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- Taylor & Francis
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- © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON).
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- 2474-9508
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- 10.1080/24749508.2021.1924441
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Abstract
GEOLOGY, ECOLOGY, AND LANDSCAPES INWASCON https://doi.org/10.1080/24749508.2021.1924441 RESEARCH ARTICLE WebGIS-based decision support system for soil erosion assessment in Legedadi watershed, Oromia Region, Ethiopia Tsedale Gebreegziabher, Karuturi Venkata Suryabhagavan and Tarun Kumar Raghuvanshi School of Earth Sciences, Addis Ababa University, Addis Ababa, Ethiopia ABSTRACT ARTICLE HISTORY Received 17 July 2020 Soil erosion is one of the major environmental issues in Ethiopia. In highlands, though the Accepted 24 April 2021 topography is rugged; settlements have increased in recent years even if cultivation is gen- erally practiced on steep landforms. Deforestation and anthropogenic activities have resulted KEYWORDS in excessive soil erosion. In this study, a webGIS-based decision support system (DSS) was DSS; GIS; Landsat; erosivity; developed to provide complete information on soil erosion. The parameters employed were erodability; RUSLE estimated using remote sensing data. Prioritization of micro-watersheds was done on the basis −1 −1 −1 −1 of soil erosion risk through GIS. Very high soil loss of 69.9 t. ha .yr to 138.4 t. ha .yr was −1 −1 noticed in four micro-watersheds while the same was medium (59.9−69.9 t. ha .yr in another micro-watershed. About 69.8% of the total micro-watershed area showed a soil loss of 0.53−18 −1 yr−1 −1 −1 t. ha . though average soil loss in each land parcel was 25.82 t. ha .yr . Thus, this webGIS helps non-technical users to access information and understand soil conservation program to find soil erosion reduction measures in the study area. Moreover, the information also assists various natural resource-based applications to identification the jeopardized areas so that required conservation measures could be initiated. Introduction been reported throughout the country however, northern and central Ethiopian highlands are more Soil erosion is a natural process that causes loss of severely affected by soil erosion. The main reasons topsoil and reduction in fertility of agricultural land reported for excessive soil erosion in the north and in mountainous terrain (Qin et al., 2018; Sharma, central Ethiopian highlands are related to the rugged 2010; Thapa, 2020). Soil erosion results from several topography, increased population settlements in parameters such as rainfall, runoff, soil characteristics, recent years, cultivation practice on the steep land- terrain features, topsoil thickness, plantation, and forms, deforestation, and other anthropogenic activ- land-cover (Fayas et al., 2019; Gete, 2000). Soil erosion ities (Kebede et al., 2021; Moges & Bhat, 2017; Yesuph is a severe environmental problem, particularly when & Dagnew, 2019). In general, the land is fragile and high-intensity rainfall is recorded and poor land man- susceptible to soil erosion. Besides, mismanagement of agement is practiced (Belayneh et al., 2019; land and inadequate soil conservation measures have Gebreyesus & Kirubel, 2009; Suryabhagavan, 2017; also resulted in excessive soil erosion in the region Trenberth, 2011). Degradation of agricultural land by (Atoma et al., 2020; Nyssen et al., 2004; Tadesse soil erosion is a worldwide phenomenon leading to et al., 2017). Thus, soil erosion has become the most loss of nutrient-rich surface soil, increased runoff from severe problem of Ethiopia’s land resources, particu- the impermeable subsoil, and decreased water avail- larly in northern and central Ethiopian highlands. In ability for crops (Ganasri & Ramesh, 2016). recent years, local administrations are geared up and Every year, 25 − 40 billion tons of surface soils are have put serious attention towards soil conservation removed worldwide due to soil erosion, causing and management in the region. In this regard, several approximately 400 billion dollars worth of direct eco- research projects are funded to conserve water nomic losses through declined crop production resources and soil retention and management. (FAOUN, 2015; Montanarella, 2015; Opeyemi et al., Soil erosion is defined as the physical degradation 2019; Paulos, 2001; Pimental, et al., 1995). of the landscape over time. The process is initiated The heavy reliance of Ethiopia’s growing popula- when soil particles are detached from their original tion on subsistence agriculture can be considered as configuration by erosive forces such as rainfall and one of the primary reasons for the current state of land wind. The soil particles may then be transported by degradation (Ayele et al., 2014; Belayneh et al., 2019; overland flow into nearby rivers and oceans Haregeweyn et al., 2017). In general, soil erosion has (Khosrowpanah et al., 2007). Important terrain CONTACT Karuturi Venkata Suryabhagavan drsuryabhagavan@gmail.com School of Earth Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 T. GEBREEGZIABHER ET AL. characteristics influencing the mechanism of soil loss Decision Support Systems (DSS) tool to assist deci- are slope length, shape, and aspect. The impact of sion-makers for program integration (Byong-Lyol slope and aspect would play a significant role in the et al., 1998; Shim et al., 2002). Thus, all factor maps runoff mechanism. Most studies have indicated that and models are an integral part of the decision-making sheet and rill erosion by water and burning of dung process. The Internet is another factor to be consid- and crop residue are the major components of land ered when developing a DSS (Yang et al., 2005). degradation that affect on-site land productivity Advances in communication networks have overcome (Woldamlak & Teferi, 2009). Soil erosion is many difficulties in using timely and spatially distrib- a naturally occurring process; however, it is acceler- uted resources in the decision-making processes. ated by human activities such as; intensive agriculture, Therefore, one of the tremendous benefits of using improper land management, deforestation, and culti- information technologies in decision making is the vation on steep slopes (Foley, 2017; Jayasekara et al., potential to overcome limited resources in terms of 2018). time, data, and communication (Carver, 2001; Pandey In the past, many scientific works have used satellite et al., 2000). data and GIS models to characterize soil erosion The majority of studies on soil erosion focus either (Grimm et al., 2003; Haile & Fetene, 2012; Kinnell, on climate or land-use changes, but little work has 2010; Lazzari et al., 2015). Different empirical and been done to combine both factors. Globally, the mechanistic models were established to predict soil Revised Universal Soil Loss Equation (RUSLE) is the loss (Atoma et al., 2020; Fullen, 2003; Lal, 2001; most commonly used empirical model for predicting Millward & Mersey, 1999; Ostovari et al., 2021; average annual soil loss (Alexandridis et al., 2015; Tadesse et al., 2017). These models provide informa- Moges & Bhat, 2017; Molla & Sisheber, 2017; Yesuph tion about eroding areas, soil types, lithological units, & Dagnew, 2019). Even though the model was land-use and land-cover, with reduced costs and sig- designed for use at runoff plot or single hillslope nificant accuracy. Soil erosion models that have been scale, the combined use of GIS and RUSLE was used developed to quantify soil erosion are; Universal Soil to estimate the magnitude and spatial distribution of Loss Equation (USLE), Revised Universal Soil Loss erosion in ungagged catchments (Angima et al., 2003; Equation (RUSLE), Modified Universal Soil Loss Erdogan et al., 2007; Fu et al., 2005; Raclot et al., 2009). Equation (MUSLE), Morgan, Morgan and Finney The present study was carried out to assess the soil Model (MMF), European Soil Erosion Model erosion rate and develop a soil erosion intensity map (EUROSEM), Griffith University Erosion System for the Legedadi watershed using the RUSLE model. Template (GUEST), Limburg Soil Erosion Model Importantly, in the current study, all factor maps for (LISEM), Water Erosion Prediction Project (WEPP), the study area were developed as a user-friendly inter- Unit Stream Power-based Erosion Model (USPED) face with easy access to users for predicting soil ero- and Soil and Water Assessment Tool (SWAT) sion. The online tool can be accessed by all users, (Arnold et al., 1998; Jha & Paudel, 2010; Laflen et al., including farmers and crop consultants, to help 1985; Maurya et al., 2021; Nasiri, 2013; Renard et al., develop better management strategies. 1997; De Roo et al., 1996; Rose et al., 2003; Van der Knijff et al., 2000; Wischmeier & Smith, 1978). Soil erosion predictions at the watershed and regional Materials and methods scales are usually regarded as the scientific basis for Study area land management and decision-making. The model computations are made in a raster-based GIS, and The study area is located about 30 km east of Addis the model provides information on potential soil loss Ababa. One of the sources of drinking water for Addis on a cell-by-cell basis (Singh, 2019). However, valida- Ababa and its surroundings is the Legedadi reservoir, tion of such spatial soil loss predictions is generally which was constructed in 1967 and is located in the difficult (Gobin et al., 2004). Very few studies in the upper northwestern part of the Awash basin. The literature have reported validation of soil erosion reservoir catchment area falls into Oromia Regional maps (Prasuhn et al., 2013). State under the Administration of North Shoa Zone in In order to support the next generation of multi- Aleltu Bereh District. The catchment is bounded by process and service-oriented computations, a web-GIS latitude 9°3ʹ0’’−9 13ʹ30’’N and longitude 38° platform is considered a viable solution for gathering 60ʹ30ʹ’−39°02ʹ30ʹ’ E, covering a total area of 234 km and sharing collected data from the watershed so that (Figure 1). The elevation ranges from 2460 − 3200 m the information flow is easily managed and inter- asl in the watershed. The study area receives an annual preted using spatial thematic maps related to specific average rainfall of 1189.8 mm for the past 32 years and levels of soil erosion information. Current advances in exhibits wet climatic conditions with a mean mini- computational speed, storage, WWW, and software mum and maximum temperature of 22.95°C and provide an excellent opportunity to develop the 27.8°C, respectively. The major portion of the GEOLOGY, ECOLOGY, AND LANDSCAPES 3 watershed is covered by cultivated land, followed by alkaline Basalt with Rhyolite and Trachyte. Since the grassland, settlement, forest, and a small fraction of catchment basin has different geological formations, it the area is a water body. Geologically, the area falls in influences the river’s base flow and the run-off coeffi - the Miocene-Pliocene and Middle Miocene periods. cient of the basins (Endalkachew, 2012). Further, the The major rocks present in the study area are con- soil texture is calcic xerosols followed by leptosols, glomerate sandstone, siltstone with basalt, and Sub orthic solonchaks, and pellic vertisols. Figure 1. The location map of the study area. 4 T. GEBREEGZIABHER ET AL. erosion intensity maThe second phase of this study Methods was to create a user-friendly web-GIS interface with The study was conducted using Remote sensing data, easy access for predicting soil erosion. The detailed topographic maps (Ethiopian Mapping Agency, on flow chart of the methodology is presented in a scale of 1:50,000), secondary data obtained from (Figure 2). various Government organizations, and primary data collected from the field observations. Landsat 8 satel- lite data (the year 2015; path 168 and row 054) were The revised universal soil loss equation (RUSLE) used to generate land-use and land-cover map, and the The RUSLE is an empirical model widely applied in Digital Elevation Model (DEM) of the area was used, estimating soil erosion in Ethiopia (Abate, 2011; with a spatial resolution of 30 m. Further, the study Amare et al., 2014; Amdihun et al., 2014; Kiflu, 2010; area’s rainfall data for the years 1984 − 2014 was Woldamlak & Teferi, 2009). Soil erosion is directly obtained for six metrological stations located in and affected by the climate (rainfall), runoff erosivity, ped- around the watershed boundary from the National ological characteristic (soil erodibility), topography Meteorological Agency (NMA) of Ethiopia. The soil (slope length and steepness), land-use and land-cover properties were described based on the soil database pattern, and anthropogenic (soil management, erosion compiled by the Food and Agricultural Organization, control practice) characteristics. RUSLE provides collected from the Ministry of Agriculture, Federal improved means of computing soil erosion factors Government of Ethiopia. Also, the soil types were (Kaltenrieder, 2007; Woldamlak & Teferi, 2009) classified to obtain the K factor values. These (Equation (1)): K factor values were adopted based on the FAO Soil Classification System (Hurni, 1985a). In the first phase A ¼ R� K � L� S� C� P (1) of the soil erosion analysis, the criteria for all the considered factors were defined. This was required to where A is the computed average soil loss per unit area assess the soil erosion rate and to develop a soil −1 −1 (t ha yr ); R is rainfall-runoff erosivity factor Figure 2. Flow chart of the methodology. GEOLOGY, ECOLOGY, AND LANDSCAPES 5 −1 −1 −1 (MJ mm h ha y ); K is soil erodibility factor (t LS factor was determined by using the following −1 −1 −1 ha MJ mm ); L is slope length factor, S is slope Equation (3): � � steepness factor, C is cover-management practice fac- tor and P is conservation practice factor. LS ¼ 0:065þ 0:045Sþ 0:0065S (3) 22:1 To identify the spatial pattern of potential soil ero- sion in the study area, all the six erosion factors, where, X is the slope length (m) and S is the slope presented in Equation (1) were estimated within gradient (% or degree). a raster grid (Kouli et al., 2009). The values of X and S were derived from DEM. To calculate the X value, Flow accumulation was derived from the DEM after conducting fill and flow in Rainfall and runoff erosivity factor (R) ArcGIS. The values were varied from 0.2 − 0.5 depend- Rainfall erosivity represents the capacity of rain to ing on the slope and slope gradient values. For this, erode the soil (Wischmeier, 1959). Soil loss is closely a value of 0.5 was taken for slopes exceeding 4.5%, 0.4 related to rainfall partly through the detaching power for 3 − 5% slopes, 0.3 for 1 − 3%, and 0.2 for slopes less of raindrops striking the soil surface and partly than 1% (Wischmeier & Smith, 1978). The LS factor through the contribution of rain through runoff map was prepared from DEM, which corresponded to (Morgan, 1995). In this study, Hurni’s empirical equa- inter-row matrix cells with the same value. tion (Hurni, 1985a) that estimates R-value for Ethiopian highlands from annual total rainfall was Cover-management practice factor (C) used (Equation (2)): The estimation of cover-management or land-use and R ¼ 8:12þ 0:562P (2) land-cover was classified by supervised digital image classification technique. Later it was supplemented where, R is the rainfall erosivity factor and P is the through ground truth data with the help of GPS field mean annual rainfall in mm. The unit of R is MJ mm points. The land-use patterns were derived from the −1 −1 t ha yr (Jiang, 2013). interpretation of Landsat 8 image dating 2015. The Similar methods of determining R values from classified land-use classes were verified by ground annual total rainfall have been used in the previous truth pixels. To perform quantitative classification studies (Kiflu, 2010; Morgan, 2005; Woldamlak & accuracy assessment, it is necessary to compare two Teferi, 2009). sources of information: first, the remote sensing derived classification data, and second, the reference test information data obtained from the field observa- Soil erodibility factor (K) tions. The relationship between these two sets of infor- The soil erodibility, the soil loss rate per R factor unit, mation is summarized in the cell array. The cell array can be computed according to the relation proposed lists class values for the pixels in the classified image by Wischmeier and Smith (1978). The higher the and the corresponding reference image (Leica erodibility value the soil has, the more erosion it will Geosystems, 2003). The reference’s class values are suffer when exposed to the same intensity of rainfall, based on ground truth data, and the cell array data is splash, or surface flow (Hudson, 1981). The unit for −1 −1 −1 retrieved from the image file. soil erodibility is t ha MJ mm (Jiang, 2013). The K-factor for the soils of the study area was taken as per Support practice factor (P) the study conducted by Helden (1987) (Table 1). The support practice factor (P) is the ratio of soil loss Further, the K factor raster map was generated for with a specific practice to the corresponding loss with different soil types. upslope and downslope tillage (Renard et al., 1997). The conservation practice factor values depend on the Slope length and steepness factor (LS) type of conservation measures implemented, and it For the present study, the Digital Elevation Model was requires the mapping of conserved areas to be quanti- used for the calculation of the LS factor. The original fied. Since permanent management is not practiced in equations proposed by Wischmeier and Smith (1978) the present study area and no P factor values were have been replaced by the upslope contributing areas available, the values suggested by Wischmeier and (Desmet & Govers, 1996; Mitasova et al., 1996, 1995). Smith (1978) were adopted. Wischmeier and Smith (1978) have considered two types of land-use classes: Table 1. Soil erodibility values for soil types (Helden, 1987). agricultural land, and other land classes. Thus, for the Soil color K− factor values present study, the P factor value for two land-use and Black 0.15 land-cover classes were defined to different slope Brown 0.2 classes. The agricultural lands were classified into six Red 0.25 Yellow 0.3 slope categories, and P factor values were assigned for 6 T. GEBREEGZIABHER ET AL. each type while all non-agricultural lands were Description of the Cloud Server assigned a P factor value of 1.00. The Cloud Server is the heart of the Web-GIS mon- itoring platform and supports data management and the security and visualization of the maps in the sys- Layout of the Web-GIS monitoring platform tem. The Cloud Server operating system is the distri- The second phase of the present study was to prepare bution of Linux (Carver, 2001; McCarty, 1999) due to a layout of the monitoring platform. The monitoring the following characteristics; it is open-source, sup- platform’s general structure comprises monitoring ports the latest release of various open-source pro- devices, data acquisition, and factors related to soil grams and libraries, it inherits security features. The erosion. Figure 3 represents the layout of the Web- following geospatial data servers were examined and GIS monitoring platform. The Cloud Server incorpo- tested for the monitoring platform’s needs; GeoServer, rates the database for storing the monitoring data of MapGuide Open Source, Mapnik, and MapServer. each factor theme related to soil erosion. The spatial Finally, the GeoServer was selected as it served most database includes geographical data related to various of the requirements of the monitoring platform. thematic layers of the factors pertaining to soil ero- sion. The Geo Server is used to display geographical data. The application server communicates the data to Web-GIS development cycle the end-user from the Front End of the soil erosion Developing a web GIS is more than only using the maps monitoring platform. appropriate hardware and software (Alesheikh et al., Figure 3. Overall layout of the Web-GIS monitoring platform. GEOLOGY, ECOLOGY, AND LANDSCAPES 7 Figure 4. Iterative model development and its life cycle. 2002; Yang et al., 2005). GIS-based project develop- system; the main challenge is selecting the appropriate ment consists of components such as; data develop- technology (Alesheikh et al., 2002). Since the system ment, data organization, and application development must be compatible with the open-source environ- that are not similar and different from the standard ment, the available options are limited. software development processes. The web-GIS devel- opment cycle is a step-by-step method from require- Results ment analysis to the ongoing use and implementation of the expected portal, the iterative RUSLE model Based on the RUSLE criteria, the required factors were (Figure 4). generated for the present study area. The factors that To develop the system, the most crucial aspect is were used to compute the average soil loss are; rainfall- defining the user’s requirements correctly. Data acqui- runoff erosivity (R), soil erodibility (K), slope length sitions and related activities are to be implemented (L), slope steepness (S), cover-management practice based on user requirements. Therefore, data is signifi - (C), and conservation practice factor (P). This cant in the fulfillment of the condition and the devel- included assigning weight to each factor based on its opment of the Web-GIS. Once the needs are relative influence on soil erosion. Later, based on these identified, the next step is to design the system. Like criteria, soil erosion was estimated for the entire study most internet applications, Web-GIS is based on the area. Further, based on the amount of soil loss and the server/client model. In a server/client system, area coverage, the entire study area was classified into a computer acts as a client that sends requests to the nine classes. Besides, a webGIS-based platform for the server computer. The server computer processes the user’s decision support system was also developed. requests and sends the results back to the client Through the website, this model facilitates exploring (Figure 5) (Ozdilek & Seker, 2004). The basic archi- soil erosion-prone areas in the watershed under the tecture of the system is shown in Figure 6. There are present study. The developed webGIS-based platform many possible ways to construct a web-based map is based on open software tools, flexible, user-friendly, Figure 5. General system architecture-Client computer and Server computer. 8 T. GEBREEGZIABHER ET AL. Figure 6. Web-GIS Architecture and process line diagram. and cost-effective for communities and decision- interpolated to generate the continuous rainfall data makers. for each grid cell within the area under study. Average annual rainfall and runoff erosivity were computed for the study area for the period 1984 to 2014. The mean Rainfall and runoff erosivity (R) factor annual rainfall of the study area ranges from 1097.5 For the present study area, mean annual rainfall data −1665.1 mm, as shown in (Figure 7(a)). Further, for 1984to 2014 was procured for five meteorological results were interpolated and converted to the contin- stations from the Ethiopian National Meteorological uous surface by using the inverse distance weight Services Agency. This data was further analyzed and method (IDW). Details of meteorological stations Figure 7. (a) Mean annual rainfall, (b) Distribution of Rainfall erosivity factor (R). GEOLOGY, ECOLOGY, AND LANDSCAPES 9 Table 2. Details of rainfall station in the Legedadi watershed. area. In the present study area, four different types of UTM Coordinates soils were identified, these are pellic vertisols, orthic Elevation Annual rainfall Rain Gauge Station Easting Northing (m) (mm) solonchaks, calcic xerosols, and leptosols (Table 3). Addis Ababa 0384451 090108 2386 1220.66 Average annual soil losses for watershed were esti- Observatory mated for each grid cell (30 × 30 m). Results indicate Addis Ababa Bole 0384500 090200 2354 1035.48 Sendafa 0390117 090908 2569 1097.49 that soil erodibility value in the study area ranges from Dire Gidib 0385635 090928 2560 1646.91 −1 −1 −1 0.22 to 0.3 t ha MJ mm . It may be noted that the Chefe Donsa 0390724 085812 2392 948.67 majority of the study area is covered by pellic vertisols (76.9%) with total area coverage of 16129.2 ha, as shown in (Figure 8(a)). The rest of the area is covered Table 3. Soil classes and respective Soil erodibility (K) factor value. by orthic solonchaks, calcic xerosols, and leptosols Area coverage Soil erodibility (K) factor with an area coverage of 1889.5 ha (9%), 2076.3 ha −1 −1 −1 Soil Class (ha) (%) (t ha MJ mm ) (9.9%) and 868.8 ha (4.2%), respectively. The soil Pellic Vertisols 16129.2 76.9 0.22 erodibility map of the study area is presented as Orthic Solonchaks 1889.5 9.0 0.175 Figure 8(b). Calcic Xerosols 2076.3 9.9 0.3 Leptosols 868.8 4.2 0.3 Total 20963.75 100 Slope length and steepness (LS) factor used to compute R factors are presented in (Table 2). The computed slope length (X), slope gradient (S), and The rainfall erosivity estimated from the mean annual LS factors for the present study area are presented in total rainfall of the respective stations ranges from Figure 9. The results clearly show that the slope length −1 −1 −1 608.7−927.7 MJ mm ha h y . The R factor is (X) values in the study area, in general, vary from 0 to directly proportional to the annual rainfall of the 57.6 m whereas, the slope gradient (S) varies from 0.06 to study area, as shown in (Figure 7(b)). 7.5. Further, the computed LS factor in the watershed ranged from 0 to 31.4, with the mean value for the entire watershed to be approximately 2.87 with ±3.0. The soil Soil erodibility (K) factor erosivity by runoff increases with the velocity of the Soil loss was spatially correlated to the vegetation, runoff water on the steeper slopes. Thus, in steeper slope, gradient, altitude, and rainfall. It is reasonable slopes, the runoff water will attain accelerated velocity, to understand that soil erosion is mostly dependent on resulting in an increased shear force on the soil surface. the topography, runoff, and land-cover of the study Figure 8. (a) Distribution of soil types, (b) Distribution of soil erodibility factor (K). 10 T. GEBREEGZIABHER ET AL. Figure 9. (a) Slope length (X), (b) slope gradient (S), and (c) slope length and steepness (LS) factors. GEOLOGY, ECOLOGY, AND LANDSCAPES 11 Therefore the soil erosion and transportation capability truth pixels. To perform quantitative classification of runoff water will be increased considerably. accuracy assessment, the remote sensing derived clas- sification data were compared with the reference test information data obtained from the field observation. Cover-management practice factor (C) The overall accuracy of land-use and land-cover clas- sification was checked by the Kappa test, and k^ was Land-use and land-cover map of the present study found to be 94.1% and 0.91, respectively. Based on area (Figure 10(a)) shows that about 44.6% of the Hurni’s suggestion (Hurni, 1985a), for Ethiopian total study area is occupied by the cultivated land highlands, the C factor value was assigned to each followed by open grassland (39.3%), settlement land-use and land-cover class, as shown in (Table 4). (8.1%), forest (6.4%), and water body (1.6%). The The lowest C factor value was assigned for the water classified land-use classes were verified by ground Figure 10. (a) Land-use/land-cover map, (b) Cover−management practice factor (C), and (c) Support practice factor (P). 12 T. GEBREEGZIABHER ET AL. Table 4. Land-use/land-cover classes with respective Cover Table 6. Soil erosion estimation in the study area. −management practice factor (C). Soil loss Area coverage −1 1 Area coverage (t ha yr ) (ha) (%) Land-use and land-cover classes (ha) (%) C factor value 0–10 18343.7 87.5 10–20 593 2.8 Water body 325.8 1.6 0 20–30 322.8 1.5 Forest 1344.9 6.4 0.05 30–45 316.1 1.5 Settlement 1707.4 8.1 0.99 45–60 201.4 1.0 Cultivated land 9346 44.6 0.15 60–70 105.7 0.5 Open grassland 8239.7 39.3 0.01 70–80 131.2 0.6 Total 20963.75 100 80–100 140.2 0.7 >100 809.7 3.9 Total 20963.8 100 Table 5. P factor value for land-use and land-cover classes with respect to slope (Wischmeier & Smith, 1978). Land-use/land-cover class Slope class Slope (%) P factor value values as suggested by Wischmeier and Smith (1978) Agricultural land I 0 − 5 0.11 5 − 10 0.12 were adopted. Accordingly, the P factor values were II 10 − 20 0.14 assigned for two land-use and land-cover classes: agri- 20 − 30 0.22 30 − 50 0.31 cultural and all non-agricultural classes. All the non- III >50 0.43 agricultural land classes were assigned with a P factor Other land-use class All 1.00 value of 1. For the agricultural land class, the P factor values were classified into two; slope class I (P values, body (0), and the highest value was assigned for the 0.11–0.12) and slope class II (P values; 0.14–0.31) settlement (0.99) (Figure 10(b)). The areas with a high (Table 5). The distribution of P factor classes in the C factor value can be changed to enhance their infil - study area is shown in Figure 10(c). tration by changing the cropping and surface management. Soil erosion estimation The Soil erosion estimates for the Legedadi Support practice factor (P) watershed were made through the RUSLE model. The permanent management is not practiced in the The results show that the soil erosion in the study −1 −1 present study area, and thus no P factor values can be area ranges from 0 to17155.7 t ha yr with a mean −1 −1 directly derived. For the present study, the P factor annual soil loss of 24.55 t ha y . The soil erosion/ Figure 11. Soil erosion estimation (a) overall study area, and (b) micro-watershed level. GEOLOGY, ECOLOGY, AND LANDSCAPES 13 Table 7. Soil erosion estimation at micro-watershed level in the study area. Soil loss Area coverage −1 1 Micro-watersheds No (t ha yr ) (ha) (%) 6,11,12,13,14,15,16,17,18,20,21,22,23,24,25,26, 0.53–18 13128.5 69.8 27, 28, 29, 30, 31, 32 and 33. 9 18–37.3 314.6 1.8 5,4,8,10 37.3–59.9 2768.5 14.7 19 59.9–69.9 38.6 0.2 1,2,3,7 69.9–138.4 2562.7 13.6 loss in the study area was divided into nine sub- stored feature, a prepared map file was added. After classes based on the amount of soil loss and the this, the codes that were written in HTML and the Java area coverage (Table 6). The perusal of results scripts were built and run. Finally, by using any web (Table 6) indicates that 18343.7 ha of area, which is browser, the application can be launched. about 87.5% of the study area, has been estimated to The Front End of the monitoring platform was −1 −1 have soil loss in between 0 to10 t ha yr and only designed to visualize the monitoring data by using 809.7 ha (3.9%) of the area may have soil loss more any web browser, such as Internet Explorer, Firefox −1 −1 than 100 t ha yr . The distribution of area with Mozilla, Google Chrome, etc. For soil data visualiza- predicted annual soil loss in the study area is pre- tion, no extra installation of software is required. To sented in Figure 11(a). With respect to the area run the application, the Front End system can be with distribution, the majority of the study area (87.5%) good memory and adequate computational power and −1 −1 shows very low soil erosion (0 to10 t ha yr ), must support various spatial data formats such as; −1 −1 whereas high soil loss areas (>100 t ha yr ) are GeoJSON, Web Map Service, ESRI Shapefile, etc. mainly distributed in the northern and north-eastern Besides, it must support complex 3D models with parts of the study area. Further, at a micro-watershed reasonable file sizes. The developed webGIS-based level, about 23 micro-watersheds show soil loss in the soil loss information dissemination user interface is −1 −1 range of 0.53–18 t ha yr . These micro-watersheds shown in Figure 12. The Web-GIS monitoring plat- constitute about 69.8% of the total study area and are form can be accessed through any web browser. distributed in the southern, central, western, and eastern parts of the study area (Table 7). Four micro- Discussion watersheds (1, 2, 3, and 7) show relatively more soil −1 −1 loss, ranging between 69.9–138.4 t ha yr . These Soil erosion is a complex process that is influenced by micro-watersheds constitute about 13.6% of the total various natural and human-induced factors (Tadesse study area and are present in the northern parts et al., 2017). In the present study, the RUSLE model (Figure 11(b)). was used to estimate soil erosion in the study area. In selecting and implementing conservation measures for micro-watersheds, the concepts of soil loss tolerance Decision support system and critical soil loss values can provide useful frame- For adequate regional land planning, real-time data works. Soil loss tolerance refers to the maximum soil and information on the extent of soil erosion and loss that can occur from a given land without leading erosion-prone areas are vital. However, such systema- to soil degradation (Hurni, 1985a; Kinnell, 2000; tic information is not available in Ethiopia due to Morgan, 2005). For conservation planning, soil loss a lack of resources, poor data organization, and man- tolerance values can be set at rates of soil formation. agement. In the present study, a systematic methodol- However, it is not practically possible to determine the ogy was followed firstly to estimate the soil erosion. rate at which soil loss equals soil formation quality Secondly, a web-based GIS decision Support System (Abate, 2011). Different values of soil loss tolerance was developed to provide organized online informa- have been proposed by various sources (Morgan, −1 tion on soil erosion for the study area. The online 2005). However, a mean annual soil loss of 11 t ha −1 system may help provide up-to-date information y is generally considered acceptable, while it may be −1 −1 about soil erosion-prone areas that may facilitate the as low as 2 t ha yr in sensitive areas (Hudson, planners in decision-making for adequate regional 1981). Further, Hurni (1985) suggested the maximum −1 −1 land planning. tolerable soil loss of 18 t ha y , estimated for For the webGIS-based decision Support System, the Ethiopia, except for flat landforms in the watershed. user interface has been developed by using HTML and The results from the present study shows that the javascript. The first step required to access this system majority of the study area (87.5%) leads to very low −1 −1 is to browse the Geo-server (http://localhost:8080/geo soil erosion (0 to10 t ha yr ), whereas soil loss >10 t −1 −1 server/web/), and later a workspace has to be created. ha yr constitutes the remaining 12.5% of the study Further, on the designed workspace by using the area. Also, the maximum tolerable soil loss limit (18 t 14 T. GEBREEGZIABHER ET AL. Figure 12. Developed WebGIS-based soil loss information dissemination user interface. −1 −1 −1 −1 ha y ) for Ethiopia, as Hurni (1985) suggested, is estimated to be 0.00 − 263.25 t ha yr with the mean −1 −1 found in less than 9% of the study area. Further, at annual soil loss rate of 58.3 t ha y (Israel, 2011). a micro-watershed level, about 23 micro-watersheds Previous studies conducted on soil erosion assess- −1 −1 show soil loss in the range 0.53–18 t ha yr . These ment in Ethiopia show a different rate of soil erosion. micro-watersheds constitute about 69.8% of the total According to the estimates made by FAO (1984), the study area. annual soil loss in the highlands of Ethiopia ranges Soil erosion has been reported from many parts of from 1248–23400 million ton per year, estimated from Ethiopia and is considered significant on account of 78 million of a hectare of pasture, ranges, and culti- soil loss, which ultimately affects crop productivity. vated fields throughout Ethiopia. Thus, the soil ero- Increased soil erosion in different parts of Ethiopia sion estimated by FAO (1984) is equivalent to about −1 −1 has been reported in the works of Haregeweyn et al. 16 − 300 t ha y . Another study conducted by Soil (2017), Tadesse et al. (2017), and Atoma et al. (2020). Conservation Research Program (SCRP) at Anjeni Further, an increase in the rate of soil erosion due to Research Station has revealed the annual soil loss −1 −1 changes in rainfall patterns is reported in the works of rate to be 131 − 170 t ha y (Bono & Seiler, 1984; Zerihuna et al. (2018). They also showed changes in SCR, 1996). Further, the Awash River basin’s average soil erosion rate due to changes in the slope of the area annual soil loss has been estimated to be in the range in the northwestern parts of Ethiopia. An increase in of 200 − 300 t ha−1 y − 1 (PDRE, 1989).Thus, as soil erosion and rainfall erosivity due to climate assessed during the present study for the Legedadi change is also observed in the other parts of the watershed, the soil erosion falls in the range of 0.53–- world, as reported in the studies by Simonneaux 138.4 t ha−1 y − 1, which very well go in line with et al. (2015) in Morocco and Maeda et al. (2010) in previous studies conducted in different parts of the Kenya. Besides, other studies also indicated the sever- country. ity of the soil loss from different parts of Ethiopia. The web-based GIS decision Support System devel- Amsalu and Mengaw (2014) estimated soil loss to be oped during the present study can provide systematic −1 −1 30.6 t ha yr in Jabi Tehinan District. The average online information on soil erosion for the study area. erosion rate for agricultural land has been estimated to The spatial soil erosion maps are placed online for the −1 −1 be about 40 t ha y in different parts of the region user’s visualization. The online system may help make (Buzuayehu et al., 2002). Further, annual soil loss of decisions for soil conservation and may help to reduce −1 −1 28.84 t ha y has been reported from the Awash soil erosion in critical regions. The online system may −1 −1 basin (Atesmachew et al., 2010) and 26 t ha yr also provide up-to-date information about soil ero- from the Wondo Genet watershed (Amare et al., sion-prone areas that may facilitate the planners’ deci- 2014). From Dire Dam watershed, soil loss has been sion-making for adequate regional land planning. GEOLOGY, ECOLOGY, AND LANDSCAPES 15 Thus, this study may be useful for various natural online system may also offer policy-makers options for resources-based applications. managing soil erosion hazards most efficiently by For the present study, the RUSEL model was fol- prioritizing regions of interest. The methodology lowed to estimate soil erosion. A general need was felt used in the present study can be applied in other to modify and improve the equations to address the parts of the country. Region-wise, similar web-based local environmental conditions for the specific char- decision support systems for soil erosion monitoring acteristics of the parameters used in the model for the and land conservation strategies may develop at the investigated watershed. Further, there is a need to regional level. calibrate the RUSEL model parameters to address relative changes in soil erosion under changing land Acknowledgments use and climate patterns within an extended period and large spatial distribution of these changes. We are thankful to the head and staff of the School of Earth However, it was beyond the present study’s scope to Sciences, College of Natural and Computational Sciences, Addis Ababa University, for providing all kinds of necessary conduct in-depth methodological differences on facilities and support during the present study. We are also RUSLE parameters, assuming that this model’s pros thankful to Addis Ababa Water and Sewerage Agency for and cons are well known and widely described in the providing all the essential data required for the present literature. Thus, it is recommended that future study. We even thankfully acknowledge data support from researchers should consider the points mentioned National Metrological Service Agency. The authors would also like to thank the reviewers for suggesting numerous above to make the necessary modifications in the improvements. RUSEL model. Disclosure of potential conflicts of interest Conclusion The authors declare that they have no competing interests. Soil erosion is a severe environmental problem, parti- cularly when high-intensity rainfall is recorded and poor land management is practiced. Soil erosion is ORCID a global issue with its significant impacts on the degra- Karuturi Venkata Suryabhagavan http://orcid.org/0000- dation of agricultural lands. 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Journal
Geology Ecology and Landscapes – Taylor & Francis
Published: Apr 3, 2023
Keywords: DSS; GIS; Landsat; erosivity; erodability; RUSLE