Soil Erosion Prediction Using Morgan-Morgan-Finney Model in a GIS Environment in Northern ...

16 Pages·2015·4.75 MB·English

Hindawi Publishing Corporation
Applied and Environmental Soil Science
Volume 2014, Article ID 468751, 15 pages
http://dx.doi.org/10.1155/2014/468751
Research Article
Soil Erosion Prediction Using Morgan-Morgan-Finney Model in
a GIS Environment in Northern Ethiopia Catchment
GebreyesusBrhaneTesfahunegn,1,2LulsegedTamene,3andPaulL.G.Vlek1
1CollegeofAgriculture,AksumUniversity,P.O.Box287,Aksum,Ethiopia
2CentreforDevelopmentResearch,UniversityofBonn,Walter-Flex-Street3,53113Bonn,Germany
3InternationalCentreforTropicalAgriculture(CIAT),ChitedzeAgriculturalResearchStation,P.O.Box158,Lilongwe,Malawi
CorrespondenceshouldbeaddressedtoGebreyesusBrhaneTesfahunegn;[email protected]
Received7February2014;Accepted14March2014;Published22April2014
AcademicEditor:YongSikOk
Copyright©2014GebreyesusBrhaneTesfahunegnetal.ThisisanopenaccessarticledistributedundertheCreativeCommons
AttributionLicense,whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkis
properlycited.
Even though scientific information on spatial distribution of hydrophysical parameters is critical for understanding erosion
processesanddesigningsuitabletechnologies,littleisknowninGeographicalInformationSystem(GIS)applicationindeveloping
spatialhydrophysicaldatainputsandtheirapplicationinMorgan-Morgan-Finney(MMF)erosionmodel.Thisstudywasaimedto
derivespatialdistributionofhydrophysicalparametersandapplythemintheMorgan-Morgan-Finney(MMF)modelforestimating
soilerosionintheMai-Neguscatchment,northernEthiopia.Majordatainputforthemodelincludeclimate,topography,land
use, and soil data. This study demonstrated using MMF model that the rate of soil detachment varied from <20tha−1 y−1 to
>170tha−1 y−1,whereasthesoiltransportcapacityofoverlandflow(TC)rangedfrom5tha−1 y−1 to>42tha−1 y−1.Theaverage
soillossestimatedbyTCusingMMFmodelatcatchmentlevelwas26tha−1 y−1.Inmostpartsofthecatchment(>80%),the
−1 −1
modelpredictedsoillossrateshigherthanthemaximumtolerablerate(18tha y )estimatedforEthiopia.Hence,introducing
appropriateinterventionsbasedontheerosionseveritypredictedbyMMFmodelinthecatchmentiscrucialforsustainablenatural
resourcesmanagement.
1.Introduction soilerosionbywaterisstillrecognizedtobeaseverethreat
to the national economy [2, 6, 7]. This indicated that the
Soil erosion is the dominant cause of soil degradation at
existingliteratureontherateofsoilerosioninEthiopiacalls
a global scale [1–3]. This is accounted for between 70 and
for a wise decision supporting tools in order to reduce the
90% of total soil degradation [2, 4]. The adverse influences
degradation level. For instance, past studies on soil erosion
of soil erosion as a cause for soil degradation have long
in the catchments of Tigray region (northern Ethiopia)
been recognized as a severe problem for sustainability of −1 −1
showedvariabilityrangingfrom7tha y [8]tomorethan
economic development [5]. This is because a large portion
−1 −1 −1 −1
24tha y [7]and80tha y [9].Accordingtothereport
of fertile soil is lost annually which negatively influences
−1 −1
thegoalofachievingfoodsecurity[3].However,estimation byFAO[10],erosionrateisestimatedupto130tha y from
−1 −1
of soil erosion rate is often difficult due to a complex cropland and 35tha y averaged over all land use types
interplayofmanyfactors,besidesthedifferencesinscaleand inthehighlandsofEthiopia.Suchdiscrepanciesintherates
methodologicalcomponentsofthestudies[6,7].Inacountry of erosion by the studies mainly attributed to changes and
like Ethiopia, with an agriculture-based economy for more differencesinlanduse,managementpractices,andmethods
than85%ofpopulation,havingreliablesoillossdataisindeed employed while developing input data and their respective
amatterofgreatconcernsandnotamatterofchoice. scale of analysis. Predominantly, previous erosion related
Regardless of the great deal of management practices input data were developed from simple point observation
undertakenaggressivelyinEthiopiacatchmentsbythegov- such as runoff plot, and data were interpolated through
ernment in the past 1-2 decades to reduce soil degradation, conventional method [8, 11, 12]. Such method poses many
2 AppliedandEnvironmentalSoilScience
limitationsintermsofcost,representation,andreliabilityof isaphysicallybased-empiricalmodel(mixmodel),itneeds
the resulting data [5]. Recently, to reduce such limitations less data than most of the other erosion predictive models
geostatistic techniques that interpolate data for an entire [23].
catchmentfromappropriatelysampledpointmeasurements Understanding the hydrophysical parameters that can
arereadilyavailable[13,14]. influenceerosionrateinacatchmentiscomplexduetothe
Mapping through conventional methods demands an combined nature of the natural processes and man-made
intensivedatacollection,whichisoftendifficulttopracticein features [7, 31]. Therefore, research to obtain quantitative
complexterrainslikeinnorthernEthiopia[7,14].TheGeo- description of hydrology/erosion in a catchment must con-
graphic Information System (GIS) techniques can provide siderthesespatialheterogeneities.Inordertotackleagainst
easyandtimeeffectivetoolstomapandanalyzeerosioninput hydrologicalrelatedproblems(runoff,sedimentation),accu-
dataofhydrophysicalparameters.Thesetechniquescoupled rate representation or locating the spatial distribution and
withtheconceptofcatchmentprioritycanhelpinidentifying variabilityoftheinfluencingparametersusingGISisneces-
areas where treatment plans should be first located. Many sary [7, 32]. This study was aimed to derive and assess the
studies (e.g., Sharad et al. [15]; Sanware et al. [16]) revealed spatial distribution of hydrophysical parameters developed
thatGIStechniquescanhaveagreatroleincharacterization using GIS technique and apply them in the MMF model
and prioritization of subcatchments. The catchment level for estimating the spatial variability of soil loss in the Mai-
assessment and mapping of hydrophysical resources can Neguscatchment,northernEthiopia.Thespatialmapcanbe
supporttheidentificationofconstraints,ecologicalproblems, usedforprioritizingareaswithinthecatchmentthatrequire
and adoption of effective management practices that sus- immediatemanagementmeasuresonthebasisoftheseverity
tain land and water resources using integrated catchment ofrunoff/soilloss.
management strategies [17]. In addition, the availability of
hydrophysicalparametersinaGISmapformatcanbeused 2.MaterialsandMethods
readilyforerosionmodelrunninginordertounderstandspa-
tialdistributionofecologicalproblemssuchassoilerosion. 2.1. Study Area. The study area, Mai-Negus catchment, is
Inmanyenvironmentalstudies,datainputsaremeasured located in the Tigray region, northern Ethiopia (Figure1).
at single points in space, even though classical statistics Thecatchmenthasanareaof1240haandaltituderangesfrom
assumethatmeasureddataareindependentandthusarenot 2060to2650mabovesealevel[33].Inthecatchment,mean
∘
sufficienttoanalyzespatiallydependentvariables[13,14,18]. annual temperature of 22 C and annual rainfall of 700mm
However, information is required for the entire catchment havebeen recorded(MeteorologyAgency-Mekellebranch).
space, which necessitates methods that interpolate data to The highest amount of rainfall (>70%) is received between
estimate the mean value within an area [13]. Geostatistics JulyandAugust.Landuseispredominantlyarable,withteff
provides the basis for interpolation spatial variability of (Eragrostis tef) being the major crop along with different
hydrophysical erosion model input parameters that affect proportions of pasture land and scattered patches of trees,
runoffandsoilloss[13,14,19–21]. bushes,andshrubs.Themajorrocktypesarelavapyroclastic
To estimate soil erosion and suggest appropriate man- and metavolcanic. Leptosols are found mainly on the very
agementplans,manyerosionmodelssuchasUniversalSoil steep positions, Cambisols on gentle to steep slopes, and
LossEquation(USLE)[22],Morgan-Morgan-Finney(MMF) Vertisolsontheflatareasofthecatchment[34].
[23],WaterErosionPredictionProject(WEPP)[24],Soiland
WaterAssessmentTool(SWAT)[25],EuropeanSoilErosion 2.2.ResearchApproach. Theresearchapproachinthisstudy
Model(EUROSEM)[26],andAnnualizedAgriculturalNon- consisted of five main steps. These are (i) identification
Point Source (AnnAGNPS) [27] have been developed and of hydrophysical parameters which are inputs of MMF
used data inputs generated through GIS. Among these model, (ii) field surveys and informal discussions in order
models, the USLE has remained the most practical method to identify representative soil sampling zones within the
of estimating soil erosion potential for more than 40 years studycatchmentforsoilsamplingandanalysisandalsothe
[28, 29], despite the fact that it has many limitations for corresponding vegetation cover conditions, (iii) application
applicationatcatchment-scale.Ontheother-hand,process- of empirical relations which are described by Morgan et
basederosionmodelsdevelopedafterwardhavelimitationsin al. [23, 35] to calculate intermediate MMF model inputs,
applicabilityduetointensivedataandcomputationrequire- (iv) application of geostatistic interpolation technique for
ments [30]. The application of process-based models is not spatial model inputs development, and (v) application of
always an easy task since these require large amounts of MMFmodelinGISenvironmentwhileestimatingspatially
information which is often not available, mainly in data distributederosionoutputssuchastotaloverlandtransport
scarce developing regions. The MMF model was selected capacityandsoildetachmentrate.
to estimate annual soil loss, since this model endeavours
to retain the simplicity of USLE and also encompasses the 2.3. MMF Model Inputs Preparation. Input data for MMF
understandingoferosionprocessesintowaterandsediment model include rainfall (mm), land use, digital elevation
phases [23]. Meaning, the MMF model was selected to be model (DEM) for slope map derivation, soil texture, soil
−1
applied inthisstudy because ofitssimplicityandflexibility moisturecontentatfieldcapacity(%ww ),soildetachability
−1 −3
ascomparedtothephysical-basemodelsandhasastronger index(gJ ),bulkdensityofsoil(Mgm ),cohesionofsoil
physicalbasethanUSLE.Inaddition,sincetheMMFmodel surface (KPa), soil moisture storage capacity (𝑅𝑐), effective
AppliedandEnvironmentalSoilScience 3
×105 260000 340000 420000 500000 580000
16.5
×102
N
16
N. western 15643
zone
15.5 Eastern
Western zone zone
Central zone
15 15634
14.5 (km) Southern
zone 15625
0 0.5 1 2
14
Mekelle
15616
(b) Tigray N
15 15607
TTTiiggirrgaaryyay
461000 462000 463000 464000 465000
13 N
(km)
0 0.4 0.8 1.6
11
9 Projection: UTM
Zone: 37N
Datum: Adindan
8 Spheroid: Clarke 1880
(c) Mai-Negus catchment
6
4
33 36 38 41 44 47
(a) Ethiopia
Figure1:Studyarea:Ethiopia(a),Tigray(b),andMai-Neguscatchments(c).Thebluecolourshadedareaisthereservoir.
hydrological top soil depth (EHD), and ratio of actual to assumedtobesimilarinthestudycatchment.Thereasonfor
potentialevapotranspiration(𝐸𝑡/𝐸𝑜)[23,35].Inputdatawere having only one weather station in the study catchment is
collectedfromdifferentsourcessuchasfieldandlaboratory thattheOfficeofMeteorologyAgencybelievedthatrainfall
determination,empiricalrelations,andtheliterature.Meteo- variabilityisnegligiblewithinsuchasmallarearegardlessof
rologicaldatasuchasrainfalland𝐸𝑡datawereobtainedfrom thedifferencesinelevation.Rainfallintensitywasassumedat
the meteorology station near the study area in 2009. Slope 25mmh−1whichiserosivefortropicalclimatessuchasMai-
wasderivedfromDEMdevelopedfromthetopographic-map Negusecatchmentbecausenoactualintensitydatawasfound
available at the EthiopianMapping Agency for Aksum area forthestudycatchment.Soildetachabilityindex(𝐾)(gJ−1)
[33]. The map was scanned, and contours and spot heights was determined from the literature that corresponds to the
were digitized and tagged with elevation values in a GIS soiltextureobservedinthestudycatchment.
environment. The vector elevation map was converted to
rasterandprojectedusingtheUniversalTransverseMercator 2.4. Soil Sampling Zones and Sample Collection. In order
37North(UTM-37N)referencesystem. to prepare MMF model soil related inputs, soil sampling
Crop and soil parameters were collected from 117 plots that considered soil variability in the study catchment was
scatteredthroughoutthecatchmentconsideringmajorland executed.Samplingapproachesthatdividedafieldintosmall
use and cover types (bush land, protected area, cultivated, units (zone sampling) can capture variability and provide
abandoned fields, grazing land, mixed-forest, and residen- more information about soil-test levels compared with one
tial). Supervised classification and visual interpretation of composite sample collected from an entire large sampling
the land satellite image of November 2009 was carried area [36]. To reduce the number of samples and sampling
out for general land use and cover mapping. In addition costs zone sampling is suggested to provide a way to group
to this, crop covers for the different crop types and their the spatial variability of soils while maintaining acceptable
corresponding geographic coordinates were collected using information about soil properties [36]. Sampling by zone
fieldsurveyinSeptember2009.Datarelatedtorainfallsuchas assumesthatsamplingareasarelikelytoremaintemporally
rainfallintensity,numberofrainydays,andtotalrainfallwere stable[36,37].
4 AppliedandEnvironmentalSoilScience
Inthisstudy,thezonesamplingtechnique(divideafield ×103
into homogenous units that allow capturing variability and 1565
N
provide more information)was used to collect soil samples
based on previous and existing knowledge of the soil and
(km)
landusesystemsintheentirestudycatchment.Thenatural
1564
andmanagementfactorsacrossthelandscapethatinfluenced 0 0.4 0.8 1.6
soilpropertiesspatialvariabilitywereconsideredwhileiden-
tifying the soil sampling zones. Three soil sampling zones
1563
that represented the soil quality (SQ) categories, long-term
landuseandsoilmanagementsystems,anddifferenterosion
status sites in the catchment were identified using farmers’
opinions and researcher and extension experts’ judgment. 1562
The data that divided the catchment into the soil sampling
zones was derived during the field reconnaissance surveys
in June 2009. The SQ sampling zone was entirely used for
1561
arablelandinthecatchmentwhereastheothertwosampling
zonesbelongedtoallthelandusesystemsinthecatchment.
461000 462000 463000 464000 465000
The sampling zones were further subdivided into different
subsampling zones considering the variability within each Catchment boundary
zoneandanalyticalcosts. Reservoir boundary
The SQ sampling zone was divided into three subzones Soil sampling point
ashigh,medium,andlowSQbasedonfarmers’knowledge.
Figure 2: The distribution of soil sampling and vegetation cover
Theyusedindicatorssuchasyieldandyieldcomponent,soil pointsinthestudycatchment.
depth, colour, and fertility conditions to divide into these
subzones.Thedetailsonhowlocalfarmers’classifiedsoilinto
differentSQcategoriesinthestudycatchmentcanbefound
in Figure2. The sampling distance was not regular, ranging
inTesfahunegnetal.[38].
from40to180m.
Eightrepresentativelong-termlandusesystemsampling
Soil samples were collected in June 2009. A total of 51
zoneswereidentifiedbasedonfarmers’historicalandpresent soil samples (3 subzones × 17 samples) were collected from
informationacquiredinthecatchment.Theseare(i)natural
the SQ based sampling zone. From the long-term land use
forest;(ii)plantationofprotectedarea;(iii)grazedland;(iv) systems,atotalof24soilsamples(8subzones×3samples)
teff (Eragrostis tef)-faba bean (Vicia faba) rotation; (v) teff-
werecollected.Itwasalsocollected42soil/sedimentsamples
wheat(Triticumvulgare)/barley(Hordeumvulgare)rotation; (3 subzones × 12 samples in the catchment and 6 sampling
(vi) teff monocropping; (vii) maize (Zea mays) monocrop-
points in the reservoir) from the erosion-status sites. The
ping; and (vii) uncultivated marginal land. The age of the
grand total of the composite samples collected across the
systemsvariedfrom5-6yearsforteffmonocroppingand20–
samplingsubzoneswas117.Eachcompositesoilsamplewas
30yearsofmaizemonocroppingsystem.Averageageofthe
collected using 5–8 samples from each representative sub-
other systems was about 10 years except for the plantation,
samplingzonedependingonthesizeandhomogeneityofthe
grazed land, and uncultivated marginal land systems with 2
samplingarea(100–300m ).Allthecompositesoilsamples
morethan15years.
werecollectedatthesoildepthof0–20cm(theploughdepth)
Theerosionstatus-basedsamplingzonewasdividedinto
sincethisiswheremostchangesareexpectedtooccurdueto
threesubzonesasstable,eroded,anddeposition(aggrading)
erosion,long-termlanduse,andsoilmanagementpractices.
sites. Information from the local farmers, extension agents,
The composite soil samples were pooled into a bucket and
and researcher’s (first author) observation on the level of mixedthoroughlytohomogenizeit.Finally,asubsampleof
topsoil depth (A-horizon), deposition, rills, pedestals, root 500g from the pooled composite samples was taken and
andsubsoilexposure,andgulliesindicatorswereconsidered soil samples were air dried and sieved to pass 2mm mesh
whileidentifyingthethreeerosion-statussamplingsubzones. sieves before analysis for soil textures. On the other hand,
Those areas having A-horizon and minimum erosion indi- two undisturbed soil samples were collected from each soil
catorswereconsideredasstablesitesandthereverseofthis sampling point for bulk density and soil moisture determi-
aserodedsites.Depositionalsiteswerealsoeasilyidentified nation.Inaddition,fieldlevelobservationandmeasurement
as they are mainly located in depression and flat areas with for parameters such as effective hydrological top soil depth
evidencesofrecentsedimentdeposition.Intotal,therewere (m),groundcover,andcoverfactorwerecarriedoutfromthe
14 subsampling zones across the erosion-status sites in the samplingpointsandgeoreferenced.
catchmentforthesoilsamplescollection.Afterdoingallthis
identificationanddivision,thesoilsamplingpointsineach 2.5. Soil Analysis. The soil samples collected in the soil
subzonewerelocatedatthecentre,consideringsoilsinthat sampling zones were determined for soil texture using the
point best represent the samples. Each sampling point was Bouyoucoshydrometermethod[39],soilbulkdensity(BD)
georeferencedastheirdistributioninthecatchmentisshown by the core method [40], and soil moisture content at field
AppliedandEnvironmentalSoilScience 5
capacity(ww−1)byequilibratingthesoilwithwaterthrough ×102
capillaryactioninKRbox[41]. 15648
N
2.6. Empirical Relations in Deriving Inputs of MMF Model.
15640
Some intermediate input parameters were estimated from
observeddatainthecatchmentusingtheempiricalrelations
describedinMorganetal.[23]as
15632
𝐸=𝑅(11.9+8.7log (𝐼)),
10
𝐸 0.5 15624
𝑅𝑐 =1000∗MS∗BD∗EHD∗(𝐸𝑡) ,
𝑜
𝑅 (1) 15616
SR=𝑅exp(− 𝐶),
𝑅
𝑜
15608
𝑅
𝑅 = ,
𝑜 𝑅𝑛 460800 461600 462400 463200 464000 464800
where 𝐸 is annual kinetic energy of rainfall (Jm−2), 𝐼 is Land use-cover
−1 Teff
intensity of rainfall which is assumed to be 25mmh in Projection: UTM
Barley/wheat
tropicalconditions,SRissurfacerunoff/overlandflow(mm), Maize Zone: 37N
𝑅𝑛isnumberofrainydays,𝑅isaverageannualrainfall(mm), Water body Datum: Adindan
𝑅𝑐 issoilmoisturestoragecapacity(mm),𝑅𝑜 isannualrain Settlement Spheroid: Clarke 1880
per rain day, MS is soil moisture content at field capacity Plantation/closure (km)
−1 −3 Pasture/grazing
(ww ), BD is bulk density of the topsoil layer (Mgm ), 0 0.5 1
Forest
EHD (m) is effective hydrological topsoil depth defined as
Pulses
the depth of soil from the surface to an impermeable or
stonylayertothebaseofAhorizonortothedominantroot Figure3:Landuseandlandcoverofthestudycatchment.
base, and 𝐸𝑡/𝐸𝑜 is the ratio of actual (𝐸𝑡) to potential (𝐸𝑜)
evapotranspiration.EHDisthetopsoildepthwithinwhich
thestorageofwateraffectsthegenerationofrunoff.
Intermediatemapsderivedonthebasisoflanduse/cover inputsderivationbecauseitwasmorereliablethantheother
map (Figure3) included ratio of actual to potential evap- interpolation methods based on the mean squared error
otranspiration (𝐸𝑡/𝐸𝑜), permanent rainfall contributing to whichcomparesthemeasuredvalueswiththepredictedones.
permanentinterception,andstreamflow(𝐴)andcropcover Moreover, since the spacing of the measured or observed
managementfactor(𝐶𝑓).The𝐶𝑓 combines𝐶and𝑃factors hydro-physical input parameters were relatively sparse and
of the Universal Soil Loss Equation to give ratio of soil randomlychosenforeachsubsamplingzone,ordinarykrig-
lossunderagivenmanagementtothatofbaregroundwith ing is the best unbiased predictor for the random process
down-slopetillage,otherconditionsbeingequal.Thesewere at specific unsampled locations [43]. Ordinary kriging also
determinedinthefield.Intermediatelayersderivedfromsoil has an additional advantage of minimizing the influence of
map(soiltexture)includedsoildetachabilityindex(𝐾)and outliers [44]. The semivariogram analyses were conducted
cohesionoftopsoil(COH)thatweregeneratedusingArcGIS beforetheapplicationofordinarykriginginterpolationofthe
9.3software.AccordingtoMorganetal.[23]andDinka[35], inputparameters.Thisisbecausethesemivariogrammodel
𝐾 is defined as the weight of soil detached from the soil determined the interpolation function [14]. Semivariogram
massperunitofrainfallenergy.Thevaluesofplantandsoil modelswerechosenbyusingthecross-validationtechnique
related hydrological parameters are shown in Tables 1 and thatcomparesstatisticalmeansquareerrorvaluesestimated
2, respectively. Inputs such as plant related (e.g., EHD, 𝐴, fromthesemivariogrammodelsandactualvalues.
CC)andsoilrelated(e.g.,𝐾,COH)parameterswereadopted
fromMorganetal.[23]andDinka[35],inwhichsuchvalues
2.8.MMFModelApplication. TheMMFmodellingprocesses
corresponded to crop type and cover conditions and soil
erosionintwophases,thatis,thewaterandsedimentphases
texturesobservedinthefield.
[23]. The water phase mainly comprises of prediction of
soil detachment by rain splash. It thus requires data related
2.7. Application of Geostatistical Interpolation Technique.
After the point data and their corresponding coordinates to intensity of rainfall (𝐼, mmh−1), number of rainy days
were entered into ArcGIS 9.3 software, maps of hydrophys- (𝑅𝑛),andaverageannualrainfall(𝑅,mm).Afterdeveloping
ical model input parameters were developed using kriging the different input spatial maps (layers), the rate of soil
interpolationtechnique[42].Ordinarykrigingwasselected detachment by rain drop impact (𝐹, kgm−2), rate of soil
asthepreferredinterpolationmethodforMMFmodelspatial detachmentbyrunoff(𝐻,kgm−2),andtransportcapacityof
6 AppliedandEnvironmentalSoilScience
Table1:PlantrelatedhydrologicalparametersfordifferentlanduseandlandcoverintheMai-Neguscatchment.
Landuse EHD(cm) A CC(%) PH(m) GC(—) 𝐸𝑡/𝐸𝑜 𝐶𝑓
Teff 5 0.15 85 0.2 0.85 0.5 0.5
Barley/wheat 12 0.3 60 0.3 0.80 0.58 0.3
Maize 12 0.25 50 1 0.75 0.68 0.25
Pulse 12 0.2 55 0.2 0.80 0.65 0.25
Grazing 12 0.2 80 0.18 0.80 0.8 0.1
Closure(grass/shrubs/bushes) 15 0.4 55 1.5 0.60 0.85 0.1
Mixedforest 20 0.5 65 1.8 0.65 0.9 0.01
Residential 19 0.1 5 0.75 0.55 0.6 0.13
EHD:Effectiverootdepth;A:thepercentageofrainfallcontributingtopermanentinterception;CC:canopycoverfraction;PH:plantheight;GC:ground
cover;𝐸𝑡/𝐸𝑜:theratioofactualtopotentialevapotranspiration;and𝐶𝑓:thecropcovermanagementfactor.
Table2:Soilrelatedhydrologicalparametersvaluesforsoiltexture Input: rainfall, soil, land use, DEM
intheMai-Neguscatchment.
Rainfall energy Surface runoff
K MS BD COH (E) (Q)
Texture −1 −1 −3
(gmJ ) (%ww ) (Mgm ) (KPa)
Sandyloam 0.7 18.6 1.87 2 Rain drop
Surface runoff
Sandyclayloam 0.3 23.7 1.72 3 detachment (F) detachment (H)
Clayloam 0.4 35.38 1.48 10
Sandyclay 0.35 21.21 1.51 1 Total soil Total sediment
Siltclayloam 0.3 28.63 1.31 9 detachment transport rate
rate (F + H = D)
(TC)
K: soil detachability index; MS: soil moisture at field capacity (1/3 bar)
tension;BD:bulkdensitytopsoil;andCOH:cohesionoftopsoil.
Compare
Take the minimum
−2 Soil erosion
overlandflow(runoff)(TC,kgm )arecalculatedintheGIS
environmentasfollows: Figure 4: Flow chart depicting the methodology used for MMF
modelling(source:Morganetal.[23]).
𝐹=10−3∗𝐾∗(𝐸∗𝑒−0.05𝐴),
𝐻=10−3∗(0.5COH)−1(SR)1.5sin(𝑆)(1−GC), (2) thereservoir.Thepercentdifference(𝐷)measurestheaverage
differencebetweenthesimulatedandmeasuredvaluesas
TC=10−3∗𝐶𝑓∗SR2∗sin(𝑆), 𝑝−𝑞
𝐷=( )100,
(3)
𝑞
where 𝐾 is soil detachability index (gJ−1), 𝐸 is annual where𝑝ismodelsimulatedvalueand𝑞ismeasurevalue.“𝐴
kineticenergyofrainfall(Jm−1),𝐴ispercentageofrainfall valuecloseto0%isbestfor𝐷;however,highervaluesof𝐷
contributingtopermanentinterceptionandstreamflow(%),
areacceptableiftheaccuracyinwhichtheobserveddatahave
COH is cohesion of the soil surface (KPa), GC is fraction
gatheredisrelativelypoor”[45].
of ground (vegetation) cover (0-1), 𝐶𝑓 is the crop cover In addition, different studies showed that soil profile
managementfactor,and𝑆isthesteepnessofthegroundslope
datasuchasdegreeoftruncationofthetopsoilhorizonby
expressedindegree.Totalparticledetachment(𝐷=𝐹+𝐻)is
erosion can be used to assess the performance of models
finallycomputedassumofsoilparticledetachmentbyrunoff
(e.g., Tamene [7]; Desmet and Govers [46]; Mitasova et
(𝐻) and soil particle detachment by raindrop (𝐹) impacts.
al. [47]; and Turnage et al. [48]). In this study, the soil
Themodelcomparesthepredictedrateofsplashdetachment
profile data was applied to evaluate the estimated erosion
(𝐷) and the transport capacity for overland flow (TC), and
resultbyMMFmodel.Forthispurpose,areaswithpossible
theminimumvalueistakenastheerosionrate(annualsoil
erosionprocesseswereselected,andsoilprofiledatarelated
loss)estimatedforfthestudycatchment(Figure4).
to the truncation level of the A horizon were documented
in the study catchment. These data were then compared
2.9.ModelEvaluation. Thepercentdifference(𝐷)wasused with the soil loss prediction made by the model. The main
asmethodsforgoodness-of-fitmeasureofmodelprediction. purpose was to evaluate whether the spatial pattern of
The model estimated soil loss rate in this study was eval- erosion predicted by MMF model correlated well with the
uated with respect to the sediment deposited surveyed in depth of soil profile data which semiquantitatively verified
AppliedandEnvironmentalSoilScience 7
the performance of the model. With regard to this, 10 soil The effective hydrological top soil depth (EHD) spa-
profiles (pits) were identified and georeferenced and then tial map (Figure5(e)) indicated higher values (>15cm) in
comparedtothecorrespondingmodelspatialerosionresults. relatively better vegetation-cover areas, high SQ and stable
The MMF model result also compared with the outputs of sites, and flat to gentle slopes. The EHD values were lower
other models which simulated in the study area and other (<15cm) in marginalized areas, cultivated and degraded
highlandsofTigrayregioninnorthernEthiopia. grazinglands.Majority(78%)ofthestudycatchmentshowed
low EHD, indicating that such sites can be the source of
higherrunoffandsoilloss.Similarly,thehighervalues(0.68
3.ResultsandDiscussion to0.90)of𝐸𝑡/𝐸𝑜correspondedtoforestland,whereaslower
values (0.05–0.33) corresponded to agricultural areas. The
3.1.SpatialDistributionofHydrophysicalParametersInfluenc- intermediate 𝐸𝑡/𝐸𝑜 values can correspond to other areas
ing Erosion. Spatial variability in slope, rainfall, vegetation, such as bush land (Figure5(f)). Such values are thus more
soil texture, and land use and cover are among the main influencedbythespatialdistributionofcropcoverandman-
factors which influence the distribution of erosion risk in agementfactor(𝐶𝑓)whichrangesfrom0.2–0.8(Figure5(g)).
a catchment [7]. Since there was only one meteorological Areasofthecatchmentwithhigher𝐶𝑓 areexpectedtohave
station,weatherdatasuchasrainfallwasassumedtobethe lower 𝐸𝑡/𝐸𝑜 values and vice versa [52]. As part of the 𝐶𝑓
samethroughoutthestudycatchment.Thevariabilityinthe factor,vegetationmaintainshighratesofevapotranspiration,
kriging interpolated maps of the other erosion influencing rainfallinterception,andrunoffinfiltration[52,53].
factors in the catchment is shown in Figure5. The slope of The soil moisture storage capacity (𝑅𝑐) calculated as a
thestudycatchment(Figure5(a))showedthatthenorthern function of MS, BD, EHD, and 𝐸𝑡/𝐸𝑜 varied spatially from
and easter∘n part of the catchment had a very steep slope 5 to 79mm (Figure5(h)). Most of the small 𝑅𝑐 values were
(up to 77 ) and slope steepness decreases in the direction locatedonsteepslopeswithshallowsoildepth,poorsurface
to the reservoir (south of the catchment). This indicated cover,andmarginallands.Farmerswhocultivatedtheirland
that sources of hydrological losses as runoff and sediment on steep slopes confirmed that they often face crop failure
yield can be higher on steep areas as compared to flat to due to moisture stress related to low 𝑅𝑐. Generally, low 𝑅𝑐
gentle slopes provided that the other factors are similar. canbeusedasanindicatorofasourceofhigherrunoff.Such
Such influence of slope can be explained by soil texture soilcouldbesusceptibletosoildetachmentbyraindropand
variability;thatis,siltandclaysoilsdominatedtowardsthe runoffimpactsasaresultoflessvegetationcover.Reduction
reservoir and flat areas whereas there was a coarser texture intheEHD(Figure5(e))canleadtolowsoilmoisturestorage
on the steep slopes of the catchment (Figure5(b)). This capacity, 𝑅𝑐 (Figure5(h)), which resulted in higher surface
could be associated with the selective behaviour of erosion runoff(Figure5(i)).
intransportingfineparticles[7,49]. Forestimatingsoildetachmentratebyrunoff(𝐻),under-
Bulk density is higher (up to 1.90Mgm−3) in the steep standing the spatial distribution of slope (𝑆) (Figure5(a)),
slopes with poor vegetation cover and soil management surfacerunoff(SR)(Figure5(i)),cohesionofthesoilsurface
−3
practices and decreased to about 1.10Mgm around the (COH) (Figure5(j)), and fraction of ground cover (GC)
reservoir and valley which are located at the foot-slope of (Figure5(k))areimportantconditions.Thesoildetachability
the catchment (Figure5(c)). From this figure, it is possible byraindropimpactisalsoinfluencedbythesoildetachability
toobservehighererosiononareasdominatedbyhigherbulk index (𝐾) which showed higher values (0.79–0.98gJ−1) on
densityastheyarelocatedonthesteepslopes,lowSQ,eroded flat areas with coarser soils and poor soil cover and lower
−1
sites,andmarginallandsoilsandsoilswithpoorvegetation values(0.11–0.33gJ )onsteepslopewithfinesoilsandgood
cover. An increase in bulk density can negatively affect the vegetation cover in the study catchment (Figure5(l)). The
circulation of air, water, and plant nutrients and their root lower𝐾valuesonsteepslopecouldbeassociatedwithrainfall
system and in-turn raises rate of soil erosion [41, 50]. Such dropimpactanglewhichisfallingtothegroundsurfaceon
higherBDincreasessurfacerunoffwhichisthedrivingforce inclination.
for soil loss by decreasing soil infiltration and soil water
holdingcapacity[51]. 3.2. Estimated Soil Loss Using MMF Model. The spatial dis-
The spatial distribution of soil moisture content at field tributionoftherateofsoildetachmentbyraindrop(𝐹)indi-
capacity (MS) showed lower value in the hilly part of cated that flat areas (south of the catchment) were exposed
catchmentascomparedtothatofflattogentleslopesunder more to 𝐹 as compared to that of hilly land (Figure6(a)).
similarmanagementandcoverconditions(Figure5(d)).This Thiscouldbeattributedtotheperpendicularfallofraindrop
isconsistentwiththereportbyBeheraetal.[52]whoreported energy(strongenergy)onflatareasascomparedtoraindrop
lowervaluesofsoilparameterssuchasMSinthetopsoillayer impact angle on inclination [2, 3]. This implied that steep
ofhillyareas.However,regardlessoftheslopesteepnessthe slope reduces the impact of rainfall drop energy because
highestMSwasfoundinsomesitesofthestudycatchment rain drop met the soil surface on inclination. Despite this,
suchasintheforestandclosedpasturelandswithrelatively the net detached and transported soil is almost the same
higherorganicmatter(datanotshown).ThelowestMSinthe on flat lands, whereas on steep surfaces more soil particles
catchmentwasassociatedwithlowSQ,poorlanduse,andsoil arethrowndownslopethanupslopeduringthedetachment
managementsystems(e.g.,marginalland,overgrazingland, process,resultinginanetmovementofmaterialdownslope
anderodedsiteswithshallowsoildepth). [3]. Generally, the 𝐹 values estimated by the model ranged
8 AppliedandEnvironmentalSoilScience
×102 ×102
15648 N N
15644
15640
15636
15632
15628
15624
15620
15616
15612
15608
461000 462000 463000 464000 465000 460300 461200 462100 463000 463900 464800
Slope (deg) Soil texture
0–4 62–73 Projection: UTM Sandy loam Projection: UTM
5–13 Reservoir Zone: 37N Sandy clay loam Zone: 37N
14–22 Datum: Adindan Clay loam Datum: Adindan
23–31 Spheroid: Clarke 1880 Sandy clay Spheroid: Clarke 1880
32–41 Silt clay loam
42–48 (km) Reservoir (km)
49–55 0 0.4 0.8 1.6 0 0.4 0.8 1.6
56–61
(a) (b)
×102 ×102
N N
15644 15644
15636 15636
15628 15628
15620 15620
15612 15612
460300 461200 462100 463000 463900 464800 460300 461200 462100 463000 463900 464800
Bulk density Moisture at field capacity
1–1.11 Projection: UTM 16–18 Reservoir Projection: UTM
1.12–1.22 Zone: 37N 19–21 Zone: 37N
1.23–1.33 Datum: Adindan 22-23 Datum: Adindan
1.34–1.44 24–26
Spheroid: Clarke 1880 Spheroid: Clarke 1880
1.45–1.56 27-28
1.57–1.67 (km) 29–31 (km)
1.68–1.78 0 0.4 0.8 1.6 32-33 0 0.4 0.8 1.6
1.79–1.89 34–36
1.90-2.00 37-38
Reservoir
(c)
(d)
Figure5:Continued.
AppliedandEnvironmentalSoilScience 9
×102 ×102
N
N
15644 15644
15636 15636
15628 15628
15620 15620
15612 15612
460300 461200 462100 463000 463900 464800 460300 461200 462100 463000 463900 464800
EHD Actual to potential ET
5.00–6.67 Projection: UTM 0.05–0.11 Projection: UTM
6.68–8.33 Zone: 37N 0.12–0.22
Zone: 37N
8.34–10.00 Datum: Adindan 0.23–0.33
10.01–11.67 0.34–0.44 Datum: Adindan
Spheroid: Clarke 1880
11.68–13.33 0.45–0.56 Spheroid: Clarke 1880
13.34–15.00 (km) 0.57–0.67
(km)
15.01–16.67 0 0.4 0.8 1.6 0.68–0.78
0 0.4 0.8 1.6
16.68–18.33 0.79–0.89
18.34–20.00 0.90–0.99
Reservoir Reservoir
(e) (f)
×102 ×102
N N
15644 15644
15636 15636
15628 15628
15620 15620
15612 15612
460300 461200 462100 463000 463900 464800 460300 461200 462100 463000 463900 464800
Cover factor (CF) SMSC
0.1 Projection: UTM 5.00–14.37
Projection: UTM
0.3 Zone: 37N 14.38–23.74
Zone: 37N
0.4 Datum: Adindan 23.75–33.10
Datum: Adindan
0.5 Spheroid: Clarke 1880 33.11–42.47
0.6 42.48–51.84 Spheroid: Clarke 1880
0.7 (km) 51.85–61.21
(km)
0.8 0 0.4 0.8 1.6 61.22–70.57
0 0.4 0.8 1.6
Reservoir 70.58–79.94
Reservoir
(g)
(h)
Figure5:Continued.
10 AppliedandEnvironmentalSoilScience
×102 ×102
N N
15644 15644
15636 15636
15628 15628
15620 15620
15612 15612
460300 461200 462100 463000 463900 464800 460300 461200 462100 463000 463900 464800
MMF, SR COH
Projection: UTM Projection: UTM
5–99 1 7
Zone: 37N Zone: 37N
100–192 2 8
Datum: Adindan Datum: Adindan
193–285 3 9
286–379 Spheroid: Clarke 1880 4 10 Spheroid: Clarke 1880
380–472 (km) 5 Reservoir (km)
0 0.4 0.8 1.6
473–566 0 0.4 0.8 1.6 6
Reservoir
(j)
(i)
×102 ×102
N N
15644 15644
15636 15636
15628 15628
15620 15620
15612 15612
460300 461200 462100 463000 463900 464800 460300 461200 462100 463000 463900 464800
GC
MMF, K Projection: UTM
0.36–0.43 Projection: UTM
0–0.11 0.57–0.67 Zone: 37N
0.44–0.50 Zone: 37N
0.51–0.57 0.12–0.22 0.68–0.78 Datum: Adindan
0.58–0.64 Datum: Adindan 0.23–0.33 0.79–0.89 Spheroid: Clarke 1880
0.65–0.78 Spheroid: Clarke 1880 0.34–0.44 0.90-1 (km)
(km) 0.45–0.56 Reservoir
0.79–0.85 0 0.4 0.8 1.6
0 0.4 0.8 1.6
Reservoir
(k) (l)
Figure5:Spatialdistributionofparametersinfluencingthehydrology/erosionofthecatchment:(a)slope;(b)soiltexture;(c)bulkdensity
(Mgm−3);(d)moistureatfieldcapacity(%);(e)EHD,effectivehydrologicaltopsoildepth(cm);(f)𝐸𝑡/𝐸𝑜,actualtopotentialevaporation;
(g)covermanagementfactor;(h)SMSC,soilmoisturestoragecapacity(mm);(i)SR,surfacerunoff(Q)(mm);(j)COH,cohesionofthesoil
surface(KPa);(k)GC,fractionofgroundcover(0-1);and(l)𝐾,soildetachabilityindex.