Volume 9, Number 2 (January 2022):3307-3315, doi:10.15243/jdmlm.2022.092.3307 ISSN: 2339-076X (p); 2502-2458 (e), www.jdmlm.ub.ac.id
Open Access 3307 Research Article
Remote sensing, GIS, and RUSLE in soil loss estimation in the Kulfo river catchment, Rift valley, Southern Ethiopia
Muralitharan Jothimani*, Ephrem Getahun, Abel Abebe
Department of Geology, College of Natural Sciences, Arba Minch University, P O Box:21, Ethiopia
*corresponding author: [email protected]
Abstract Article history:
Received 20 October 2021 Accepted 2 December 2021 Published 1 January 2022
Quantification of soil is crucial for maximizing the advantages of land resources while minimizing the negative consequences of land degradation in the long term. It will also make it possible to identify locations that need immediate soil erosion management. The present study was carried out in the Kulfo river catchment, Rift valley, Southern Ethiopia. The Revised Universal Soil Loss Equation (RUSLE) method was utilized to estimate the mean yearly soil loss in the research region using remote sensing, other collateral data. The RUSLE model inputs were mapped and integrated into the ArcGIS software, and the results show that 0 and 1211 t ha−1year−1 are the minima and maximum soil loss in the present study area. Soil erosion- prone regions were divided into three categories: 0-42 t ha−1year−1 (low), 43-128 t ha−1 year−1 (medium), and > 128 t ha−1 year−1 (high). And the average rate of soil erosion is 68.47 t ha−1year−1. Low, medium, and high soil erosion areal extent and area percentages in the current research area is 270 km2 (77 %), 61 km2 (17 %), and 19 km2 (6%), respectively. A high rate of soil erosion was found where high steep slope, barren land, and high precipitation occurred in the present study area. The current study's outcomes were confirmed by comparing soil loss estimates in the same geo- environmental conditions found in Ethiopia's highlands. The outcome of this study is important for decision-makers and policymakers.
Keywords:
Ethiopia GIS
Kulfo River catchment remote sensing Rift valley RUSLE soil erosion
To cite this article: Jothimani, M., Getahun, E. and Abebe, A. 2022. Remote sensing, GIS, and RUSLE in soil loss estimation in the Kulfo river catchment, Rift valley, Southern Ethiopia. Journal of Degraded and Mining Lands Management 9(2):3307- 3315, doi:10.15243/jdmlm.2022.092.3307.
Introduction
Land degradation takes several forms, the most common of which is soil erosion. It is caused by a combination of natural and human factors (Rosas and Gutier- rez., 2019; Teng et al., 2019; Bhattacharya et al., 2020). Soil erosion is a severe threat to the food security of Africa, Asia, and other developing countries (Shiferaw, 2011). In Ethiopia, rainfall impacts on soil erosion are notably severe (Jothimani et al., 2020a; Yisehak et al., 2020). In the Ethiopian highland, 27 million hectares of land have been significantly degraded; among these, 2 million hectares of land are classified as an unrecoverable state (Hurni, 1988). Ethiopia loses 1,493 t yr-1 of soil every
year, according to (Mengistu et al., 2021). It is well- known as the leading cause of land degradation in the nation's rain-fed agricultural areas (Hurni et al., 2015), with significant consequences for on-site and off-site ecosystems (Yaekob et al., 2020).
Soil erosion affects the physical and chemical characteristics of the soil, especially its fertility.
Ethiopian farmers have been driven to seek out more fertile territory, resulting in the more cultivated area at the risk of forest ecosystems (Mengistu et al., 2021).
Soil erosion is a critical problem over a wide geographical and temporal scale in Ethiopia (Shiferaw, 2011; Meshesha et al., 2012; Erkossa et al., 2015;
Desta and Lemma, 2017; Haregeweyn et al., 2017;
Open Access 3308 Welde and Gebremariam, 2017). Several models have
been created to estimate the amount of water that caused soil erosion. Revised Universal Soil Loss Equation (RUSLE) is the most frequently used technique for estimating soil loss. Publicly accessible data sets and interface with Geographic Information System (GIS) are often utilized to develop the RUSLE model (Gashaw et al., 2017; Kidane et al., 2019). The soil loss estimation and its geographical extent of soil erosion can be predicted using the RUSLE model (Modeste et al., 2016). Soil erosion may be calculated using the RUSLE model in small and large areas, and it is one of the major merits of this model. The RUSLE model was developed by the United States of America- Department of Agriculture and Land Use Planning (Renard et al., 1997; Tamene and Vlek, 2008).
The following scholars used the RUSLE model to estimate soil erosion in the different parts of Ethiopia (Wolka et al., 2015; Kayet et al., 2018;
Zerihun et al., 2018; Alewell et al., 2019). Most of the research listed above was carried out on highlands and plateaus, where soil erosion and degradation are more severe. An understanding of soil erosion patterns is crucial to developing methods for managing soil erosion. Remote sensing data and GIS tools permit more precise estimations of the extent and amount of soil erosion by using RUSLE model (Wilson and Lorang, 2000; Bartsch et al., 2002; Gelagay and Minale, 2016). Mainly land use and cover (LULC) data can be extracted from remote sensing images, and a cover-management factor (C-factor) map can be generated, and this is one of the major inputs for the RUSLE model (Tesema et al., 2020).
The following research has demonstrated that soil erosion concerns persist in the watersheds of the rift valley. 81,864 tons of soil erosion were projected to occur annually in Ethiopia's south-central rift valley basin, located in the Berta watershed (Bekele et al., 2019). (Bekele and Gemi, 2021) calculated that the probable soil loss rate in Ethiopia's Rift Valley Basin's Dijo watershed ranged from 0.89 to 5.29 tons per hectare per year. There is severe land degradation in the Kulfo river catchment due to soil erosion and flooding. It causes sedimentation and nutrient intrusion into Lake Chamo (Blumberg and Schütt, 2004; Jothimani et al., 2020b). Extreme flooding occurrences in and around the basin reduce agricultural production by encouraging the development of nonfarming operations (Wolde et al., 2018). Around 70% of the rural community's safe water is sourced from the Kulfo River, making it a potential water source for the urban population as well (Teklemariam, 2005). This study was aimed to quantify soil loss in the Kulfo river catchment in the Southern Ethiopian Rift Valley by utilizing GIS tools, remote sensing data, and the RUSLE model. The present study is significant in the identification of erosion-prone locations and soil and water conservation strategies. And so far, no similar research has been undertaken in the current study area.
Materials and Methods Study area
The Kulfo River originates in the Guge highlands, located on southern Ethiopia's western escarpment of the Main Ethiopian Rift. Chamo Lake surrounds the Guge and Amaro Mountains. And the lake Chamo is mainly fed by the Kulfo river. The isthmus that connects Lake Chamo and Lake Abaya passes through Arba Minch to Lake Chamo. Lake Chamo is located at 1,110 meters elevation on the Main Ethiopian Rift Valley, Arba Minch town, Southern Ethiopia. Kulfo river generally drains into Lake Chamo; however, after heavy rains, it can also flow to Lake Abaya via a bifurcation immediately southwest of Arba Minch Airport, located approximately southwest of the airport. The current research was conducted in the Kulfo river watershed of the Southern Ethiopian Rift Valley, situated around 60 02' and 6016'N latitude and 37018' and 37032'E longitude an altitude of 1224 to 3547 m above sea level and a total area of 353 km2. (Figure 1) shows the study area's location map. The mean annual temperature is between 14 and 23°C. The catchment receives 620–1250 mm of rain per year. The mean annual flow is estimated to range between 1.8 and 22.8 m3/s (for the observation period 1985–2014) (Yisehak et al., 2020). Cambisols, ferralsols, regosols, and fluvisols are the four primary soil types found in the Kulfo watershed. Agricultural activities are the primary land use in the Kulfo river catchment. The bushlands, shrublands, water bodies, forests, and cultivated land are the significant LULC type of the present study area (Wolde et al., 2018).
Data sets and their sources
The RUSLE model's input data is obtained from various sources, including field observations and different digital and non-digital data sets. (SRTM- DEM) - Shuttle Radar Topographic Mapper- digital elevation model with 30 m X 30 m spatial resolution was downloaded from the earth explorer website and file entity id: SRTM1N06E037V3 and, for the current study, and the same was employed to create elevation and slope layers. The Optical Land Imager (OLI) image from Landsat-8 was also downloaded from the earth explorer website, and the image acquisition date is 12-March-2019, and 159-66 is the path and row of the image.
The present study's LULC layer was created using the Landsat-8 OLI's false-color composite (FCC). Rainfall data for the current research catchment was obtained from https://chrsdata.eng.uci.edu/, as a grid format with a spatial resolution of 4 km * 4 km from 2001 to 2019. The rainfall data in grid format was obtained for an individual year from the above source, and average rainfall was calculated. The present study area soil data was downloaded from the Ethiopian Soil Information System (EthioSIS) website (https://wle.cgiar.org). To confirm the present study's
Open Access 3309 LULC map accuracy, fifty ground truth data were
gathered from the field utilizing a global positioning system (GPS). The current study employed ArcGIS
10.8 software to process and analyze the various data sets. The methodology flow chart of the present study is shown in (Figure 2).
Figure 1. Study area location map.
Figure 2. Methodology flow chart.
Open Access 3310 RULSE model
RUSLE is a soil conservation planning and evaluation tool created by the United States Department of Agriculture (Nearing et al., 2005). RUSLE is deemed simple because it demonstrates how LULC, climate, soil, and other variables affect soil erosion using publicly available/accessible data (Alexakis et al., 2013). RUSLE model calculation is shown in the following equation 1.
A = R×K×LS×C×P --- equation 1
where A is an average annual soil loss in tons per hectare per year, R is rainfall-runoff erosivity factor, K is soil erodibility factor, LS is topographic or slope length/steepness factor, C is cover and cropping management factor. P is supporting practices (land use) factor.
Rainfall-Runoff erosivity factor (R- Factor)
According to (Foster et al., 2003), precipitation intensity levels are the primary properties of rainfall, and they exhibit the most significant annual changes.
Hurni (1985) developed R-calculation for Ethiopia (equation 2), which many Ethiopian scholars have used to build the RULSE model in different catchments of Ethiopia (Meshesha et al., 2012; Kidane et al., 2019; Negash et al., 2021).
R = −8.12+(0.562∗P) --- equation 2 where P denotes the mean annual rainfall in millimeters (mm), calculated from 19 years of rainfall
data was downloaded from
https://chrsdata.eng.uci.edu/ from 2001 to 2019. The
current study area's 4 km * 4 km rainfall grid was retrieved from the aforementioned site, and the contour was derived using ArcGIS software's inverse distance weighted technique. Correspondingly, the present catchment's average lowest and maximum rainfall is 1983 mm and 1029 mm. The highest rainfall was observed in the study area's highlands (northwest), whereas the lowest was recorded in the valley section (south-east). Equation 2 was used to calculate the R- factor, and the results reveal that the lowest and highest R-factor values are 541.27 and 1,066 ha−1 h−1 year−1. Soil (K) factor
Soil (K) factor indicates how susceptible the soil is to erosion by rain (Lee et al., 2017; Pradhan et al., 2018).
When it comes to erosion resistance, different soil types have varying degrees of resistance. The erodibility rate is influenced by the soil's different physical and chemical properties (Wischmeier and Smith 1978). The current study's soil data was acquired as a shapefile from EthioSIS. Orthic acrisols are the most common soil type in the present research region, accounting for 73 percent of the entire study area. Chromic vertisols, dystric fluvisols, dystric nitisols, and eutric nitisols are other soil types. The values of the K factor for Ethiopian soil conditions were proposed by (Hurni, 1985; Hellden, 1987). The K-factor values for each soil type were allocated as follows: chromic vertisols (0.24), dystric fluvisols (0.36), dystric nitisols (0.26), eutric nitisols (0.34), orthic acrisols (0.34). (0.2). The soil type and K-factor maps for the current research region are shown in Figure 3.
Figure 3. Soil and K- factor maps.
Open Access 3311 Slope length‑steepness (LS) factor
The slope length steepness (LS) is an important element that impacts the flow velocity of the surface water. Hence, it is significant in RUSLE model as a topography component (Kaltenrieder, 2007; Nekhay et al., 2009; Prasannakumar et al., 2012). The higher the slope's steepness and length, the more energy is available to cause soil erosion. Moore and Burch (1986a, b) have proposed a method of estimating the LS factor utilizing flow accumulation and slope steepness used in the present study. Equation 3 shows the LS-factor computation.
LS = ([flow accumulation] * [cell size]/22.13)0.4 *
¬sin [slope gradient]/0.0896)1.3 --- equation 3 where cell size is the DEM's spatial resolution (in this case, 30 m). Flow accumulation was calculated using arc hydro techniques. The Ls-factor was calculated, and the results were obtained employing ArcGIS
software's "raster calculator" tool to process equation 3 in the spatial analyst tool. The LS-factor values vary from 0.03 to 98.14, with the highest LS-factor values observed in areas with a steep valley (Figure 4).
Cropping and land cover factor (C factor)
The C-factor represents LULC, especially in mountainous areas; it is crucial in preventing soil erosion (Shiferaw, 2011). Soil erosion on vegetated land is lower than on unvegetated land because of vegetation's better capacity to protect the soil surface from erosion (Alexakis et al., 2013). As a result, changing LULC types to more vegetative surface coverings can aid in soil erosion prevention. The current study's C factor was obtained using the LULC map. A false-color optical satellite image of Landsat-8 OLI was used for mapping the LULC for the present study. The LULC map of the present catchment was created using a supervised image classification technique.
Figure 4. LS and land use/land cover maps.
The forest area covers 17%, and other significant LULC types are agricultural land, bare and bushland, and settlements are found in the present study area (Figure 4). LULC map's accuracy was defined by the user's exactness and the producer's precision. Fifty points were selected randomly from the classified LULC map to perform the accuracy assessment using Google earth's image archives and field verification.
The current study's average user, producer, overall accuracies, and Kappa coefficients are 83%, 78%, and
71%, respectively. The C factor values for Ethiopia were given by different scholars, including (Wolka et al., 2015; Degife et al., 2021). The C-factor value varies from 0 to 1, with a value close to 0 suggesting lower soil erosion susceptibility and more successful conservation efforts. A value approaching 1 implies no conservation efforts, and that area is highly susceptible to soil erosion (Wolka et al., 2015; Degife et al., 2021).
Different LULC types were allocated different C factor values, ranging from 0.01 to 1 (Figure 5).
Open Access 3312 Conservation support practice factor (P)
The P factor measures how well soil–water conservation reduces soil erosion. In other words, it's the difference between natural erosion and erosion when preventative measures are applied (Wischmeier and Smith 1978). The P factor has shown the effects of preventive methods such as close-growing plants, terracing, and contouring on a specific geographical location for soil erosion. The P factor varies from 0 to 1, with 0 indicating well-established soil–water conservation strategies and 1 showing no such measures have been adopted. Despite particular government watershed management programs, field
observations have revealed that virtually all land covers in the current research region are empty of conservation measures. (Wischmeier and Smith, 1978) recommended that the P-value by using LULC and slope %. In different areas of Ethiopia (Hurni, 1985;
Bewket and Teferi, 2009; and Moges et al., 2020) utilized the same approach to determine the P-factor value. Initially, LULC and slope % maps were intersected in the ArcGIS environment, and P-factor values were allocated to the agricultural and nonagricultural areas depending on the slope percentage. The values of the P-factor in this study ranged from 0.63 to 0.9 (Figure 5).
Figure 5. P and C factor maps.
Results and Discussion Estimation of soil loss
RUSLE inputs like R, K, LS, C, and P factors were converted to a raster format, and then all layers were combined using eq 1. The final results reveal that the current study area's soil loss varies from 0 to 1211 t ha1year1. Soil erosion-prone regions were divided into three categories: 0 – 42 t ha−1year−1 (low), 43 – 128 t ha−1year−1 (middle), and > 128 t ha−1year−1 (high).
(Figure 6) depicts the soil loss map. And the average rate of soil erosion is 68.47 t ha−1year−1. Low, medium, and high soil erosion areal extent and area percentages in the current research area is 270 km2 (77 %), 61 km2 (17 %), and 19 km2 (6%), respectively. Erosion proneness has varied greatly throughout the landscape, where steep slopes, high drainage occurrences, and places where poor land management is present. The present analysis results were classified into low,
medium, and high zones for soil erosion proneness.
The high soil erosion-prone locations were identified in the northwest and central regions of the current research area (Figure 6).
An intersection analysis of the soil loss estimation results and the LULC layer revealed that agricultural land is responsible for 1.41% of high soil erosion, whereas bushland is responsible for 1.13%.
Shrubland, agricultural land, and bare land cause 9.85%, 5.15%, and 2.49% of soil erosion in medium soil erosion zones. Overgrazing and seasonal mass burning of shrubs in the upper region of the present catchment induce soil loss. Increased agricultural techniques in the very erodible soil exacerbated the situation due to a lack of plant cover. Further, the high amount of soil loss was exacerbated by overgrazing and deforestation. The annual soil loss in Ethiopia's highlands ranges from 1248 to 23,400 million t ha−1year−1. In the present study area, the acceptable
Open Access 3313 limit of soil loss for the Ethiopian highlands is
surpassed, as stated by (Wolka et al., 2015) as less than 10 t ha−1year−1 for the central rift valley and (Hurni, 1993) as 6 t ha−1year−1 to 10 t ha−1 year−1 for the Ethiopian highlands.
Validation
It is essential to validate the model used to predict soil erosion to check the correctness of the findings and assess the model's effectiveness. However, the research region's lack of field-level observation data could not compare projected erosion rates with field determined values. Average soil loss estimated in high lands of Ethiopia was considered to validate and compare the present study results. Gete (2000) found a mean soil loss rate of 243 t ha−1year−1 in Ethiopia's north-western highlands (Bewket and Teferi, 2009) calculated 93 t ha−1year−1in the Chemoga watershed.
Yihenew and Yihenew (2013) reported t ha−1year−1 in Northwestern Ethiopia. Gelagay and Minale (2016) estimated 47.4 t ha−1year−1 in the Koga watershed.
And, mean soil erosion value of the present study area is 68.47 t ha−1year−1. The projected soil loss rate and the geographical distribution of erosion categories in the current watershed are thus more realistic when compared to previous studies.
Figure 6. Soil loss map.
Conclusion
The present study adopted remote sensing data, GIS tool, and RUSLE model to estimate the soil loss with the severity and variability in the Kulfo river catchment, Rift Valley, Southern Ethiopia. The
catchment has a significant problem with water- induced soil erosion. The Kulfo River's tributaries and deep gullies are transporting sediments into the main river channel's catchment region, a core factor for soil erosion. The RUSLE model, when used in conjunction with remote sensing data and ArcGIS tools, can forecast soil erosion and create spatial distribution maps. In general, the climate (R- factor) significantly impacts the geographical distribution of soil erosion, and the R-factor is the indicative factor of climatic conditions in the RUSLE model. And, the R-factor affects the plant cover, which impacts the C-factor and the present study area. The mean soil erosion value is 68.47 t ha−1year−1. The current study results were validated by comparing the soil loss estimation in the same geo-environmental settings present in Ethiopian highlands. As a result, the research area's soil loss values per year exceeded the tolerable limits of soil loss, necessitating the implementation of sufficient water and soil conservation strategies in the study region. The present study shows the effectiveness of remote sensing data, GIS tools, and the RULSE model in soil erosion estimation. The current study results are significant for decision-makers and planners.
Acknowledgement
The authors would like to acknowledge the United States Geological Survey (USGS), Center for Hydrometeorology and Remote Sensing (CHRS), University of California, Irvine (UCI), and Ethiopian Soil Information System (EthioSIS) for providing optical and DEM satellite products, rainfall, and soil data conduct this present study.
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