J
OURNAL OFD
EGRADED ANDM
ININGL
ANDSM
ANAGEMENTVolume 10, Number 4 (July 2023):4749-4759, doi:10.15243/jdmlm.2023.104.4749 ISSN: 2339-076X (p); 2502-2458 (e), www.jdmlm.ub.ac.id
Open Access 4749 Research Article
Modeling the impact of land use/land cover change on soil erosion: in Suluh River Basin, Northern Ethiopia
Hailay Hagos Entahabu1,2*, Amare Sewnet Minale1,3, Emiru Birhane4,5
1 Department of Geography and Environmental Studies, Bahir Dar University, Bahir Dar, Ethiopia
2 Department of Geography and Environmental Studies, Debre Tabor University, Debre Tabor, Ethiopia
3 Bahir Dar University, Spearheads Climate Resilient Green Economy Research in North Western Ethiopia (BDU-IUC Programme), Bahir Dar University, Bahir Dar, Ethiopia
4 Department of Land Resources Management and Environmental Protection, Mekelle University, Mekelle, Ethiopia
5 Faculty of Environmental Sciences and Natural Resource Management Norwegian, University of Life Sciences, Åas, Norway
*corresponding author- [email protected]
Abstract Article history:
Received 13 December 2022 Accepted 17 March 2023 Published 1 July 2023
The Suluh river basin is subjected to soil erosion due to land use and land cover change. Yet, the impact of land use/land cover change soil erosion has not been applied in the study area. Thus, the current study focused on the modeling of the impact of land use/land cover changes on soil erosion in the Suluh river basin, the northern highland part of Ethiopia. Landsat image data sources were used to achieve the objectives. Ancillary data was also used. The nearest neighbor's fuzzy way of classification and the land change modeler for modeling, and the revised universal soil loss equation model for estimating the soil erosion rate were employed. Both qualitative and quantitative data were analyzed qualitatively and quantitatively. The study's findings confirmed that cultivated land, bare land, and built-up areas significantly increased while areas occupied by natural vegetation such as forest land, shrub lands, and grazing lands decreased at a rapid rate in between 1990 to 2018. The predicted results suggest the continuation of the trend up to 2048 if business as usual is continued. The annual mean of soil lost in the study area was about 36.31, 43.32, and 47.78 in the years 1990, 2002, 2018, and will be 56.54, and 71.62 tons per hectare per year in 2028, and 2048, respectively. When we consider 15 t ha−1 year−1 as the maximum tolerable soil loss (TSL) rate for ease of presentation, the areas above the TSL have increased from 88.3% in 1990 to 88.6% in 2002 and to 89.6% in 2018, and are expected to increase to 89.9% and 99.8% in 2028 and 2048 periods, respectively. Thus, land use and land cover change information and its impact on soil erosion should be taken under consideration by land use planners to apply sustainable land management activities in the Suluh river basin.
Keywords:
land change modeler land use/land cover soil erosion rate
Universal Soil Loss Equation
To cite this article: Entahabu, H.H., Minale, A.S. and Birhane, E. 2023. Modeling the impact of land use/ land cover change on soil erosion: in Suluh River Basin, Northern Ethiopia. Journal of Degraded and Mining Lands Management 10(4):4749- 4759, doi:10.15243/jdmlm.2023.104.4749.
Introduction
Land use/ cover changes (hereafter LULCC) (Sharma et al., 2011) and soil erosion (Pimentel 2006; Blanco and Lal, 2008; Pimentel and Burgess, 2013) are major
global environmental issues. Soil erosion is mainly caused by anthropogenic factors (Mekuria, 2005) one is LULCC and natural factors. Globally, ten million hectares of croplands have been lost every year due to soil erosion (Pimentel, 2006). Africa is more severely
Open Access 4750 affected by soil erosion with about 500,000 affected by
it (Blanco and Lal, 2008). According to Lambin and Geist (2006) in Ethiopia, about 57% and 28% of the area are moderately and severely affected by soil erosion. Hurni (1993) also revealed that in the highlands of Ethiopia, rates of annual soil loss reached as high as 200-300 t ha-1 year-1, which could amount to 23.4 × 109 tons of soil annually. But, Abate (2011) disclosed that the rate of soil erosion increases on the steeper slope and scant vegetation coverage. Ethiopian highlands soil erosion rate was estimated to be 35 t ha-1 year-1 (Mitiku et al., 2006); where steep topography, rapid deforestation, and early settlements existent (Reusing et al., 2000; Hurni et al., 2005;
Bewket and Teferi, 2009).
Regarding the soil erosion estimation rate in Ethiopian highlands, there are inconsistent findings may be because of spatial and temporal variations.
Hurni (1988) indicated that soil loss in cultivated lands reaches 16-300 t ha−1 year−1 while Zeleke (2000) revealed 130-170 t ha−1 year−1. A study conducted by Hurni (1993) revealed that 42 t ha−1 year−1 in cultivated land while FAO (1986) had shown 100 t ha−1 year−1. EFAP (1993) showed about 1.9-3.5 billion tons of yearly topsoil loss while Taddese (2001) estimated 1.5 billion. Sonneveld and Keyzer (2003) predicted and warned that Ethiopia's potential production capacity of land will be reduced by 30% from 2010 to 2030 in the highlands of Ethiopia. Having such different findings needs an empirical investigation into the problem at the local catchment level.
In the Suluh River basin (hereafter SRB), LULCC, population growth, and lack of alternative livelihood strategies led the dwellers to aggravate the soil erosion. To avert the global environmental disaster being brought about by soil erosion, it is imperative to estimate the soil erosion rate at the basin level which it has a baseline for sustainable land management stakeholders. Additionally, relating LULCC with an estimation of soil loss in the SRB is very important for land use planners. However, the lack of baseline data on the estimation of the annual soil erosion rate in relation to LULCC was not yet investigated in the SRB. This led to aggravating soil erosion rate with the rapid expansion of LULC in the study area. Therefore, the current study focuses on modeling the impact of LULC changes on soil erosion in SRB for the years
2028 and 2048. The intention of the present research is motivated to fill this gap in scanty data and to inform local policy for better implementations of sustainable land management.
Materials and Methods The study area: Suluh River Basin
SRB is found in the northeastern part of the Tigray region, northern Ethiopia. The geographic location of SRB extends from 39°24'59.06" E to 39°26'22.73" E latitude and 13°38'18.27" N to 14°13'53.29" N longitude (Figure 1). The total area coverage of SRB is about 930 km2 and its elevation varies from 1700 to 3,298 meters above sea level. The study watershed falls in four districts (Tsaeda Emba, Hawuzen, Kiltie Awlealo, and Degua Tembien) of the Eastern and South Eastern zone of Tigray.
Sources and processing of data for LULC change detection and prediction
Free satellite images (Landsat-5 TM of 1990, Landsat- 7 ETM+ of 2002 and Landsat-8 OLI-TIRS of 2018) were used for the LULC change analysis of SRB (Table 1). The land sat images that were used for this study are freely available. The Landsat-7 ETM+ 2002 and Landsat-8 OLI-TIRS 2018 of 30 meters pixels were resampled to a 15 meters pixel size. 30-meter Digital Elevation Model (DEM) based on Aster imagery and ancillary data was also employed. A field survey was carried out in 2018. The pre-processing and processing were made using ERDAS 2014 software. We applied nearest neighbor fuzzy classification (eq1) in eCognition Developer 9.2 software as per the LULC types listed in Table 2.
A = {(X, µA(x)); x ϵ X}, where µA → [0,1] (1) where A = fuzzy set, X = a space of objects, X = elements belonging to space X, µ – membership function.
In this study, overall accuracy (Eq2) and Kappa coefficient (Eq3) were used to assess the accuracy of the classified images. As a result, we found an excellent accuracy, in which the kappa coefficient results reveal 0.886, 0.883 and 0.852 for the years 1990, 2000, and 2018, respectively (Table 3).
Table 1. The characteristics of land sat satellite data.
Sensor Path / row Acquisition
Time
Spatial Resolution Resolution
Sensor
Landsat TM 169/50 February/1990 30 m TM
Landsat ETM+ 169/50 February/2002 15 m ETM+
Landsat OLI-TIRS 169/50 February/2018 15 m OLI-TIRS
Aster DEM 30 m
Topo-sheet map 1:50,000
Field data Nov.2017-Jan 2018
Road, rivers and town map District boundary
Village boundary
Open Access 4751 Figure 1. Location map of the study area, Suluh river basin in Tigray, northern Ethiopia.
Table 2. Land cover, Land use types and their descriptions.
LU/LC Classes Descriptions
CL Areas covered by crops in both irrigation and subsistence farming.
FL Areas covered by forests mainly better canopy
GL Areas covered by grasses including the closed and free grazing land
SBL This category contains low woody plants that typically grow vertically and are less than three meters tall with many stems.
BL Vacant spaces with little to no vegetation cover that may also have exposed soil or bedrock.
BUL Areas for construction sites and towns
PL Areas composed of Cactus, Eucalyptus globules and Cupresus spp.
WB Includes lakes (both man-made and natural lakes), rivers, and reserves, among other things.
Note: Forest land (FL), cultivated land (CL), shrub-bush land (SBL), built up land (BU), grazing land (GL), bare land (BL), plantation land (PL) and water body (WB).
Since the values fall above the cut point of the standard overall classification accuracy level of 85% (Fleiss et al., 2003; Doxani et al., 2008; Congalton and Green, 2019) with no class less than 70% (Kumar et al., 2018).
OA= ∗ 100 (2)
where OA is overall accuracy, x is the number of correct values in diagonals of the matrix, and y is a total number of values taken as a reference point.
K= ∑ − ∑ ( × + 1)/ 2 − ∑ − ∑ ( ∗ + 1) (3)
where: r is the number of rows in the matrix, Xii is the number of observations in rows i and column I (along the major diagonal), Xi+ is the marginal total of row i (right of the matrix), Xi+1 is the marginal totals of column i (bottom of the matrix), N is the total number of observations, and K is Kappa coefficient.
Open Access 4752 Table 3. Summary of error matrixes for the classified images of 1990 and 2002.
LULC Classes 1990 2002
User Accuracy Producer Accuracy User Accuracy Producer Accuracy
BL 56% 100% 65% 100%
BUL 88% 100% 89% 100%
CL 100% 55% 100% 56%
FL 100% 94% 100% 100%
GL 100% 94% 100% 100%
PL 86% 100% 88% 100%
SBL 78% 100% 79% 100%
WB 94% 100% 100% 100%
Overall Accuracy 87.12121212 89.79592
Kappa Accuracy 0.852591473 0.883327
LULC Modeling using Land Cover Modeler (hereafter LCM) was applied in IDEISI software. The LCM was originally designed to manage biodiversity influences, and to analyze and forecast LULC changes (Ramachandra et al., 2013; Dzieszko, 2014; Mishra et al., 2014; Roy et al., 2014; Megahed et al., 2015;
Gibson et al., 2018; Ansari and Golabi, 2019). This model predicts the LULC changes from the thematic raster images having the same number of classes in the same sequential order (Mas et al., 2012). In this study, the LCM is used to forecast the future LULC changes SRB for 2028 and 2048.
Sources and processing of data for RUSLE factor values
This model originally developed by Wischmeier and Smith (1978) and modified by Renard et al. (1997), was used to predict soil loss in the domain in the Basin (Eq4).
A= R × K × LS × C × P (4)
where A is the annual soil loss (Mg ha-1 year-1), R is the rainfall erosivity factor (MJ mm ha-1 h-1 year-1), K is the soil erodibility factor (Mg h MJ-1 mm-1), L is the slope length factor (unit less terrain factor), S is the slope steepness factor (unit less terrain factor), C is the crop management or land cover factor (unit less vegetation cover factor), and P is a dimensionless erosion control practice factor.
Annual soil loss (A) was computed by overlaying five raster layers over the SRB using Eq4. The raster layers represented rainfall erosivity factor (R), soil erodiblity factor (K), topographic factor (LS), cover management factor (C), and land management factor (P).
In this study, the analysis of each process factor was derived as follows:
(A) Rainfall erosivity (R) factor: - rainfall erosive quantifies the effect of rainfall impact and also reflects the amount and rate of runoff likely to be associated with precipitation events (Xu et al., 2008). Erosivity can be predicted by a suitable regression equation in the case of insufficient rainfall records (Hellden, 1987;
FES, 2009). The R-factor (eq 5) adapted from Hurni (1985a, 1985b) is given by a regression equation:
R = -8.12 + 0.562P (5)
where R is the erosivity factor and P is the mean annual rainfall in mm.
The mean annual rainfall data of the area from the years 1988 to 2018 was taken from five local stations (Adigrat, Edaga Hamus, Frewainy, Hawuzen Wukro, and Hagere Selam) and interpolated using the Ordinary Kriging method in ArcGIS10.5 software.
Then, the R-value of each grid cell was calculated using Eq(5), and the raster calculator Geo-processing tool. Hence, the maximum and minimum rainfall in SRB was 353.83 and 316.22 mm per year (Figure 3(A)).
(B) Soil Erodibility (K) factor: Hurni (1985a); Hellden (1987), and Renard et al. (1997) recommended the K values (ranges from 0 to 1) based on easily observable soil color as an indicator for the erodibility of the soil in the highlands of Ethiopia. Thus, the soil types of SRB (Table 4) were classified based on their color by referring to the soil database of the Tekeze river master plan. As indicated in Figure 3 (B) the K- value in the study area ranges from 0.163 to 0.126.
(C) Topographic parameters (LS factor):-This study employed the following advanced LS factor computation method (eq6) based on the upslope contributing area suggested by (Desmet and Govers, 1996a; Moore and Burch, 1985, 1986, 1992;
Mitasova and Mitas, 1999; Simms et al., 2003).
LS = (As/22.13)0.6 (sinB/0.0896) 1.3 (6) where: LS is slope steepness–length factor, As is the specific catchment area (flow accumulation*cell size), and B is the slope angle. LS-factor (Eq.7) was done based on an indication of Mitasova and Mitas (1999) and Simms et al. (2003).
POW ([flow accumulation]* cell size /22.13, 0.6)*
POW (sin ( [slope] * 0.01745)/0.0896, 1.3) (7).
This study, therefore, used the above-mentioned modified and advanced approach to determining slope length and gradient (LS) factor. The value of S was directly derived from 20m resolution DEM. Similarly, flow accumulation was derived from the DEM after conducting fill and flow direction processes in Arc GIS 10.5 in line with the Arc Hydro tool. The flaw accumulation grid represents several grid cells that are contributing for down ward flow and cell size
Open Access 4753 represents 20 m*20 m contributing area. As indicated
in Figure 3 (C) the LS value in the study area ranges from 6.23 to 0.344.
(D). Crop Management Factor (C) :- represents the ratio of soil loss under a given crop to that of the bare soil (Morgan, 1994) ranging from 0 to 1. In this
research, the C-factor is the variable to show its potential impact on soil erosion. After changing the coverage to the grid, a corresponding C-value was given to each LULC class (Table 5 and Figure 2).
Then, Rasterization and Reclassification method in ArcGIS10.5 was used to make it suitable for model input and thematic map generation.
Table 4. Estimated K-values for some soils in Ethiopia.
Soil Types Area
(km2)
Area (%)
Estimated K value (metric tons ha–1 mj–1 mm–1)
Reference
Chromic Cambisols 23.6 2.5 0.2 Moges and Bhat (2017), Gashaw
et al. (2019)
Eutric Leptosols 165.9 17.8 0.2 Zerihun et al. (2018) and Gashaw
et al. (2019)
Haplic Lixisols 384.7 41.4 0.25 Gelagay and Minale (2016) and
Zerihun et al. (2018)
Lithic Leptosols 210.9 22.7 0.2 Worku (2010)
Vertic Cambisols 144.93 15.6 0.15 Gelagay and Minale (2016),
Zerihun et al. (2018)
Table 5. The C-value for different LULC types as given by different authors.
LULC types C-value References
Barland 0.18
Builtup land 0.004 SWCS (2003)
Forest land 0.001 Hurni (1985a)
Cultivated Land 0.17 Hurni (1985); Brhane and Mekonen (2009)
Shrubs and bush land 0.014 Wischmeierand Smith (1978)
Grazing Land 0.01 Eweg and van Lammeren (1996)
Plantation land 0.13 SWCS (2003)
Water body 0.0 Ongsomwang and Thinley (2009)
Figure 2. The C-value of A (1990), B (2002), C (2018), D (2028) and E (2048).
A B C
D E
Open Access 4754 Figure 3. R-factor (A), K-factor (B), LS-factor (C) and LS-factor D (1990), E (2002), F(2018), G (2028),
H (2048).
(E) Conservation Practices Factor (P) :- is the ratio of soil loss with a specific support practice to the corresponding loss with upslope and downslope cultivation (Wischmeier and Smith, 1978) ranges from 0-1(Table 6 and Figure 2).
Table 6. Values of erosion control practices (P-value) for the two LULC categories.
LULC type Slope (%) P-factor
Cultivated land 0-5 0.1
5-10 0.12
10-20 0.14
20-30 0.19
30-50 0.25
50-100 0.33
Other Land All 1.00
Source: Wischmeier and Smith (1978).
The classified LULC and slope thematic map format were changed to vector format and the corresponding P -values were assigned to the combination of each land use/land cover and slope classes, and a raster map of the P factor was produced. As indicated in Figure 3 (D-H) the P value in the study area ranges from 1 to 0.
In this research, the P- factor is the variable to show its potential impact on soil erosion.
Results
LULC change Analysis and Prediction
Table 7 indicates that BL by 8.1%, BUL by 17.2%, PL by 20.42, and CL by 199.9% showed increased trends between 1990 and 2002 years. On the opposite, FL by 14.04%, GL by 90.35%, SBL by 135.27%, and WB by 6.01% revealed reduction trends. The predicted results suggest a continuation of the trend up to 2048 if business as usual is continued. Table 7 indicated that BL by 2.2%, PL by 5%, BUL by 5.7%, and CL by 6.1% will be shown an increasing trend between 2018 and 2048 years. On the opposite, FL by 2%, GL by 7.7%, SBL by 9%, and WB by 0.3% will be revealed a reduction trend. Hence, such like LULC change could have an impact on soil erosion.
The impact of LULC change on soil erosion The LULC changes that happened from 1990 to 2018 periods had increased the mean annual soil erosion rate from about 36.31 t ha−1 year−1in 1990 to 47.78 t ha−1 year−1 in 2002 and to 56.54 t ha−1 year−1 in 2018 (Table 8). The prediction result also shows that the process of soil erosion is expected to continue in the 2028 and 2048 periods if the trends of rapid LULC changes have been continuing. Cultivated land shows highest mean annual soil loss rate 37.75 t ha−1 year−1in 1990 to
A B C D
E F G H
Open Access 4755 59.25 t ha−1 year−1 in 2018, which is greater than the
soil loss rate estimated for the whole basin (e.g., 36.31 t ha−1 year−1in 1990 and 47.78 t ha−1 year−1 in 2018) (Table 8). In contrast, the average annual soil loss rate estimated from the forest, and shrub-bush lands is lower than the erosion rate estimated from cultivated land. The main reason for this increase in the soil erosion potential of the catchment over the studied
period could be attributed to expansions of cultivated, built-up, and bare land at the expenses of vegetation (like forest and shrub-bush) land and grazing land.
According to Figure 4, the estimated soil erosion rate was classified into five severity classes such as very slight (0-5 t ha−1 year−1), slight (5-15 t ha−1 year−1), moderate (15-30 t ha−1 year−1), severe (30-50 t ha−1 year−1) and very severe (> 50 t ha−1 year−1).
Table 7. LULC Change trends in from 1990 to 2048.
LULC LULC Area (km2) Trends of Change(%)
Classes 1990 2002 2018 2028 2048 1990-02 1990-18 2018-28 2018-48
BL 82.5 91.2 90.6 92 94.2 8.1 11 0.6 2.2
BUL 12.7 16.4 29.9 33 38.7 17.2 29 1 5.7
C L 351 580.5 551 586 592 199.94 65 0.6 6.1
FL 24.4 0.7 10.3 8 6 -14.04 -97 -2 -2
GL 186 18.9 95.6 73 65.4 -90.35 -90 -2 -7.7
PL 46.7 5.1 67.2 71 76 20.42 8.9 0.6 5
SBL 219 216.1 84.1 66 57 -135.27 -1.5 -2 -9
WB 7.6 1.2 1.54 1.02 0.68 -6.01 -8.5 -3 -0.3
Table 8. The contributions of each LULC type to the total soil loss in the study area in the 1990-2048 periods.
Study Period LULC Types
BL BUL CL FL GL PL SBL WB
Soil Loss (tons)
1990 36797.0 5683.4 156605.1 10869.0 82969.6 20825.4 97696.7 3401.8
2002 48553.5 8710.9 308940.6 396.0 10047.3 2722.2 114974.2 643.4
2018 53061.0 17577.8 323442.6 6059.4 56118.0 39468.2 49348.9 928.0 2028 63885.9 22931.8 407022.6 5555.3 50708.3 49287.1 45863.5 710.6 2048 82883.7 34037.1 520866.4 5318.3 59401.4 65128.8 50155.7 572.7 Percent (%)
1990 8.87 1.37 37.75 2.62 20.00 5.02 23.55 0.82
2002 9.81 1.76 62.42 0.08 2.03 0.55 23.23 0.13
2018 9.72 3.22 59.25 1.11 10.28 7.23 9.04 0.17
2028 9.89 3.55 63.01 0.86 7.85 7.63 7.10 0.11
2048 10.13 4.16 63.66 0.65 7.26 7.96 6.13 0.07
Table 9. The estimated mean annual soil erosion rate in each LULC type and the entire basin during 1990-2048 periods.
Year Mean Annual Soil Erosion Rate (t ha-1 year-1)
BL BUL CL FL GL PL SBL WB Entire Basin
Soil Loss (tons)
1990 8.87 1.37 37.75 2.62 20.00 5.02 23.55 0.82 36.31
2002 9.81 1.76 62.42 0.08 2.03 0.55 23.23 0.13 43.32
2018 9.72 3.22 59.25 1.11 10.28 7.23 9.04 0.17 47.78
2028 9.89 3.55 63.01 0.86 7.85 7.63 7.10 0.11 56.54
2048 10.13 4.16 63.66 0.65 7.26 7.96 6.13 0.07 71.62
The result revealed that areas with a severe moderate, very slight, and slight level of erosion intensity were diminished from the 1990 to 2048 periods, while the very severe category increased during the same periods.In SRB, the highest soil loss-affected areas are mainly found in the upper and steepest slopes and the lowest was mostly on the gentle slopes. Due to the LULC changes, areas above the tolerable soil loss (TSL) rate were increased throughout the 1990-2048
periods. When we consider 15 t ha−1 year−1 as the maximum TSL rate for ease of presentation, the areas above the TSL have increased from 88.3% in 1990 to 88.6% in 2002 and to 89.6% in 2018, and are expected to increase to 89.9% and 99.8% in 2028 and 2048 periods, respectively (Table 9). The very slight and slight erosion intensities were 9.1-0.1% of the SRB between 1990-2048 years whereas moderate, severe and very severe erosion categories were 24.3-59.4%.
Open Access 4756 Table 10. Soil erosion severity classes (adopted from Haregeweyn et al., 2017) in the SRB from 1990-2048
periods.
Study Years Soil erosion severity classes
Very Severe (>50 t ha-1
year-1)
Severe (30–50 t ha-1
year-1)
Moderate (15–30 t ha-1
year-1)
Slight (5-15 t ha-1
year-1)
Very Slight (0-5 t ha-1
year-1) Area (ha)
1990 184.2 394.1 219.8 81.8 23.3
2002 205.4 379.4 215.4 80.3 22.5
2018 239.2 355.0 189.8 71.2 17.0
2028 295.3 344.1 169.4 70.1 24.2
2048 454.5 257.0 51.8 1.4 0.6
Percent (%)
1990 20.4 43.6 24.3 9.1 2.6
2002 22.7 42.0 23.9 8.9 2.5
2018 27.4 40.7 21.8 8.2 1.9
2028 32.7 38.1 18.8 7.8 2.7
2048 59.4 33.6 6.8 0.2 0.1
Figure 4. Soil erosion severity classes (adopted from Haregeweyn et al., 2017) in the SRB; A (1990), B (2002), C (2018), D (2028) and E (2048).
Discussion
In the current research BL, BUL and CL show increasing trends in the expense of WB, FL, GL, and SBL shows in the years 1990, 2002, 2018, 2028 and
2048. The LULC changes that happened from 1990 to 2018 periods had increased the average annual soil erosion rate from about 36.31 t ha−1 year−1 in 1990 to 47.78 t ha−1 year−1 in 2002 and to 56.54 t ha−1 year−1 in 2018. The prediction result also shows that the process
A B C
D E
Open Access 4757 of soil erosion is expected to continue in the 2028 and
2048 periods if the trends of rapid LULC changes have been continuing. The estimated soil erosion rate of the SRB is related to the previous estimate made in the Ethiopian highlands by FAO (1986), and other studies which were carried out in the same region (i.e., the highlands) such as Meshesha et al. (2012), Gelagay and Minale (2016) Zerihun et al. (2018) and Gashaw et al. (2019).
The study comes up with the finding of that there were shifts from the smallest levels of erosion intensity to the next higher level of erosion category. The result of the present research is generally consistent with the findings of previous studies in the highlands of Ethiopia (Hurni, 1993; Lakew et al., 2000; Gete and Hurni, 2001; Woldeamlak and Sterk, 2005; Mitiku et al., 2006; Gizachew, 2014; Tadesse and Abebe 2014;
Berhan et al., 2015; Moges and Bhat, 2017, and Gashaw et al., 2019). The highest soil loss affected parts were in the upper and steepest areas of the study area are also allied with other findings in the Ethiopian highlands, such as those of Bewket and Teferi (2009), Gelagay and Minale (2016) and (Gashaw et al. 2019).
Moreover, the trend in soil erosion rate in the study area during the 1990-2048 periods was associated with the increase in erosion vulnerable LULC category.
Consistency with the study, the increase in the mean annual soil erosion rate due to the expansions of cultivated land and reductions of vegetation covers was also reported by Sharma et al. (2011), Meshesha et al. (2012) and Moges and Bhat (2017) periods.
Conclusions
In conclusion, an increase of bar land, cultivated and built-up land at the expense of water bodies, forests, shrub-bush and grazing lands have occurred from 1990 to 2018 periods, and the same trends are expected to continue up to 2048. Soil erosion has increased over the past from 1990-2018, and is expected to continue up until 2048. Thereby, the annual mean of soil lost in the study area has about 36.31, 43.32, 47.78, 56.54, and 71.62 tons per hectare per year in the years 1990, 2002, 2018, 2028 and 2048, respectively. The expansions of cultivated land are the foremost contributor to these increases. The tolerable soil loss (TSL) rate was increased from 1990 to 2048 periods due to LULC changes. Therefore, finding urges for concerted efforts toward sustainable land management and diversifying the livelihoods of the local communities in SRB.
Acknowledgements
The authors thank Bahir Dar University and Debre Tabor University in Ethiopia for their financial support. The authors greatly acknowledge farmers and the agricultural development agents of the Watershed for their interest to focus group discussions and for providing necessary information.
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