Prioritization of soil erosion-prone sub-watersheds using geomorphometric and statistical-based weighted sum priority approach in the middle Omo-Gibe River basin, Southern Ethiopia

ABSTRACT

Soil erosion, a major factor contributing to land degradation, poses significant environmental challenges across sub-Saharan Africa, including Ethiopia. This study identifies erosion-prone sub-watersheds in southern Ethiopia's middle Omo-Gibe River basin using digital elevation model data, analyzed using a geomorphometric and statistically weighted sum priority approach in a Geographic Information System environment. Additionally, hypsometric analysis, lithology, land use/cover analysis, and field observations were integrated to enhance methodological reliability in assessing soil erosion patterns and subsequent land degradation in the basin. Results highlight sub-watersheds with varying degrees of erosion sensitivity, emphasizing morphometric parameters and landscape characteristics. Furthermore, hypsometric analysis confirms a moderate to high susceptibility of sub-watersheds to soil erosion. Moreover, half of the river basin consists of shrubland, encompassing scrubs, range lands, and small wooded areas. These are less efficient in mitigating erosion compared to forests, especially in the face of significant summer rainfall and ensuing runoff. Statistical prioritization identified 19 sub-watersheds, covering 43% of the basin's total area, as highly prone to soil erosion and land degradation. The study's findings are valuable for Ethiopian watershed management decision-makers, enabling informed resource allocation and targeted interventions to control soil erosion and protect water resources by utilizing the outputs of this study.

KEYWORDS:

• Soil erosion
• land degradation
• middle Omo-Gibe River
• geomorphometry
• weighted sum priority

1. Introduction

Land degradation is a growing concern globally, particularly impacting the economies and environments of developing regions due to its close association with ecological resource utilization (Berkhout et al. 2021; IPBES 2018). This, coupled with desertification and drought, has already impacted over three billion people, especially disadvantaged rural communities, small-scale farmers, indigenous groups, and other vulnerable populations worldwide (Hossain, Krupnik, and Timsina 2020; Nachtergaele et al. 2011). Considering the extent and impacts of problems incurred through land degradation, the United Nations Convention to Combat Desertification has recognized it as a significant socioeconomic and environmental challenge, particularly in the world's arid regions (UNCCD 2020). Subsequently, the global discourse on environmental issues prioritizes addressing land degradation due to its adverse environmental impact and potential to increase threats to global food security. Soil erosion, influenced by natural elements such as water and wind, which are further modulated by human activities, stands as a primary contributor to land degradation (Ziadat et al. 2021).

Conversely, land degradation can worsen soil erosion by compromising soil structure and reducing its capacity to retain moisture and nutrients, making the land more prone to erosion by water and wind (Kirui, Mirzabaev, and von Braun 2021). Furthermore, the accelerated global rate of soil erosion degrades the depletion of agricultural productivity by removing topsoil from farmland, particularly impacting impoverished developing regions such as the African continent. For instance, the global erosion rate was estimated at 35.9 peta-grams per year using a global potential soil erosion model (Mosbahi, Benabdallah, and Boussema 2013). Moreover, according to Africa's Economics of Land Degradation report, about 375.6 and 61.5 million ha of land have experienced loss of topsoil and chemical degradation from soil erosion, respectively (Pushpam Kumar et al. 2015). The African continent, particularly Sub-Saharan Africa (SSA) is mainly vulnerable to land degradation, and it is the most severely affected region, with around 55% of the land areas at high risk of further degradation (UNEP 2023).

Among the SSA countries, Ethiopia is particularly threatened by land degradation due to high dependence on agriculture, population growth, frequent famine, and poverty. In Ethiopia, soil erosion by water severely threatens the national economy and costs around US$ 100 billion annually (Bewket and Teferi 2009; Yesuf et al. 2007). Ethiopia's measured average soil loss ranges between 35–300 tons per hectare per year (Fenta et al. 2021). The Ethiopian highlands are mostly influenced by severe land degradation due to the combination of deforestation, rainfall erosivity, steep slopes, overgrazing, and agricultural systems like stagnation agricultural techniques (Nyssen et al. 2015). Consequently, land degradation affects over 85% of the population in the highlands and over 40 million hectares of land in Ethiopia (Samuel Gebreselassie and Mirzabaev 2016). The Omo-Gibe River (OGR) basin is a significant part of the highlands, and shows mean annual soil loss exceeds 60 tons per hectare per year (Girma and Gebre 2020; Keddi Lencha and Moges 2016; Tesfaye 2018). As a result, the problem of soil erosion and land degradation is still a significant issue in much of the Ethiopian highlands, and it is likely to get worse in the future due to projected population growth and the recurrence of extreme precipitation events during the twenty-first century (Nyssen et al. 2015). Therefore, soil erosion assessment is a primary goal in a decision support system for reversing the ever-increasing problem of land degradation. Many studies worldwide, including Ethiopia, have started to look at the situation and have developed various assessment and monitoring methods to study issues at the watershed scale.

Moreover, the study of soil erosion and consequential land degradation at the watershed scale is not considered simply as the hydrological unit but also as a socio-economic and ecological entity. It is crucial to comprehend the optimum interaction and synergetic effect of land and water resources in the context of natural and human factors (Arabameri et al. 2020; Hossain, Krupnik, and Timsina 2020; Kant, Kumar, and Meena 2023). In recent years, the use of watersheds as the fundamental unit in morphometric analysis has gained prominence. Remote sensing (RS), in conjunction with Geographic Information Systems (GIS), has emerged as a powerful tool for capturing and expressing various aspects of the current climate, natural resource degradation, structural characteristics, geomorphology, and geology of watersheds. The geospatial-based analysis of geomorphometric parameters is a widely used technique for characterizing, classifying, mapping, and presenting various geological, topographic, and hydrological characteristics of watersheds and their geophysical and environmental processes (Anshumali1 2019; Hossain, Krupnik, and Timsina 2020; Subhash Chavare 2013). It also provides a quantitative description of the drainage system, relief features, hydrological processes, shape, and geological formations of the watersheds (Strahler 1957).

Moreover, the morphometric analysis also helps to identify the relationship between geomorphometric parameters and their effects on soil erosion, erodibility, and land degradation in watersheds (Gajbhiye, Mishra, and Pandey 2014; Malik, Kumar, and Kandpal 2019; Rahaman et al. 2015; Rai et al. 2017; Verma, Patel, and Choudhari 2023). The morphometric parameters can be measured by grouping them into essential characteristics, linear features, drainage area, and landscape/relief aspects of morphometry. These aspects, directly and indirectly, determine the degree of soil erosion and land degradation and are important indicators for the planning and implementation of watershed development strategies and programs (Boitt and Bebeto 2020; Kumar et al. 2022; Rahaman et al. 2015; Wagh, Manekar, and Vallabhbhai 2021). Moreover, the land degradation and topological changes within watersheds, accomplished by weathering processes, stream erosion patterns, and sediment transportation by surface runoff, are determined by the hypsometry of drainage basins (Strahler 1957). The hypsometric curve is related to the volume of the soil mass in the basin and the amount of erosion that had occurred in a basin against the remaining mass (Francis Lucazeau 1997). Besides, examining land use practices through land use or cover maps aids in understanding land use conversion resulting from unsustainable practices such as excessive wood harvesting, overgrazing, and intensified agriculture on arable lands and steep slopes. These practices accelerate the depletion of ecosystem services and the process of land degradation within the watershed (Bajocco et al. 2012; Choto and Fetene 2019; Mirzabaev et al. 2016). Therefore, combining hypsometric analysis, land use/cover analysis, and geomorphometric analysis supported by field observations can strengthen the methodological reliability when assessing soil erosion patterns, a preference evident in recent studies.

The OGR basin, a significant part of Ethiopia's south-central highland, has been attracted to observe soil erosion severity in recent years. Moreover, the majority of the catchments in the river basin are occupied and cultivated by a vast number of smallholder farmers. The poor land management methods, combined with the area’s rugged topography, erosive rainfall pattern, and nutrient depletion, pose severe to the farmers’ livelihood (Dagnachew et al. 2020; Dagne, Assefa and Teferi 2024). Besides, the Middle Omo-Gibe River (MOGR) basin is currently renowned for its Omo-Gibe III hydroelectric power plant, which has a substantial supply of energy sources for the country. However, the reservoir is continuously threatened by soil erosion and subsequent sedimentation from dense stream networks and sub-watersheds of the basin, resulting in siltation and nutrient enrichment, creating significant problems in the reservoir (Kanito, Bedadi, and Feyissa 2023).

In recent years, efforts have been made to quantify and map soil erosion-prone areas using various remote sensing-based analyses in the Ethiopian highlands, including the OGR basin. However, most previous studies have mainly focused on the physical forms of degradation and have applied single parameters to monitor the risks of soil erosion and land degradation. These studies primarily analyzed the cause and consequences of land degradation using field surveys, and few of them analyzed morphometric factors for soil erosion assessment (Berkhout et al. 2021; Meseret 2016; Wubie 2015). However, there were no significant previous research studies in the river basin that addressed the inter-consequential relationships between geomorphometric factors and soil erosion in the context of prioritizing sub-watersheds prone to soil erosion (Dagne et al. 2024; Fenta et al. 2021; Mohammed, Adugna, and Takala 2018). The present study aims to fill the gaps mentioned above by analyzing the detailed geomorphometric parameters of more than 90 sub-watersheds of the MOGR basin, using the geospatial and statistical techniques with the supplement of field observations to prioritize the sub-watershed in terms of their erosional potential for the sustainability of agriculture, natural resource management, and livelihood activities. By leveraging morphometric and hypsometric features and weights derived from the correlation matrix, the current research highlights the effectiveness of a non-subjective statistically computed weighted sum priority (WSP) method for ranking sub-watersheds prone to soil erosion. The study also highlights the influences of morphometric factors on soil erosion and land degradation, which have been neglected in previous studies. The findings from this research significantly contribute to the knowledge of assessing, quantifying, and mapping the distribution and severity of soil erosion, thus addressing the growing concerns of land degradation in the MOGR basin of southern Ethiopia.

2. Regional settings of the study area

The study is carried out in southern Ethiopia's sub-watersheds of the MOGR basin. It is extended between the geographic coordinates of 6° 06’ 23'‘ N -7° 23’ 15” N latitude and 36° 17’ 35” E – 37° 56’ 23” E longitude (Figure 1(a)). The MOGR basin covers a total basin area of 14,382.3 km². The basin, part of the Omo River, holds economic significance for the country, supplying approximately 2,474 megawatts (MW) of hydroelectric power through its series of power plants constructed within the basin. The physiography of the river basin is characterized by mountains, sloppy hills, dissected terrain features, steep-sided river valleys, small plateaus, and prolonged gulleys influenced by the river and its tributaries. The relief of the basin ranges from 491 meters (m) to 3554 m above mean sea level (a.m.s.l) (Figure 1(a)). It spans the south-central highlands of Ethiopia, including Hambaricho, Damota, Guge, Dawuro, and Konta highlands. The basin exhibits dense stream networks, deep gorges, and waterfalls. For instance, the Ajora waterfalls, located between the Kambata and Wolaita administrative zones, with substantial volume of rivers flow contribute significantly to the water and sediment flow of the MOGR basin (Figure 2(c)). The climate of the MOGR basin typically includes tropical, sub-humid, and semi-arid environments. It varies from hot and semi-arid climates in the southern part of the basin to tropical and humid in the highlands (Chaemiso, Abebe, and Pingale 2016). The mean annual rainfall ranges from 800 millimeters (mm) to over 1800 mm, and the mean yearly temperature is around 23°C. The geology of the MOGR basin is dominated by fault feature origin associated with the Great Rift Valley of Ethiopia (Gathogo 2017). It comprises the geological processes and events of the Precambrian to the Quaternary period of the Cenozoic era (Figure 1(b)). It dominantly comprises the tertiary extrusive and intrusive rocks followed by Quaternary extrusive and intrusive rocks and Precambrian rocks, which are relatively soft and weathering rocks particularly susceptible to soil erosion (Figure 1(b,c)). Most of the geomorphology of the river basin was highly disturbed by exogenesis processes such as landslides, weathering and erosion, and human activities. The dominant soil types covering the study area are Eutric Nitosols, Eutric Combisols, Orthic Acrisols, Ochric Andosols, Calcaric Fluvisols, Heplic Yermosols and Plinthic Ferralsols (Montanarella Luca et al. 2015).

Figure 1. Study area description: (a) Location of study area, (b) Geology, and (c) Lithology.

Figure 2. Drainage system of the MOGR basin: (a) drainage network map, (b) Shapa River, (c) Soke and Ajacho Rivers falling into Ajora twin waterfalls, and (d) Omo Gibe River.

3. Databases and methodology

3.1. Data sources and characteristics

A better resolution and accurate Digital Elevation Models (DEMs) are essential prerequisites for delineating watersheds and sub-watersheds, conducting morphometric analysis, and prioritizing sub-watersheds. This data consists of a grid of elevation values, enabling detailed quantitative studies of landforms and related topographic features. Several global open-source DEMs are available and usually used for morphometric and watershed prioritization studies, e.g. SRTM3v4 DEM, ASTER GDEM V2, ALOS World 3D (AW3D), ALOS PALSAR RT1, TanDEM-X DEM, NASADEM, Copernicus DEM (Adiri et al. 2022; Chymyrov 2021). The current study preferred and used the AW3D DEM among available open-source DEMs because of its better vertical and horizontal accuracy. It is also known as a world-leading accurate and precise global-scale elevation data source. AW3D DEM is a global DEM dataset derived from data collected by the Advanced Land Observing Satellite (ALOS), operated by the Japan Aerospace Exploration Agency (JAXA). This dataset provides global coverage with a spatial resolution of ∼ 30 meters. Numerous researchers across various global regions, including different areas within Africa, have evaluated the accuracy or suitability of this DEM in comparison to locally available or obtained datasets (Buckley et al. 2020; Ghannadi et al. 2023; Rahman et al. 2022; Santillan, Makinano-Santillan, and Makinano 2016). Their findings consistently highlight the AW3D DEM as their regions’ most accurate elevation model (Rahman et al. 2022; Tadono et al. 2014). Besides, the mean, standard deviation, and RMSE of AW3D DEMs versus 5121 control points distributed across 127 image tiles were −0.44, 4.38, and 4.40 m, respectively (Takaku, Tadono, and Tsutsui 2014). This DEM was downloaded from open sources of Opentopography databases (https://portal.opentopography.org/datasetMetadata). The dataset was transformed from the Geographic coordinates of the World Geodetic System (WGS)-1984 to the Universal Transverse Mercator (UTM) Zone-37 North (N) projected coordinate system of the study region. This conversion facilitated further processing and extraction of morphometric parameters in the metric system. In addition, the geomorphology/geology, soil maps, published reports provided by different organizations and researchers, and multi-spatial resolution satellite dataset of Sentinel-2A MultiSpectral Instrument (MSI) and Google Earth maps were taken as ancillary datasets for improving the results and extracting other parameters like land use/cover, soil, and lithology to support the inferences calculated in this study.

3.2. Delineation of stream network and sub-watersheds

The spatial analysis tool built within ArcGIS 10.8.2 is used to compute the flow accumulation threshold (FAT), generate stream networks, and delineate sub-watersheds. The FAT limit for extracting the stream network and then delineating sub-watersheds has been determined using Equation 1. (1) $\mathrm{FAT}={\log }_{10}(F{A}^{\ast }\mathrm{CS}{)}^{\ast }\text{\%}\mathrm{of}\mathrm{MFA}$(1) Where FA refers to flow accumulation, CS is DEM cell size, and MFA refers to maximum flow accumulation limit, a user-defined value. The user-defined FAT value is determined based on the density of the drainage raster. This value is instrumental in hydrological modeling and watershed analysis, as it determines the delineation of watersheds, identification of stream networks, and various hydrological assessments. Upon defining the user-defined FAT limit, the stream raster is processed, and the stream network of the watershed is defined (Figure 2(a)). Hence, this study meticulously selected and calibrated the FAT to accurately depict flow patterns and comprehensively analyze watershed dynamics, including associated environmental processes.

3.3. Geomorphometric analysis

Geomorphometry, a quantitative analysis of digital topographic surfaces, is a widely available tool crucial for watershed prioritization and assessment of soil erosion and land degradation (Pike and Wilson 1971). It relies on the assumption that a form or surface contains valuable information about functionality, inner composition, and genesis. Morphometric analysis, initially introduced by Horton (1945) in hydrology, has been pivotal in understanding stream characteristics. The stream network, essential for comprehending landform processes, physical soil traits, and land degradation states, can be more effectively analyzed by considering the drainage basin's areal, linear, shape, and relief features (Biswas 2015; Schumm 1956). Moreover, integrating morphometric data with other geospatial datasets, such as hypsometry, land cover, soil types, precipitation, and so on, enhances understanding of the complex interactions between landforms and hydrological processes (Raja Shekar and Mathew 2024). Accordingly, more than 30 morphometric parameters, i.e. basic, linear, drainage area, shape, and landscape/relief aspects, have been calculated and examined over 96 sub-watersheds within the study region (Figure 3).

Figure 3. Sub-watersheds of the MOGR basin with the central part along the Omo Gibe River referring to the areas of the river valley and its main river channel.

The linear aspect denotes the orientation of stream channels and drainage lines within a watershed, which is crucial for delineating water flow paths and identifying erosion hotspots (Table 1). Meanwhile, the relief aspect encompasses elevation variations, including slope steepness and elevation differences, offering insights into terrain ruggedness that influences hydrological processes (Figure 4). Additionally, the shape aspect refers to geometric characteristics like size, compactness, and drainage pattern, affecting water flow and sediment transport distribution within the watershed. These parameters were employed to prioritize soil erosion in the study watersheds. The ArcGIS 10.8.2 hydrology toolbox automatically extracted these parameters. Additionally, the study utilized the natural break technique, or the Jenks optimization method, to classify and group data into categories. This method is ideal for situations with distinct breaks or clusters in data distribution, facilitating meaningful classification into discrete categories or classes commonly applied in GIS and spatial analysis for data like soil erosion rates.

Figure 4. The methodology workflow for geomorphometric analysis and prioritization of sub-watersheds.

Table 1. Morphometric parameters and their standard definitions.

Morphometric Parameters		Definitions/Estimation	References
Basic Parameters	Basin Area (A)	The projected area enclosed by the watershed boundary	(Schumm 1956)
	Basin Length (Lb)	Distance from the outlet to the farthest point	(Schumm 1956)
	Perimeter (P)	The outer boundary of the drainage basin is measured in kilometers.	(Schumm 1956)
	Mean basin width (Wb)	$W_b={\displaystyle \frac{A}{L_b}}$	Horton (1955)
	Maximum Elevation	Maximum Elevation of the watershed above mean sea level	DEM
	Minimum Elevation	Minimum Elevation of the watershed above mean sea level	DEM
	Basin Relief (Bh)	$B_h=\mathrm{Hmax}-\mathrm{Hmin}$	(Schumm 1956)
Linear parameters	Stream Order (U)	Hierarchical Rank	(Strahler 1957)
	Stream Number ($N_u$)	$N_u=N1+N2+\dots \dots +\mathrm{Nn}$	Horton (1945)
	Stream Length ($L_u$)	Length of the Stream	(Horton 1945)
	Mean Stream Length (Lsm)	$L_{\mathrm{sm}}={\displaystyle \frac{L_u}{N_u}}$	(Horton 1945)
	Stream Length Ratio (RL)	$R_L={\displaystyle \frac{L_u}{N_u-1}}$	(Strahler 1957)
	Bifurcation Ratio (Rb)	$R_b={\displaystyle \frac{N_u}{N_u+1}}$	(Strahler 1957)
	Mean Bifurcation Ratio (Rbm)	$R_{\mathrm{bm}}=\left(\mathrm{Rb}1+\mathrm{Rb}2+\dots +{\displaystyle \frac{\mathrm{Rbn}}{n}}\right)$	(Strahler 1957)
	Rho coefficient (ρ)	$\rho ={\displaystyle \frac{L_u}{R_b}}$	Horton (1945)
Drainage areal parameters	Drainage Density (Dd)	$D_d={\displaystyle \frac{L_u}{A}}$	(Horton 1945)
	Stream Frequency (Fs)	$F_s={\displaystyle \frac{N_u}{A}}$	(Horton 1945)
	Drainage texture (Dt)	$D_t={\displaystyle \frac{N_u}{P}}$	Horton (1945)
	Constant of channel maintenance (C)	$C={\displaystyle \frac{1}{D_d}}$	(Schumm 1956)
	Infiltration number(If)	$I_f=\mathrm{Fs}\ast D_d$	(Horton 1955)
Shape Parameters	Shape factor (Sf)	$S_f={\displaystyle \frac{L_b^2}{A}}$	(Horton 1945)
	Form factor (Ff)	$F_f={\displaystyle \frac{A}{L_b^2}}$	(Horton 1945)
	Circulatory ratio (Rc)	$R_c=4\pi \ast {\displaystyle \frac{\mathrm{A}}{{\mathrm{P}}^2}}$	(Schumm 1956)
	Elongation ratio (Re)	$R_e=2\ast {\left({\displaystyle \frac{A}{\pi }}\right)}^{0.5}/L_b$	(Strahler 1957)
	Length of overland flow (Lo)	$L_o={\displaystyle \frac{1}{2D_d}}$	(Melton 1957)
	Compactness coefficient (Cc)	$C_c={\displaystyle \frac{0.2821\ast P}{\sqrt{A}}}$	(Horton 1945)
Landscape Morphometric Parameters	Relief Ratio (Rh)	$R_h={\displaystyle \frac{B_h}{L_b}}$	(Schumm 1956)
	Absolute Relief (Ar)	$\mathrm{Maximum}\mathrm{height}\mathrm{in}\mathrm{refence}\mathrm{to}\mathrm{mean}\mathrm{sea}\mathrm{level}$	(Horton 1945)
	Relative relief (RR)	$R_R={\displaystyle \frac{B_h}{P}}$	(Melton 1957)
	Dissection Index (DI)	$D_I={\displaystyle \frac{R_R}{A_R}}$	(Melton 1957)
	Ruggedness Index (RI)	$R_I=\left({\displaystyle \frac{B_h}{1000}}\right)\ast A^{-0.5}$	(Melton 1957)
	Ruggedness Number (Rn)	$R_n=D_d\ast \left({\displaystyle \frac{B_h}{1000}}\right)$	(Schumm 1956)
	Average slope (Sa)	$S_a={\displaystyle \frac{B_h}{\sqrt{A}}\ast 100}$	(Strahler 1957)
	Hypsometric Analysis (HI)	$\mathrm{HI}={\displaystyle \frac{\mathrm{Hmean}-\mathrm{Hmin}}{\mathrm{Hmax}-\mathrm{Hmin}}}$	(Strand and Pemberton 1982)

3.4. Hypsometric analysis

Hypsometric analysis assesses the landscape's morphometric characteristics, including elevation distribution and relief variability, aiding in understanding terrain complexity. This information influences geomorphological processes like soil erosion and sediment transport (O. Singh, Sarangi, and Sharma 2008). The basin hypsometry is computed using AW3D DEM, involving the estimation of the hypsometric integral (HI) and generating a hypsometric curve (Figure 4). It deals with the relationship between the horizontal cross-sectional area of the watershed and its elevation in the dimensionless form (Shekar et al. 2023). The HI value of the basin and its sub-watersheds are computed using the Elevation-Relief Ratio (ERR) method proposed by (Pike and Wilson 1971). It is estimated by classifying the basin elevation into equal intervals and computing zonal statistics in ArcGIS 10.8.2. Moreover, the hypsometric curve is plotted on a graph using the ratios of relative elevation and relative area of the basin. The HI ratio value and the hypsometric curve shape are used to assess basin morphology, topographic complexity, and stage of watershed evolution.

3.5. Weighted sum priority (WSP) approach for sub-watershed prioritization

The prioritization of sub-watersheds in this study is accomplished using the weighted sum priority (WSP) approach (Figure 4). The WSP approach integrates rigorous statistical methods with geospatial technologies to identify critical parameters necessary for the comprehensive analysis and prioritization of sub-watershed (Kulimushi et al. 2022). This approach efficiently generates weights, enabling the objective ranking of sub-watersheds while minimizing subjective biases in the results (Boitt and Bebeto 2020; Kadam et al. 2019; Kulimushi et al. 2022). The approach involves two key steps: the initial priority ranking (IPR) of sub-watersheds based on ranking selected morphometric parameters, and the final priority ranking (FPR) determined using weighted compound factors (WCF) derived from a cross-correlation matrix (Kulimushi et al. 2022).

3.5.1. Initial priority ranking (IPR)

Sub-watersheds’ IPR is based on morphological characteristics, encompassing linear, drainage area, shape, and landscape/relief parameters (Table 1). Linear, aerial, and landscape/relief parameters are directly proportional to soil erodibility factors, while shape parameters are inversely related to soil erosion (Biswas 2015; Gajbhiye, Mishra, and Pandey 2014; Malik, Kumar, and Kandpal 2019; Wagh, Manekar, and Vallabhbhai 2021_A*{). The compound factor (CF) for each sub-watershed is calculated by summing the rank values of linear, areal, shape, and landscape/relief parameters and then dividing by the number of parameters, as expressed in equation 2(2) $\mathrm{CF}={\displaystyle \frac{\mathrm{Sum}\mathrm{of}\mathrm{ranking}}{\mathrm{Number}\mathrm{of}\mathrm{parmeters}}}$(2) . The IPR is given based on the calculated CF value, where the sub-watershed with the lowest CF value is assigned the highest priority. (2) $\mathrm{CF}={\displaystyle \frac{\mathrm{Sum}\mathrm{of}\mathrm{ranking}}{\mathrm{Number}\mathrm{of}\mathrm{parmeters}}}$(2)

3.5.2. Weighted compound factor (WCF)

The correlation matrix among all selected morphometric parameters is estimated to measure the linear relationship between morphometric pairs for calculating WCF. The present study uses the Pearson correlation coefficient method to perform a correlation matrix using an open-source R software environment. Then, the WCF used for computing the WSP factor is calculated as a fraction between the sum of the correlation coefficient and the total correlation value, as expressed in Equation 3. (3) $\mathrm{WCF}={\displaystyle \frac{\mathrm{Sum}\mathrm{of}\mathrm{Correlation}\mathrm{Cofficient}}{\mathrm{Grand}\mathrm{Total}\mathrm{Correlation}}}$(3)

3.5.3. Final priority ranking (FPR)

The WSP approach assesses watershed prioritization using the previously calculated WCF for each sub-watershed. The results obtained from the IPR are then multiplied by the WCF to determine the WSP factor for the final prioritization of sub-watersheds. Accordingly, the final prioritization is determined by summing the multiplication values of all parameters, as outlined in Equation 4. (4) $\mathrm{WSP}=\sum (\mathrm{WC}{F}^{\ast }\mathrm{IPR})$(4)

4. Results

4.1. Basic morphometric parameters of basin and sub-watersheds

The extracted morphometric parameters, including basin area, length, width, perimeter, elevation (maximum and minimum), relief, aspect, and slope (Table 1), are vital for prioritizing watersheds. They provide essential insights into watersheds’ physical characteristics and hydrological behaviour, thus influencing the calculation and assessment of other morphometric factors discussed in subsequent sub-sections (Gajbhiye, Mishra, and Pandey 2014; Saha 2020).

4.1.1. Basin area (A)

The total drainage area of the MOGR is 14,382.3 km². The sub-watersheds with the largest and smallest basin are SWS38 (1961.5 Km²) and SWS57 (4.67 Km²), respectively (Supplementary Table 1). Among the sub-watersheds of the basin, 12.5% of the sub-watersheds are medium-sized sub-watersheds in their size of watershed classification. However, the sub-watersheds vary in size, ranging from small areas of 4.67 km² to larger watersheds with an area of 207.76 km² (Figure 5(a)). A spatial distribution pattern was observed concerning basin extent, as the largest basins are grouped and located mainly on the eastern side compared to the western side.

Figure 5. Basic morphometric and topographic parameters: (a) Basin area & width, (b) Basin length & perimeter, (c) Maximum elevation, (d) Basin relief, (e) Basin slope, and (f) Basin aspect.

4.1.2. Basin length (L_b), width (W_b), and perimeter (p)

The basin length, or hydrologic length, signifies the distance over which surface drainage flows. It represents the longest dimension parallel to the main drainage line within the basin and is utilized to measure water travel time from the watershed outlet to the basin boundary (Arefin, Islam Mohir, and Alam 2020). The basin length of the MOGR basin is 198.78 km (Figure 5(b)). The sub-watersheds with the lowest and highest basin lengths are SWS53 (3.56 km) and SWS68 (65.40 km). The mean basin width of the river basin computed as the ratio of basin area to basin length is 72.35 km (Figure 5(a)). Meanwhile, the basin width of the sub-watersheds varies from 1.20 km (SWS6) to 29.99 km (SWS38) (Supplementary Table 1). The perimeter of the MOGR basin is 814.18 km (Figure 5(b)). Moreover, the sub-watershed perimeter ranges from 9.71 km in SWS57 to 273.8 km in SWS38 (Supplementary Table 1). It influences the interaction between the watershed and its surrounding landscape, including land use practices, surface runoff patterns, and ecological connectivity.

4.1.3. Basin relief (B_h)

The relief of the watershed significantly influences its erosional characteristics. It plays a crucial role in shaping landforms, drainage patterns, surface runoff, groundwater recharge, permeability, and erosional properties of the terrain (Magesh, Chandrasekar, and Soundranayagam 2011). In the MOGR basin, elevations range from 3554 m a.m.s.l to a minimum of 491 m a.m.s.l (Figure 5(c)). Among the 96 sub-watersheds, 25 (26%) have elevations exceeding 2500 meters, with SWS56, SWS38, and SWS50 having the highest peaks, while only 10% have maximum elevations below 1500 meters a.m.s.l (Supplementary Table 1). Moreover, the basin relief of the MOGR basin is 3063 m, with relief values ranging from 563 m in SWS85 to 2839 m in SWS38 (Figure 5(d)). The 48% of sub-watersheds exhibit relief exceeding 1500 m, indicative of steeper terrain associated with rapid runoff and concentrated flow paths. These conditions may elevate susceptibility to erosion and sedimentation.

4.1.4. Basin aspect and slope

The aspect of the watershed typically denotes the direction a mountain slope faces, impacting the local climate and precipitation regime. In the MOGR basin, the majority of the area is categorized as northwest (13.2%), southwest (13.1%), and south (13.1%) (Figure 5(f)). Moreover, the basin slope indicates the steepness of the terrain within the watershed, influencing surface runoff, erosion rates, and sediment transport. In the MOGR basin, slopes range from less than 5° to a maximum of 75° (Figure 5(e)). Approximately 12% of the basin areas feature slope gradients exceeding 30°, while around 50% have slopes ranging from 10° to 30°. Consequently, steeper slopes across the basin and sub-watersheds typically result in accelerated runoff velocities and heightened erosion potential, contributing to increased sedimentation in downstream water bodies.

4.2. Linear morphometric parameters of the basin and sub-watersheds

4.2.1. Stream order (U) and stream number (N_u)

The stream order (U) is the first step in the morphometric analysis of drainage basins and is a measure of the position of streams in the hierarchy of tributaries (Strahler 1957). The MOGR basin consists of seven stream orders, with the Omo-Gibe River (OGR) representing the seventh-order stream (Table 2). This river typically divides the MOGR basin into two distinct parts (Figure 2(a)). Most streams fall within the first and second-order categories, comprising approximately 94.66% of the total number of streams. Sub-watersheds featuring the second-highest stream order, i.e. sixth-order streams, are primarily situated on the left side of the Omo-Gibe River (Figure 6(a)). Additionally, more than 50% of basin sub-watersheds are linked to minimum stream orders, corresponding to third-order streams. The highest stream number (N_u) are observed in SWS38, SWS56, and SWS74, with total stream numbers of 1,684, 1,195, and 1,107, respectively, and suggest a denser and more interconnected river network (Supplementary Table 1).

Figure 6. Linear Morphometry of sub-watersheds: (a) Stream number, (b) mean stream length, (c) Mean bifurcation ratio, and (d) Rho coefficient.

Table 2. Linear parameters of the MOGR basin.

Stream Order (U)	Stream number (Nu)	Stream length (Lu)	Mean St. length (Lsm)	Stream length ratio (RL)	Mean RL	Bifurcation ratio (Rb)	Mean Bifur. ratio (Rbm)	Rho coefficient (ρ)
1^st order	9745	8670.17	0.89	2.07	2.36	4.28	4.66	0.48
2^nd order	2275	4186.22	1.84	2.27		4.40		0.51
3rd order	517	2155.14	4.17	1.96		4.14		0.47
4th order	125	1019.16	8.15	1.95		4.31		0.45
5th order	29	459.97	15.86	3.27		4.83		0.68
6th order	6	311.51	51.92	2.65		6.00		0.44
7th order	1	137.47	137.47	14.16				0.51
Total/Av.	12698	16939.64	1.33

4.2.2. Stream length (L_u), mean stream length (L_sm), and stream length ratio (R_L)

The stream length (L_u) indicates the behavior of surface runoff in the basin, which has a significant role in the drainage basin system. The stream with longer lengths typically indicates a flatter gradient, whereas the smaller length is characteristic of areas with more extensive slopes and finer textures (Adhikari 2021). The total L_u of the MOGR basin is 16,939.64 km (Table 2). The total L_u of first-order streams of the river basin is 8,670.17 km. In contrast, the total L_u of the seventh order is 137.47 km, indicating more significant numbers of first-order streams with maximum cumulative distance. Moreover, the first and second-order streams contribute to 76% (12,856.4 km) of the total L_u of the river basin, while the remaining higher-order streams account for only 24% of the total L_u (Figure 6(a)). There is an inverse relationship between stream order and length, and a direct positive relationship exists between a stream number and length in a corresponding stream order. The mean stream length (L_sm) of the MOGR basin is 1.33 km, which varies from 0.89 km in first-order streams to 51.92 km in sixth-order streams (Figure 6(b)). The L_sm is higher for the left bank tributaries (northern section) of the main OGR than the right bank tributaries, further suggesting the well-developed drainage basin or watershed in the eastern section of the MOGR basin (Figure 6(b)). The analysis of L_sm illustrates that the L_sm increases as the stream number decreases and vice versa, harmonizing with the low of Horton's stream order and length geometric relationship. The sub-watershed stream length ratio (R_L) varies from 1.95 km to 3.27 km, and the mean R_L of the MOGR basin is 2.36 km (Table 2).

4.2.3. Basin bifurcation ratio (R_b)

The bifurcation ratio (R_b) is the ratio between the total stream segments of one order and that of the next higher order in a drainage basin (Schumm 1956). If the R_b differs from one order to the next, these irregularities depend on the drainage basin's geological and lithological development (Strahler 1957). The R_b of the MOGR basin varies from 4.14 in the third order to 6 in the sixth order (Table 2). The sub-watersheds with the highest R_b are SWS38 (20.93), SWS74 (20.19), and SWS56 (19.734), whereas the lowest R_b are recorded at SWS3, SWS6, SWS36, SWS51, SWS57, SWS9, and SWS44 with less than five ratios (Supplementary Table 1). Strahler suggested that the R_b characteristically ranges between 2 and 5 for basins where the geologic structures do not disturb the drainage pattern. At the same time, structural controls play dominant roles where it is higher than 10 (Strahler 1957). The mean bifurcation ratio (R_bm) of the MOGR basin is 4.66, with variations observed among sub-watersheds ranging from 2 in SWS44 to 6.44 in SWS80 (Figure 6(c)). The higher range in the variation of the R_bm of the sub-watersheds is ascribed to the geometrical dissimilarities among the sub-basins.

4.2.4. Rho coefficient (ρ)

The Rho coefficient (ρ) is a proportion of the stream length and bifurcation ratio (Horton 1945). The ρ is an important parameter that links drainage density to the physiographic growth of the basin, which is helpful for the assessment of the storage capacity of a stream network in watersheds (W. R. Singh, Barman, and Tirkey 2021). Thus, it is a determinant parameter that controls the ultimate degree of drainage growth in a given watershed. The ρ of the MOGR basin is 0.51 (Figure 6(d)), while the ρ values of sub-watersheds range from 0.35 at SWS15 to 1.98 at SWS95 (Supplementary Table 1). As the ρ value increases, the probability of flood storage and the degree of soil erosion increases (Horton 1945). The approximate 70% of sub-watersheds exhibit a ρ value exceeding 0.55, indicating higher flood storage capacity and an amplified soil erosion effect within these sub-watersheds (Figure 6(d)).

4.3. Drainage areal morphometric parameters and characterization

The drainage area aspects of parameters are a basin's two-dimensional properties that deal with the total projected area contributing overland flow to the channel area.

4.3.1. Stream frequency (F_s)

Stream frequency (F_s) is the ratio of the total number of stream segments of all the orders in the watershed to the total area of the watershed (Horton 1945). It is also known as stream density and is controlled by the watershed’s physical characteristics, climate, vegetation cover, lithology (rock and soil types), surface infiltration capacity, and area relief (Adhikari 2021). It is directly related to erodibility, as the higher the F_s, the higher the erodibility, and vice versa (Wagh, Manekar, and Vallabhbhai 2021). The F_s of the MOGR basin is 0.88 streams /km² (Supplementary Table 2). Sub-watershed F_s vary from 0.62 streams/km² in SWS13 to 1.71 streams/km² in SWS57 (Figure 7(a)). The 30% sub-watersheds exhibit F_s exceeding one stream/km², suggesting a higher susceptibility to erosion processes.

Figure 7. Drainage area morphometry: (a) Stream frequency, (b) Drainage density, (c) Drainage texture, and (d) Constant of channel maintenance.

4.3.2. Drainage density (D_d)

Drainage density (D_d) is the ratio of total channel segment lengths cumulated for all orders within a basin to the entire basin area (Horton 1945). It depends on sub-surface elements’ permeability, vegetation cover types, and terrain relief. The D_d of the MOGR basin is 1.18 km/km², which indicates that the river basin has a highly coarse surface and lower relief, with a higher recharge of subsoil in the basin (Figure 7(b)). The D_d of sub-watersheds ranges from the lowest at SWS68 (0.92 km/km²) to the highest (1.6 km/km²) recorded at SWS47 (Supplementary Table 2). The lower D_d of the study area indicates that the area has permeable subsoil, dense vegetative cover, and coarse texture, whereas the higher D_d shows an inverse to these (Arabameri et al. 2020). In some cases, drainage patterns in different litho-units have demonstrated that the streams flowing over various rock units exhibit multiple types of channel development (Ghosh, Mukhopadhyay, and Jana 2021).

4.3.3. Drainage texture (D_t)

Drainage texture (D_t) is the ratio of stream number and perimeter of the basin (Horton 1945). It depends on several physical factors such as climate, rainfall, vegetation, rock, soil type, infiltration capacity, and soil evolutionary stage (Arabameri et al. 2020). The D_t of the river basin is classified into four categories: very coarse (<2), coarse (2-4), moderate (4-6), and fine or highland topography (>6) (Wischmeier and Smith 1978). The D_t of the MOGR basin is 15.6, which means that the basin is categorized into highland topography classes (Figure 7(c)). The sub-watersheds with the highest record of D_t are at SWS38, accounting for 6.15, whereas the lowest D_t is 0.39, recorded at SWS44 (Supplementary Table 2). The results indicate that 79% of sub-watersheds are characterized by lower values of D_t, falling into the very coarse to coarse category, while the remaining 21% exhibit higher values of D_t, classified as moderate to fine D_t (Figure 7(c)). Moreover, finer D_t often results in more concentrated flow paths, increasing the erosive power of water and leading to accelerated erosion rates within the watershed.

4.3.4. Constant of channel maintenance (C)

The constant of channel maintenance (C) parameter indicates infiltration and the control of flow to the basin outlet. It depends on the rock type and permeability, climatic regime, vegetation cover and relief, and the duration of erosion and climatic history (Raja Shekar and Mathew 2024). The C value is meager in areas of close dissection relief. In the present study, the value of C for the MOGR basin is 0.85 (Figure 7(d)). The sub-watershed value ranges from a minimum of 0.63 at SWS47 to a maximum of 1.09 at SWS68 (Supplementary Table 2). The result indicates that all sub-watersheds of the basin have a C value >0.5, referring to a higher degree of susceptibility to soil erodibility and consequent land degradation. Accordingly, about 40 sub-watersheds of the river basin are the most erodible with their C values > 0.9 (Figure 7(c)).

4.3.5. Infiltration number (I_f)

The infiltration number (I_f) is the outcome of drainage density and stream frequency, reflecting the infiltration potential of a surface (Prabhakaran and Jawahar Raj 2018). Higher I_f indicates lower runoff and less susceptibility to soil erosion and vice versa (Adhikari 2021; Arabameri et al. 2020). The I_f for the MOGR Basin is 1.04, whereas the I_f of the sub-watersheds ranges from a minimum of 0.59 (SWS68) to a maximum of 2.01 (SWS8) (Supplementary Table 2). Moreover, the higher I_f generally corresponds to lower erosion potential, while lower I_f may indicate higher erosion risk.

4.4. Shape morphometric parameters of basin and sub-watersheds

Shape morphometry is defined as the geometric features of a landmark configuration (Klingenberg 2016). Unlike other morphometric aspects, the shape parameters have an inverse relationship with erosion potential and land degradation process. The higher values of shape parameters illustrate the less likely erosional status and vice versa (Kadam et al. 2019).

4.4.1. Shape factor (S_f) and form factor (F_f)

The shape factor (S_f) can be defined as the ratio of the square of the basin length to the basin area (Horton 1945). The S_f value for the MOGR basin is 2.75 (Supplementary Table 2). Comparatively, the sub-watersheds exhibit a range of S_f, with the minimum and maximum values recorded as 1.49 (SWS93) and 6.89 (SWS94), respectively (Figure 8(a)). Approximately 54% of the sub-watersheds possess an S_f below 3, categorized as having lower S_f values. Meanwhile, 34% fall into the medium category, with S_f ranging between 3 and 5. The remaining 12% of sub-watersheds have higher S_f values (Supplementary Table 2). Moreover, the form factor (F_f) quantifies the relationship between a watershed's area and perimeter. It's computed by dividing the area of the watershed by the square of its perimeter. The F_f describes the shape of the basin and its flow intensity (Kulimushi et al. 2022). The smaller the value of the F_f, the more it will elongate the shape, and the more excellent value of the F_f indicates the circular shape of the basin. For the MOGR basin, the F_f is 0.36 (Supplementary Table 2). In comparison, sub-watersheds exhibit F_f values ranging from a minimum of 0.15 in SWS94 to a maximum of 0.67 in SWS93 (Figure 8(b)). Basins with a high F_f value encounter more increased peak flow with a shorter duration and vice-versa.

Figure 8. Shape morphometry of sub-watersheds: (a) Shape factor, (b) Form factor, (c) Circulatory ratio, and (d) Elongation ratio.

4.4.2. Circulatory ratio (R_c)

Circulatory ratio (R_c) is defined as the ratio of the basin area to the area of the circle having the same circumference as the basin's perimeter (Schumm 1956; Strahler 1957). The value of the R_c varies from 0 (a line) to 1 (a circle). The computed R_c for the MOGR basin is 0.27, which indicates that the river basin is more of an elongated shape with impermeable and rough surfaces for enhancing soil erosion (Supplementary Table 2). However, the R_c for sub-watersheds ranges from 0.3 (SWS22) to 0.75 (SWS85) (Figure 8(c)). Moreover, a higher R_c suggests a denser network of stream channels, improving drainage efficiency and dispersing runoff effectively, which can reduce surface runoff and erosion. However, excessively high R_c may increase erosion risk by promoting aggressive soil erosion, particularly during high-flow periods, due to excessive drainage density and closely spaced stream channels (Horton 1945; Udaipur, Singh, and Yadav 2020).

4.4.3. Elongation ratio (R_e)

The elongation ratio (R_e) refers to the ratio of the diameter of a circle having the same area as the basin and the maximum basin length (Schumm 1956). The value of the R_e ranges between 0 and 1, where the weight closer to zero suggests the elongated shape, and closer to 1 confirms the circular shape of the watersheds (Topno et al. 2022). The R_e of the MOGR basin is 0.68, which comparatively has a moderately elongated watershed shape. The R_e for all sub-watersheds ranges between 0.43 and 0.93, indicating that watersheds are moderately elongated to oval (Figure 8(d)). Sub-watersheds such as SWS1, SWS6, SWS18, SWS22, SWS25, SWS44, SWS67, SWS68, SWS90, and SWS94 demonstrate a R_e value below 0.5, indicating a more elongated shape with steeper slopes. This configuration can accelerate runoff and intensify water erosive force (Rai et al. 2017; Shekar et al. 2023). Conversely, sub-watersheds like SWS19, SWS37, SWS40, SWS50, SWS53, SWS63, SWS72, SWS80, SWS85, and SWS93 exhibit a R_e exceeding 0.8, indicating an oval-shaped form, which reduces susceptibility to erosion by minimizing concentrated runoff along the longer axis (Supplementary Table 2).

4.4.4. Length of overland flow (L_o)

The length of overland flow (L_o) is half of the reciprocal of drainage density (Melton 1957). It affects a basin's hydrological and physiographical processes because it suggests the length of water flow over the ground before it reaches a definite stream channel (Kulimushi et al. 2022). Generally, a higher value of the L_o reveals low relief, longer flow paths, and less surface runoff, and vice versa. The L_o for the MOGR basin is 0.59 km/km², whereas the sub-watersheds value ranges from 0.46 km/km2 at SWS68 to 0.8 km/km² at SWS47 (Supplementary Table 2). Among the sub-watersheds, 65 (68%) have less than 0.6 km/km² L_o, referring to higher relief with steep slopes and shorter flow paths, which consequently causes high surface runoff and soil erosion.

4.4.5. Compactness coefficient (C_c)

The compactness coefficient (C_c) is defined as the ratio of the watershed perimeter to the circumference of the circular area equal to the watershed area (Arefin, Islam Mohir, and Alam 2020; Bendjoudi and Hubert 2002). The C_c closer to 1 indicates a more compact, circular shape, while values further from 1 suggest a more irregular or elongated shape. The C_c value of the river basin is 1.92 (Figure 9). Moreover, sub-watershed C_c varies from the slightest record of 1.16 at SWS22 to the highest value of 1.82 at SWS85 (Supplementary Table 2). The C_c values for all sub-watersheds deviate from 1, indicating variations in the erosional potential of the land. Approximately 30% of sub-watersheds exhibit C_c values exceeding 1.5. Thus, the sub-watersheds with lower C_c (i.e. more circular or compact shapes) may have a more uniform distribution of water flow and sediment transport, potentially reducing the likelihood of erosion compared to sub-watersheds with higher C_c (i.e. more irregular or elongated shapes) where water may concentrate in specific areas, increasing erosion susceptibility.

Figure 9. Landscape/relief morphometry: (a) Absolute relief, (b) Relative relief, (c) Dissection index, (d) Relief ratio, (e) Ruggedness index, and (f) Ruggedness number.

4.5. Landscape/relief morphometric parameters of the basin and sub-watersheds

Landscape/relief morphometric parameters refer to watersheds’ elevation and topographic features to analyze terrain characteristics. They are directly related to soil erosion, where the high rate of the parameters designates a higher risk of soil erosion and vice versa. The relief or landscape morphometry deals with three-dimensional parameters.

4.5.1. Basin absolute relief (A_r) and relative relief (R_r)

Absolute relief (A_r) refers to the maximum elevation of any region at the mean sea level. The A_r of the MOGR basin is 3554 m a.m.s.l (Figure 9(a)). Relative relief (R_r) is the ratio of basin relief to the perimeter of the drainage basin (Melton 1957; W. R. Singh, Barman, and Tirkey 2021). It is the most reliable potential predictor of denudation rates (Gouri et al., 2013). There is a direct relationship between R_r and the denudational process of the watershed, where the more significant the R_r, the higher the denudational rate of the surface and vice versa. The calculated R_r of the MOGR river basin is 3.76, whereas the minimum and maximum R_r values of the sub-watersheds are recorded at SWS38 (10.37) and SWS9 (120.17), respectively (Figure 9(b)). These varying R_r provide information about the local topographic variation within a landform feature and help assess its ruggedness or steepness (Melton 1957).

4.5.2. Relief ratio (R_h)

The relief ratio (R_h) is the ratio between basin relief and basin length. The higher R_h ratio represents the hilly relief and erodibility (Suresh and Krishnan 2022). The R_h of the MOGR basin is 15.41 (Figure 9(d)). In comparison, the R_h for all sub-watersheds ranges from the lowest value, 39.25, to the highest value, 348.08, at SWS46 and SWS40, respectively (Supplementary Table 2). The 41% of sub-watersheds show R_h exceeding 150, indicating an increased susceptibility to soil erosion and land degradation.

4.5.3. Dissection index (D_I)

Dissection Index (D_I) is the ratio between relative relief and absolute relief of the watershed (Melton 1957). On average, the values of D_I vary between 0 (complete absence of vertical dissection/erosion and hence the dominance of flat surface) and 1 (in exceptional cases, cliffs, it may be at the steep escarpment of hill slope or the seashore) (Salvi et al. 2023). The D_I for the river basin is 0.86, indicating the higher vertical escarpment and vertical dissection with a higher degree of runoff and soil erosion (Figure 9(c)). Meanwhile, the D_I of the sub-watershed varies from 0.42 at SWS3 to 0.87 at SWS2 (Supplementary Table 2). The result confirms that all sub-watersheds except SWS3, SWS48, and SWS14 of the MOGR basin have higher D_I, indicating steeper escarpment and consequent soil erosion.

4.5.4. Ruggedness index (R_I) and number (R_n)

The Ruggedness index (R_I) is the ratio between the mean minimum ruggedness and the subtracted value of maximum and minimum ruggedness (Melton 1957). The R_I expresses the elevation difference between adjacent cells of a DEM (Riley, DeGloria, and Elliot 1999). The quantitatively calculated R_I of the MOGR basin is 0.03 (Supplementary Table 2). Moreover, sub-watershed R_I ranges from 0.06 to 0.54 at SWS74 and SWS9, respectively (Figure 9(e)). The Ruggedness number (R_n) is computed from the watershed's basin relief and drainage density (Schumm 1956). It expresses the terrain's structural complexity and measures the surface's unevenness. The higher the R_n, the higher the surface unevenness and higher erosional intensity (Strahler 1957). The R_n of the MOGR basin is 3.61, which confirms that the river basin has higher structural complexity and rough surface with higher erosion sensitivity (Figure 9(f)). The R_n value of the sub-watersheds ranges from 0.59 to 3.37(Supplementary Table 2).

4.6. Land use/land cover and soil erosion

The classified land use/land cover types indicate that more than 50% of the river basin is dominated by shrubland, which includes scrubs, range lands, and small woody and bush grounds (Figure 11). Scrublands typically have shallow root systems and sparse canopy cover, making them less effective at preventing erosion than forests or grasslands. Moreover, intense rainfall and runoff, especially during intense rain storms, lead to significant soil erosion in the different sub-watersheds of the MOGR basin. Furthermore, the rangelands in various areas are primarily used for grazing livestock in the MOGR basin. Thus, overgrazing and improper management of rangelands can lead to vegetation degradation, soil compaction, and increased vulnerability to erosion in different sub-watersheds (Figure 10). The agricultural land use is the following dominant coverage, accounting for 26% of the MOGR basin. This land use is mainly distributed across the upper parts of the MOGR basin, where dense rural settlements are dominated.

Figure 10. Land use land cover types and their effects on soil erosion.

Moreover, intensive agricultural practices such as monoculture cropping and overcultivation in different sub-watersheds can lead to increased soil erosion. Additionally, bare soil between crop cycles is highly susceptible to erosion. The sub-watersheds with dominant bare grounds, cultivation, grazing lands, and settlement land uses exhibit higher rates of soil erosion and land degradation problems (Figure 10). In the MOGR basin, the sub-watersheds around the northern, northeastern, and northwestern sides have higher effects of land cover and management practices, indicating a higher rate of soil erosion occurrences. However, dense trees and forest cover predominantly occupy the central, southern, and southwestern areas, resulting in lower soil erosion and land degradation effects. These results are consistent with the geomorphometric parameters of the MOGR basin.

4.7. Hypsometry analysis of the basin and sub-watersheds

Hypsometry encompasses measuring and analyzing the altitude and basin area relationship to comprehend the degree of surface dissection and watershed development stages (Strahler 1957). Differences in the shape of the curve and the HI value are related to the degree of disequilibrium in the balance of erosive and tectonic forces (Weissel, Pratson, and Malinverno 1994). Hypsometric curves are non-dimensional measures of the proportion of the catchment area above a given peak (Strahler 1957). It is premeditated by considering relative height and relative area. It is obtained by plotting the relative area along the abscissa (x-axis) and relative elevation along the ordinate (y-axis). The hypsometric curve of the MOGR basin and most of the sub-watersheds with HI below 0.45 are S-shaped (Figure 11(b)), which shows that the basin is at the equilibrium stage of development. Whereas some sub-watersheds with HI values greater than 0.5 have convex shapes that are more sensitive to soil erosion (O. Singh, Sarangi, and Sharma 2008). Therefore, HI helps prioritize the sub-watersheds based on the degree of soil erodibility when proposing soil and water conservation activities. Strahler (1957) states that the HI value ≥0.6 shows youth stage/convex upward curves. The HI value between 0.35-0.6 represents the mature or equilibrium stage of the watersheds with an S-shaped curve line (Figure 11(d)). The HI value ≤0.35 shows the old (monadnock) or peneplain stage of the watersheds with concave or downward curve shapes (Figure 11(c)). The watershed in the youthful stage is more susceptible to erosion than the equilibrium or mature stage. The HI value of the MOGR basin ranges from 0.26-0.7, and the mean value is 0.45 referring to the increased susceptibility of sub-watersheds to soil erosion (Figure 11(b)). The lowest values of HI and more concave curves are located in the southern part of the MOGR basin (Figure 11(b)). Therefore, this area could represent a more mature stage perhaps due to a longer time of fluvial activity according to Strahler's landscape evolution model (Strahler 1957).

Figure 11. Hypsometry of the study area: (a) Hypsometric curve, (b) Hypsometric Integral, (c) Sub-watersheds with HI < 0.3, (d) Sub-watersheds of HI 0.4-0.45, and (e) Sub-watersheds with HI >5.

4.8. Prioritization of sub-watersheds

The study prioritizes sub-watersheds based on geomorphometric parameters and correlation coefficient matrix. The IPR is based on 17 geomorphometric parameters directly and inversely related to soil erosion and land degradation (Supplementary Table 3). Linear, areal, and landscape/relief morphometric parameters are directly related to soil erodibility, while shape morphometric parameters are inversely related. The last IPR ranking is determined by computing CF values from the preliminary ranking using the procedure expressed in Equation 2, and the results are presented in Supplementary Table 3. The top ten sub-watersheds with the highest IPR ranking are SWS75, SWS2, SWS56, SWS25, SWS24, SWS94, SWS55, SWS32, SWS68, and SWS67 (Figure 12(a)). These sub-watersheds have the highest soil erosion susceptibility and land degradation, requiring critical watershed management practices. The least prioritized sub-watersheds based on IPR are SWS9, SWS59, SWS49, SWS14, SWS37, SWS27, SWS36, SWS26, SWS53, SWS48, and SWS85 (Supplementary Table 3). The final prioritization of sub-watersheds in the MOGR basin involves computing a WCF for each parameter, estimating WSP ranking values, and assigning soil erosion priority classes as calculated using equation 4. The highest priority ranking ‘1’ is assigned for the lowest summation compound value, while the lowest priority ranking is assigned for the highest summation value, and so on. Therefore, in the MOGR basin, the highest priority ranking is assigned to SWS75 with a WSP value of 31.27, followed by SWS25, SWS2, SWS32, SWS94, SWS68, SWS13, SWS12, SWS67, and SWS17. The lowest priority ranking is assigned to SWS85, with a WSP value of 71.8 (Figure 12(a)). The sub-watersheds within the MOGR basin are categorized into five priority classes, ranging from very high to very low soil erosion proneness, based on the WSP factor. This classification is determined using a GIS-based natural break technique or the Jenks method to minimize the variance within each group while maximizing the variance between groups. Thus, the sub-watersheds of the river basin can be classified as very high (31.27–44.94), high (44.55–49.72), medium (49.73–52.41), low (52.42–56.93), and very low (56.94–71.8) prioritization classes of soil erosion proneness and consequent land degradation (Figure 12(c)). The WSP factor-based final priority classifications of sub-watersheds illustrate that 19 of all sub-watersheds fall under the very high soil erosion priority classes, which account for 43% of the land area, equivalently about 5,693 km² of total area (Figure 12(c)). Similarly, another 19 sub-watersheds corresponding to 1,673 km² (13%) of the river basin's total area are classified under high-priority classes, and 17% of the basin's total area is categorized under medium-priority classification (Supplementary Table 5).

Figure 12. Soil erosion severity classification: (a) Initial priority ranking of sub-watersheds, (b) Final prioritization based on proneness to soil erosion, and (c) Soil erosion severity classes.

4.9. Result validation of sub-watershed prioritization

The validation of geomorphometric analysis and conclusive outcomes of sub-watershed prioritization are authenticated through the utilization of both correlation coefficient matrix (CCM) and general linear model (GLM). The descriptive statistical parameters, such as minimum, maximum, means, and standard deviations, were computed for all morphometric variables to assess statistical deviations and variations. The multivariate analysis of morphometric parameters using a GLM uses the partial Eta-squared (η²) value, which measures the effect size of different variables in the analysis of variance (ANOVA) models. Hence, most parameters utilized for analysis exhibit significant variance and effect size in influencing the concurrent output of individual variables. Moreover, the results of the prioritization of sub-watersheds are also validated by using a CCM computed between morphometric parameters as well as the inter-correlation among the effects of CF and WCF approaches applied for prioritization of sub-watersheds (Table 3). Upon examination of the correlation analysis of the variables, it is evident that there are strong positive and negative relationships among the morphometric parameters used in this study.

Table 3. Inter-correlations among prioritization variables.

Variables	CF	IPR	WSP	FPR
CF	1	0.983**	0.970**	0.951**
IPR	0.983**	1	0.948**	0.961**
WSP	0.970**	0.948**	1	0.978**
FPR	0.951**	0.961**	0.978**	1

** Correlation is significant at the 0.01 level (2-tailed).

Note: CF = Compound Factor, IPR = Initial Priority Ranking, WSP = Weighted sum priority, and WSP = Weighted sum priority.

Additionally, a strong, significant positive correlation exists between the CF and WCF and between the IPR and FPR ranking. This result arises from the significant positive correlation between morphometric parameters and the effectiveness of the CCM. This reaffirms the importance of the selected parameters in categorizing soil erosion and subsequent land degradation within the sub-watersheds. It is worth noting that SWS75 and SWS85 have been assigned the same priority ranking, with SWS75 ranked first ‘1’ and SWS85 ranked last ‘96’ in both the CF and WSP approaches (Supplementary Tables 3 & 5). This consistency highlights the effectiveness of the variables in classifying soil erosion severity. Overall, the results of this analysis demonstrate the strong inter-relationships among the geomorphometric parameters and the suitability of certain variables for assessing soil erosion and land degradation in the sub-watersheds.

5. Discussion

Several studies have been conducted in different regions of Ethiopia, e.g. Blue Nile Basin, Angered Watershed, Wondo Genet watershed, Dera Woreda, Omo-Gibe upper basins, and so on in North-Western, southern, and central parts of Ethiopia to prioritize sub-watersheds based on their susceptibility to soil erosion and land degradation (Bogale 2021; Ermias 2024; Gudu Tufa 2018; Mekuria 2023; Jothimani, Lawrence, and Dawit 2020; Mohammed, Adugna, and Takala 2018). These studies typically utilize morphometric parameters, GIS, and remote sensing technology to assess soil erosion risk and prioritize intervention areas. Moreover, in the upcoming years, Ethiopia is expected to face heightened vulnerability to soil erosion and land degradation, primarily due to the worsening of various factors such as climate change, population growth, anthropogenic pressure leading to deforestation, intensified inappropriate agricultural activities, inadequate land management practices, and uncontrolled urbanization. In light of this evidence, the purpose of the present study is to prioritize sub-watersheds of the MOGR basin, located in the south-central Ethiopian Highlands, based on various geomorphological characteristics. The geomorphological features were calculated using a more realistic geospatial technique and analyzed using the most appropriate non-subjective statistically computed WSP and morphometric techniques with supplementing soil/lithology, land use data, and field observations. Recognition of sensitive areas vulnerable to soil erosion is essential if appropriate land and water management strategies are established and enforced.

The MOGR basin, shaped by geological and geomorphological processes linked to the formation of the Great Rift Valley of Ethiopia, features rugged terrain influenced by ongoing endogenic and exogenic forces (Gathogo 2017). This terrain, characterized by fault features, is highly susceptible to soil erosion and warrants further investigation (Figure 13(a)). The mean HI value of 0.45 indicates the MOGR basin's later youth stage, reflecting the varied impact of fluvial and denudational processes on the terrain. The MOGR basin, with a total area exceeding 2,500 km², falls within the category of a large watershed according to Strahler's classification criteria (Strahler 1957). However, the MOGR basin predominantly consists of small sub-watersheds with 3rd order streams vulnerable to soil erosion due to their sensitivity to high-intensity, short-duration rainfalls, posing a heightened risk of land degradation (Bogale 2021).

Figure 13. Relief features and associated effects: (A) Satellite image depicting the highlands and river valley, (B) Degraded gulley around Ajora waterfalls, and (C) Landslides along Hambaricho mountain.

Moreover, the majority of sub-watersheds, along with the main OGR basin, exhibit shorter basin lengths and narrower widths, known to accelerate runoff and increase soil erosion potential in river channels, as evidenced by previous research (Anshumali1 2019; Topno et al. 2022). The relief of the MOGR basin is characterized by the main river valley in the center, the Hambaricho, Damota, and Guge Mountain chains in the east, and the Dawuro-Konta highlands on the other side of the river basin (Figure 13(a)). Significant variations in relief indicate increased surface runoff, weathering, and erosion rates within the sub-watersheds (Raja Shekar and Mathew 2024; Topno et al. 2022). Additionally, the orientation of the basin influences moisture distribution and evaporation rates, with west-facing slopes typically drier than east-facing slopes, which receive more rainfall and support greater vegetation (Bogale 2021; Schumm 1956). Moreover, over 60% of the study area exhibits slope gradients exceeding 20 degrees, accelerating runoff and soil erosion rates (Figure 13(b)). This heightened slope steepness correlates with increased soil weathering and erosion, posing a significant risk of soil loss and degradation in most sub-watersheds (Mosbahi, Benabdallah, and Boussema 2013).

The linear morphometric parameters investigated in the MOGR sub-watersheds provide valuable insights into their physical structure, drainage network connectivity, and river system dynamics, influencing hydrological and geomorphological processes like erosion (Shekar et al. 2023). With higher proportions of U and N_u, the MOGR basin exhibits significant topographic influence on stream development. This configuration of drainage characteristics contributes to increased overland flow, indicated by higher R_b in the sub-watershed stream networks (Boitt and Bebeto 2020; Raja Shekar and Mathew 2024; Shekar et al. 2023). Specifically, 77% of sub-watersheds have R_bm exceeding 3, suggesting a higher stream branching, flash flood probability, and soil erodibility (Ojha et al. 2023). Similar research in Ethiopian watersheds, including the Upper Gibe catchment and Gilgal Abay watersheds, corroborates these findings, where R_bm ranged from 2.45 to 7.43 (Bogale 2021; Jothimani, Lawrence, and Dawit 2020; Mekuria 2023; Mohammed, Adugna, and Takala 2018; Tassew, Belete, and Miegel 2021). The study characterized the morphometric factors of drainage areas, highlighting their substantial contribution to soil erosion processes by directly shaping water flow dynamics and sediment transport within a watershed. For instance, the calculated D_d of the sub-watersheds ranges from 0.92 km/km² to 1.60 km/km², indicating lower to moderate D_d. This suggests slower water movement and reduced erosion potential, enabling water to spread over a larger area and infiltrate the soil more effectively. The prevalence of scrubland, range land, and agriculture areas with less forest cover in the MOGR basin suggests increased surface runoff and erosion despite lower D_d. This is due to fewer channels for water flow, leading to accumulation and concentration in certain areas (Arabameri et al. 2020; Wagh, Manekar, and Vallabhbhai 2021). As a result, localized erosion hotspots may develop, necessitating further investigation in future studies. Moreover, 30% of sub-watersheds exhibit higher F_s, suggesting a higher susceptibility to erosion processes. The findings reveal that 79% of sub-watersheds have lower D_t values, categorized as very coarse to coarse, with the remaining 21% having moderate to fine D_t values (Figure 7(c)). Finer D_t creates more concentrated flow paths, intensifying water's erosive potential and accelerating erosion rates within the watershed (Mohammed, Adugna, and Takala 2018; Udaipur, Singh, and Yadav 2020). The study also revealed that most sub-watersheds possess a C value exceeding 0.5, suggesting a higher degree of susceptibility to soil erodibility and consequent land degradation as noted in Kulimushi et al. (2022).

The examined shape morphometric parameters in the MOGR basin further elucidate characteristics influencing erosion susceptibility in the sub-watershed. For instance, approximately 60% of sub-watersheds have lower S_f, indicating higher water discharge in shorter time intervals and potentially efficient sediment disposal. Moreover, about 54% of sub-watersheds have an R_c of less than 0.5, potentially leading to irregular water flow distribution and increased erosion vulnerability (Figure 14(b)). Furthermore, about one-third of sub-watersheds show lesser R_e, suggesting steeper slopes that intensify runoff and erosive potential (Figure 14(a)). The L_o ranging from 0.46 to 0.8 km/km², affects hydrological processes, with 68% of sub-watersheds having L_o below 0.6 km/km², indicating steep slopes and increased surface runoff and soil erosion in these sub-watersheds (Figure 13(c)). The C_c varying from 1.16 to 1.82 indicates erosional potential, with higher C_c potentially leading to more uniform water flow and reduced erosion likelihood (Kadam et al. 2019).

Figure 14. Soil erosion status: (a) Degraded gully at Dubo village, (b) Satellite image depicting soil erosion, (c) Erosion-prone area at Wormuma village, and (d) degraded land after Deforested land in Senbeta catchment.

The study also examined landscape morphometric parameters concerning watershed erosion, focusing on elevation and topographic features. Results indicate that nearly half of the sub-watersheds have a D_I exceeding 0.5, indicating steep slopes and elevation variations that increase erosion susceptibility. On the other hand, approximately 31% of the sub-watersheds exhibit higher topographic roughness and irregularities. Moreover, 30% of the sub-watersheds have a HI value >0.45, indicating a nearly immature basin development with a higher susceptibility to erosion and land degradation. Field observations across the MOGR basin confirm widespread challenges, including increased soil erosion, surface runoff, galleys, landslides, deforestation, and land degradation, primarily near river courses, mountain slopes, bare terrain, grazing areas, and farmlands (Figure 14(a–c)).

The study utilized morphometric parameters and correlation coefficient matrices to prioritize sub-watersheds based on their susceptibility to soil erosion and land degradation. Initially, sub-watersheds on the eastern side of the main river channel were identified as highly susceptible, attributed to unstable landscapes, diverse slopes, and anthropogenic pressures. Further analysis using weighted sum factors and correlation matrices identified sub-watersheds on the northern, northeastern, eastern, and southeastern sides of the MOGR basin as having the highest soil erosion prioritization rankings. These areas exhibit increased surface runoff, topographic diversity, and soil erosion susceptibility. Out of the 96 sub-watersheds analyzed, 38 (56% of the total study area) located around the geographic coordinates of 6^o15'58.79"- 7^o17'16.76” N latitude and 37^o15'24.87"- 37^o38'05.04"E longitude are very high to highly susceptible to soil erosion and land degradation, requiring robust land management plans. Besides, 17% of the sub-watersheds fall under a medium-priority classification, while 20 sub-watersheds (5.5% of the basin) have very low soil erosion susceptibility due to dense vegetation and limited human activity. The intercorrelations between the morphometric parameters using GLM and a CCM validated the efficiency of morphometric parameters for sub-watershed prioritization. The results revealed a significant positive and negative correlation among various geomorphometric parameters with significance levels of 0.01 and 0.05 (Supplementary Table 4). These relationships validate their impact on geophysical processes and mutual reliance. The correlation analysis of the study supports the use of morphometric indicators in evaluating soil erosion and land degradation in the sub-watershed. Additionally, the multivariate analysis of the morphometric parameters conducted in the ANOVA's partial η² reveals a significant proportion of variations accounted for by these parameters, demonstrating their strong influence.

Unlike previous studies, this study is the first in the region to analyze the maximum morphometric parameters, including hypsometric analysis to describe geophysical, hydrological, and environmental factors, and utilized a non-subjective statistically computed WSP approach for sub-watershed prioritization. Furthermore, the results of this study are consistent with research findings from other basins conducted in Ethiopian highlands (Ermias 2015; Gudu Tufa 2018; Jothimani, Lawrence, and Dawit 2020; Mekuria 2023; Mohammed, Adugna, and Takala 2018; Tassew, Belete, and Miegel 2021). For example, the study revealed that 38 sub-watersheds in the MOGR basin exhibit significant susceptibility to soil erosion and subsequent land degradation. Similar results were observed in the upper Gibe catchment of the OGR basin, where approximately 49% of sub-watersheds were at higher risk of soil erosion (Mohammed, Adugna, and Takala 2018). In addition, a study conducted in the Gojeb River basin, which is one of the sub-watersheds of the OGR, also classified over 30% of the research areas as being at a higher risk of soil erosion based on morphometric parameters.

Thus, these studies offer a valuable way of defining the priority areas to be identified for intervention to mitigate soil erosion. It provides a dynamic, cost-effective, sustainable alternative to conventional watershed prioritization approaches. The high to very high soil erosion-prone sub-watersheds identified in this study urgently require comprehensive watershed management strategies to mitigate the escalating risk of soil erosion and land degradation in the MOGR basin. It is recommended that government and non-government organizations implement various soil and water conservation practices such as terraces, soil bands, traditional rainwater harvesting methods like fanayaju, agronomic measures, vegetative practices, reforestation, and area closure techniques, mainly targeting farm and grazing lands, as well as bare or degraded lands. Furthermore, policy measures and exploring alternative options to alleviate population pressure and reduce dependency on limited land resources are essential considerations.

6. Conclusion

This study is the first in the region to analyses over 30 detailed morphometric parameters across 96 sub-watersheds in the MOGR basin to prioritize those prone to soil erosion and subsequent land degradation. It utilizes a geo-morphometric and statistical-based WSP approach in the GIS environment by integrating hypsometric analysis, lithology, land use/cover analysis, and field observations to enhance methodological reliability in assessing overall soil erosion patterns across the MOGR basin. The MOGR basin is situated in the southern part of the Ethiopian Highlands, where diverse lithological, land use/cover, and hydro-geomorphological characteristics cause the prevalence of soil erosion. This susceptibility is evaluated through various morphometric parameters and hypsometric analysis. Firstly, the linear geomorphometric parameters investigated in the study shed light on the dynamics of the river system and affected hydrological and geomorphic processes, including erosion. For instance, the MOGR basin's topography significantly influences stream development, increasing overland flow, as indicated by higher R_b in the sub-watershed stream networks. Notably, 77% of sub-watersheds have R_bm values exceeding 3, suggesting a higher degree of stream branching, flash flood probability, and soil erodibility. The studied drainage areal morphometric parameters emphasize their role in soil erosion by shaping water flow and sediment transport within watersheds (Ojha et al. 2023). For instance, the calculated D_d values for sub-watersheds range from 0.92 km/km² to 1.60 km/km², indicating slower water movement and reduced erosion potential. However, despite lower D_d, the prevalence of scrubland, range land, and agriculture areas with less forest cover in the MOGR basin suggests increased surface runoff and erosion and needs further investigation. Besides, 30% of sub-watersheds show higher F_s values, indicating increased susceptibility to erosion processes. The 21% of sub-watersheds have moderate to fine D_t values. Finer D_t intensifies water's erosive potential, accelerating erosion rates within the watershed (Boitt and Bebeto 2020). Moreover, the shape morphometric parameters studied in the MOGR basin reveal key characteristics influencing erosion susceptibility in sub-watersheds. For example, about 54% of sub-watersheds have R_c values below 0.5, potentially leading to irregular water flow distribution and increased erosion vulnerability. Moreover, one-third of sub-watersheds show lesser R_e, indicating steeper slopes that intensify runoff and erosive potential. The 68% of sub-watersheds have L_o below 0.6 km/km², signifying steep slopes and increased surface runoff and soil erosion. Regarding landscape morphometric parameters, nearly half of the sub-watersheds have a D_I exceeding 0.5, indicating steep slopes and elevation variations that increase erosion susceptibility. 30% of sub-watersheds have a HI value >0.45, indicating nearly immature basin development with a higher susceptibility to erosion and land degradation. The integrated geomorphometric and statistical-based WSP approach prioritized 38 sub-watersheds among the studied 96 sub-watersheds as being very high to highly susceptible to soil erosion and land degradation in the MOGR basin. Most of the sub-watersheds with very high to high susceptibility to soil erosion are situated in the northeastern (e.g. SWS12, SWS13, SWS17) and eastern (SWS22, SWS25, SWS32, SWS38) part of the MOGR basin. However, certain sub-watershed (e.g. SWS46, SWS58, SWS67, SWS68, SWS75, SWS94) in the southern part are also classified within this very high to high susceptibility category of soil erosion potential. These identified sub-watersheds urgently need comprehensive watershed management strategies to combat escalating risks of soil erosion and subsequent land degradation in the MOGR basin. Moreover, the study's findings are valuable for water management decision-makers, enabling informed decisions, resource allocation, and targeted interventions to control soil erosion and protect water resources across this region. Furthermore, the study holds considerable importance in achieving various sustainable development goals in Ethiopia, including promoting sustainable land management, conserving natural resources, enhancing climate resilience, improving livelihoods, and aligning with the United Nations’ 2030 agenda for global sustainable development. Finally, the study recommends that forthcoming research should concentrate on quantifying soil erosion in the highly prioritized sub-watersheds across the MOGR basin. This entails utilizing soil loss estimation models in conjunction with field observations to improve the formulation of enhanced adaptive measures for combating soil erosion and land degradation throughout this region.

Acknowledgments

This work is supported by the fellowship of the Indian Council for Cultural Relations (ICCR), New Delhi, India. The authors gratefully acknowledge the support from the Central University of Punjab, Bathinda, for facilitating the execution of this research. Special thanks are extended to Prof. Omvir Singh (Dr) and Rajan Bhandari (Dr) for their invaluable contributions in critically editing the language of the research paper. The authors also express their gratitude to Wachemo University, Hossana, Ethiopia, for supporting the field data collection for this research endeavor.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The dataset for generating digital elevation data sources for this research was obtained from freely available Open topography databases (https://portal.opentopography.org/datasetMetadata).