1

BuildingjointIndia-UKcapacity,capability,researchand

innovationintheenvironment:Finalreport

AmandaJ.Robinson

JulianR.Thompson

MikeAcreman

KatjaLehmann

EgonL.Dumont

NathanJ.Rickards

T.ThomasManishNemaPrabhashMishraShikhaGaurPushpendraAgarwalYatveerSinghSharadJain

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TableofContents

1.Introduction............................................................................................................................................32.HydrologicalmodellingoftheUpperNarmadaBasin..............................................................52.1.MIKESHEmodellingoftheUpperNarmadaBasin............................................................................52.1.1.Modeldevelopment.................................................................................................................................................62.1.2.Modelcalibrationandvalidation....................................................................................................................142.1.3.Results:Modelcalibrationandvalidation...................................................................................................14

2.2.GWAVAmodellingoftheUpperNarmadaBasin.............................................................................202.2.1.Modeldevelopment..............................................................................................................................................202.2.2.Modelcalibrationandvalidation....................................................................................................................262.2.3.Results:Modelcalibrationandvalidation...................................................................................................27

2.3.HydrologicalmodellingoftheUpperNarmada:Summaryandongoingwork.....................313.GangaMetagenomePilotStudy.....................................................................................................323.1.Methods.........................................................................................................................................................323.1.1.Sampling....................................................................................................................................................................323.1.2.DNAextraction........................................................................................................................................................343.1.3.Sequencing................................................................................................................................................................35

3.2.Preliminaryresults...................................................................................................................................353.3.Summaryandongoingwork..................................................................................................................35

4.Conclusions..........................................................................................................................................365.References............................................................................................................................................36Appendix1.Expenditure.....................................................................................................................42

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1. Introduction

This project involved collaboration betweenUCL and the Centre of Ecology andHydrology

(CEH) in the UK and the Indian National Institute of Hydrology (NIH), with the aim of

supportingtheimprovementofwater-relatedscienceinIndia.

Therearetwomainstrandstotheproject.Thefirstfocussedonhydrologicalmodellinginthe

UpperNarmadaBasin.Hydrologicalmodellingisakeytoolforimprovingourunderstanding

ofwater resources and assessing the potential impacts of a variety of scenario types upon

waterresources, fromclimate (e.g.Thompson etal.,2013;2014a;Ho etal.,2015)and land

cover change (e.g. Kalantari et al., 2014; Wijesekara et al., 2014), to irrigation and dam

regulation scenarios (e.g. Ahrends et al., 2008; Singh et al., 2011; Räsänen et al., 2012). It

thereforehasanimportantroletoplayincatchmentmanagementandplanning(Loucksand

van Beek, 2005; McCartney and Acreman, 2009). However, uncertainties are inevitably

introduced intoallhydrologicalscenario impactassessments(e.g.RefsgaardandHenriksen,

2004;Goslingetal.,2011),anditisimportantthattheresultantuncertaintyinthemodelling

resultsisrecognisedandtakenintoaccount.

Onesourceofuncertaintythathasbeenrelativelyunder-studiedisinter-hydrologicalmodel

uncertainty (Prudhomme and Davies, 2009; Thompson et al., 2013). There are numerous

hydrological model codes and these vary in terms of their representation of hydrological

processes,spatialdiscretizationanddatarequirements.Inter-hydrologicalmodeluncertainty

arises because although different hydrological models of the same catchment, developed

usingalternativemodel codes,mayallperformacceptably foranobservedbaselineperiod,

they may still respond differently under scenario conditions (e.g. Gosling et al., 2011;

Hagemann etal.,2013;Thompson etal.,2013;Velázquez etal.,2013).Oneobjectiveof this

project was therefore to develop alternative models of the same catchment using two

differentmodelcodes, inordertoenable future inter-modelcomparisons.Morespecifically,

thesemodelswill beused for scenariomodelling aspart of ongoing collaborationbetween

UCL,CEHandNIH,anduseofalternativemodelcodeswillallowamorerobustassessmentof

therangeofpotentialimpacts.ThisongoingworkisdiscussedfurtherinSection2.3.

TheNarmadaBasinwasselectedasasuitablecatchment for theapplicationofhydrological

modelling and subsequent inter-hydrological model uncertainty assessment. With a

populationofover16million(GovernmentofIndiaMinistryofWaterResources,2014)anda

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drainageareaof98,796km2(India-WRIS,2015),theNarmadaBasinisanexampleofariver

basinfacingnumerousmanagementchallenges.Inparticular,therearemultipleongoingand

planneddamandirrigationdevelopmentprojectsforthebasin(GovernmentofIndiaMinistry

ofWaterResources,2014).Atthesametime,itisvitalthatenvironmentalflowrequirements

(the flow needs of the river ecosystem; Richter et al., 1997; Acreman and Dunbar, 2004),

continuetobemet, inordertosustaintheeconomically,sociallyandecologically important

ecosystemservicesprovidedby theriver. Importantly,NIHwereable tomakeavailable to

UCL and CEH observed river discharge records for multiple sites within the basin. Such

recordsareaprerequisiteforhydrologicalmodelcalibration/validationandaccesstorecords

forsomeriverbasinsinIndia,mostnotablytheGanges,canbeproblematical(Johnstonand

Smakhtin,2014;Masoodetal.,2015).

Thesecondstrandofthisprojectcomprisesapilotstudytoassesstheecologicalstateofthe

upperGangausingametagenomicapproach.OnlyfewmicrobialdatasetsoftheGangaexistto

date, partly because it is difficult to sample eDNA (environmental DNA) under often

unfavourable conditions (e.g. heat, long journey times to sampling points, inaccessibility,

insufficient laboratory facilities) and partly because whole metagenome sequencing is a

relativelyyoungmethod.Thisbio-assessmentwillprovideasnapshotofthecurrentcondition

of the microbial, invertebrate and vertebrate components of the river ecosystem from its

confluencetothefirstsizeableurbancentres.Overandabovethat,itwillprovideacontextfor

spatial and temporal studies inyears to come,which is likely tobeextremelyuseful in the

lightofinitiativessuchastheCleanGangacampaign,launchedbytheIndianGovernmentlast

May. It will assess the potential of eDNA as a cost effective tool to biomonitormicro- and

macrobiota and investigate water quality issues (Tan et al., 2015) and ecological state

(Thomsen et al., 2015) of the river and its tributaries. The pilot study involved four field

campaignstosample13samplingsites(orasubsamplethereof,Figure3.1)fromtheriver’s

confluence at Devprayag past the cities Rishikesh and Haridwar, where discharge of

pollutantsandabstractionofwaterincreases,totheMadhyaGangabarrage,wheretheflowof

theriverisblockedandthenpartlydivertedintoacanalbeforeitenterstheplains.Thiswill

helptoinvestigatethedegreeofecologicaldeteriorationthattheriverexperiencesinitsfirst

100km.

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2. HydrologicalmodellingoftheUpperNarmadaBasin

TheNarmadaRiverislocatedincentralandwesternIndiaandisthelargestwesternflowing

river of thepeninsula India (Government of IndiaMinistry ofWaterResources, 2014). The

basin largely falls within the State of Madhya Pradesh, but also covers parts of Gujarat,

MaharashtraandChhattisgarh(Figure2.1).Thehydrologicalmodellingstrandoftheproject

focuseson theUpperNarmadaBasindowntoHoshangabad,which lieswithin theStatesof

Madhya Pradesh and Chhattisgarh only. Hydrological models of the Upper Narmada were

developedusingtwodifferentmodelcodes:MIKESHEandGWAVA.Thedevelopmentofthese

models isdiscussedinSections2.1and2.2,respectively.Section2.3providesasummaryof

theoutcomesofthisstrandoftheprojectandanoutlineofongoingwork.

Figure2.1.TheNarmadaBasin(left)andtheUpperNarmadaBasin(right).

2.1. MIKESHEmodellingoftheUpperNarmadaBasin

In consultationwithNIH, aMIKESHEhydrologicalmodel of theUpperNarmadaBasinhas

beendevelopedatUCL,usingapproachesdevelopedfortheMekongRiverBasin(Thompson

et al., 2013; Thompson et al., 2014a; 2014b).MIKE SHE is a comprehensive, deterministic,

distributedmodellingsystem,capableofsimulatingthemajorprocessesofthelandphaseof

thehydrologicalcycle(GrahamandButts,2005). Ithasamodularstructureandalthoughit

was originally designed as physically-basedmodel code,many of themodules now offer a

rangeofprocessdescriptions,someofwhichareconceptualandsemi-distributed.Theseare

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particularlyapplicableforlargebasinssuchastheNarmadawherethefocusisthesimulation

ofriverflowandwheredetaileddata,suchashydrogeologicalcharacterisation,requiredfor

morephysically-basedapproaches,arenotavailable(Andersenetal.,2001;Stisenetal.,2008;

Refsgaardetal.,2010).

2.1.1. Modeldevelopment

Table2.1summarisesthecomponentsoftheMIKESHEmodeloftheUpperNarmadaandthe

dataemployedwithinthem.Themodeldomainwasspecifiedusingashapefilebasedupona

catchmentshapefileforthewholeoftheNarmadaBasinthatwasprovidedbyNIH.Watershed

delineation was undertaken using topographic data (see below) and this shapefile within

ArcSWAT(Winchelletal.,2013)todefinethecatchmentextentoftheUpperNarmadaBasin

to just downstream of Hoshangabad discharge station. This provided a catchment area of

44,725km2.Themodelgridsizewassetto2km×2kminordertoretainabalancebetween

representingcatchmentcharacteristicsandefficientcomputationtime(Vázquezetal.,2002;

Thompson et al., 2013). Consequently, although most spatial inputs to the model, such as

topographyand landcover,havea resolutionof 1km×1km,duringmodel runs, all input

dataareautomaticallyresampledtothe2km×2kmmodelgrid.Topographywasspecified

usingSRTM(ShuttleRadarTopographyMission)baseddata (Figure2.2;available fromthe

USGSEarthExplorer:http://earthexplorer.usgs.gov/).

Thespatialdistributionoffivedifferentlandcoverclasseswithinthebasinwasobtainedfrom

NIH(Figure2.2).Thesedatawerebasedonremotesensing.LeafAreaIndex(LAI)valuesfor

the different land cover classeswere based on a combination of those used in theMekong

MIKE SHEmodel and values from Jain et al. (1992), a study thatmodelled the Kolar sub-

catchmentoftheNarmadaBasin.RootDepth(RD)valuesforthedifferentlandcoverclasses

werebasedonthoseusedintheMekongMIKESHEmodel(seeTable2.2).Overlandflowin

theUpperNarmadamodel iscalculatedusinga finite-differenceapproach tosolve the two-

dimensional Saint–Venant equations (Graham and Butts, 2005). The land cover data were

employedtospatiallydistributeManning’sM(theinverseofManning’sn)valuesforoverland

flowresistance(Table2.2),withvaluesbasedonVieux(2004).

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Table 2.1. Summary of key data (and data processing) required for each component of the

coupledMIKESHE/MIKE11modeloftheNarmada.

Modelcomponent

Keyinputs/datarequired

Initialdataformat ProcessingundertakenexternallytoMIKEZero*

ProcessingundertakeninMIKEZero

Modeldomain Catchmentextent–thebasinareaupstreamofHoshangabad

ESRIpolygonshapefile

InArcGIS:Convertedtoadifferentcoordinatesystem(ProjectedcoordinatesystemWGS1984UTMZone44N).EditedtheedgetopreventgriddingerrorsinMIKESHE.

N/A

Topography Topography ESRIgridrasterfilewitharesolutionof0.0008333°×0.0008333°.Datasource:SRTM(ShuttleRadarTopographyMission)

InArcGIS:ConvertedtoUTM44Ncoordinatesystemandtoaspatialresolutionof1km×1kmtomatchtheinitialmodelgrid.Finalmodelgridsize:2km×2km.

ConvertedtoMIKEZero’sdfs2gridfileformat.

Landuse/vegetation

Landusedistribution

Imagineimagerasterfile.Therearefivelanduseclasses:Forest,Shrub,Waterbodies,BaresoilandAgriculture.

InArcGIS:ConvertedtoUTM44Ncoordinatesystemandtoaspatialresolutionof1km×1km.Buffergeneratedaroundavailabledata.

ConvertedtoMIKEZero’sdfs2gridfileformat.

LeafAreaIndexes

N/A ValuesforthedifferentlandcoverclassesarebasedonthoseusedinsimilarworkundertakenbyUCL(Thompsonetal.,2013;Thompsonetal.,2014).

Rootdepths N/A Asabove.

Overlandflow:modelledusingthe2Dfinite-differencemethod

Manning’sMforoverlandflowresistance

N/A Manning’sMvaluesdistributedaccordingtolanduse/landcover,usingadfs2gridfilebasedonthelandusedfs2file.ValuesforthedifferentlandcoverclassesbasedonVieux(2004).

Unsaturatedzone:modelledusingthetwo-layerwaterbalancemethod

Soilclasses ArcGISpolygonshapefileandrastersthatprovidesoilclassdataforthemajority(~80%)ofthebasin.

InArcGIS:ConvertedtoUTM44Ncoordinatesystemandtoaspatialresolutionof1km×1km.Buffergenerated.

ConvertedtoMIKEZero’sdfs2gridfileformat.

Soilhydraulicproperties

N/A Valuesforthedifferentsoilclassesderivedfromtheliterature(ClappandHornberger,1978;NormanandDixon,1995).

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Table2.1.(cont.)

Modelcomponent

Keyinputs/datarequired

Dataobtained/ReceivedfromNIH–Dataformat

ProcessingundertakenexternallytoMIKEZero

ProcessingundertakeninMIKEZero

Saturatedzone:modelledusingtheconceptual,linearreservoirmethod

Spatialdistributionofground-watersub-catchments,interflowreservoirsandbaseflowreservoirs

NIHconsultedwithonthenumberandlocationofgaugingstationstobeemployedduringmodelcalibration.

PolygonshapefilehasbeengeneratedinArcGIS.Thebasinwasdividedintofivegroundwatersub-catchmentsbasedontopographyandthelocationsofthefivecalibrationgaugingstations.

N/A

Catchmentmeteorology:Precipitationandevapo-transpirationmodules.

Spatialdistributionofprecipitation

N/A Polygonshapefileof0.25°×0.25°gridsquarescoveringthebasinextent,generatedinArcGIS.

N/A

Precipitation 0.25°×0.25°griddeddailyprecipitationforIndiafortheperiod1901–2013.Dataforeachyearisinadifferentfile,inagriddedbinaryformat.

DatafirstconvertedtoASCIIfileformatusingCscriptprovidedwithdata.DatathenprocessedinMatlabintothenecessaryformatforinputtoMIKEZero.

DataconvertedintoMIKEZero’sdfs0timeseriesfileformat.

Spatialdist.ofPET

N/A Generated1°×1°polygonshapefile.

N/A

Potentialevapotrans-piration

Gridded(1°×1°)temperaturedatareceivedfromNIHforthecalculationofPET.

PETdatacalculatedinMatlab–forthecellscoveringthebasindowntoHoshangabadonly.

DataconvertedintoMIKE’sdfs0timeseriesfileformat.

MIKE11one-dimensionalhydraulicmodelforsimulatingchannelflow(usingKinematicrouting)

Planofthemainriverchannels

AnESRIpolylineshapefile.Thisdenserivernetworkisdividedintosevenstreamorders.

InArcGIS:Differentstreamordersextractedtoseparateshapefiles.Newfieldgenerated:lengthofeachbranch.

RivernetworkdigitisedinMIKEZero/MIKE11.

Syntheticcross-sections

N/A Syntheticcross-sectionsgeneratedinExcel.

DatainputtoMIKE11model.

Manning’snforbedresistance

N/A Representativevaluebasedontheliterature(Chow,1959)andpreviousmodellingexperience.

Reservoirregulationdata/rules

NIHwillbeconsultedastowhetherreservoir/damregulationdatacanbemadeavailableforfuturemodeldevelopment.Currently,modelcalibrationhasbeenundertakenwithoutdamsincludedinthemodel.

N/A Riverdischargetimeseriesformodelcalibration/validation.

Dataforthecalibration/validationperiodof2002–2013receivedforfivegaugingstations.

DataforthefivestationshavebeenprocessedinExcelandMatlabintothenecessaryformatforinputtoMIKEZero.

DataconvertedintoMIKEZero’sdfs0timeseriesfileformat.

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Figure2.2.TopographicdatafortheUpperNarmadaBasin.

Figure2.3.LandcoverclasseswithintheUpperNarmadaBasin.

Table2.2.Summaryoflandcoverclassesandrelatedparameters.

Code Landcover Rootdepth(mm)1 LAI2 Manning’sM32 Forest 900 1.0–6.0 103 Shrub 500 2.6–3.8 84 Waterbodies 0 0 255 Baresoil 10 0 506 Agriculture 500 0.3–6.9 29

1BasedontheMIKESHEmodeloftheNarmada.2BasedontheMIKESHEmodeloftheNarmadaandJainetal.(1992).3BasedonVieux(2004).

Land cover

AgricultureBare SoilForestShrubWater Bodies

maslHigh : 1317

Low : 279

0 10050 km¯

0 10050 km¯

Land cover

AgricultureBare SoilForestShrubWater Bodies

maslHigh : 1317

Low : 279

0 10050 km¯

0 10050 km¯

10

Following initial calibration attempts that revealed overestimation of discharge at

downstream discharge stations (Barmanghat and Hoshangabad), irrigation was included

within the model over two command areas: Bargi (1570 km2) and Barna (579 km2). The

locationsofthecommandareas(seeFigure2.4)werebasedonafigurefromGovernmentof

IndiaMinistryofWaterResources(2014)thatwasgeorectifiedanddigitisedinArcGIS.Data

on the location of the culturable command area (land actually irrigated) within the gross

commandarea(theoverallregioncontainingirrigatedland)werenotavailable.However,the

acreagesofthecommandareasincludedintheMIKESHEmodelweremadetomatchthose

reported on the India-WRIS (Water Resources Information System) website (India-WRIS,

2013a,b)and inGovernmentof IndiaMinistryofWaterResources(2014). Irrigationwater

for the Bargi and Barna command area was specified as being abstracted from the river

sectionsatthelocationsofBargiDamandBarnaDam,respectively.Duringmodelcalibration,

anevapotranspirationcropcoefficient(Kc)of1.2wasspecifiedoverthecommandareasfor

themonthsofMay–September.ThismeansthattheinputPETovertheseareasismultiplied

by 1.2 in these months. Crop coefficients are commonly employed to adjust potential

evapotranspirationestimatesspecificallyforcropland,andaKcof1.2iswithintherangeof

normalKcvaluesaccordingtoAllenetal.(1998).

The two-layer water balance method was employed for the unsaturated zone. For this

module,thespatialdistributionofsoilclasseswasspecifiedusinga1km×1kmgridbasedon

a georectified and digitised version of a soil map that was provided by NIH. Soils were

aggregated intosixclasses (Figure2.5)and therequiredhydraulicparameters foreachsoil

classweretakenfromtheliterature(Table2.3).

Figure2.4.GrosscommandareasandassociateddamsincludedinMIKESHE.

!

!

Barna DamBargi Dam

Soil classClayClay loamLithosol

SandSandy loamSilt loam

Bargi command area

Barna command area

0 10050 km¯

0 10050 km¯

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Figure2.5.SoilclassdistributionwithintheNarmadaBasin.

Table2.3.Soilclassesandassociatedparametervalues.

Soil_code

Soil_class Watercontentatsaturation*

Watercontentatfieldcapacity+

Watercontentatwiltingpoint+

Saturatedhydraulicconductivity(m/s)

1 sand 0.40 0.14 0.04 0.000182 sandyloam 0.43 0.26 0.09 3.5e-0053 siltloam 0.49 0.34 0.16 7.2e-0064 clayloam 0.47 0.34 0.18 2.5e-0065 clay 0.48 0.42 0.25 1.3e-0066 lithosol 0.45 0.30 0.13 7e-006

*fromClappandHornberger(1978) +fromNormanandDixon(1995)

Formodelling thesaturatedzone, theconceptual, semi-distributed, linear reservoirmethod

was selected. Advantages of this method include lower data requirements and reduced

computationtimecomparedtophysicallybasedsolutions(Andersenetal.,2001;Stisenetal.,

2008;Thompson etal.,2013;2014a;2014b).Using theapproachemployed for theMekong

(Thompson et al., 2013), theUpperNarmadawas divided into five sub-catchments (Figure

2.7)basedupontopographyandthelocationofthedischargegaugingstationsforwhichdata

for model calibration/validation were available. Within each sub-catchment, the saturated

zoneisrepresentedbyashallowinterflowreservoir,andtwobaseflowreservoirstosimulate

fasterandslowerbaseflowstorage.Exchangesbetweenreservoirs,andultimatelytheMIKE

11hydraulicmodel,arecontrolledbytimeconstants(DHI-WE,2009).Thetwotimeconstants

(interflowandpercolation) for each interflowreservoir and thebaseflow time constant for

eachbaseflowreservoirwerevariedduringmodelcalibration.

!

!

Barna DamBargi Dam

Soil classClayClay loamLithosol

SandSandy loamSilt loam

Bargi command area

Barna command area

0 10050 km¯

0 10050 km¯

12

Figure 2.6. Conceptual structure of the sub-catchment based linear reservoir saturated zone

module.Source:GrahamandButts(2005).

Figure2.7.Sub-catchmentdistributionandriverdischargegaugingstationlocations.

##

##

#

Manot

DindoriGadarwaraBarmanghat

Hoshangabad

# Discharge stations

River network

Sub-catchments

1 Narmada to Dindori2 Narmada to Manot3 Narmada to Barmanghat4 Shakkar to Gadarwara5 Narmada to Hoshangabad

1

2

3

45

0 10050 km¯

13

Daily griddedprecipitation data for theUpperNarmadawere derived from the IMD (India

MeteorologicalDepartment)/NCC(NationalClimateCentre)HighSpatialResolution(0.25°×

0.25°)LongPeriod(1901–2013)DailyGriddedRainfallDataSetOverIndia(Paietal.,2014),

obtained from the IMD.Duringmodel calibration (see Sections 2.2.2–2.2.3), a precipitation

lapse ratewas introduced over the spatial extent of sub-catchments 1, 2 and 4,which are

upstream sub-catchments located at higher elevations. The three lapse rates were then

subjecttocalibration.Forthecalculationofdailygriddedpotentialevapotranspiration(PET),

IMD/NCChigh resolution (1°×1°) griddeddaily temperaturedata (Srivastava et al., 2009)

wereused.PETwascalculatedusingtheHargreavesmethod,asthismethodisrecommended

by the FAO for use in situations where there are insufficient data to calculate Penman-

Monetith (Allen et al., 1998).Parameters for the equationwerebasedupon thoseobtained

from ECALTOOL, a computer program that provides calibrated values for the CH and EH

parametersoftheHargreavesequation(Pateletal.,2014).Theparametersvarythroughthe

year, with the values for somemonths subjected to further calibration independent of the

ECALTOOL.ThespatialdistributionsofprecipitationandPETinputsareshowninFigure2.8.

Figure2.8.a)Spatialdistributionofa)precipitationinputsandb)PETinputs.

Forthesimulationofchannelflow,MIKESHEisdynamicallycoupledtoMIKE11(Havnøetal.,

1995),aone-dimensionalhydraulicmodel.Aplanofthemainrivernetworkwasdigitisedin

MIKE11.For thegenerationofsyntheticcross-sections,channelwidthmeasurementswere

taken from satellite imagery in Google Earth. A generalised cross-section profile and a

relationshipbetweenchannelwidthandmaximumchanneldepthwerebasedonlimiteddata

available from NIH (a single cross-section for the river channel at Hoshangabad discharge

station)andtheliterature(Rajaguruetal.,1995;Payasi,2015).Cross-sectionswerespecified

as depths relative to the bank, with bank elevations taken from the SRTM DEM (digital

elevationmodel).Thekinematicroutingmethodwasemployed.

a) b)

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2.1.2. Modelcalibrationandvalidation

Modelcalibrationwasundertakenagainstdischargerecordsfromfivegaugingstationsforthe

period 2002–2008. The calibration parameters were the time constants of the saturated

zone’sinterflowandbaseflowlinearreservoirsandtheprecipitationlapseratesoverselected

sub-catchments(seebelow).Asdescribedabove,irrigationcommandareaswereaddedtothe

modelintheearlystagesofcalibrationandaKcof1.2wasspecifiedovertheseareasforthe

monthsofMay–September.Modelperformanceateachdischargestationwasevaluatedboth

qualitatively,throughvisualcomparisonofgraphsofobservedandsimulateddischarge,and

quantitatively, using model performance statistics. The indicators used were the Nash–

Sutcliffecoefficient(NSE;NashandSutcliffe,1970),thePearsoncorrelationcoefficient(r)and

the percentage deviation in simulated mean flow from the observed mean flow (Dv;

Henriksenetal.,2003).NSEcanvarybetween-1and1,whilstrcanvarybetween0and1;in

both cases, the closer the value to 1, the better the model performance according to that

criteria.InthecaseofDv,thecloserthevalueto0,thebetter.Modelperformanceaccordingto

the NSE and Dv values was classified using the scheme of Henriksen et al. (2008). Model

validationwas subsequently undertaken for the period 2009 toMay 2013 using the same

stationsandperformancestatistics.

2.1.3. Results:Modelcalibrationandvalidation

Table2.4summarisestheoptimisedvaluesofthecalibrationparameters.Precipitationlapse

rates were employed over sub-catchments 1, 2 and 4, following initial model runs that

displayedconsistentunderestimationofdischargeat gauging stationsdownstreamof these

sub-catchments(Dindori,ManotandGadarwara,respectively).Furthermore,thesearethree

upstream sub-catchments that are located at higher elevations and exhibit relatively large

rangesinelevation.Raingaugenetworksinmountainousregionsoftendisplayabiastowards

stationsbeing locatedat lowerelevations,which can lead to systematicunderestimationof

precipitation(e.g.FreiandSchär,1998;Frei etal.,2003;Lietal.,2016).Precipitation lapse

ratescanbeemployedtotryandaddressthisissue(e.g.Immerzeeletal.,2012b;Wijesekara

etal.,2012).Thefinallapseratevaluesarewithintherangeofthosepreviouslyreportedin

mountainousregions(e.g.Immerzeeletal.,2012a;2012b).

15

Table2.4.Finalcalibrationparametervalues.

Sub-catchmentnumber 1 2 3 4 5Sub-catchmentname Din Man Barm Gad HoshPrecipitationlapserate(%/100m) 6 6 7 Interflowtimeconstantforinterflowreservoir 4 6 14 4 14Percolationtimeconstantforinterflowreservoir 4 14 14 4 14Timeconstantforbaseflowreservoir1(days) 35 35 65 65 65Timeconstantforbaseflowreservoir2(days) 250 200 1500 120 350

Model performance statistics for the calibration period are provided in Table 2.5. As

indicated, a shorter period of 2001–2006was employed atManot, due to data availability.

Observed and simulated daily, monthly and mean monthly discharges are presented in

Figures2.9,2.10and2.11,respectively.Theannualriverregimeisrepresentedfairlywellby

themodel, as aremonthly discharges, with good sequencing of the annualmonsoon flood

pulseachievedatalldischargestations.

Table 2.5.Model performance statistics for the calibration and validation periods (validation

shaded).ModelperformanceindicatorsaretakenfromHenriksenetal.(2008).

Station Period Dv DailyNSE

Dailyr

MonthlyNSE

Monthlyr

Dindori Cal:01/02–12/08 -10.22 *** 0.40 ** 0.64 0.83 **** 0.91

Val:01/09–05/08 0.73 ***** 0.58 *** 0.79 0.84 **** 0.93

Manot Cal:01/02–12/06 -8.98 **** 0.53 *** 0.73 0.93 ***** 0.97

Barmanghat Cal:01/02–12/08 3.89 ***** 0.60 *** 0.80 0.82 **** 0.93

Val:01/09–05/10,06/11–05/13 10.39 *** 0.64 *** 0.82 0.79 **** 0.92

Gadarwara Cal:01/02–12/08 -6.77 **** 0.35 ** 0.59 0.64 *** 0.80

Val:01/09–05/10,06/12–05/13 -21.14 ** 0.76 **** 0.89 0.86 ***** 0.97

Hoshangabad Cal:01/02–12/08 6.66 **** 0.63 *** 0.82 0.84 **** 0.95

Val:01/09–05/08 23.16 ** 0.66 **** 0.84 0.77 **** 0.94

Performanceindicator

Excellent*****

Verygood****

Fair***

Poor**

Verypoor*

Dv <5% 5–10% 10–20% 20–40% >40%

NSE >0.85 0.65–0.85 0.50–0.65 0.20–0.50 <0.20

16

Figure 2.9. Observed and simulated daily discharge for the calibration and validation periods

(separatedbydashedline).

17

Figure2.10.Observedandsimulatedmonthlymeandischargeforthecalibrationandvalidation

periods(separatedbydashedline).

18

Figure2.11.Observedandsimulatedriverregimesfortheperiod2002–2008.

The simulated river regimes at Barmanghat andHoshangabad (Figure 2.11) display a bias

towards underestimation of dry season discharges and overestimation of peak monthly

discharges, although year-to-year variation in the pattern and magnitude of monthly

discharges iswellreproduced(Figure2.10).Observeddryseasonflowsmaybehigherthan

thosesimulatedduetodryseasonreleases fromdamsintheUpperNarmadaBasinsuchas

the Bargi, Barna and Tawa Dams. Similarly, observed wet season discharges at a monthly

resolutionmay be lower than those simulated due to these dams. Dams are not currently

includedintheMIKESHEmodelduetoalackofdataondamregulationandreleases.Future

workwillseektoexplorethepotentialofincludingthemajordamsusingtherangeofcontrol

structureapproachesthatareavailablewithinMIKE11,withdamoperationbeingvariedto

improvemodelperformance in theabsenceofdetailed recordsof actualoperation.Despite

these issues,meandischarges arewell representedby themodel,withDv classed as “very

good”to“excellent”atallstations(Table2.5).

19

Model performance at a daily resolution is notably weaker compared to at a monthly

resolution,asdemonstratedinFigure2.9andTable2.5.Usingmonthlydischarges,NSEvalues

forthecalibrationperiodareclassedas“fair”to“excellent”andrvaluesof0.80andaboveare

achieved.Incomparison,atadailyresolution,NSEisclassedas“poor”(fourstations)to“fair”

(atHoshangabadonly, themostdownstreamstation)and r values rangebetween0.59and

0.82.Thisweakerperformanceatadailyresolutionmaypartlyberelatedtothequalityand

spatialresolutionofthegriddedprecipitationandPETdata.

Model validationwas undertaken using the period 2009 toMay 2013. However, observed

dischargerecordswereunavailableforthestationatManot,anddatawereonlyavailablefor

threeandahalfyearsatBarmanghatandtwoandahalfyearsatGadarwara,asindicatedin

Table 2.5 and demonstrated visually in Figure 2.10. As for the calibration period, good

sequencing of the annual monsoon flood pulse is achieved at all fours stations for the

validationperiod(Figure2.10).Furthermore,dailyriscloseto0.8orhigherandmonthlyris

over 0.9 at all stations (Table 2.5), representing a strong positive correlation between

observedandsimulateddischarges.

AtDindori,themodelperformancestatisticsforthevalidationperiodarebetterthanduring

calibration.ForexampleDv is classedas “verygood”, rather than “fair” (aspreviously)and

dailyNSE is classed as “fair”, instead of “poor”. At Barmanghat,Dv displays an in increase,

representing greater overestimation ofmean discharge for the validation period.However,

the daily NSE value indicates an improvement inmodel performance at a daily resolution,

whilstthemonthlyNSEcontinuestobeclassedas“verygood”despiteasmallreduction.At

Gadarwara,althoughmeandischargeshowsgreaterunderestimationforthevalidationperiod

(amorenegativeDv),NSEand r indicateanoverall improvement inmodelperformanceat

thisstation,atbothadailyandmonthlyresolution.Finally,atHoshangabad,Dvdisplaysan

increase, leading to its classification falling from “very good” to “poor”. Despite this,

performanceatadailyresolutionaccordingtoNSEimprovesfrom“fair”to“verygood”,and

themonthlyNSEvalueremains“verygood”.AtBarmanghatandHoshangabad,thetendency

towards overestimation of peak monthly discharges and underestimation of dry season

monthly discharges that was exhibited during the calibration period is repeated for the

validationperiod(Figure2.10).Thisis,again,likelytobeduetotheeffectsoflargedams.

20

Overall, performance of themodel is considered appropriate to allow use of themodel in

furtherinvestigations,suchasclimatechangescenariosimulation(e.g.Thompsonetal.,2013)

andtheassessmentoftheimpactsofclimatechangeuponenvironmentalflows(Thompsonet

al., 2014b), particularly as comparisons between baseline and scenario discharges would

typically bemade at a temporal resolution lower thandaily, such asmonthly or annual. In

addition, and as discussed above, the potential for including dams within the model to

enhancemodelperformancecanbeexplored.

2.2. GWAVAmodellingoftheUpperNarmadaBasin

2.2.1. Modeldevelopment

In consultationwithCEH (with financial supportprovidedby funds external to the current

project),aGWAVAmodeloftheNarmadabasinhasbeendevelopedatNIH.Thisprocesswas

startedwith a 3-day GWAVA training given by CEH staff at NIH Roorkee (India) inMarch

2015.Thiswasfollowedbyan8-dayGWAVAmodellingworkshopinCEHWallingford(UK)in

June 2015 during which CEH gave further guidance to NIH staff. Between and after these

meetings,GWAVAwassetupandcalibratedbyNIHwithcontinuedsupportfromCEH.

Inputdatahasbeenpreparedfortheentirebasin,althoughmodellingiscurrentlylimitedto

theupperbasin.TheGlobalWaterAVailabilityAssessmentmodel(GWAVA)wasdevelopedby

CEHandtheBritishGeologicalSurveyinordertoprovideanimprovedmethodologyforthe

assessmentofwaterresourcesattheglobalscale.Themodelhasthecapabilitytoincorporate

additionalwater resourcecomponentssuchas reservoirs, abstractions,andwater transfers

thatmodifywater quantity and flow regime.Itwasdevelopedwith funding fromDFID (UK

Department for InternationalDevelopment).Themodelwas initiallydevelopedandapplied

onagridwitharesolutionof0.5°latitude×0.5°longitude,butitcannowbeappliedatfiner

resolutions.Thechoiceofgridsizeisacompromisebetweentheneedtorepresentthespatial

variabilityandtheavailabilityofsuitabledata.

GWAVAprovidesacomparisonofwateravailabilityanddemandatthescaleofthegridcell

for the comprehensive assessment of the spatial and temporal variability of the water

resources over a basin. Model outputs include simulated daily flows and a cell-by-cell

comparison of water availability. The model is also capable of carrying out the combined

21

assessmentofbothsurfacewaterandgroundwater.Policymakersandstakeholderscanuse

model results for taking appropriate resource allocation decisions. GWAVA also has the

capabilitytoassessfuturewaterresourcesunderprojectedchangesindriverssuchasclimate

andlanduse.ThemodelisaFORTRAN90programandrunsonaPCunderWindows7,ina

MS-DOS box. GWAVA is based on the PDM (Probability Distributed Model) rainfall-runoff

model(Moore,1985,2007)anditsconceptualstructureisgiveninFigure2.12.

Figure 2.12. Conceptual structure of the PDM-based GWAVA Rainfall-Runoffmodule. Adapted

from:Moore(2007).

Themodel iscomprisedofthreecomponents: i)pre-processor, ii)coreengineandiii)post-

processor.Asthemodelinputsarerequiredinbinaryformat,thepre-processorconvertsthe

ASCII input files intobinary formatandalso simultaneously checks forerroneousvalues in

thedatasets.Thecoreengineisthemaincomponentofthemodel.Itreadsthepre-processed

filesandrunsthewaterbalancemodel.Thecoreenginealsogeneratesthewateravailability

indices and can be run both in normalmode and in calibrationmode. The post processor

interpretsthedataproducedbythecoreengineandgivesoutputinthedesiredformats.

Thedatarequirement for theGWAVAmodelapplication includesadrainagemapandbasin

boundary, Digital Elevation Model (DEM), Land use/Land cover (LULC) map, soil map,

22

reservoir command area maps, crop type map in command areas and population and

livestock maps. The required climate inputs include daily temperature, precipitation and

potentialevapotranspiration(PET).Otherpotentialinputs,dependingonthewaterresource

components includedwithin themodel, are reservoir area-elevation-capacity data, average

water consumption rate forhumanand livestockpopulation, industrialdemands, irrigation

demands and inter-basinwater transfers. Themodel is capable of simulating the effects of

natural features suchas lakes,wetlands, glaciers and snowalongwith the incorporationof

reservoirs and their impacts on the water availability scenario. Based on these data

requirementsandtheobjectivesof themodellingexercise,multiple input filesneededtobe

preparedintheformatdesiredbythepre-processoroftheGWAVAmodel.Twomajortypesof

inputfilesinclude,i)compulsoryinputfilesandii)optionalinputfiles.Compulsoryinputfiles

include the physical parameter file, general water demands file and the climate data file,

whereas optional files include the sub-catchment calibration file, mountain region

information, glacier information, groundwater information, daily water demands, water

transfersandclimatescenarios.

The physical parameter file contains the information pertaining to area of grid cell, flow

directioninthegridcell,soilclass,landcover,gridcoordinates,channel-routing,percentage

of lake/wetland and lake characteristics such as surface area capacity, shape parameter,

outflow constant and value of the outflow power. The soil and land-use data are used to

determinethemajorparametersofthemodel.AspertherequirementsoftheGWAVAmodel,

thesoilmaphasbeenreclassifiedintosevenmajorclasses:sand,sandyloam,silt loam,clay

loam,clay,lithosolandorganic.ThesoilmapofthestudyareaisgiveninFigure2.13.Thearea

underthevarioussoilclassesisgiveninTable2.6.Similarly,theGWAVAmodelconsidersthe

effectsofonlyfourtypesoflandusecategories,thesebeingforest,shrub,grassandbaresoil.

Although, for the simulationof abstractions for irrigation,detailed informationon irrigated

crop-typesisused.TheLULCmapofthestudyareahasbeenreclassifiedaccordinglyintofour

majorclassesandisgiveninFigure2.14.TheareaunderthevariousLULCclassesisgivenin

Table2.7.

The SRTM (Shuttle Radar TopographyMission) Digital ElevationModel (DEM) data,which

wasalsoemployedintheMIKESHEmodeloftheNarmada,hasbeenresampledatthedesired

resolutionofGWAVAmodelat0.125°latitude´0.125°longitude.Theflowdirectionmaphas

beenpreparedfromtheprocessedDEMandisgiveninFigure2.15.Thephysicalparameter

23

filefortheNarmadabasinhasbeenpreparedbasedontheinformationextractedfromthese

GISfilesandotherparametervalueswereassignedsuitedtothestudyarea.Themodeluses

the Muskingum method of routing the channel flows between the cells and has two

parameters ‘x’ and ‘k’. The parameter ‘x’ is fixed at 0.5 whereas the parameter ‘k’, which

representsthetimedelayindays,canbeassigned.

Figure2.13.SoilmapofNarmadabasininMadhyaPradesh.

Table2.6.Areaundervarioussoilclasses

Soiltype Area(km2) Area(%)Sand 25333 28.4Sandyloam 5650 6.3Siltloam 13486 15.1Clayloam 182 0.2Clay 44469 49.9

Thewaterdemandfilecontainsthegridcellinformationpertainingtototalpopulation,urban

population,cattlepopulation,populationofsheepandgoats,urbanwaterdemandrate,and

ruralwaterdemandrate,intheappropriateunitsrecognisedbythemodel.Theinformation

pertaining to crop type, crop area, planting month etc., is also given in the general water

demandsfile.Themodelhasthecapabilityofincludingeighttypesofcropsforanygridcell.

24

Crop water demands are calculated based on the crop details and PET for the grid cell.

Thereafterthecropwaterdemandsofall thecropsaresummedupforeachcell.Thewater

demand file has also been prepared based on information extracted from theGIS files and

generalinformationpertainingtothecropcharacteristicsinthestudyarea.

Figure2.14.LanduselandcovermapoftheNarmadabasin

Table2.7.AreaundervariousLULCclasses

LULCclasses Area(km2) Area(%)Forest 20551 21.5Shrub 22643 23.7Waterbodies 1523 1.6Baresoil 3008 3.1Agriculture 47802 50.0

TheclimatedatafiletobeusedintheGWAVAmodelcanbeoftwotypes,i)monthlyclimate

normalsandanomaliesorii)dailyclimate.Thedailyclimatedataneedstobeinbinaryformat

whereasthemonthlyclimatedatacanbeeitherinASCIIorbinaryformat.AswiththeMIKE

SHEmodel of theNarmada, the high resolution gridded precipitation data set prepared by

IndiaMeteorologicalDepartment (IMD)of0.25° latitude´0.25° longitudehasbeenused to

extracttheprecipitationovertheNarmadabasin.Similarly,thegriddedtemperaturedataof

25

IMDataresolutionof1°latitude´1°longitudehasbeenusedtoextractthetemperatureover

Narmadabasin.PEThasbeencomputedusingtheThornthwaitemethod.Thereafterthedaily

climatedatafilehasbeenpreparedinthebinaryformat.

Figure2.15.FlowdirectionmapuptoHoshangabadoftheNarmadabasin.

The GWAVAmodelwas run at a resolution of 0.125° latitude´ 0.125° longitude to have a

detailed understanding of the spatial variability of the water availability in the basin. The

Narmadabasinup to its confluencewith theArabianSeacomprisesof661gridcellsat the

selectedresolution.Allthemandatoryaswellastheoptionalinputfileshavebeenprepared

at this resolutionby resampling theoriginaldatasets.Even though the inputdatahasbeen

preparedforthecompletebasin, themodellingexercisewas initiallybe limitedtoNarmada

upto theHoshangabadgaugingsite.TheNarmadaBasinup toHoshangabadcontains three

major dams: Bargi multipurpose dam, Tawa dam and Barna dam. Also manymore major,

mediumandminorprojectsarebeingplanned in thispartof thebasin. Subsequently, after

gaining enough confidence in the model, the modelling exercise will be extended for the

completeNarmadaBasin,comprisingofmanymorewaterresourceprojects.

26

2.2.2. Modelcalibrationandvalidation

TheGWAVAmodelcanberun inthenormalmodeor inthecalibrationmode.Theoptional

sub-catchmentcalibrationfilecanbecreatedtoenabletheuseofcalibratedparametersinthe

user-selectedsub-catchments.Someofthecriticalmodelparametersthatneedtobegivenfor

the various sub-catchments in the sub-catchment calibration parameter file include the

following:thePDMparameterofpowerlawprobabilitydistribution(b)whichdescribesthe

spatialvariations in soilmoisture storagecapacity, surface runoff routingparameter (Srout),

groundwaterroutingparameter(Grout),multipliertoadjustrootingdepths(fact).

Forthepresentstudy,themodelhasbeenruninthenormalmodetounderstandtheabilityof

themodelinsimulatingtheflowsinthebasinbasedonthemanualcalibrationbyfinetuning

theparametersofthemodelwithintheprescribedparameterranges.Themanualcalibration

has been carried out with the compulsory input datasets andmodel simulated flows have

beencompared toobserved flowsat theHoshangabadgauging site. Several trial runswere

performedand themodel resultshavebeen comparedwith thegauged flowseries and the

modelparametersadjustedaccordinglytoobtainbetterresults.Thelikelyrangesofthemajor

parametersaregiveninTable2.8.

Table2.8.Likelyrangeofthekeyparameters.

Parameter upperbound expectedvalue lowerboundb 4 1 0.25fact 4 1 0.25Srout 12 - 0Grout1 100

1:AnegativevalueofGroutisallowed.However,thismeansthatthereisnoroutingdelay,i.e.allwater

thatdrainsfromthesoilstorebecomesbaseflowimmediatelywithoutbeingtemporarilystoredinthe

groundwaterstore.

2:Sroutvaluesabove1areallowed,howeverthensurfaceroutingismodelledasifSroutis1.

Themodelwasinitiallyrunfortheperiod2001–2006andthemodelparameterscalibrated

using themanual calibration procedure. The post-processorwas run subsequently and the

localflowsaswellasthetotalflowswereobtainedforeachgridcellinthebasin.Themodel

performancehas been evaluated through visual comparisonof the observed and simulated

discharge plots at the grid cell pertaining to the Hoshangabad gauging site. Model

performancehasalsobeenevaluatedusingtheindicatorofpercentagedeviationinsimulated

27

flow from the observed flow at various time steps (Dv; Henriksen et al., 2003). Model

validationwassubsequentlyundertakenfortheperiod2007–2010.

2.2.3. Results:Modelcalibrationandvalidation

Table 2.9 summarises the optimised values of the calibrated parameters obtained by fine

tuning the sensitiveparametersusing themanual calibrationapproach. Comparisonof the

observedandsimulateddailystreamflowatHoshangabadgaugingsiteduringthecalibration

isgiveninFigure2.16.Similarly,comparisonoftheobservedandsimulatedmonthlystream

flowatHoshangabadgaugingsiteduringthecalibrationisgiveninFigure2.17.Thesegraphs

show that themodel has been able to simulate the flows at themonthly scale with a fair

degreeofaccuracycomparedtothedailyflowvalues.Themodelisabletosimulatethepeaks

aswell as the recession curveof thedailyhydrographs reasonablywellwith theminimum

datainputsusingmanualcalibrationbyconsideringthestudyareaasasinglesub-catchment.

Table2.9.Parametervaluesobtainedduringmanualcalibration.

Parameter Calibratedparametervaluesb 0.0065Srout 0.08Grout 0.25bfpower 4bfpower:baseflowrecessionpower

Figure2.16.Comparisonoftheobservedandsimulateddailyflowduringcalibrationat

Hoshangabad.

28

Figure2.17.Comparisonoftheobservedandsimulatedmonthlyflowduringcalibrationat

Hoshangabad.

Theperformanceof themodelhasbeenevaluatedbasedonthepercentagedeviation inthe

simulatedmonthlyflowsfromtheobserved,theresultsofwhicharegiveninTable2.10.The

comparisonofthemeanmonthlystreamflowduringcalibrationperiod2001–2006isgivenin

Figure2.18.

Table2.10.Differenceinvolumeintheobservedandsimulatedmonthlyflowsduringcalibration.

Month Dvcalibration(%)Jan -24.70Feb 33.57Mar 22.24Apr 11.29May -4.53Jun -11.92Jul 58.63Aug 89.64Sep 80.43Oct 151.13Nov 56.03Dec -9.18

29

Figure2.18.Comparisonoftheobservedandsimulatedmeanmonthlyflowduringcalibrationat

Hoshangabad.

Based on the visualisation of Figures 2.17 and 2.18 aswell as the percentage difference in

volumesgiveninTable2.10,itisveryclearthatthemodelisoverestimatingtheflowsduring

themonthsofthemonsoonseason.Thereasonforthiscanbeattributedtothefactthatthe

modelhasbeenruninthenormalmodewithoutincorporatingthereservoircharacteristics.

As the three major dams store large volumes of water, the water ultimately reaching the

gaugingsiteatHoshangabadonlycomprisesofthedamreleasesintotheriverandtheflows

fromtheintermediatesub-catchments.Assuch,theobservedflowsarealwayslessthanthe

simulatedflowswhichconsidersthebasintobevirginwithouttheimpactofanydams.The

samepatternisobservedduringthevalidationperiod2007–2010asdepictedbyFigure2.19,

which gives the comparison of the daily observed and simulated flows during validation;

Figure2.20,whichgivesthecomparisonofthemonthlyobservedandsimulatedflowsduring

validation; Table 2.11,which gives the percentage difference in volumes during validation;

andFigure2.21,whichgivesthemeanmonthlyflowduringvalidation.

Themodelresultsareencouragingconsideringtheminimaldatainputs.Furthermore,thereis

ample scope for substantial improvements in the simulation of stream flow in the basin,

through the incorporation of reservoir characteristics and adopting the multi-site auto-

calibrationapproach.

30

Figure 2.19. Comparison of the observed and simulated daily flow during validation at

Hoshangabad.

Figure 2.20. Comparison of the observed and simulated monthly flow during validation at

Hoshangabad.

31

Table2.11.Differenceinvolumeintheobservedandsimulatedmonthlyflowsduringcalibration.

Month Dvvalidation(%)Jan 20.88Feb 8.43Mar 7.00Apr -31.63May -42.73Jun -48.81Jul 32.94Aug 185.73Sep 93.97Oct 262.93Nov 135.05Dec 85.14

Figure2.21.Comparisonoftheobservedandsimulatedmeanmonthlyflowduringvalidationat

Hoshangabad.

2.3. HydrologicalmodellingoftheUpperNarmada:Summaryandongoingwork

AMIKE SHEmodel of the UpperNarmada (up to theHoshangabad gauging site) has been

developed that simulatesobserved flows reasonablywell, followingamulti-site calibration.

Likewise, the GWAVA model has been applied over the same area, with the minimal

compulsory inputs of physical parameters, general water demands and climate data. The

manualcalibrationapproachhasbeenusedbyvaryingoneparameteratatimeandobtaining

32

abestfitofthesimulatedflowswiththeobservedflows.InthecaseoftheMIKESHEmodel,

ongoingworkwillimprovetherepresentationofirrigationwithinthemodel,throughgaining

moredetailedinformationonirrigationwithinthebasinfromCEH.Informationpertainingto

theirrigatedcropsinthecommandareaswillalsobeincorporatedwithintheGWAVAmodel.

Inbothmodels,theinclusionofreservoirpropertiespertainingtothethreemajordamswill

be investigated inorder toprovidemore realistic simulationofwater resourceswithin the

basin.Thiswill also give an insight into the change in the flow regime, if any, due to these

interventions in theriverbasin.Further improvements to theGWAVAmodelwillbesought

throughuseoftheauto-calibrationapproach.Multi-sitecalibrationofthemodelwillalsobe

undertakenforthebettersimulationofflowsatalltheavailablegaugingsites.Aftergaining

confidenceinboththeMIKESHEandtheGWAVAmodelsbasedonthevariousperformance

criteria, these models shall be used thereafter to assess the potential impacts of climate

changeonthefuturewaterresourcesoftheNarmadabasin,usinganinter-modelcomparison

approach.Dependingon theavailabilityof resources, themodellingexercise canbe further

extended to the whole of Narmada basin, which, however shall be a challenging task,

consideringthelargenumberofexistingandproposedlargedamsinthebasin.

3. GangaMetagenomePilotStudy

TheGangaformsinUttarakhandastheconfluenceofAlaknandaandBhagirati,twomountain

streamsthatoriginateatHimalayanglaciers.Ittravelsalengthof2525kmthroughtheNorth

Indian plain to the Bay of Bengal (Jain et al., 2007) serving major urban centres such as

Haridwar, Kanpur and Varanasi on its course. This metagenomic pilot study covers 13

sampling points (Figure 3.1) on the first 120 km of the Ganga to capture four seasonal

snapshots of the river ecology upstream and downstream of expected pollution hotspots.

MethodsandpreliminaryresultsaredescribedinSections3.1and3.2.

3.1. Methods

3.1.1. Sampling

Samplingpoints(Figure3.1,Table3.1)werelocatedup-anddownstreamoflikelypollution

hotspotssuchasvillagesandtowns,andwheretributariesjointhemainriverchannel.

33

Figure3.1.SamplingpointsontheGanga.

Watercolumnsamplesweretakenatfourpointsintime:beforetheMonsooninAprilwhen

theriver’swater levelwasat its lowest,aftertheMonsooninOctoberwhenthewater level

wasatitshighestandbetweenthosetwopointsinJuneandDecember.InAprilandOctober,

all 13 siteswere sampled over three days. InDecember and June, a subset of 5 siteswere

sampledononedayfortechnicalreasons. Paralleltotakingwatercolumnsamples inApril

andOctober,thesedimentwassampledatall13sites.

Toperformthewatercolumnsampling,a low-costsetupwasused,comprisingapressure-

resistantwaterbottle,pressurizedwiththehelpofacyclepump,0.22μsterivexfiltersandthe

preservativeRNAlater(ThermoFisherScientific,Loughborough,UKorAMBIONInc.,Austin,

Texas),asdescribedinLehmann(2016).Thefilterwasputinasterilesamplingbag(Thermo

FisherScientific,Loughborough,UK)andstoredinacoolbox.

34

Table3.1.GangaRiversamplingpointlocations.

River/SamplingPoint Lat/Long FurtherinformationRiverBhagirathi/DevPrayag 30.147319,

78.597530aboveconfluence

RiverAlaknanda/DevPrayag 30.15222,78.60012

aboveconfluence

RiverGanga/Bageshwar 30.11375,78.58815

belowconfluence

RiverHanvel/Shivpuri 30.133964,78.389662

Gangatributary

RiverGanga/Shivpuri 30.131812,78.390954

belowHanvel/aboveRishikesh

RiverGanga/TriveniGhat/Rishikesh

30.102782,78.299777

belowRishikesh

RiverGanga/ShantiKunjGhat,Bhupatwala

29.963564,78.180650

aboveHaridwar

RiverGanga/Bisanpour 29.85628,78.14922

belowHardiwar(leftbranch)

RiverGanga/SheetalKhedra 29.82186,78.18161

belowHaridwar(rightbranch)

RiverGanga/Balawali 29.64122,78.10194

abovebarrage(flowing)

RiverGanga/MadhyaGangaBarrageu/s

29.37458,78.04128

abovebarrage(reservoir)

RiverGanga/MadhyaGangaBarraged/s

29.37227,78.04141

belowbarrage

RiverSolani/Mukeempur 29.780465,77.961902

comparisonriver

Forthesedimentsampling,sedimentwasretrievedusingacleanbucketattachedtoarope.

Thebucketwasthrownintothewateranddraggedalongtheriverfloortocatchandretrieve

surface sediment as described in Clark (2003). The sediment sample, pooled andmixed in

that way, was then scooped out of the bucket with a sterile spatula, placed into a sterile

samplingbag,coveredinRNAlaterandstoredinacoolbox.Onarrival inthelaboratorythe

sampleswerestoredat-20°Cuntilextraction.

3.1.2. DNAextraction

ToextractDNAfromthesedimentsamples,thefollowingwereaddedtothepooledsample:

300μloflysisbuffer(100mMNaCl,500mMTris(pH8),10%(w/v)sodiumdodecylsulfate,

2mgml-1proteinaseK,2mgml-1lysingenzymemix(bothSigma-Genosys,Gillingham,UK))

and300μlofNaH2PO4(pH8.0).TheDNAwasincubatedina55°Cwaterbathfor30minand

mixed every 10 min., before adding 80 μl of prewarmed 10% CTAB solution (65°C),

35

incubating at 65°C for 10 minutes and adding 680μl chloroform:isoamyl alcohol (24:1

vol/vol).Thetubeswerecentrifugedfor5minutesat14000rpm.Theaqueoustoplayerwas

aspiratedintoanewtubeandtheDNAprecipitatedbyaddinga300%(w/v)PEG/NaClmix

(30%(w/v)PEG8000,1MNaCl), leavingthesamplesonthebenchfor1h(afterPaithankar

and Prasad, 1991). The samples were then centrifuged (12 per treatment) for 10 min at

14000rpm.ThesupernatantwasdiscardedandtheDNApelletswerewashedbyadding300

μl70%chilledethanol.Thetubeswerecentrifugedagain,theethanolwasdiscardedandthe

tubes were left to dry in a laminar flow cabinet until the ethanol had evaporated. 50 μl

ultrapurewaterwasaddedandtheDNAwaslefttoresuspendfor1honthebench.Forthe

watercolumnsamples,theMoBioPowerwaterDNAextractionkit(MoBio,Carlsbad,US)was

used, followingtheprotocolbutaddingthesamePEGprecipitationstepasdescribedabove

afteritem14oftheextractionprotocol,tocleantheDNAfromRNAlaterresidues.

3.1.3. Sequencing

Afterextraction,thesamplesweresenttoLGCGenomics(Berlin,Germany)formetagenomic

shotgunand16SsequencingontheIlluminaNextSeqplatform.

3.2. Preliminaryresults

The field sampling methods were successfully tested and all samples yielded sufficient

amounts of high-quality DNA. The DNA is currently being sequenced. The DNA library

preparationwaswithoutcomplication.

3.3. Summaryandongoingwork

Samplinghasbeenundertakenonthefirst100kmoftheUpperGangabasintoinvestigatethe

impactofthefirstmajorurbancentresandengineeringprojectsontheecologyandmicrobial

functioningoftheGanga.TheeDNAsamplingcampaign,carriedoutusinglow-costmethods

suitable to theunfavourable conditionsencountered in Indiadue tohigh temperaturesand

remotelocation,yieldedgoodresults.Thedataresultingfromthesequencingthatiscurrently

underway will be pre-processed and analysed on the NERC EOS cluster with the Qiime

(qiime.org) and Kraken (https://ccb.jhu.edu/software/kraken/) pipelines, followed by

analysis tools such as MaAsLin (https://huttenhower.sph.harvard.edu/maaslin), LEfSe

36

(https://bitbucket.org/biobakery/wiki/lefse) and MEGAN (http://ab.inf.uni-tuebingen.de

/software/megan/)fortaxonomicandfunctionalprofiling.

4. Conclusions

Thisprojecthassuccessfullydeveloped twomodelsof theNarmadaBasinusingalternative

hydrologicalmodelcodes.Performanceofthesemodelsisgenerallygoodalthoughon-going

work will seek to fine tune performance through the incorporation of dams (although as

noted above details of how existing dams operate are not yet available). The models are

providing the foundations for subsequent work, already underway with additional NERC

funding,whichfocusesonassessingtheimpactsofalternativefutureclimatechangescenarios

usingtheCMIP5GCMensemble.Modelresultswillbeassessedusingtheenvironmentalflow

approach described by Thompson et al. (2014b). Thiswork also expands the geographical

focustoincludetheBarak-KushiyaraRiverBasinthatspanstheIndian/Bangladeshiborder

(using a modified SWAT model – Rahman et al., in press). Both the Namada and Barak-

Kushiyara(aswellastheMekongandUpperNiger)featureinaproposal inpreparationfor

thecurrentNERCcall“UnderstandingthePathwaystoandImpactsofa1.5°CRiseinGlobal

Temperature”.Thisproposedprojectwilluseexistingmodelstocomparefutureprojections

of river flowandenvironmental flow characteristics for climate change scenarios involving

1.5°Cand2.0°Cchangesinglobalmeantemperature.

TheeDNAsamplingcampaignfortheUpperGangaisthefirstof itskindandhasdeveloped

approachesthataresuitabletotheIndiancontext.Metagenomicshotgunand16Ssequencing

arecurrentlyunderwayandresultswillinformtheuseoftheseapproachesforbiomonitoring

ofmicro-andmacrobiotaand investigatewaterquality issueswithin this, andother Indian

riversystems.

5. References

Acreman,M. and Dunbar,M.J. (2004) 'Defining environmental river flow requirements – areview',HydrologyandEarthSystemSciences,8,861–876.

Ahrends,H.,Mast,M.,Rodgers,C.andKunstmann,H.(2008)'Coupledhydrological–economicmodellingforoptimisedirrigatedcultivationinasemi-aridcatchmentofWestAfrica',EnvironmentalModelling&Software,23,385–395.

Allen,R.G.,Pereira,L.S.,Raes,D.andSmith,M.(1998)Cropevapotranspiration-Guidelinesforcomputingcropwaterrequirements-FAOIrrigationanddrainagepaper56,Rome:FAO.

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