ARTICLE

International Journal of Water Sciences

Assessing the Potential Impacts of Four Climate Change Scenarios on the Discharge of the , Using the SWAT Model

Research Article

Alain Lubini1 and Jan Adamowski2,*

1 Department of Civil Engineering, Ecole Polytechnique de Montréal, Canada 2 Department of Bioresource Engineering, McGill University, Ste. Anne de Bellevue, Quebec, Canada * Corresponding author E-mail: [email protected] ȱ Received 13 Feb 2013; Accepted 26 Mar 2013

DOI: 10.5772/56453

© 2013 Lubini and Adamowski; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

AbstractȱTheȱSoilȱandȱWaterȱAssessmentȱToolȱ(SWAT)ȱ dailyȱbaselineȱtemperatureȱandȱprecipitation.ȱUnderȱallȱ wasȱ usedȱ toȱ exploreȱ theȱ potentialȱ impactȱ ofȱ fourȱ scenarios,ȱ Simiyuȱ Riverȱ dischargeȱ increasedȱ (24Ȭ45%),ȱ climateȱchangeȱscenariosȱonȱdischargeȱfromȱtheȱSimiyuȱ showingȱ theȱ highestȱ increaseȱ inȱ theȱ rainyȱ seasonȱ Riverȱ inȱ Tanzania,ȱ locatedȱ inȱ theȱ Lakeȱ Victoriaȱ (Marchȱ toȱ May),ȱ withȱ theȱ greatestȱ increaseȱ occurringȱ watershedȱ inȱ Africa.ȱ Theȱ SWATȱ modelȱ usedȱ inȱ thisȱ duringȱ theȱ rainyȱ seasonȱ (Marchȱ toȱ May).ȱ Dischargeȱ studyȱwasȱcalibratedȱandȱverifiedȱbyȱcomparingȱmodelȱ wasȱ influencedȱ toȱ aȱ muchȱ greaterȱ degreeȱ byȱ increasesȱ outputȱwithȱhistoricȱstreamȱflowȱdataȱforȱ1973Ȭ1976ȱasȱ inȱ precipitationȱ ratherȱ thanȱ byȱ temperature.ȱ Theȱ wellȱ asȱ 1970Ȭ1971.ȱ SWATȱ wasȱ operatedȱ atȱ dailyȱ andȱ increaseȱinȱriverȱflowȱpredictedȱbyȱtheȱmodelȱsuggestsȱ monthlyȱ timeȱ stepsȱ duringȱ bothȱ calibrationȱ andȱ thatȱ theȱ potentialȱ increaseȱ inȱ heavyȱ floodȱ damageȱ verification.ȱ Forȱ theȱ dailyȬtimeȱ stepȱ verification,ȱ theȱ duringȱtheȱ rainyȱ seasonȱ willȱ increase,ȱ whichȱ could,ȱ inȱ modelȱ hadȱ aȱ Nashȱ Sutcliffeȱ coefficientȱ ofȱ efficiencyȱ turn,ȱhaveȱsignificantȱadverseȱeffectsȱonȱinfrastructure,ȱ (NSE)ȱofȱ0.52ȱandȱaȱcorrelationȱcoefficientȱ(R2)ȱofȱ0.72.ȱ humanȱ health,ȱandȱtheȱenvironmentȱinȱtheȱwatershed.ȱ Forȱ theȱ monthlyȱ timeȬstepȱ verification,ȱ theȱ recordedȱ Theȱ SWATȱ predictionsȱ provideȱ anȱ importantȱ insightȱ NSEȱ andȱ R2ȱ valuesȱ wereȱ 0.66ȱ andȱ 0.70.ȱ Inȱ developingȱ intoȱtheȱmagnitudeȱofȱstreamȱflowȱchangesȱthatȱmightȱ climateȱ changeȱ scenariosȱ withinȱ theȱ generalȱ patternsȱ occurȱ inȱ theȱ Simiyuȱ Riverȱ inȱ Tanzaniaȱ asȱ aȱ resultȱ ofȱ definedȱ byȱ theȱ Intergovernmentalȱ Panelȱ onȱ Climateȱ futureȱclimaticȱchange.ȱ Change,ȱ predictedȱ increasesȱ inȱ CO2ȱ concentrationsȱ ȱ wereȱ implementedȱ withinȱ theȱ constraintsȱ ofȱ theȱ Keywordsȱ Climateȱ Change,ȱ Modelling,ȱ Riverȱ Flow,ȱ model’sȱ parameterisationȱ byȱ raising,ȱ inȱ aȱ seasonallyȬ SWAT,ȱSimiyuȱRiver,ȱAfricaȱ specificȱ manner,ȱ theȱ valuesȱ ofȱ twoȱ proxyȱ parameters:ȱ ȱ www.intechopen.com Alain Lubini and Jan Adamowski: Assessing the PotentialInt. j. Impactswater sci., of Four2013, Climate Vol. 2, Change 1:2013 1 Scenarios on the Discharge of the Simiyu River, Tanzania Using the SWAT Model 1.ȱIntroductionȱ creatingȱ regionalȱ climateȱ changeȱ scenariosȱ combiningȱ GCMȱ resultsȱ withȱ theȱ newlyȱ publishedȱ IPCCȱ IS92ȱ Fromȱ politicalȱ andȱ socialȱ scientistsȱ toȱ engineersȱ andȱ emissionȱ scenariosȱ andȱ demonstratedȱ theȱ applicationȱ ofȱ hydrologists,ȱ theȱ impactȱ ofȱ globalȱ warmingȱ hasȱ theȱmethodȱinȱAfrica.ȱInȱHulme’sȱstudy,ȱchangesȱinȱmeanȱ demandedȱtheȱattentionȱofȱresearchȱcommunitiesȱaroundȱ annualȱ temperatureȱ andȱ precipitationȱ underȱ anȱ IS92aȱ theȱworldȱasȱwellȱasȱofȱtheȱgeneralȱpublic.ȱClimateȱchangeȱ emissionȱscenarioȱwereȱpredictedȱforȱtheȱperiodȱofȱ1990ȱtoȱ isȱcharacterizedȱnotȱonlyȱbyȱrisesȱinȱsurfaceȱtemperaturesȱ 2050.ȱ andȱ seaȬlevels,ȱ butȱ alsoȱ changesȱ inȱ precipitationȱ andȱ ȱ decreasesȱinȱsnowȱcoverȱ(IPCC,ȱ2007).ȱMoreover,ȱclimaticȱ Hulmeȱetȱal.ȱ(1996)ȱtookȱaȱmoreȱselectiveȱapproachȱtoȱtheȱ shifts,ȱasȱaȱresultȱofȱglobalȱwarming,ȱhaveȱbeenȱlinkedȱtoȱ useȱ ofȱ GCMȱ experimentsȱ inȱ describingȱ theȱ 2050ȱ humanȱ healthȱ problems,ȱ waterȱ supplyȱ shortages,ȱ andȱ consequencesȱofȱthreeȱfutureȱclimateȱchangeȱscenariosȱforȱ biodiversityȱandȱecosystemȱdamage.ȱTheseȱclimaticȱshiftsȱ theȱ Southernȱ Africanȱ Developmentȱ Communityȱ (SADC)ȱ haveȱ alreadyȱ createdȱ problemsȱ relatedȱ toȱ humanȱ health,ȱ regionȱ ofȱ southernȱ Africa.ȱ Threeȱ differentȱ GCMȱ waterȱ supplyȱ shortages,ȱ andȱ lossȱ ofȱ biodiversityȱ andȱ experiments,ȱ selectedȱ toȱ spanȱ theȱ rangeȱ ofȱ precipitationȱ ecosystemȱintegrity.ȱManyȱstudiesȱconcludeȱthatȱAfricaȱisȱ changesȱ predictedȱ byȱ theȱ GCMȱ forȱ theȱ SADCȱ region,ȱ notȱonlyȱtheȱcontinentȱthatȱisȱtheȱmostȱvulnerableȱtoȱtheȱ allowedȱforȱanȱassessmentȱofȱsomeȱpotentialȱimpactsȱandȱ impactsȱ ofȱ globalȱ warming,ȱ butȱ alsoȱ theȱ oneȱ thatȱ isȱ theȱ implicationsȱofȱclimateȱchangeȱonȱagriculture,ȱhydrology,ȱ leastȱ equippedȱ (financiallyȱ andȱ managerially)ȱ toȱ handleȱ health,ȱ biodiversity,ȱ wildlifeȱ andȱ rangelands.ȱ However,ȱ themȱ (Perlis,ȱ 2009).ȱ Theȱ majorityȱ ofȱ Africa’sȱ populationȱ considerableȱ uncertaintyȱ remainsȱ regardingȱ theȱ largeȬ reliesȱ toȱ aȱ largeȱ extentȱ onȱ naturalȱ resources.ȱ Thisȱ scaleȱ precipitationȱ changesȱ simulatedȱ byȱ GCMsȱ forȱ dependency,ȱcoupledȱwithȱfragileȱgovernanceȱcapacities,ȱ Africa.ȱ Basedȱ onȱ suchȱ models,ȱ Joubertȱ andȱ Hewitsonȱ couldȱ resultȱ inȱ severeȱ problemsȱ inȱ addressingȱ theȱ (1997)ȱ neverthelessȱ concludedȱ that,ȱ inȱ general,ȱ challengesȱ ofȱ climateȱ changeȱ (IISD,ȱ 2009).ȱ Thisȱ willȱ precipitationȱ wouldȱ increaseȱ overȱ muchȱ ofȱ theȱ Africanȱ necessitateȱ effectiveȱ andȱ sustainableȱ waterȱ resourcesȱ continentȱ byȱ theȱ yearȱ 2050.ȱ Asȱ aȱ specificȱ example,ȱ theirȱ managementȱ andȱ adaptationȱ strategiesȱ inȱ theȱ short,ȱ modelȱ predictedȱ thatȱ byȱ 2050ȱ partsȱ ofȱ theȱ Sahelȱ mightȱ medium,ȱandȱlongȬterm.ȱȱȱ expectȱ precipitationȱ ratesȱ asȱ muchȱ asȱ 15%ȱ overȱ 1961Ȭ90ȱ ȱ means.ȱ Inȱ lightȱ ofȱ water’sȱ importanceȱ inȱ agriculture,ȱ fisheries,ȱ ȱ andȱmanufacturing,ȱitȱisȱimportantȱtoȱassessȱtheȱpotentialȱ Moreȱ recently,ȱ Jungȱ etȱ al.ȱ (2007)ȱ usedȱ theȱ mesoȬscaleȱ impactsȱofȱclimateȱchangeȱonȱtheȱhydrologicalȱprocessesȱ meteorologicalȱ modelȱ MM5ȱ andȱ theȱ hydrologicalȱ modelȱ inȱaȱwatershed.ȱTherefore,ȱtheȱgoalȱofȱthisȱresearchȱwasȱtoȱ WaSimȱ toȱ performȱ aȱ jointȱ regionalȱ climateȬhydrologyȱ useȱ theȱ Soilȱ andȱ Waterȱ Assessmentȱ Toolȱ (SWAT)ȱ toȱ simulationȱ toȱ estimateȱ theȱ effectsȱ ofȱ anthropogenicȱ measureȱ theȱ likelyȱ impactsȱ ofȱ fourȱ climateȱ changeȱ influencesȱonȱtheȱwaterȱbalanceȱinȱtheȱVoltaȱBasin,ȱlocatedȱ scenariosȱ onȱ theȱ dischargeȱ inȱ Tanzania’sȱ Simiyuȱ River,ȱ inȱ Westȱ Africa.ȱ Theirȱ resultsȱ indicatedȱ aȱ decreaseȱ inȱ locatedȱ inȱ theȱ Lakeȱ Victoriaȱ watershedȱ inȱ Africa.ȱ Lakeȱ rainfallȱatȱtheȱbeginningȱofȱtheȱrainyȱseason,ȱanȱincreaseȱ Victoriaȱ isȱ notȱ onlyȱ theȱ largestȱ freshwaterȱ lakeȱ onȱ theȱ inȱ rainfallȱ atȱ theȱ heightȱ ofȱ theȱ rainyȱ season,ȱ andȱ aȱ clearȱ continent,ȱbutȱalsoȱoneȱofȱtheȱmajorȱsubȬbasinsȱwithinȱtheȱ increaseȱinȱtemperature.ȱ Nileȱbasin.ȱTheȱSimiyuȱRiverȱwasȱselectedȱbecauseȱofȱitsȱ ȱ economic,ȱsocial,ȱenvironmental,ȱandȱpoliticalȱimportanceȱ Lumsdenȱetȱal.ȱ(2009)ȱassessedȱpresent,ȱintermediate,ȱandȱ inȱ easternȱ Africaȱ andȱ becauseȱ fewȱ studiesȱ haveȱ beenȱ futureȱ climateȱ scenariosȱ forȱ Southernȱ Africaȱ toȱ evaluateȱ conductedȱ inȱ thisȱ areaȱ onȱ thisȱ topic.ȱȱInȱ thisȱ study,ȱ theȱ potentialȱ changesȱ inȱ hydrologicallyȱ relevantȱ statisticsȱ ofȱ SWATȱ modelȱ wasȱ usedȱ toȱ evaluateȱ fluctuationsȱ inȱ rainfallȱthatȱcouldȱbeȱobservedȱthisȱcenturyȱasȱaȱresultȱofȱ seasonalȱandȱannualȱdischargesȱinȱresponseȱtoȱfourȱfutureȱ climateȱ change.ȱ Theȱ scenariosȱ thatȱ wereȱ accountedȱ forȱ wereȱ developedȱ byȱ aȱ downscalingȱ techniqueȱ simulatedȱ climateȱ scenariosȱ inȱ theȱ Simiyuȱ River.ȱ Theȱ modelȱ wasȱ byȱ GCMs.ȱ Accordingȱ toȱ theirȱ study,ȱ moreȱ rainfallȱ isȱ calibratedȱ andȱ verifiedȱ usingȱ historicalȱ climateȱ andȱ projectedȱ inȱ theȱ eastȱ ofȱ theȱ region,ȱ inȱ theȱ formȱ ofȱ moreȱ streamȱflowȱdataȱinȱtheȱSimiyuȱRiver.ȱ rainȱdaysȱandȱmoreȱdaysȱwithȱgreaterȱintensityȱrainfalls.ȱ 1.1ȱPreviousȱstudiesȱofȱclimateȱchangeȱimpactsȱinȱAfricanȱ Atȱ theȱ sameȱ time,ȱ theȱ resultsȱ indicateȱ lessȱ projectedȱ watershedsȱ rainfallȱ alongȱ theȱ westȱ coastȱ andȱ theȱ adjacentȱ interior,ȱ withȱ theȱ possibilityȱ ofȱ aȱ slightȱ increaseȱ inȱ interȬannualȱ ApartȱfromȱTyson’sȱ(1991)ȱearlyȱresearch,ȱrelativelyȱlittleȱ variability.ȱ workȱ hasȱ emergedȱ onȱ futureȱ climateȱ changeȱ scenariosȱ ȱ focusedȱ onȱ Africa.ȱ Tyson’sȱ climateȱ changeȱ scenariosȱ forȱ UsingȱtheȱoutputȱdataȱofȱGCMsȱinȱorderȱtoȱaddressȱtheȱ southernȱ Africaȱ wereȱ constructedȱ usingȱ resultsȱ fromȱ theȱ impactsȱofȱclimateȱchangeȱinȱWestȱAfricaȱwasȱstudiedȱbyȱ firstȱ generationȱ ofȱ GCMȱ (Generalȱ Circulationȱ Model)ȱ ArdoinȬBardinȱetȱal.ȱ(2009).ȱUsingȱfourȱdifferentȱmodels,ȱ equilibriumȱ 2uCO2ȱ experiments.ȱ Inȱ aȱ furtherȱ theyȱ wereȱ unableȱ toȱ reproduceȱ rainfallȱ volumesȱ inȱ theȱ development,ȱ Hulmeȱ (1994)ȱ presentedȱ aȱ methodȱ forȱ Sahelianȱzone,ȱandȱtheyȱhadȱdifficultyȱinȱsimulatingȱtheȱ

2 Int. j. water sci., 2013, Vol. 2, 1:2013 www.intechopen.com seasonalȱ dynamicsȱ ofȱ rainfallȱ inȱ theȱ Guineanȱ zone.ȱ InȱotherȱwatershedsȱinȱAfrica,ȱGithuiȱetȱal.ȱ(2007)ȱusedȱtheȱ Runningȱ theȱ HadCM3ȱ(Hadleyȱ Centreȱ CoupledȱModel,ȱ SWATȱmodelȱtoȱinvestigateȱtheȱimpactȱofȱclimateȱchangeȱ versionȱ3)ȱmodelȱwithȱnewȱscenariosȱforȱfutureȱclimateȱ onȱtheȱrunoffȱofȱtheȱNzoiaȱRiverȱwatershedȱinȱKenya.ȱTheȱ conditions,ȱtheyȱcameȱtoȱtheȱconclusionȱthatȱtheȱpossibleȱ resultsȱ showedȱ anȱ acceptableȱ matchȱ betweenȱ theȱ futureȱ changesȱ inȱ runoffȱ areȱ highlyȱ dependentȱ onȱ measuredȱandȱtheȱpredictedȱdischargeȱ(R²ȱ>ȱ0.7),ȱwithȱanȱ rainfallȱ and,ȱ hence,ȱ onȱ theȱ qualityȱ ofȱ theȱ outputȱ ofȱ aȱ increaseȱinȱrainfall,ȱtemperatureȱandȱsurfaceȱrunoffȱforȱallȱ givenȱGCM.ȱ theȱ consideredȱ scenarios.ȱ Theȱ studyȱ suggestedȱ thatȱ theȱ ȱ 2020sȱ areȱ likelyȱ toȱ experienceȱ moreȱ floodingȱ eventsȱ asȱ aȱ Milzowȱetȱal.ȱ(2011)ȱexploredȱtheȱuseȱofȱcombinedȱsatelliteȱ resultȱofȱincreasedȱrainfallȱthanȱinȱtheȱ2050s.ȱ radarȱaltimetry,ȱsurfaceȱsoilȱmoistureȱestimatesȱ(SSM)ȱandȱ ȱ GravityȱRecoveryȱandȱClimateȱExperimentȱ(GRACE)ȱtotalȱ Doktorgradesȱ(2009)ȱstudiedȱtheȱimpactȱofȱclimateȱchangeȱ storageȱ changeȱ datasetsȱ forȱ hydrologicalȱ modelȱ inȱtheȱsubȬsurfaceȱhydrologyȱofȱtheȱVoltaȱBasinȱusingȱtheȱ calibrationȱ usingȱ aȱ SWATȱ modelȱ inȱ theȱ poorlyȱ gaugedȱ MM5ȱ mesoscaleȱ climateȱ modelȱ andȱ theȱ hydrologicalȱ Okavangoȱ watershedȱ inȱ Southernȱ Africa.ȱ Theȱ studyȱ WaSiMȱ (Waterȱ Flowȱ andȱ Balanceȱ Simulationȱ Model)ȱ foundȱ thatȱ theȱ useȱ ofȱ theȱ combinationȱ ofȱ theȱ threeȱ dataȱ modelȱ usingȱ theȱ IPCCȱ 2001ȱ climatologicalȱ data.ȱ Inȱ hisȱ setsȱ improvedȱ theȱ parametrizationȱ ofȱ theȱ model.ȱ Theȱ detailedȱ study,ȱ Doktorgradesȱfoundȱ thatȱtheȱ 2007ȱ reportȱ modelȱshowedȱpoorȱperformanceȱatȱaȱdailyȱtimeȱstepȱdueȱ ofȱ theȱ IPCCȱ lackedȱ newȱ informationȱ onȱ Africanȱ climateȱ toȱ absenceȱ ofȱ inȬsituȱ precipitationȱ measurementsȱ andȱ changeȱ(butȱhighlightedȱmodelȱuncertainties,ȱparticularlyȱ largeȱ variationȱ inȱ theȱ threeȱ precipitationȱ datasets,ȱ butȱ overȱtropicalȱAfrica).ȱHeȱalsoȱfoundȱthatȱtheȱinconsistencyȱ performedȱwellȱforȱlongȬtermȱscenarios.ȱ ofȱdifferentȱmodelȱprojectionsȱreflectedȱinȱtheȱlowȱvaluesȱ ȱ ofȱ theȱ regionalȱ climateȱ changeȱ indexȱ inȱ Giorgiȱ (2006),ȱ Inȱtheȱpastȱdecade,ȱstudiesȱcarriedȱoutȱinȱtheȱSimiyuȱRiverȱ whichȱ reliesȱ onȱ regionalȱ temperatureȱ andȱ precipitationȱ watershedȱ(theȱstudyȱsiteȱofȱtheȱresearchȱdescribedȱinȱthisȱ changesȱ inȱ theȱ IPCCȱ multiȬmodelȱ ensembleȱ framework.ȱ paper)ȱofȱtheȱLakeȱVictoriaȱwatershedȱofȱTanzania,ȱwereȱ Thisȱ modelȱ discrepancyȱ cloudsȱ theȱ interpretationȱ ofȱ theȱ mainlyȱ aimedȱ atȱ forecastingȱ streamȱ flow.ȱ Aȱ Geographicȱ resultsȱofȱuncertainȱmodelȱparameters,ȱwhichȱmayȱimpactȱ Informationȱ Systemȱ (GIS)ȱ basedȱ distributedȱ modelȱ specificallyȱonȱtheȱsimulationȱofȱAfricanȱclimate.ȱInȱlightȱ developedȱ byȱ combiningȱ aȱ gridȬbasedȱ waterȱ balanceȱ ofȱallȱofȱthis,ȱIPCCȱ2001ȱdataȱwasȱusedȱinȱDoktorgrades’ȱ modelȱ andȱ aȱ flowȱ accumulation/routingȱ model,ȱ andȱ studyȱtoȱavoidȱmodelȱdiscrepancyȱandȱuncertaintyȱinȱtheȱ operatingȱ onȱ aȱ monthlyȱ timeȱ stepȱ atȱ aȱ spatialȱ gridȱ cellȱ interpretationȱ ofȱ theȱ modelȱ parametersȱ forȱ Africanȱ resolutionȱofȱ0°10’,ȱperformedȱreasonablyȱwellȱ(rȱ3%ȱerrorȱ climateȱ simulations.ȱ Forȱ theȱ sameȱ reasonȱ asȱ theȱ onȱanȱannualȱbasis)ȱinȱpredictingȱstreamȱflowȱinȱthisȱriverȱ Doktorgradesȱ(2009)ȱstudy,ȱourȱstudyȱalsoȱusesȱtheȱIPCCȱ watershedȱ(Moges,ȱ1998),ȱbutȱfaredȱpoorlyȱinȱsimulatingȱ 2001ȱclimateȱdataȱforȱsimulationsȱforȱstudyingȱtheȱclimateȱ theȱtimingȱandȱmagnitudeȱofȱhydrographs.ȱ changeȱ impactsȱ onȱ theȱ flowȱ inȱ theȱ Simiyuȱ Riverȱ ȱ watershed.ȱ Inȱ aȱ moreȱ recentȱ study,ȱ Mulunguȱ etȱ al.ȱ (2007)ȱ exploredȱ ȱ dischargeȱestimationȱinȱtheȱSimiyuȱRiverȱwatershedȱusingȱ Mostȱrecently,ȱMangoȱetȱal.ȱ(2011)ȱusedȱtheȱSWATȱmodelȱ theȱSWATȱmodel.ȱUsingȱhighȱresolutionȱdata,ȱtheyȱusedȱ toȱinvestigateȱtheȱimpactsȱofȱlandȱuseȱandȱclimateȱchangeȱ theȱmodelȱtoȱestimateȱtheȱdischargeȱatȱNdangaluȱgaugingȱ inȱtheȱhydrologyȱofȱtheȱMaraȱRiverȱBasinȱinȱKenya.ȱDueȱ stationȱ(112012ȱatȱ33.568°Eȱ2.639°S).ȱSensitivityȱandȱautoȬ toȱ theȱ scarcityȱ ofȱ climateȱ dataȱ inȱ theȱ region,ȱ rainfallȱ calibrationȱ analysisȱ showedȱ thatȱ surfaceȱ modelȱ estimatesȱ wereȱ derivedȱ fromȱ theȱ remoteȱ sensingȱ dataȱ parametersȱ suchȱ asȱ theȱ curveȱ numberȱ (CN2)ȱ andȱ providedȱ byȱ theȱ Famineȱ Earlyȱ Warningȱ Systemȱ (FEWS).ȱ saturatedȱhydraulicȱconductivityȱ(SOL_K)ȱwereȱtheȱmostȱ Theȱ NSEȱ valuesȱ forȱ theȱ observedȱ andȱ simulatedȱ flowsȱ sensitive,ȱwithȱaȱlowȱlevelȱofȱmodelȱperformanceȱforȱtheȱ usingȱ theȱ RFEȱ modelȱ wasȱ 0.43,ȱ whileȱ theȱ R2ȱ valueȱ wasȱ verificationȱperiod.ȱ 0.56.ȱThoughȱtheȱcorrelationȱresultsȱwereȱnotȱsufficientȱtoȱ ȱ predictȱaccurateȱflowsȱinȱtheȱ watershed,ȱtheȱdataȱcanȱbeȱ Rwetabulaȱetȱal.ȱ(2007)ȱusedȱaȱspatiallyȱdistributedȱmodelȱ usedȱ forȱ betterȱ understandingȱ ofȱ theȱ hydrologicalȱ (WetSpa)ȱ toȱ estimateȱ dailyȱ riverȱ waterȱ dischargeȱ inȱ theȱ processesȱinȱtheȱwatershed.ȱ Simiyuȱ River.ȱ Threeȱ yearsȱ ofȱ dailyȱ observedȱ dischargeȱ ȱ valuesȱ measuredȱ atȱ theȱ mouthȱ ofȱ theȱ riverȱ atȱ Lakeȱ Inȱtheȱresearchȱdescribedȱinȱthisȱpaper,ȱtheȱSoilȱandȱWaterȱ Victoriaȱ wereȱ usedȱ toȱ calibrateȱ globalȱ parametersȱ ofȱ theȱ Assessmentȱ Toolȱ (SWAT)ȱ wasȱ usedȱ toȱ exploreȱ theȱ model.ȱResultsȱshowedȱthatȱ38.6%ȱandȱ61.4%ȱofȱtheȱtotalȱ potentialȱ impactsȱ ofȱ fourȱ climateȱ changeȱ scenariosȱ onȱ runoffȱwereȱprovidedȱrespectivelyȱbyȱsurfaceȱrunoffȱandȱ dischargeȱ fromȱ Tanzania’sȱ Simiyuȱ River,ȱ locatedȱ inȱ theȱ interflow,ȱ whileȱ theȱ groundwaterȱ flowȱ contributionȱ wasȱ LakeȱVictoriaȱwatershed.ȱTheȱpurposeȱofȱtheȱresearchȱisȱtoȱ nil.ȱ Theȱ modelȱ revealedȱ thatȱ theȱ totalȱ outflowȱ toȱ Lakeȱ provideȱ insightȱ intoȱ theȱ magnitudeȱ ofȱ streamȱ flowȱ Victoriaȱoccursȱmainlyȱinȱtheȱwetȱseason,ȱfromȱMarchȱtoȱ changesȱthatȱmightȱoccurȱinȱtheȱSimiyuȱRiverȱwatershedȱ MayȱandȱfromȱNovemberȱtoȱJanuary.ȱ asȱ aȱ resultȱ ofȱfutureȱ climaticȱchange,ȱandȱ toȱassessȱwhatȱ www.intechopen.com Alain Lubini and Jan Adamowski: Assessing the Potential Impacts of Four Climate Change 3 Scenarios on the Discharge of the Simiyu River, Tanzania Using the SWAT Model variablesȱ affectȱ dischargeȱ theȱ most.ȱ Thisȱ informationȱ isȱ Februaryȱ representȱ aȱ periodȱ ofȱ transitionȱ betweenȱ theȱ criticalȱforȱaȱbetterȱunderstandingȱofȱtheȱpotentialȱeffectsȱ two.ȱTheȱdryȱseasonȱoccursȱbetweenȱJuneȱandȱSeptember.ȱ ofȱ climateȱ changeȱ onȱ theȱ Simiyuȱ River,ȱ includingȱ Meanȱ annualȱ precipitationȱ generallyȱ variesȱ betweenȱ 750ȱ potentialȱadverseȱeffectsȱonȱinfrastructure,ȱhumanȱhealth,ȱ mmȱandȱ1100ȱmm,ȱtheȱlatterȱoccurringȱinȱtheȱupperȱpartȱ andȱtheȱenvironment.ȱȱThisȱinformationȱisȱalsoȱcriticalȱforȱ ofȱtheȱwatershed.ȱTheȱhistoricalȱmaximumȱandȱminimumȱ theȱ developmentȱ ofȱ waterȱ resourcesȱ strategiesȱ andȱ annualȱ rainfallsȱ areȱ 1500ȱ mmȱ yrȬ1ȱ andȱ 400ȱ mmȱ yrȬ1,ȱ policiesȱasȱwellȱasȱpossibleȱadaptationȱstrategies.ȱȱ respectively.ȱEstimatesȱofȱminimumȱandȱmaximumȱmeanȱ actualȱ evapotranspirationȱ withinȱ theȱ watershedȱ rangeȱ 2.ȱMethodsȱ fromȱ500ȱtoȱ700ȱmmȱyrȬ1ȱandȱfromȱ1200ȱtoȱ2700ȱmmȱyrȬ1.ȱ Onȱ average,ȱ theȱ monthlyȱ dischargeȱ variesȱ fromȱ 0ȱ toȱ 34ȱ 2.1ȱTheȱSimiyuȱwatershedȱ m³/s,ȱ whileȱ duringȱ theȱ dryȱ seasonȱ (Juneȱ toȱ October),ȱ Roughlyȱ 5320ȱ km²ȱ inȱ area,ȱ theȱ Simiyuȱ Riverȱ watershed,ȱ minimumȱorȱnoȱdischargeȱisȱsometimesȱrecorded.ȱ locatedȱwithinȱtheȱMwanza,ȱKwimbaȱandȱMwasaȱdistrictsȱ 2.2ȱDataȱ ofȱ Tanzania,ȱ liesȱ withinȱ theȱ confinesȱ 33°15’—35°00’Eȱ longitudeȱandȱ2°30’–3°30’Sȱlatitudeȱ(Figureȱ1).ȱȱArisingȱinȱ Providedȱ withinȱ theȱ frameworkȱ ofȱ theȱ “Friendsȱ ofȱ theȱ theȱSerengetiȱPlainsȱatȱanȱaltitudeȱofȱaboutȱ1882ȱmȱAMSL,ȱ ”ȱ project,ȱ theȱ dataȱ requiredȱ toȱ conductȱ thisȱ studyȱ theȱriverȱflowsȱinȱaȱwesterlyȱdirectionȱandȱdischargesȱintoȱ includedȱaȱdigitalȱelevationȱmodel,ȱlandȱuse,ȱandȱsoilȱtypeȱ Lakeȱ Victoria.ȱȱTheȱ waterȱ qualityȱ ofȱ Lakeȱ Victoriaȱ hasȱ maps,ȱalongȱwithȱweatherȱdata.ȱDailyȱdataȱrecordsȱforȱtheȱ beenȱ steadilyȱ decliningȱ dueȱ toȱ pointȱ andȱ nonȬpointȱ climaticȱ variablesȱ (precipitation,ȱ relativeȱ humidity,ȱ pollutionȱ sourcesȱ fromȱ domestic,ȱ industrialȱ andȱ temperature,ȱ windȱ speed,ȱ solarȱ radiationȱ andȱ potentialȱ agriculturalȱactivities.ȱTanzanianȱriverȱbasinsȱthatȱpolluteȱ evapotranspirationȱ (ETp),ȱ drawnȱ fromȱ theȱ twoȱ stationsȱ Lakeȱ Victoriaȱ areȱ mainlyȱ theȱ Mara,ȱ Kagera,ȱ andȱ Simiyuȱ locatedȱ downstreamȱ ofȱ theȱ Simiyuȱ Riverȱ withinȱ theȱ watershedsȱ(Crul,ȱ1995).ȱȱTheȱSimiyuȱcatchmentȱisȱoneȱofȱ watershed,ȱ wereȱ checkedȱ forȱ reliability,ȱ consistencyȱ andȱ theȱ mainȱ contributorsȱ toȱ theȱ deteriorationȱ ofȱ Lakeȱ homogeneity.ȱ Theȱ SWATȱ modelȱ usedȱ inȱ thisȱ studyȱ wasȱ Victoria,ȱ becauseȱ itȱ isȱ relativelyȱ large,ȱ andȱ hasȱ manyȱ calibratedȱandȱverifiedȱbyȱcomparingȱmodelȱoutputȱwithȱ agriculturalȱ activitiesȱ thatȱ useȱ agrochemicalsȱ (Ningu,ȱ historicȱ streamȱ flowȱ dataȱ forȱ 1973Ȭ1976ȱ asȱ wellȱ asȱ 1970Ȭ 2000),ȱgeneratingȱhighȱyieldsȱofȱsedimentsȱ(Lugomelaȱandȱ 1971ȱdueȱtoȱtheȱavailabilityȱofȱcoexistingȱflowȱandȱclimateȱ Machiwa,ȱ2002).ȱȱ dataȱduringȱtheȱtimeȱperiod.ȱGivenȱthatȱdataȱseriesȱforȱtheȱ timeȱ periodȱ underȱ concernȱ wereȱ incompleteȱ andȱ ofȱ variableȱlength,ȱaȱneedȱclearlyȱexistedȱforȱeitherȱapplyingȱ someȱ sortȱ ofȱ patchingȱ techniqueȱ orȱ calculatingȱ anȱ arealȱ meanȱ rainfall.ȱ Havingȱ chosenȱ theȱ latterȱ approach,ȱ dailyȱ dataȱ fromȱ pairȱ meteorologicalȱ stationsȱ wereȱ usedȱ inȱ aȱ doubleȱ massȱ curveȱ analysisȱ overȱ aȱ commonȱ periodȱ ofȱ timeȱ inȱ orderȱ toȱ assessȱ theȱ homogeneityȱ ofȱ theȱ rainfallȱ data.ȱRainfallȱdistributionȱwasȱfoundȱtoȱbeȱhomogeneousȱ withinȱ theȱ Simiyuȱ region.ȱ However,ȱ theȱ resultingȱ distributionȱ ofȱ residualsȱ suggestedȱ thatȱ theȱ outliersȱ beȱ removedȱ inȱ orderȱ toȱ calculateȱ theȱ arealȱ meanȱ rainfallȱ usingȱtheȱThiessenȱpolygonȱmethod.ȱ ȱ Figureȱ1.ȱSimiyuȱWatershedȱ(Rwetabulaȱet.al,ȱ2005)ȱ 2.3ȱTheoreticalȱbackgroundȱ

Theȱ watershedȱ isȱ mainlyȱ dominatedȱ byȱ graniteȱ (SynȬ Developedȱ byȱ theȱ USDAȱ Agriculturalȱ Researchȱ Serviceȱ orogenicȱandȱMagmatite),ȱsedimentaryȱandȱmetamorphicȱ (ARS)ȱtoȱpredictȱtheȱimpactȱofȱlandȱmanagementȱpracticesȱ rocksȱ (Nyanzian),ȱ withȱ volcanicȱ rocksȱ (Neogene)ȱ onlyȱ onȱwater,ȱsedimentȱandȱagriculturalȱchemicalȱyieldsȱoverȱ coveringȱ aȱ smallȱ portionȱ ofȱ theȱ watershedȱ aroundȱ theȱ extendedȱ periodsȱ ofȱ timeȱ inȱ largeȱ complexȱ watershedsȱ Serengetiȱ plainsȱ inȱ northȬcentralȱ Tanzania.ȱ Coveredȱ byȱ (e.g.,ȱonȱaȱwatershedȱscale)ȱofȱvaryingȱsoil,ȱlandȱuseȱandȱ grassesȱ andȱ otherȱ herbaceousȱ plantsȱ overȱ mostȱ ofȱ itsȱ managementȱ conditions,ȱ theȱ physicallyȱ basedȱ andȱ semiȬ extent,ȱtheȱwatershed’sȱeasternȱandȱnorthernȱportionsȱareȱ distributedȱ SWATȱ modelȱ operatesȱ onȱ aȱ dailyȱ timeȱ stepȱ dominatedȱ byȱanȱ openȱ coverȱ ofȱ trees,ȱ whereȱ theȱ crownsȱ (Neitschȱetȱal.,ȱ2002).ȱSWATȱdividesȱwatershedsȱintoȱsubȬ doȱnotȱformȱaȱthicklyȱinterlacedȱcanopy.ȱ watershedsȱandȱattendantȱstreamȱreaches;ȱyieldsȱforȱeachȱ ȱ subȱ basinȱ areȱ thenȱ computed,ȱ routedȱ throughȱ streamȱ Characterizedȱ byȱ aȱ tropicalȱ wetȱ andȱ dryȱ climate,ȱ theȱ reachesȱandȱreservoirs,ȱandȱcombinedȱasȱappropriate.ȱȱ watershedȱisȱsubjectȱtoȱtwoȱwetȱseasons:ȱtheȱshortȱrainsȱofȱ ȱ OctoberȱthroughȱDecember,ȱandȱtheȱlongȱrainsȱofȱMarchȱ Inputȱ informationȱ forȱ eachȱ subȬbasinȱ isȱ groupedȱ orȱ throughȱ May.ȱ Theȱ intermediateȱ monthsȱ ofȱ Januaryȱ andȱ organizedȱ intoȱ theȱ followingȱ categories:ȱ climate;ȱ

4 Int. j. water sci., 2013, Vol. 2, 1:2013 www.intechopen.com hydrologicȱ responseȱ unitsȱ orȱ HRUs;ȱ ponds/wetlands;ȱ §·1000 ȱ ȱȱȱȱȱ S25.4 ¨¸ 10 ȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱ(3)ȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱ groundwater;ȱ andȱ theȱ mainȱ channel,ȱ orȱ reach,ȱ drainingȱ ©¹CN theȱ subȬbasin.ȱ Hydrologicȱ responseȱ unitsȱ areȱ lumpedȱ ȱȱȱȱȱȱ landȱ areasȱ withinȱ theȱ subȬbasinȱ thatȱ areȱ comprisedȱ ofȱ TheȱGreenȱandȱAmptȱequationȱwasȱdevelopedȱtoȱpredictȱ uniqueȱ landȱ cover,ȱ soil,ȱ andȱ managementȱ combinations.ȱ infiltrationȱ basedȱ onȱ theȱ soilȱ parameters.ȱ Theȱ methodȱ Anȱ assumptionȱ isȱ madeȱ thatȱ thereȱ isȱ noȱ interactionȱ assumesȱ thatȱ theȱ soilȱ profileȱ isȱ homogenousȱ andȱ betweenȱ HRUsȱ withinȱ aȱ singleȱ subȬbasin.ȱ Loadingȱ fromȱ antecedentȱ moistureȱ isȱ uniformlyȱ distributedȱ inȱ theȱ eachȱ HRUsȱ areȱ calculatedȱ separatelyȱ andȱ thenȱ summedȱ profile.ȱ Theȱ GreenȬAmptȱ MeinȬLarsonȱ infiltrationȱ rateȱ isȱ togetherȱ toȱ determineȱ theȱ totalȱ loadingsȱ fromȱ theȱ subȬ definedȱas:ȱ basinȱ(Neitschȱetȱal.,ȱ2002).ȱ <'T ȱ ȱȱȱȱȱȱ wf v ȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱ(4)ȱȱȱȱȱ ȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱ fK[1inf e  ] 2.4ȱHydrologyȱ t F inft

TheȱhydrologicalȱprocessesȱinȱSWATȱareȱsimulatedȱinȱtwoȱ where,ȱ phases:ȱ landȱ phaseȱ andȱ routingȱ phase,ȱ theȱ formerȱ beingȱ Ȭ1 finf isȱtheȱinfiltrationȱrateȱatȱtimeȱtȱ(mmȱhr ),ȱ basedȱonȱtheȱwaterȱbalanceȱequation,ȱexpressedȱinȱmmȱofȱ t Finf isȱtheȱcumulativeȱinfiltrationȱatȱtimeȱtȱ(mm),ȱ waterȱ(Neitschetȱet.al,ȱ2005):ȱ t Keisȱtheȱeffectiveȱhydraulicȱconductivityȱ(mmȱhrȬ1),ȱ

in < wf isȱtheȱwettingȱfrontȱmatricȱpotentialȱ(mm),ȱandȱ day  surf  a seep gw ȱ ȱ SWt0 SW¦ (Ri Qii E Wi Qi ) ȱȱ(1) 'T isȱ theȱ changeȱ inȱ volumetricȱ moistureȱ contentȱ acrossȱ i0 v theȱwettingȱfrontȱ(mmȱmmȬ1)ȱȱ where,ȱ 2.5ȱClimateȱchangeȱ Eai ȱisȱtheȱamountȱofȱevapotranspirationȱonȱdayȱi,ȱ Qgw ȱisȱtheȱamountȱofȱreturnȱflowȱonȱdayȱi,ȱ i ClimateȱchangeȱsimulationsȱthoughȱSWATȱcanȱbeȱcarriedȱ Qissurf ȱtheȱamountȱofȱsurfaceȱrunoffȱonȱdayȱiȱ(mm),ȱ i outȱ byȱ manipulatingȱ theȱ climateȱ inputsȱ suchȱ asȱ Rday ȱisȱtheȱrainfallȱdepthȱonȱdayȱi,ȱ i precipitation,ȱ temperature,ȱ solarȱ radiation,ȱ relativeȱ SWtȱisȱtheȱfinalȱsoilȱmoistureȱcontentȱ(dayȱn),ȱ humidity,ȱ windȱ speed,ȱ ETpȱ andȱ weatherȱ generatorȱ SW0ȱisȱtheȱinitialȱsoilȱmoistureȱcontentȱ(dayȱ0),ȱandȱ parametersȱ thatȱ areȱ fedȱ intoȱ theȱ modelȱ (Neitschetȱ etȱ al.,ȱ seep Wisi ȱ theȱ amountȱ ofȱ percolationȱ bypassȱ flowȱ exitingȱ 2005).ȱ Forȱ someȱ parametersȱ (e.g.,ȱ CO2ȱ levelȱ andȱ theȱbottomȱofȱtheȱsoilȱprofileȱonȱdayȱiȱ(mmȱH2O).ȱ temperature)ȱadjustmentsȱcanȱbeȱmadeȱwithinȱindividualȱ ȱ subȬwatershedsȱbyȱaȱsimpleȱpercentȱincreaseȱorȱdecreaseȱ Toȱ modelȱ thisȱ phase,ȱ SWATȱ requiresȱ climaticȱ variablesȱ inȱtheȱparticularȱparameter.ȱ suchȱ asȱ dailyȱ precipitation,ȱ maximum/minimumȱ airȱ ȱ temperature,ȱ solarȱ radiation,ȱ windȱ speedȱ andȱ relativeȱ Changesȱ inȱ carbonȱ dioxideȱ levelsȱ areȱ particularlyȱ criticalȱ humidity.ȱ becauseȱtheyȱhaveȱanȱimpactȱonȱplantȱgrowth.ȱAsȱvolumeȬ ȱ basedȱatmosphericȱcarbonȱdioxideȱconcentrationsȱ([CO2]volȱ)ȱ Surfaceȱ runoffȱ canȱ beȱestimatedȱeitherȱbyȱtheȱSCSȱcurveȱ increase,ȱ plantȱ productivityȱ increasesȱ andȱ plantȱ waterȱ numberȱ procedureȱ orȱ theȱ Greenȱ andȱ Amptȱ infiltrationȱ requirementsȱ goȱ down.ȱ Consequently,ȱ theȱ canopyȱ method.ȱ Theȱ SCSȱ curveȱ equationȱ isȱ anȱ empiricalȱ modelȱ resistanceȱtermȱcanȱbeȱmodifiedȱtoȱreflectȱtheȱimpactȱofȱtheȱ developedȱtoȱprovideȱaȱconsistentȱbasisȱforȱestimatingȱtheȱ changeȱ inȱ [CO2]volȱȱ onȱ leafȱ conductance.ȱ Morisonȱ andȱ amountȱofȱrunoffȱunderȱvaryingȱlandȱuseȱandȱsoilȱtypes,ȱ Giffordȱ(1983)ȱfoundȱthatȱwhenȱtheȱinitialȱ[CO2]volȱȱrangedȱ andȱisȱgivenȱby:ȱ betweenȱ330ȱandȱ660ȱppm,ȱaȱdoublingȱinȱCO2ȱconcentrationȱ resultedȱ inȱ aȱ 40%ȱ reductionȱ inȱ leafȱ conductance.ȱ 2 RI Consequently,ȱ Easterlingȱ etȱ al.ȱ (1992)ȱ proposedȱ theȱ ȱ ȱȱȱȱȱȱ day a ȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱ(2)ȱȱȱȱȱȱȱȱȱȱ Qsurf followingȱ modificationȱ toȱ theȱ leafȱ conductanceȱ termȱ forȱ RISday a simulatingȱtheȱeffectsȱofȱ[CO2]volȱȱonȱevapotranspiration:ȱ where,ȱȱ § [CO ] · 2vol (5)ȱȱȱȱȱȱȱȱȱȱȱ Iaȱrepresentsȱ theȱ initialȱ abstractionsȱ –ȱ includingȱ surfaceȱ ȱ gAA g¨ 1.4 0.4 ( )¸ ȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱ CO2 © 330 ¹ storage,ȱinterceptionȱandȱinfiltrationȱpriorȱtoȱrunoffȱ(mm)ȱ Qsurfȱisȱ theȱ accumulatedȱ runoffȱ and/orȱ rainfallȱ excessȱ whereȱ gȱ l,ȱ CO2ȱ isȱ theȱ leafȱ conductanceȱ modifiedȱ toȱ reflectȱ (mm)ȱ CO2ȱeffectsȱ(mȱsȬ1)ȱandȱCO2ȱisȱtheȱconcentrationȱofȱcarbonȱ Rdayȱisȱtheȱrainfallȱdepthȱforȱtheȱdayȱ(mm),ȱandȱ dioxideȱinȱtheȱatmosphereȱ(ppmv).ȱ Sȱisȱtheȱretentionȱparameterȱ(mm),ȱwhichȱvariesȱspatiallyȱ dueȱtoȱchangesȱinȱsoils,ȱlandȱuse,ȱ managementȱ andȱ slopeȱ 3.ȱCalibrationȱandȱverificationȱ andȱtemporallyȱdueȱtoȱchangesȱinȱsoilȱwaterȱcontent.ȱItȱisȱ relatedȱ toȱ theȱ curveȱ numberȱ (CN)ȱ accordingȱ toȱ theȱ Inȱ orderȱ toȱ assessȱ theȱ impactȱ ofȱ climateȱ changeȱ onȱ relationship:ȱ dischargeȱfromȱtheȱSimiyuȱRiver,ȱtheȱSWATȱmodelȱhadȱtoȱ www.intechopen.com Alain Lubini and Jan Adamowski: Assessing the Potential Impacts of Four Climate Change 5 Scenarios on the Discharge of the Simiyu River, Tanzania Using the SWAT Model firstȱbeȱcalibratedȱandȱverified.ȱTheȱmodelȱwasȱcalibratedȱ inputsȱforȱtheȱsameȱsimulationȱperiodȱinȱorderȱtoȱprovideȱ byȱ comparingȱ predictedȱ andȱ observedȱ hydrographsȱ forȱ aȱconsistentȱbasisȱforȱtheȱcomparisonȱofȱscenarioȱimpacts.ȱ theȱperiodȱofȱJanuaryȱ1973ȱtoȱDecemberȱ1976.ȱ Theȱ 4Ȭyearȱ periodȱ fromȱ 1973ȱ toȱ 1976ȱ wasȱ usedȱ asȱ aȱ ȱ baselineȱcondition.ȱ Theȱ SCSȱ CNȱ methodȱ forȱ partitioningȱ ofȱ precipitationȱ betweenȱ surfaceȱ runoffȱ andȱ infiltration,ȱ theȱ variableȱ 3.2ȱClimateȱchangeȱscenarioȱ storageȱmethodȱforȱchannelȱrouting,ȱandȱtheȱHargreavesȱ Variousȱmodelingȱtechniquesȱhaveȱbeenȱusedȱtoȱdevelopȱ methodȱ forȱ calculatingȱ ETpȱ wereȱ modelȱ optionsȱ aȱ wideȱ rangeȱ ofȱ climateȱ changeȱ scenarios.ȱ AtmosphereȬ implementedȱ throughȱ theȱ study.ȱ Theseȱ modelȱ optionsȱ oceanȱ generalȱ circulationȱ modelȱ simulationsȱ (AOGCMs)ȱ wereȱ suitableȱ givenȱ theȱ levelȱ ofȱ dataȱ availability;ȱ forȱ areȱ oftenȱ usedȱ toȱ helpȱ buildȱ theseȱ scenarios,ȱ asȱ areȱ example,ȱ theȱ temperatureȱ timeȱ seriesȱ wasȱ incompleteȱ regionalȱ climateȱ modelsȱ thatȱ useȱ downscaledȱ AOGCMȱ (maximumȱ andȱ minimumȱ temperaturesȱ wereȱ onlyȱ simulatedȱ data.ȱ Inȱ additionȱ toȱ modelingȱ techniques,ȱ availableȱ fromȱ 1970ȱ toȱ 1974).ȱ Thoughȱ theȱ Penmanȱ scenarioȬbuildingȱ requiresȱ aȱ physicalȱ understandingȱ ofȱ Monteithȱmethodȱisȱmoreȱaccurateȱwhenȱcompleteȱdataȱisȱ theȱ processesȱgoverningȱ regionalȱ responses,ȱasȱ wellȱ asȱaȱ available,ȱtheȱHargreavesȱ methodȱrequiresȱlessȱdataȱ andȱ comprehensiveȱunderstandingȱofȱrecentȱhistoricalȱclimateȱ canȱbeȱappliedȱinȱinstancesȱwhenȱsomeȱweatherȱdataȱareȱ changeȱ events.ȱȱAlthoughȱ theȱ preciseȱ patternȱ ofȱ theȱ missingȱ(Heuvelmansȱetȱal,ȱ2005).ȱ changesȱ inȱ temperature,ȱ precipitation,ȱ andȱ extremeȱ ȱ eventsȱ isȱ notȱ wellȱ known,ȱ simulationsȱ byȱ globalȱ climateȱ Alongȱ withȱ theȱ curveȱ number,ȱ theȱ fractionȱ ofȱ availableȱ modelsȱhaveȱledȱtoȱanȱagreementȱonȱtheȱfollowingȱtrends:ȱ waterȱ(AW)ȱisȱanȱimportantȱparameterȱinȱcalibratingȱtheȱ i)ȱ Theȱ globalȱ meanȱ surfaceȱ temperatureȱ isȱ projectedȱ toȱ surfaceȱrunoff:ȱ increaseȱbetweenȱ1.1ȱ°Cȱandȱ6.4ȱ°Cȱbyȱ2100;ȱii)ȱGlobalȱseaȱ levelsȱareȱprojectedȱtoȱriseȱbyȱ18ȱtoȱ59ȱcentimetersȱbyȱtheȱ TTmpwp AW ȱȱȱȱȱ(6)ȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱyearȱ 2100.ȱ Furthermore,ȱ climateȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱ changeȱ scenariosȱ forȱ TTfc pwp ȱ ȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱ Africaȱindicateȱfutureȱwarmingȱacrossȱtheȱcontinent,ȱwithȱ decadalȱincreasesȱthatȱrangeȱfromȱ0.2°Cȱ(lowȱscenario)ȱtoȱ where,ȱȱ moreȱ thanȱ 0.5°Cȱ (highȱ scenario)ȱ (Hulmeȱ etȱ al.ȱ 2001;ȱ ȱ DesankerȱandȱMagadzaȱ2001).ȱ ߠ T ȱ isȱ theȱ soil’sȱ volumetricȱ soilȱ moistureȱ contentȱ atȱ ௙௖ m ȱ fieldȱcapacityȱ Inȱ orderȱ toȱ examineȱ theȱ sensitivityȱ ofȱ streamȱ flow,ȱ bothȱ T Ʌ ȱ fc ȱ ୬ȱisȱtheȱmeasuredȱvolumetricȱsoilȱmoistureȱcontent,ȱ alteredȱ temperaturesȱ andȱ rainfallsȱ wereȱ usedȱ asȱaȱ proxyȱ andȱ forȱincreasesȱinȱ[CO2]vol.ȱToȱachieveȱyearȬlongȱequivalentsȱ ȱT Ʌ ȱ isȱ theȱ soil’sȱvolumetricȱsoilȱmoistureȱcontentȱ pwp ȱ ୮୵୮ ofȱrisesȱinȱ[CO2]volȱofȱ479,ȱ492,ȱ555ȱorȱ559ȱppmȱrelativeȱtoȱ atȱtheȱpermanentȱwiltingȱpoint.ȱ theȱ baseline,ȱ inȱ combinationȱ withȱ temperaturesȱ beingȱ ȱ raisedȱ overȱ theȱ entireȱ yearȱ byȱ 1.5,ȱ 2.5,ȱ orȱ 4.5°C,ȱ rainfallȱ Modelȱ performanceȱ duringȱ theȱ calibrationȱ andȱ wasȱincreasedȱtoȱdifferentȱdegreesȱonȱaȱmonthȬtoȬmonthȱ verificationȱ phasesȱ wasȱ evaluatedȱ usingȱ bothȱ theȱ basisȱ (2,ȱ 3,ȱ 4,ȱ 5,ȱ 10,ȱ 15,ȱ orȱ 20%)ȱ exceptȱ inȱ Juneȱ throughȱ correlationȱ coefficientȱ betweenȱ measuredȱ andȱ predictedȱ Augustȱ whenȱ itȱ remainedȱ unchangedȱ (Tableȱ 1).ȱ Theȱ parameterȱvaluesȱ(R²)ȱandȱtheȱNashȬSutcliffeȱcoefficientȱofȱ combinedȱ increasesȱ inȱ temperatureȱ andȱ precipitationȱ ofȱ efficiencyȱ (NSE).ȱ Theȱ formerȱ measuresȱ theȱ strengthȱ andȱ theȱ fourȱ scenariosȱ implementedȱ wereȱ basedȱ onȱ putativeȱ directionȱ ofȱ aȱ linearȱ relationshipȱ betweenȱ twoȱ variables,ȱ risesȱ inȱ [CO2]volȱ andȱ IPCCȱ (2001)ȱ predictionsȱ ofȱ futureȱ thusȱ representingȱ theȱ proportionȱ ofȱ theȱ varianceȱ ofȱ oneȱ warmingȱ acrossȱ Africa.ȱ Asȱ discussedȱ earlier,ȱ variableȱ thatȱ isȱ predictableȱ fromȱ theȱ otherȱ variable;ȱ theȱ Doktorgradesȱ (2009)ȱ foundȱ thatȱ theȱ IPCCȱ 2007ȱ dataȱ forȱ latterȱ indicatesȱ howȱ wellȱ theȱ plotȱ ofȱ observedȱ versusȱ theȱAfricanȱcontinentȱlackedȱnewȱinformation,ȱwhichȱwasȱ predictedȱdataȱfitsȱaȱ1:1ȱline.ȱAȱperfectȱmatchȱofȱobservedȱ reflectedȱ inȱ theȱ inconsistentȱ modelȱ projectionsȱ duringȱ andȱpredictedȱvaluesȱwouldȱresultȱinȱanȱNSEȱofȱ1.0,ȱwithȱ simulations.ȱ Hence,ȱ IPCCȱ 2001ȱ dataȱ wasȱ usedȱ inȱ theirȱ valuesȱ graduallyȱ approachingȱ zeroȱ asȱ theȱ qualityȱ ofȱ theȱ studyȱtoȱavoidȱmodelȱdiscrepancyȱandȱuncertaintyȱinȱtheȱ matchȱ deterioratesȱ toȱ theȱ pointȱ (NSEȱ <ȱ 0.0)ȱ whereȱ theȱ interpretationȱ ofȱ theȱ modelȱ parametersȱ forȱ Africanȱ meanȱ observedȱ parameterȱ isȱ aȱ betterȱ predictorȱ thanȱ theȱ climateȱ simulations.Dataȱ fromȱ theȱ IPCCȱ Thirdȱ modelȱ(Singhȱetȱal.,ȱ2004).ȱValuesȱofȱ1.0ȱtȱNSEȱtȱ0.5ȱareȱ Assessmentȱ Reportȱ(2001)ȱ wasȱ alsoȱ usedȱ throughoutȱtheȱ generallyȱ consideredȱ toȱ representȱ goodȱ modelȱ presentȱresearchȱprojectȱforȱtheȱsameȱreasons.ȱ performance.ȱ ȱ 3.1ȱScenarioȱbaselineȱ Basedȱ onȱ theȱ resultingȱ riseȱ inȱ CO2ȱ theȱ firstȱ scenarioȱ describesȱaȱfutureȱworldȱwithȱrapidȱchangeȱinȱeconomicȱ Aȱ baselineȱ scenario,ȱ assumedȱ toȱ reflectȱ currentȱ structuresȱalongsideȱreductionsȱinȱmaterialȱintensityȱandȱ conditions,ȱ wasȱ executedȱ priorȱ toȱ performingȱ scenarioȱ theȱ introductionȱ ofȱ cleanȱ andȱ resourceȱ efficientȱ simulations,ȱwhichȱwereȱthenȱrunȱwithȱmodifiedȱclimateȱ technologies.ȱ Theȱ secondȱ scenarioȱ isȱ characterizedȱ byȱ

6 Int. j. water sci., 2013, Vol. 2, 1:2013 www.intechopen.com intermediateȱ levelsȱ ofȱ economicȱ developmentȱ withȱ lessȱ Overȱ theȱ interior,ȱ semiȬaridȱ regionsȱ ofȱ theȱ Saharaȱ andȱ rapidȱandȱmoreȱdiverseȱtechnologicalȱchangeȱthanȱinȱtheȱ centralȬsouthernȱ Africa,ȱ predictedȱ risesȱ inȱ temperaturesȱ firstȱ scenario.ȱ Theȱ lastȱ twoȱ scenariosȱ incorporateȱ theȱ rangedȱ fromȱ 1.5°Cȱ upȱ toȱ 4.5°Cȱ (IPCCȱ 2001).ȱ Indeed,ȱ theȱ rapidȱ introductionȱ ofȱ newȱ andȱ moreȱ efficientȱ intermodalȱrangeȱȬȱanȱindicatorȱofȱtheȱextentȱofȱagreementȱ technologiesȱandȱplaceȱaȱspecialȱemphasisȱonȱcreatingȱaȱ betweenȱ differentȱ GCMsȱ Ȭȱ isȱ smallestȱ overȱ Northȱ andȱ balancedȱportfolioȱofȱenergyȱsourcesȱ(i.e.,ȱnotȱrelyingȱtooȱ Equatorialȱ Africaȱ andȱ greatestȱ overȱ theȱ interiorȱ ofȱ heavilyȱ onȱ oneȱ particularȱ sourceȱ ofȱ energy).ȱ Theȱ thirdȱ southernȱAfrica.ȱTheȱscenariosȱdescribedȱaboveȱareȱdrawnȱ scenarioȱ inȱ particularȱ focusesȱ onȱ theȱ useȱ ofȱ nonȬfossilȱ fromȱ historicalȱ dataȱ thatȱ showsȱ anȱ increaseȱ ofȱ energyȱsources.ȱ approximatelyȱ0.7°CȱinȱtemperatureȱacrossȱmostȱofȱAfricaȱ ȱ duringȱ theȱ 20thȱ century,ȱ aȱ decreaseȱ inȱ rainfallȱ overȱ largeȱ Theȱfourȱscenariosȱtestedȱinȱthisȱstudyȱwere:ȱ portionsȱofȱtheȱSahelianȱregion,ȱandȱanȱincreaseȱinȱrainfallȱ ȱ inȱeastȱcentralȱAfrica.ȱȱ 2.5 ȱ S492 Aȱ2.5°Cȱriseȱinȱtemperatureȱoverȱtheȱentireȱyear.ȱȱ Fileȱ Soilȱ Adjusted Increasesȱinȱprecipitation:ȱȱ Parameterȱ Descriptionȱ Unitsȱ typeȱ layer valueȱ SeptȬNov:ȱ3%ȱ SOL_AWCȱ Soilȱavailableȱ mmȱH20ȱmmȬ1ȱȱ.solȱ 1ȱ 0.12ȱ waterȱcapacityȱ 2ȱ 0.03ȱ DecȬFeb:ȱ5%ȱ 1ȱ 0.03ȱ MarȬMay:ȱ15%ȱ 2ȱ 0.50ȱ SOL_Kȱ Saturatedȱhydraulicȱȱ mmȱhrȬ1ȱ .solȱ 1ȱ 20ȱ ResultingȱRiseȱinȱ[CO2]vol:ȱ492ppmȱ conductivityȱ(Ksat)ȱ 2ȱ 12ȱ 1ȱ 7ȱ 1.5 S479 Aȱ1.5°Cȱriseȱinȱtemperatureȱoverȱtheȱentireȱyear.ȱȱ 2ȱ 23ȱ CN2ȱ Curveȱnumberȱ ȱȱ .mgtȱ ȱȱ 40ȱ Increasesȱinȱprecipitation:ȱȱ SLSUBBSINȱ Meanȱslopeȱ mȱ .hruȱ ȱȱ 10ȱ SeptȬNov:ȱ1%ȱ Lengthȱ SLOPEȱ Meanȱslopeȱȱ mȱmȬ1ȱ .hruȱ ȱȱ 0.129ȱ DecȬFeb:ȱ20%ȱ Steepnessȱ MarȬMay:ȱ5%ȱ OV_Nȱ ManningȱȈnȈȱ ȱȱ .hruȱ ȱȱ 0.01ȱ Valueȱ ResultingȱRiseȱinȱ[CO2]vol:ȱ479ppmȱ LAT_TIMEȱ Lateralȱflowȱȱ dayȱ .hruȱ ȱȱ 0.001ȱ 2.5 Travelȱ S555 Aȱ2.5°Cȱriseȱinȱtemperatureȱoverȱtheȱentireȱyear.ȱȱ CH_Nȱ1ȱȈnȈȱvalueȱforȱ ȱȱ .subȱ ȱȱ 29.9ȱ theȱtributaryȱ Increasesȱinȱprecipitation:ȱȱ CH_Kȱ1ȱ Effectiveȱhydraulicȱ mmȱhrȬ1ȱ .subȱ ȱȱ 11ȱ SeptȬNov:ȱ4%ȱ conductivityinȱtributaryȱchannelȱ CH_Kȱ2ȱ Effectiveȱhydraulicȱȱ mmȱhrȬ1ȱ .Rteȱ ȱȱ 0.019ȱ DecȬFeb:ȱ15%ȱ conductivityȱinȱȱ MarȬMay:ȱ10%ȱ mainȱchannelȱ CH_Nȱ2ȱ ManningȱȈnȈȱvalueȱȱ ȱȱ .Rteȱ ȱȱ 0.05ȱ ResultingȱRiseȱinȱ[CO2]vol:ȱ555ppmȱ forȱtheȱmainȱchannelȱ 4.5 CH_Sȱ2ȱ Averageȱslopeȱofȱȱ mȱmȬ1ȱ .Rteȱ ȱȱ 0.001ȱ S559 Aȱ4.5°Cȱriseȱinȱtemperatureȱoverȱtheȱentireȱyear.ȱȱ mainȱchannelȱ GW_DELAY Groundȱwaterȱ dayȱ .GWȱ ȱȱ 30ȱ Increasesȱinȱprecipitation:ȱȱ Delayȱ SeptȬNov:ȱ2%ȱ ALPHA_BFȱ Baseflowȱalphaȱȱ dayȱ .GWȱ ȱȱ 0.3ȱ factorȱȱ DecȬFeb:ȱ10%ȱ GWQMNȱ Thresholdȱdepthȱȱ mmȱwaterȱ .GWȱ ȱȱ 2575ȱ MarȬMay:ȱ20%ȱ ofȱwaterȱ PND_FRȱ Fractionȱofȱȱ ȱȱ .PND ȱȱ 0.09ȱ 2 vol ResultingȱRiseȱinȱ[CO ] :ȱ559ppmȱ subwatershedȱareaȱȱ ȱ thatȱdrainsȱȱ intoȱpondsȱ ȱ 2.5 2.5 PND_PSAȱ Surfaceȱareaȱȱ haȱ .PND ȱȱ 720ȱ Seasonȱ S492 ȱ S555 ȱ ofȱpondsȱ ȱ 'Tȱ 'Pȱ 'CO2ȱ 'Tȱ 'Pȱ 'CO2ȱ PND_Kȱ Hydraulicȱȱ mmȱhrȬ1ȱ .PND ȱȱ 0.005ȱ (°C)ȱ (%)ȱ (ppm)ȱ (°C)ȱ (%)ȱ (ppm)ȱ conductivityȱȱ Dec.ȬFeb.ȱ 2.5ȱ 5ȱ 492ȱ 1.5ȱ 20ȱ 479ȱ throughȱbottomȱȱ Mar.ȬMayȱ 2.5ȱ 15ȱ 492ȱ 1.5ȱ 5ȱ 479ȱ ofȱpondsȱ Jun.ȬAug.ȱ 2.5ȱ 0ȱ 492ȱ 1.5ȱ 0ȱ 479ȱ WET_FRȱ Fractionȱofȱȱ ȱȱ .PND ȱȱ 0.05ȱ Sep.ȬNov.ȱ 2.5ȱ 3ȱ 492ȱ 1.5.ȱ 1ȱ 479ȱ subwatershedȱthatȱȱ ȱ drainsȱintoȱwetlandsȱ WET_NSAȱ Surfaceȱareaȱofȱȱ haȱ .PND ȱȱ 20ȱ 2.5 4.5 wetlandsȱatȱ S492 ȱ S559 ȱ ȱnormalȱwaterȱlevelȱ 'Tȱ 'Pȱ 'CO2ȱ 'Tȱ 'Pȱ 'CO2ȱ WET_MXSA Surfaceȱareaȱofȱȱ haȱ .PND ȱȱ 700ȱ (°C)ȱ (%)ȱ (ppm)ȱ (°C)ȱ (%)ȱ (ppm)ȱ wetlandsȱatȱȱ 2.5ȱ 15ȱ 555ȱ 4.5ȱ 10ȱ 559ȱ maximumȱwaterȱȱ 2.5ȱ 10ȱ 555ȱ 4.5ȱ 20ȱ 559ȱ levelȱ 2.5ȱ 0ȱ 555ȱ 4.5ȱ 0ȱ 559ȱ WURCȱ Averageȱdailyȱȱ m3ȱdayȬ1ȱ .wusȱ ȱȱ 5u104ȱ 2.5ȱ 4ȱ 555ȱ 4.5ȱ 2ȱ 559ȱ removalȱfromȱtheȱȱ 200u104ȱ Tableȱ1.ȱIncreasesȱrelativeȱtoȱtheȱbaseȱscenarioȱinȱairȱtemperatureȱ reachȱforȱtheȱmonthȱ ('T),ȱ precipitationȱ ('P),ȱ andȱ atmosphericȱ carbonȱ dioxideȱ Tableȱ2.ȱSWATȱmodelȱparametersȱafterȱcalibrationȱandȱ concentrationȱ('CO2)ȱonȱaȱvolumetricȱbasis,ȱforȱtheȱfourȱclimateȱ verification.ȱ changeȱscenariosȱinvestigated.ȱ ȱ

www.intechopen.com Alain Lubini and Jan Adamowski: Assessing the Potential Impacts of Four Climate Change 7 Scenarios on the Discharge of the Simiyu River, Tanzania Using the SWAT Model FutureȱchangesȱinȱmeanȱseasonalȱrainfallȱinȱAfricaȱareȱlessȱ 250 0 wellȱdefined.ȱUnderȱtheȱlowestȱwarmingȱscenario,ȱupȱtoȱ 200 50 2050ȱ fewȱ areasȱ wouldȱ experienceȱ changesȱ inȱ Decemberȱ ) throughȱFebruary,ȱorȱJuneȱthroughȱAugustȱprecipitation,ȱ 150 100 exceedingȱtwoȱstandardȱdeviationsȱofȱnaturalȱvariability.ȱȱ Q(m3/s) 100 150 However,ȱ inȱ partsȱ ofȱ equatorialȱ Eastȱ Africa,ȱ rainfallȱ isȱ Rainfall (mm predictedȱ toȱ increaseȱ byȱ 5Ȭ20%ȱ fromȱ Decemberȱ throughȱ 50 200 February,ȱ andȱ decreaseȱ byȱ 5Ȭ10%,ȱ fromȱ Juneȱ throughȱ 0 250 Augustȱ(IPCCȱ2001b).ȱ 1/1/1973 5/1/1973 9/1/1973 1/1/1974 5/1/1974 9/1/1974 1/1/1975 5/1/1975 9/1/1975 1/1/1976 5/1/1976 9/1/1976 4.ȱResultsȱandȱdiscussionsȱ years Observed discharges Simulated discharges Rainfall ȱ Inȱ orderȱ toȱ createȱ anȱ accurateȱ simulationȱ ofȱ theȱ Simiyuȱ Figureȱ2.ȱSequentialȱplotȱofȱtheȱdailyȱobservedȱandȱmodeledȱ River’sȱ flowȱ usingȱ SWAT,ȱ itȱ wasȱ necessaryȱ toȱ adjustȱ dischargesȱandȱtheȱrainfallȱatȱNdagaluȱforȱtheȱcalibrationȱperiodȱ severalȱ modelȱ parametersȱ toȱ bestȱ matchȱ observedȱ andȱ 1973Ȭ1976ȱ simulatedȱ valuesȱ (Tableȱ 2).ȱ Followingȱ thisȱ calibration,ȱ TheȱR2ȱwasȱ0.73,ȱandȱtheȱNSEȱwasȱ0.53,ȱforȱtheȱcalibrationȱ specificȱ modelȱ parametersȱ wereȱ setȱ andȱ remainedȱ periodȱasȱshownȱinȱTableȱ5.ȱ unchangedȱ forȱ theȱ verificationȱ period.ȱȱTheȱ comparisonȱ ȱ betweenȱ theȱ predictedȱ andȱ observedȱ flowsȱ wasȱ carriedȱ Statistics Dailyȱtimeȱstepȱ outȱforȱbothȱdailyȱandȱmonthlyȱtimeȱstepsȱ(Tableȱ3).ȱ ȱ Calibrationȱ Verificationȱ ȱ Dailyȱ Monthlyȱ Nȱ 1461ȱ 730ȱ Statistics*ȱ ȱ Calibrationȱ Verificationȱȱ Calibrationȱ Verificationȱ VarQobs 403.06ȱ 1004.68ȱ

nȱ 1461ȱ 730ȱ 48ȱ 24ȱ SSQ 278374.3ȱ 355261.8ȱ R 0.7314ȱ 0.7232ȱ VarȱQobsȱ 403.66ȱ 1004.7ȱ 178755ȱ 447067ȱ NSE 0.5272ȱ 0.5156ȱ SSQȱ 278374ȱ 355262ȱ 2584101ȱ 3659731ȱ n,ȱnumberȱofȱvalues;ȱVarȱQobs,varianceȱofȱtheȱobservedȱflow;ȱSSQ,ȱsumȱofȱ 2 R2ȱ 0.73ȱ 0.72ȱ 0.83ȱ 0.83ȱ squareȱdeviationsȱbetweenȱobservedȱandȱpredictedȱflowȱvalues;ȱR ,ȱlinearȱ correlationȱcoefficientȱbetweenȱobservedȱandȱpredictedȱflowȱvalues;ȱNSE,ȱ NSEȱ 0.53ȱ 0.52ȱ 0.70ȱ 0.66ȱ NashȬSutcliffeȱcoefficientȱofȱmodelȱefficiency.ȱ Tableȱ5.ȱStatisticsȱforȱdailyȱdataȱ *n,ȱnumberȱofȱvalues;ȱVarȱQobs,varianceȱofȱtheȱobservedȱflow;ȱSSQ,ȱsumȱofȱ squareȱdeviationsȱbetweenȱobservedȱandȱpredictedȱflowȱvalues;ȱR2,ȱlinearȱ correlationȱcoefficientȱbetweenȱobservedȱandȱpredictedȱflowȱvalues;ȱNSE,ȱ 4.1.2ȱVerificationȱ NashȬSutcliffeȱcoefficientȱofȱmodelȱefficiency.ȱ Theȱ verificationȱ periodȱ encompassedȱ theȱ hydrologicalȱ Tableȱ3.ȱSWATȱmodelȱaccuracyȱstatisticsȱforȱcalibrationȱandȱ yearsȱ ofȱ 1970ȱ andȱ 1971;ȱ duringȱ thisȱ twoȬyearȱ simulationȱ verificationȱphasesȱforȱbothȱdailyȱandȱmonthlyȱtimeȱsteps.ȱ period,ȱ noȱ changesȱ wereȱ madeȱ toȱ theȱ calibratedȱ 4.1ȱDailyȱTimeȱstepȱ parameters.ȱ Theȱ resultsȱ showȱ thatȱ theȱ dischargesȱ predictedȱ fromȱ theȱ modelȱ wereȱ notȱ veryȱ differentȱ fromȱ 4.1.1ȱCalibrationȱ thoseȱ observedȱ duringȱ theȱ calibrationȱ periodȱ (Figureȱ 3).ȱ Theȱ calculatedȱ flowȱ isȱ lessȱ thanȱ whatȱ wasȱ measuredȱ forȱ Inȱ general,ȱ theȱ modelȱ wasȱ ableȱ toȱ accuratelyȱ simulateȱ bothȱ years,ȱ theȱ differencesȱ beingȱ 37%ȱ andȱ 20%ȱ forȱ 1970ȱ dischargeȱratesȱbelowȱ80ȱm3/s,ȱbutȱitȱfailedȱtoȱsimulateȱhigherȱ andȱl971,ȱrespectively.ȱTheȱR2ȱwasȱ0.72,ȱandȱtheȱNSEȱwasȱ peaks.ȱThisȱmayȱbeȱpartlyȱattributableȱtoȱinconsistenciesȱinȱ 0.52,ȱforȱtheȱverificationȱperiodȱasȱshownȱinȱTableȱ5.ȱ theȱ dataȱ set,ȱ sinceȱ someȱ peaksȱ areȱ notȱ explainedȱ byȱ theȱ 350 magnitudeȱ ofȱ theȱ precipitationȱ recordedȱ forȱ theȱ 300 correspondingȱ period.ȱ Furthermore,ȱ givenȱ thatȱ theȱ riverȱ Measured ) 250 Calculated watershedȱisȱmainlyȱdominatedȱbyȱsandyȱloamȱsoilȱ(whichȱ 200 hasȱ aȱ highȱ infiltrationȱ rateȱ evenȱ whenȱ thoroughlyȱ wetted),ȱ 150 theseȱ resultsȱ suggestȱ thatȱ groundȱ waterȱ storageȱ mayȱ beȱ Discharge (m3/s 100 abstractingȱwaterȱfromȱeitherȱrunoffȱorȱfromȱtheȱriverȱitself.ȱȱ 50

ȱ 0 Aȱ plotȱ ofȱ predictedȱ andȱ measuredȱ dailyȱ streamȱ flowsȱ 3/8/70 5/2/71 2/6/72 5/17/70 7/26/70 10/4/70 2/21/71 7/11/71 9/19/71 (Figureȱ2)ȱoverȱtheȱ4Ȭyearȱȱcalibrationȱperiodȱ(1973Ȭ1976)ȱ 12/28/69 12/13/70 11/28/71 years showsȱ thatȱ theȱ modelȱ underestimatedȱ theȱ flowȱ forȱ theȱ ȱ yearsȱ 1973ȱ andȱ 1974ȱ byȱ 18%ȱ andȱ 23%,ȱ respectively,ȱ andȱ Figureȱ3.ȱPredictedȱandȱsimulatedȱstreamȱflowȱforȱtheȱ overestimatedȱitȱbyȱ15%ȱandȱ21%ȱinȱ1975ȱandȱ1976.ȱȱ verificationȱperiodȱ1970Ȭ1971ȱ

8 Int. j. water sci., 2013, Vol. 2, 1:2013 www.intechopen.com 4.2ȱMonthlyȱtimeȱstepȱ 2500 ) 4.2.1ȱCalibrationȱ 2000 1500 Theȱmonthlyȱstreamȱflowȱdataȱthatȱwasȱcollectedȱduringȱ 1000 theȱfourȬyearȱcalibrationȱperiodȱ(1973Ȭ1976)ȱrevealedȱthatȱ 500 Discharge (m3/s Discharge predictedȱ flowsȱ closelyȱ followedȱ observedȱ flowsȱ (Figureȱ 0 4).ȱ Theȱ R2ȱ wasȱ 0.83,ȱ andȱ theȱ NSEȱ wasȱ 0.70,ȱ forȱ theȱ 1357911131517192123 calibrationȱperiodȱasȱshownȱinȱTableȱ6.ȱ Month number ȱ Observed Simulated Statisticsȱ Monthlyȱtimeȱstepȱ ȱ

Calibrationȱ Verificationȱ Figureȱ5.ȱMonthlyȱobservedȱandȱcalculatedȱdischargeȱforȱtheȱ verificationȱperiodȱ1970/01ȱȬȱ1970/12ȱ Nȱ 48ȱ 24ȱ

2 VarQobsȱ 178754.96ȱ 447066.8ȱ RespectiveȱR ȱandȱNSEȱvaluesȱofȱ0.83ȱandȱ0.66ȱ(Tableȱ6)ȱ

SSQȱ 2584100.52ȱ 3659731ȱ confirmȱ thisȱ conclusionȱ asȱ theyȱ areȱ similarȱ toȱ correspondingȱ valuesȱ forȱ theȱ calibrationȱ periodȱ (R2=0.83ȱ R ȱ 0.8346ȱ 0.8273 andȱNSE=0.70).ȱTheseȱverificationȱresultsȱindicateȱthatȱtheȱ NSEȱ 0.6988ȱ 0.6589 SWATȱ modelȱ wasȱ ableȱ toȱ accuratelyȱ replicateȱ monthlyȱ n,ȱnumberȱofȱvalues;ȱVarȱQobs,varianceȱofȱtheȱobservedȱflow;ȱSSQ,ȱsumȱofȱ cumulativeȱ streamȱ flowȱ levelsȱ atȱ theȱ outletȱ ofȱ theȱ squareȱdeviationsȱbetweenȱobservedȱandȱpredictedȱflowȱvalues;ȱR2,ȱlinearȱ watershedȱoverȱtheȱtimeȱperiodȱsimulated.ȱ correlationȱcoefficientȱbetweenȱobservedȱandȱpredictedȱflowȱvalues;ȱNSE,ȱ ȱ NashȬSutcliffeȱcoefficientȱofȱmodelȱefficiency.ȱ Inȱorderȱtoȱdevelop,ȱverify,ȱandȱimproveȱtheȱaccuracyȱofȱ Tableȱ6.ȱStatisticsȱforȱmonthlyȱdataȱ theȱmodel,ȱaȱsensitivityȱanalysisȱwasȱusedȱtoȱidentifyȱtheȱ factorsȱ thatȱ hadȱ theȱ greatestȱ impactȱ onȱ predictedȱ riverȱ Theȱ timeȬseriesȱ ofȱ predictedȱ andȱ monthlyȱ measuredȱ flowȱ (seeȱ Appendix).ȱ Theseȱ factorsȱ wereȱ foundȱ toȱ be:ȱ i)ȱ streamȱ flowsȱ thatȱ wereȱ usedȱ toȱ produceȱ theseȱ resultsȱ showȱthatȱthereȱwasȱaȱslightȱoverȱpredictionȱduringȱhighȱ Waterȱ capacityȱ ofȱ theȱ soilȱ layerȱ (SOL_AWC);ȱ ii)ȱ Meanȱ flowȱmonthsȱ(i.e.,ȱMarchȬMay)ȱinȱ1975ȱandȱaȱlargeȱunderȱ slopeȱ lengthȱ (SLSUBBSN);ȱ iii)ȱ Meanȱ slopeȱ steepnessȱ estimationȱduringȱtheȱsameȱmonthlyȱperiodȱinȱ1974;ȱthisȱ (SLOPE);ȱandȱiv)ȱThresholdȱdepthȱofȱwaterȱ(GWQMN).ȱȱȱ isȱ mostȱ likelyȱ becauseȱ ofȱ theȱ uncertaintiesȱ inȱ theȱ ȱ measuredȱ values.ȱ Theȱ dataȱ showedȱ aȱ remarkableȱ Scenarioȱ 2.5 1.5 2.5 4.5 differenceȱ inȱ theȱ magnitudeȱ ofȱ extremeȱ events;ȱ forȱ Monthȱ Baselineȱ S492 ȱ S479 ȱ S555 ȱ S559 ȱ example,ȱ theȱ recordedȱ streamȱ flowȱ duringȱ Marchȱ 1975ȱ Janȱ 1.32ȱ 1.67ȱ 2.86ȱ 2.44ȱ 2.05ȱ wasȱ 1176.2ȱ m3/s,ȱ whileȱ streamȱ flowsȱ inȱ Marchȱ 1974ȱ Febȱ 0.97ȱ 1.28ȱ 2.54ȱ 2.09ȱ 1.67ȱ Marȱ 19.40ȱ 22.87ȱ 21.59ȱ 22.35ȱ 24.27ȱ 3 measuredȱonlyȱ319.79ȱm /s.ȱȱ Aprȱ 26.84ȱ 32.45ȱ 29.24ȱ 30.95ȱ 34.40ȱ Mayȱ 12.15ȱ 14.90ȱ 13.38ȱ 14.25ȱ 16.25ȱ OBS MOD Junȱ 0.27ȱ 0.42ȱ 0.31ȱ 0.36ȱ 0.48ȱ Julȱ 0.01ȱ 0.01ȱ 0.01ȱ 0.14ȱ 2.39ȱ 3000 Augȱ 0.00ȱ 0.26ȱ 0.67ȱ 0.76ȱ 0.80ȱ ) 2500 Sepȱ 0.00ȱ 0.00ȱ 0.00ȱ 0.00ȱ 0.87ȱ 2000 Octȱ 0.00ȱ 0.93ȱ 0.84ȱ 1.49ȱ 2.90ȱ 1500 Novȱ 2.29ȱ 3.14ȱ 3.99ȱ 4.37ȱ 4.26ȱ 1000 Decȱ 3.68ȱ 6.03ȱ 7.71ȱ 7.22ȱ 6.62ȱ 500 Yearlyȱ Discharges (m3/s Discharges cumulativeȱ 66.95ȱ 83.96ȱ 83.14ȱ 86.42ȱ 96.95ȱ 0 Dec-72 May-74 Sep-75 Jan-77 Jun-78 Tableȱ4.ȱPredictedȱrelativeȱchangesȱinȱriverȱflowsȱ(m3ȱdayȬ1)ȱforȱ Month theȱ4ȱclimateȱchangeȱandȱbaselineȱscenarios.ȱ ȱ Figureȱ4.ȱMonthlyȱobservedȱandȱcalculatedȱdischargeȱforȱtheȱ Inȱ additionȱ toȱ theȱ curveȱ number,ȱ theȱ fractionȱ ofȱ calibrationȱperiodȱJanuary1973ȱȬȱDecemberȱ1976ȱ availableȱ waterȱ (AW)ȱ isȱ anȱ importantȱ parameterȱ inȱ calibratingȱ theȱ surfaceȱ runoff.ȱ Forȱ sandyȱ loamȱ soil,ȱ 4.2.2.ȱVerificationȱ predictedȱ riverȱ flowȱ wasȱ veryȱ sensitiveȱ toȱ AW;ȱ aȱ 0.1ȱ Whenȱ analyzingȱ theȱ performanceȱ onȱ aȱ monthlyȱ timeȬ increaseȱ inȱ theȱ latterȱ resultedȱ inȱ aȱ 0.31Ȭ0.53ȱ increaseȱ ofȱ scale,ȱ theȱ verificationȱ periodȱ showsȱ thatȱ theȱ modelȱ theȱ NSEȱ thatȱ indicatedȱ aȱ betterȱ fit,ȱ whileȱ R2ȱ remainedȱ performedȱwell,ȱalthoughȱitȱslightlyȱunderȱpredictedȱtheȱ quiteȱstable.ȱHowever,ȱaȱsimilarȱchangeȱinȱAWȱforȱclayȱ flowȱ forȱ theȱ wetȱ season.ȱ However,ȱ thereȱ wasȱ littleȱ loamȱ soilȱ didȱ notȱ significantlyȱ affectȱ theȱ performanceȱ differenceȱ betweenȱ predictedȱ andȱ measuredȱ valuesȱ forȱ criteria.ȱTheȱoptimalȱvaluesȱofȱ T m ȱforȱsandyȱloamȱandȱ Novemberȱ andȱ Decemberȱ 1970ȱ orȱ forȱ Januaryȱ 1971ȱ clayȱ loamȱ soilsȱ wereȱ 0.6ȱ andȱ 0.5ȱ respectively,ȱ bothȱ

(Figureȱ5).ȱȱ greaterȱthanȱtheȱrespectiveȱsoil’sT fc ,ȱindicatingȱthatȱoneȱ www.intechopen.com Alain Lubini and Jan Adamowski: Assessing the Potential Impacts of Four Climate Change 9 Scenarios on the Discharge of the Simiyu River, Tanzania Using the SWAT Model mayȱexpectȱsignificantȱpercolationȱfromȱtheȱtopȱlayersȱtoȱ composition,ȱwhichȱisȱmainlyȱmadeȱofȱclayȬloamȱ(73.05%)ȱ theȱunderlyingȱlayers.ȱȱ andȱ sandȱ clayȱloamȱ (25.73%)ȱ (Mulunguȱ etȱ al.ȱ 2007).ȱ Theȱ ȱ increaseȱinȱflowȱasȱshownȱaboveȱwillȱrequireȱadjustmentȱ Comparatively,ȱ theȱ followingȱ factorsȱ eachȱ hadȱ aȱ lesserȱ fromȱpolicyȱmakersȱinȱorderȱtoȱaddressȱtheȱriskȱrelatedȱtoȱ effectȱ onȱ overallȱ modelȱ performance:ȱ theȱ curveȱ numberȱ moreȱ rainfall,ȱ withȱ measuresȱ needingȱ toȱ beȱ takenȱ toȱ (CN),ȱbaseȱflowȱalphaȱfactorȱ(ALPHA_BF),ȱgroundȱwaterȱ protectȱ citiesȱ againstȱ likelyȱ floodingȱ andȱ waterborneȱ delayȱ (GW_DELAY),ȱ saturatedȱ hydraulicȱ conductivityȱ diseasesȱ(suchȱasȱmalaria,ȱcholera,ȱandȱdysentery).ȱȱȱȱ (SOL_K),ȱ groundwaterȱ “revap”ȱ coefficient,ȱ andȱ REVAPMNȱ (thresholdȱ depthȱ ofȱ waterȱ inȱ theȱ shallowȱ 40 aquiferȱforȱrevap).ȱForȱexample,ȱdespiteȱtheȱhydrologicalȱ m 35 responseȱ unitȱ (HRU)ȱ chosenȱ withinȱ theȱ watershed,ȱ anȱ 30 increaseȱinȱcurveȱnumberȱfromȱ35ȱtoȱ79ȱresultedȱinȱneitherȱ 25 theȱ NSEȱ norȱ theȱ R²ȱ beingȱ drasticallyȱ affected;ȱ withȱ theȱ 20 15 NSEȱdroppingȱfromȱ0.53ȱtoȱ0.51ȱandȱR²ȱdecreasingȱbyȱ1%.ȱ 10

Anȱ optimalȱ CNȱ valueȱ ofȱ 40ȱ wasȱ obtainedȱ forȱ soilsȱ Avg monthly wyld (m 5 allowingȱ moderateȱ waterȱ transmissionȱ andȱ infiltrationȱ 0 whenȱ thoroughlyȱ wettedȱ (U.Sȱ Naturalȱ Resourceȱ 123456789101112 ConservationȱServiceȱSoilȱHydrologicȱGroupȱB).ȱ Month Baseline Scenario1 Scenario2 Scenario3 Scenario4 ȱ 4.3ȱClimateȱchangeȱ Figureȱ6.ȱChangeȱinȱaverageȱmonthlyȱstreamȱflowsȱpredictedȱforȱ scenariosȱ1ȱȬȱ4ȱrelativeȱtoȱtheȱbaselineȱoverȱtheȱ4ȱyearȱsimulationȱ Cumulativeȱ monthlyȱ streamȱ flowsȱ (Tableȱ 4)ȱ predictedȱ periodȱ 1.5 4.5 underȱ theȱ fourȱ scenariosȱwereȱ24%ȱ( S479 )ȱ toȱ45%ȱ ( S559 )ȱ greaterȱ thanȱ thoseȱ underȱ theȱ baselineȱ scenario.ȱ Theȱ 5.ȱConclusionsȱandȱrecommendationsȱ greatestȱincreasesȱoccurredȱinȱtheȱrainyȱseasonȱ(Marchȱtoȱ 2.5 2.5 BecauseȱofȱtheȱstrategicȱimportanceȱofȱtheȱSimiyuȱRiverȱinȱ May).ȱ Scenariosȱ S492 ȱ andȱ S555 ȱ hadȱ identicalȱ risesȱ inȱ 2.5 relationȱ toȱ Africa’sȱ vulnerabilityȱ toȱ climateȱ change,ȱ thisȱ temperature,ȱbut S555 representedȱaȱgreaterȱequivalentȱriseȱ 2.5 2.5 studyȱchoseȱtoȱanalyzeȱtheȱimpactȱofȱfourȱpossibleȱfutureȱ inȱ [CO2]ȱ than S492 ,ȱ whileȱ S555 ȱ representedȱ anȱ overallȱ 2.5 climateȱchangeȱ scenariosȱonȱtheȱ dischargeȱ ofȱ theȱ Simiyuȱ greaterȱincreaseȱinȱprecipitationȱthan S492 .ȱApartȱfromȱtheȱ longȱrainyȱperiod,ȱ theȱpredictedȱ streamflowȱ wasȱgreaterȱ Riverȱ watershed,ȱ asȱ measuredȱ atȱ theȱ Ndangaluȱ gaugingȱ 2.5 2.5 station.ȱ Thisȱ studyȱ usedȱ aȱ SWATȱ modelȱ thatȱ wasȱ basedȱ underȱ S555 ȱ than S492 ,ȱ indicatingȱ thatȱ theȱ dischargeȱ wasȱ stronglyȱ influencedȱ byȱ theȱ increaseȱ inȱ precipitation.ȱ Forȱ onȱ aȱ fourȱ yearȱ calibrationȱ periodȱ andȱ aȱ twoȱ yearȱ allȱ fourȱ scenarios,ȱ theȱ magnitudeȱ ofȱ theȱ dischargesȱ verificationȱperiod,ȱwithȱbothȱcalibrationȱandȱverificationȱ recordedȱ duringȱ theȱ dryȱ periodȱ (Juneȱ throughȱ August)ȱ beingȱ performedȱ onȱ aȱ dailyȱ andȱ monthlyȱ basis.ȱ Theȱ increasedȱ slightlyȱ comparedȱ toȱ theȱ baselineȱ despiteȱ theȱ simulatedȱ climaticȱ scenariosȱ usedȱ inȱ thisȱ studyȱ wereȱ factȱ thatȱ theȱ precipitationsȱ wereȱ heldȱ atȱ 0ȱ mm.ȱ Anȱ hypotheticalȱ inȱ nature,ȱ andȱ thusȱ cannotȱ beȱ viewedȱ asȱ 4.5 illustrationȱ ofȱ thatȱ increaseȱ occurredȱ under S559 ,ȱ whereȱ assessmentsȱ ofȱ absoluteȱ futureȱ climaticȱ conditions.ȱ seasonalȱdischargeȱ[JulyȬAugust]ȱwasȱ13Ȭfoldȱgreaterȱthanȱ Nevertheless,ȱ theȱ SWATȱ predictionsȱ derivedȱ fromȱ thisȱ theȱ correspondingȱ baseline.ȱ Underȱ suchȱ circumstances,ȱ studyȱ provideȱ insightȱ intoȱ theȱ potentialȱ magnitudeȱ ofȱ waterȱ shortageȱ problemsȱ wouldȱ beȱ alleviatedȱ withȱ streamȱflowȱchangesȱthatȱcouldȱoccurȱasȱaȱresultȱofȱfutureȱ possibleȱbenefitsȱforȱagriculturalȱactivities.ȱ climaticȱchanges,ȱandȱtheȱcalculatedȱdischargesȱmayȱserveȱ ȱ asȱ aȱ comparativeȱ baselineȱ forȱ variousȱ otherȱ climateȱ Thisȱ resultȱ isȱ inȱ accordanceȱ withȱ theȱ conclusionsȱ drawnȱ changeȱscenarios.ȱȱ fromȱ theȱ studyȱ conductedȱ byȱ Githuiȱ etȱ al.ȱ (2008)ȱ inȱ ȱ westernȱKenya,ȱwhichȱisȱinȱtheȱsameȱregionȱasȱtheȱSimiyuȱ Inȱthisȱstudy,ȱtheȱmostȱsensitiveȱparametersȱidentifiedȱ Riverȱ watershed.ȱ Theyȱ foundȱ thatȱ streamflowȱ responseȱ wereȱ theȱ SOL_AWC,ȱ SLSUBBSN,ȱ SLOPE,ȱ andȱ wasȱ notȱ sensitiveȱ toȱ shiftsȱ inȱ temperatureȱ withȱ 6Ȭ115%ȱ GWQMN,ȱ andȱ notwithstandingȱ apparentȱ changeȱ (inȱ streamflow)ȱ whenȱ rainfallȱ variedȱ fromȱ 2.4Ȭ inconsistenciesȱ inȱ theȱ dataȱ seriesȱ used,ȱ theȱ modelȱ wasȱ 23.2%.ȱ fairlyȱaccurateȱinȱpredictingȱlowȱflows,ȱbutȱdidȱpoorlyȱinȱ ȱ predictingȱextremeȱpeakȱflowȱevents.ȱThisȱisȱlikelyȱdueȱtoȱ Theȱ predictedȱriseȱ inȱriverȱ flowȱ(Figureȱ 6)ȱ indicatesȱ thatȱ highȱ annualȱ andȱ variability,ȱ whichȱ createsȱ higherȱ theȱ potentialȱ forȱ heavyȱ floodȱ damageȱ duringȱ theȱ rainyȱ varianceȱ inȱ theȱ simulatedȱ streamflow.ȱ Evenȱ thoughȱ seasonȱofȱMarchȱthroughȱMayȱisȱpossible.ȱAȱcomparisonȱ sensitivityȱ analysesȱ onȱ individualȱ parametersȱ wereȱ betweenȱtheȱbaselineȱandȱscenariosȱ1ȱandȱ4ȱsuggestsȱthatȱ conducted,ȱ itȱ wouldȱ alsoȱ beȱ advisableȱ toȱ performȱ thereȱwillȱbeȱanȱincreaseȱofȱ17%ȱandȱ22%ȱofȱstreamȱflowȱ advancedȱuncertaintyȱanalysisȱinȱtheȱfutureȱtoȱdecreasingȱ duringȱthisȱperiod.ȱStreamȱflowȱisȱhighȱasȱaȱresultȱofȱanȱ theȱ influenceȱ ofȱ parameterȱ uncertaintyȱ onȱ theȱ modelingȱ increaseȱinȱrainfall;ȱthisȱimpliesȱhighȱrunoffȱdueȱtoȱtheȱsoilȱ results.ȱ Bothȱ approachesȱ shouldȱ beȱ consideredȱ duringȱ

10 Int. j. water sci., 2013, Vol. 2, 1:2013 www.intechopen.com watershedȱ delineation.ȱ Futureȱ studiesȱ usingȱ theȱ SWATȱ 7.ȱReferencesȱ modelȱ shouldȱ alsoȱ beȱ doneȱ withȱ longerȱ timeȱ seriesȱ inȱ orderȱtoȱimproveȱtheȱoutputȱmodelsȱforȱtheȱwatershed.ȱ [1] ArdoinȬBardinȱS.,ȱDezetterȱA.,ȱServat,ȱE.,ȱPaturelȱJ.E.,ȱ ȱ Maheȱ G.,ȱ Nielȱ H.,ȱ Dieulinȱ C.,ȱ (2009),ȱ Usingȱ generalȱ Forȱeachȱscenario,ȱincreasesȱinȱprecipitationȱhadȱaȱgreaterȱ circulationȱ modelȱ outputsȱ toȱ assessȱ impactsȱ ofȱ impactȱ onȱ theȱ river’sȱ dischargeȱ thanȱ increasesȱ inȱ climateȱ changeȱ onȱ runoffȱ forȱ largeȱ hydrologicalȱ temperature,ȱ andȱ thisȱ wasȱ foundȱ toȱ beȱ trueȱ onȱ bothȱ aȱ catchmentsȱ inȱ Westȱ Africa,ȱ Hydrologicalȱ Sciencesȱ seasonalȱbasisȱasȱwellȱasȱoverȱlongerȱperiodsȱofȱtime.ȱThisȱ 54(1),ȱ77Ȭ89ȱ supportsȱ theȱ findingsȱ ofȱ Wigleyȱ andȱ Jonesȱ (1984),ȱ whoȱ [2] Crul,ȱ R.C.M.,ȱ (1995),ȱ Limnologyȱ andȱ hydrologyȱ ofȱ evaluatedȱ theȱ influencesȱ ofȱ precipitationȱ changesȱ andȱ Lakeȱ Victoria.ȱ Verhandlungenȱ Internationaleȱ directȱ [CO2]ȱ effectsȱ onȱ streamȱ flowȱ andȱ foundȱ thatȱ forȱ VereinigungȱLimnologie,ȱ25,ȱ39–48.ȱ mostȱ riversȱ inȱ theȱ worldȱ annualȱ streamȱ flowȱ wasȱ moreȱ [3] Desankerȱ P.V.,ȱ Magadzaȱ C,ȱ (2001),ȱ Africa.ȱ In:ȱ sensitiveȱ toȱ precipitationȱ changesȱ thanȱ toȱ theȱ rateȱ ofȱ McCarthy,ȱJ.J.,ȱO.F.ȱCanziani,ȱN.A.ȱLeary,ȱD.J.ȱDokenȱ evapotranspiration.ȱ Theȱ forecastedȱ trendsȱ forȱ theȱ longȱ andȱK.S.ȱWhiteȱ(eds.).ȱClimateȱChangeȱ2001:ȱImpacts,ȱ rainyȱ seasonȱ thatȱ wereȱ producedȱ duringȱ theȱ differentȱ AdaptationȱandȱVulnerability.ȱIPCCȱWorkingȱGroupȱ simulationȱ scenariosȱ indicatedȱ thatȱ underȱ increasedȱ II,ȱ Thirdȱ Assessmentȱ Report,ȱ Cambridgeȱ Universityȱ precipitation,ȱfloodingȱwouldȱintensify.ȱTheȱconsequencesȱ Press.ȱ ofȱ severeȱ floodȱ damageȱ couldȱ negativelyȱ impactȱ [4] Doktorgradesȱ D.E.,ȱ (2009),ȱ Modellingȱ impactsȱ ofȱ communicationȱ infrastructure,ȱ farms,ȱ humanȱ health,ȱ climateȱchangeȱonȱwaterȱresourcesȱinȱtheȱVoltaȱBasin,ȱ humanȱ settlements,ȱ andȱ biodiversity.ȱȱInȱ someȱ areas,ȱ Westȱ Africa,ȱ Dieseȱ Dissertationȱ istȱ aufȱ demȱ commercialȱranchingȱmayȱmarginallyȱimproveȱasȱaȱresultȱ HochschulschriftenȱserverȱderȱULBȱBonn.ȱȱ ofȱincreasedȱrainfall,ȱwhereasȱcommunalȱranchingȱmightȱ http://hss.ulb.uniȬbonn.de/diss_onlineȱ elektronischȱ beȱ exposedȱ toȱ greaterȱ riskȱ becauseȱ ofȱ increasedȱ erosionȱ publiziertȱȱȱ andȱtheȱincursionȱofȱwoodyȱweeds.ȱȱ [5] Easterlingȱ W.E.,ȱ Rosenbergȱ N.J.,ȱ McKenneyȱ M.S.,ȱ ȱ Allanȱ Jonesȱ C.,ȱ Dykeȱ P. T.,ȱ Williamsȱ J.R.,ȱ (1992),ȱ Predictingȱ futureȱ rainfallȱ patternsȱ isȱ extremelyȱ difficultȱ Preparingȱtheȱerosionȱproductivityȱimpactȱcalculatorȱ andȱthisȱcreatedȱuncertaintiesȱinȱthisȱstudy.ȱȱThisȱisȱdueȱtoȱ (EPIC)ȱ modelȱ toȱ simulateȱ cropȱ responseȱ toȱ climateȱ theȱfactȱthatȱtheȱmodelsȱusedȱinȱthisȱstudyȱdidȱnotȱaccountȱ changeȱandȱtheȱdirectȱeffectsȱofȱCO2,Agriculturalȱandȱ forȱtheȱroleȱofȱtheȱElȱNinoȱsouthernȱoscillationȱ(ENSO)ȱorȱ ForestȱMeteorology,ȱ59ȱ(1992),ȱpp.ȱ17–34.ȱ otherȱ atmosphericȱ processesȱ affectedȱ byȱ landȱ coverȱ [6] FicklinȱD.ȱL.,ȱLuoȱY.,ȱLuedelingȱE.,ȱZhangȱM.,ȱ(2009),ȱ changeȱ andȱ dustȱ loadings.ȱ Suchȱ limitationsȱ makeȱ itȱ Climateȱ changeȱ sensitivityȱ assessmentȱ ofȱ aȱ highlyȱ difficultȱ toȱ quantifyȱ someȱ ofȱ theȱ impactsȱ ofȱ climateȱ agriculturalȱ watershedȱ usingȱ SWAT,ȱ Journalȱ ofȱ change,ȱ whichȱ isȱ whyȱ futureȱ researchȱ shouldȱ focusȱ onȱ Hydrology,ȱ Volumeȱ 374,ȱ Issuesȱ1–2,ȱ30,ȱ Pagesȱ16Ȭ29,ȱ integratedȱ approaches,ȱ asȱ wellȱ asȱ investigatingȱ theȱ linksȱ ISSNȱ0022Ȭ1694.ȱ betweenȱ climate,ȱ hydrological,ȱ andȱ ecosystemȱ models.ȱ [7] Githuiȱ F.,ȱ Gitauȱ W.,ȱ Mutuaȱ F.,ȱ Bauwensȱ W.,ȱ (2009),ȱ Furthermore,ȱ becauseȱ climateȱ changeȱ hasȱ veryȱ regionalȱ Climateȱ changeȱ impactȱ onȱ SWATȱ simulatedȱ impacts,ȱ climateȱ changeȱ scenariosȱ generatedȱ byȱ otherȱ streamflowȱ inȱ westernȱ ,ȱ Internationalȱ Journalȱ climateȱmodelsȱshouldȱbeȱcomparedȱwithȱscenariosȱsetȱupȱ ofȱClimatology,ȱ29,ȱ1823–1834.ȱ byȱtheȱGCMsȱbecauseȱitȱwillȱincreaseȱourȱunderstandingȱ [8] HeuvelmansȱG.,ȱGarciaȬQujanoȱJ.F.,ȱMuysȱB.,ȱȱFeyenȱJ.,ȱ ofȱ potentialȱ impactsȱ onȱ waterȱ resourcesȱ inȱ Africaȱ inȱ theȱ Coppinȱ P.,ȱ (2005),ȱ Modelingȱ theȱ waterȱ balanceȱ withȱ 21stȱcentury.ȱȱThereȱisȱaȱspecificȱneedȱtoȱdevelopȱregionalȱ SWATȱasȱpartȱofȱtheȱlandȱuseȱimpactȱevaluationȱinȱaȱ modelsȱthatȱincorporateȱlocalȱclimateȱaspectsȱ(fromȱeitherȱ lifeȱcycleȱstudyȱofȱCO2ȱemissionȱreductionȱscenarios,ȱ sparseȱ observationsȱ orȱ lowȱ resolutionȱ numericalȱ HydrologicalȱProcessesȱ19,ȱ729Ȭ748ȱȱȱȱȱȱ simulations)ȱ thatȱ canȱ beȱ usedȱ inȱ constructingȱ variousȱ [9] Houghtonȱ J.T.,ȱ Dingȱ Y.,ȱ Griggsȱ D.J.,ȱ Noguerȱ M.,ȱ Vanȱ climateȱ changeȱ scenarios.ȱ Researchȱ shouldȱ alsoȱ focusȱ onȱ derȱ Lindenȱ P. J. , ȱ Daiȱ X.,ȱ Maskellȱ K.,ȱ Johnsonȱ C.A.,ȱ evaluatingȱ strategiesȱ toȱ sustainȱ andȱ improveȱ theȱ (2001),ȱ Climateȱ Changeȱ 2001:ȱ Theȱ Scientificȱ Basis.ȱ developmentȱofȱtheȱSimiyuȱRiverȱandȱitsȱwatershedȱinȱaȱ Contributionȱ ofȱ Workingȱ Groupȱ Iȱ toȱ theȱ Thirdȱ changingȱenvironment.ȱStrategiesȱthatȱincorporateȱclimateȱ Assessmentȱ Reportȱ ofȱ theȱ Intergovernmentalȱ Panelȱ changeȱadaptationȱoptionsȱwillȱalsoȱenhanceȱthisȱprocess.ȱȱ onȱ Climateȱ Change.ȱ IPCCȱ (Intergovernmentalȱ Panelȱ onȱ Climateȱ Change),ȱ Cambridgeȱ Universityȱ Press,ȱ 6.ȱAcknowledgmentsȱ Cambridge,ȱNewȱYorkȱ [10] HulmeȱM.,ȱ(1994)ȱRegionalȱclimateȱchangeȱscenariosȱ TheȱadviceȱofȱProf.ȱWillyȱBauwensȱofȱVrijeȱUniversityȱisȱ basedȱ onȱ IPCCȱ emissionsȱ projectionsȱ withȱ someȱ appreciated.ȱ Theȱ financialȱ assistanceȱ ofȱ theȱ Flemishȱ illustrationsȱforȱAfricaȱarea,ȱBlackwellȱPublishingȱonȱ Interuniversityȱ Council,ȱ theȱ IUPWAREȱ program,ȱ theȱ behalfȱ ofȱ Theȱ Royalȱ Geographicalȱ Societyȱ (withȱ theȱ KatholiekeȱUniversiteitȱLeuven,ȱandȱanȱNSERCȱDiscoveryȱ InstituteȱofȱBritishȱGeographers)ȱ26(1):ȱ33Ȭ34ȱȱ GrantȱheldȱbyȱJanȱAdamowskiȱareȱacknowledged.ȱ

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