Exploring and Modeling the Spatial Variability of Soil Erosion in Tana Basin, Northwestern Ethiopia

Abstract

Soil degradation exacerbates the depletion of soil resources and poses a threat to agricultural productivity and environmental sustainability. To address this issue, initiatives focusing on soil and water conservation (SWC) were carried out in the Tana Basin watershed located in Ethiopia. However, their spatial distribution, status, and effects of SWC activities on soil erosion were not evaluated in the entire study watershed. Thus, a revised universal soil loss equation (RUSLE) was adopted to identify soil erosion prone areas and evaluate the effect of SWC activities on soil erosion in Ethiopia. The finding shows the average soil loss of the catchment is 18.65 t/ha/year (moderate), and the detached and transported soil is 7934 tons/year. By implementing bunds in croplands and restricting grazing in degraded bushlands, it is feasible to reduce current soil erosion by 47% of the overall soil loss. The catchment area shows varying degrees of soil erosion, with a minimal rate in some areas, while others experience slight, moderate, severe, and very severe rates of soil loss, which make up 15.5%, 13.3%, 12.8%, and 10.9%, respectively. The prediction efficiency of the RUSLE using ground-measured soil erosion data results ($R^2$ = 0.90) revealed reliability. Consequently, the initial situation (soil bund and enclosure) results in a 47% reduction in erosion, the second situation involves increased expenses in materials and labor by 53% compared to the first scenario, and the third situation converts all deteriorated bushlands into forest land by 10%. Therefore, recommended SWC practices should be adopted in the Kecha and similar watersheds to minimize soil erosion and sustain soil productivity.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Sub-Saharan African nations are greatly vulnerable to serious soil erosion and crop nutrient loss that impacts crop production and environmental quality [1, 2]. Soil degradation is a constraint to prosperity for most developing countries [3] and hampers productivity in rural farming communities of the country [4]. Moreover, the level of soil erosion in Africa, particularly in Ethiopia, is significant, with elevated regions being particularly susceptible to soil erosion [5, 6]. Additionally, in Ethiopia, where agricultural production is crucial to the economy, soil erosion is exacerbated by excessive farming and grazing practices [6], leading to detrimental consequences [5] and decline in soil fertility [7–10]. Consequently, soil erosion results in limiting soil fertility and environmental sustainability [11, 12] and poor environmental quality [13, 14].

The average soil loss on cultivated lands of the highlands of Ethiopia is estimated at 40 t/ha/year [15], and it varies from 1 t/ha/year to more than 300 t/ha/year [16]. The soil loss was 30.4 t/ha/year [17] and 23.7 t/ha/year [18]. Besides, Teshome et al. [19] were 64.3 t/ha/year and 122.3 t/ha/year in Anjeni and Debre Mewi, respectively. Thus, soil losses were greater than the acceptable soil loss for Ethiopia that varies from 2 to 18 t/ha/year [20]. The outcome of soil erosion is persistent in the highlands of Ethiopia which shares 43% of the country’s area, 95% of the cropland, and rainfed agriculture is the major activity for 87% of the country’s people [21]. It created land loss and food shortages [22, 23]. The soils call for implementing soil and water conservation (SWC) practices [24].

So far, the government has been implementing various strategies for conserving and managing land resources in the country [6, 25]. These practices have played a crucial role in preserving soil nutrients and preventing soil erosion [26], enhancing soil fertility [27], increasing soil moisture content [28], and revitalizing land productivity [27]. Moreover, these SWC practices have proven to be beneficial by improving soil physicochemical properties [28], boosting crop productivity [29], and creating opportunities for reusing cultivated land that was previously occupied by physical structures such as planting fodder [30].

Nonetheless, several studies conducted by monitoring runoff at plot scales in Ethiopian highlands have found a notable decline in soil loss [31]. Kecha catchment is extremely impacted by soil erosion [22, 32]. The soil loss increases sedimentation and water quality problems in Lake Tana. SWC activities have been implemented to reduce soil erosion [33]. On the contrary, the spatiotemporal soil loss investigation in the Kecha catchment is restricted. Additionally, the assessment and evaluation of different SWC structures within the watershed and their overall impact on soil loss have not been extensively conducted. Consequently, the outcomes of this study could provide valuable insights into the geographical variability of SWC and its influence on soil erosion in the northwestern highlands of Ethiopia.

Furthermore, SWC measures can yield both positive and negative consequences on soil properties [34], the hydrological cycle and hydraulic characteristics of the soil [35], water and soil losses [36], water infiltration, and bio-macropore connectivity. Several researchers have documented the impacts of SWC on soil loss, runoff, and crop yields in different regions of Ethiopia [37]. However, these studies did not account for the spatial scale effects of SWC. Thus, the specific objectives of the research were to predict soil erosion in the watershed; identify soil erosion hotspot areas for further intervention by planners, and evaluate the efficiency of varying land management scenarios to diminish soil erosion in Kecha catchment, Tana Basin.

2. Materials and Methods

2.1. Location and Climate of the Research Site

The study was employed in the Kecha, situated in the Amhara boundary, Ethiopia. Kecha is a catchment within Tana Basin where the altitude varies from 1911 to 2115 m above mean sea level. It is located between 11°38′45″ N to 11°40′30″ N latitude and 37°29′30″ E to 37°30′45″ E longitude (Figure 1). Based on data collected from meteorological stations in the vicinity between 1994 and 2021, the average annual precipitation in the research area ranges from 1076 to 1953 mm. The average monthly maximum temperature is 27.0°C, while the average monthly minimum temperature is 12.6°C. The primary rainfall period is from June to September, with dry conditions prevailing otherwise [38].

[figure(s) omitted; refer to PDF]

In the study watershed, precipitation is erosive, according to Fenta et al. [39]; and water-induced soil erosion poses a significant environmental challenge [40]. Additionally, the study area exhibits a notable spatial variability in rainfall, as highlighted by Rientjes et al. [41]; which leads to variations in storm kinetic energy and subsequently rainfall erosivity (factor R), as mentioned by Fenta et al. [39]. These variations greatly impact soil erosion rates [42]. The significance of this issue is further compounded by the potential for high erosion rates due to steep topography [43]. Among the various SWC practices, soil bunds are the most commonly implemented physical structures in cultivated lands, particularly in areas with high rainfall. These structures enhance both soil water retention and groundwater recharge [44]. Furthermore, they mitigate the effective steepness and length of natural slopes, reduce soil erosion and water loss, alleviate the severity of droughts, and ultimately contribute to ecosystem restoration [45].

2.2. Major Land-Use Types and Soils of the Research Site

The primary means of sustenance in this region is the crop farming system, in which smallholder farmers engage in both annual crop production and livestock management. The main crops cultivated in this area include teff (Eragrostis tef. (Zucc.) Trotter), finger millet (Eleusine coracana (L.) Gaertn.), and maize (Zea mays L.). The croplands are planted with various annual crops and undergo intensive plowing at least four times a year, although the land coverage is reduced during the early rainy season. However, this intensive cultivation makes the croplands more susceptible to erosion. In order to maximize their economic returns with minimal investment, farmers are transitioning from crop production to cultivating Khat on their arable lands. To enhance crop yields, farmers utilize inorganic fertilizers, farmyard manure, and compost. The predominant soil types in the area consist of Acrisols, Luvisols, Vertisols, and Leptosols [46].

2.3. Experimental Setup and Plot Features

The landscape in the study watershed has become fragmented due to various traditional land use practices. The main types of land use include cultivated lands, grazing lands, and degraded bushlands [47]. However, there are a higher proportion of cultivated lands compared to noncultivated ones such as grazing lands and degraded bushlands. The farming system in this area is a mix of crop and livestock production, with both rainfed and continuous cropping practices. Ethiopia has seen a significant decrease in soil loss, as reported by studies conducted by Herweg and Ludi [48]; Adimassu et al. [49]; and Amare et al. [50]. However, these assessments mainly focused on croplands, while efforts to reduce soil erosion through area exclosure and vegetation restoration in noncroplands are also underway at various scales. Previous studies have primarily concentrated on structural measures used in croplands, neglecting promising approaches such as the use of exclosures and combined measures. Relevant research is scarce in areas with intense rainfall and highly erodible soils [51]. This lack of research may be attributed, at least in part, to the financial challenges associated with establishing experimental facilities and collecting data over large spatial and temporal scales.

Consequently, a total of 16 runoff plots were created on cropland (CL), grazing land (GL), and degraded bushland (DBL). CL plots were divided into two treatments at all sites: (1) control (C) plots with no conservation practice, and (2) soil bund (SB) (Figure 2). Teff (Eragrostis tef (Zucc.) Trotter) were sown in all CL lands. In the noncropland (GL and DBL) plots, (1) control (C) and (2) plots where animals were grazing and browsing, and (2) exclosure (E) plots. The plot (6 m × 30 m) has a 10 m³ trapezoidal trench to collect runoff and is lined with geo-membrane plastic (Figure 2). The workflow followed for the present study is depicted in Figure 3.

[figure(s) omitted; refer to PDF]

2.4. Estimating RUSLE Parameters

The current research used RUSLE as given in the following equation [52]:$$\begin{array}{}\text{(1)}& A=R\ast K\ast L\ast S\ast C\ast P,\end{array}$$where A = estimated mean annual soil loss (t/ha/year), R = erosivity factor (MJ mm ha⁻¹ h⁻¹ yr⁻¹), K reflects soil erodibility factor (Mg h MJ⁻¹ mm ⁻¹), LS, C and P, are the slope length and gradient factors, cover management factor and soil erosion control practice factor, respectively.

We computed the R factor using rainfall data, which were collected from a manually installed rain gauges and automatic rain gauges in the study watershed (Figure 4). The R value was computed with equation (2) which was adapted for Kecha watershed and similar areas [53].$$\begin{array}{}\text{(2)}& R={\displaystyle \sum }\text{KE}\ast {\mathrm{I}}_{30},\text{(3)}& {\text{KE}}_{\text{vol}}=6.8\ast \ln \mathrm{I}+5.96,\end{array}$$where R is rainfall erosivity; KE = total storm kinetic energy in thousands of joules per hectare; I₃₀ = maximum 30-minute rainfall intensity; KE_vol = kinetic energy volume (j/m²/mm); I = average rainfall intensity of a given storm (mm/hr).

[figure(s) omitted; refer to PDF]

We calculated K value with equation (4) and obtained soil properties from Table 1. The K value ranges from 0 to 1, where the lower value indicates less and the higher value indicates venerability to erosion risk [54].$$\begin{array}{}\text{(4)}& K=2.77\ast \mathrm{M}1.14\ast {10}^{-7}\ast 12-\text{OM}+4.28{10}^{-3}\ast \text{St}-2+3.29{10}^{-3}\ast \text{Pt}-3,\end{array}$$where M = silt plus fine sand content (%), OM = organic matter content (%), St = soil structure code (very fine granular = 1, fine granular = 2, coarse granular = 3, blocky, platy, or massive = 4 and Pt = permeability class (rapid = 1, moderate = 2, slow = 3, very slow = 4).

Table 1

Different soil type parameters for soil erodibilty (K) value determination.

Soil types	Parameters to determine K value
	OM	M	ST	PT
Nitic Luvisols	3.10	45.0	2	3
Haplic Luvisols	3.37	50.3	2	3
Lithic Leptosols	2.24	82.0	3	2
Eutric Regosols	2.75	56.0	2	3

OM = organic matter content (%), M = silt plus fine sand content (%), St = soil structure code, and Pt = permeability class. Source: [54].

The LS factor was computed from a DEM which was obtained from https://earthexplorer.usgs.gov/ and calculated by the following equation [52]:$$\begin{array}{}\text{(5)}& \text{LS}={\text{FA}\ast \frac{\text{RES}}{22.13}}^{0.4}\ast \text{sine}{\mathrm{S}\ast \frac{0.01745}{0.09}}^{1.3},\end{array}$$where L reflects the slope length factor; FA flows accumulation, RES is the resolution (cell size) of the grid (30 m), and S is the slope in degrees.

Contour tillage, terracing, and grassed waterways reduce runoff and soil erosion and P factor [27]. In this study, we conducted a detailed field surveys using GPS and investigated the type and grade of different SWC structures available in the catchment). Thereby, we collected the spatial location of the SWC structure with Garmin 64 handheld geographical positioning system (GPS, S1 Table). Then, $P$ value was allocated for each SWC practice (Table 2). Runoff sampling was made every day in the morning from each experimental plot (Figure 5) and filtered with Whatman filter paper.

Table 2

The management factor ($P$) of the Kecha catchment.

Land use	Slope (%)	$P$ value
		Agricultural land	Nonagricultural land
Cultivated land	0–5	0.1	1
	5–15	0.12
	15–30	0.14
	30–50	0.19
	50–75	0.25
	>75	0.33

Source [52].

[figure(s) omitted; refer to PDF]

3. Results and Discussion

3.1. Soil Erosion Factor Estimation

The rainfall extent within the study catchment varied from 1024.93 to 1345.45 mm (S2 Table). Meshesha et al. [53] reported a range of 7.4 to 32.43 J/m²/mm for the Bahir Dar area; Meshesha et al. [55] found a range of 11.52 to 36.82 J/m²/mm for the Central Rift Valley and Nyssen et al. [56] found a range from 15 to 36 J/m²/mm for Hagereselam northern Ethiopia. The range of erosivity of Kecha watershed, 0.08 to 107.31 MJmm/ha/hr) is comparable to Meshesha et al. [53] report of 0.32 to 103.92 MJmm/ha/hr and higher than that of Meshesha et al. [55] which has obtained a range of 1.05 to 72.9 MJmm/ha/hr in the Central Rift Valley.

Soil texture, drainage conditions, depth, structure, and organic content influence soil erodibility [54, 57]. Stone covers have been shown to decrease soil erodibility [57], while soils with higher erodibility values are more prone to erosion [54]. The K values for Humic Nitisols, Eutric Vertisols, Eutric Leptosols, Haplic Luvisols, and Lithic Leptosols were 0.1, 0.14, 0.21, 0.26, and 0.49 Mg/ha⁻¹ MJ⁻¹ mm⁻¹, respectively (Table 3 and Figure 6). Humic Nitisols in the eastern part of the catchment, had the least (0.1) K value. However, about 80.6% of the catchment had a K value of 0.49 Mg ha⁻¹ MJ⁻¹ mm⁻¹, which could have a greater contribution to soil erosion.

Table 3

Main soil and land use types and the corresponding erodibility (K) and cover (C) factor in the study watershed.

No.	Soil type	Area	Percent	K value	Cover type	C value
1	Nitic Luvisols	219	52	0.01	CL	0.24
2	Haplic Luvisols	104	25	0.02	GL	0.19
3	Eutric Regosols	84	20	0.06	FL	0.03
4	Lithic Leptosols	17	4	0.03	DBL	0.07
	Total	425	100		S	0.01

[figure(s) omitted; refer to PDF]

About 32.8% of the catchment was categorized within a slope of 8−16° and has a soil erosion rate of 18.6 t/ha/year. Slope steepness has more effect on soil erosion than slope length [58]. The LS map is generated for the research site (Figure 6). The result indicates that most of the area (43%) is categorized as moderate followed by gentle (30%), steep (17%), and very steep (10%) areas. The topographic factor (LS) factor characterizes the combined influence of slope length (L) and slope gradient (S), which play a significant role in regulating the movement of soil particles. The greater the steepness and length of the slope, the greater the velocity and erosive potential of runoff across different types of land positions [17, 18, 52, 59–63]. In this study, the LS factor for the current catchment ranged from 0 to 22 (Figure 6), indicating a high vulnerability to erosion risk [54].

Less crop cover and improper cultivation worsen soil erosion [27, 64]. Thus, 70% of the Kecha attachment is enclosed by the farming areas. The C-factors were computed. The C-factor map indicates that the study area consists of a high vegetation cover, which largely helps to control soil erosion. The result of the C-factor value of the area ranges from 0.01 to 0.24 (Figure 7).

[figure(s) omitted; refer to PDF]

3.2. Spatial Soil Loss Estimation

The soil loss of the study catchment is 1,399,210 t/year and 32.84 t/ha/year. It has ranged from 0.03 t/ha/year in the eastern, outlet, and plain areas to 500 t/ha/year in the gorged valley, and steep slope lands (Figures 8 and 9). Consequently, the calculated average soil loss for the watershed is now 18.65 t/ha/year, with a total of 7934.04 (t) of detached and transported soil). This suggests that the erosion status of the watershed is moderately significant. The result is greater than the acceptable soil loss (5–12 t/ha/year) [66]. The erosion map reveals geographical disparities in the extent of erosion (Figures 8 and 9). Most portions have clay loam soil textural class [66].

[figure(s) omitted; refer to PDF]

Accordingly, the mean soil loss from Kecha watershed is lower than the estimates reported in several parcels of the country including 56 t/ha/year in Rift Valley [67], 26 to 71 t/ha/year in Debiremwe watershed [50], 68 t/ha/year in rib area [68], 24.3 t/ha/year in Gelana sub-watershed [69], and 37 t/ha/year in Anjeni watershed [70]. It might be due to the land management and the topography is dominantly moderate to gentle slope. However, there is a significant spatial difference between soil loss within the watershed, which ranges between 0.5 and over 100 t/ha/year. Most of the severe soil erosion conditions are found from central to the eastern side of the watershed (Figures 8 and 9).

The study area exhibits a higher average soil loss compared to previous research conducted in other regions of the country, as documented by Tufa et al. [71] and Tilahun et al. [72], who reported 8.25 t/ha/year for Ajema watershed. In the Ethiopian highlands, an acceptable soil erosion rate falls within the range of 2 to 18 t/ha/year [20]. The spatial analysis reveals that nearly half (47%) of the study catchment experiences very slight rates of soil erosion, while areas with slight, moderate, severe, and very severe rates of soil loss make up 15.5%, 13.3%, 12.8%, and 10.9%, respectively (Table 4). Therefore, particular attention and intervention are required for areas with severe and very severe erosion status. The findings also show that regions with severe and very severe erosion status account for only 24% of the total area, while moderate and lower erosion cover nearly 75% of the total area, indicating good news for the region (Table 4).

Table 4

Soil erosion rate and severity class.

No.	Soil erosion severity class	Soil erosion rates (t/ha/year)	Area (ha)	Percent
1	Very slight	0–5	194.6	47.3
2	Slight	5–10	63.9	15.5
3	Moderate	10–20	54.9	13.3
4	Severe	20–50	52.8	12.8
5	Very severe	>50	45.1	10.9
	Total		411.3

The current discovery yielded similar results to other studies conducted in different catchments. In Gumara, the soil loss was estimated to be 42.67 t/ha/year [73], while in the Beshillo catchment it was 37.5 t/ha/year [74]. These studies utilized the same basin and model as our research [22]. However, our findings indicated a lower soil loss compared to previous studies conducted in a similar geographical area. For instance, Weldu Woldemariam and Edo Harka [75] estimated soil loss to be 70.5 ton/ha/year, 107 ton/ha/year, and 50 ton/ha/year. Furthermore, Munye [76] reported soil loss of 223 ton/ha/year in Dengora and 256 ton/ha/year in Meno catchment. Teshome et al. [77] found soil loss to be 4735 ton/ha/year, Zeleke [78] reported 243 ton/ha/year, Eniyew et al. [79] estimated 576 ton/ha/year, and in Syria, Mohammed et al. [23] discovered an average soil loss of 137.4 ton/ha/year.

3.3. Proposed Soil Conservation Scenarios

Different land use and management scenarios were developed and evaluated with the calibrated model to mitigate soil erosion. The outcomes of these scenarios are presented in Table 5 and Figure 9. Upon implementation, the first scenario is projected to reduce erosion by 47%, the second scenario by 53%, the third scenario by 9%, and the fourth scenario by 13% (Table 5). Therefore, it is imperative to improve management practices and Sustainable Land Management (SLM) to effectively control soil erosion and optimize the utilization of watershed resources.

Table 5

Different scenarios proposed for the study area.

No.	Minimum	Maximum	Mean soil loss (t/ha/yr)	Total soil loss (tons)	Difference (tons)	Change (%)
Current	0	197	18.7	7934.04	0	0
S1	0	130	10	4242.80	−3691.24	−47
S2	0	130	8.8	3733.66	−4200.38	−53
S3	0	197	17.1	7255.19	−678.85	−9
S4	0	220	21.1	8952.31	1018.27	13

Based on S1’s recommendations, implementing bunds in croplands and restricting grazing in degraded bushlands can potentially reduce soil erosion by almost 50%. This shift could transform the watershed’s soil loss from moderate to slight, ensuring sustainable resource management that can be passed on to future generations. Given that this approach aligns with sustainable land management practices already in place across the country, including in this specific watershed, scaling up its implementation is both feasible and achievable.

The S2 model closely resembles S1, but it represents a more advanced version of SLM, leading to increased material and labor costs. Despite this, it is a recommended approach for SLM, making its feasibility and practicality unquestionable. Consequently, the implementation of S2 results in a slightly higher reduction in soil loss compared to S1 (53%). On the other hand, S3 focuses on converting degraded bushlands into plantations or forests, a feasible strategy given the widespread practice of plantation establishment driven by government initiatives. Finally, scenario S4 assumes no intervention in the catchment area, resulting in a 13% increase in existing soil loss and exacerbating the watershed’s soil erosion condition.

4. Conclusion

About 23.7% of the Kecha watershed was categorized as at high risk of soil erosion. It was more serious in the northern, northwestern, and southeastern parcels of the catchment. The soil erosion map guides to bring improved SWC practices such as stone-faced graded soil bund, check dams, terracing trenches, and expanding biological measures. It gives witness that the RUSLE model associated with the ArcGIS is a reliable method to check out the spatial variability of soil erosion for proper management interventions.

This illustrates the need to prioritize land management actions for the study area’s high-risk areas for soil erosion. The anticipated rate of soil loss and its geographical distribution can serve as a foundation for the watershed’s comprehensive management and sustainable land use. The installation of control measures should be given special emphasis in areas with high and severe soil erosion hotspots to improve the local inhabitants’ standard of living. Hence, to preserve the local people’s access to food in the study region, the government and local community should put in place mechanisms to protect against soil erosion-related topsoil loss and to reduce climate change.

Authors’ Contributions

Anteneh Wubet wrote the original manuscript. Derege Tsegaye Meshesha, Anteneh Wubet Belay, Gizachew Ayalew Tiruneh, Enyew Adgo, Tiringo Yilak Alemayehu, Chandrakala M., and José Miguel Reichert conceived and designed the experiments, performed the experiments and analyzed and interpreted the data, and reviewed the paper [79].

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