Agricultural Water Management

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j our na l h o me p a ge :w w w . e l s e v i e r . c o m / l o c a t e / a g w a t

Impact of rainwater harvesting on water resources of the modder river basin, central region of South Africa

W.A. Welderufael, Y.E. Woyessa

^∗

, D.C. Edossa

SchoolofCivilEngineeringandBuiltEnvironment,CentralUniversityofTechnology,FreeState,PrivateBagX20539,Bloemfontein9300,SouthAfrica^1,2

a r t i c l e i n f o

Articlehistory:

Received2May2012 Accepted27July2012 Available online 18 August 2012

Keywords:

Hydrology Catchment Landuse Waterharvesting

a b s t r a c t

Alongthepathofwaterflowinginariverbasinaremanywater-relatedhumaninterventionsthatmodify thenaturalsystems.Rainwaterharvestingisonesuchinterventionthatinvolvescollectinganduseof surfacerunofffordifferentpurposeintheupstreamcatchment.Increasedwaterconsumptionatupstream levelisanissueofconcernfordownstreamwateravailabilitytosustainecosystemservices.Theupper ModderRiverbasin,locatedinasemiaridregioninthecentralSouthAfrica,isexperiencingintermittent droughtscausingwatershortagesforagriculture,livestockanddomesticuses.Toaddressthisproblem atechniquewasdevelopedforsmallscalefarmerswiththeobjectiveofcollectingandconcentratingof rainwaterforcropproduction.However,thehydrologicalimpactofawideradoptionofthistechniqueby farmershasnotbeenwellquantified.Inthisregard,theSWAThydrologicalmodelwasusedtosimulate potentialhydrologicalimpactofsuchpractices.Thescenariosstudiedwere:(1)baselinescenario,based ontheactuallanduseof2000,whichisdominatedbypasture(combinationofnaturalandsomeimproved grasslands)(PAST);(2)partialconversionofactuallanduse2000(PAST)toconventionalagriculture (Agri-CON);and(3)partialconversionofactuallanduse2000(PAST)toin-fieldrainwaterharvesting whichwasaimedatimprovingtheprecipitationuseefficiency(Agri-IRWH).

SWATwascalibratedusingbothobserveddailyaswellasmonthlystreamflowdataofasub-catchment (419km²)inthestudyarea.SWATperformedwellinsimulatingthestreamflowgivingNashandSutcliffe efficiencyof0.57forthemonthlystreamflowcalibration.Thesimulatedwaterbalanceresultsshowed thatthehighestpeakmeanmonthlydirectflowwasobtainedundertheAgri-CONlanduse(18mm), followedbyPAST(12mm)andAgri-IRWHlanduse(9mm).Thesewere19%,13%and11%ofthemean annualrainfall,respectively.TheAgri-IRWHscenarioreducedtheannualdirectflowby32%compared toAgri-CONwhichissignificantatp<0.02level.OntheotherhanditwasfoundthattheAgri-IRWH contributedtomoregroundwaterrecharge(40mm/year)comparedtoPAST(32mm/year)andAgri-CON (19mm/year)scenarios.Althoughtherewasobservableimpactoftherainwaterharvestingtechniqueon thewateryieldwhenconsideredonamonthlytimeframe,theoverallresultsuggeststhattheannual wateryieldofoneoftheupperModderRiverBasinquaternarycatchmentwillnotbeadverselyaffected bytheAgri-IRWHlandusescenariodespiteitssurfacerunoffcapturedesign.

1. Introduction

In ariver basin, thereare manywater-related humaninter- ventions,suchaswaterstorage,diversion,regulation,distribution, application,pollution,puriﬁcationandotherassociatedactsthat modifythenaturalwatersystems.Thecommoneffectofallofthese isthattheyimpactonthosewholivedownstream(Sunaryo,2001;

Ngigietal.,2006,2008),hencetheneedforaholisticapproachofa riverbasinscaleanalysisandmanagement.Thisapproachshould

∗Correspondingauthor.

E-mailaddress:[email protected](Y.E.Woyessa).

1 Tel.:+27515073241;fax:+27515073254.

2 www.cut.ac.za.

enhancethecommonunderstandingoftheimpactsofthedifferent activitiesontheoverallproductivityofwaterandsustainabilityof naturalresourceuse.

Rainwater harvesting, which involves collection of surface runoffintheupstreamcatchmentandisdesigned forupstream water consumption, may have hydrological impacts on down- streamcatchmentwateravailability(Ngigi,2003;Ngigietal.,2008;

Makuriraetal.,2009).Increasedwaterconsumptionatupstream level is an issue of concernfor downstream water availability, butitisgenerallyassumedthatthereareoverallgainsandsyn- ergiesbymaximizingtheefﬁcientuseofrainwateratfarmlevel (Rockstrometal.,2002;Ngigietal.,2008).However,expansionof rainwaterharvestingpracticescouldhaveunintendedhydrolog- icalconsequencesonriverbasinwaterresourcesandmayhave negativeimplicationsondownstreamwateravailabilitytosustain 0378-3774/$–seefrontmatter© 2012 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.agwat.2012.07.012

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Runoff

Basin

Mulch No-till

1 m 2 m

Runoff

Maize

Fig.1. DiagrammaticrepresentationoftheIRWHtechnique.

Source:AdaptedfromHensleyetal.(2000).

hydro-ecological and ecosystem services (Andersson et al., 2011).

Theexpectedupstreamshiftsinwaterﬂowsmayresultincom- plexandunexpecteddownstreameffectsintermsofquantityand qualityofwater.Ingeneral,though,increasingtheresidencetimeof waterinacatchmentthroughrainwaterharvestingmayhavepos- itiveenvironmentalaswellashydrologicalimplications/impacts downstream(Rockstrometal.,2002).However,itmayalsoresult inuninformeddecisionsbypolicymakers.Forinstance,Rajasthan IrrigationDepartmentinIndiaorderedthedestructionofcommu- nityrainwaterharvestingstructures,fearingthatitwouldthreaten the supply of irrigation water to downstream users (Agarwal etal.,2001).Therefore,there isaneedforfurtherresearchand understandingonthepossibleimpactofwiderexpansionofrain- water harvestingtechnologiesonthewater resource of a river basin.

TheModderRiverbasin,locatedinthesemi-aridregionsofcen- tralSouth Africa, is experiencingintermittent droughts causing watershortagesforagriculture,livestockanddomesticpurposes.

Theirrigatedagricultureinthebasindrawswatermainlybypump- ing out of river, poolsand weirs. However, many of the rural developingfarmersrelyonrain-fedagricultureforcropproduction.

Intherecentpast,theInstituteforSoil,ClimateandWater(ISCW) oftheAgriculturalResearchCouncil(ARC),SouthAfrica,introduced amicro-basintillagetechniquewhichcanbeusedasinsiturain- waterharvestingtechnique.Itisalsoknownasinfieldrainwater harvesting(IRWH).Itwasdevelopedforsmall-scalefarmersinthe basinwiththeobjectiveofcollectingandconcentratingofrain- waterforcropproduction(Hensleyetal.,2000).Itwasfoundthat withtheuseof theIRWHtechnique(Fig.1)thesurfacerun-off wasreducedtominimum.Withthistechnique,evaporationfrom thebasinsoilsurfacewasreducedconsiderablywhenmulchingis usedinthebasin.Thetechniquealsoenhanceshighwaterinfil- trationintothesoilbycapturingsurfacerunoffinthebasinofthe IRWH(Figure1).Thetechniqueshowedasignificantincreasein cropyieldsofmaize,sunflowerandbeans(30–50%)comparedto conventionaltillagepractices atGlen, SouthAfrica(Bothaetal., 2003).Makuriraetal.(2007)alsoreportedmaizeyieldincreaseof upto80%byusingacombinationofrainwaterharvestingandcon- servationagriculturecomparedtotheconventionaltillagepractice inMakanyacatchment,TanzaniawhileNgigietal.(2006)reported beans,wheatandMaizegrainyieldincreaseof30–150%bycon- servation tillagecompared tothe traditional tillage practice in Kenya.

Basedonthespeciﬁcbiophysicalandsocioeconomicrequire- mentsof IRWH,somestudieswerecarried outtoestimatethe suitableareasforIRWH.Forinstance,Woyessaetal.(2006b)esti- mated27%oftheupperModderriverbasinareaassuitablefor IRWHbased on biophysical conditions. MwengeKahinda et al.

(2008a,b)estimated79% ofthebasinassuitablefor IRWHcon- sidering both biophysical and socioeconomics criteria in their assessment.InoneofthequaternarycatchmentsoftheupperMod- derriverbasin (C52A),however, MwengeKahinda etal.(2009) foundonly14%ofthebasinareaassuitableforIRWH.Mwenge Kahindaetal.(2009)alsoconductedastudyonthehydrological impactofIRWHbyconsideringthemonthlymedianflow(wettest seasonflow)ofC52Acatchmentwhen100%oftheestimatedsuit- ableareasareunderIRWH.Theyreportedthatthe100%adoption scenariosignificantlyreducedthehighflowcomparedtotheactual landuseof2000or0%adoption.Theyalsoshowedthat“themost likelyscenario”,whichisabout10%oftheareabeingadoptedfor IRWH,gavenosignificantdifferencecompared tothe0%adoption.Mostrecently,Anderssonetal.(2011)assessedthepotential impactofinsituwater harvestingscenariowithotherlanduse scenarios in theThukelaRiverbasin, South Africa, using SWAT hydrologicalmodelandreportedanonsignificantimpactofthe waterharvestingtechniqueonthestreamflowofthebasin.How- ever,this study isaimed atevaluating theimpacts ofdifferent landusescenariospracticedaroundtheModderRiverbasinespe- cially,onseveralstreamflowcomponentsandwaterbalancesof theC52AquaternarycatchmentsimultaneouslybyapplyingArcGIS andSWAThydrologicalmodel.

Numerousmodellingapproacheshavebeendevelopedtosim- ulatetheimpactsandconsequences oflandusechangesonthe environmentingeneralandwaterresourcesinparticular.Oneof thesemodelsistheSoilandWaterAssessmentTool(SWAT),which wasdevelopedbytheUSDAtosimulatetheimpactsofland-use changesandlandmanagementpracticesonwaterbalanceofcatch- ments,especiallyforungaugedcatchments(Arnoldetal.,1998).

Manyresearchreportshavedemonstratedtherobustnessofthe modelinsimulatingsatisfactorilymostofthewaterbalancecom- ponentsofcatchments(Gassmanetal.,2007;Shimelisetal.,2008;

Ouessaretal.,2009;Anderssonetal.,2011).SWAThasalsoproven tobeaneffectivetoolforunderstandingpollutionsfromfertilizer applicationsandpointsources(Arnoldetal.,1998;Fohreretal., 2005)andforwiderenvironmentalstudies(Gassmanetal.,2007).

Themodelisalsousedasadecisionsupporttoolinlanduseplan- ningbysimulatingtheimpactofdifferentlandusescenarioson

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Fig.2.LocationoftheModderriverbasin(C52)andthestudycatchment(C52A).

waterresources(Fohreretal.,2001;Chanasyketal.,2003;Conan etal.,2003b;Mapfumoetal.,2004;Linetal.,2007;Weietal.,2008;

ChoiandDeal,2008).Similarly,Gargetal.(2011)appliedthecal- ibratedandvalidatedSWAT2005modellingtooltoacommunity watershedatKothapallyinIndiatocomparetheimpactsofvarious soilandwatermanagementinterventionsinthewatershedduring a30-yearsimulationperiod.

Taking into account its wider application in assessing the impacts of land use changes onwater resources,SWAT model

(version 2005) was applied in the Modder river basin of Cen- tralSouthAfricatoevaluatetheimpactoflandusechangeson water resources,withparticularemphasis ontheﬂow ofwater intoRustfonteindam.Themainaimofthisstudywastoassessthe hydrologicalimpactofatillagetechniquewhichisusedasinsitu rainwaterharvestingintheUpperModderRiverBasin(C52A)of centralSouthAfrica.Thisresearchhypothesizesthatexpansionof IRWHintheupstreamofthecatchmentwillhaveimpactsonthe differentcomponentsofcatchmentstreamﬂow.

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Fig.3.Soiltype,landuseandtopographyofthestudysite:soiltype(a),landuse2000(b),slopeclasses(c)andpastureon0–3%slopechangedtoagriculture(d).(Note:all landtypelegendsareasdefinedbySoilClassificationWorkingGroup(1991).)ThelanduseclassesaredefinedinTable1.

2. Materialsandmethods 2.1. Studysite

TheModderRiverbasin isa largebasin withatotal areaof 17,380km².Itisdividedintothreesub-basins,namelytheUpper Modder,theMiddleModderandtheLowerModder.Thestudywas carriedoutintheUpperModderRiverBasinspeciﬁcallyinthequa- ternarycatchment,C52A(Fig.2),whichislocatedbetween26.48^◦ and26.87^◦Eastand29.25^◦and29.62^◦South.TheC52Aquaternary catchmentreceivesmeanannualrainfallof537mm/yearandhas anareaof927.6km².Thedominantsoiltypesofthestudycatch- mentaresandyclayloamandsandyclay.Accordingtolanduse map2000,thedominantlandusetypeinthecatchmentispasture (seeFig.3b).

2.2. Inputdata

SWATmodel needslanduseinputdatain shape ﬁleformat orrasterformatwhichshouldbeprocessedfromsatelliteimages.

Theprocessedshapeﬁledatafortheyear2000wasobtainedfrom theInstituteforSoil,ClimateandWater(ISCW)oftheAgricultural

ResearchCouncil(ARC).OtherinputdataneededbySWATmodel areasfollows:

•Soiltypemapinshapeﬁle.

•Soildatabasewhichincludessoilphysicalandchemicalproper- tiesforthedifferentsoiltypesinthecatchment.

•Climatedata(rainfall,minimumtemperature,maximumtemper- ature,windspeed,relativehumidity,andsunradiationwhichall canbeperday,permonthorperyear).

•LandscapedataintheformofDigitalElevationModel(DEM).

•Basiccropmanagementpractices dataincludingtypeof crop, plantingdate,tillagetypeandfertilizermanagement,etc.

TheDEMfortheModderriverbasin(C52)wasobtainedfrom ISCWataresolutionof90m×90m.Rainfalldataatthreeweather stationsinthestudybasinduring1993–2007wereobtainedfrom SouthAfricanWeatherService(SAWS)(Fig.4)whereastemper- aturedatawiththesamelengthof recordswereobtainedfrom SAWS measured at three nearby stations (Bloemfontein-STAD, Bloemfontein-W.O.andKnellportDam).Otherclimaticdatawere generatedbySWATbyusingWXGEN weathergeneratormodel (SharpleyandWilliams,1990).Statisticalparametersusedinthe

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Fig.4. Studysub-catchmentinC52Aandlocationsofrainandstreamﬂowgauging stations.

weathergeneratingmodulewerecalculatedfrom50yearsclimatic dataofC52Acatchment(1951–1999)obtainedfromSouthAfrican AtlasofAgrohydrologyandClimatologydevelopedbySchulzeetal.

(2001).Rainfallfromthethreestationswasspatiallydistributed tothecatchmentsub-basinsbySWAT.SWATusesskewednormal distributionmethodtocalculaterainfallamountsineachsub-basin.

Thesoilmapofthecatchment(C52A),inprocessedshapeﬁle format,wasobtainedfromISCW.Thesoilofthecatchmentiscov- eredbylandtypeDc17(90.3%)andDb89(8.3%)(Fig.3a).Bothland typesare dominatedbyValsrivier soilforms (Soil Classiﬁcation WorkingGroup,1991).Therefore,thewholeareaofC52Acatch- mentwastakenasValsrivirseriesforthisstudy.

2.3. Modelsetup

The study was carried out using the comprehensive, semi- distributed and physically based hydrological model, Soil and WaterAssessmentTool(SWAT)version2005.SWATwasdevel- opedby theUSDAtosimulatetheimpacts ofland-usechanges andlandmanagementpracticesonwaterbalanceofcatchments (Arnoldetal.,1998).TheSWATmodelisprimarilydevelopedfor ungaugedcatchments(Arnoldetal.,1998).Detailsofmodelfunc- tionanddescriptionweregiveninArnoldetal.(1998).Themodel wassetupusingthefollowinginputdatafromthestudycatchment:

•DEM

•Soilmapinshapeﬁle

•Landusemapof2000inshapeﬁle

•Rainfallandtemperaturedailydata(1993–2007)

Thestudyareawas,then, delineated bySWATmodel based onthe DEMand the geographic coordinates of theﬂow gaug- ingstationattheoutletofthecatchment.AsindicatedinFig.4, there are two gauging stations in the catchment. The gaug- ingstationC5R003measures dischargefromthewholeareaof C52Acatchment(927km²).ThedischargeatC5R003isregulated by Rustfontein dam at the outlet. The second gauging station (C5H056)measuresthedischargefroma sub-catchmentwitha

contributingareaof412km².Thedailystreamﬂowrecordsdur- ingtheyears 2002–2006for thestationC5H056wereobtained fromtheDepartmentofWaterAffairs(DWA)ofSouthAfrica.Dur- ingthedelineationprocess,sixsub-basinswerecreatedwithinthe catchmentC52A.

Inthisstudy,landusedataoftheyear2000wasusedasabench- markagainstwhichtwolandusescenarioswerecompared.The ﬁrstparameterizationwasdonebasedonthelandusedataof2000.

SWATuses27parametersallofwhich,exceptsoilparameters,were derivedinternallybythemodelduringthedatainput,boundary delineation,andsub-basinandhydrologicalunit(HRU)creation processes(Arnoldetal.,1998).Threeslopeclasses(0–3%,3–8%and

>8%)wereusedduringsuper-positioningoflanduse,soilandslope mapstodeﬁnedifferentHRUs.Theslopelayermap,whichwascre- atedbySWAT,waschangedtoshapeﬁleandusedasonecriterion forcreatingthedifferentlandusescenarios.

Maizeagriculture under the conventional tillageand inﬁeld rainwaterharvesting(IRWH)scenarioswerecreatedusing‘edit’

toolofSWATandArcGIS.

Thecurvenumberforantecedentsoilmoistureconditiontwo (CN2)and tillagemanagementweremodiﬁed forAgri-IRWHin ordertosatisfythesurfaceconditioncreatedbyIRWH.Afterparam- etercalibration,theCN2undertheIRWHscenariowasreducedto 35,which istheminimumdefault valueinthemodel,byusing

“edit”menuintheSWAT.Thechangewasmadeinordertoreduce thesurfacerunofftobesimulatedtominimum.CN2isthemost sensitiveparameterwhichinﬂuencesthesurfacerunoffinSWAT model(Anderssonetal.,2011).Undertheconventionalscenario, agenerictillagepracticewasselectedtorunthesimulationwhile undertheIRWHscenarioaconservationtillagepracticewascho- senfromthedatabaseoftillagepracticesinSWATmodel.Weather datafrom1993to2007wasusedformodelsetup.

2.4. Sensitivity,calibrationandvalidation

Sensitivityandcalibrationanalysesforparametersusedinthe modelwerecarried outusingSWATstatistical module.Calibra- tionwascarriedoutonthemostsensitiveinputparametersofthe model(Table3)byusingtheauto-calibrationmoduleofSWAT.The ﬂowdatarecordedatthegaugingstationC5H056onC52Acatch- mentduringtheyear2002wasusedtocalibratethemodel’stop sevensensitiveparameters(Table3).Thiswasconductedinorder tooptimizethevaluesofthoseparametersranked1–7duringsen- sitivityanalysis.ThecalibrationmoduleinSWATcalculatesonly theobjectivefunctiondescribedasNashandSutcliffeefﬁciency.

Theobjectivefunctions,coefﬁcientofdetermination(R²),D-index, residualmeansquareerror(RMSE)werecalculatedaccordingto Willmott(1981).Modelvalidationwasnotperformedduetounre- liableobservedﬂowdatabeyondtheyear2002.

Followingmodelcalibration,anassessmentoflandusechange impactonthewaterbalancesofcatchmentC52Awasundertaken byusingpresentlanduse(landuse2000)andtwolandusescenar- ios.Duringthestreamﬂowsimulationprocess,theﬁrsttwoyears datawereusedtowarmuptheSWATmodel.Oncethemodelwas setupandcalibrated,thewaterbalanceofthecatchmentwassimu- latedwiththeSWATmodelforeachlandusescenario.Simulations wereconductedondailyaswellasonmonthlytimesteps,butthe resultswereinterpretedusingmeanmonthlyvalues.

2.5. Scenariodeﬁnition

Thetwolandusescenariosconsideredwere:(1)conventional landusewhichrepresentsthecurrentlandusepracticeinthearea, and(2)in-ﬁeldrainwaterharvesting,basedontheworkofHensley etal.(2000),whichwasaimedatimprovingtheprecipitationuse

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Table1

ActuallanduseofC52Ain2000andthetwolandusescenarios.

Landusetype Areaandpercentage AreaandpercentageunderAgri-CONor

Agri-IRWH

Area(km²) (%) Area(km²) (%)

Agriculture(AGRR) 72.4 7.8 492.4 53.1

Evergreenforest(FRSE) 2.2 0.2 2.2 0.2

Pasture(PAST) 780.0 84.1 360.0 38.8

Rangeplusbrushland(RNGB) 42.0 4.5 42.0 4.5

Urban(URBN) 6.1 0.7 6.1 0.7

Waterbodies(WATR) 10.5 1.1 10.5 1.1

Wetland(WETN) 14.0 1.5 14.0 1.5

Total 927.2 100.0 927.1 100.0

efﬁciencybyreducingsurfacerunoff. The2000landusedataof C52Ashows that84% ofthelandis coveredby pasture(PAST).

Thiswastakenasabase-casescenarioagainstwhichtheothertwo scenarioswerecompared(Fig.3bandTable1).Tocreatethefirst scenario(Agri-CON),achangewasmadetotheoriginalpasture (PAST)landinsuchawaythattheareacoveredbypastureonslopes of0–3%wasconvertedtoagriculturalland(croppedwithmaize) withconventionaltillagepractices(Fig.3c,dandTable2).Theslope rangeswereselectedinsuchawaythatitsatisfiestheFAOslope classificationstandard(FAO,1990)andthesuitablesloperangefor IRWH(MwengeKahindaetal.,2008a).Thischangebroughtabout aconversionof420km²(54%)ofthepastureareatoagricultural landthusincreasingtheareaoftheagriculturallandfrom8%to 53%anddecreasingthepastureareafrom84%to39%.Thesecond scenario(Agri-IRWH)wasobtainedbychangingthepastureland (PAST)locatedonslopesof0–3%toanagriculturallandplanted withmaizeusinganinfieldrainwaterharvesting(IRWH)(Fig.3c, dandTable1).Inbothscenarios,socio-economicfactorswerenot consideredaspartofarequirementtothelandusechangesmade.

Fig.3c,theslopemapandFig.3b,thechangedlandusemapshows thespatialareawherethechangeofpasturelandtoagricultureis made.

2.6. Statisticalanalysis

Percentagechanges of the differentstreamﬂow components underthevariousscenariosfromthebase-casescenario(PAST)and amongthemselveswerecomputedusingthefollowingformula:

%change= (ScenarioA−ScenarioB) ScenarioB ×100

whereAandBarestreamﬂowdatageneratedunderthedifferent scenarios.

Besides,statisticaltest(F-test)wasconductedtoseewhether therearesigniﬁcantdifferencesamongthescenariosintermsof thegeneratedmeanmonthlystreamﬂowcomponents.

3. Resultsanddiscussion

3.1. Sensitivityanalysisandcalibration

Streamﬂow simulations wereconducted using SWAT model and the parameters were analysed for their sensitivity on the Table2

C52Asloperangesandtheirareacoverage.

Sloperange(%) Area(km²) (%)

0–3 524.1 56.5

3–8 319.0 34.4

>8 84.0 9.1

Total 927.1 100.0

totalstreamflowdischargeusingSWAT’ssensitivityanalysismod- ule.These arerankedand presentedinTable3.Thetopranked parametershasveryhighinfluenceonthestreamflowamountand occurrencespatiallyaswellastemporally.

ResultsofthecalibrationanalysisrevealedanR²(coefficientof determination)of0.68 andaD-index(agreementindex)of0.86 (Table4).Thesystematicandunsystematicrootmeansquareerrors (RMSEsandRMSEu)arealsominimal.Theratiooftheunsystem- atic rootmean square error(RMSEu) tothe root mean square errorprovidedavalueof0.87,indicatinggoodcorrelationbetween theobservedandsimulatedwater yieldand indicatingthatthe erroris notpossibly ofa systematic nature(Welderufaeletal., 2009).TheNashandSutcliffeefficiencyrevealedavalueof0.57 forthemonthlystreamflowcalibration,describingasatisfactory correlationbetweentheobservedandsimulatedmonthlystream discharges.Fig.5showstheplotofobservedandsimulatedstream- flowdata.

Althoughthestatisticalperformancewasfoundtobesatisfac- toryonmonthlyresolution,simulationofthedailystreamﬂowor

Table3

Resultsofsensitiveanalysis.

Parameter Rank

Curvenumberforlanduse(CN2) 1

Soilavailablewatercapacity(SolAWC) 2

Thresholddepthofwaterintheshallowaquiferrequiredfor returnﬂowtooccur(Gwqmn)

3 Soilevaporationcompensationfactor(Esco) 4

Soillayerdepth(SolZ) 5

Groundwater‘revap^a’coefﬁcient(GwRevap) 6 Soilsaturatedhydraulicconductivity(SolK) 7

Averageslopelengthofsubbasin(Slope) 8

Thresholddepthofwaterintheshallowaquiferfor‘revap’tooccur (Revapmn)

9

Surfacelagtime(Surlag) 10

Effectivehydraulicconductivityinmainchannelalluvium(ChK2) 11

Moistsoilalbedo(SolAlb) 12

Averageslopeofsubbasin(Slsubbsn) 13

aRevap:SWATmodelsthemovementofwaterintooverlayingunsaturatedlayers asafunctionofwaterdemandforevapotranspiration.Toavoidconfusionwithsoil evapotranspirationthisprocesshasbeentermed‘revap’.

Table4

Calibrationperformancestatistics.

Indicesfordailystreamﬂow Value

RMSE 0.18

RMSEs 0.09

RMSEu 0.16

R² 0.68

D-index 0.86

RMSEu:RMSE 0.87

NashandSutcliffeefﬁciency,NS^a 0.57

aValueformonthlystreamﬂowcalibration.

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0

10

20

30

40

50

60

70 0.0

0.5 1.0 1.5 2.0 2.5 3.0

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191

Rainfall (mm/day)

Total Stream flow (mm/day)

Day of the year (DoY) during 2002

Rainfall Simulated Observed

Fig.5. Observedandsimulateddailystreamﬂow(Q)aftercalibrationatgaugingstationC5H056.

thewateryieldofthesub-basinusingthecalibratedparameters provideda resultthat failedtocapturesomeof thepeakﬂows (Fig.5).

3.2. Waterbalanceofthecatchment(C52A)

Theimpactsofthedifferentlandusescenarios onthewater balance of the catchment are presented in Figs. 6 and 7 and Tables 5 and 6. The simulated mean monthly water yield (WY=directflow(DIRQ)+groundwaterflow(GWQ)-transmission loss) during the period of 1995–2007 showed non-significant changein peak flow when PASTland on 0–3% slope was con- vertedtoAgri-CONandAgri-IRWHlandusetypes.Thesimulated monthlymeanpeakWYswere20mm/month,18mm/monthand 16mm/month forAgri-CON, Agri-IRWH and PAST,respectively.

ThemeanmonthlyWYundertheAgri-CONlandusescenariowas higherthantheothertwoscenariosduringtherainymonthsof DecembertoMarchonly(Fig.7a).Duringtheremainingmonths, the two land use types (Agri-IRWH and PAST) recharged the groundwaterbetterandhadhigherWYsthantheAgri-CONland usescenario.Agri-IRWHshowedahigherpeakWYvalue(12.5%) thanPASTprobablyduetothehighgroundwaterﬂowcontribution

Table5

Simulatedannualdeepwaterpercolationunderthedifferentlandusescenarios.

Year Precipitation(mm/year) Annualdeeppercolation(mm/year) Agri-IRWH PAST Agri-CON

1995 590.7 0.6 3.3 0.6

1996 755.5 110.3 67.1 45.4

1997 452.8 20.3 22.2 11.6

1998 811.5 78.3 59.0 28.0

1999 433.0 0.0 0.0 0.0

2000 591.3 7.9 14.2 4.3

2001 934.3 122.2 135.3 70.5

2002 531.3 28.3 21.4 12.4

2003 425.6 4.0 11.6 3.1

2004 403.7 0.0 0.0 0.0

2005 541.9 1.3 2.9 1.3

2006 910.8 168.7 174.3 104.4

2007 396.1 0.2 0.2 0.2

Mean 598.4 41.7^a 39.3^a 21.7^b

a,bNumbersfollowedbydifferentlettersaresigniﬁcantlydifferentatP<0.05

bytheIRWHtechniqueduringthesamemonthastheoccurrence ofthepeakﬂow.TheF-testfortwosamplevariancesofthemean monthlyWYsrevealednosigniﬁcantdifferencesamongthethree landusescenarios.

The effect of the different land use scenarios on the water balanceofC52Aiswelldemonstratedbythedirect flow(DIRQ) componentoftheWY.TheDIRQcombinessurfacerunoff(SURQ) andlateralflow(LATQ)components.Thelateralflowortheinter- flowispartofthequickresponseofastreamflowtoarainfallevent thatinfiltratesintothesoilandmakesitswaytothestreamchan- nelthroughthesub-soilaboveaclayeyorsemi-imperviouslayer.

Fig.6apresentsthedirectﬂowcomponentofthethreelandusesce- narios.ThehighestmeanmonthlypeakﬂowofDIRQwasobtained underAgri-CONlanduse,amountingtoabout18mm/monthfol- lowedbyPASTwith12mm/month.Agri-IRWHlandusescenario generatedthelowestDIRQwhichamountedto9mm/month.Sim- ilarly,themeanannualDIRQswere71,52,and45mm/yearunder Agri-CON,PAST,andAgri-IRWHlandusescenarios,respectively.

The F-test for the DIRQ gave a significant difference (P<0.02) betweenAgri-IRWHandAgri-CONlandscenarioswhiletherewas nosignificantdifferencebetweenAgri-IRWHandPASTaswellas betweenPASTandAgri-CON.AlltheDIRQsgeneratedunderthe Agri-IRWHscenariocamefromthelateralflow(LATQ)component of thedirect flow (Table5).The surface runoff(SURQ) compo- nentfromIRWHportionoftheAgri-IRWHscenarioshowsnoor insignificantrunoff duringthewholestudyperiod(1995–2007) (Table5).Ngigietal.(2006)alsoestimatedtheamountofrainfall capturedbyrainwaterharvestingandconservationtillagepractices thatwouldhavebeenchangedtosurfacerunoffbyusingarainfall- runoffmodel as50–85%,75–100%and100%forheavy,medium andlightstorms,respectively,comparedtotraditionaltillagewhich capturedandstored25%,50–60%and75–100%,respectively,from similarstorms.

Generally,theresultsofthesimulationdemonstratedthatthe annual WY didnot show signiﬁcant differenceamong the different landusescenarios, which were89mm/year, 84mm/year and83mm/yearforAgri-CON,PASTandAgri-IRWH,respectively (Fig.7a).MwengeKahindaetal.(2008a)alsoreportedthatthere wasnosigniﬁcantchangeintheoverallWYbytheintroduction ofIRWHinthequaternarycatchmentC52A.Similarly,Ngigietal.

(2008)afterconductingacomprehensivestudyonthehydrologi- calimpactofﬂoodstorageandirrigationwaterabstractioninthe

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0 20 40 60 80 100 120

0 2 4 6 8 10 12 14 16 18 20

Aug Jul Jun May Apr Mar Feb Jan Dec Nov Oct Sep

Rainfall (mm/month)

DIRQ (mm/month)

Month

Agri-IRWH Agri-CON PAST Rainfall

(a)

0 20 40 60 80 100 120

0 2 4 6 8 10 12

Rainfall (mm/month)

GWQ (mm/month)

Month

Agri-IRWH Agri-CON PAST Rainfall

(b)

Fig.6.Simulatedstreamflowcomponents:(a)directflowand(b)baseflowinthequaternarycatchment(C52A)underthreelandusescenarios.

UpperEwasoNg’iroRiverBasin,Kenya,reportedanon-signiﬁcant effectontheamountofﬂowdownstream.

Agri-IRWHtechniquereducedthedirectﬂowby37%andthe surfacerunoffcomponentbyalmost100%comparedtotheAgri- CON land use scenario. It also showed a 10% increase of the LATQ compared toAgri-CON. This obviously improves the soil wateravailabilitywithinthecroprootzone.Rain-fedagriculture usingAgri-IRWHtechniqueinthisareahasbeenreportedtohave increasedproductionofmaizeandsunﬂowerbyabout50%com- paredtoAgri-CONproduction(Hensleyetal.,2000;Bothaetal., 2003,2007).Woyessaetal.(2006b)havealsodemonstratedthat

IRWHimprovedbothcropproductionandmonetaryincomeofa farmermorethantheconventionallandpreparationmethodthat usessupplementalirrigationsystembyharvestingthedirectrunoff insmalldamsorponds.

Theotherinterestingresultontheimpactoflandusechange wasrelatedtothegroundwaterflow (baseflow) componentof the WY. Fig. 6b presents the groundwater flow component of thestreamflow.Agri-IRWH,duetoitssurfacerunoffharnessing design,collectstherunoffgeneratedfromthetwo-meterstripand storesitintheonemeterwidebasin.Bydoingsoitallowsmore watertoinfiltrateintothesoilandpercolateasignificantamount

Table6

Componentsofthedirectﬂowunderthethreelandusesscenariosinmm/year.

Year PREC PAST Agri-CON Agri-IRWH

SURQ LATQ DIRQ SURQ LATQ DIRQ SURQ LATQ DIRQ

1995 590.7 10.6 18.2 28.8 16.5 30.2 46.7 0.0 31.1 31.1

1996 755.5 61.2 27.5 88.6 85.9 42.5 128.4 0.0 51.4 51.4

1997 452.8 8.1 16.0 24.1 12.2 25.6 37.7 0.0 26.6 26.6

1998 811.5 75.1 29.0 104.1 86.5 45.2 131.8 0.0 54.2 54.2

1999 433.0 1.5 12.8 14.3 3.3 22.3 25.6 0.0 22.4 22.4

2000 591.3 6.4 20.4 26.8 10.9 31.4 42.4 0.0 32.1 32.1

2001 934.3 118.8 38.0 156.9 98.3 54.9 153.2 3.9 66.9 70.8

2002 531.3 14.4 18.7 33.1 26.5 29.9 56.2 0.0 32.2 32.2

2003 425.6 19.2 13.5 32.7 23.4 22.3 45.7 0.0 24.1 24.1

2004 403.7 0.1 12.0 12.1 0.7 19.6 20.4 0.0 19.7 19.7

2005 541.9 0.8 16.3 17.1 1.7 25.2 26.9 0.0 25.2 25.2

2006 910.8 104.8 42.4 147.1 112.3 58.2 170.5 0.0 70.4 70.4

2007 396.1 4.1 11.4 15.6 7.0 18.5 25.5 0.0 18.8 18.8

Mean 598.3 32.7 21.2 53.9 37.3 32.8 70.1 0.3 36.5 36.8

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0 20 40 60 80 100 120

0 5 10 15 20 25

Rainfall (mm/month)

WY (mm/month)

Month

Rainfall Agri-IRWH Agri-CON PAST

(a)

0 20 40 60 80 100 120

0 10 20 30 40 50 60 70 80

Rainfall (mm/month)

ET (mm/month)

Month

Rainfall Agri-IRWH Agri-CON PAST

(b)

Fig.7. Simulatedwateryield(a)andevapotranspiration(b)inthequaternarycatchment(C52A)underthreelandusescenarios.

furtherdeepintothegroundwatertablethantheAgri-CONland usescenario(Table6).Improvedsoilinfiltrationisalsoreportedby Makuriraetal.(2009)underrainwaterharvestingtillagetechnique knownbyfanyajuusinTanzania,Makanyacatchment.Vohlandand Burry(2009)reportedthatinfieldrainwaterharvestingenhances infiltrationandgroundwaterrecharge.

Thus,theAgri-IRWHwasfoundtorechargethegroundwater tablesignificantly(P<0.03)morethantheAgri-CONscenario.The buildupofthewatertableundertheAgri-IRWHwillinturncon- tributetotherechargeoftheC52Astreamasabaseflow.Thus,the highestmeanmonthlypeakgroundwaterflowwasproducedby Agri-IRWHamountingto10mm/month,followedby7mm/month and4mm/monthbyPASTandAgri-CONlandusescenarios,respectively.Thestatisticaltestalsoshowedthatthereishighsignificant difference(P<0.01) betweenAgri-IRWH and Agri-CON in their monthlymeanGWQ.Incaseoftheannualgroundwaterflow,the resultsofthescenarioswereinreversesequencecomparedtothe directflow. Thehighestannualgroundwaterflowwasobtained fromAgri-IRWHwhichwas37mm/year,followedby32mm/year underPASTand18mm/yearunderAgri-CONlandusescenarios.

The base flow showed an increase of about 105% under Agri- IRWHcomparedtoAgri–CONlandusescenario.TheF-testforthe meanannualdeeppercolation(1995–2007)alsorevealedasignifi- cantdifference(P<0.03)betweenAgri-IRWHandAgri-CON.There wasalsoasignificantdifference(P<0.04)betweenPASTandAgri- CONintermsofannualdeeppercolation.However,therewasno significantdifferencebetweenAgri-IRWHandPAST.Theresults demonstratethattherewashigherinfiltrationofwaterunderAgri- IRWHandPASTthanundertheAgri-CONlandusescenario.The Agri-IRWHtechniquecreatesapond ofwaterinsidethefurrow thatlaterinfiltratesintothesoilprofile.Moreover,Agri-IRWHand PASTscenarioswerefoundtoincreasetheresidencetimeofrunoff

flow in acatchment whichin turnhad aneffect ontheoccur- renceofthemonthlyWYpeakflows.Thus,theincreaseddryseason WYunderAgri-IRWHmayhavepositiveenvironmentalaswellas hydrologicalimplications/impactsdownstreambyprovidingmore streamflow duringthedryseason.Thiscontributes tomaintain theenvironmentalflowinthestreamaswellasprovideswater downstreamduringthedryseason.

Withregardtothesimulatedevapotranspiration(ET),therewas nosigniﬁcantdifferenceinthetotalannualamount,buttherewas amarkeddifferencebetweenthemonthlyETdistributionofgrass andmaizecrops(Fig.7b).TheETsfromAgri-CONandAgri-IRWH landusesfollowedthesamepatternduetothefactthatthesame typeofcrop(maize)wasconsideredinbothcases.

4. Conclusions

TheSWAThydrologicalmodelwasusedtoanalysetwolanduse scenariosincomparisontothe2000baselinelandusetype.The modelwasabletoillustratethepotentialimpactofdifferentland usetypesonthewaterresourcesofquaternarycatchmentC52A.

Theresultsofthescenarioanalysisrevealedthatconventionalagri- culturallandusetypegeneratedthehighestdirectflowcompared totheonesdominatedbypastureorIRWHlandusetypes.Thecon- ventionalagriculturemaynotsupportfavourablecropproduction onrain-fedsemi-aridareas,suchastheModderriverbasin,dueto thedecreasedinfiltrationofwatertothesub-soilwhichultimately influencesthesoilwatercontentwithintherootzone.

Theresultsalsoimpliedthattherewasimprovementofwater infiltrationintothesoilbyAgri-IRWHlanduse.Bothresultedin higherbaseflowthanAgri-CONlandusetypeanddemonstrated increaseddeepwaterpercolationwithasignificantdifferencein

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annualamountscompared toAgri-CON.TheAgri-IRWHshowed 105%higherbaseﬂowcomparedtotheAgri-CONlandusescenario.

Overall,theresultssuggest thattheWYof C52Awillnotbe adverselyaffectedbytheAgri-IRWHlandusescenariodespiteits designforsurfacerunoffabstraction.Itisexpectedthatthisresult willassistintakingaproactivemeasureregardingwaterresources managementingeneralandastrategicallocationanduseofwater inparticular.

However,stillthereremainssomeuncertaintiesinsimulating thelateralandgroundwaterﬂowcomponentsofthewateryield duetothelimiteddatain thesub-soil physicalpropertiessuch assoiltexture,soilhydraulicconductivity,andsoilwaterholding capacity,whichhavemajorinﬂuencesonthewateryieldcompo- nents.

Acknowledgements

Theauthorswouldliketoacknowledgetheﬁnancialsupport receivedfromtheWaterResearchCommissionandtheNational ResearchFoundationofSouthAfrica.

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