Method
Article
An
approach
to
validate
groundwater
contamination
risk
in
rural
mountainous
catchments:
the
role
of
lateral
groundwater
flows
F.A.L.
Pacheco
a,b,*
,
L.M.O.
Martins
d,
M.
Quininha
b,
A.S.
Oliveira
a,
L.F.
Sanches
Fernandes
c,da
DepartmentofGeology,UniversityofTrás-os-MonteseAltoDouro,Ap.1013,5001-801VilaReal,Portugal
bChemistryResearchCentre,VilaReal,Portugal
cDepartmentofCivilEngineering,UniversityofTrás-os-MontesandAltoDouro,Ap.1013,5001-801VilaReal,
Portugal
d
CentrefortheResearchandTechnologyofAgro-EnvironmentandBiologicalScience,VilaReal,Portugal ABSTRACT
Computermodelsdedicatedtothevalidationofgroundwatercontaminationriskintheruralenvironment,namely
theriskofcontaminationbynitrateleachatesfromagriculturefertilizers,arefrequentlybasedondirectcomparison
of riskyareas (e.g., cropland,pastures usedfor livestockproduction) and spatial distributionsof contaminant (nitrate)
plumes.Thesemethodsarefatedtofailwherelateralflowsdominateinthelandscape(mountainouscatchments)
displacingthenitrateplumesdownhillandfromtheriskyspots.Inthesecases,thereisnoconnectionbetweenthe
spatiallocationofriskyareasandnitrateplumes,unlessthetwolocationscanbelinkedbyacontaminanttransport
model.Themainpurposeofthispaperisthereforetointroduceamethodwherebyspatio-temporallinkscanbe
demonstratedbetweenriskyareas(contaminantsources),actualnitrateplumes(contaminantsinks)andmodeled
nitratedistributionsatspecificgroundwatertraveltimes,therebyvalidatingtheriskassessment.Themethod
assemblesacoupleofwellknownalgorithms,namelytheDRASTICmodel [1,2]andtheProcessingModflowsoftware
(https://www.simcore.com),buttheircombinationasriskvalidationmethodisoriginalandprovedefficientinits
initialapplication,thecompanionpaperofPachecoetal.[3].
©2018TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense(http://
creativecommons.org/licenses/by/4.0/).
ARTICLE INFO
Methodname:Anapproachtovalidategroundwatercontaminationriskinruralmountainouscatchments:theroleoflateral groundwaterflows
Keywords:groundwatercontaminationrisk,ruralmountainouscatchment,contaminanttransport,traveltime
Articlehistory:Received16September2018;Accepted2November2018;Availableonline7November2018
*Correspondingauthorat:DepartmentofGeology,UniversityofTrás-os-MonteseAltoDouro,Ap.1013,5001-801,VilaReal, Portugal.
E-mailaddress:[email protected](F.A.L. Pacheco).
https://doi.org/10.1016/j.mex.2018.11.002
2215-0161/©2018TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense(http:// creativecommons.org/licenses/by/4.0/).
ContentslistsavailableatScienceDirect
MethodsX
SpecificationsTable
Subjectarea EnvironmentalScience Morespecificsubjectarea Hydrology
Waterresources
Methodname Anapproachtovalidategroundwatercontaminationriskinruralmountainouscatchments:the roleoflateralgroundwaterflows
Nameandreferenceof originalmethod
Thismethodarticleispresentedforthefirsttimeinthecompanionpaper[3]
Resourceavailability LinkstodataresourcesareprovidedinTable1.DatausedinPachecoetal.[3]isprovidedas SupplementaryMaterial
Methoddetailsandapplicability
The workflow to validate groundwater contamination risk in mountainous catchments is composedofthreeconnectedmodules(Fig. 1):(1)theriskassessmentmodule,whichusestheDRASTIC model[1,2]toquantifyaquiferintrinsic vulnerability,landuse/ landoccupationdatatoquantify specificvulnerability [4], and an algebraic combination of intrinsic and specific vulnerability to quantifygroundwatercontaminationrisk;(2)thegroundwaterflow-contaminanttransportmodule, which uses (a) stream flow models at catchment scale to estimate average aquifer properties (hydraulic conductivity and effective porosity) and groundwater travel time, (b) the Processing Modflowsoftware(https://www.simcore.com)tomodelthespreadingofnitrateloadsinjectedathigh riskspots;(3)thevalidationmodule,whichcomparesmeasuredandmodelednitrateconcentrations alongcatchmentprofiles,withthemainpurposeofverifyingifthemeasuredconcentrationscanbe relatedtotheinjectionpointsviaadvective-dispersivetransportduringaspecifictimespan.Thethree modulesaredescribedindetailinthenextsubsections.Inallcases,theyarecomplementedwiththe useof Geographic Information Systems (the ArcMap and ArcHydro computer packages of ESRI company),namelytoperformnecessaryterrainmodelinganalyses,spatialinterpolations,rastermap computations,finalthematicmaps,amongothergeographicrelatedtasks.TheArcMapsoftwareis widelyusedinenvironmentalstudies(e.g.,[5–9]).
Theriskmapsproducedwiththepresentmethodcanbeincludedinmanagementplanstohelp waterauthorities protecting localgroundwater resources,as proposed inValle etal. [10]. Other applicationsmayincludetheembeddingofvulnerabilitymapsinhydrologicspatialdecisionsupport systemssuchasMikeBasin(e.g.,[11,12]),toimprovescenarioanalyses focusedongroundwater protection.Besidesapplicationonwaterresourcesmanagementandaquiferprotection,theresults fromthepresentmethodcanbecoupledwithoutcomesfromgeochemicalmodels,toimprovethe interpretationofgroundwatercompositioninaquiferswithsubstantialanthropogenicinfluence(e.g., [13–15]).
Inmountainouswatershedswhereaquifersystemsareprimarilycomposedofcarbonaterocks,the useofDRASTICmodelintheriskassessmentmodulemaynotbeadequate.Inthesecases,itwillhave beenpreferabletodescribeintrinsicvulnerabilityusingmorespecificmethods,suchastheEPIK[16], RESK[17],RISKE[18]orSINTACS[19],amongothers.
Data
ThesourcesofinformationanddatarequiredtoimplementtheworkflowaredepictedinTable1. Thistablereferstoaspecificapplication,namelytheonepresentedinthecompanionpaperwherethis methodisappliedforthefirsttime[3],butitcanbeusedasreferencetoanysimilarapplication.
TheSupplementaryMaterialcomposedofnumericandgraphicaldatawasassembledpermodule andsub-moduleandisprovidedinExcelformat.Besidesrawdatausedintheworkflow(e.g.,depthsto watertablemeasuredin dugwells),theseExcelworksheetscontaintheimplementationof most equationsofmodules(1),(2a)and(3)presentedbelow.
Riskassessmentmodule
TheestimationofaquifervulnerabilityisbasedontheDRASTICmodel[1,2].Thismethodrelates aquifervulnerabilitytosevenfeaturesthatformtheDRASTICacronym:D-Depthtothewatertable,R -Recharge,A-Aquifermaterial,S-Soiltype,T-Topography,I-ImpactofthevadosezoneandC -Hydraulicconductivity.Somefeaturesarenumerical(e.g.,D,R)whileothersarecategorical(e.g.,A,S). Regardlessthetype,featuresareevaluatedwithinthestudiedareaandthenconvertedintoacommon dimensionless scale (ratings) to become consistently aggregable and mutuallycomparable. The ratings vary from 1 (lowest vulnerability) to 10 (highest vulnerability). The weighted sum (aggregation)oftheseratingsformstheDRASTICindex(V).
V¼ðDwDrÞþðRwRrÞþðAwArÞþðSwSrÞþðTwTrÞþðIwIrÞþðCwCrÞ ð1Þ
wheresubscriptrpointstothefeatureratingandsubscriptwtothefeatureweight.Factorweights were originallyset up byan Environmental Protection Agency (EPA) committee using a Delphi (consensus)approach.Theweightswereassumedconstant,withvaluesDw=5,Rw=4,Aw=3,Sw=2,
Tw=1,Iw=5,andCw=3.
Theconsensusonfeatureweightsresultedfromtheunderstandingofreal-worldconditionsat variouswell-studiedregions.Inmanyplaces,however,theimportancegiventothefeaturesisnot understoodbecauseweightsdonotreproducelocalconditions[20,21].Forthatreason,applicationof DRASTICmodelisfrequentlysucceededbyastagecalledsensitivityanalysis,wherebyoriginalweights arereplacedbyoptimizedweightscalculatedbyanobjectivefunction.Themostcommonapproaches tosensitivityanalysisarethesingle-parameter[22]andthemapremoval[23]methods.The single-parametermethodhasthepurposetocheckthespatialsignificanceofDRASTICweights.Whilethe originalweightsweregivenhigherorlowervaluesignoringthespatialdistributionofcorresponding ratings,NapolitanoandFabbri[22]arguethateffectiveweightsshouldresultfromanassessmentand judgmentofwjRjproducts acrosstheentirestudiedregion.Firstly, theauthorsidentifiedunique
conditionsubareas,whichareparcelswithspecificcombinationsofweights(w)andratings(R).Then, theeffectiveormodifiedweight(W)offeaturejwascalculatedby:
Wj¼ Pn i¼1 wjRij Vi n ð2Þ
whereViisthevulnerabilityindexofparceliandnisthenumberofparcels.Themapremovalmethod
evaluateswhether or not is necessary to use all theparameters in theassessment of DRASTIC vulnerability.Firstly, Lodwicketal.[23] definedthe“unperturbed”and “perturbed”vulnerability indices.TheDRASTICindexcalculatedbyEquation1istheunperturbedindex(V)basedonNfeatures, whilethevaluescomputedusingalowernumberoffeatures(n)aretheperturbedindices(V’).Then,
Table1
Sourcesofinformationanddatarequiredtoimplementthecross-profilingalgorithm..Symboldescription(institutionnamesin Portuguese):SNIRH–SistemaNacionaldeInformaçãoemRecursosHídricos;DGT–DireçãoGeraldoTerritório.
Code Parameter Unit Information/data Source URLof internet website Riskassessmentmodule(DRASTICintrinsicvulnerability+landuse-basedspecificvulnerability)
D Depthtothe watertable
m Hydraulicheaddatafrom1261dugwells locatedintheMoraisMassif,northern Portugal
Pacheco,(2000)
R Recharge mmyr–1 Rechargedatafrom23springwatersheds locatedaroundtheLimãos,Assureiraand Amedosub-basins
Pacheco,(2000)
A Aquifer material
dimensionless Spatialdelineationanddescriptionof aquifersystemslocatedintheAncient Massif,publishedinthehandbookof Almeidaetal.(2000)entitledTheaquifer systemsofContinentalPortugal
WaterInstitute, National informationSystem onWaterResources http://snirh. apambiente. pt/
S Soiltype dimensionless Spatialdistributionofsoiltypes Agroconsultores andCoba(1991) T Topography % Hillsideslopesobtainedfromanalysisofa
DigitalElevationModel(DEM)
DGT http://
www.igeoe. pt
I Impactofthe vadosezone
dimensionless Descriptionofaquifersystemslocatedin theAncientMassif
WaterInstitute, National informationSystem onWaterResources http://snirh. apambiente. pt/ C Hydraulic Conductivity
mday–1 Transmissivityandthicknessdatafromthe 23springwatersheds
Pacheco,(2000) L LandUse dimensionless Specificvulnerabilityevaluation Antonakos&
Lambrakis,[4] Groundwaterflowandcontaminanttransportmodule(ProcessingModflow)
h Hydraulichead m Spatialdistributionofhydraulicheads estimatedfromdigitalelevation(H)and depthtothewatertable(D)data
DGT;Pacheco, (2000) http:// www.igeoe. pt K,ne Hydraulic conductivity andeffective porosity mday–1, dimensionless
Hydraulicconductivityandeffective porosityof23springwatersheds calculatedbytheBrutsaertMethod
Pacheco,(2000)
Q Spring dischargerate
m-3
.s-1
Dischargeofspringwatermeasuredona monthlybasis
Pacheco,(2000) NO3 Nitrate
concentrations
mgL–1 Nitrateconcentrationinthe23spring
watersheds,assessedatthespringsitein April1997,September1997andJanuary 1998
thesensitivitytowardsremovingoneormorefeaturesfromthevulnerabilityanalysisiscalculatedby: S¼absolutevalue 100V VNV 0 n ! ( ) ð3Þ whereSisthesensitivityexpressedintheformofindexvariation.
Havingcompletedtheestimationofaquifervulnerability,theoriginaloroptimizedVvaluesare combinedwithaspecificvulnerabilityindexbasedonlandusesoroccupations(L),adaptedfrom AntonakosandLambrakis[4],toobtainthefinalscoreongroundwatercontaminationrisk:
Risk=V+4L (4)
Accordingtotheseauthors,themostriskyareascomprisethefarmlandswherefertilizersareused thatcanleachandcontaminatethewatertableaquifer,whilethelessriskyareascomprehendthe forestspotswherenopotentialsourcesofcontaminationareanticipated.
Groundwaterflow-contaminanttransportmodule
The running of a groundwater flow-solute transport model such as the Processing Modflow (https://www.simcore.com)requirestheavailabilityofdataonaquiferproperties,namelyhydraulic conductivity(K)andeffectiveporosity(ne).ThevaluesofKandnecanbeestimatedbytheso-called
methodofBrutsaert[24–26],whorelatedthesepropertiestostreamflowconstantsandcatchment’s geometricalfeatures.Accordingtothismethod,inaplotofln(
D
Q/D
t)versus.ln(Q),whereQ(m3s–1)is
thestreamflowdischargerateandt(s)thecorrespondingtime,thelowerenvelopetothescatter pointsisrepresentedbytwostraightlines,onewithaslopeb=1representingthelong-timeflows (baseflows),andtheotherwithaslopeb=3describingtheshort-timeflows(interflows).They-values wherethelinesinterceptln(Q)=0(orQ=1)arethestreamflowconstantsa1anda3,whicharerelated
toKandneinEquations5a,b:
K¼0:57 ffiffiffiffiffi a1 a3 r A3 V2zL 2 ! ð5aÞ ne¼ 1:98 Vzpa1a3 ð5bÞ Vz¼
g
V ð5cÞwhereA,VandLarethecatchment’sgeometricfeaturesarea,volumeandlengthofwaterchannels, respectively,whileVzistheportion(
g
)ofVactuallyinvolvedingroundwaterflowdefinedaseffectivewatershedvolume.AccordingtoSantosetal.[27],
g
=0.25forcatchmentslargerthanA=10km2.Themethod of Brutsaert hasbeen successfully applied tospring and streamwatershedsshaped on fracturedrocks[28,29,30,27,31].
Althoughnotessentialtogroundwaterfloworsolutetransportmodeling,theturnovertimeof groundwaterintheaquifercanbeestimatedwiththepurposetocompareturnovertimeswithstress periodsofcontaminantplumedispersionresultingfromProcessingModflowruns.Theturnovertime (t,s)canbeestimatedthroughcombinationofstreamflowconstantsandgroundwaterdischarge(Q; m–3.s–1)asproposedinPacheco[31]:
t¼Q1p:98a
1a3 ð6Þ
TherunningofProcessingModflowstartswiththecreationofanewmodelandproceedswith generationofagrid(mesh)consistingofanarrayofnodesandassociatedfinite-differenceblocks (cells).Preparatorystepsalsoincludespecificationofaquifertypesandboundaryconditions,andthe
definitionofhydrostratigraphicunitsrepresentedbyoneormoremodellayers.Thespecificationof boundaryconditionsismeanttoidentifyno-flow,constant-headoractive-head(time-dependent) cells.Havingdefinedthemodel’senvironment,itisnecessarytopopulatethegridwithtopographic, hydrologic,aquiferproperty,andcontaminantdata.ProcessingModflowrequirestheuseofconsistent unitsthroughoutthemodelingprocess.ThefeedingofProcessingModflowwithtopographicdata aimsthedefinitionofaquifermodelgeometry,namelytheelevationofmodellayerlimits(topand bottom).Usually,thetoplayerelevationsaresettothealtitudesofalocalDigitalElevationModel (Htop),whilethebottomlayerelevations(Hbot)canbesetto:
Hbot¼Htopb ð7aÞ
with b¼ Vz
A ð7bÞ
Whereb (m)is theaverage aquifer thicknessestimated as ratio of effective watershedvolume (Equation 5c)over catchmentarea. The requiredhydrologicand aquifer property datacomprise estimatesonhydraulichead(h),recharge(R),(horizontal)hydraulicconductivity(K)andeffective porosity(ne).Hydraulicheadsarecalculatedfromtopographicelevations(Htop)anddepthstothe
watertable(D),whicharetheDrvaluesusedinDRASTICbeforetheirconversionintodimensionless
ratings:
h¼HtopD ð8Þ
Asregardsrecharge,ProcessingModflowcanbesourcedwiththesameestimatesasusedinthe riskassessmentmodule,whileforKandnethesoftwarecanusetheoutcomesfromEquations5a,b.In
thecaseofRandK,theoriginalunits(mm
yr–1andms–1,respectively)needpriorconversionintothe selectedProcessingModflowunits(e.g.,mday–1).ProcessingModflowisreadytorunwhentemporalparametersandcontaminantloadsarefinally specified.Inthissoftware,thesimulationtimeisdividedintotheso-calledstressperiods,whichare timeintervalscharacterizedbytheconstancyofexternalexcitationsorstresses.Inthecontextof contaminanttransportinruralcatchments,thecontaminantloadsaredefinedwheregroundwater contaminationderivedfromagricultureorlivestockactivitiesisexpected,called“injectionareas”.For thecurrentworkflow,contaminantloadswereassumedtooccurinarbitrarypointsdistributedwithin theareasofhighspecificvulnerabilitydeterminedonthebasisoflandusebytheriskassessment module.Besidesdefinitionofinjectionareasandsites,itisnecessarytosetuptheinjectedvolume(Vi;
m3
day–1)andthecontaminant’smaximumnumberoftotalmovingparticles.Theinjectedvolume wasestimatedby:Vi ¼RA ð9Þ
whereA(m2)representsinjectionareaandRtheaveragerechargewithinthatarea.Themodeled
contaminantisnitrate,withthemaximumnumberoftotalmovingparticlesadjustedtothelargest nitrateconcentrationobservedinthestudiedarea.
ThesimulationsarebasedontheMODFLOW(forgroundwaterflow)andMT3D(forcontaminant transport)toolsofProcessingModflowsoftware.Thesimulationsinvolvethehandlingofadvection and dispersionprocesses, but neglect any effect of chemical reaction. The parameters used in advectionanddispersionpackagesarethedefaultparameters (seeSupplementaryMaterial).The initialcontaminantconcentrationsaresetuptonullvalues.
Validationmodule
In this module,measurednitrate concentrations arecompared withmodeled nitrate concen-trationsalongthecatchment’slongestflowpath.Firstly,themeasuredandmodeled(foreachstress period)aredigitallysampledalongthelongestflowlinevertices.Thesampledvaluesaresavedintable format(seeSupplementaryMaterial)andopenedinMicrosoftExcel.Thesampledvaluescomprisethe distancefromflowlineoriginandthenitrateconcentrationattheflowlinevertex.Subsequently,
nitrateconcentrationsevaluatedattheverticesareplottedasfunctionofdistance.Thisplotiscalleda crossprofile.Measuredandmodeledconcentrationsarerepresentedinthesamediagramwiththe purposeofidentifyingthebestmatchbetweenrealandpredictedprofiles.Theprocessisrepeatedif nitrateconcentrationshavebeenmeasuredatdifferenttimes(e.g.winter,summer)indifferentflow components (e.g., interflow / return flow or base flow). For the best matches, a coefficient of determinationisestimatedbetweenmeasuredandmodeledconcentrationstobeusedasgoodnessof fitmeasure.
Insightsonmodelimplementation
Thebasedatarequiredtoimplementtheworkflowisdemandinganddiverse.Usually,itneedsto be compiled from various sources, as demonstrated in Table 1. Module 1 uses a great deal of geographical data.Most intrinsic / specific vulnerability parametersare spatially represented by polygonshapefilesoftenrelatedtoqualitativeparameters(e.g.,parameterA-aquifermaterial),points linkedtonumericalparameters(e.g.,parameterD–depthtothewatertableassessedindugwells),or raster files also linkedto numerical parameters (e.g. parameter T – topography). Lines are less frequentlyusedunless,forexample,fracturesarecombinedwithlithologictypestorepresenttheA parameter.Theattributetableofpolygonshapefiles(e.g.,lithology)needstocontainacolumnwhere theassociatedparameter(i.e.,A)israted.Therastermapdisplayingthecategoricalparameterinspace is obtained through polygon to raster conversion within the ArcMap software, using the aforementionedcolumnasconversionfield.Thehandlingofpointshapefilesisdifferent.Ingeneral, theoriginalvariable(e.g.recharge)isfirstinterpolatedacrossthestudiedareaandthenrecastas ratings (i.e., R) in keeping with the DRASTIC model reclassification tables. Interpolation and reclassificationtoolsareavailablefromtheArcMaptoolbox.Digitalelevationmodelsinrasterformat areusuallythesourcedataforparameterT.BeforeconvertingelevationdataintoTratingstheuser needstousetheslopetoolofArcMaptoobtainslopevaluesinpercentrise.
ThestageofsensitivityanalysisiscarriedoutinMicrosoftExcel.Firstly,therastermapsdisplaying theDRASTICparametersare digitallysampled(SampletoolfromtheSpatial Analyst>Extraction toolbox)usingameshofregularlydistributedpointsasinputlocationfeature.Secondly,theresultis exportedastableandsubsequentlyimportedintoExcel.Finally,Equations2and3areimplementedin thissoftwareascanbeconsultedintheSupplementaryMaterial.
TheDRASTIC(Equation1)andrisk(Equation4)mapsareobtainedthroughthemapalgebratoolof ArcMap.
Module2acombinestheuseofArcHydroandExcel.TheArcHydrotoolsareembeddedinArcMap andareusedtodrawwaterlinesandcatchmentboundariesaswellastoevaluatetheirgeometric parameters(A,V,L).ThescoresofA,VandLarethenusedinEquations5a-ctoestimateKandne.
However,beforecompletingthistask,streamflowconstantsa1anda3needtobeestimatedfromthe
ln(
D
Q/D
t)versus.ln(Q)diagrams.TheSupplementaryMaterialcontainsindicationonhowthelower envelopes tothe scatterpointsareusedtoobtainthesevalues. Havingestimateda1and a3,theturnovertimeofgroundwateriscalculatedstraightforwardlybycombiningthesevalueswithaverage springdischargesinEquation6.
ImplementationofModule2brequirestheexportofanumberofparameters(e.g.,Htop,Hbot,b,h;
Equations7and 8)inASCIIformattotheProcessingModflowsoftware,usingtherastertoASCII conversiontoolofArcMap.TheASCIIfilesareheadedbythenumberofrowsandcolumnsdefiningthe modelinggrid,followedinthesucceedinglinesbytheparametervaluesatthegridnodes(pleasesee SupplementaryMaterial).
ImplementationofModule3requiresnofurtherinsightsbesidestheonespresentedabove. Acknowledgements
Thisresearchwas sponsoredbytheINTERACTproject–“Integrated ResearchinEnvironment, Agro-ChainandTechnology”,no.NORTE-01-0145-FEDER-000017,initslineofresearchentitledBEST –“Bioeconomyandsustainability”,financedbytheEuropeanRegionalDevelopmentFund(ERDF) throughNORTE2020(NorthRegionalOperationalProgram2014/2020).
AppendixA.Supplementarydata
Supplementarymaterialrelatedtothisarticlecanbefound,intheonlineversion,atdoi:https://doi. org/10.1016/j.mex.2018.11.002.
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