spatial delineation of soil erosion vulnerability in the lake

HYDROLOGICAL PROCESSESHydrol. Process. (2009)Published online in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/hyp.7476

Spatial delineation of soil erosion vulnerability in the LakeTana Basin, Ethiopia

Shimelis G. Setegn,1,2* Ragahavan Srinivasan,3 Bijan Dargahi1 and Assefa M. Melesse2

1 Division of Hydraulic Engineering, Department of Land and Water Resources Engineering, The Royal Institute of Technology (KTH), Stockholm,Sweden

2 Department of Earth and Environment, Florida International University, Miami, FL, USA3 Spatial Science Laboratory, Texas A & M University, College Station, TX, USA

Abstract:

The main objective of this study was to identify the most vulnerable areas to soil erosion in the Lake Tana Basin, BlueNile, Ethiopia using the Soil and Water Assessment Tool (SWAT), a physically based distributed hydrological model, and aGeographic Information System based decision support system that uses multi-criteria evaluation (MCE). The SWAT modelwas used to estimate the sediment yield within each sub-basin and identify the most sediment contributing areas in the basin.Using the MCE analysis, an attempt was made to combine a set of factors (land use, soil, slope and river layers) to make adecision according to the stated objective. On the basis of simulated SWAT, sediment yields greater than 30 tons/ha for eachof the sub-basin area, 18Ð4% of the watershed was determined to be high erosion potential area. The MCE results indicatedthat 12–30Ð5% of the watershed is high erosion potential area. Both approaches show comparable watershed area with highsoil erosion susceptibility. The output of this research can aid policy and decision makers in determining the soil erosion ‘hotspots’ and the relevant soil and water conservation measures. Copyright 2009 John Wiley & Sons, Ltd.

KEY WORDS soil erosion; Lake Tana; SWAT; MCE; GIS; hydrologic modeling

Received 11 November 2008; Accepted 12 August 2009

INTRODUCTION

Soil erosion and loss of agricultural soils is a majorproblem in Blue Nile River Basin, Ethiopia. The high rateof surface erosion in the basin and the rate of sedimenttransport in the river system contributes to increasedsedimentation problems in the Lake and reservoirs aswell as the downstream areas. Poor land use practices,improper management systems and lack of appropriatesoil conservation measures have played a major rolefor causing land degradation problems in the country.Because of the rugged terrain, the rates of soil erosion andland degradation in Ethiopia are high. The soil depth ofmore than 34% of the land area is already less than 35 cm[Zemenfes, 1995; Soil Conservation Research Project(SCRP, 1996)]. Hurni (1989) indicated that Ethiopia losesabout 1Ð3 billion metric tons of fertile soil every year andthe degradation of land through soil erosion is increasingat a high rate. According to Kruger et al. (1996), 4% ofthe highlands are now so seriously eroded that they willnot be economically productive again in the foreseeablefuture. The SCRP (1996) has estimated an annual soil lossof about 1Ð5 billion tons from the highland. Accordingto the Ethiopian Highlands Reclamation Study (EHRS,1984), soil erosion is estimated to cost the country$1Ð9 billion between 1985 and 2010. These call for

* Correspondence to: Shimelis G. Setegn, Division of Hydraulic Engi-neering, Department of Land & Water Resources Engineering, The RoyalInstitute of Technology (KTH), Teknikringen 76-3tr, 100 44 Stockholm,Sweden. E-mail: [email protected]; [email protected]

immediate measures to save the physical quality of soiland water resources of the country.

The Lake Tana Basin is one of the most affectedarea by soil erosion, sediment transport and land degra-dation. The land and water resources of the basin andthe Lake Tana ecosystem are in danger due to the rapidgrowth of population, deforestation and overgrazing, soilerosion, sediment deposition, storage capacity reduction,drainage and water logging, flooding, pollutant transport,population pressure and overexploitation of specific fishspecies. The available land and water resources are notused effectively to improve the livelihood and socioeco-nomic conditions of the inhabitants. Sediments, organicand inorganic fertilizers from the agricultural fields thatenter the lake by runoff may result in eutrophication.So far no effective measures have been taken to com-bat flooding, soil erosion and sedimentation problems.The lack of decision support tools and limitation of dataconcerning weather, hydrological, topographic, soil andland use are the factors that significantly hinder researchand development in the area. To solve the existing soilerosion problems there is a need to identify the mosterosion sensitive areas in the region, so that effectiveconservation measures can be taken. Appropriate toolsare needed for the better assessment of the hydrologyand soil erosion processes as well as decision supportsystem for planning and implementations of appropri-ate measures. The tools involve various hydrological andsoil erosion models as well as geographical informa-tion system (GIS). Many hydrological and soil erosion

Copyright 2009 John Wiley & Sons, Ltd.

S. G. SETEGN ET AL.

models are developed to describe the hydrology, ero-sion and sedimentation processes. Hydrological modelsare tools which describe the physical processes control-ling the transformation of precipitation to runoff. Erosionmodeling is based on understanding the physical lawsof landscape processes that occur in the natural envi-ronment. There are different soil erosion models suchas Chemicals, Runoff, and Erosion from AgriculturalManagement Systems (CREAMS) (Knisel, 1980), Ero-sion Productivity Impact Calculator (EPIC) (Williamset al., 1984), Agricultural Non-point Source PollutionModel (AGNPS) (Young et al., 1987, 1989), EuropeanSoil Erosion Model (EUROSEM) (Morgan et al., 1998)and Soil and Water Assessment Tool (SWAT) (Arnoldet al., 1998). These models allow evaluation of man-agement practices that influence factors contributing toerosion (Srinivasan and Engel, 1994). SWAT model isone of the appropriate watershed models for long-termimpact analysis. It is widely applied in many parts ofUnited States (Bingner, 1996; Arnold et al., 1998; Peter-son and Hamlett, 1998; Srinivasan et al., 1998; Benamanet al., 2005; Neitsch et al., 2005) and many other coun-tries (Heuvelmans et al., 2004; Bouraoui et al., 2005;Alamirew, 2006; Setegn et al., 2008). A comprehensivereview of SWAT model applications is given by Gassmanet al. (2007).

Analysis of spatial information is becoming an emerg-ing approach which is capable of acquiring, managingand analyzing complex problems of river basins and lakewatersheds. In recent years, GIS has shown to be a goodalternative to serve as a better decision support tool in theplanning, management and implementation of soil andwater resources. GIS is a very useful tool for storing,processing and manipulating and visualization of spatialdatabases. Consequently, the integration of multi-criteriaevaluation (MCE) within a GIS context could help usersto improve decision making processes. The main pur-pose of the MCE technique is to investigate a numberof alternatives in the light of multiple criteria and con-flicting objectives (Voogd, 1983). To carry out that, it

is necessary to generate compromise alternatives and aranking of alternatives according to their degree of attrac-tiveness (Janssen and Rietveld, 1990). In the last decade,MCE has received renewed attention in the context of aGIS-based decision making (Pereira and Duckstein, 1993;Heywood et al., 1995; Malczewski, 1996). Different stud-ies have been conducted using MCE technique in thearea of the natural resources management (e.g. Tecle andYitayew, 1990; Ceballos-Silva and Lo’pez-Blanco, 2003;Leskinena and Kangas, 2005; Bello-Pineda et al., 2006;Hajkowicz and Higgins, 2006). In this study, MCE seemsto be applicable to GIS-based spatial delineation of ero-sion vulnerability which helps to carry out the delineationof the most erosion prone area in the Lake Tana Basin.

Generally, this article explains decision support sys-tem with MCEs and physically based SWAT model inidentifying erosion hazard areas in the Lake Tana Basin.SWAT calculates the sediment yield within each hydro-logical response units (HRUs) and sub-basin. The GIStool combines the slope, land cover, soil and river layersas the factors which contribute to soil erosion. Hence,the main goal of this study is to delineate the erosionvulnerable areas through physically based SWAT modeland the MCE technique within a GIS context.

METHODS

Study area

The Lake Tana watershed, which is one of the sub-basin of Blue Nile (Abbay River Basin Integrated Devel-opment Master Plans Project, 1999) River Basin, has adrainage area of 15 096 km2. It is located in the coun-try’s north–west highlands (latitude 12 00N, longitude37°150E) (Figure 1). This basin is one of the major basinsthat significantly contribute to the livelihoods of tensof millions of people in the lower Nile River basin.The mean annual rainfall of the catchment area is about1280 mm. The mean annual actual evapotranspirationand water yield of the catchment area are estimated

Figure 1. Location map of the study area (Setegn et al., 2008)

Copyright 2009 John Wiley & Sons, Ltd. Hydrol. Process. (2009)DOI: 10.1002/hyp

SPATIAL DELINEATION OF SOIL EROSION PRONE AREAS

to be 773 mm and 392 mm, respectively (Setegn et al.,2008). It is rich in biodiversity with many endemic plantspecies and cattle breeds; it contains large areas of wet-lands; and it is home to many endemic birds and culturaland archaeological sites. This basin is of critical nationalsignificance as it has great potentials for irrigation, hydro-electric power, high value crops and livestock production,ecotourism and others. The lake covers 3000–3600 km2

area at an elevation of 1800 m and with a maximum depthof 15 m. It is approximately 84 km long and 66 km wide.It is the largest lake in Ethiopia and the third largest inthe Nile Basin. Gilgel Abay, Ribb, Gumera and Megechare the main rivers feeding the lake contributing morethan 93% of the inflow. It is the main source of the BlueNile River that is the only surface outflow for the Lake.The climate of the region is ‘tropical highland monsoon’with main rainy season between June and September. Theair temperature shows large diurnal but small seasonalchanges with an annual average of 20 °C. Major soils inthe basin are Chromic Luvisols, Eutric Cambisols, EutricFluvisols, Eutric Leptosols, Eutric Regosols, Eutric Verti-sols, Haplic Alisols, Haplic Luvisols, Haplic Nitisols andLithic Leptosols (Figure 2a). The majority of the landarea, 51Ð3%, of the Lake Tana Basin is used for agricul-ture, 29% is agropastoral area, 20% of the basin is cov-ered by the lake water (Figure 2b) (Setegn et al., 2008).

Models description

In this study, two approaches were used to identifyerosion prone areas to improve the decision making inplanning and implementing soil and water conservationmeasures. The approaches involved the integration of thespatially distributed SWAT model, GIS and MCE.

Description of SWAT model. SWAT is a river basinscale, continuous time and spatially distributed modeldeveloped to predict the impact of land managementpractices on water, sediment and agricultural chemicalyields in large complex watersheds with varying soils,land use and management conditions over long periodsof time (Arnold et al., 1998; Neitsch et al., 2005).The detail description of the model can be found inSWAT2005 theoretical document (Neitsch et al., 2005).As a physically based model, SWAT uses HRUs to

describe spatial heterogeneity in terms of land cover, soiltype and slope within a watershed. SWAT simulates thehydrological cycle based on the water balance equation.

SWt D SW0 Ct∑

iD1

�Rday � Qsurf � Ea � Wseep � Qqw�i

�1�In which SWt is the final soil water content (mm), SW0

is the initial soil water content on day i (mm), t is thetime (days), Rday is the amount of precipitation on dayi (mm), Qsurf is the amount of surface runoff on day i(mm), Ea is the amount of evapotranspiration on day i(mm), Wseep is the amount of water entering the vadosezone from the soil profile on day i (mm) and Qgw is theamount of return flow on day i (mm).

SWAT calculates the surface erosion caused by rainfalland runoff within each HRUs with the Modified Univer-sal Soil Loss Equation (MUSLE) (Equation 2) (Williams,1975). MUSLE is a modified version of the UniversalSoil Loss Equation (USLE) developed by Wischmeierand Smith (1965, 1978). USLE predicts average annualgross erosion as a function of rainfall energy. In MUSLE,the rainfall energy factor is replaced with a runoff factorto simulate erosion and sediment yield. This improvesthe sediment yield prediction accuracy, eliminates theneed for delivery ratios (the sediment yield at any pointalong the channel divided by the source erosion abovethat point) and single storm estimates of sediment yieldscan be calculated. Sediment yield prediction is improvedbecause runoff is a function of antecedent moisture con-dition and rainfall energy. In MUSLE, the crop manage-ment factor is recalculated every day that runoff occurs.It is a function of above ground biomass, residue on thesoil surface and the minimum C factor for the plant.

sed D 11Ð8�Qsurf ð qpeak ð areahru�0Ð56 ð KUSLE

ð CUSLE ð PUSLE ð LSUSLE ð CFRG �2�

where sed is the sediment yield on a given day (metrictons), Qsurf is the surface runoff volume (mm/ha), qpeak isthe peak runoff rate (m3/s), areahru is the area of the HRU(ha), KUSLE is the soil erodibility factor [0Ð013 metricton m2 h/(m3 metric ton cm)], CUSLE is the cover andmanagement factor, PUSLE is the support practice factor,

Figure 2. (a) Left—soil types (b) right—land cover maps of Lake Tana Basin


S. G. SETEGN ET AL.

LSUSLE is the topographic factor and CFRG is the coarsefragment factor. The details of the USLE factors and thedescriptions of the different model components can befound in study of Neitsch et al. (2005).

The hydrological model component estimates therunoff volume and peak runoff rate that are in turn usedto calculate the runoff erosive energy variable. SWATcalculates the peak runoff rate with a modified ratio-nal method. The sediment routing model (Arnold et al.,1995) that simulates the sediment transport in the channelnetwork consists of two components operating simulta-neously: deposition and degradation. To determine thedeposition and degradation processes the maximum con-centration of sediment calculated by Equation 3 in thereach is compared with the concentration of sediment inthe reach at the beginning of the time step.

The maximum amount of sediment that can be trans-ported from a reach segment is a function of the peakchannel velocity and is calculated by Equation 3.

concsed,ch,mx D Csp ð vspexpch,pk �3�

where concsed,ch,mx is the maximum concentration ofsediment that can be transported by the water (ton/m3

or kg/l), Csp is a coefficient defined by the user, vch,pk

is the peak channel velocity (m/s) and spexp is exponentparameter for calculating sediment reentrained in channelsediment routing that is defined by the user. It normallyvaries between 1Ð0 and 2Ð0.

The maximum concentration of sediment calculated inEquation 3 in the reach is compared with the concentra-tion of sediment in the reach at the beginning of the timestep, concsed,ch,i. If concsed,ch,i > concsed,ch,mx, deposi-tion is the dominant process in the reach segment andthe net amount of sediment deposited is calculated fromEquation 4.

seddep D �concsed,ch,i � concsed,ch,mx� Ð Vch �4�

where seddep is the amount of sediment deposited inthe reach segment (metric tons), concsed,ch,i is the initialsediment concentration in the reach (kg/l or ton/m3),concsed,ch,mx is the maximum concentration of sedimentthat can be transported by the water (kg/l or ton/m3) andVch is the volume of water in the reach segment (m3).

If concsed,ch,i < concsed,ch,mx, degradation is the dom-inant process in the reach segment and the net amount ofsediment reentrained is calculated as Equation 5.

sed0deg D �concsed,ch,mx � concsed,ch,i� Ð Vch Ð Kch Ð CCH

�5�where seddeg is the amount of sediment reentrained in thereach segment (metric tons), concsed,ch,mx is the maximumconcentration of sediment that can be transported by thewater (kg/l or ton/m3), concsed,ch,i is the initial sedimentconcentration in the reach (kg/l or ton/m3), Vch is thevolume of water in the reach segment (m3), KCH isthe channel erodibility factor (cm/h/Pa) and CCH is thechannel cover factor.

The final amount of sediment in the reach is determinedfrom Equation 6.

sedch D sedch,i � seddep C seddeg �6�

where sedch is the amount of suspended sediment in thereach (metric tons), sedch,i is the amount of suspendedsediment in the reach at the beginning of the timeperiod (metric tons) and seddep is the amount of sedimentreentrained in the reach segment (metric tons).

The amount of sediment transported out of the reachis calculated by Equation 7.

sedout D sedch Ð Vout

Vch�7�

where sedout is the amount of sediment transported out ofthe reach (metric tons), sedch is the amount of suspendedsediment in the reach (metric tons), Vout is the volume ofoutflow during the time step (m3) and Vch is the volumeof water in the reach segment (m3).

Model input. Digital elevation model: A 90 m by90 m resolution Digital Elevation Model (DEM) forBlue Nile Basin (Figure 3) was downloaded from Shut-tle Radar Topography Mission (SRTM) website (Jarviset al., 2006). A 2 m by 2 m resolution DEM for Anjeniwatershed was also obtained from soil and water conser-vation programme (SCRP), University of Bern, Switzer-land. The DEM was used to delineate the watershedsand to analyze the drainage patterns of the land surfaceterrain.

Figure 3. Digital Elevation Model (DEM) of the Lake Tana Basin (meter above sea level)



Land use and soil data: Land use is one of the mostimportant factors that affect runoff, evapotranspirationand surface erosion in a watershed. The land use andsoil map of the study area was obtained from ministryof water resources Ethiopia and SCRP, University ofBern, Switzerland. Figure 2b shows that more than 50%of the Lake Tana watershed is used for agriculture. SWATmodel requires different soil textural and physicochemi-cal properties such as soil texture, available water content,hydraulic conductivity, bulk density and organic car-bon content for different layers of each soil type. Thesedata were obtained from different sources (Setegn et al.,2008).

Weather data and river discharge: In this study, theweather variables used for driving the hydrological bal-ance are daily precipitation, minimum and maximumair temperature for the period of 1978–2004. Thesedata were obtained from Ethiopian National Meteorolog-ical Agency for stations located within and around thewatershed. Weather data were also obtained from SCRPproject office Addis Ababa, Ethiopia (SCRP, 2000). Dailyriver discharge values for Ribb, Gumera, Gilgel Abay,Megech rivers and the outflow river Blue Nile (Abbay)were obtained from the Hydrology Department of theMinistry of Water Resources of Ethiopia. These dailyriver discharges at four tributaries of Lake Tana wereused for model calibration (1981–1992) and validation(1993–2004). Ten years precipitation, air temperature,river discharge and sediment measurements on MinchetRiver were used for the simulation of the stream flow andsediment yield in Anjeni gauged watershed. The periodfrom 1984 to 1988 was used for calibration of the model.Whereas the period from 1989 to 1993 was used for val-idation of the SWAT model.

Description of multi-criteria evaluation

GIS-based analysis of spatial data is capable of analyz-ing complex problem of evaluating and allocating naturalresources for targeting potential or sensitive areas. MCEmodel (under IDRISI GIS environment) is a method fordecision support where a number of different criteria arecombined to meet one or several objectives (Voogd, 1983;Carver, 1991). An objective is thus a perspective thatserves to guide the structuring of decision rules, whichis the procedure whereby criteria are selected and com-bined to arrive at a particular evaluation, and evaluationsare compared and acted upon. Many GIS software sys-tems provide the basic tools for evaluating such a model.For this study, GIS software called IDRISI which hasan MCE module was used. A detailed description of themethod can be found in IDRISI32 Guide to GIS andImage Processing (Eastman, 2001). The GIS tool com-bines the slope, land use and soil layers as a major factor,which contributes for soil erosion and sediment transport.

The GIS-based MCE procedures involve a set ofgeographically defined alternatives and a set of evaluationcriteria represented as map layers. The problem is to

combine the criterion maps according to the criterion(attribute) values and decision maker’s preferences usinga decision rule (combination rule).

The primary issue in MCE is concerned with how tocombine the information from several criteria to form asingle index of evaluation. In the case of Boolean cri-teria (constraints), the solution usually lies in the union(logical OR) or intersection (logical AND) of conditions.However, for continuous factors, a weighted linear com-bination (WLC) (Voogd, 1983) is most commonly used.With a WLC, factors are combined by applying a weightto each followed by a summation of the results to yielda suitability map, i.e.

S D∑

wixi �8�

where S stands for suitability, wi for weight of factor iand xi for criterion score of factor i.

This procedure is not unfamiliar in GIS and has a formvery similar to the nature of a regression equation. Incases where Boolean constraints also apply, the procedurecan be modified by multiplying the suitability calculatedfrom the factors by the product of the constraints, i.e.

S D∑

wixi ð cj �9�

where cj is the criterion score of constraint j and Dproduct.

Because of the different scales upon which criteriaare measured, it is necessary that factors be standard-ized before combination using the formulas above, andthat they be transformed, if necessary, such that all fac-tors maps are positively correlated with suitability. Voogd(1983) reviews a variety of procedures for standardiza-tion, typically using the minimum and maximum valuesas scaling points. The simplest is a linear scaling suchas:

xi D �Ri � Rmin�/�Rmax � Rmin� ð standardized range�10�

where R is the row score. However, if the continuousfactors are really fuzzy sets, we easily recognize this asjust one of many possible set membership functions. InIDRISI, the module named FUZZY is provided for thestandardization of factors using a whole range of fuzzy setmembership functions. The module provides the optionof standardizing factors to either a 0–1 real number scaleor a 0–255 byte scale. The latter option is recommendedbecause the MCE module has been optimized for speedusing a 0–255 level standardization. Importantly, thehigher value of the standardized scale must represent thecase of being more likely to belong to the decision set. Acritical issue in the standardization of factors is the choiceof the end points at which set membership reaches either0Ð0 or 1Ð0 (or 0 and 255).

Breaking the information down into simple pairwisecomparisons in which only two criteria need to beconsidered at a time can greatly facilitate the weightingprocess and will likely produce a more robust set ofcriteria weights. A pairwise comparison method has the


S. G. SETEGN ET AL.

added advantages of providing an organized structurefor group discussions and helping the decision makinggroup hone in an areas of agreement and disagreementin setting criterion weights. The technique implementedin IDRISI is that of pairwise comparisons developed bySaaty (1977) which is known as the Analytical HierarchyProcess (AHP).

In the procedure for an MCE using a WLC, itis necessary that the weights sum to one. In Saaty’stechnique, weights of this nature can be derived bytaking the principal eigenvector of a square reciprocalmatrix of pairwise comparisons between the criteria.The comparisons concern the relative importance of thetwo criteria involved in determining suitability for thestated objective. Ratings are provided on a nine-pointcontinuous scale.

Calibration, validation and application of SWAT model

The SWAT model was calibrated and validated for flowin Lake Tana Basin on a daily basis and for flow andsediment yield in Anjeni gauged watershed on a monthlybasis. Before the calibration exercise was implanted, 26hydrological parameters were tested for sensitivity analy-sis for the simulation of the stream flow in the study area.The details of all hydrological parameters are found in theArcSWAT interface for SWAT user’s manual (Neitsch,et al., 2004; Winchell et al., 2007). Both manual andautocalibration methods were implemented for minimiz-ing the difference between measured and predicted flowand sediment yield. In this study, Sequential UncertaintyFitting (SUFI-2) (Abbaspour et al., 2004, 2007) calibra-tion and uncertainty analysis method was used for auto-calibration of the flow and sediment parameters. In SUFI-2, Latin hypercube sampling is used to draw independentparameter sets (Abbaspour et al., 2007). The goodnessof fit was quantified by the coefficient of determination(R2) and Nash–Sutcliff coefficient (NSE) (Nash and Sut-cliffe, 1970) between the observations and the final bestsimulation. This efficiency is commonly used quantitativemeasure of hydrograph prediction performance that helpsto evaluate between the predicted and observed flow aswell as observed and predicted sediment yield. NSE iscalculated using Equation 11.

NSE D 1 �

(n∑

iD1

Oi � Pi

)2

(n∑

iD1

Oi � O

)2 �11�

where NSE is the prediction efficiency, Oi is the observedcondition at time i, Oi is the mean of the observed valuesover all times, Pi is the predicted value at time I and P isthe mean predicted value over all times. The index i referto storm number for calculating the prediction efficienciesfor sediment yield, and refer to time during the storm forcalculating the efficiency of a hydrograph for a particularstorm.

Root mean square error (RMSE) observations standarddeviation ratio (RSR): RMSE is one of the commonlyused error index statistics (Chu and Shirmohammadi2004; Singh et al., 2005; Moriasi et al., 2007). RSR stan-dardizes RMSE using the observations standard devia-tion, and it combines an error index (Moriasi et al., 2007).RSR is calculated as the ratio of the RMSE and standarddeviation of measured data, as shown in Equation 12.

RSR D RMSE

STDEVobsD

√√√√ n∑iD1

�Qobsi � Qsim

i �2

√√√√ n∑

iD1

�Qobsi � Qmean

i �2

�12�

Percent bias (PBIAS): PBIAS measures the averagetendency of the simulated data to be larger or smaller thantheir observed counterparts (Gupta et al., 1999). PBIASis calculated with Equation 13.

PIBIAS D

n∑iD1

�Qobsi � Qsim

i � ð �100�

n∑iD1

�Qobsi �

�13�

In which PBIAS is the deviation of data being evalu-ated, expressed as a percentage.

After setting up of the model, the default simulationsof stream flow, using the default parameter values, werecarried out in the Lake Tana Basin for the calibrationperiod (1978–1992). An independent precipitation, tem-perature and stream flow dataset (1993–2004) was usedfor validation of the model in the four river basins. Peri-ods 1978–1980 and 1990–1992 were used as ‘warm-up’periods for calibration and validation purposes, respec-tively. The warm-up period allows the model to get thehydrologic cycle fully operational.

The sediment yield was simulated from 1992 to 2004in the Lake Tana Basin. SWAT calculates the soil erosionand sediment yield within each HRUs in each sub-basinwithin the watershed. The model gives the magnitudeof sediment yield in each sub-basin so that the rate ofsoil erosion within each sub-basin can be understood.First, the calculated sediment yield was converted fromSWAT project file into VIZSWAT (a visualization andanalysis tool developed by Baird & Associates for SWATmodel output). VIZSWAT is a customized version ofSpatial Data Analyzer, a GIS-based data visualizationand analysis tool that animates time series and spatialdata over GIS maps with impressive display speed. TheVIZSWAT aggregated the simulated sediment yield intoannual average. Second, the aggregated annual sedimentyield was taken to ArcGIS and added as a separate fieldto the watershed attributable to produce a map. Finally,the map was reclassified into four erosion categories aslow, medium, high and very high erosion potential. Thisshows the areas in the watershed which produce high andlow annual sediment yields.



Application of MCE

In this approach, the selection of potential soil erosionprone areas through integration of various of GIS layers,spatial analysis and multi-criteria based evaluation ispresented. The decision was made after combination offour criteria (factor maps) using MCE decision wizard.The first factor considered is slope factor. Steeper andlonger slopes result in high erosion rates. The secondcriterion is the land cover which controls the detachabilityand transport of soil particles and infiltration of waterinto the soil. Soil types also play significant role inerosion and sediment transport process depending upontheir physical properties and sensitivity to erosion. Alayer which contains all rivers within the catchments wasalso considered as a contributing factor in the study. Itwas assumed that places close to streams or rivers aremore easily washed especially during high flow seasons.

Development of factor maps. Slope factor map: TheDEM was first imported from ArcGIS to IDRISI soft-ware. The slope map was generated from the DEM usingsurface analysis tool. The decision wizard requires thecriteria maps in raster form. First, a raster image initial-ization layer was created by defining the spatial parame-ters. The vector form slope map was converted to rastermap using the initialized image layer and a raster conver-sion module. The raster map consists of the slope classfrom 0 to 205Ð57%. This slope range was reclassified tosix major slope classes depending on the Food and Agri-culture Organization (FAO) slope classification (Table I).Each slope category was given an index for their sen-sitivity to erosion (Table I). The sensitivity index wasgiven values from 0 to 20. This value is relative valuewith respect to each subfactor. Zero value means lesssensitive and 20 means extremely sensitive.

Land cover factor map: The land cover map was storedin shape file format and imported from ArcGIS to IDRISI.It was in vector form and converted to raster form (inthe same way as for the slope map). The major landcover types were originally subclassified into 151 classesdepending upon the specific type of cover, the type ofcrops and degree of cultivation. The 151 classes of covertype were reclassified into 12 main land cover types. The12 classes of land cover types were in turn reclassified asper its sensitivity to erosion. Values were set from 0 to20 to categorize the factor map into common sensitivity

Table I. FAO slope categories and assigned sensitivity index

Newclass

Slopecategories (%)

Characteristics Sensitivityindex

1 0–2 Flat or almost flat 02 2–5 Gently undulating 43 5–10 Undulating 84 10–15 Rolling 125 15–30 Moderately steep 166 >30 Steep 20

index. For instance the highest value of 20 was assignedto the most sensitive land cover type and zero to lesssensitive one (Table II).

Soil factor map: The soil layer were imported fromshape file to IDRISI format and then converted into raster.Originally the soil layer had 200 subclasses and they werereclassified to 12 major soil types. Each soil type wasassigned values from 0 to 20 depending upon their degreeof sensitivity to soil erosion. The sensitivity of the soilto erosion was based on the soil physical characteristics(texture and structure). These characteristics are alsostudied by different organization and their characteristicson erosion vulnerability were listed in Abay master planstudy (Table III.).

Rivers factor map: The GIS layer which consists ofall rivers in the Lake Tana watershed was imported toIDRISI. For this river layer, a distance analysis wasperformed so that the decision wizard could consider thedistance from the river with a certain value dependingupon the criteria set. In this case, it is considered thatthe area nearest to the river may have the chance to beeasily washed because of the high flood current especiallyduring high flow seasons.

Table II. Land cover type in the Lake Tana catchments

Land cover Area (km2) Area(%)

Sensitivityindex

assigned

Afro Alpine 100Ð04 0Ð7 1Dominantly cultivated 7732Ð79 51Ð0 20Moderately cultivated 3364Ð88 22Ð2 15Forest 13Ð11 0Ð1 2Grassland 424Ð96 2Ð8 1Water body 3041Ð42 1 0Swamp 19Ð82 0Ð1 3Plantations 8Ð93 0Ð1 5Shrub land 429Ð40 2Ð8 7Urban 27Ð36 0Ð2 10Woodland open 11Ð18 0Ð1 5

Table III. Major soil types in the Lake Tana Catchments (sourcefor soil map: Ethiopian Ministry of water resources)

No. Soil type Area (km2) Area (%) Sensitivityindex

assigned

1 Chromic Luvisols 4240Ð9 18Ð3 62 Eutric Cambisols 2Ð1 0Ð1 43 Eutric Fluvisols 1847Ð6 8Ð0 84 Eutric Leptosols 4913Ð0 21Ð2 205 Eutric Regosols 44Ð9 0Ð2 46 Eutric Vertisols 2064Ð8 8Ð9 47 Haplic Alisols 1498Ð8 6Ð5 128 Haplic Luvisols 4507Ð3 19Ð4 69 Haplic Nitisols 544Ð4 2Ð3 1210 Lithic Leptosols 456Ð6 2Ð0 211 Urban 10Ð5 0Ð0 212 Water 3042Ð6 13Ð1 0


S. G. SETEGN ET AL.

Integration of different layers. Once the criteria maps(factors and constraints) have been developed, an eval-uation or aggregation stage is undertaken to combinethe information from the various factors and constraints.The MCE module offers three logics for the evalua-tion/aggregation of multiple criteria: Boolean intersec-tion, WLC, and the ordered weighted average. The mostsimplistic type of aggregation is the Boolean intersec-tion or logical AND. This method is used only whenfactor maps have been strictly classified into Booleansuitable/unsuitable images with values 1 and 0. The eval-uation is simply the multiplication of all the images.However, for continuous factors, a WLC (Voogd, 1983)is a usual technique. The details of the different logic forthe MCE are described in IDRISI32 help tutorial manual(Eastman, 2001).

Before combining the four factor maps they werefirst standardized to a scale of 0–255, where 0 isnot sensitive and 255 is more sensitive. The wizardfacilitates standardization of quantitative factor imagesusing the module FUZZY. The membership functionshape and type and set control points from 0 to 20were determined for each factor maps. Factor weights areassigned to specify the relative importance of each onein determining the aggregate output value using AHP.The comparisons concern the relative importance of thetwo criteria involved in determining suitability for thestated objective. In AHP analysis, ratings are providedon a nine-point continuous scale (Table IV).

The factor weights were derived by taking the prin-cipal eigenvector of a square reciprocal matrix of pair-wise comparisons between the criteria for final analy-sis (Table V). In this study, six scenarios were adapteddepending upon the priority given to the factors. Forinstance, in the first scenario slope factor was given thehighest priority followed by land cover, soil and rivermaps. As the complete pairwise comparison matrix con-tains multiple paths, it is also possible to determine thedegree of consistency that has been used in developingthe ratings. The consistency ratio (CR) indicates the prob-ability that the matrix ratings are randomly generated.Saaty (1977) suggested that matrices with CR ratingsgreater than 0Ð10 should be reevaluated. After complet-ing the whole process, the final MCE erosion potentialarea map was produced which show the degree of ero-sion sensitivity of each area. The final map was rankedfrom 0 to 255 scales and it was reclassified into fourcategories (nil, slight, moderate and severe) dependingupon the combined degree of sensitivity to erosion. Thereclassified map was exported from IDRISI to ArcGISfor visualization and mapping.

Table V. A pairwise comparison matrix for assessing the com-parative importance of factors to identify erosion sensitive areas

Scenario 1

Criteria Slope Land cover Soil Rivers

Slope 1Land cover 1/3 1Soil 1/5 1/3 1Rivers 1/9 1/5 1/3 1

RESULTS AND DISCUSSION

SWAT model calibration and application

The Lake Tana Basin was divided into 34 sub-basinsand 284 HRUs. Surface runoff volume and peak runoffrate (m3/s) are the flow components which determinethe rate of soil erosion and sediment yield. First, themodel was calibrated for flow in the Lake Tana Basin.The sediment parameters were generated after calibratingthe SWAT model in Anjeni gauged watershed which islocated very near to the basin in a similar topography andagroclimatic zone.

The parameter sensitivity analysis was carried outusing the ArcSWAT interface for the whole Lake TanaBasin and Anjeni watershed. Twenty-six hydrologicalparameters were tested for sensitivity analysis for thesimulation of the stream flow in the study area. The mostsensitive parameters considered for calibration in bothwatersheds were soil evaporation compensation factor,initial SCS Curve Number II value, base flow ˛ factor(days), threshold depth of water in the shallow aquiferfor ‘revap’ to occur (mm), (days), available water capac-ity (mm WATER/mm soil), groundwater ‘revap’ coef-ficient, channel effective hydraulic conductivity (mm/h)and threshold depth of water in the shallow aquifer forreturn flow to occur (mm). Table VI shows sensitiveparameters for flow and calibrated flow parameters val-ues for both Anjeni gauged watershed and Lake TanaBasin. Whereas the most sensitive parameters for pre-dictions of sediment yield are linear parameter for cal-culating the maximum amount of sediment that can beentrained during channel sediment routing, channel coverfactor, USLE equation support practice factor, exponentparameter for calculating sediment reentrained in channelsediment routing and minimum value of USLE C factorfor land cover/plant. These sediment parameters are listedin Table VII with their calibrated values.

SWAT model was first calibrated and validated forflow in Anjeni watershed. The model was calibratedfor sediment after calibrating the flow parameters in the

Table IV. The continuous rating scale (Eastman, 2001)

Rating scale

1/9 1/7 1/5 1/3 1 3 5 7 9Extremely Very strongly Strongly Moderately Equally Moderately Very strongly Strongly Extremely

Less important More important



Table VI. Sensitive parameters for flow and calibrated flow parameters values for both Anjeni gauged watershed and Lake TanaBasin

No. Sensitive flow Lower and Final fitted valueparameters upper bound

Gilgel Abay River Megech River Ribb River Gumera River Anjeni watershed

2 CN2 š25%a �10 �9 �10 �8 �51 ESCO 0–1 0Ð8 0Ð8 0Ð8 0Ð8 0Ð753 ALPHA BF 0–1 0Ð1 0Ð1 0Ð1 0 0Ð0484 REVAPMN 0–500 300 289 372 4465 SOL AWC š25%a 20 �20 �10 20 506 GW REVAP š0Ð036 20 0Ð1 0 0Ð17 CH K2 0–5 4Ð6 3Ð2 1Ð9 0Ð7 0Ð7178 GWQMN 0–5000 108 17 333 989 Gw Delay 0–500 2810 Gw Revap š0Ð036 0Ð08711 Sol Z š25 C21%Ł

a The percentage with which the original values changed.ESCO-Soil evaporation compensation factor, Sol Awc-Available water capacity (mm WATER/mm soil), Gw Delay-Groundwater delay (days),Gw Revap-Groundwater ‘revap’ coefficient, Ch K2-Channel effective hydraulic conductivity (mm/h), Sol Z-Soil depth (mm), REVAPMN-Thresholddepth of water in the shallow aquifer for ‘revap’ or percolation to the deep aquifer to occur (mm, H2O), GWQMN-Threshild depth of water in theshallow aquifer required for return flow to occur (mm, H2O), Sol Z- Soil depth (mm).

Table VII. Sensitive and calibrated sediment parameters and forAnjeni gauged watershed

Parameter Lower andupperbound

Rankbased onrelative

sensitivity

Calibratedvalue

Linear parameter forcalculating themaximum amountof sediment that canbe reentrainedduring channelsediment routing(Spcon)

0Ð0001–0Ð01 5Ð09 0Ð005

Channel cover factor(Ch Cov)

0–1Ð00 4Ð08 0Ð35

Channel erodibilityfactor (Ch Erod)

0–1Ð00 3Ð12 0Ð50

USLE equationsupport practicefactor (USLE P)

0–1Ð00 1Ð44 0Ð8

Exponent parameterfor calculatingsedimentreentrained inchannel sedimentrouting (Spexp)

1–2Ð00 1Ð06 1Ð39

Minimum value ofUSLE C factor forland cover/plant(USLE C)

š25 0Ð09 0Ð27

USLE, universal soil loss equation.

watershed. The main reason not able to calibrate sedimentparameters in the Lake Tana Basin is that there is nomeasured sediment data with in Lake Tana Basin. Thecomparison between the flow parameters calibrated bothat Anjeni and Lake Tana Basin indicated a reasonablesimilarity between the parameters in the two basins. Inboth areas the most sensitive parameters for flow and

Table VIII. Model evaluation statistics for calibration and val-idation result for flow in the Lake Tana Basin (Setegn et al.,

2008)

Objective Riversfunction

Gilgel Abay Gumera Megech Ribb

Cal Val Cal Val Cal Val Cal Val

NSE 0Ð73 0Ð69 0Ð62 0Ð60 0Ð18 0Ð04 0Ð51 0Ð48R2 0Ð75 0Ð80 0Ð69 0Ð70 0Ð19 0Ð32 0Ð59 0Ð55

Cal, calibration; Val, validation; NSE, Nash–Sutcliff coefficient; R2,coefficient of determination.

sediment are similar which indicate that the hydrologicalresponse to flow and sediment as a result of land use,soil and topographic characteristics are related, so thatwe were able to upscale the sediment parameters to theLake Tana Basin.

The comparison between the observed and simulatedflow discharge values for 12 years of simulations indi-cated that there is a good agreement between the observedand simulated flows using SUFI-2 algorithms, whichwere verified by higher values of R2 and NSE for GilgelAbay, Gumera and Ribb inflow rivers. Calibrated andvalidated model predictive flow performance for all LakeTana inflow rivers on daily flows is summarized inTable VIII.

The monthly calibration and validation of the SWATmodel for flow and sediment yield in Anjeni watershedhave shown that the model can predict the flow andsediment yield as well as indicated by model perfor-mance evaluation measures, the, R2 the Nash–Suttcliffesimulation efficiency, PBIAS and standardized RMSE.The statistical comparison between the measured monthlysediment yield and best simulation result from SUFI-2 algorithms showed a good agreement. The resultwas verified by NSE D 0Ð81, PBIAS D 28%, RSR D


S. G. SETEGN ET AL.

0Ð23 and R2 D 0Ð85 for calibration and NSE D 0Ð79,PBIAS D 30%, RSR D 0Ð29 and R2 D 0Ð80 for validationperiods. Both the NSE and RSR results show good resultboth for calibration and validation periods. The PBIASvalues are good for both periods. The R2 statistics alsoshow a good correlation between measured and simulatedsediment yields.

The simulated sediment yield output of the SWATmodel has shown that 18Ð4% of the watershed area hashigh potential for soil erosion (Table X) which producesan average annual sediment yield of 30 to 65 tons/ha. Onthe basis of the classes assigned to the annual sedimentyield, the map was reclassified into four major categoriesof soil erosion hazards region such as very low, low,moderate and severe erosion conditions (Figure 4).

The result of the SWAT output indicated that signifi-cant portions of the area which are known to be highlycultivated area are more vulnerable to soil erosion. More-over, areas at a higher slope condition have shown highercontribution of sediment yield. Some parts of the water-shed which have higher erodibility characteristics becauseof poor soil physical properties contributed for a highersediment yield than others. Many of the places which arevery near to rivers and stream has shown a considerablecontribution for higher soil erosion and sediment yield.The Ribb and Gumera inflow rivers shown to contributelarge amount of sediment yield production to the LakeTana with respect to their size.

Figure 4. Spatially delineated erosion sensitive areas using Soil and waterassessment tool (SWAT) model (predicted sediment yield with in each

sub-basin area) in the Lake Tana Basin

MCE analysis

In the MCE analysis, six scenarios were tested to findthe main factors which play a major role for the rateof soil erosion in the watershed. In the first and secondscenarios, the main consideration was given for slopefactor followed by land cover and soil, respectively. In allcases river factor networks was given the lowest priorityin comparison with others (Table IX). It was assumedthat the position of rivers in the watershed play less rolefor the rate of soil erosion than the other factors.

On the basis of the foregoing assumptions, the weightswere derived by entering the ratings into a pairwise com-parison matrix for each scenario. The pairwise compar-ison matrix for scenario one indicates that the rating ofland cover factor relative to slope gradient is 1 : 3. Soilis less important than slope, river factor is less importantthan slope factor and so on. The computed CR is 0Ð03which is within the acceptable range (<0Ð10).

In the first scenario, which gives high priority to slopegradient followed by land cover, soil and river factor,25Ð5% of the land area is erosion prone area and 12%of the land areas are moderately erosion prone. But 22%of the land area is less erosion prone. In this scenario,the first factor considered is slope factor. This factor isknown to be the main driving force for the movementof surface water. Steeper and longer slopes result in higherosion rates. The second criterion is the land cover whichcontrols the detachability and transport of soil particlesand infiltration of water into the soil. The types of the soilalso play a significant role for erosion depending upontheir physical properties and sensitivity to erosion.

A layer which contains all rivers within the catchmentswas also considered as a contributing factor in the study.Here it was assumed that places close to streams orrivers are more easily washed especially during high flowseasons. In the third scenario which gives high priorityto land cover followed by slope, soil and river factor,only 28Ð5% of the watershed has high potential for soilerosion. Scenario 4 which gives higher priority to soildata, 30Ð4% of the watershed has high potential for soilerosion (Table X).

Finally, the MCE erosion potential map was pro-duced using GIS incorporating the MCE model results(Figure 5). The final map is a posterior probability map,showing erosion sensitive areas. The MCE maps werecompared with the map developed based on the SWAToutput. It can be observed that Scenarios 1 and 3 pro-duce a similar result with SWAT output. This indicates

Table IX. Scenarios showing different factor map combinationorder

Scenario Factors in order of priority

1 Slope Land cover Soil Rivers2 Slope Soil Land cover Rivers3 Land cover Slope Soil Rivers4 Land cover Soil Slope Rivers5 Soil Land cover Slope Rivers6 Soil Slope Land cover Rivers



Table X. Percentage of sub-basin areas and sediment yield in percent using SWAT, and erosion potential areas using MCE

Class SWAT Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Remarks

Area(%)

Sedimentyield

(tones/ha)

Area (%) Area (%) Area (%) Area (%) Area (%) Area (%)

1 44Ð3 0–9 22Ð5 33Ð6 20Ð6 20Ð9 22Ð8 23Ð2 Nil2 18Ð8 9–17 39Ð5 27Ð8 5Ð5 5Ð2 25Ð9 40Ð3 Slight3 18Ð5 17–30 12Ð5 25Ð1 45Ð8 43Ð5 40Ð7 24Ð0 Moderate4 18Ð4 30–65 25Ð5 13Ð5 28Ð1 30Ð4 10Ð6 12Ð5 High

MCE, multi-criteria evaluation; SWAT, Soil and Water Assessment Tool

(a) (b) (c)

(d) (e) (f)

Figure 5. Spatially delineated erosion sensitive areas in the Lake Tana Basin using multi-criteria evaluation (MCE)—(a) Scenario 1, (b) Scenario 2,(c) Scenario 3, (d) Scenario 4, (e) Scenario 5 and (f) Scenario 6

that land cover and slope factors contribute more to therate of soil erosion than the other factors.

The identification of the most erosion prone areas willhelp the local government and other stakeholders whoare interested and involved in soil and water conserva-tion activities in the Lake Tana Basin to successfully planand implement appropriate soil and water conservationmethods. Soil and water conservation methods requirecareful background information for decision makers toselect the target area and type of intervention as per theexisting land use, soil type, topography and degree oferosion hazard. The result of the two model approach

can help as a guide to spatially locate the erosion vulner-able area in the Lake Tana Basin. The spatial distributionof the soil erosion sensitivity predicted in both of mod-eling approach indicated relatively comparable results inshowing erosion vulnerable area in the Lake Tana Basin.The analysis had shown that more than 14% of the areasshown sensitivity in both the modeling approaches.

CONCLUSION

Land resource evaluation and allocation is one of thefundamental concerns to a resource development. With


S. G. SETEGN ET AL.

the advent of GIS, there is ample of opportunities forbetter soil and water resources assessments. The mainaim of this study was to identify erosion vulnerable areasin the Lake Tana Basin. In this study, two modelingapproaches, SWAT and MCE, were applied for theidentification of erosion potential areas in the Lake TanaBasin. The advantage of using the two approaches isto use the output of the SWAT model, which havebeen tested and verified with the measured dataset,as a guide for verification of the result of differentMCE scenarios tested in the watershed. The model wassuccessfully calibrated and validated for flow in the fourmain tributaries of Lake Tana. Moreover, it was validatedfor flow and sediment yield in Anjeni gauged watershedusing SUFI-2 calibration and uncertainty algorithms. Themodel evaluation statistics for both stream flows andsediment yield gave good results that were verified byNSE and R2 > 0Ð50.

The SWAT model showed that 18Ð5% of the watershedis high erosion potential areas. The MCE result forscenarios that gives high priority to land cover and slopefactor, indicated 28% and 25% of the land area arevulnerable to soil erosion, respectively. The comparisonof the maps produced by the two approaches showeda considerable similarity indicating areas with possibleerosion risk. The result indicated that land use factorplay a significant factor in the rate of soil erosion andland degradation. The output of this study may supportplanners and decision makers to take relevant soil andwater conservation measures and thereby reduce thealarming soil loss and land degradation problems in thebasin.

ACKNOWLEDGEMENTS

The authors would like to thank the Applied TrainingProject of the Nile Basin Initiative for the financial sup-port of this research. They extend thanks to Meteorologi-cal Agency of Ethiopia and Ministry of Water Resourcesfor the data used in the study. They also acknowledgethe Associate Editor and the two anonymous reviewersfor their constructive comments and suggestions.

REFERENCES

Abbaspour KC, Johnson CA, van Genuchten MT. 2004. Estimatinguncertain flow and transport parameters using a sequential uncertaintyfitting procedure. Vadose Zone Journal 3(4): 1340–1352.

Abbaspour KC, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J,Zobrist J, Srinivasan R. 2007. Modelling hydrology and water qualityin the pre-alpine/alpine Thur watershed using SWAT. Journal ofHydrology 333: 413–430.

Abbay River Basin Integrated Development Master Plans Project. 1999.Volume IX—Land Resources Development, Part II—Semi-DetailedSoils Survey , May 1999.

Alamirew, CD. 2006. Modeling of hydrology and soil erosion in upperAwash river basin. PhD thesis, University of Bonn, Institut furStadtebau, Bodenordnung und Kulturtechnik, Garmany; 235p.

Arnold JG, Srinivason R, Muttiah R, Williams JR. 1998. Large areahydrologic modeling and assessment part I: model development.Journal of the American water Resources Association 34(1): 73–89.

Arnold JG, Williams JR, Maidment DR. 1995. Continuous-time waterand sediment routing model for large basins. Journal of HydraulicEngineering 121(2): 171–183.

Bello-Pineda J, Ponce-Hernandez R, Liceaga-Correa MA. 2006. Incor-porating GIS and MCE for suitability assessment modelling of coralreef resources. Environmental Monitoring and Assessment 114(1–3):225–256.

Benaman J, Christine AS, Douglas AH. 2005. Calibration and validationof soil and water assessment tool on an agricultural watershed in upstateNew York. Journal of Hydrologic Engineering, ASCE 10(5): 363–374.

Bingner RL. 1996. Runoff simulated from Goodwin Creek watershedusing SWAT. Transactions of the American Society of AgriculturalEngineers 39(1): 85–90.

Bouraoui F, Benabdallah S, Jrad A, Bidoglio G. 2005. Application ofthe SWAT model on the Medjerda river Basin (Tunisia). Physics andChemistry of the Earth 30: 497–507.

Carver SJ. 1991. Integrating multi-criteria evaluation with geographicalinformation systems. International Journal of Geographical Informa-tion Systems 5(3): 321–339.

Ceballos-Silva A, Lo’pez-Blanco J. 2003. Delineation of suitable areasfor crops using a multi-criteria evaluation approach and land use/covermapping: a case study in Central Mexico. Agricultural Systems 77:117–136.

Chu TW, Shirmohammadi A. 2004. Evaluation of the SWAT model’shydrology component in the piedmont physiographic region ofMaryland. Transactions of the ASAE 47(4): 1057–1073.

Eastman JR. 2001. Idrisi32—release 2. Guide to GIS and ImageProcessing , vol. 2. Clark Labs, Clark University: Worcester, MA.

Ethiopian Highlands Reclamation Study (EHRS). 1984. Annual ResearchReport (1983–984). Ministry of Agriculture: Addis Ababa.

Gassman PW, Reyes MR, Green CH, Arnold JG. 2007. The Soiland Water Assessment Tool: historical development, applications,and future research directions. Transactions of the ASABE 50(4):1211–1250.

Gupta HV, Sorooshian S, Yapo PO. 1999. Status of automatic calibrationfor hydrologic models: comparison with multilevel expert calibration.Journal of Hydrologic Engineering 4(2): 135–143.

Hajkowicz S, Higgins A. 2006. A comparisons of multiple criteriaanalysis techniques for water resource management. European Journalof Operational Research 184(1): 255–265.

Heuvelmans G, Muys B, Feyen J. 2004. Analysis of the spatial variationin the parameters of the SWAT model with application in Flanders,Northern Belgium. Hydrology and Earth Systems Sciences 8(5):931–939.

Heywood I, Oliver J, Tomlinson S. 1995. Building an exploratorymulti-criteria modeling environment for spatial decision support. InInnovations of GIS 2 , Fisher P, (ed). Taylor and Francis: Leicester;127–136.

Hurni H. 1989. Soil for the Future. Environmental Research forDevelopment Cooperation, Uni Press 62, University of Berne: Berne;42–46.

Janssen R, Rietveld P. 1990. Multi-criteria Analysis and GIS: anapplication to agriculture land use in The Netherlands. In GeographicalInformation Systems for Urban and Regional Planning , Scholten H,Stilwell J. (eds.) Kluwer Academic Press: Dordrecht, The Netherlands;129–138.

Jarvis A, Reuter HI, Nelson A, Guevara E. 2006. Hole-Filled SeamlessSRTM data V3 , International Centre for Tropical Agriculture (CIAT),available from http://srtm.csi.cgiar.org.

Knisel WG. 1980. CREAM: A Field Scale Model for Chemicals,Runoff, and Erosion from Agricultural Management Systems . USDA,Conservation Research Report No. 26, 643pp.

Kruger HJ, Berhanu F, Yohannes GM, Kefane K. 1996. Creating aninventory of indigenous soil and water conservation measuresin Ethiopia. In Sustaining the Soil: Indigenous Soil and WaterConservation in Africa, Reij C, Scoones I, Toulmin C (eds). EarthScanPublications: London; 170–180.

Leskinena P, Kangas J. 2005. Multi-criteria natural resource managementwith preferentially dependent decision criteria. Journal of Environmen-tal Management 77(3): 244–251.

Malczewski JA. 1996. GIS-based approach to multiple criteria groupdecision-making. International Journal of Geographical InformationScience 10(8): 321–339.

Morgan RPC, Quinton JN, Smith RE, Govers G, Poesen JWA, Auer-swald K, Chisci G, Torri D, Styczen ME. 1998. The European SoilErosion Model (EUROSEM): a dynamic approach for predicting sed-iment transport from fields and small catchments. Earth Surface Pro-cesses and Landforms 23(6): 527–544.



Moriasi DN, Arnold JG, Van Liew MW, Binger RL, Harmel RD,Veith T. 2007. Model evaluation guidelines for systematic quantifica-tion of accuracy in watershed simulations. Transactions of the ASABE50(3): 885–900.

Nash JE, Sutcliffe JV. 1970. River flow forecasting through conceptualmodels. Part I—a discussion of principles. Journal of Hydrology 10:282–290.

Neitsch SL, Arnold JG, Kiniry JR, Williams JR, King KW. 2004. Soiland Water Assessment Tool, User’s Manual: Version 2005 . USDAAgricultural Research Service and Texas A&M Blackland ResearchCenter: Temple.

Neitsch SL, Arnold JG, Kiniry JR, Srinivasan R, Williams JR. 2005. Soiland Water Assessment Tool, Theoretical Documentation: Version 2005 .USDA Agricultural Research Service and Texas A&M BlacklandResearch Center: Temple.

Pereira JMC, Duckstein L. 1993. A multiple criteria decision-makingapproach to GIS-based land suitability evaluation. InternationalJournal of Geographical Information Systems 75: 407–424.

Peterson JR, Hamlett JM. 1998. Hydrologic calibration of the SWATmodel in a watershed containing Fragipan Soils. Journal of theAmerican Water Resources Association 34(3): 531–544.

Saaty TL. 1977. A scaling method for priorities in hierarchical structures.Journal of Mathematical Psychology 15: 234–281.

SCRP (Soil Conservation Research Programme). 1996. Soil ConservationResearch Project Database Report 1982–1993 . Ministry of Agricultureand University of Berne, Series Report III. Hundelafto Research Unit,Institute of Geography, University of Berne: Switzerland.

SCRP (Soil Conservation Research Program). 2000. Soil Erosion andConservation Database. Area of Anjeni, Gojam, Ethiopia: Long-TermMonitoring of the Agricultural Environment, 1984–1994. Centre forDevelopment and Environment in association with the Ministry ofAgriculture, Ethiopia: Berne, Switzerland.

Setegn SG, Srinivasan R, Dargahi B. 2008. Hydrological modeling in theLake Tana Basin, Ethiopia using SWAT model. The Open HydrologyJournal 2: 49–62.

Singh J, Knapp HV, Demissie M. 2005. Hydrologic modeling of theIroquois River watershed using HSPF and SWAT. Journal of theAmerican Water Resources Association 41(2): 343–360.

Srinivasan R, Engel BA. 1994. A spatial decision support system forassessing agricultural non point source pollution. Journal of AmericanWater Resources Association 30(3): 441–452.

Srinivasan R, Ramanarayanan TS, Arnold JG, Bednarz ST. 1998. Largearea hydrologic modeling and assessment part II: model application.Journal of the American Water Resources Association 34(1): 91–101.

Tecle A, Yitayew M. 1990. Preference ranking of alternative irrigationtechnologies via a multicriterion decision making procedure.Transactions of ASAE 3(5): 1509–1517.

Voogd H. 1983. Multi-Criteria Evaluation for Urban and RegionalPlanning . Pion, Ltd.: London.

Williams JR. 1975. Sediment-yield prediction with universal equationusing runoff energy factor. Present and Prospective Technology forPredicting Sediment Yield and Sources: Proceedings of the SedimentYieldWorkshop 1975 , USDA Sedimentation Lab., Oxford, November28–30, 1972. ARS-S-40. 244–252.

Williams JR, Jones CA, Dyke PT. 1984. A modeling approach todetermine the relationship between erosion and soil productivity.Transactions of the ASAE 27(1): 129–144.

Winchell M, Srinivasan R, Di Luzio M, Arnold J. 2007. ArcSWATInterface for SWAT User’s Guide. Blackland Research Center, TexasAgricultural Experiment station and USDA Agricultural ResearchService.

Wischmeier WH, Smith DD. 1965. Predicting rainfall-erosion lossesfrom cropland east of the Rocky Mountains. Agriculture Handbook ,vol. 282. USDA-ARS.

Wischmeier WH, Smith DD. 1978. Predicting rainfall erosion losses:a guide to conservation planning. Agriculture Handbook , vol. 282.USDA-ARS.

Young RA, Onstad CA, Bosch DD, Anderson WP. 1987. AGNPS; Agri-cultural Non-point Source Pollution Model: A large watershed Analysistool . Conservati Research. Report35, USDA-ARS: Washington, DC;77pp.

Young RA, Onstad CA, Bosch DD, Anderson WP. 1989. AGNPS: a nonpoint Sourse Pollution Model for Evaluating Agricultural Watersheds.J. Soil and Water Conservation 44(2): 168–173.

Zemenfes T. 1995. The political economy of land degradation in Ethiopia.Northeast African Studies 2: 71–98.


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