Estimation of Annual Average Soil Loss Rate from Hangar River Watershed Using RUSLE through the Application of GIS Technique
a Mahmud Mustefa*, a,b Fekadu Fufa (PhD) and a,cWakjira Takala
a,b,c Faculty of Civil and Environmental Engineering, Jimma University, P.O.Box 378, Ethiopia. (*corresponding Author: [email protected])
This study was aimed to estimate the spatially distributed mean annual soil loss rate and map the most vulnerable areas in Hangar watershed using Revised Universal Soil Loss Equation (RUSLE) with the aid of Geographical Information System (GIS) techniques. The RUSLE parameters; rainfall erosivity (R-factor), soil erodibility (K-factor), slope steepness and slope length (LS-factor), vegetative cover (C-factor) and conservation practice (P-factor), which consists of a set of logically related geographic features and related attribute data were generated for the analysis. A 30 x 30 DEM was used for catchment delineation and analysis of the LS-factor. The land use/ land cover map of 2013 was used for the analysis of C-factor, Soil map of the study area for the analysis of the K-factor, mean annual rainfall data of the nearby rain gauge stations for analysis of R-factor. By integrating these five map layers in GIS raster calculator, the required spatially distributed annual average soil loss rate was determined. The result of the analysis depicted that the amount of soil loss from the Hanger catchment ranges from 1 to 500 t ha-1 yr-1 with an average annual soil loss rate of 32t ha-1yr-1 from the whole catchment. About 84.2% of the total area experienced soil loss above tolerable limit of 11t ha-1yr-1. The total annual soil loss from the entire watershed was found to be about 24.93 Mtons. This indicates maintaining the sustainability of the soil productivity will be difficult if the specified amount of soil is removed annually. To evaluate the effect of watershed management, contour ploughing with terracing was evaluated in this study and the result indicates if it is fully developed, the average annual soil loss rate will decrease from 32 to 19.2tha-1yr-1. Consequently, applying effective watershed management reduces the vulnerability of the watershed by 40%. Based on the spatial vulnerability of the watershed, most critical soil erosion areas were situated in the steepest upper part of the watershed due to intensive agricultural activities.
Key words: Annual soil loss, Hangar watershed, RUSLE, Soil erosion
Soil erosion is a natural process resulting from the removal of soil particles from the surface of the earth by water and wind, transporting and depositing elsewhere (Hurni, 1988). And it is one of the reasons of soil degradation which leads to the deteriorations of physical, chemical and biophysical properties of the soil (FAO, 1978).
The action of soil erosion is triggered by a combination of factors such as steeply slopes, heavy rainfall after long dry period, inappropriate use of land cover patterns and ecological disasters (Oldeman, 1998). Moreover, some intrinsic features of a soil can also make it more prone to erosion. such intrinsic features are a thin layer of topsoil being silty textured and low organic matter content (Kosmas, 1997).
Soil erosion is one of the biggest global environmental problems resulting both on-site
effects such as; loss of top fertile soil, minimize water holding capacity of the soil, nutrients and minerals carried off by water and off-site effects such as; silting up of dams, disruption of lake ecosystems, contamination of drinking water and increased downstream flooding (Tamene and Vlek, 2008). Even though these effects have been identified as a global problem in the 20th century, the trend of Soil erosion has continued to increase throughout the whole nation (Adugna et al., 2015). Studies show that in the whole globe, about 80 % of agricultural lands suffered from moderate to severe soil erosion which is a cause of loss of productivity of agricultural lands (Hurni, 1998; Gete, 2000). Pimentel et al. (2009) and Jahun (2015), also reveal a shocking figures about the erosion phenomenon, that is, most of the soil from farmlands is washed away about 10 to 40 times faster than it is being replaced, citing examples that in some parts of United States was losing a soil of 10 times faster than the regular replacement rate. On the other hand, Pimentel et al. (2009) and Jahun (2015) present that, China and India are also said to be losing soil of 30 to 40 times faster than its formation.
In Ethiopia, a number of studies indicate the existence of sever soil erosion in the highland areas and sedimentation in the low land areas of the country (Bewket and Teferi, 2009; Kebede et al., 2015). For instance some of the evidence research shows that an average annual soil loss rate of 35 t ha-1 yr-1 (FAO, 1986); 42 t ha-1 yr-1 (Hurni, 1993) and 57t ha-1 yr-1 (Girmay, 2009) were reported. In addition to this, other researches also show that soil erosion rate ranges from 16 to 300 t ha-1 yr-1 (Hurni, 1986) and 130 to 170 t ha-1 yr-1 (Gete, 2000) in the highland areas of the country. Related study also indicates the existence of sever problems on agricultural lands due to removal of fertile soil and sedimentations on the water bodies and reservoirs in Ethiopia (Kebede, 2012). The study area, Hangar River Watershed, is one of the catchments suffering with this sever soil erosion problem as well (Jemal, 2010).
Studies indicate that splash, sheet and rill erosion by water are the major components of land degradation that affect land productivity in the Ethiopia (Desta et al., 2005; Haregeweyn et al., 2015). In general, Soil erosion and transportation by water due to rain drop impact is the most common erosion agent in the country (Zeleke and Hurni, 2001).
The severity of soil erosion in Ethiopia is due to most part of the country is being steep sloped and mountainous, and the existence of higher and frequent rainfall amount with higher intensities. In addition to this; human activities, rapid population growth, poor cultivation system and poor land use practices, deforestation and overgrazing, has a great contribution to soil degradation in the country (Hurni, 1993; Kebede, 2012). Loss of fertile soil, rapid degradation of natural systems, significant sediment depositions in the lakes and reservoirs and sedimentation of irrigation infrastructures are generally, due to poor watershed management system in the country (Akalu et al., 2009).
The main River in the study area (Hangar River) is one of the major tributaries of Didesa River, which finally joins to Blue Nile River. This River has a length of more than 200 km and has its own medium scale tributary rivers, which consists of 11sub-catchments larger than 500 km2 each. The exposure to erosion and Sediment contribution from those tributary Rivers varies depending up on the existed situation of the sub-catchments. Therefore, conducting this research contributes to identify the most sever soil erosion areas in the specified catchment. Knowing and identifying the most prone area, is very important to take interventions measures in line with the identified erosion vulnerable area.
In order to predict and evaluate soil erosion quantitatively, different prediction models have been efficiently developed and employed by different soil scientists in the last few decades (Gelagay and Minale, 2016). Using these models now a day different researches are undertaken in different parts of the world to estimate the rate of soil erosion and mapping of erosion risk areas. One of the most widely used empirical models is universal soil loss equation (RUSLE), with remotely sensed data and GIS software (Renard et al., 1997). The result of this model has been checked by different researcher and showed its efficiency in estimating rate of soil erosion and mapping of erosion risk areas throughout the world. For instance, Millward and Mersey (1999) show the potential of using a combination of remote sensing, GIS, and RUSLE in estimating soil erosion loss on a cell-by-cell basis.
Among the soil loss estimation models, only few are used to measure soil loss in Ethiopian conditions, because of data limitations. One of these few soil erosion prediction models, RUSLE is mostly used model because of its simplicity relative to other conceptual and process based models, relative data availability for this model and integration with GIS. (Temesgen, 2017; Gelagay and Minale, 2016). Even though, this model has been developed after the parameters are tested and validated under diverse soil, climate and management conditions of United State of America, several efforts has been made to calibrate and validate the use of RUSLE model for other countries including Ethiopia. Among those studies for instance (Hurni, 1988; Helden, 1998; in Ethiopia; Angima et al., 2003 in Kenya; Prasannakumar et al., 2012 in India). Specifically as sited by Alemayehu (2012), Mulugeta, 2004 has calibrated RUSLE for Andit Tid watershed while Serkalem (2005) for Mayebar and Mesfin (2008) for Anjeni watersheds in Ethiopia. In all these studies RUSLE was publicized that the model shows satisfactory result. Therefore, the objective of this research is to quantify the amount of annual soil loss rate from Hager River watershed using this most applicable model RUSLE, through the application of GIS technique and to identify the most vulnerable areas of the watershed.
1.1. Statements of the problems
Ethiopia has been described as one of the most seriously affected nation in the world by soil erosion (Hurni, 1988; Mitiku et al., 2002; Gizachew, 2015). Soil erosion and sediment yield from catchments are therefore key limitations to achieve sustainable land use and maintaining water quality in rivers, lakes and other water bodies (Benedict and Andreas, 2006).
Many of Ethiopia’s hydroelectric power and irrigation reservoirs such as Aba-Samuel, Koka, Angerib, Melka Wonka, Borkena, Adarko and Legedadi has been threatened by the heavy sedimentation. Therefore, these dams have been suffered from reduction in their capacity and life span, quality of water and require costly operation for removal and operation and thus these dams loss their intended services (Kebede, 2012; Gelagay, 2016).
The degradation of large part of the Ethiopian highlands has reached a scale where it has become increasingly difficult even to maintain the current level of production of basic food which is already insufficient in many regions of the country (Bekele, 1998). Hence, Soil erosion affects the socio-economic condition of a country directly or indirectly; especially countries like Ethiopia whose economy is extremely dependent on agriculture (Angima et al., 2003; Abate, 2011). Therefore, the economic implication of soil erosion is more series in such countries because of the capacity to cope with it and also to replace the lost nutrients. As sited in Gashaw et al. (2017), Sonneveld and Keyzer (2003) estimates through modeling work and suggests that soil erosion in Ethiopia will reduce the potential production of the land by 10% in 2010 and by 30% in 2030. As a result, the value added per capita per annum in the agricultural sector goes down from US$372 in 2010 to US$162 in 2030.
The top fertile soil which is naturally abundant resource plays a vital role for the agricultural productivity. But, the removal of this top fertile soil leads to reduction in crop production. This reduction of crop production results poverty on the major population in the country. At the same time, sedimentation problems occur in the water bodies and reservoirs and minimize the life span of reservoirs (MoARD, 2010).
Studies conducted by using Water Evaluation and Planning (WEAP) model, and assessed the future potential of irrigation and hydropower in Blue Nile River Basins shows that, Hangar River has a potential of developing more than 14000 ha of irrigation and 1.8 to 9.6 MW of hydroelectric power (Matthew et al, 2005). Accordingly, Federal Government of Ethiopia (FDRE) has a plan of implementing this project. However, the large part of this area is degraded due to deforestation for intensive agricultural activities like; farm expansion, extraction of fuel, constructional wood, overgrazing and for other related purpose which are the consequences of population growth and expansion over the area, as other parts of the country. Now a day agricultural lands in the study area is less productive due to soil degradations. Farmers use different fertilizers for agricultural lands in order to compensate some of the lost nutrients in the soil due to soil erosion, which is costly. This condition was seen during site visit. According to FAO (1986), Rapid population growth, cultivation on steep slopes, clearing of vegetation, and overgrazing are the main factors that accelerate soil erosion in Ethiopia. These, the damages associated with excessive soil erosion problem thought to be severe in this area as there are intensive agricultural activities, rapid population growth, cultivation on steep slope and related activities mentioned by FAO (1986) are common on the study area. Therefore, this study was done to estimate the annual soil erosion rate and add the soil erosion information for the decision makers to plane appropriate soil Conservation practice in the watershed so that reducing fertile soil loss from farm lands and sedimentation in the proposed multi-purpose hydraulic structure on Hangar River.
2. Study area
The study area was Hangar River watershed, which is located in North West part of Ethiopia. The major part of the catchment is found in East Wollega Zone Oromia National Regional State Taking the outlet near the confluence points of Didesa River, she study area covers an area of 7790 km2. The geographical location of the study area extends from 36O 02′ 21″ to 37O 58′ 50″ E longitude and 9O 01’26” to 9o 59′ 50″ N Latitude. Anger River is one of the largest tributaries of Didesa River which emerges from near Horo district and flows towards South-West direction to join with Didesa River.
Figure 2 Location map of the study area Ethiopian River Basins (A) Blue Nile River Basin (B) Hangar River Watershed (C)
3. Materials and Methodology
In this study Arc GIS 10.3 was used for Analyzing, Displaying and viewing spatial data, Arc Hydro extension was used for Watershed delineation and RUSLE was used to quantify the soil loss rate.
To analyze the soil erosion vulnerability condition in the study area, RUSLE in GIS environment with factors obtained from metrological data, soil data, topographic map, satellite image, digital elevation model and other relevant studies were used. In addition to this, field observation were carried out to collect the primary data which were a key information regarding the current land management practice exercised in the study area.
Figure 2 Flow chart of the determinations of soil loss using RUSLE in Arc GIS
4. Results and Discussions
4.1. RUSLE Model Parameters
4.1.1. R – Factor
In the study area, the long-term mean annual rainfall amount was varied between 1570 to 2130 mm. Owing to this variation in mean annual rainfall amount within the study area, variation in rainfall erosivity was observed. Accordingly, the rainfall erosivity values estimated from mean annual rainfall of the selected rainfall stations ,varied from 882 MJ mm ha-1 hr-1 yr-1 at Shambu to 1196 MJ mm ha-1 hr-1 yr-1 at Nakamte. The calculated values in (Table220.127.116.11, Section 18.104.22.168) show that as the mean annual rainfall increases, the rainfall erosivity also increases. Following this, the study area faces highly erosive rainfall at Southern part of the study area Around Nakamte and gradually decreases towards the central and eastern parts of the study area around Hanger Gute and Shambu respectively. The areas in between the two extremes (Shambu and Nakamte), shares the values of erosivity in between the maximum and minimum erosivity value distributed spatially. Figure 4.1 shows the spatial variation of erosive power of rainfall in the study area.
Figure 4.1 R-factor map of the study area
From the digital soil map of the study area, eight different soil types with different characteristics were identified (Table 3.7, Section 22.214.171.124). The dominant soil type, Haplic Alisols covers the larger area which accounts about 38.2 % of the total area. Mostly this soil type exists in the Southern, central parts and northern boundary of the catchment (Figure 4.2 A). Haplic Acrisols, which is the second largest coverage area, is found at central to West and North-Western parts of the catchment. Haplic Arenosols which is highly resistance to erosion is found at the northern parts of the catchment and covers too small areas, about 0.004 % which is even invisible on the soil map unless zoomed in it in the GIS windows.
The erodibility characteristics of the existed soils in the study area were varied with the range of K-factor value of 0.15 to 0.35 t ha hr ha-1 MJ-1 mm-1. As the K-factor values approaches to 1, it indicates the susceptibility of the soil to erosion and as the K- factor values close to 0, it indicates the soil having good erosion resistance capacity. Hence, Dystric Leptosols, Eutric Leptosols and Haplic Arenosols which accounts about 12.8, 2.3 and 0.004 % of the total area respectively, have highest K-factor values of 0.35. Eutric Vertisols, which covers smaller area about 0.1 %, has the lowest K-factor values of 0.15, which indicates that the soil is less susceptible to erosion (Table 3.7).
Generally, Figure 4.2 (B) shows that > 60 % of the total area of the catchment was covered with soils which have lower to moderate K-factor values of 0.2 and 0.3 t ha hr ha-1 MJ-1 mm-1. Such soil types were found mostly at the central and South-Western parts of the catchment with some coverage at Northern part as well. Therefore, in terms of soil erodibility condition, the catchment characterizes with moderately vulnerable to erosion.
Figure 4.2 Major Soil types in the study area (A) respective K-factor map (B)
The values of LS-factor in the study area varies between 0 (flatter and lower part) to 61(steeper and upper part). As illustrated in Figure 4.3, most of the central and South-Western parts of the study area show a lower LS-factor value of 0 to 0.05. The higher LS- factor values of 10 to 61 were mostly observed at the mountainous and hilly region of the study area and along the side (bank) of the rivers. This is because, as the slope gradient increases, the value of LS-factor also increases. Consequently, soil erosion also increases. Therefore, at the area, where smaller LS-factor values existed, the expected soil erosion due to this factor would be less and at the area where, larger LS-factor values existed, the expected soil erosion would be more.
Figure 4.3 LS- factor map of the study area
From the classified LU/LC image, the area of each LU/LC class was calculated and presented in (section 126.96.36.199, Table 3.8). Based on the calculation, it was observed that the highland area was covered with open forest about 14 %, grazing land about 0.83%, grass lands about 12% and dense forest of 4.5% which corresponds with lower C-factor values. Over the study area, dense forest and grass land which have C-factor values of 0.01, collectively covers an area of only 16.5 % and about 59.5 % of the study area was covered with agricultural land which exposes to direct rainfall during the time of farm preparation. Soil erosion from this area was expected to be high because of the soil is exposed to the first rainfall events without any cover. For this area, the maximum C-factor value of 0.15 was assigned for agricultural lands next to bare soil with C-factor values of 0.6. As it is seen from the map (Figure 4.4 A) the cultivated land covers most of the central parts with some scattered distribution at Southern and Northern part of the study area. Therefore, the contribution of this factor for erosion at the Central and South-Western part is high and the contribution at the Western and at the highland areas of the watershed is less. This can be seen clearly on C-factor map for respective land use and land cover class (Figure 4.4B).
Figure 4.4 Maps of LU/LC (A) and corresponding map of C- factor (B)
Depending on the land management practice employed in the study area currently on varied slope gradient, the value of P-factor ranges from 0.4 to 0.9 (Figure 4.5 A). Based on the result, the central part of the study area characterizes with lower P-factor values and the whole other part of the study area shows the higher P-factor values. As shown in slope map of the study area (Section 188.8.131.52, Figure 3.9), the central part of the study area is highly flat and gentle slope from 2 to 16 % and the Southern, Eastern and Northern parts of the study area is steeper slope which is more than 16 % slope. Because of the P-factor values are highly influenced by slope steepness conditions, this upper part of the study area was characterizes with higher value of P-factor. Figure 4.5 shows P-factor values for existed conditions (A) and for imagined watershed management practice (B). Considering an implementation of watershed management practice such as contouring with terracing fully developed, the P-factor values ranges from 0.1 to 0.18 (Figure 4.5 B). In this condition also, the lower values of P-factor was concentrated at the central part of the study area and the higher values of P-factor was shown at upper and outer parts of the study area. Therefore, from the central parts of the study area, the expected soil erosion would be lesser due to the lesser LS-factor values in this particular area and the outer upper sloppy part of the study area contributes larger erosion due to larger LS-factor values in this area.
Figure 4.5 Map of P-factor for existing condition (A) for the imaged conditions (B)
4.2. Estimated average annual soil loss for existing condition
The pixel-based modeling results show that the spatial distribution of the annual soil loss rate varied from 1 t ha-1 yr-1 in low land and flat area to 500 t ha-1 yr-1 in degraded sloppy area with average annual soil loss rate of 32 t ha-1 yr-1 for the entire study area (Figure 4.6). On annual basis, the total soil loss of the watershed was found to be 24.93 Mtons of sediment from 7790 Km2 of land.
The result showed that the catchment is experiencing quit large spatial variation of soil loss due to quit large difference in topographical condition, land use land cover variation and higher rainfall variation. It is because; these factors are the major factor affecting soil erosion in the study area. Accordingly, the watershed was classified in to six severity classes to identify the most prone area to erosion, moderately affected area, list affected area and other respective trends of erosion conditions. In terms of exposure to the risk of erosion, about 15.8 % of the watershed was characterized by low to moderate soil erosion problem, which was from 1 to 11 t ha-1 yr-1 and such area can be considered as areas with tolerable soil erosion risk area. The remaining percentage area was categorized under, high, very high, sever and very sever soil erosion risk areas of 39.3, 31.8, 12.1 and 1% of the study area respectively (Table 4.1).
According to FAO (1985) and Renard et al. (1996), soil loss tolerance refers to the maximum soil loss that can occur from a given land without leading to degradation of the soil, and this is estimated to be 5- 11 t ha-1 yr-1. In line with this, the central parts of the study area which covered about 15.8% of the total area, could be considered as low soil erosion risk area. This is because; the result of soil erosion rate in this area was found to be in a range of maximum tolerable erosion limit of 11 t ha-1 yr-1. As it has been clarified by Yahya (2013), LS-factor, R-factor and K-factor, have a significant effect on the process of erosion in decreasing order. Therefore, the lower values of soil loss vulnerability was because of the central parts of the study area is characterized with relatively flat and gentle slope having lower LS-factor of 0 to 0.1 and the lower rainfall erosivity values of 882-973 MJ mm ha-1 hr-1 yr-1 as well as the lower K-factor values shown in (Figures 4.1, 4.2 and 4.3).
Based on the result found, about 84.2% (> 4480 km2) of the study area was identified to be highly suffered in soil erosion. The severity of erosion ranges from high (12-25 t ha-1 yr-1), very high (25-50 t ha-1 yr-1) and sever soil erosion class (50-100 t ha-1 yr-1) (Table 4.2). About 1% of the total area (77.9 ha) was exposed to very sever soil erosion risk (>100 t ha-1 yr-1). This part of the area is found mostly at the South corner of the catchment and some parts to Western part as well as Eastern part of the catchment. This is due to the higher erosive power of rainfall that comes from higher rainfall intensity around the specified area and the higher LS- factor values of 10 to 60, resulted from cultivation on steep slope lands (Figures 4.3 and 4.4). Table 4.1clearly indicates the area coverage and relative percentage of each soil erosion severity class for current condition of the study area.
The estimated soil loss rate and the spatial patterns are generally realistic, compared to previous studies on some of Ethiopian basins and watersheds. For instance, soil loss rate estimated by Hurni (1985) for Ethiopian highlands ranges from 0.0 to 300 t ha-1 yr-1. Temesgen (2017) also reveals that the soil loss rate ranges from 0 to 237 t ha-1 yr-1. Other related study conducted by Kebede (2014) shows that the soil loss rate ranges between 0 and 203 t ha-1 yr-1 from neighboring catchment of the study area, using the same model and from 0 to 150 t ha-1 yr-1 was presented by Betrie (2011) for the whole Blue Nile Basin.
In line with this, the average soil loss rate of the whole watershed in this study (32 t ha-1 yr-1) is comparable with similar findings reported in Amare et al. (2014) for Wondo Genet watershed about 26 t ha-1 yr-1, in Tadesse (2014) for the Jabi Tehinan watershed in the North-Western highlands about 30.6 t ha-1 yr-1 and Haregeweyn et al. (2017), also for the whole Upper Blue Nile Basin, reports to an average of 27.5 t ha-1 yr-1 with the same prediction model.
Table 4.1 Soil erosion severity class and corresponding percent coverage area.
Current soil erosion status of the study area
(t ha-1 yr-1 ) Area (km2) Area coverage (%) Severity Class
100 80 1.0 Very Sever
Total 7790 100
Unlike the findings of this study, some studies however, report a rather higher rate of erosion in different parts of Ethiopian watersheds. For instance, Bewket and Teferi (2009) report high rate of erosion with an average soil rate of 93t ha-1 yr-1 for Chemoga watershed of Blue Nile Basin in North-Western highland. Gelagay and Minale (2016) also report for Koga watershed of Blue Nile basin, the average soil loss rate to be 47.4 t ha-1 yr-1. In Contrary to the current and other studies in the highlands, few other studies report very low average soil erosion rate. For instance, in Medego watershed in the northern highlands with a rate of 9.63 t ha-1 yr-1 is observed by Gebreyesus and Kirubel (2009) and in Zingin watershed with a rate of 9.10 t ha-1 yr-1 is reported by Gizachew (2015). This variation of results comes from the actual existing condition of the watersheds. The lower results were due to the large portion of their study area being flat and gentle slope. For instance, about 49.77% of Medego watershed was covered with a slope less than 15% (Gebreyesus and Kirubel, 2009). Figure 4.6 shows total soil loss rate and its distribution over the study area.
Figure 4.6 Total soil loss rate map of the study area
4.3. Impacts of proposed interventions measures
In this study, two cases of P-factor values were tasted to check the effects of watershed management on soil erosion rate. The first case, when P-factor values are taken for existed condition of the study area, discussed in Section 4.2. And the second case was what if there was an implementation of effective terracing with contour ploughing of farm lands in the study area? For this case, the result shows that, the annual average soil loss rate was reduced from 32 to 19.2 t ha-1 yr-1, which means it was reduced the annual soil loss rate by 40%. This was checked by taking the recommended values of P-factor for both contour ploughing with terracing and considering only contour ploughing, activities separately and comparing the result of the two conditions (Figure 4.7). Due to this physical intervention, the area, with soil erosion rate existed in the limit of soil loss tolerance of 11 t ha-1 yr-1, increases from 15.80 to 41.81 % of the total area. On the other hand, the severely affected area, which shows a soil loss rate of >50 t ha-1 yr-1 would be reduced from 13.1 to 5.61 % of the total area, which indicates the reduction of severely affected area and the increment of least affected area by soil erosion.
Generally, the result of this section shows that the implementation of integrated watershed managements specifically terracing with contour ploughing, significantly reduces the vulnerability of soil erosion in the study area. The comparison of these two conditions (current existed condition and imagined contour ploughing and terracing) is presented on Table 4.2 and Figure 4.7 with the same color codding to easily understand the reduction of soil erosion rate due to this intervention.
Table 4.2 The comparisons of soil loss for current management practice and imagined management practice
Current soil erosion status of the study area Soil erosion status considering contour ploughing and terracing
(t ha-1 yr-1) Area (Km2) Area coverage
(%) Area (Km2) Area coverage
(%) Severity Class
100 80 1.0 47 0.59 Very Sever
Total 7790 100% 7790 100%
Figure 4.7 Map of total soil loss rate for current condition (A) considering the imagined management practice (B)
4.4. Prioritization of soil erosion vulnerable area
The minimum, maximum and average annual soil loss rate for each of the district in the study area were analyzed and presented in Table 4.3. Figure 4.8 shows the boundary of the districts and the color coding severity class of soil erosion for each district in the Hangar River watershed. Based on the result, Wayu district was identified to be a sever soil erosion prone area. From this district, the rate of erosion was found to be minimum of 2 and maximum of 500 t ha-1 yr-1 with annual average of 47.2 t ha-1 yr-1 which is the maximum rate of the entire study area. Seyo and Horo districts show relatively a lesser vulnerability with an annual average soil loss of 23.8 and 21.2 t ha-1 yr-1, respectively. Thus, some parts of the study area, were affected by sever soil erosion than other regions due to various reasons. One of the major reasons was the variation of existed physical condition of the areas. Table 4.3 shows the minimum, maximum and annual average soil loss rate for each district.
Table 4.3Table shows the vulnerability of soil erosion at district level
Districts Soil loss rate (t ha-1 yr-1)
Minimum Maximum Annual Average
Limu 4 320 39.2
Belew Jiganfoy 4 160 31.5
Sasiga 2 320 36.4
Wayu 2 500 47.2
Sire 2 250 27.7
Seyo 2 100 23.8
Horo 1 100 21.2
Dengoro 2 160 24.3
Jarte 2 300 30.3
Yaso 6 200 27.7
Gida Ayana 2 100 29.5
Figure 4.8 Boundaries of districts in the study area and severity class map
5. CONCLUSIONS AND RECOMMENDATIONS
This study attempted to present a comprehensive over view of the status of erosion and its distribution in the watershed under present watershed condition and with proposed watershed management practices. The findings of this study reveal that the study area is currently experiencing severe soil erosion by water. The result of this study indicates that the annual soil loss rate for existed conditions ranges from 0 to 500 t ha-1 yr-1 with average annual soil loss of 32 t ha-1 yr-1, which is far larger than the maximum tolerable soil loss of 11 t ha-1 yr-1. Such losses could threaten the sustainability of land productivity in the study area and at the same time, excessive sedimentation and eutrophication problem at the downstream proposed reservoirs on Hangar River and also on Ethiopian Great Renaissance Dam.
Implementing conservation practice such as contour ploughing with terracing effectively could reduce the annual average soil loss from 32 to 19.8 t ha-1 yr-1. Due to the imagined intervention, the area which was in a range of maximum soil los tolerance could be improved from 15.8 to 42.34% of total area.
In the steep slope areas of the watershed especially in wayu district, the rate of soil erosion extends up to 500 t ha-1 yr-1 with annual average soil loss of 47.2 t ha-1 yr-1. Central and Eastern parts of the study area including Horo and Seyo districts, show a lesser rate of erosion of 21.2 and 23.8 t ha-1 yr-1 respectively which shows a lesser vulnerability to erosion.
The computed soil erosion rate was compared with previous estimates and reports of nearby areas in order to validate the result of this study, and found to be reasonable. The predicted amount of soil loss and its spatial distribution could facilitate to implement a comprehensive and sustainable land management through conservation planning for the prioritized soil erosion risk areas in the study area.
Based on the findings of this study, the following recommendations are forwarded.
• Intensive sustainable soil and water conservation practices should be carried out by taking each stream order and agricultural field as management unit especially in the upper part where most critical sediment source areas are situated.
• Areas characterized by high to very sever soil loss should be given special attention before the area is changed to irreversible land degradation.
• The watershed management for moderate soil erosion area should also be provided in order to protect them from further degradation and erosion.
• The local communities should adopt immediate soil conservation measures in their cultivated lands by applying different soil protective methods like mulching, strip cropping, terracing, contour plowing, multiple cropping and other indigenous means of soil conservation techniques.
• Local stake holders and decision makers should implement both long and short-term timely updated natural resource management systems.
• From the result, the effects of implementation of physical soil conservation measure terracing with contour ploughing reduces soil loss rate by 40% , therefore, other physical and biological conservation measures should be tested by how much it could be reduced if fully developed.
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First of all, I would like to thank the almighty God, for the accomplishments of this work and all good and bad things in my life. Then, I wish to express my deepest gratitude to my main advisor Dr. Eng. Fekadu Fufa and co- advisor Mr. Wakjira Takala for their guidance, advice, criticism, encouragements and motivation during this study. My sincere appreciation also goes to the Ministry of Water and Energy, National Meteorological Authority and to all my instructors and staff members of the department of Hydraulic and water resource engineering for sharing their experience, materials and valuable cooperation throughout my study. All of my friends, I will never forget your support and love during the study time and my entire life. Finally, my grateful thanks belong to my families, for their moral support and belongingness throughout the study.