. In addition, soil and water conservation (SWC) practices pay off by enhancing the physicochemical characteristics of the soils [37] and creating the possibility for reusing cultivated land that was previously occupied by physical structures, such as planting fodder [38]. Identifying erosion hotspots is a mechanism to spatially pinpoint out high-risk sites, which require SWC planning and management [39–41]. However, critical erosion areas have not been prioritized [42].
The emergence of advanced technologies such as remote sensing and Geographic Information Systems (GISs) has significantly enhanced the accuracy of estimation processes [43]. While various models exist for predicting soil erosion, the revised universal soil loss equation (RUSLE) remains the most commonly utilized model in the soil erosion estimation research [44–46]. The integration of geospatial technologies, including GIS, remote sensing, and RUSLE methodologies, proves to be cost-effective, efficient, and more accurate compared to many alternative models when assessing soil erosion [47–50].
Moreover, RUSLE is a prominent model employed to calculate and forecast the spatial and temporal variations in soil erosion within a GIS framework [51]. It estimates the long-term average annual soil loss in specific regions characterized by particular ground slope conditions [52]. This model is recognized for its simplicity and relevance, requiring minimal data, which contribute to their widespread application globally due to their practicality and straightforward computational methods [53]. In addition, it is primarily utilized to evaluate erosion risk and its potential applications [52, 54, 55]. However, its application has not been tested in Ethiopia. Thus, predicting the spatial soil loss potential of the Gumara catchment using a reliable and applicable model was essential.
2. Study Area Characterization
2.1. Location
Gumara, a major catchment of the Blue Nile Basin, is located between latitudes 11°34′-12° N and longitudes 37°33′-38°11′ E in Ethiopia. It merges many drainage lines through its route from its origin, Guna Mountain, to its terminal, Lake Tana, forming Gumara watershed. It has altitudes varying from 1758 to 3683 m above sea level (Figure 1). Areas at higher elevations experience lower air temperatures, a phenomenon attributed to the decline in temperature with increasing altitude. Given that the watershed encompasses a range of altitude classes, the significant differences in elevation play a crucial role in determining the regional variations in agricultural practices.
[figure(s) omitted; refer to PDF]
The watershed encompasses an area of 141,173 ha. It is situated within the northern highland region, primarily composed of Oligo–Miocene volcanic trap basalt rock, which is underpinned by early tertiary volcanic formations [56]. This watershed is noted for its substantial rainfall. The mean annual temperature recorded in this area is 16.6°C. Historically, this watershed has been a vital source of water for the surrounding regions and is located within a crucial agricultural zone that has experienced considerable growth in agricultural activities over the years.
2.2. Watershed Morphology
Watershed morphology involves the measurement and mathematical evaluation of the watershed’s surface configuration, including the shape and dimensions of its landforms. The hydrologic responses, particularly regarding runoff and sediment yield, are primarily influenced by the topology and geometric characteristics known as geomorphologic parameters [57]. Key watershed attributes, such as size, slope, and shape, significantly impact runoff characteristics, making them crucial for hydrologic analysis. The quantitative assessment of geomorphologic parameters is highly valuable for watershed evaluation and natural resource management, as it provides insights into basin characteristics [58]. To characterize the geomorphic parameters of the watershed, a variety of dimensionless parameters were utilized, derived from the watershed divide and digital elevation model (DEM) using ArcGIS. Topography is closely linked to relief features and landscape positioning. The watershed exhibits considerable geographic diversity, ranging from flat plains to mountainous regions in terms of surface slope, and lowlands to highlands in elevation. The terrain map, generated from STRM 30 m data using Global Mapper and GIS in three dimensions (3D), indicates significant variability in elevation relative to slope length and highlights the elevation and direction of surface gradients.
3. Methodology
3.1. Estimating RUSLE Parameters
3.1.1. Rainfall–Runoff Erosivity (R) Factor
Rainfall denotes the amount of water that descends upon a watershed, serving as a primary climatic factor influencing surface erosion. The rate at which soil can absorb water is contingent upon the intensity of rainfall; when this intensity surpasses the soil’s capacity to absorb, surplus rainwater initiates overland flow [57]. The ability of surface runoff to transport soil particles escalates with increasing flow depth and velocity, thus enhancing this capacity in correlation with the volume and intensity of precipitation. Similar to temperature, rainfall exhibits both spatial and seasonal variability within the watershed due to regional disparities. The rainfall distribution is characterized as unimodal, categorizing the seasons into summer and winter, with average annual rainfalls reaching a minimum of 1274.06 mm and a maximum of 1593.53 mm, respectively.
The climatic parameter known as the rainfall erosivity (R) factor affects soil loss through agents that detach and transport soil [59]. Rainfall kinetic energy and the maximum 30-min intensity are multiplied to determine the R factor [60]. An empirical equation (Equation (1)) was established to predict R values from rainfall [26, 61–63]. The inverse distance weighted (IDW) technique was used to calculate and map the R value [64].
3.1.2. Soil Erodibility Factor (K) and Topographic Factor (LS)
Vectorized soil map was converted into a raster map using ArcGIS software version 10.5. The estimation of the soil erodibility factor was carried out as per the relationship between K and soil type [65]. The topographic factor (LS) was determined from a slope gradient factor (S) and a slope length factor (L) estimated from the ASTER DEM dataset. We computed L and S using Equations (2) and (3) under the GIS environment, respectively [60, 66].
The soil’s ability to prevent soil erosion is indicated by its soil erodibility (K). When compared to exposed soil, it shows how much soil is lost for every unit of erosive energy [54]. The primary factors for measuring the K factor are textural content and organic carbon (OC) [67, 68]. We used HWSD to derive the K factor as per Equation (4), which was developed by Sharpley and Williams [69] and utilized by Balabathina et al. [70], Hu et al. [71], and Mohammed et al. [68].
The effect of slope length and steepness on soil erosion is expressed by the LS-factor. The real horizontal slope length is divided by a slope length of 22.1 m to generate the L value, and the S-factor is the actual slope-to-slope ratio (9%) [72]. The length of the slope affects drainage [73]. It was used by Saha et al. [14] and Belayneh et al. [74]. The LS-factor was extracted from DEM (https://earthexplorer.usgs.gov) using following equation [60].
3.1.3. Land-Cover Factor (C) and Support and Conservation Practices Factor (P)
The ratio of soil loss through vegetation cover to soil loss from continuously farmed land is known as the “cover factor” (C) [75]. With dense woods, it has a value of 0.001, whereas for barren terrain, it has a value of 1 [54]. Cover and management practices include agricultural operations, surface roughness, vegetation cover, and crop rotation [68]. C-factor values were derived from Abay River Basin Master Plan [23, 41, 76]. The derived LULC raster map was assigned the corresponding C-factor values [23, 54, 60, 62, 77–81], and the raster map of C-factor was produced.
The ratio of soil loss with erosion control practices to a similar soil loss if the farming system is up and down a slope is known as the support practice (P) factor [60]. For soils with no support activities, the
3.2. Soil Loss Estimation
The yearly soil erosion was estimated by Equation (4) (Figure 2) superimposing the respective RUSLE factor value under ArcGIS software version 10.5 [82].
[figure(s) omitted; refer to PDF]
4. Results and Discussion
4.1. Estimating RUSLE Parameters
4.1.1. Rainfall–Runoff Erosivity Factor (R) Estimation
The R-factor, which represents the erosive potential of raindrops, is influenced by the distribution, intensity, and volume of rainfall and is likely a significant contributor to erosion. This factor is determined by calculating the kinetic energy of rainfall based on the maximum intensity recorded over a 30-min period [74, 83]. R describes the intensity of precipitation at a particular location [84, 85]. The R-factor is an input in the RUSLE model [60]. The average rainfall of the study area varied from 1315 to 1565 mm with the R factor varying from 745 to 857.4 R factor (MJmmha−1 yr−1), respectively (Figure 3).
[figure(s) omitted; refer to PDF]
4.1.2. Soil Erodibility Factor (K) Estimation
Soil susceptibility to erosion caused by runoff, known as the erodibility factor, is influenced by the cohesive forces among soil particles and can fluctuate based on factors such as the extent of vegetation cover and the moisture levels present in the soil [60]. K expresses the soil loss rate per rainfall erosion index (R) for bare soil, tilled-up and -down slope with no conservation work and on a slope of 5° and 22 m length [15, 82]. A soil erodibility nomograph estimates K [86, 87]. Twelve soil series in the watershed were identified and their corresponding K values ranged from 0.15 (least susceptibility to erosion) to 0.25 (high susceptibility to soil erosion) MghMJ−1 mm−1 (Figure 4).
[figure(s) omitted; refer to PDF]
4.1.3. Topographic Factor (LS) Estimation
The characteristics of a watershed’s topography play a crucial role in determining the rates of erosion and the movement of sediment. Various issues concerning SWC arise from the topographical elements of the watershed, especially the arrangement of uplands, valley slopes, and floodplains. The surface features are essential for accurately modeling soil loss and sediment yield [39]. The steepness and length of the slope determine the velocity of runoff in an area [63]. The LS factor has a strong contribution in developing sheet, rill, and inter-rill forms of erosion by water. The LS factor of this study was calculated as suggested by Moore and Burch [88] and which has been applied in the studies in [42, 89, 90]. Generally, areas characterized by low-lying lands and plains experience minimal soil loss, whereas regions with elevated terrain and steep slopes are prone to higher soil loss. Slope serves as the primary topographical characteristic, derived from the DEM.
The research has showed that erosion rates can increase by two to four times when the slope rises from 5° to 25°, assuming consistent vegetation cover [91]. Steep terrains facilitate rapid water runoff, with the highest risk for erosion and runoff occurring on slopes exceeding 12.29%, which account for 50.86% of the Gumara watershed area. Conversely, fields with gentle slopes of less than 5.24%, representing 19.21% of the watershed, are less susceptible to runoff and erosion. However, even gentle slopes can experience water accumulation, particularly when the slope is extensive and the infiltration rate is slow.
Wischmeier and Smith [60] defined the slope length (L) as the distance from the point of origin of the surface flow to the point where each slope gradient (S) decreases enough for the beginning of deposition. Soil loss is much less sensitive to changes in slope length than to changes in slope steepness [92]. The LS-factor incorporates both slope length (L; Figure 5) and steepness (S; Figure 6). Slope length calculation was insufficient due to the variation of topography, land-use practices, and related land covers [88]. Particularly, the influence of the slope on soil erosion is assigned by its length and steepness [60, 93].
[figure(s) omitted; refer to PDF]
In the watershed, the LS-factor significantly contributes to soil erosion. The L and S-factors were more pronounced in the southeast than in the center. Higher slope length results in larger drainage, while greater slope steepness results in faster runoff. More of an impact on soil erosion comes from slope steepness than slope length [94]. Besides, sub-Himalayan areas experienced a high rate of soil erosion due to their spread on high elevations and steep slopes [95–97].
The LS-factor is important in soil erosion estimation [98]. The identified flow length of the study watershed was 0.008–0.72 km and their corresponding slope length factors ranged from 0.88 to 8. The slope steepness varied from 0% in the area to 143.1% on the parts of the steep slope of the watershed. S-factor was found to be in the range of 0.34–11.76.
4.1.4. Land-Cover and Management Factor (C) Estimation
The cover-management factor (C) in USLE-type equations measures the combined effect of all interrelated cover and management measures [60]. C-factor represents the impact of crop cover on soil erosion [99]. This factor represents a nondimensional number between zero and one [60]. The land-cover type of the watershed area is classified into eight major units and 17 subunits (Figure 7). C-values varied from zero for well-managed soil and permanent wetlands to 0.85 for areas having more runoff and erosion, agricultural fields, and urban [100, 101]. The soil loss due to the cover of vegetation and management is denoted as the C-factor [102]. The cover of the land has a significant impact on the C-factor. It has values varying between 1, a completely devoid of vegetation, and 0, well-covered ground [103]. Studies by authors in [79] used satellite images to characterize land covers to derive the C-factor.
The values for the C-factor were assigned after the categories of land use and cover (Figure 7). Hurni and Hellden’s categorization was followed by this C-factor classification [104]. Near-surface features have greater values to reduce soil loss and erosion [105]. Soil erosion is made worse by inadequate crop cover and poor cultivation [35, 106].
[figure(s) omitted; refer to PDF]
4.1.5. Support and Conservation Practices Factor (P) Estimation
The P-factor quantifies the soil loss resulting from cultivation practices that run both up and down the slope, in contrast to the soil loss experienced when employing conservation methods [33]. Conservation techniques such as strip cropping, contour farming, and terracing notably modify surface runoff, as well as the processes of soil detachment and removal [82]. Specifically, the P-factor represents the ratio of soil loss associated with conservation efforts to the soil loss incurred from conventional straight-row cultivation on sloped terrain. The P-factor shows the impacts of measures to minimize runoff and thus soil erosion. According to McCool et al. [87], the P-factor refers to how surface conditions affect flow paths and flow hydraulics. Generally, the lower the
[figure(s) omitted; refer to PDF]
It evaluates the impact of SWC practices on erosion rates [60]. The p value represents several human practice elements and land management. p values therefore depend on the kind and state of a specific erosion control strategy. A lower p value indicates that SWC is in a good condition and can therefore prevent runoff and erosion to a greater extent, and the opposite is also true.
For the current research, a p value was used from Wischmeier and Smith’s study [60] where the slope of the catchment was associated with management activities, which are highly influenced by the slope of the area. It was implemented in Ethiopia, as cited by various literature studies [25, 62, 63, 107]. Agricultural lands located at slope 0%–100% were classified into six classes and the assigned class value of p varied from 0-0.33 [60]. The p values for the rest cultivated lands, located at slopes above 100%, were calculated as 0.33 to 0.53 (Figure 8).
4.2. Soil Loss Estimation
A soil erosion map of Gumara watershed was produced using a RUSLE model that estimates the soil erosion on slopes with five factors. RUSLE is usually used for soil loss estimation [44, 108, 109]. Besides, the hypsometric analysis is useful to evaluate the runoff and associated erosion in the hilly area [28]. It predicts soil loss under similar topography and agroecology [110] and classifies hot spots for minimizing soil loss.
As shown in Table 1, the soil loss was categorized into four. It showed that 53.11% of the total area was included in slight, moderate (24.21%), high (17.24%), and very high classes (5.45%). The average annual soil loss of the study catchment was 35.83 ton ha−1 (Figure 9). The Ethiopian Highland Reclamation Study estimated an average soil loss rate of 35 ton ha−1 yr−1 in the highlands of the country [22]. Gebreyesus and Kirubel [111] found the highest soil loss in Medego watershed. The soil loss rate in Ethiopia mainly relays on the slope gradient, rainfall amount and distribution, and land cover [112].
Table 1
Soil loss and severity class of Gumara watershed based on FAO [22].
| Class | Soil loss rating | Area coverage | |||
| ton·ha−1 yr−1 | mm/yr | Descriptions | ha | % | |
| I | 0–15 | 0-1 | Slight | 74971.39 | 53.11 |
| II | 15–50 | 1–4 | Moderate | 34177.08 | 24.21 |
| III | 50–200 | 4–16.5 | High | 24337.24 | 17.24 |
| IV | > 200 | > 16.5 | Very high | 7687.65 | 5.45 |
[figure(s) omitted; refer to PDF]
4.3. Applications of the Findings
The results from RUSLE erosion models’ simulation of processes have been the primary information for land-use planning in the watershed basins, especially to better understand how the hydrological cycle, soils, and vegetation interact among themselves. A holistic methodology that integrates GIS with the RUSLE for assessing the spatial patterns of soil erosion and sediment transport at the watershed level has been introduced. The incorporation of GIS has augmented the spatial examination of the various inputs and outputs within the watershed. RUSLE is the best model available for estimating soil loss. This model is particularly useful for developing nations with limited data resources because of its ability to predict soil loss with minimal input. The findings have a wide range of applications because of their accessibility to data and simplicity [113–115]. It can compute and estimate the data to determine how much soil has been lost for river basins, individual farm fields, or other areal units.
The sources for the other studies may contain data that have been manipulated. If the appropriate modifications are made, the model can be applied to other similar studies. Broadly speaking, each factor’s mean value has increased proportionately with soil erosion, indicating a correlation between the factors and soil erosion. Furthermore, the study’s findings are correlated with information from the literature and ground-level data. Validating is required for further local testing and refining of the RUSLE subfactors to improve the model results [101, 116]. The approach not only ensures continuous improvement but also fosters a deeper understanding of the model’s adaptability in varying conditions, making it a valuable tool in studying erosion and soil losses worldwide.
Demonstrating the possible impacts (negatives or positives) generated by the removal of species or changing soil management from agricultural to forestry and livestock activities has helped to understand how better to conserve the environment. Based on the advent of new computational technologies, the outputs from RUSLE models can provide a much better understanding by scientists and land managers, and consequently, give more support for the actions of the field level engineers. Moreover, RUSLE is easy to use and requires low data. Its widespread adoption speaks to its enduring practicality and versatile application. Since soil erosion has increased in frequency and intensity, additional research, better legislation, and mitigating measures are necessary. Accordingly, RUSLE parameterization and application in various climatic regimes and locales are still being improved through studies conducted all over the world.
5. Conclusion
The RUSLE model in combination with the GIS technique in this study could provide ways of computing the factors of erosion factors (R, K, LS, P, and C) and demonstrate its importance for predicting soil loss. The erosion factors are site-specific and require validation, especially with Ethiopian conditions. Areas with high to very high soil erosion call for SWC practices. The erosion map serves as a guide for implementing better SWC techniques, such as terracing trenches, check dams, stone-faced graded soil bunds, and extending biological measures. It attests to the validity of the RUSLE model’s association with ArcGIS as a dependable technique for examining the spatial variability of soil erosion in order to determine the need for appropriate management actions.
The estimated severity can offer evidence-based conservation and planning processes and implementation for the land users. The research outputs could provide soil resource allocation and productivity increase and ensure environmental sustainability in Gumara and similar areas. This demonstrates the necessity of giving high-risk regions for soil erosion in the study area priority when it comes to land management interventions. The projected rate of soil loss and its distribution geographically can be used as a basis for sustainable land-use and comprehensive management of the watershed. Improving the quality of life for residents should be the primary goal of installing control measures in areas with high and severe soil erosion hotspots. The sustainability of soil and other natural resources in the study area is judged to require the protection and conservation of existing vegetation cover and/or the replanting of forests in cultivated lands for long-term soil resource conservation and erosion prevention, particularly in steeper slopes. If these actions were included in government plans for SWC, they would be especially successful.
The study shows that the RUSLE is a valuable tool for estimating soil loss over areas and facilitating sustainable land management through conservation planning, when combined with satellite remote sensing and GISs. The approach makes effective use of the few resources available. It is recommended that empirical data be used to verify policy-relevant erosion risk maps, and that thresholds for policy guidelines derived from these maps be adjusted to account for regional differences. Thus, the technique can be used to evaluate and identify erosion-prone areas to prioritize conservation areas in other regions of Ethiopia. In addition, the data used in the present study were of coarser resolution and of a larger scale. There is a wider scope for further studies at microwatershed levels using high-resolution satellite imageries and more accurate rainfall datasets derived from different sources and validated with field-based models. More precise soil-loss estimations would help various stakeholders in taking suitable measures for the prevention of soil erosion.
Author Contributions
Mersha Ayalew, Gizachew Ayalew Tiruneh, and Mamaru Ayalew designed the method, collected data, and analyzed and wrote the first draft manuscript. Mersha Ayalew, Gizachew Ayalew Tiruneh, Mamaru Ayalew, T. D. Mequanent, Chandrakala M., Dessie Tibebe, and José Miguel Reichert interpreted the results, reviewed, and commented the manuscript. All authors had equal contributions to the paper.
Funding
The research was funded by the Amhara Design and Supervision Works Enterprise and Bahir Dar University.
Acknowledgments
Mersha Ayalew has received a Master of Science in hydraulic engineering from Bahir Dar University and is gratefully acknowledged. The authors would like to thank Amhara Design and Supervision Works Enterprise for providing funding, access, and assistance with the datasets. The authors gratefully thank the anonymous reviewers and editors whose valuable suggestions and comments have helped enrich the quality of this article.
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Abstract
Agriculture output and environmental sustainability are threatened by land degradation, which deteriorates soil resources. In response, soil and water conservation (SWC) initiatives were implemented in Ethiopia. However, soil erosion remains a main challenge to soil productiveness and sedimentation in the country. To determine erosion-prone areas and assess the impact of SWC activities on soil erosion in the country, a revised version of the universal soil loss equation (RUSLE) was implemented. Gumara catchment is an erosion-prone area in Ethiopia. Spatial estimation of soil erosion is essential to conserve, manage, and use soil and water resources. Therefore, research was conducted to evaluate the soil erosion of the catchment using the RUSLE with ArcGIS environment. The results indicated that the soil erosion extent was classified into four categories as per the erosion rate, and 5.45% of the catchment explained very high erosion (> 200 ton ha−1 yr−1) followed by 17.24% classified as high (50–200 ton ha−1 yr−1). And, 24.21% and 53.11% of the catchment had a moderate and slight class, respectively. The spatial distribution of soil erosion in the Gumara watershed could be used to better use soil resources, increase agricultural production, and ensure environmental sustainability. The country’s soil erosion is characterized by various estimates, indicating spatiotemporal dynamics. This is primarily due to the heterogeneity of the different sites, which is primarily linked to varying cover values and management factors. With this information, conservation decisions can be made with greater knowledge by concentrating on important hotspots. Therefore, RUSLE applied with ArcGIS across various land management practices and climate zones is a potential tool for SWC demanding site identification. This continues to be beneficial in the pursuit of sustainable land management techniques for the local people’s long-term well-being.
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Details
; Mersha Ayalew 2 ; Mamaru Ayalew 3 ; Mequanent, T D 4
; Chandrakala, M 5
; Tibebe, Dessie 4
; José Miguel Reichert 6
1 Department of Natural Resource Management Debre Tabor University P.O. Box 272, Debre Tabor Ethiopia
2 Amhara Design and Supervision Works Enterprise (ADSWE) P.O. Box 1921, Bahir Dar Ethiopia
3 Amhara Water Irrigation and Energy Development Bureau P.O. Box 88, Bahir Dar Ethiopia
4 College of Natural and Computational Sciences University of Gondar P.O. Box 196, Gondar Ethiopia
5 Regional Center National Bureau of Soil Survey and Land Use Planning Bengaluru India
6 Soils Department Universidade Federal de Santa Maria (UFSM) Av. Roraima 1000, Santa Maria 97105-900 State of Rio Grande do Sul, Brazil