The Indus Basin of Pakistan - COnnecting REpositories · the system is unable to generate more economic benefits in the basin given the current constraints, including water allocation

119 The Indus Basin of Pakistan • http://dx.doi.org/10.1596/978-0-8213-9874-6

Key Messages

• Theintegratedmodelismostsensitivetoinflowsintothesystem,cropwaterrequirements,andthedepthtogroundwater.

• Thewaterallocationsperthe1991ProvincialAccordandwithinprovincesarethemostcriticalconstraintintheIndussystem.ByrelaxingtheAccordcon-straintandallowingoptimaleconomicallocationbetweenandwithinprov-inces,bothPunjabandSindhprovincesstandtogain.Theabilitytomanageextremeevents(forexample,drought)bymorereliablymeetingsystem-widedemandsisalsoenhanced.

• Climate futures were examined representing a plausible range of climatechanges within the next 80 years consistent with recent observationsandtheory.

• Gross domestic product (GDP),Ag-GDP, and household income are esti-matedtodecreaseby1.1,5.1,and2.0percent,respectively,onanannualbasisasaresultofplausibleclimatechanges. Inthemostextremefuture—wheninflow is 90 percent exceedance probability and the temperature increases+4.5°C—GDP,Ag-GDP,andhouseholdincomeareestimatedtodecreaseby2.7,12.0,and5.5percent,respectively,onanannualbasis.

• Climate impactsoncropproductionaregreatest inSindh(–10percentonaverage).

• Irrigatedrice,sugarcane,cotton,andwheatdemonstratedthegreatestsensi-tivitytoclimate,andchangesrepresentbothresponsetoclimateanddynamicresponses to water availability and price changes. Milk revenues are alsoexpectedtodecrease.

• Three possible adaptation investments were evaluated: improvements tosystem-wideefficiency,constructionofnewstorage,andinvestmentsinagri-culturetechnologiestoincreasecropyield.

• Fromasystemperspective,additionalstorageprovidesagriculturalbenefitsbymitigatingtheeffectsofdroughts,butitprovideslittleadditionalagriculturalbenefit(assumingnoexpansionofthecurrentirrigatedarea)inotheryears.

C h a p t e r 6

Sensitivity and Scenario Results

120 Sensitivity and Scenario Results

The Indus Basin of Pakistan • http://dx.doi.org/10.1596/978-0-8213-9874-6

Thisisatleastpartiallyduetothecurrentconstraintsonagriculturalproduc-tion,includingallocationconstraintssuchasthe1991Accord.

• Although the model does not optimize for hydroelectricity production,additional storage does result in increased hydropower and consequenteconomicbenefit.Floodriskreductionwasnotconsideredinthisreportbutispotentiallysignificant.

• Canalefficiencyandcropyieldinvestmentsshowpotentialtominimizetheimpacts of future climate risks and meet food self-sufficiency objectives,increasingproductionby5–11percentonaverageandoffsettingfutureclimatelosses.

• Withoutspecificinterventions,environmentalconsiderations,suchasflowtothesea,changesindepthtogroundwater,andtheoverallsalinitysituation,areprojected to worsen. Potential adjustments to climate and food risks needadditionalinvestigation.

Sensitivities of hydrologic parameters and the DIVaCrD Constraint

The most sensitive parameters in the Indus Basin Model Revised (IBMR)(figure6.1a)arestreaminflowintotheIndus,cropwaterrequirement,anddepthtogroundwater.Theobjectivevaluecanchangesignificantlywithdifferentavail-ableinflows.Thelowesttotalinflowtested(90percentexceedanceprobability)is101millionacre-feet (MAF)and thehighestvalue (10percentexceedanceprobability)is209MAF.Theobjectivevaluedecreasestoalmost60percentofthebaseline(seechapter5,section“Baseline:Year2008–09”)whentheinflowsdropto101MAF.Whentheinflowincreasesto209MAF,theobjectivevaluechangeissmall(+0.1percentfromthebaseline).Thatis,forhigh-flowsettings,thesystemisunabletogeneratemoreeconomicbenefitsinthebasingiventhecurrent constraints, including water allocation requirements from the 1991Accord and physical capacity and land area constraints (that is, irrigated areaservedbytheIndusBasinIrrigationSystem[IBIS]isfixed).

Increasingtemperaturesareexpectedtoincreaseevaporativedemandfromcropsandsoils,whichwouldincreasetheamountofwaterrequiredtoachieveagiven levelofplantproduction(BrownandHansen2008).ThecropwaterrequirementparametersinIBMRarebasedontheoreticalconsumptiverequire-ments,surveydata,andmodelexperimentsofwaterbalancesoftheentirebasin(Ahmad,Brooke,andKutcher1990).AlocalstudybyNaheedandRasul(2010)isusedtolinkcropwaterrequirementandairtemperaturechangeundertheassumptionthatcropphenologyandmanagementwillremainthesameunderdifferentair temperatureconditions.Themodelingresults indicate thatwhenthecropwaterrequirementincreasesmorethan5percentabovethebaselineirrigation requirements (corresponding to a temperature increase larger than2°C), the objective value drops significantly. Figure 6.1a shows that thistemperatureincreasewillresultina42percentdecreaseintheobjectivevalue(fromthebaseline).Thehighesttestedcropwaterrequirementis+35percentmore than the baseline which corresponds to a 6.5°C temperature increase.

Sensitivity and Scenario Results 121


Thisisanunlikelytemperaturechangeinthenextseveraldecadesbutillustra-tiveofthedynamicsandsensitivityofthesystem.

Theobjectivevalueisalsosensitivetothedepthtogroundwater,whichvariesacross agro-climatic zones (ACZs). Figure 6.1a shows that the objective valuedecreasesbyabout4percentwhenaveragegroundwaterdepthdoublesthrough-outthesystem.Notethattheunitpumpingcostisconstant(afunctionofvolumeonly)anddoesnotincreasewithdepth.Thisisalimitationinthecurrentmodel.Groundwaterissuesarediscussedfurtherinthe“EnvironmentIssues”section.

The historical canal diversion constraint (DIVACRD) simulates the 1991ProvincialAccord requirement (described inchapter2).Thiswaterallocation

Figure 6.1 IBMr Sensitivity analysis results

a. Sensitivity analysis results on hydrologic parameters

–60 –50 –40 –30 –20 –10 0

Rainfall (150–350 mm)

Depth to groundwater (0–100% increase)

Water requirement (0–35% increase)

Inflow (10–90% exceedance)

Objective value (percentage change)

Note: IBMR = Indus Basin Model Revised. Objective value baseline = PRs 2,850 billion.

0

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Without

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Obj

ectiv

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lue

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, bill

ions

)

DIVACRD (%)

Note: Empty data points indicate the use of imaginary water. The “±” means tolerance range, the “(B)” means baseline and “Without” means runs without the Accord.



constraint is the most critical constraint in the model. Figure 6.1b showstheobjectivevalueforvaryinglevelsoftheconstraint—fromstrictadherencetonoconstraint.Fora±xpercentdeviation,canaldiversionscanvarybetweena(1–x) to (1+x) fractionof thehistorical canal allocations.As the constraint isincreasinglyrelaxed,moreobjectivevalue(thatis,economicbenefit)ispossible.The objective value ranges by a factor of 2.When the deviation allowed issmallerthan±15percent,theobjectivevalueshowsthelargestchanges.Byfullyrelaxing this constraint, the largest objective value is achieved. Under thesecircumstances,theonlybindingconstraintbecomestheactualphysicalcapacityofthesystem,bothcanalandlandcapacities.Notethatforsubsequentscenarios,a ±20 percent deviation is used as the baseline. This is the point at whichsufficientirrigationwaterisavailable.

Table6.1showstheimpactofDIVACRDacrossthedifferentprovinces.Bycompletelyrelaxingthisconstraint,theoptimizedallocationresultsinadditionalcanalwatertobothPunjabandSindhandaconsequent increase ineconomicbenefits toboth. In absolute terms, the additionalnet revenues inPunjab areestimatedtobePRs83,464million(US$1.04billion);forSindhtheyareesti-matedtobePRs82,584million(US$1.03billionin2009).TheincreaseinSindh(18percent)islargerinpercentagetermsthanPunjab(4percent).Moreover,amarginal analysis was done on this constraint over the entire basin whichrevealed that the incremental value-added per acre-foot (AF) is greatest inPunjab.ThisreflectsthehighernetreturnsfromtheagriculturesectorinPunjab.

TheaggregategainsfromrelaxingDIVACRDinvolverelativegainsbysomeACZs,canalcommands,andcropsascomparedwithothers.ThisresultsfromtheallocationofwatertoitsmosteconomicallyproductiveusesattheACZlevel.Thus,themodelsimulatesoptimaleconomicallocationboth between and within provinces.Forexample,whilePunjabwouldgaina5percent increase incanaldiversionswiththerelaxationofDIVACRD,themodelalsoshowsthatwithinPunjabsomeACZswouldloseupto5percent.Thus,inordertoimplementandrealize the fullbenefitsof relaxing the1991Accord, considerationofhow toprovideincentivesforwinnersandloserswithinprovincesmaybeasimportant

table 6.1 analysis of Impact across provinces with and without the provincial accord(PRs, millions)

ProvinceRevenue

(PRs, millions)Cost

(PRs, millions)Net

revenueaProfit change if

remove DIVACRDCanal diversion

(MAF)Change in

canal diversion Marginal (PRs/AF)

Fixed provincial allocationPunjab 2,390,054 433,072 1,956,982 55.8 n.a n.aSindh 573,822 116,339 457,482 45.6 n.a n.aOthers 104,218 27,257 76,961 9.3 n.a n.a

Optimized allocationPunjab 2,503,663 463,216 2,040,447 83,464 (4%) 61.4 5.6 14,904

Sindh 718,849 178,782 540,067 82,584 (18%) 57.0 11.4 7,244Others 103,913 34,038 69,876 –7,086 (–9%) 6.6 –2.7 –2,624

Note: n.a. = not applicable, DIVACRD = 1991 Provincial Water Allocation Accord, MAF = million acre-feet, AF = acre-foot.a. Net revenue = revenue−cost.



(ormore)thantheneededincentivesbetweenprovinces,whichforSindhandPunjabareself-evidentattheprovinciallevel.

ThepotentialbenefitsofrelaxingtheDIVACRDconstraintlookpromising.However,theseoptimizationresultsmustbeweighedinrelationtothecurrentstateofinterprovincialwaterrelationsandadministration.Asnotedinchapter2,the1991Accordwasestablishedtoclarifyinterprovincialshares,thatis,withtheaimofincreasingthereliabilityofprovincialsharesanddeliveries,andtherebyincreasing theprospect forconsensuson future infrastructuredevelopmentofthe sort envisioned by theWater Sector Investment Planning Study (WSIPS,WAPDA 1990). Unfortunately, neither aim has been sufficiently achieved.Briefly, Sindh did not trust Punjab’s diversions before independence in1947,letalonebeforetheAccordof1991(seeMichel1967);neitherprovincehastrustedtheotherorIndusRiverSystemAuthority(IRSA)undertheAccordascurrentlyadministered;andpresumablytheywouldnotexpecttotheothertotakeorreceivetheir“optimalshares”underarelaxedAccord.IRSA’stechnicaland administrative limitations have been discussed in previous studies (seereviewbyTariqandUlMulk2005,forBriscoeandQamar2006).

Furthermore, it isworthwhilenoting that themeasurementof actual canalflows,watercoursediversions, andwateruses remainsuncertain anddisputed.Provincialdepartmentshavesoughtinvariouswaystoraisetheempiricalstan-dardsformonitoringirrigationwaterdiversionsanduse.Attheinterprovinciallevel, however, Pakistan made an unsuccessful effort to install telemetryequipment to improve real-time data quality for deliveries under the 1991Accord,whichIRSAeventuallyhadtoabandon.Therehavebeenrecentcallsforrenewedinvestmentinanadvanced,high-qualitymeasurementsystem.

Finally,eventhoughit isunlikelyandprobablyunwisethattheDIVACRDconstraintshould—byitself—berelaxed,thereisroomforflexiblepolicyadjust-mentsandmechanismswithinthewiderframeworkofthepresentAccord(forexample,interprovincialexchangeofsurplusallocations,waterbanking,andleas-ingarrangements),whichtheIBMRmodelingresultssuggestshouldbepursuedonagro-economicgrounds.Thesecould includemechanismswithinprovincesfor exchanging water for compensation and also mechanisms for exchangebetweenprovinces.Theresults suggest that theremaybesignificantgainsnotonly intermsofrelaxingtheprovincialAccordbutalso in implementingeco-nomicallocationwithinprovinces.Infact,neitherismutuallyexclusive,andthegreatestgainswouldresultfromeconomicallocationatbothlevels.

Future Climate risk Scenarios

Climatescenariosweredevelopedtoexaminetheeffectsofpossiblehydrologicalorclimaticchangesthatmayoccurinthefuture.Giventhelowconfidenceingeneral circulationmodel (GCM)projections in this region (Immerzeel et al.2011), amore robust approachwouldbe toevaluate responses across awiderangeofplausibleclimatefutures.Notethatsomeofthefuturescenariosincludegreaterprecipitation,butallfeaturewarmertemperatures.Afutureyearisnot



specifiedsincetheIBMRisasingle-yearmodel.Allresultsarecomparedwithabaselinebasedoncurrentclimate.Thus,resultsforinvestmentsarepresentedas percent changes to those same investments under the current climate.ThebaselineincludestheDIVACRDconstraint.Resultsarepresentedasbox-whiskerplotsandshow1st,25th,50th,75th,and99thpercentiles.Itmustbenotedthat,sincethesearesingle-yearruns, impactsare likelytobeunderesti-matedsincethemodelassumesreservoirsareatfullstorageatthebeginningoftheyear.Moreover,thedepthtothewatertableisthesameacrossscenarioruns.Forthesereasonsalimitedmultiyearversionofthemodelwascreatedtoallowtheseresourcestodynamicallyvarytoillustratethebenefitsoftheinvestmentscenariosexamined(discussedlaterinthesection,“Long-TermCharacteristicsofInvestmentsandWaterProductivity”).

Climate Risk ScenariosTo generate a wide range of potential climate scenarios, combinations ofcorrespondinginflowandcropwaterrequirementparametersareused.Inflowisvaried from 10 to 90 percent exceedance probability using 10 percent incre-ments,andthecropwaterrequirementisvariedfrom+2.5,+5,to+20percent,correspondingto1°–4.5°Ctemperatureincreases(possiblyoccurringaroundthe2020s and 2080s, respectively (based on the GCM outputs from chapter 4).Furthermore,sincemuchofthewatersinthesystemoriginatefromtheUpperIndusBasin(UIB)intheHimalaya,climatechangeimpacts(usingcorrespondingtemperature and precipitation changes) on snow and ice in the UIB, andultimately on the inflows into the Indus main-stem basin (as described intable4.10)areincluded.Fromthese,atotalof70differentclimatefuturesaregenerated.Thesescenariosrepresentaplausiblerangeofclimatechangefutureswithin the next 80 years consistent with recent observations and theory.Theimpactsoftheseclimatefuturesonthecomputablegeneralequilibrium(CGE)andIBMRoutputsareshowninfigure6.2.

Generally, negative impacts are estimated under these climate riskscenarios.Grossdomesticproduct (GDP),Ag-GDP, andhousehold incomeareestimatedtodecreaseby1.1,5.1,and2percent,respectively.Inthemostextreme climate future (when inflow is 90 percent exceedance probabilityand the temperature increases +4.5°C), GDP, Ag-GDP, and householdincomeare estimated todecreaseby2.7,12, and5.3percent, respectively.Figure6.3alsodemonstrates thatmostof thenegative impactson incomeswilloccurforthosehouseholdsoutsideoftheagriculturesector(exceptforthoselivinginprovincesotherthanPunjabandSindh).Sincetheincreaseinpricesislargerthanthedecreaseinproduction,farm-relatedhouseholdswilllikelybenefit.However,non-farmhouseholds(forexample,urban)willhavetopaymoreforfood,thusresultingindecreasinghouseholdincomes.Whentheaggregatedhouseholdincomeiscalculatedatthenationallevel,themodelweighted each household against their baseline incomes. Since non-farmhouseholdshavehigherweights, the aggregatedhousehold income showsanegativeimpact.



Figure 6.2 CGe and IBMr Outcomes under Climate risk Scenarios

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GDP Ag-GDP Household income

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Note: CGE = computable general equilibrium, IBMR = Indus Basin Model Revised. The upper error bar represents 99 percent, the upper box represents 75 percent, the middle line of the box represents 50 percent, the lower box represents 25 percent and the lower error represents 1 percent.

Figure 6.3 household Income Changes under Various Climate Scenarios for Different households

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Punjab

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others

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Baseline(PRs, billions)

1,706 488 518 1,425 5,295



Figure 6.2b shows that total crop production is estimated to decrease0–13percent.Thechangeinpowergenerationvariesthemost,from+22percentto–34percent.Increasesareduetomoresurfacewaterbecomingavailablefrommoresnow-melt.

Figure6.4breaksdownthecropproductionintodifferentprovincesandcrops.Theproductionchangesaregreatest inSindh(around10percenton average).Inthemostextremeclimatefuture,a36percentdecreaseincropproductionisestimated inSindhanda5percentdecrease inPunjab.Figure6.4bshowsfivecropsthatcontributemosttothetotalcroprevenue(seetable5.2).Thelargestprojectedproductiondecreasewillbeforirrigatedriceandsugarcanewhere,intheworstcasescenario,almost25percentand20percentdecreases,respectively,are estimated (6 percent and 5.7 percent average decrease). The worst-casescenarios for cotton and wheat are reductions of 2 percent and 7 percent,respectively.Basmatiricehasaverysmallnegativeimpact(lessthan1percent)undertheseclimatefutures.Notethattheseimpactsdonotconsiderchangesinthebiologicalcropyieldresponse(beyondthosechangesduetowaterrequire-ments)inthesesimulations.AccordingtoIqbaletal.(2009),forinstance,usingabio-physiological based model, wheat yields are expected to decrease about3percentundertheA2scenarioand5percentunderB2inthe2080s.Thus,thesechangeswouldbeinadditiontowhatthisstudy’smodelcurrentlypredicts.

Figure 6.4 Crop production Changes under Climate risk Scenarios

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Basmati rice Irrigated rice Cotton Sugarcane Wheat

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Figure6.5showsthatthelargestchangesinrevenueareestimatedforwheat,sugarcane, irrigated rice, and cow and buffalo milk. In the extreme climatefutures,sugarcaneandirrigatedricerevenuesmaydropbyasmuchas13percent.Irrigatedriceunderthebestcircumstancesmaymarginallyincreaseinrevenue.

Hydrograph Monthly Shift ScenarioTheclimaterisk scenariospresent the inflow,precipitation,andtemperaturechange impacts under the assumption that the intra-annual hydrologicalpatternwillremainthesame.Thissectionpresentsanevaluationoftheeffectofashiftofthehydrographonemonthforward(AprilinflowbecomesMarchinflow) and backward (April inflow becomes May inflow).A monthly shiftforward is consistent with what a warming climate might do as describedearlier (see chapter 4) that is, earlier snow melt and peak flow. Figure 6.6ashows that a forward monthly shift can have a larger negative impact ontheeconomythanabackwardshift.Thisimpactislargerinmagnitudethantheaverageclimateriskscenario.Figure6.6bshowsalsothecropproductionandhydropower generation impacts. Less power is generated with these hydro-graphshiftssincelesswaterisstored.

Figure 6.5 Commodities revenue under Climate risk Scenarios

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Basmati rice Irrigated rice Cotton Sugarcane Wheat

Perc

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Baseline(PRs, millions) 749,694 170,466 674,609 245,950 418,049

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Cow milk Buffalo milk



“Worst” and “Best” Case Climate ScenariosThepossible“worst”and“best”caseclimatefuturesrepresentlowprobabilitybutpossiblyhighimpactscenarios(“surpriseevents”).Theworstcaseisdefinedasthe90percentexceedanceprobabilityinflow(only10percentofflowsarelessthan this level), a forward monthly hydrograph shift, 20 percent less rainfall,20 percent more water requirement (consistent with a +4.5°C change), andgroundwatertabledepths20percentdeeperthroughoutthebasin.Thebestcaseisdefinedasthe10percentexceedanceprobabilityinflow(90percentofflowsarelessthanthislevel),20percentmorerainfall,nochangeintheexistingcropwaterrequirements,andgroundwatertabledepths20percentshallower.AlmostallGCMprojectionsindicateincreasingtemperaturesinthefutureandahighuncertainty in thedirection andmagnitudeofprecipitation change.However,

Figure 6.6 CGe and IBMr Outcomes under hydrograph Monthly Shift Scenarios

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given thecounteractingnature that temperatureandprecipitationcanplay inoverall water availability in the Indus system, the probability of the best andworstclimatescenariosisbelievedtobequitesmall.Thatisnottosay,however,thatextremeeventsmayincreaseinfrequencyinthefuture,apredictionwhosescienceisyetinconclusive.

In the worst case, GDP,Ag-GDP, and household income decrease by 3.1,13.3,and6.1percent,respectively,onanannualbasis(figure6.7a).Inthebestcase,GDP,Ag-GDP,andhouseholdincomeincreaseby1.0,4.2,and1.3percent,

Figure 6.7 CGe and IBMr Outcomes under the “Worst” and “Best” Case Scenarios

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respectively.Theserangesrepresentarangeofpossibleeconomicfuturesinthebasin.Figure6.7bshowsthatthecropproductiondecreasesby13percentandincreasesby3percentundertheworstandbestscenarios,respectively.Similarly,powergenerationdecreasesby32percent and increasesby23percentunderworstandbestscenarios,respectively.Theseresultsindicatethatpowergenera-tion is more sensitive to climate changes than crop production.This in partreflects the alternative sourceof irrigation (that is, groundwater) available forcropproductionandalternativecroppingpatterns.

adaptation Investment Scenario analyses

Threedifferentinvestmentsarediscussedinthissection:canalandwatercourseefficiencyimprovements(CANEFF),newreservoirconstruction,andcropyieldimprovementinvestments.Toexaminetheroleplayedbyeachoftheseinvest-mentsovertimetheoriginalIBMRismodifiedforalimitedmultiyearanalysis(IndusBasinMulti-Year: IBMY).The IBMYuses the entire 50 yearhistoricalinflowtimeseries (1961–2010). In the IBMY,theDecemberreservoir storagefromthepreviousyearbecomestheinitialstorageforthecurrentyear.Moreover,depthtowatertableisrevisedeachyear.Allotherdata,includingthecropwaterrequirements,precipitation,price,anddemanddata,areassumedtobethesameeachyear.Thus,onlythewaterresourceendowmentisdynamic.

Improving System-Wide EfficiencyThefirstadaptationinvestmentistoimprovesystem-wideefficiency(CANEFF).The current canal and watercourse efficiency is estimated to be only 76 and55 percent, respectively.Thus, only about 40–50 percent of the water in thesystemisactuallyavailableforfieldlevelirrigation.Severalpreviousstudieshaveaddressedthis issue(PRCEngineering1986;WorldBank1996).Forexample,Cooley,Christian-Smith,andGleick(2008)evaluatedfourdifferentwater-savingscenariosfortheirrigationsystemsinCalifornia.Twocommonlyusedtechnolo-giesaresprinkleranddrip/micro-irrigationsystems.Sprinklerirrigationdeliverswatertothefieldthroughapressurizedpipesystemanddistributesitviarotatingsprinklerheads,spraynozzles,orasinglegun-typesprinkler.Thefieldefficiencyforsprinklerirrigationsystemisabout70–75percent(Cooley,Christian-Smith,andGleick2008).Dripirrigationistheslowapplicationoflow-pressurewaterfromplastictubingplacedneartheplant’srootzone.DripsystemscommonlyconsistofburiedPVCpipemainsandsubmainsattachedtosurfacepolyethylenelateral lines. The field efficiency for sprinkler irrigation system is about87.5–90 percent (Cooley, Christian-Smith, and Gleick 2008). Canal lining isanother traditional approach to improving irrigation system efficiency. It cancontrolseepagetosavewaterforfurtherextensionoftheirrigationnetworkandalsoreducewaterlogginginadjacentareas(Swamee,Mishra,andChahar2000).Skogerboeetal.(1999)estimatedthatfortheFordwahEasternSadiqiaprojectin Punjab, different types of canal lining can reduce the seepage losses by50 percent. This study models an adaptation investment scenario whereby



the system-wide efficiency is improved to 50 percent (from the existing35percent)—primarilythroughcanalandwatercourseimprovements.

New Storage in the Indus BasinThe second adaptation investment is the construction of new reservoirs(NEWDAM).Theconstructionoflargedamscanincreasethecountry’swaterstoragecapacityandbettermanagescarcity.Newdamswillalsoaddpowergen-eration;therebyhelpingtomeetthecountry’sexpandingelectricityneeds.Inthisanalysis,additionalstorageisprimarilyevaluatedintermsofitsabilitytoimproveagriculturalproductionfortheexistingirrigationsystemunderclimatechangeconditions.The potential economic value of storage for flood risk reduction,improved drought management, and expansion of the irrigated area is notincluded.Althoughhydropowerproductionisestimated,thevalueofthatelec-tricityisnotincludedhere,andthusdoesnotfactorintoGDPorobjectivefunc-tionresults.Thus,thisevaluationshouldnotbeseenasacost-benefitanalysisofnewdamconstruction.Theadaptationinvestmentusedhereintroducesabout13MAFintothemodelingstructure.Theoperationrulesandstorage-levelrela-tionshipisassumedthesameastheexistingreservoirsinthesystem.

Improving Crop Technologies and YieldsThethirdadaptationinvestmentisnewcroptechnologiestoimprovecropyields(CYIELD).Asnotedearlier,itisassumedthatcropyieldisconstantovertimeforeachcropateachACZ.However,itisreasonabletoassumethatcropyieldswill improve in the futureas a resultofnew technologies andon-farmwatermanagementimprovements(asshowninfigure2.10).Forexample,biotechnol-ogyinvestmentsingeneticallymodified(GM)cropspromisegreatbenefitsforbothproducers and consumers of agricultural products, although the applica-tionsofGMarealsoassociatedwithpotentialrisks(FAO2002).

OneofthemostsuccessfulexamplesofbiotechnologyistheapplicationofBTcotton inChina.Basedon surveydata,Huanget al. (2002) reported thatfarmerswhousedBTcottonobservedincreasedoutputperhectareandincreasedtheirincomesduetoreducedpesticidesandlaborinputs.SincenodetailedGMcropdataisavailable,aroughestimationwasmadeofcropyieldimprovementbasedonFAO(2002)dataonyieldtrendsfordifferentareasanddifferentcrops.For example, for developing countries the wheat yield improvement is about2.0 percent per year and for rice is about 1.1 percent per year (over the1989–99period).Themodelincludesanadaptationinvestmentthatassumesa20 percent yield improvement,whichwill represent thepossible yield in thenext10–20yearsaccordingtoFAOestimates.

Long-Term Characteristics of Investments and Water ProductivityThecumulativedistributionfunctions(CDF)oftheIBMRobjectivevalueforthethreeinvestmentscenariosjustdescribedfromthe50-yearhistoricalrecordarepresentedinfigure6.8.TheCDFisagraphthatdescribestheprobabilityoffindinganobjectivevalueatthatvalueorless.This50-yearsimulationincludes



droughtyearsandcorrespondinglylowvaluesoftheobjectivevalue.TheCANEFFandCYIELDinvestmentsshifttheCDFtotherightofthebaseline,indicatingthattheaverageobjectivevaluetendstoincreaseundertheseinvestments.Thelong left-side tailsof theCDFsof these two investmentsaredue tovery lowvaluesthatoccurredindifficultyears,suchasthedroughtsthatoccuroverthe50yearsimulation.TheNEWDAMinvestmentisuniqueinthatiteliminatestheleft-sidetail,showingthatadditionalstoragereducestheprobabilityofverylowobjectivevalues,thusmitigatingtheeffectsofdroughtyears.However, itdoesnot increase the objective value under normal and high flow years. This isprimarilybecausetheobjectivefunctiondoesnotincludetheeconomicbenefitsfromadditionalhydropowergenerationandfloodcontrol.Inaddition,whiletheincreasedreservoirvolumemaysupplymorewaterasaresultoftheconstraintsoftheAccord,thatwatercannotbeputtouseeffectively.“Combo”isallthreeinvestmentscombined.SummarystatisticsfortheseCDFsareshownintable6.2.Noticethatallmeanvaluesarehigherthanthebaselineandthatthestandarddeviationisreduced,especiallysofortheNEWDAMinvestment.

Performance of Adaptation InvestmentThissectionisanevaluationoftheperformanceoftheadaptationinvestmentsunder the range of future climate risk scenarios.The DIVACRD constraint isenforcedinallmodelruns.Figure6.9andtable6.3showthattheCANEFFandCYIELD investments can significantly improve macroeconomic performanceandhouseholdincomeunderaclimatechangefuture.Insteadoflossesof1,5,and2percentforGDP,Ag-GDP,andhouseholdincome,respectively,withthese

Figure 6.8 Cumulative Distribution Functions of IBMr-2012 Objective Value for Different adaptation Investments (without Climate risk Scenarios)

Note: CANEFF = canal and watercourse efficiency improvement, CYIELD = crop yield, NEWDAM = construction of new reservoirs. The cumulative distribution function (CDF) is a graph of the value of the objective function versus the probability that value will occur.

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table 6.2 Mean and Standard Deviation Objective Value for 50 Years from Different Investments

Objective value (PRs, billions)

With DIVACRD

Mean SD

Baseline 2,619 491 CANEFF 2,802 465 NEWDAM 2,843 38 CYIELD 3,085 466Combo 3,451 34

Note: CANEFF = canal and watercourse efficiency improvement, CYIELD = crop yield, NEWDAM = construction of new reservoirs.

Figure 6.9 economic Outcomes from CGe under Different Investments

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adaptationinvestmentsimpactsarepositive.Forexample,theaverageAg-GDPwillincreasebyabout4percentand11percentwiththeCANEFFandCYIELDinvestments,respectively.TheCANEFFandCYIELDinvestmentsshowaclearpositive shift with very low probabilities of observing negative changes. TheNEWDAMinvestment showsminor improvementandreduces the impactofthe1stpercentileclimatefuture.Asdiscussedintheprevioussection,thisfindingreflectsprimarilythecontributionofadditionalstoragetoanexistingirrigationsystemanddoesnot incorporateother potentialbenefits to theeconomyandhouseholds.

Examining the impactof these investmentsoncropproductionandpowergeneration(figure6.10)showsthattherelativeefficacyoftheseinvestmentsoncrop production is similar.The CANEFF and CYIELD investments result ingreatercropproduction(5–11percentmoreonaverage)thantheNEWDAMinvestment.TheNEWDAMinvestment,ontheotherhand,canminimizetheimpactsofextremeclimateimpactlossesandreducevariability.Moreover,thepowergenerationbenefitscanbequitelargewiththeNEWDAMinvestment.The highest power generation increase is 130 percent. Even under the worstclimatescenariopowergenerationstill increasesby20percent.Theeconomicvalueofnewreservoirsunderthisanalysiswouldbealmostentirelyfromthesepowerbenefitsandfromareductionintheimpactsofextremeevents.

Investment CostsThecostofasystem-widecanalefficiencyprogram(toachievethe50percentscenario) is difficult to quantify because of different approaches used anddiversityingeographicconditions.Skogerboeetal.(1999)reportthatthecostforcanallininginPakistan(fortheFordwahEasternSadiqiaProject)rangedPRs608–3,725 per foot of canal (in 1999 PRs).The reduction in seepage rangedfrom a factor of 2–10, depending on the prevalent conditions. Using theseindicative costs estimates and assuming that all 60,000 km of watercoursesin the IBIS are lined, a cost range of PRs 180–1,107 billion is calculated.Similarly,system-wideefficiencycanbeimproveddirectlyattheon-farmlevels.Cooley, Christian-Smith, and Gleick (2008) estimated the unit cost forsprinkler (US$1,000–3,500 per acre) and for drip/micro-irrigation systems

table 6.3 Impact of Different adaptation Investments under Climate risks

GDP Agri-GDP Household income

Average change without investments (%)No investment –1.1 –5.1 –2Average gain with investments (%)CANEFF 2.04 9.32 3.21NEWDAM 0.29 1.5 0.64CYIELD 3.66 16.7 5.42Combo 6.05 27.4 7.45

Note: CANEFF = canal and watercourse efficiency improvement, CYIELD = crop yield, NEWDAM = construction of new reservoirs.



(US$500–2,000peracre).ThecostfornewstoragecanbefoundontheWaterandPowerDevelopmentAuthority(WAPDA)website.TheestimatedcostforBashaandKalabagh(twooftendiscussedreservoirs)isPRs390,000millionandPRs366,000millionin2005value,respectively.Therefore,thetotalestimatedcostfornewreservoirsconstructionisPRs390,000+366,000=PRs756billion(US$9.54billion).Finally,estimatingtheinvestmentcostrequiredfornewtech-nologies and research and development to raise crop yields by 20 percent isdifficultbecauseoftheinherentcomplexityassociatedwiththeseinvestments.According to a study by Menrad, Gabriel, and Gylling (2009), the additionalcosts per tons for GM and non–genetically-modified rapeseed oil, sugar, andwheatarePRs32,400,20,160,and15,680,respectively,inGermany.Therefore,theaverageadditionalcostpertonsisPRs22,746.

Effect of Investments on Food Self-SupplyHavingaccesstosufficientquantitiesoffoodisanindicatoroffoodsecurity.Thus,changes incropproductionaredirectly related toPakistan’sability tobe foodself-sufficient,notwithstandingtherolethatfoodimportsmayplay.Wheat-basedproducts(flourandbread)areamajorpartofthedietinPakistan.Theseprovideupwardsof60percentoftheproteinandcarbohydrateintheaveragePakistanidiet (Bastin, Sarwar, and Kazmi 2008). Supply and demand of wheat areused to estimate the impact that climate change may have on the nutritional

Figure 6.10 IBMr Outcomes under Different Investments

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requirementsinPakistan.Bastin,Sarwar,andKazmi(2008)calculatedaconver-sionfactorfromwheatproductiontocombinedproteinandcarbohydratesupplyin flour. The average value is 70 percent, which means that 1,000 tons ofwheatcanprovide700tonsofproteinandcarbohydrateinflour.Thisconvertingfactoristhenmultipliedbythe50-yearaveragewheatproductionandtheproteinandcarbohydratesupplyarecalculatedunderbaselineandalladaptationinvest-mentscenarios.UsingtheGCMprojectionsfromchapter4,sixtemperatureandprecipitation combinations were selected for the 2020s, 2050s, and 2080s(seetable6.4).

Theproteinandcarbohydraterequirementisestimatedbasedonthepopula-tion. Historical data show a strong linear relationship between Pakistan totalpopulationandtotalrequirementofproteinandcarbohydrate.Thisequationisusedtoestimatetheproteinandcarbohydraterequirementin2020s,2050s,and2080s,basedonfuturepopulationestimates.Theresultsofproteinandcarbohy-dratesupplyandrequirementinalltheseyearsunderallinvestmentscenariosaregiven in table6.5.The supply ishigher than thedemandduring thebaselineand2020stimeperiod.However, thesupplywillbe less thandemandbythe2050s without any investment. Only the CYIELD investment can maintainthe production to meet the future protein and carbohydrate requirements.

table 6.4 projected temperature, precipitation, and Inflow Changes

YearsProjected

temperature (°C)Projected

precipitation (%)Crop water requirement

change (%) Inflow change (%)

2020s +1.5 Low: 0 +4 –4High: +10 +4 +4

2050s +3 Low: –10 +10 –8High: +20 +10 +17

2080s +4.5 Low: –10 +20 +1High: +10 +20 +18

Note: Temperature and precipitation projection follow the average general circulation model (GCM) results in chapter 4; inflow changes from current condition are calculated by the model in chapter 3.

table 6.5 protein and Carbohydrate Supply and requirements under Climate Change estimates

Pakistan population (millions)

Cereal-based protein and carbohydrate demand

(tons, millions)

Protein and carbohydrate supply (tons, millions)

Baseline CANEFF NEWDAM CYIELD

Baseline 167.4 10.1 16.3 18.0 16.4 19.82020-low P 227.8 13.7 16.1 17.7 16.2 19.42020-high P 16.2 17.8 16.3 19.52050-low P

307.2 18.415.8 17.2 15.9 19.0

2050-high P 15.9 17.4 15.9 19.12080-low P 386.7 23.1 15.5 16.8 15.6 18.62080-high P 15.5 16.8 15.6 18.7

Note: CANEFF = canal and watercourse efficiency improvement, CYIELD = crop yield, NEWDAM = construction of new reservoirs. Shaded cells mean supply is less than demand.



Bythe2080s,noneoftheinvestmentscansupplysufficientproteinandcarbo-hydratesforthecountry.DisaggregatingthesefindingsbyprovinceshowsthatPunjabisabletomeetitsproteinandcarbohydratedemands,evenoutto2080.TherealfoodsecuritychallengewillbeinSindh,evenasearlyas2020.Notethatitisassumedthatinterprovincialtradingdoesnotchangeandthatfoodimportsarenotconsidered.

environmental Issues

TheprimaryenvironmentalissuesrelatedtowateruseintheIndusBasinincludeflow requirements to the sea, groundwater over pumping, and groundwatersalinity.TheseissueswerediscussedandanalyzedinAhmadandKutcher(1992).Theseoutcomesarereevaluatedhereundertheclimateriskscenariosandinvest-mentscenariosdescribedearlier,andthecostforasustainablegroundwaterusagesituationisalsoevaluated.

Current Environmental ConditionsEnvironmentalflowstotheseaarerequiredtosustainthediversedeltaicecosys-temsandminimizesalineintrusion.Aminimum10MAFtotheseaisrequiredper the 1991 ProvincialAccord.This minimum flow is difficult to maintainduringdroughtyears(forexample,2002–04).HaqandKhan(2010)estimatethatoverthelast20years,atleast2millionacresofarablelandhavebeenlostin Sindh as a result of salt water intrusion. On average, over the long-termhistoricalrecord,almost30MAFisavailabletothesea(figure6.11).This,how-ever,maybeanissueinthefutureifcurrenttrendscontinue.Figure6.12showsthattheflowsbelowKotriBarrage(thelastbarrageinthesystem)havedecreasedover time.Theannualaverage from1936 to1960was87MAFcompared to41 MAF over the 1977–2000 time period. For future analysis, this modeling

Figure 6.11 Multiyear Flow to the Sea, 1961–2009

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table 6.6 Baseline environmental Conditions

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Fresh Saline Fresh Saline Fresh Saline

Punjab 18.1 4.8 –9.6 4.4 +35.9 +4.2Sindh 3.4 9.4 2.7 4.6 +5.4 +29.3Others 3.0 0.4 –2.5 0.2 +4.5 +0.7

Note: MAF = million acre-feet.

framework can be used to test the system-wide implications of variousscientifically-basedmonthlyminimumflowrequirements.

GroundwaterquantityandqualityissuesarealsoaprominentenvironmentalissueintheIndusBasin.PunjabfacesunsustainablepumpingrateswhileinSindhthe dominant issue is related to problems of salinity and waterlogging. Onaverage, the net recharge in freshwater areas in Punjab (groundwater inflowminusoutflow) is–9.6MAFandthusthewatertable isdeclining(1–6ftperyear).Thissituationisworstduringdroughtyears.Onaverage,thenetrechargeinthesalineareasinSindhis+4.6MAF(moregroundwaterisflowinginthanout);asaresultthenetsaltaccumulationonthesurfaceintheseareasismorethan 29 million tons (per year).The reason for this net accumulation is thatgroundwaterpumpingdoesnotexistinthesesalineareas,sofreshwaterisnotrechargedintotheseaquifers.Meanwhile,theevaporationratesinsalineareasareusually higher than in fresh areas (due to the higher water table), generatingsubstantialquantitiesofsaltnearthegroundsurface(rootzone).Thenetaccu-mulationofsaltinthefreshwaterareasinPunjabisalsoquitelargebecauseofthelargevolumesofwaterbeingappliedforirrigation(whichhavesomeback-groundsalinity).Thebaselinegroundwaterconditionsaregivenintable6.6.

Sustainable Groundwater UsageToevaluatethesustainabilityofcurrentgroundwaterusage,assuminganenergycostofPRs5perkwh(WAPDA)andpumpingdepthsaround80ft(thisdepth

Figure 6.12 historical Flows below Kotri Barrage, 1938–2004

Note: Solid line represents 10 million acre-feet (MAF) established under the 1991 Provincial Water Allocation Accord.

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togroundwateristhedeepestvalueanywhereinthemodel),thetotalpumpingcostusedinthemodelisPRs800perAFforthebaselinecase.Theminimumgroundwaterrequiredisabout4.8MAFwherefarmershavelittlechoicebuttopumpfromtheaquifer,evenwhen thepumpingcostsareveryhigh.Becausegroundwaterisalwaysneededtoaugmentsurfaceirrigationsupplies,thereduc-tion ingroundwaterpumpingalsoservesasacaponproductivityand,conse-quently,surfacewateruse.Thetotalpumpinginwhichthenetrechargeiszero(thatisnodropinwatertable)iscalculatedinthemodeltobeabout48.6MAF.Thismaybeconsideredasthe“safeyield”andmatchesearlierreportednumbers(forexample,51MAFbyQureshi2011).

Table6.7showstheresultsofrestrictingthemodeltoagroundwaterabstrac-tionatthesafeyieldofabout50MAF.Thetableshowstheeconomiccostforsustainablegroundwaterusage.TheobjectivevaluedecreasesbyPRs38billion(US$0.47billion).Thisrepresentsonlya2percentreduction,whichsuggeststhatprudentpolicyongroundwatermanagementmaybecost-effective,dependingonanassessmentofresourcevalues.Punjabwillhavethemostimpactsintermsofcrop production. On the other hand, these“costs” of sustainable groundwaterusagewillbemoreequalbetweenPunjabandSindhwhentheprovincialalloca-tionconstraintisrelaxed.ThepumpingreductionsaregreatestinPunjab.Notethattheactualdepthtogroundwaterdoesnotdirectlyaffecttheoptimization.Thus,theseresultsmaybeoptimistic.

Climate and Investment ScenariosThissectionpresentschangesintheseenvironmentalparametersunderdifferentadaptationinvestments.Figure6.13showstheresultsofflowtotheseaandfreshgroundwater net recharge with and without adaptation investments.Theflowtotheseadoesnotsignificantlychangewhenadaptationinvestmentsareintroduced.Partofthiscanbeexplainedbyexamininghowmuchsurfaceandgroundwaterisused(table6.8).TheCYIELDinvestmentusesalmostthesameamountofsurfacewaterasthenoinvestmentscenario.Thus,theremainingflow

table 6.7 Baseline vs. Sustainable Groundwater Usage Model

Baseline Capped pumping Difference Percentage of baseline

Objective value (PRs, billions) 2544 2506 38 98Total production (1,000 tons) 94,047 89,385 –4,662 95 Punjab 64,983 61,428 –3,555 95 Sindh 24,225 23,434 –791 97Canal diversion (MAF) 109.6 109.5 –0.1 .. Punjab 58.1 58.3 0.2 101 Sindh 43.3 42.8 –0.5 99Groundwater pumping (MAF) 57.9 50.0 –7.9 86 Punjab 54.1 47.0 –7.1 87 Sindh 3.2 2.6 –0.6 82

Note: .. = negligible, MAF = million acre-feet.



totheseaisessentiallythesame.TheCANEFFinvestment,ontheotherhand,divertslesssurfacewaterduringthehighandnormalflowsituationsbutalmostthesameamountduringthelowflowsituations.Thisisbecausecanalwateris“free”comparedtogroundwater.Therefore,whenCANEFFmakesmoresurfacewater available, the system will divert the same amount of surface water anddramaticallyreducethegroundwaterusage(sincegroundwaterhasacost).Sincecanaldiversionsarealmostthesame,theflowtotheseavaluewillalsobethesame.TherangeofvaluesofflowtoseafortheNEWDAMinvestmentincreases.Duringhigh flow situations, the1991ProvincialAccord limits the amountofwaterthatcanbedivertedandutilized.Thus,additionalwaterprovidedbynewstoragecannotbeusedandescapestothesea(sincethemodeldoesnotallowfor

table 6.8 Irrigation Mix under Different adaptation Investments

Average water uses under climate risk scenarios (MAF) Canal Tubewell Total

No investment 109.8 66.4 176.2CANEFF 108.1 51.6 159.7NEWDAM 115.4 64.2 179.6CYIELD 109.5 62.6 172.1

Note: MAF = million acre-feet, CANEFF = canal and watercourse efficiency improvements, CYIELD = crop yield, NEWDAM = construction of new reservoirs.

Figure 6.13 environmental related Outcome under Different Investments

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theexpansionofirrigatedareas).Duringlowflowsituations,theadditionalstor-agewillallowthesystemtodivertmorewaterfromcanalandresultinlessflowtothesea.ThisisthereasonforawiderrangeinNEWDAM.

Similarly, the groundwater net recharge does not change significantly withthese investments.Onlyunder theNEWDAMinvestment is thegroundwaternet recharge improved.This is because more water is made available for thesurfacesystem,particularlyduringdroughtconditions.Waterlossesfromcanalsand watercourses are treated as the major groundwater inflow in the model.Whenefficiencyimproves(asintheCANEFFinvestment),theamountofcanaldiversion decreases and the losses also decrease. This is a negative effect ingroundwaternet recharge.Moreover, pumping is reduced,which is apositiveeffectingroundwaternetrecharge(table6.8).Thus,thesetwoeffectsoffseteachother.WiththeCYIELDinvestment,slightlylessgroundwaterisusedandnetrechargemarginallyimproves.

Figure6.14showsthesaltbalanceinbothfreshandsalineareasresultingfromthe adaptation investments. In this study, we follow the approach taken by

Figure 6.14 Salt accumulation in Soil Layer in Fresh and Saline areas

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AhmadandKutcher(1992)inthesecalculations.Forfreshareas,thelargestcom-ponent of salt accumulation is from pumping groundwater with backgroundsalinity.OnlyCANEFF,whichreducesoverallgroundwateruse,canreducethesaltaccumulationinthesefreshareas.Withreducedfreshwaterflowsintothesalineareas(underCANEFF),thesaltconcentrationsincrease.NEWDAMandCYIELDhavenosignificanteffectonsaltbalanceineitherfreshorsalineareas.

Insummary,thischapterhasexaminedthepotentialagro-economicimpactsofsomeofthepressingchallengesintroducedinchapters1and2.IthasalsoassessedpotentialinvestmentandmanagementalternativesfortheIndusBasin.This analysiswas accomplishedby linking anupdated IBMRmodelwith aneconomically broader CGE–social accounting matrix (SAM) analysis. TheanalysisfirstidentifiedsomeofthekeysensitivitiesandmorerobustaspectsoftheIBIS.Ateverystep,keydataandmodelingissues,andfurtherIndusirriga-tion management questions were encountered, which will be priorities forfurther analysis. The concluding chapter presents these main findings, theirsignificance,andprioritiesforfutureresearch.

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Ahmad, M., and G. P. Kutcher. 1992. “Irrigation Planning with EnvironmentalConsiderations:ACaseStudyofPakistan’sIndusBasin.”WorldBankTechnicalPaper166,WorldBank,Washington,DC.

Bastin, G. Q., S. Sarwar, and Z. A. Kazmi. 2008. “Wheat-Flour Industry in Pakistan.”DiscussionPaper,CompetitivenessSupportFund,Islamabad.

Briscoe,J.,andU.Qamar,eds.2006.Pakistan’s Water Economy: Running Dry(includesCDofbackgroundpapers,2005).Washington,DC:WorldBank.

Brown, C., and J.W. Hansen. 2008. Agricultural Water Management and Climate Risk: Report to the Bill and Melinda Gates Foundation. IRI Techical report 08-01,InternationalResearchInstituteforClimateandSociety,Palisades,NY.

Cooley,H.,J.Christian-Smith,andP.H.Gleick.2008.More with Less: Agricultural Water Conservation and Efficiency in California—A Special Focus on the Delta.” PacificInstitute,Oakland,CA.

FAO(FoodandAgricultureOrganizationoftheUnitedNations). 2002.World Agriculture: Towards 2015/2030 Summary Report.Rome.

Haq,N.U., andM.N.Khan.2010.Pakistan’s Water Concerns. IPRIFactfile, IslamabadPolicy Research Institute, Islambad. http://ipripak.org/factfiles/ff127.pdf (accessedMay17,2012).

Huang,J.,R.Hu,C.Fan,C.E.Pray,andS.Rozelle.2002.“BtCottonBenefit,CostsandImpactinChina.”AgBioForum5(4):153–66.

Immerzeel, W. W., F. Pellicciottti, A. Gobiet, and S. Ragetti. 2011. “The Impact ofUncertaintyinClimateChangeScenariosonProjectionofFutureWaterSupplyfromthe Asian Water Towers.” Eos Trans. AGU 92 (52) Fall Meeting. Suppl., AbstractC53E-0721.



Iqbal,M.M.,M.A.Goheer,S.A.Noor,H.Sultana,K.M.Salik,andA.M.Khan.2009.Climate Change and Wheat Production in Pakistan: Calibration, Validation and Application of CERES-Wheat Model.GCISC-RR-14,GlobalChangeImpactStudiesCentre,Islamabad.

Menrad,K.,A.Gabriel, andM.Gylling.2009.“CostsofCo-ExistenceandTraceabilitySystems in the Food Industry in Germany and Denmark.” Paper presented at theFourthInternationalConferenceonCoexistencebetweenGeneticallyModifiedandnon-GMbasedAgriculturalSupplyChains,Melbourne,Australia,November10–12.

Michel,A.A.1967.The Indus Rivers: A Study of the Effects of Partition.NewHaven,CT:YaleUniversityPress.

Naheed, G., and G. Rasul. 2010.“Projections of CropWater Requirement in PakistanunderGlobalWarming.”Pakistan Journal of Meteorology7(13):45–51.

PRCEngineering.1986.Technical Criteria for Rehabilitation of Canal Systems in Pakistan.Islamabad:GovernmentofPakistan/USAIDIrrigationSystemsManagementProject.

Qureshi,A.S.2011.“WaterManagementintheIndusBasininPakistan:ChallengesandOpportunities.”Mountain Research and Development31(3):252–60.

Skogerboe,G.W.,M.Aslam,M.A.Khan,K.Mahmood,S.Mahmood,andA.H.Khan.1999. Inflow-Outflow Channel Losses and Canal Inning Cost-Effectiveness in the Fordwah Eastern Sadiqia (South) Irrigation and Drainage Project. Report R-85,InternationalWaterManagementInstitute,Lahore,Pakistan.

Swamee,P.,G.C.Mishra,andB.Chahar.2000.“MinimumCostDesignofLinedCanalSections.”Water Resources Management12:1–12.

Tariq,S.M.,andS.UlMulk. 2005.“Sustainable,AccountableInstitutions.”Backgroundpaper7forPakistan’s Water Economy: Running Dry.Washington,DC:WorldBankandOxfordUniversityPress.

WAPDA (Water and Power Development Authority). 1990. Water Sector Investment Planning Study (WSIPS).5vols.Lahore,Pakistan:GovernmentofPakistanWaterandPowerDevelopmentAuthority.

WorldBank.1996.Pakistan Impact Evaluation Report.Report15863-PAK,WorldBank,Washington,DC.

Documents

The Indus Basin of Pakistan - COnnecting REpositories · the system is unable to generate more economic benefits in the basin given the current constraints, including water allocation