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To link to this article : DOI :
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To cite this version :
Macary, Francis and Morin, Soizic and Probst,
Jean-Luc and Saudubray, Frédéric A multi-scale method to assess
pesticide contamination risks in agricultural watersheds. (2014)
Ecological Indicators, 36 . pp. 624-639. ISSN 1470-160X
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A
multi-scale
method
to
assess
pesticide
contamination
risks
in
agricultural
watersheds
Francis
Macary
a,∗,
Soizic
Morin
b,
Jean-Luc
Probst
c,d,
Frédéric
Saudubray
aaIrstea,URADBXAménitésetdynamiquesdesespacesruraux,50avenuedeVerdun,F-33612CestasCedex,France bIrstea,URREBXRéseaux,épurationetqualitédeseaux,50avenuedeVerdun,F-33612CestasCedex,France
cUniversitédeToulouse,INPT,UPS,LaboratoireEcologieFonctionnelleetEnvironnement(EcoLab),ENSAT,Avenuedel’Agrobiopôle,31326 Castanet-Tolosan,France
dCNRS,EcoLab,31326Castanet-Tolosan,France
Keywords: Agriculture GIS Remotesensing Pesticides Phytopixal Spacescaling
Watercontaminationrisk
a
b
s
t
r
a
c
t
Theprotectionofwaterisnowamajorpriorityforenvironmentalmanagers,especiallyarounddrinking pumpingstations.Inviewofthenewchallengesfacingwateragencies,wedevelopedamethoddesigned tosupporttheirpublicpolicydecision-making,atavarietyofdifferentspatialscales.Inthispaper,we presentthisnewspatialmethod,usingremotesensingandaGIS,designedtodeterminethe contami-nationriskduetoagriculturalinputs,suchaspesticides.Theoriginalityofthismethodliesintheuse ofaverydetailedspatialobject,theRSO(ReferenceSpatialObject),whichcanbeaggregatedtomany workingandmanagingscales.Thishasbeenachievedthankstothepixelsizeoftheremotesensing,with agridresolutionof30m×30minourapplication.
Themethod–calledPHYTOPIXAL–isbasedonacombinationofindicatorsrelatingtothe environmen-talvulnerabilityofthesurfacewaterenvironment(slope,soiltypeanddistancetothestream)andthe agriculturalpressure(landuseandpracticesofthefarmers).Thecombinationoftheseindicatorsforeach pixelprovidesthecontaminationrisk.Thescoringofvariableswasimplementedaccordingknowledge inliteratureandofexperts.
Thismethodisusedtotargetspecificagriculturalinputtransferrisks.Theriskvaluesarefirstcalculated foreachpixel.Afterthisinitialcalculation,thedataarethenaggregatedfordecisionmakers,according tothemostsuitablelevelsoforganisation.Thesedataarebasedonanaveragevalueforthewatershed areas.
InthispaperwedetailanapplicationofthemethodtoanareainthehillsofSouthwestFrance.We showthepesticidecontaminationriskbyinareaswithdifferentsizedwatersheds,rangingfrom2km2
to7000km2,inwhichstreamwateriscollectedforconsumptionbyhumansandanimals.Theresults
wererecentlyusedbytheregionalwateragencytodeterminetheprotectionzoningforalargepumping station.Measureswerethenproposedtofarmerswithaviewtoimprovingtheirpractices.
Themethodcanbeextrapolatedtodifferentotherareastopreserveorrestorethesurfacewater.
1. Introduction
Forseveraldecades,powerfulagriculturalpressureson envi-ronmentsofvaryingvulnerabilityhaveledtothecontaminationof waterbodiesbyarangeofdifferentpollutants:fertilisers, primar-ilynitrogen(Henin,1980;Schröderetal.,2004;Perrinetal.,2008) andpesticides(VanderWerf,1996;Barriuso,2004)areamongthe bestknown.Atthesametime,societyisdemandinganevermore stringentprotectionoftheenvironment.Inresponsetothis,public authoritieshaveimplementednumerousregulatoryandincentive
∗ Correspondingauthor.Tel.:+33557890845. E-mailaddress:francis.macary@irstea.fr(F.Macary).
measures,someofwhich directlyconcernagriculturalactivities
(CEE,1991a,b,1998).
In1992,theEuropeanEconomicCommunityimplementedthe first majorreform of the EuropeanAgricultural Policy (Deneux
andEmorine,1998;Weyerbrock,1998).Theaimwastodecrease
theagriculturalbudgetof ECCwithalimitationofproductions, and to increase the environmental protection (surface waters, groundwater,soils,andbiodiversity).Thisnewpolicyledto agri-environmentalmeasures,withsubsidiesforfarmerswhodecided toadoptenvironmentalprotectionmeasures.Theyear2000saw thearrivaloftheEuropeanFrameworkDirectiveonWater,which requiresallbodiesofwatertobeinagoodecologicalcondition by2015.TheDirectivesetoutchemicalandbiologicalnormstobe achievedbyEUstates(EC,2000).
Differentassessmentsofthesepoliciesindicatethatthe sus-tainabilityofwatermanagementinruralareascanbeimproved. Accesstodecision-makingtoolsatdifferentspatialscaleswillallow abetteranalysisofrisks,andhelpintheselectionofthosezones wherecertainmeasuresshouldbeprioritised.Changesin agricul-turalpracticestendtotakeplaceatfarmandlandparcellevel.This intermediatescale(smallarea)isusefulfortheimplementation ofactionsbylocalgroups(farmerswithinthesamesmall water-shed,localfishingassociations,etc.).Onthecontrary,itisatthe scaleofwateragencyhydrologicalareasthatdifferentmeasures forwaterprotectionaredefined.Forinstance,Frenchpolicy mak-ersrecentlydecidedtoprotectpumpingzonesforsurfacewaterby creatingterritorialactionplans(TAP).ATAPincludesmeasuresin whichparticipants(principallyfarmers,butalsomunicipalitiesand inhabitants)arerequiredtoreducethecontaminantstransferred intosurfacewater.SomeTAPsareconcernedwiththeextensionof culturesuccessionsonthesameparcelofland(fourorfivedifferent cultures,beforereturningtothefirst),reducingdosesoffertilizers andpesticides,adoptionofintermediatecropsinwinterbank pro-tection,theBestManagementPracticesfortillage,etc.Somegood environmentalpractices,liketheintroductionofvegetativefilter stripsalongstreams,canalsobemademoreeffectivebythe devel-opmentofriparianzones,whenpossible.Inthesecases,managers givesubsidiestofarmers,andthereforeneedaccurateinformation aboutpotentialriskzoningbeforedrawinguptheirTAP.
Upuntilnow,agri-environmentalindicatorsandhydrological modelsusedtoassesstheconditionofwaterresourcesandthe potentialforcontaminationbypollutants(cf.Section2.1),haveonly providedinformationatasinglespatiallevel.Thesebasic indica-torswereusuallyappliedattheagriculturalparcelscaleand–more rarely–atthewatershed levelespeciallythose oflarge dimen-sion,wherethereareimplicationsforenvironmentalmanagement
(Devillersetal.,2004).Thesemodelscannotbeappliedtoother
scales,becausedatafortheindicatorsaregenerallycalculatedatthe parcellevel.Thiskindofmodelstendstobeapplicabletobest man-agementpracticesinfarming.However,forpublicmanagersand wateragencies,theimplementationofagri-environmentalpolicies requiresdecision-makingtoolsforlargerscales(riverbasin,region, etc.)(Gardi,2001).
Toachievethis, thevariables, geographicalobjectsandbasic indicatorsused,mustbeadaptedtothetransferofspatialscales.In termsofdefinitions,weconsiderthatthevulnerabilityofan envi-ronment(surfacewaterforinstance)representsthelikelihoodthat itwillbeaffectedbypollutants.Ontheotherhand,thesensitivityof theenvironmentallowsustoevaluatehowthisenvironmentmay respondtocontamination.
Thekeyenvironmentalissuesconcerntheconservation, preser-vationandrehabilitationofthevarioususesofawaterresource. Indeed,differentusesofaquaticenvironmentsmaybeaffectedby occasionalanddiffusepollutions:abstractionsfordrinkingwater supplies,waterresourcesforirrigationandrecreationaluses (fish-ingandbathing). Theriskarisesfromthecombinationofthese environmentalissues, thevulnerabilityofaquaticenvironments andthedifferentpressuresbeingexperienced.
Thevulnerabilityofanenvironmentplaysanimportantrolein thequalityofsurfacewaterusedbyhumansandanimals.Pollution observedinruralwatershedsmainlyoriginatesfromagricultural inputs(pesticides,nitrogenfertilizers).Environmentalrisk assess-mentdependsonthelevelofobservationbythepeopleinvolved andthuscanbecarriedoutatdifferentspatialscales,according totheissuebeingconsidered:e.g.decision-makingscales, agricul-turalpracticesorwatercontamination.Thus,arisk(CORPEN,2003) allowsanestimationofthepossibilityofchangestospecificuses, suchasthequalityofwaterforconsumption.
Thesedifferentspatialentitiesmaybesuperimposedand fit-tedtogether:nested watershedswhen eachentity iscontained
in anotherlargerentity(a “Russiandoll” approach),or become embedded,when onlyapartoftheterritoryorspatialentityis includedinanotherterritoryorentity.Particularlyforagricultural managemententities,e.g.farmingparcels,farmsandoperational infrastructures,the sametypes of spatialentities very oftenfit together.
The problem of scale changes associated with agri-environmental risks is now a recurrent topic in sustainable development discussions; it is both a research concern and a prerequisiteforsupportingstakeholdersindecision-making.
Theaimofthispaperistopresentanewspatialisedmethod, for assessing thecontamination risk of surface waters by agri-culturalpesticideoverinterconnectedareasofdifferentsizesin differentcountries,implementedbycombiningdifferent indica-tors.Ourmainobjectiveistoprovideaspatialisedmethodusinga GIS,whichmustbepreciseenoughtoobtainefficientresults,and alsotobesuitableforenvironmentalmanagersandstakeholdersin theirdecisionmaking.Unlikehydrologicalmodels,wedonotconsider theassessmentofcontaminantflux,whichneedaresolutionof com-plexequations,butadiscriminationofriskareasbyasuitablemethod forwaterandenvironmentalmanagers.
Afterareviewofthedifferentmethodsusedforassessingwater contaminationrisks,andforchangingspatialorganisational lev-els,wewillexplainthestructureofthemethodthatwedeveloped (Section2.2).Wewillthendetailthetestapplication,carriedoutin southwestFrance,whereweusedourmethodto,determine pesti-cidecontaminationrisksforsurfacewaterdestinedforhumanand animalconsumption.
2. Consideringlevelsofspatialorganisationalinrisk assessment:areview
2.1. Modelsandindicatorsusedforcharacterisingwater contaminationrisks
Bio-physicalmodelsandagri-environmentalindicatorsare gen-erally used for assessing the contamination risk to water in watersheds(TongandChen,2002).However,theseapproachesare specifictoasingleobservationscaleandtheycannotbedirectly transferredtootherscales.
Bio-physicalmodelsevaluatetheinfluenceofafactorandits modalities on thepollutants fluxes observed at theoutlet of a riverbasin.Scenariotestingisfrequentlyusedtoprovideanswers instudiesofcomplex phenomena.Thesemodelsprovideuseful resultsfortheestimationofpollutantsflows,e.g.theEPICmodel (ErosionProductivityImpactCalculator)(Williams,1990),SWAT model(Soil&WaterAssessmentTool)(Arnoldetal.,1998),STICKS model(Brissonetal.,2003)andTNT2model(Topography-based NitrogenTransfersandTransformations)(Beaujouanetal.,2002).
Wohlfahrt et al.(2010) implemented theMHYDAS model
(dis-tributed hydrological model for agrosystems) to carry out risk assessmentsfor diffusepollution in diagnosticsas wellas test-mitigationstrategies ina smallwatershed.Ferrantetal.(2011)
haverecentlydevelopedtheSWATandTNT2modelsfor measur-ingnitrogen fluxesin South-WestFrance.Boithias etal.(2011)
andLescotetal.(2011)usedtheSWATmodeltoassesspesticides
fluxesinthesamearea.LimandEngel(2003)refinedtheNAPRA (NationalAgriculturalPesticideRiskAnalysis)modelintheUSA. Othermodelsarecurrentlyunderdevelopmentforthesameor similarpurposes(Gascuel-Odouxetal.,2009a,b).Thesecombine agronomical-hydrologicalmodels(whichtakeintoaccount agricul-turalpracticesatrelevantspatio-temporalscales),theinteraction withthephysicalenvironment,andtheclimate(Vazquez-Amabile
andEngel,2008).However,itisoftendifficulttosetparameters
usefulaswayofhelpingresearcherstobetterunderstandphysical processesthanasadecision-makingsupporttoolformanagersof publicservices.
Agri-environmentalindicatorsarebasedondescriptive param-eters thatsummarise the complexityof situations or processes
(Bockstaller et al., 1997).They are used tomonitor and assess
the impacts of agriculture on the environment (Buczko and
Kuchenbuch,2010).
Mostofthemarebuiltatthe“farmingparcel”level(Devillers etal.,2004),for exampleADSCOR(AdditiveScoring),EIQ (Envi-ronmentalImpactQuotient),EPRIP(EnvironmentalPotentialRisk IndicatorforPesticides),GUS(GroundwaterUbiquityScore),I-Phy (IndicateurPhytosanitaire),PEI(PesticideEnvironmentalIndex),or thePhosphorusIndex(Bechmannetal.,2005),thoughsomecanbe usedatthe‘farm’level:GUS,PEI,RMI(RiskManagementIndicator). Bycombiningtheseindicatorswithgeographicalinformation sys-tems(GIS)–normallyatthesmallwatershedscale–itispossibleto establishenvironmentalriskzonesforsensitiveenvironments.GIS havebeencommonlyusedsincethe1990stodeterminethe rela-tionshipsbetweenagriculturalactivities,includinglanduseand waterqualityatthewatershedscale(SrinivasanandArnold,1994;
XinhaoandYin,1997;AspinallandPearson,2000).Theythus
repre-sentadecision-makingsupporttoolforthechoiceofmeasurestobe implementedbymanagersofpublicservices(Schröderetal.,2004;
AlkanOlssonetal.,2009).Additionally,someindicatorsassess
agri-culturallandscapesforwater qualityprotection(Gascuel-Odoux
etal.,2009a,b).
However,alloftheseindicatorsarebuiltata certainspatial organisationallevelanddonotallowforchangingofspatialscales. Hence,theygenerallycannotaddressthequestionsof environmen-talmanagers.
2.2. Methodsforchangingspatialorganisationallevels
Scalereferstothespatialdimensionsatwhichentities,elements andprocessescanbeobservedandcharacterised(Dumanskietal., 1998).Thisconceptualspacerelatestostudycompartmentsand threelevelsaretraditionallyincluded:theglobalscale,the inter-mediatescaleandthelocalscale.
Theglobalscaleconcernstheenvironmentandmajorsystems (oceans,atmosphereandcontinents)forwhichthedescriptiongrid isapproximatelyonehundredkilometres.
Theintermediateor“regional”scalecorrespondstotheregional engineeringscaleinparticular,andallowstheanalysisof interac-tionsbetweenmankindandtheenvironment.Thisscaletakesinto accountthecharacteristicvalues relatingtodifferentprocesses, thegeomorphological and hydrologicalorganisation ofareas at theirbestlevelofconsistency(e.g.awatershed)andthelevelof anthropogenicactivities.Therefore,agri-environmentalissuesare generallysituatedatthisintermediatescalewhichvariesfroma fewsquarekilometrestoseveralthousand.Thisexplainswhy dif-ferentmethodscanbeused.
Thelocalscaleconcernstheprocessesthemselves;thesemay bebiological,suchasplantgrowth,orphysico-chemical,suchas riverinetransportsofsoluteandparticulateelements.
Thespatialscaletransmitstheideaofcontinuity.Incontrast, a spatialorganisationlevel (SOL)corresponds toa spatialentity andthecharacteristic values of aprocess orof a phenomenon. Thisnotionoforganisationallevelscansimultaneouslytakeinto accountthehandlingofecological,agriculturaland administra-tiveprocesses.Fourtypesofspatialorganisationallevelscanbe identifiedintheagri-environmentalfield:
-Thelevelofeco-hydrologicalorganisation(watershed,landscape unit);
-Thelevel of socio-economicorganisation:agriculturalactivity (farmingparcel,farm,operationalinfrastructure);
-Thelocalterritoryorganisationwithacollectiveidentity(towns andotherurbanareas)
-The politico-administrative organisation or decision-making levelforenvironmentalmanagers(hydrographicareaorbasin, region).
In this paper, we will look at the eco-hydrological and politico-administrativeorganisation,asthisisthelevelatwhich contaminationriskassessmentmustbedevelopedtoaid environ-mentalmanagersintheirdecisionmaking.
2.2.1. Relationshipsbetweendatasourcesandspatialscales Therelationshipsandinterconnectionsbetweenspatialentities without commongeographical boundaries are sources of com-plexityinagri-environmentalapproaches(Dumanskietal.,1998;
Dalgaardetal.,2003).Forexample,statisticaldataderivedfrom
theFrenchagriculturalcensus(RecensementAgricole)cannotbe superimposedoverahydrographicdivision.
Specificsourcesofdata(parcelsurveysandfarmsurveysin addi-tiontosite,hydrological,statisticalandremotesensingsurveys) areobtainedatdistinctobservationscales(Bocketal.,2005).The sameappliestodifferentenvironmentaldiagnosismethods(parcel reports,specificfarmreports,indicators,spatialanalyses,models). Ratherthanremainingatagivenlevel,achangeofscaleisthe processofchangingfromoneorganisationalleveltothenext.To doso,thevariationsinoperation,schematisation,descriptorsand thepossiblelinksbetweentheconsecutivelevelsmustbe
spec-ified(Puechetal.,2003).ThedevelopmentofGIShasfacilitated
theprocessingofthesemulti-sourceand multi-scalardatabases
(Balrametal.,2009).
The key aim is to have access to relevant information at thedifferentspatialscales,whileprocessingdatawhichcanaid decision-makingforthemanagersofruralareas.Relationshipsexist betweenthestudyscales,data,methodsused,theexpectedresults andthedataacquisitionmodes(Cotteretal.,2003;DixonandEarls,
2009).
2.2.2. ReferenceSpatialObject
The complex interaction between the spatial organisational level(establishedbythedecision-makinglevel)andthespatial het-erogeneity(tobemeasuredorrepresented)isamajordifficultyin theagri-environmentalfieldandespeciallyinhydrology.This diffi-cultyismagnifiedifspatialheterogeneitiesneedtobeevaluatedat multiplescalesofdecision-making.Woodetal.(1988)proposedthe “representativeelementaryarea”REAconcept,orReferenceSpatial Object(RSO),whichtakesintoaccountthataphenomenonbeing studiedcorrespondstoaworkingareawithanassociatedscale. Whenthere isa goodmatch,themodellingor explanationofa phenomenonisbothbetterandeasiertounderstand.Thismethod isproposedfordefiningthesizeofawatershedthatisadaptedto hydrologicalstudies.
AReferenceSpatialObjectordiscriminationunitcanthenbe associatedwitheachofthesescales.ThechoiceofanRSOisthe resultofacompromisebetweenitsappropriatenessatthescalein question(optimalresolution),anditsabilitytoaccuratelydefine theproblemsofdiffusepollution.
Changingfromonescaletoanothercausesmajorproblems.For example,thisoccurswhentheobservationsofagricultural pollu-tiononbasicparcelsarerelatedtothemeasurementsofpollutant productsattheoutletsofmajorwatersheds(Puechetal.,2003).This problemmaybeaccentuatedifweconsiderthetemporal dimen-sion(difficultyofcomparingindicatorsofpressureandindicators ofconditionduetotheretentionandsaltingoutofmoleculeswhich maypersistoverseveraldecades).
Fig.1.Differentspatialorganisationallevels,dataandmethods.
Onewaytoresolvetheproblemofscalechangingistoinclude severalorganisationallevelswhencreatinganindicator(Bocketal.,
2005).
Indicatorsconstituteanimportantelementinthechangefrom onescaletoanotherand arebecomingincreasinglywidespread inthehandlingofagri-environmentalissues(Steinetal.,2001;
Riley,2001).Theymayhave asingleormultiplecomponent(s).
Riley(2001)emphasisesthefactthatintheagriculturalandnatural
resourcesector,thecreationofasingle-componentindicatoristhe dominantapproach.Incontrasttothesingle-componentindicator, multi-componentindicators(VanCauwenberghetal.,2007)may becoefficientsofvariationoracomplexfunctionofthesedifferent components(Riley,2001).
2.2.3. Methodsforchangingspatialscales
Differentmethodscanbeusedaccordingeachcontext(Macary
andVernier,2007a).
2.2.3.1. Specificindicatorsforeachscale. Thelevelofspatial organi-sationbeingstudieddeterminesthemethodsthathavetobeused. Foreachsituation(Fig.1), theresponsetothequestionofscale changeisprovidedbyachangeofdataand,consequently,ofthe environmentalindicatorsspecifictoeachspatiallevel(Riley,2001;
Steinetal.,2001).Thisisthemaintypeofstudycarriedoutto
datebythedifferentauthors.Indicatorsarebuiltatdifferentlevels: parcelsofland,farms,watersheds,andadministrativeterritories
(Macaryetal.,2007).
2.2.3.2. Aggregativeanddisaggregativeapproaches. Thedescription ofphenomenawithinaspatialobjectoftenrequiresworkingat theprevious or following organisational level. Aggregative and disaggregative methods often originate from these inter-scale approaches(BlöschlandSivapalan,1995).
Disaggregation is used to determine the behaviour of con-stituentcomponentsbasedonthebehaviourofthewholeentity
(Riitters,2005).BlöschlandSivapalan(1995)describe
disaggrega-tionasbeingthetransitionfroma“meanvalue”inaparticularfield toitsdetaileddistributionwithinasectionofthisfield.
Aggregationisusedtomakethetransitionfromtheconstitutive componentstothewholeentity.However,inthiscase,the transpo-sitionoftheprocessesidentifiedatonescaleoffersnoguaranteesof validityatanotherscale.Furthermore,anotherdifficultyrelatesto theinteractionbetweenseveralprocesses.Whilethe representa-tionofasingleprocessmaybeenvisaged,theinclusionofmultiple processesismorecomplex.
Aggregationfollowsthesamelogicasanindicatorwhich con-densesrawinformationintosyntheticinformation.
2.2.3.2.1. Method of direct aggregation at each RSO observed. Thefirstmethodconsistsofaggregating theelementarydataat eachRSOobserved.
Forexample,withagri-environmentalinformation,scores qual-ifyingsimple indicators allocatedtoeach parcelof landcan be combinedatthescaleobserved(eachwatershedlevel).Thisform ofaggregationcanbeusedtotakeintoaccountdifferent physi-calprocessesatthescalesconsidered.Themaindisadvantageof thisapproachisthepotentiallossofinformationduetopremature aggregation.Thebestsolutionforkeepingasmuchofthe informa-tionaspossibleistochooseasmallRSO,calculatetheindicatorsat thisscale,andultimatelyachieveaggregationattheorganisation levelselectedbymanagersandactors.
Some authors have previously used this kind of aggrega-tion,throughaGISprocess,ofsimulationunits,assessedbythe agro-hydrologicalmodelSWAP(soil-water-atmosphere-plant),for upscalingofwaterproductivity,fromfieldstoregions(Wesseling
andFeddes,2006).
2.2.3.2.2. Choiceofacommonelementforthedivisionofspace ateach scale(Aggregationanddisaggregation). Anothermethod considersacommonRSOatthedifferentscalesfor environmen-talmanagersandlocalparticipants.Theadoptionoftheunitat thesmallestpossibleresolutionallowsfortheconservationofthe mostdetailedinformation,regardlessofthescalethatisused,thus avoidingtheprematureaggregationofinformation.
Eachelementisassociatedwiththevaluecalculated,basedon theinformationrelatingtothesimpleindicators.Theaggregation, accordingtoanarithmeticalorstatisticalprocess,isthen exclu-sivelybasedontheseelementsforeachofthescalesdeterminedin ordertoallowforthereadingandinterpretationoftheresults.The
accuracyofthisspatialisedmethodvariesaccordingtothelevelof spatialorganisation.Itisallthemoreimportantwhenthereisa smallarea,giventheavailabledataandtheirdetails.Thismeans thatitispossibletosupplementtheinformationaccordingtothe dataavailableateachorganisationallevel.
Dalgaardetal.(2003)distinguishedthreeaggregation
proce-dures: the linear procedure, the non-linear procedure and the “hierarchical”procedure.Thelattermakesiteasiertoovercome thedifficultiesofinteractionsbetweenprocesses.
Thelinearaggregationprocedureisthemostcommonlyknown. Itconsistsofthesumof itsparts.In thenon-linearaggregation procedure,thevariabletakenintoaccount,cantakedifferent val-ues,accordingtoseveralthresholds.Theaggregationmusttake intoaccounttheintrinsiccharacteristicsofthefundamentalunits. Thehierarchicalproceduretakesintoaccounttheintrinsic charac-teristicsofeachelementofthesystemandalsotherelationships betweentheseelements.
Disaggregativeapproachesdependlargelyonthedataset avail-able.Thegeneralmethodforthedisaggregationofinformationis spatialinterpolation.Thismethodreliesontoolsdirectly character-isingtheformofspatialorganisationandincludesalargenumber oftechniques.Someindicatorsareusedtomeasuretheshapeofa spatialdistribution.Here,theautocorrelationindicesarethemore traditional.Othermethodsofgeostatisticsusespaceinamorelocal way,suchasvariograms.Thesecanidentifybreaksinthe distribu-tionofdataorinterpolationtechniquesandkriging(Ernoultetal.,
2003).
2.2.3.2.3. Other aggregation processes, with multi-criteria approaches. Methods called Multi-Criteria Decision Aiding (MCDA) methods, or Multi-Criteria Decision Analysis methods, weredevelopedinthe1970s(Roy,1990).Theyhavebeenusedin severaldomains(Schärlig,1996).Sincetheearly1980s,theyhave beentestedwithsuccessinthefieldofenvironmental manage-mentissues(Simos,1990).Inmostcases,thecriteriaaggregation processwasbasedonasystemofoutranking(Maystreetal.,1994). Later,theMCDAwascoupledwithGIS,duetothespatialnatureof environmentalissues.
Malczewski(1999,2006)carriedoutanimportantsurveyof
lit-eraturewithregardtoGIScombinedwithMCDAandtheirmany applications.Twomaintrendsweredeveloped.Thefirstinvolved combiningGISsoftwarewithMCDAmethods,whichmakesbest useof theperformance of each system. Althoughvery precise, this method involves many implementations between the two processes.Then,whenthesemethodswereappliedonlarge ter-ritories,anintegratedcouplingwithGISsoftwarewasdeveloped, byadditionintheGISofaMulti-CriteriaEvaluation(MCE)function
(Eastman,2001,1993).TheMCEtakeintoaccountafuzzylogic,
andacriteriaweighting(Saaty,1990),likeinclassicalmethods: bythisway,theGISbecomesGAS(GeographicAnalysisSystem). ClarklabsofClarkUniversityinUSAdevelopedtheIDRISIsoftware
(Eastmanetal.,1993).Itwasusedinmanyenvironmental
applica-tions,likeanassessmentoflanduseinalargeregion(Paegelowand
Olmedo-Camacho,2005;PaegelowandCamacho-Olmedo,2008).
3. Materialsandmethods
3.1. Studyareaanddevelopmentcontextoftheriskassessment method
In South western France, the Coteaux de Gascogne area is drainedby17rivers.Thefivelargestriversinlengthandfloware fromWesttoEast:Baïse,Gers,Arrats,GimoneandSave.Theyare alllefttributariesoftheGaronneRiver,locatedbetweenthe Pyre-neesMountainsandtheAtlanticOcean.Alltheserivershavetheir sourceintheLannemezanplateau,andtheyaredraining.Acanal
betweentheNesteRiverandthesourcesoftheseGascognerivers wascreatedinthesecondpartofthe19thcenturytoreplenish theseriverswithwaterduringthedryperiod.Theirwatersheds coverseveralthousandsofsquarekilometres.Overthelasttwo decades(1985–2004),themeanannualrainfallwas691mmwith evapotranspirationof819mm.
Thenaturalhydrologicalregimeoftheseriversispluvialand thelowflowperiodcorrespondstosummer(JulytoSeptember) andtheinterannualvariabilityoftheirmoduleisveryhigh(Probst
andTardy,1985).
Thisgeographicalarea(Fig.2)covers7000km2,whichjustifies theuseofsatelliteimagesinordertoobtaindetailedcoverageof thelandsurface.DatafromtheCorineLandCoverdatabasearenot preciseenoughintermsofthematicresolutiontoestablish poten-tialrisksofsurfacewatercontamination,aseachkindofcrophas tobeconsidered,andnotonlyarablelandsasawhole.
Rawwaterforconsumptionbyhumansandanimalsis essen-tially extracted directly from theserivers by pumping stations (Fig.2)andthisisthemainresponsibilityofwatermanagers.Given theproblemofrelativelylimitedflows,contaminationduetothe transferoffertilisers(mainlynitrogen)andpesticidesof agricul-turaloriginismagnified.
Theeffectsoftheagri-environmentalmeasurestowhich farm-ers subscribed voluntarily after the new European agricultural policy,in 1992 (seeSection 1) werevery limited. Indeed, they werespreadverythinlyacrosstheregionandtheymade there-forelittleornocontributiontotherequiredimprovementonthe pressuresandtheirimpactsonsurfacewaters.Thishasprompted wateragencymanagers,asintheAdour-Garonneriverbasins,to makespecialprovisionsfortheprotectionofwatershedareas.For example,theimplementationoffinancialincentiveshadtobemore preciselytargeted,andwerethusdesignedwiththespecificaimof reducingthepollutionofriverwaters.Firstly,limitsforthe protec-tionofwaterpumpingareashadtobepreciselydefined,andthere wasaneedtodelineatehigh-riskwaterareas.
Thiscalledforthescientificdevelopmentofaspecificmethodto spatialisesurfacewaterrisks,easiertousethanclassical hydrolog-icalmodelsandadaptedtodifferentspatialscales.Inthiscontext, wecarriedoutresearchintoamethodofriskdetermination,at dif-ferentspatialscalesandapplicabletootherregionsinwhichsurface waterisexposedtodifferentcontaminants.
In this area, we considered nested watersheds as different spatial organisational levels (SOLs). For example, the largest watershed(Save,1150km2)istheresultofinterconnectionsof sec-ondarywatershedsdrainedbytributaries(i.e.Boulouzeriverbasin, 70km2), which are themselves interconnectionsof elementary watershedsdrainedbysub-tributaries(i.e.Montoussécatchment, 6km2).
3.2. Implementationofaspatialcognitivemethod
3.2.1. OverallstructureofthemethodanditsdifferentSOLs
Our generic spatialised pixel-based method (called PIXAL method) is based on a combination of spatialised agri-environmentalindicators,calculatedatthepixellevelofremote sensing.The application inthis paper focusesonphytosanitary issues,andthemethodisthereforecalledPHYTOPIXAL.
PHYTOPIXALtakesintoaccountfactorsofsurfacewater vulner-ability(Section3.2)andagriculturalpressure(Section3.2.3).Ina firststep,eachfactorprovidesabasisindicator,usabletodiscuss withstakeholders.Then,theseindicatorsarecombinedtoobtain riskvalues,calculatedforeachpixel(30m×30m).
Inasecondstep,riskvaluesareaggregatedtodeterminerisk zones,accordingtotheleveloforganisationandmanagement cho-senbythepublicmanagementbodiesandstakeholders.
Fig.2.LocationofthestudyareainSouthwestFrance.
Fig.4.OverallstructureofthePHYTOPIXALspatialisedmethodanditsdifferentscalesofresults.
Fromthesedata,thecontaminationriskofsurfacewatersby pollutantswascalculatedatdifferentSOLs(Fig.3),andexpressed fromthescaleofthesmallelementaryagriculturalwatershedto thatofthelargesurroundingwatershed.Thegeneralflowchartof thismethodappearsinFig.4.
3.2.2. Creationofsurfacewatervulnerabilityindicators
Thefactorswhichcontributetosurfacewatervulnerabilityare mainlyrelatedtothenatureofthesoil,thegeologicalsubstratum, theslope,andtheconnectivitywiththebodyofwaterinquestion. Someofthesewerediscardedbecausetheywerenotrelevantfor thecharacterisationofdifferentscalesofinterest,ordidnotprovide datasufficientlyaccurateanddiscriminatinginthestudyarea(i.e. geologicalsubstratum).Theparametersretainedweretopography, drainageandsoil.Thisinformationfullysatisfiestheneedto incor-poratethechangeofscale.Dataweregatheredoverthefullareaat thesmallestscale,allowingforamoreprecisedescription.
Allvariableswerescored(Devillersetal.,2004)priortothe cal-culationoftheindicators.Fromtherangeofvariationofeachfactor, classesofvaluesweredeterminedaccordingtoprevious knowl-edge(baseduponscientificliterature,agronomists’expertopinion, andourexperienceinthisarea)regardingtheircontributionto con-taminationrisks.Thatexplainswhyweconsideredthismodelasa cognitivemodel(LeBissonnaisetal.,1998).Weattributedthe high-estscorewheretherewasthehighestlevelofrisk,andidentical weightingwasusedforallscores.
Table1summarisesthescoresofthethreevulnerability
indica-tors.
3.2.2.1. Slope. Slopeisan essentialfactorin thetransferof pol-lutants likepesticides (Beaujouan et al., 2002; Barriuso, 2004), influencessurfacerun-off,andhasadirecteffectontransfertimes fromsoilstowater.Furthermore,ithasdirectconsequenceson physical erosion processes and facilitates the transportation of activesubstancesadsorbedontosuspendedmattersparticularly
duringthefloodevents(Taghavietal.,2011).ADigitalElevation Model(DEM)fromtheFrenchNationalMappingAgencyata reso-lutionof25mwasacquiredtocoverthestudyarea.Extrapolation toaresolutionof30mallowedthesamespatialresolutionasthat ofasatelliteimage.
Thethresholdsweredeterminedaccordingthespecific litera-tureabouterosioninFrance(LeBissonnaisetal.,1998).Theywere manuallycalculated,basedonthefrequencyhistogramoftheslope values,atthebasicpixellevel.Astheextremevalueswerelimited innumber,weincorporatedthemintothemajorclasses.Fiveslope classesadaptedforsurfacerunofftocalculatescoresforthisfactor, takingintoaccountthemaincharacteristicsofeach slopeclass: <3%; 3–7%;7–12%;12–25%; >25%.In this study area,crops are mainlygrowninparcelswithlessthana12%gradient.Beyondthis value,wefoundmainlygrasslands.Inthisstudy,weusedscores
from1to5(Table1)correspondingtothepreviousslopesclasses
Table1
Scoresofvulnerabilityindicatorsregardingslopesclasses,soilnature,distanceto hydrographicnetwork.
Vulnerabilityindicators Score/pixel
Slopesclasses >25 5 [12–25[ 4 [7–12[ 3 [3–7[ 2 [0–3[ 1 Soilnature
Rendosolsandthincalcosols 4
Thickcalcosols 3
Luvisolsandcambisols 2
Fluviosols 1
Distancetohydrographicnetwork
>100m 5
30–100m 3
Fig.5.Distancetowatercourses.
(1forslopes<3%,5forslopes>25%).Thesescoresmustbeadapted todifferentgeographicalcontexts,inotherareasandcountries.
3.2.2.2. Distance to watercourses. The contamination of surface
watersbypollutantsishighlydependentonthedistancebetween
theapplicationsiteandtherelevantbodyofwater.Twoparameters
wereconsideredwhendevelopingtheindicatorofdistancetothe
drainagenetwork:theexactdefinitionoftheactivenetworkand
thechoiceof“distance”adopted.Theprocessingofthedatabasefor
thedrainagenetworkconsistedfirstofextractingthepermanent
drainagesections.Wethendivideduptheresultinglayer,basedon thesuperficialareaofthesatelliteimage.Wecreatedtwobuffers, situatedatdistancesof30and100maroundthedrainagenetwork inordertoobtainthreedistinctzones(Fig.5).
Thethresholdof30mwasbasedonthespatialresolutionof thepixel.The100mthresholdwasbasedonlocalexpert opin-ion, takinginto account thevery tormented topography inthe field,especiallyinthishillyarea:this wasafirsttestof thresh-oldimplementation. We allocated a scoreto eachof thezones
(Table 1) after the merging and rasterisation of the extracted
layer.Thisscoresimplyshowstheincreasedriskwithproximity tothenetworkin3distanceclasses:<30m(score5);from30to 100m(score3);>100m(score1).Thesevalueswereassignedto accountfortheimportanceofthiscriterion,regardingotherstudies
(Aurousseauetal.,1998).Thecontext(connectivity)ofthisregion
requiredanadaptationofthescoresdependingonthespatialscale considered.Theycanbeeasilychangedaccordingeachphysical context.
3.2.2.3. Soiltypes. Soilcharacteristicsplayahighlysignificantrole intheretentiontimeofresiduesintheuppersoilhorizontothe streamwaters.Humuscontentandnatureoforganic–mineral com-plexescanplayveryimportantrolesintheretentionofmolecules.
ThroughouttheOligoceneandMioceneperiods,this areaofthe Coteauxde Gascognereceivedsandy,clayand calcareous sedi-mentsderivedfromtheerosionofthePyreneesMountains(Revel
andGuiresse,1995).Theheterogeneousmaterialsproducedathick
detritalformation,calledmolasse,duringtheMiocene.Fromthe Quaternaryperiodonwards,theriversbecamechannelized, cut-tingbroadvalleysinthemolassicdepositsandleavingterracesof coarsealluvialsediments.Thesubstratumofthedifferent water-shedsconsistsofimperviousMiocenemolassicdeposits.
Aweakerosionhasallowedthedevelopmentofcalcic Luvi-solsonthetertiarysubstratumandlocalRendosols(FAO,2006)on thehardcalcareoussandstonebeds.Amoderateerosionaffected soilcalcicCambisolsonhillsideswithverygentleslopes(lessthan 12%).Calcicsoils(Calcisols)aredominatedbyaclaycontent ran-gingfrom40%to50%andcalledlocally“Terreforts”:theyarelargely representedonthehillsandbecauseoftheirimpermeability,they greatlycontributetosurfacerunoff.Non-calcicsoils(Luvisols)are silty(50–60%)andlocallycalled“Boulbènes”(RevelandGuiresse,
1995).
In this area, 1/50,000 scale maps were drawn by pedolo-gistsfromtheCACG(Compagnied’AménagementdesCoteauxde Gascogne)inthe1960sandhavesincebeentransposedto1/80,000. ThissoilmapwasdigitisedfortheSavewatershedonly(Cemagref, 2006),limiting theuseof thisinformation tothiswatershed. It revealedasignificantbutvarying,soildepthintheriverSavearea, whichcouldinfluencesurfacerunoff.Thesoiltypesweregrouped intofourmaincategories,andscoredaccordingtotheirimportance withregardstothetransferofpollutantsbysurfacerunoff(Table1). Thiswasbasedonthestudiescarriedoutbypedologists (Revel
andGuiresse,1995)andprofessionaltechniciansbelongingtoa
RegionalActionGroupagainstWaterPollutionbyPesticide Prod-ucts(GRAMIP).Soiltypesarerankedfromthelowesttothehighest potentialsurfacerunoffprocessbelow:
◦Fluviosols(“Alluvions”):Theproximitytothesuperficialwater tableleadstodirecttransfertowardsitandthereforeanabsence oftransformation/degradationintheenvironment.
◦Luvisols and cambisols (“Boulbènes”): Permeable soils which facilitate infiltrations, but favour the formation of superficial crusting,whichisconducivetosurfacerunoff.
◦Thick calcosols(“Terrefort épais”), soils >40cm: Impermeable, clayeysoils;runoffispredominantlyhypodermic.Thesesoilsare composedofhydromorphicsandypocketswhichslowdownthe transferofproductstothewatertable.
◦Rendosolsand thin calcosols(“Terrefort mince”),soils<40cm: Theyfavourhortonian(surface)runoffwhichoccurswhenrain intensityexceedstheinfiltrationcapacityofthesoil.
3.2.3. Compositionoftheagriculturalpesticidepressureindicator 3.2.3.1. Landuse. Landusewithinawatershedinfluencesrunoff, hydrologicalregime,andstreamwaterquality.Landcoverbycrops canbeusedtoassessthepesticidepressuresexertedbyagricultural additives.Theclassificationofsatelliteimagesconstitutesthebasic informationlayerofthemethod.
TheCorineLandCoverdatabaseisnotaccurateenoughto dif-ferentiatethecropstypesonfarmland,thusimpedingtheprecise determination of risk levels. Because of this, we carried out a supervisedclassificationfromsatelliteimagesusingErdasImagine software®.Annuallandcoverisgivenbytheclassificationofaseries offourLandsat5TMsatelliteimages,takenonmultiplerelevant dates,tocharacteriseeachtypeofplantperseason,withthe low-estrateofclouds:0–20max;2004/12/01;2005/05/26;2005/07/13; 2005/08/30.
TheareaoftheseimagescoverstheCoteauxdeGascognearea, ataspatialresolutionof30m×30m.
Classificationinsupervisedmode,i.e.underthedirectcontrol oftheoperator, locatesthecrops in asufficiently accurateand exhaustivemanner.Thesamplesweregroupedintotencategories ofmajorcropsthatconsumeagriculturaladditives:wheat, bar-ley,rapeseed,sunflower,corn,sorghum,peasandbeans,soybean, grasslandsandfallow.Classificationalsodifferentiatesotherland use:fallow,hardwood,softwood,water,soilsandbuildings.We appliedthemaximumlikelihood ruletotheclassification algo-rithm:eachpixelwasallocatedtotheclasswiththehighestmean probabilityofallofthebatches.
3.2.3.2. Theagriculturalpressureindicatorforpesticides. Thelevelof pesticidepressureexertedbyagriculturalactivitiescanbederived fromlanduseforthestudiedyear,aseachcropcanbeassociated withameannumberofpesticidetreatments.Giventhelarge num-berofpesticidesubstancesandvariouscommercialformulations, weattributedameannumberoftreatmentstoeachcrop (herbi-cides,fungicidesandinsecticides)accordingtodataprovidedby theFrenchRegionalDepartmentforPlantProtection(SRPV).The treatmentofseeds,somethingwhichremainsverylocalised,was notconsideredhere.
Therefore,thepesticidepressureindicatorresultedfromthe crossingoftheclassifiedimageoflandusewiththenumberof pes-ticidetreatments.ToeasilyimplementthescoresintheGIS,these valuesweremultipliedbytwo,inordertoavoiddecimals. 3.2.4. Determinationofthecontaminationrisk(CRk)
Weperformedaspatialanalysisinordertooptimisethe dis-criminationofpesticidepressureareasataregionalscale,according toadivisionintoelementarywatershedsorhydrographicareas: levelsofdecisionmakingformanagersandstakeholders.
Thelayersofeachsingleindicatorwererasterisedbydefining acommonresolutioncellsizeof30m×30m.Thecontamination riskwasthenobtainedbycross-referencingthem.
Environmental vulnerability indicators were added with an identicalweightforeachfactor.Basedoncurrentknowledge,rising indicatorvaluesshowanincreasingrisk.Next,thecontamination risk wasobtainedbymultiplying thepressure indicatorbythe environmentalvulnerabilityindicators (Eq.(1)).Thisallowsthe calculationofazeroriskinareasinwhichnotreatmenthasbeen applied.
3.2.4.1. Calculationofthecontaminationriskforeachpixel. The fol-lowingformulawasadoptedforthespatialcognitivemodel:
CRk=[SL+D+So]×[Ap] (1)
where CRk,contaminationriskfor each crop,SL,values forthe slopelevel,D,valuesforthedistancefromeachpixeltothestream, So,valuesforthesoiltype,Ap,agriculturalpressure(numberof pesticidetreatmentsbycrop).
Weusedfiveriskcategories,whichallowedforgreater discrim-inationatthescaleofthisregion.Analystsanddecisionmakers generallyusethisnumberofcategoriesinriskstudies.
We maintained discretisation according to Jenks (Lepinard, 2008)naturalthresholds,thusobtainingthemost“homogeneous” categoriesofpossiblevalues.Thethresholdsvariedaccordingto thenumberofdiscriminatedunits.Thelevelofcontaminationrisk allocatedto a given unit (individualstatistic) dependedonthe performancesoftheotherunits.Theseunitsarecompared,with measurementsalwaysperformedusingthesameindicator. 3.2.4.2. Aggregation of information according to the investigation scale.Inordertoidentifypriorityareasforaction,theinformation obtainedmaybesubsequentlyaggregated.Accordingtothe differ-entsolutionsdescribedinSection2.2.3,wechosethepixelorgrid ofasatelliteimageasourcommonRSO(ReferenceSpatialObject). Weaggregatedthemusingalinearprocedure(Section2.2.3.2.2), atthedifferentspatialorganisationallevels,inordertobeusable byenvironmentalpublicmanagersandstakeholders(farmersand theiradvisers).Todothis,weusedspatialstatisticstools(Spatial AnalystofArcGISsoftware®,whichdoesnotincludeMCEfunction) accordingtothefollowingformula(Eq.(2))
CRkwatershed=
scoresforpesticideriskinpixels
surfaceareainpixels (2) Thisisanarea-weightedaverageofriskvaluescalculatedatthe chosenscale.
4. Results
4.1. Aconceptualmodeltogeneralisethecontaminationrisk assessment
Themethoddeveloped hereaimsat assessingsurface water contaminationrisks,atdifferentorganisationallevels.Itprovides valuableinformationatscalesappropriateforenvironmental man-agersandstakeholders,inordertosupportdecisionmaking.Itis suitable indifferentareas andcountrieswhere themain water transfersaresuperficial.Thismethodisunapplicableinitspresent forms,forexampleinkarsticenvironments.Fig.6showsthe con-ceptualmodelofthegenericPIXALmethod.
Ourassessmentmethodwasusedtodeterminethelevelof pes-ticidecontaminationriskbyinthestudyarea.
4.2. Resultoftheclassificationoftheremotesensingimagesand pesticidepressureindicator
Resultsofthelanduseandconsequentlythepesticidepressure appearinFig.7andTable2.
Fig.6.ConceptualmodelofthegenericPIXALmethod.
Inthisareaof7000km2:water,buildingsandwoodsrepresent 15%,andfarmlanduse85%(6000km2).Fourcategoriesaccountfor 83%offarmlanduse:Grasslands(35%)essentiallyinthepiedmont ofPyrenees,sunflower(19%)andwheat(18%)innorthpart,where agricultureisintensive;andfallow(12%)inallthearea.
Theoverallqualityoftheclassificationwasgivenbythemean percentageofcorrectlyclassifiedpixels.Thiscorrespondstoa mea-surementoftheseparabilityofthecategoriesaccordingtovarious channels.TheKappacoefficient(Congalton,1991,citedbyDucrot, 2005)estimatestheaccuracyofaclassification,whichtakesinto
accounterrorsinrowsandcolumns.Weobtainedamean percent-ageofcorrectlyclassifiedpixelsof86%andaKappacoefficientof 0.83forthe2005landuse.Thisallowedustoaccuratelyemploythe landusecoverageobtained.TheKappacoefficientis0.92forthe wheat,0.77forthesunflower,and0.87forgrasslandsandfallow. 4.3. Levelofpesticidecontaminationriskinsurfacewater
Weobtainedasurfacewatercontaminationriskforeachpixel. Butthismosaicof7,653,255pixelscannotbeeasilyinterpretedby
Fig.8. Estimatedriskatthescaleoftheelementarywatershed.
environmentalmanagers.Itshowsthelevelofdetailofthe calcu-lation,butcannotbeusedforagri-environmentaldecisionmaking byitself.Thisisthelevelofinterventiononthefarmingparcels (farmersandadvisers).Then,weaggregatedthepixelsatthescale ofelementaryandintermediatewatersheds.
4.3.1. Aggregationn◦1
Thepixelswereaggregatedfirsttoelementarywatersheds,and thentohydrologicalareas.Consequently,Table3andFig.8 repre-sentthecontaminationrisk(afteraggregation)atthescaleofthe 2871elementarywatersheds,coveringseveralsquarekilometres
Table2
Farminglanduseandscoresofpesticidepressureindicator.
Pesticidepressureby landusetype
Farminglanduse
(km2and%) Score/ pixel Rapeseed 161 2.7 12 PeasBeans 79 1.3 11 Wheat 1078 18.0 8 Corn 430 7.2 7 Barley 188 3.1 6 Soybean 101 1.7 5 Sunflower 1153 19.2 4 Sorghum 39 0.7 2 Grasslands 2075 34.6 0 Fallow 696 11.6 0 TOTAL 6000 100.0
(severalkm2).Thisisprimarilythelocaldecision-makinglevelfor
agri-environmentalwaterpreservationmeasures.
Highandveryhighcontaminationrisks(27%ofthesurface)are locatedintheareaswhereagriculturalpressureisatitshighest (Fig.6),incontrasttoareaswhereitislower(59%ofsurface), includ-inggrasslandsorcropswithfewpesticides,likeinthePiedmontof Pyreneesmountains(southofthemap).InPiedmont,water vul-nerabilityishigherthandownstream, mainlydue toslopesand smallvalleyswhichprovideshortdistancesfromlandparcelsto streams.Theland coverisdominated bygrasslandsand forests withoutdirectpesticideapplication.Afewcerealsarecultivated, onsmallplateaux.
4.3.2. Aggregationn◦2
Table3andFig.9representtheriskvalueatthescaleofthe
125intermediatewatersheds(tensofkm2).Thisisthe decision-makingscaleforenvironmentalmanagersinFrance.Atthislevel, theaggregationofpixelsiseasiertoreadformanagers.Ontheother hand,theinformationofferedisofamoregeneralnatureandsuited fortheimplementationofenvironmentalprotectivemeasures.
Thesecondaggregationprovidesanincreasingnumberofrisk assessmentsforhighandveryhighlevelsandattheopposite,a decreasingofthelowrates.
ThenumberofReferenceSpatialObjectsbetweenelementary watershedsandhydrologicalareasisdividedby23.
Inthisstudyarea,themanagersoftheFrenchregionalwater agency(Adour-Garonne)atthescaleofthelargewatershedcovered
Table3
Surfacecorrespondingtothelevelsofcontaminationrisks,dependingonaggregationscale:(i)2871elementarywatershedsand(ii)125hydrologicareas.
Risklevel Elementarywatershed%totalsurface Hydrologicarea%totalsurface
Verylow 28.5 58.6 12.5 31.2
Low 30.1 18.7
Intermediate 24.3 24.3 25.2 25.2
High 14.5 27.1 32.6 43.6
Fig.9.Estimatedriskatthescaleoftheintermediatewatersheds.
bythis study, decidedto choosea protective boundary forthe
zoningofanabstractionpointfordrinkingwatersupplyinga
pop-ulationof 20,000 inhabitants(the town of L’IsleJourdain). The
zoningoftheTerritorialActionPlan(TAP)ofBoulouze-Save Lis-loisewasestablishedonthebasisofthiscontaminationriskmap foranintermediatewatershedof112km2(Fig.9).TheTAPoffered
theopportunity for farmers in this areatosign upfor specific agri-environmentalmeasures,supportedbyFrenchwateragency subsidiesfrom2008to2012.OtherRegionalActionProgrammes have been created under the same conditions in order to re-establishor maintain thesurface water quality for humanand animalconsumption.
Thismethodconstitutes a step forwardin theresearchinto changesofscaleand theevaluation ofpollutioninalargeriver basin. It can therefore act as a useful decision-making sup-porttool for managersof publicservicesin differentcountries. Then, we are applying it with success, in other watersheds of differentsize (>1000km2 and <100km2) in Spainand Portugal within the framework of a European Project, Aguaflash (see
http://www.aguaflash-sudoe.eu/fordetailsMacaryetal.,2011).
5. Discussion
5.1. ApplicabilityofthePHYTOPIXALmethod
The PHYTOPIXAL method can be easily and quickly imple-mentedbyenvironmentalmanagersinanycountry,byadapting scores to each local context. However, this method has been designed predominantly for surface water contamination risks withsurfacerunofffluxes, andnoimportantinfiltration,likein karstic areas. While spatial scalesfor aggregation dependon a countries’size,thismethodologyissuitabletoallthemainfarming countriesinEurope,andevenotherregionswithsimilar character-istics,indifferentcontinentsandindeedoutsideEurope,inregions withsimilarcharacteristicsEachaggregationlevelofriskresults
will,ofcourse,dependonthesizeofcountriesandregions,but alsoonthegeographiclevelofpublicpoliciesapplication.
TheReferenceSpatialObject(gridof10m,20m,and30m)can giveverypreciseresultsforwatervulnerability,agricultural pres-sure,andcontaminationrisks.Eachstepoftheassessmentscale, withthecorrespondingindicators,leadstoagreater understand-ingofagri-environmentalprocesses,andequipsdecisionmakers tomake themostinformedchoicespossible.Aggregationofthe RSOsatappropriatedecisionlevelscanhelpmanageprioritiesin complexareas.
Thismethodcanbeusedtocompareindividualwatershedsand rankthemaccordingtotheirstreamwatercontaminationrisk.The mapsacquiredusingthismethodhelptodefinethepriorityareas foractionandfacilitatethedialoguebetweenstakeholdersby giv-ingthemcommonmethodsanddocumentation.Inaddition,these mapsallowfortheidentificationoftargetareasatdifferent lev-elsoforganisation,whichcanthenbestudiedmorespecifically (hydrologicalmeasurements,waterqualitymeasurements,specific monitoringofinputsofpesticidesinputs,cultivationmethods),or ofpriorityactionsinthefightagainstnon-pointpollution.They contributetoarisk diagnosisofpollutanttransfersintosurface waters,andhelpinidentifyingthebestremedialandprotective action.Thesebenefitshavebeenemphasisedbydifferentauthors
(Misraetal.,1996;Pattyetal.,1997;Mérotetal.,1999;Schmitt
etal.,1999;GouyandGril,2001;Carlueretal.,2009).
5.2. Comparisonwithothermethods
Differentmethodsbasedonacombinationofvariablesare gen-erallyusedatthefarmingparcelleveloratsmallwatershedlevel butrarelyonalargeterritorywiththecapacitytoaggregaterisk assessmentatthedifferentorganisationallevelsusedby stakehol-dersandenvironmentalmanagers.
Nevertheless, someauthors(Eastmanet al., 1993;Jiang and
GIS’integratedmulticriteriaevaluationfunctiontoanalyselarge territoriesandtoassessaprospectivelandcover(Section2.2.3.2.3). Theuseofsoftware likeIDRISI–a GISwhichincorporates a multi-criteriaevaluationfunction–couldthereforebeuseablefor analternativeapplicationforassessingcontaminationrisks.
Agro-hydrologicalmodellingisgenerallyusedtoimprovethe knowledgeprocessbyresearchers(Arnoldetal.,1998;Ferrantetal., 2011),butrequirealotofdata,whichareoftenunavailableina largearea.Theirimplementationisnoteasyandtime-consuming due tothecomplexity of thesoftware. Moreover,theyprovide resultswithin theirpre-programmed scales, and do not adapt thedatatosuit theneedsofenvironmentalmanagers.Someof them,likeSTICKS (Brissonet al.,2003),TNT2 (Beaujouan etal., 2002), provide results at farming parcel level, which is useful forfarmers,andinunderstandinghydrologicaltransfersinsmall watersheds. Other programs, like SWAT (Ferrant et al., 2011) deliverresultsbasedonHRUs(HydrologicalResponseUnits),which representhydrographicareasofwatersheds,withoutany connec-tionwithmanagement ReferenceSpatialObjects.Theyarealso usedbyresearchers,butseldombyenvironmentalmanagers,who prefermethodsthatcanhelpthemmakefastandeffective deci-sions.
5.3. ValidationofthePHYTOPIXALmethod
Decision-makingsupporttoolsareintendedtooffer scientif-icallyvalidatedinformationwherepossible.Theyaredesignedto conformtoarigorousapproachandtobeusedtosupportmanagers whooftenlacktheresourcesandtoolsformanagingsituationswith differentlevelsofspatialorganisation.
Like mostmethods for determiningspatial risk,it is always difficulttovalidatetheresultsproducedonalargescale,where theinstitutionalsurveynetworksforpesticidecontentsarehugely inadequate.Finally,comparisonswiththeresultsobtainedbyother researchprojectscannotbeachieved,becausethegoalsand meth-odsusedforcreatingtheindicatorsaredivergent(spatialdivisions, pressuremaps,etc.).Nevertheless,avalidationmethod,oratthe veryleastameansofcomparingtheresultsobtainedwiththoseof bio-physicalmodels,couldbeworthexploring,butdifferencesin scalescannotreallyprovideavalidation,orarelevantcomparison oftheresults.
With this in mind, we compared risk zonings at different organisationallevelswithbiologicalindicators,whicharetakeinto accounttheimpactsofpollutioninaquaticenvironments.As pesti-cidecontaminationeventsareverytransientanddifficulttocatch byspotsamplings,wecomparedPHYTOPIXALresultstobiological datathatareexpectedtoinformontheir“exposurehistory”. Com-parisonsmadetodeterminediatom(fixedmicroalgae)responses to pesticidepollution led to encouraging results (Morin et al., 2009).Weusedadatasetofabout439diatomsamples,collected fromartificialsubstrates(immersedinthewatersunder compa-rablehydrologyandlightconditions)at16sitesintheCoteauxde Gascognearea(Figs.8and9)inspring2005and2006.Biological samplingswereperformedondifferentoccasions(samplingdates) tocharacterisethediatomcommunitiesthatsettledinthestreams duringtheperiodcoveredbythepresentPHYTOPIXALcalculations. Atthesesites,PHYTOPIXALvaluesrangedfromlowtoveryhigh, with31 samples(7%) in low, 214(48.7%) inintermediate, 147 (33.5%)inhighand47(10.8%)inveryhighpotentialriskof contam-ination(nodiatomsampleintheverylowPHYTOPIXALlevel).From thisbiologicaldata,wecalculatedanindexofgeneralalteration,the IPS(IndicedePolluosensibilitéSpécifique,CosteinCemagref,1982) andspeciesdiversity(ShannonandWeaver,1949).Anovas(Fig.10) wereperformedonthedatasetusingSTATISTICAv.5.1(StatSoft, France).IPSvaluesdifferentiatedtwosetsofdata:samplescollected fromsiteswithlowcontaminationrisk(n=31)hadgoodbiological
Fig.10. Distributionof(A)IPSvaluesand(B)Shannondiversityindicesinthe4 Phytopixalcategoriestested.Valuesareminimum–lowerquartile–average–upper quartile–maximum.StatisticallysignificantdifferencesafterANOVA:**p<0.01, ***p<0.001.
quality (as shown by high index values, IPS=17.2±0.2/20), whereas those located in areas withhigher pollution risk had significantlylowerindexvalues(IPS=14.5±0.1/20,n=408).
Specificdiversity,which isknowntodecreasewithpesticide pollution(e.g.Seguinetal.,2001;Morinetal.,2009),wasfairlyhigh insiteswithloworintermediatepollutionrisk(div=4.13±0.05, n=245)anddramaticallydecreasedinthemostcontaminated loca-tions(div=3.53±0.14, n=47). Althoughrough,this comparison betweenriskzoningandrealfieldobservationsconstitutesafirst steptowardsthevalidationofthePHYTOPIXALmethod.
5.4. Limitationsandpotentialimprovementsofthemethod 5.4.1. Determinationofpesticidepressure
It would have been especially useful to have differentiated betweenthelargefamiliesofpesticidemoleculesandtheir mix-tures in commercial formulas, given their different degrees of harmfulness. In the same way, unidentified inert ingredients are often forgotten in commercial preparations, whereas they canhave long-lastingtoxicologicalconsequences (Surgan etal.,
2010).
However,atascaleofseveralthousandkm2,thisdegreeof accu-racycannotbeachievedforamethoddestinedforusebymanagers, becauseitwouldbetoodifficulttointegratealltheactive ingredi-ents.
5.4.2. Incompatibilityofthescalesofanalysisandthewater contaminationprocesswiththeavailabilityofdata
Certaindataarenotavailableatthescalesincorporating vari-ables essential for the analysis of diffuse pollution risks from agriculturaloriginconcerningsuperficialwater.Forexample,some datawhichareusefulforcharacterisingenvironmental vulnera-bilityarenotavailableorareunsuitablefordiscriminatingspace (geologyorsoilatasufficientlyaccuratescale).Likewise,someof theinformationrequiredforcalculatingpressureremains impre-cise.Therefore,agriculturalproductiondataweretakenfromlocal surveysandtheresultswerethenextrapolatedtocovertheentire studyarea.
Theperformanceoffieldsurveysinordertoobtainthisdataat thelevelofalargewatershedwouldbetooconsuminginterms ofhumanresources.Appliedtoalargearea,ourmethoddoesnot takeintoaccountlocalphysicalpointssuchasgrassystripssituated alongsidewatercourses.Itwouldbeworthexploringthepossibility ofacquiringhigh-resolutionsatelliteimages(e.g.Ikonos)over a restrictedtestareainanyfutureresearch.Furthermore,withregard toagriculturalpractices,eachpixelforagivencropisprocessedin thesamewayoverthewholearea,accordingtothehypothesisthat practicesforeachcrop,arehomogenousinthisarea.
5.4.3. Transferofinformationfromonescaletoanother: limitationsoftheaggregativeapproach
Wehavepreviouslyseen(Cf.Section2.2.3)thatthe aggrega-tiveapproachreducestheperformanceoffunctionalrelationships. Thisisduetothearrivalofnewdominantprocesseswhichobscure theimpactoflocalknowledgeormeasures,whichtherefore no longerhavea numericalsignificance ontheresult.Accordingto
Puechetal.(2003),theproblemconcernstheallocationofa
numer-icalvaluetoagridcellandtherelationshipbetweenthedifferent grids.Ontheonehand,fluxesofwaterormatterareassociated withgeometricallyshapedcellswhichareoftensquareshaped.On theotherhand,thespatialaspects(descriptionofcells)and tem-poralconsiderations(link betweenthecells)areconfused.“The aggregationisoftenrenderedcomplex,orindeedimpossible,dueto non-linearitiesandlocalheterogeneitiesexacerbatedbyachange ofseveralorganisationallevels,fromm2towatershedof100km2, i.e.jumpsofseveralordersofmagnitude.Whenseverallevelsare skipped,thereisnolongeradirectlinkbetweencauseandeffect.”
(Puechetal.,2003).
Thisapproachwould probablybenefitfromfurther develop-ment,especiallyintermsofscalechange;neverthelesswemanaged heretoaggregatevulnerabilityandpressureinformationatthe dif-ferentorganisationallevelsinquestion.Theresultingestimationof pesticidecontaminationriskinthis studyareawasusedbythe regionalwateragency,andalsotestedinSpanishandPortuguese watersheds(Macaryetal.,2011).
5.4.4. Subjectivityofzoningcriteriaandchoicesofmapping representation
Duringthemodellingphase,weallocatedscorestothe indica-torsandoptedforadiscretisationmethodtoproducefinalmaps showingpotential contaminationrisks. Thesechoices were not maderandomly,butweremainlybasedonexpertopinionsand assuch,remainopentoquestion.Themostimportantpoint con-cernsthequestforoperatingthresholds.Thedifficultyresidesin identifyingthosethatappeartobevariablewithinthesamearea accordingtothecontextandthevariables orprocesses studied. Scoredindicatorsoffersbenefitsliketheirmodularityandthefact thattheyareeasytounderstandbyusers(Devillersetal.,2004). Thereareadvantagesanddisadvantagesassociatedwiththe calcu-lationofthelimitsoftheclasses.Indeed,thisstagemaysometimes berendereddifficultwhenthereislimitedknowledge.Expert opin-ionshavetobesought,withthesubjectivitythatthisimplies.The
otherlimitationofthismethodislinkedtopotentialexistenceofa “thresholdeffect”.Forvaluesclosetothethreshold,asmall varia-tioninclasschangecouldleadtoalargevariationinthefinalresult. Conversely,alargevariationthatisconfinedtothesameclasswill havenoeffectonthisresult.
Thisexplainsmostofmajordifficultiesthatconfrontedusinthis methodwithregardtothevalidationofmappingresults.The aggre-gationprocesscouldbeimprovedwiththeprecisequalificationof eachcategoryofrisk.Includingmulti-criteriaevaluationprocesses in aGIS softwaremayconsequentlybeuseful,leading tosome potentiallyinterestingfutureresearchtopicsoftheseresearches. 5.4.5. Amethodrestrictedtoaspecificnaturalcontext
Ourmethodisrestrictedtoareasoflowpermeability substra-tum where water and pollutant fluxes aremainly exportedby surfaceandsubsurfaceflows.Itcannotbeusedinitscurrentformin karsticareas,forexample,whereinfiltrationismorepredominant. Itmustthereforebeadaptedbyincorporatingvulnerability vari-ablesfortheenvironmentitselfintothepollutionofgroundwater andnotjustsurfacewater.Itwouldnolongerbemerelyaquestion ofconsideringthedistancetowatercourses,buttothe groundwa-teraswell.Thedifficultywouldresideinevaluatingtheproportions oftransferintosurfacewaterandgroundwater.Thevariablesfor geologicalsubstratumandsoilswouldthereforebecomeessential.
6. Conclusionsandoutlooks
This researchhasfocused on thedevelopment ofa method forestimatingagriculturalpesticidecontaminationrisksinsurface waters,andtheaggregationofriskdatatocorrespondtovarious spatial organisational levels. The method provides a decision-makingtool for publicmanagersin chargeof water qualityfor human consumption and environmentalsectors. Different spa-tialscaleswereinvestigated,conservingtheinitialdataresolution throughouttheentireprocess.Classificationthresholdsforthe lev-elsof riskwere usedfor therelative rankingofrisk areas.The finalmapsproducedinourstudyareaarebasedonprecisedata collectedinthefield and aredesigned tocorrespondto appro-priatescaleforpublicenvironmentalmanagers(allowingforthe definitionofpriorityactionzones).Agri-environmentalmeasures requiredtore-establishthewaterqualityintheenvironment,can thenbetargeted.
Thevalidation ofthemethodwasperformedbycomparison withfieldbiologicaldata,ameasurementofpollutioneffectson theaquaticenvironment.Wecompared thecontaminationrisks calculated withbiological (diatom)responses,determined from simultaneouslyperformedsurveys.Biologicaldatawerecorrelated withtherisklevelsdeterminedbythePHYTOPIXALmethod, pro-vidinganindirect,butsignificant,validationofthemethod.
Thelarge-scale,highspatialresolutionofthemethodallowsfor thecoverageofpollutionrisksatdifferentscalesandtherefore con-stitutesaninterestingalternativetotraditionaldiagnosticstudies, whicharetime-consumingandcostly.
Future developments would be to improve the aggregation processof criteria,conceivablyusinga multicriteriaevaluation functionintegratedinGIS,tobeabletoassesscontaminationrisks overalargearea.Thelinksbetweentheresultsoftheriskzoning fornon-pointpollutionofagriculturaloriginandaquaticorganisms couldbeconfirmed,eitherusingabatteryoftestorganisms(e.g. combiningdiatomsandbenthicmacro-invertebrates)thatare inte-grativeofvariousanthropogenicimpactsatdifferenttimescales, orbyconfirmingthepresentresultsinotherwatersheds.
However, this work can be already of practical interest to theenvironmentalmanagers indifferentcountries, to establish zoningprioritiesfortheimplementationofpreciselytargeted agri-environmentalwaterprotectionmeasures.
Acknowledgements
ThisresearchwascarriedoutwithintheIMAQUEProject sup-portedbyregional(CPER)andEuropean(FEDER)fundsandwithin INSOLEVIEInter-regionalprojectfundedbytheAquitaineand Midi-Pyrénéesregions.
WethankDanielUny,geomaticianatIrstea/URADBX,forhis contributiontotheworkbysupportingthedevelopmentof geo-maticsapplications.
WewouldalsoliketothankJamesEmeryforhislinguistic sup-port.
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