Predicting sediment yield and transport dynamics of a cold climate region watershed in changing climate

2018·5.2 MB·English

by  Narayan Kumar Shrestha

ScienceoftheTotalEnvironment625(2018)1030–1045
ContentslistsavailableatScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Predicting sediment yield and transport dynamics of a cold climate
region watershed in changing climate
⁎
NarayanKumarShrestha,JunyeWang
AthabascaRiverBasinResearchInstitute(ARBRI),AthabascaUniversity,1UniversityDrive,Athabasca,ABT9S3A3,Canada
H I G H L I G H T S G R A P H I C A L A B S T R A C T
• Climatechangeimpactanalysisonero-
sionandsedimenttransportoftheAth-
abasca River Basin (ARB) using the
SWATmodel
• Future(mid-andlate-century)climate
datageneratedbythreeclimatemodels
forRCP4.5and8.5emissionscenarios
• Impactsduetoclimatechangearefound
toberegionspecificandland-usetype
dependent.
• Channelerosionanddepositionwere
thedominantprocessesoverhillslope
erosion.
• Incrementonsoilerosionrateduetocli-
matechangeisgreaterthanreported
soilformationrates.
a r t i c l e i n f o a b s t r a c t
Articlehistory: Theeffectsofclimatechangeonsedimentyieldandtransportdynamicsincoldclimateregionsarenotwellun-
Received3November2017 derstoodorreported.Inthisstudy,theSoilandWaterAssessmentTool(SWAT)hasbeenbuilt-up,calibrated,and
Receivedinrevisedform31December2017 validatedagainststreamflowandsedimentloadatseveralmonitoringstationsinacoldclimateregionwatershed
Accepted31December2017
-theAthabascaRiverBasin(ARB)inAlberta,Canada.Themodelwasthenfedwithbias-correctedspatialdisag-
Availableonline5January2018
gregatedhigh-resolution(~10km)futureclimatedatafromthreeclimatemodelsfortwoemissionscenarios
(RCP4.5and8.5),andtwoperiods(mid-andend-century).Resultsshowthatchannelerosionanddeposition
Keywords:
Coldclimateregions arethedominantprocessesoverhillslopeerosioninthebasin.Onaverage,apredictedwarmerandwetterfuture
Climatechange climatehasbothsynergeticandoffsettingeffectsonsedimentyield.Changesaresub-regionspecificandland-use
Sedimentyieldandtransport typedependent,thusreflectingamarkedspatialandtemporalheterogeneitywithinthebasin.Increasesonsed-
Swat imentyieldinfutureperiodsintheagriculturalareasareupto0.94t/ha/yr,andaregreaterthanreportedsoilfor-
AthabascaRiverbasin mationratesintheregion.Similarly,whilesubstantialincreases(bymorethantwofold)inthesedimentload
transportthroughtheriverreacheswereobtained,thechangesshowbothtemporalandspatialvariability,
andarecloselyalignedwiththetrendofstreamflows.Webelievethatavailabilityofsuchmodelsandknowledge
oftheeffectoffutureclimaticconditionswouldhelpwatermanagersformulateappropriatescenariostomanage
suchbasinsinaholisticway.However,significantuncertaintiesinfuturesedimentyieldandtransport,asaresult
ofvariationsinclimaticforcingofdifferentclimatemodels,needtobeconsideredinanyadaptationmeasures.
©2018ElsevierB.V.Allrightsreserved.
⁎ Correspondingauthor.
E-mailaddress:[email protected](J.Wang).
https://doi.org/10.1016/j.scitotenv.2017.12.347
0048-9697/©2018ElsevierB.V.Allrightsreserved.
N.K.Shrestha,J.Wang/ScienceoftheTotalEnvironment625(2018)1030–1045 1031
1.Introduction patches,increasederosivepowerofextremerainfallwouldaccelerate
soilerosionbyseveralfold.Similarly,inthelowerregionsoftheARB,
Itisnowevidentthatclimatechangeisarealphenomenon(IPCC, wheretheriver-bedslopeisratherflat,depositionoflargeamountsof
2014).Climatechangeinducedhydrologicalaltercationsmayposea erodedsedimentfromupstreamsub-basinswouldmakenavigationim-
threattowaterresources,canleadtoexcessivelanddegradation,and possible,especiallyduringlowflowperiods.
posesathreattoenvironmentalsustainabilityofariverbasin.Climate WhilethereareanumberofstudiesinwhichtheARBisassessed
changeaffectserosionfromuplandcatchmentsandthesedimenttrans- forclimatechangeimpacts,thesestudieshavefocusedprimarilyon
portintheriverreachesinmanyways(IPCC,2007).Therefore,thereisa the variability and/or trends of stream flows. For instance, Eum
pressingneedtoassesstheimpactsofachangingclimateintheerosion etal.(2017)assessedimpactsofclimatechangeconsideringvarious
andsedimenttransportdynamicsofariverbasin.Severalstudieshave hydrologicindicators(seasonalflows,timingandmagnitudeofmax-
beenconductedonthissubjectandwidelyreportedinliterature.Ingen- imumandminimumflows,anddateandflowatspringfreshet)at
eral,studieshaveshownthatprojectedincreasesintemperatureand upper,middleandlowerreachesoftheARB.Similarly,Leongand
projectedincreasesordecreasesinprecipitationhaveresultedincon- Donner (2015) evaluated impacts of climate change on annual
trastingsituations.Forinstance,Ademetal.(2016)foundasynergetic flow,centroidofflow,andtimingofspringrunoffatastationdown-
responseofincreasesintemperatureandprecipitationonmeanannual streamofoil-sandsregionoftheARB,includingimpactsonlow-flow
sedimentyieldintheUpperGilgelAbaycatchment,BlueNileBasin, frequency.Inarecentstudy,Shresthaetal.(2017)assessedgreen
Ethiopia.However,Rodríguez-Blancoetal.(2016)foundnon-linearre- and blue water resources at different regions of the ARB. To the
sponsesbetweenchangesinclimaticvariablesandsedimentconcentra- bestofourknowledge,therehasbeenlittleassessmentofclimate
tionsintheCorbeiracatchment,Spainduetocomplexinteractions changeimpactsonsedimentyieldandtransportdynamicsincold
betweenclimaticforcing,atmosphericcarbondioxidelevel,biomass, climatebasins,ingeneralandintheARBinparticular.Becauseof
wateryield(andstreamflow),andsoildetachmentandtransportdy- theconsequencesoflanddegradation,itisnecessarytoperforman
namics.Similarobservationshavealsobeenmadebyotherresearchers assessment of climate change impacts on erosion and sediment
(Ficklinetal.,2013;Nunesetal.,2008;Serpaetal.,2015;Zabaletaetal., transportintheARB.Assuch,thisstudywascarriedoutwiththepri-
2014).Interestingly,Serpaetal.(2015)observedtwocontrastingsitua- maryobjectiveofquantifyingtheimpactsofclimatechangeonthe
tions:decreasesinerosionratesinahumidMediterraneancatchment sedimentyieldandtransportdynamicsindifferentspatialandtem-
withapermanentvegetationcover,andincreasesinerosionratesina poralscales.Forthis,wecalibratedandvalidatedtheSoilandWater
dry Mediterranean catchment with a perennial vegetation cover, Assessment Tool (SWAT) for this cold climate, snow dominated
whichhighlightstheimportanceofvegetationcoveronerosionrates. basin.Then,highresolution(~10km)biascorrected,spatiallydisag-
BasedontheresultsonUKandEuropeancatchments,Dadsonetal. gregated future climate data from three climate models for two
(2010)alsoobservedcomplexinteractionsbetweeninvolvedvariables. emission scenarios (RCP 4.5 and 8.5) and for two future periods
However,theysuggestedthatprecipitationandland-coverwouldexert (mid-andend-century)wasfedintothemodel.Webelievethat
thestrongestcontroloversoilerosiondynamics.Similarly,Ficklinetal. theresultsofthisstudycanbetranslatedfortheholisticwatershed
(2013)highlightedtheroleofbiomassgrowthasaresultofawarmer managementoftheARBasawhole,andisapplicabletoothercold
temperatureanditsimpactonsurfacerunoffanderosionratesinanag- climateregionsaswell.
riculturalwatershedinCalifornia,USA.IntheNamOubasin,LaoPDR,
Shresthaetal.(2013a)foundsignificantuncertaintyinsedimentyield
andfluxasaresultofvariationsinclimateforcingofdifferentmodels 2.Materialsandmethods
andstressedtheneedtoconsidertheuncertaintiesinadaptationmea-
sures.Hence,itisevidentthatprojectedchangesinerosionandsedi- 2.1.Studyarea
ment transport are region specific, and are the result of various
underlyingfactors.Consequently,thereisaneedtoassesstheclimate TheAthabascaRiverBasin(ARB)istypicalofcoldclimatebasins.It
changeimpactsonerosionandsedimenttransportinnewwatersheds, liesinthecentreoftheprovinceofAlbertainCanadaandcontributes
includingcoldclimateregions. significantlytotheprovincialeconomybyprovidingacontinuousand
Coldclimateregionsservicemultipleecosystemfunctions,suchas dependablewatersupplytovariousindustriesandlocalcommunities
climateregulation,waterinfiltration,andbiodiversityconservation (AWC, 2011). The river originates in the Columbia Icefields in
(AWC,2011;Black,1997).However,climatechangeandhumanactivi- Canada's Rocky Mountains and flows about 1500 km North-East,
ties,suchasever-increasingindustrializationandagrowingpopulation, drainingintoLakeAthabascainthenorth(Fig.1).Thecatchmentarea
disturbtheseimportantecosystemsleadingtoseverelanddegradation. oftheARBatthemouthisabout161,000km2.Therivercrossesthrough
Thishasresultedinwaterqualityimpairmentsinreceivingwaterbod- variousurbancentresintheprocess(AWC,2011).Majorindustriesin
iesandhavelimitedtheirecologicalfunctions(Crabilletal.,1999).In thebasinincludeagriculture,forestry,pulpmills,coalmining,tradition-
ordertounderstandandanalyzesuchaninterrelatedandcomplexen- aloilandgasextraction,andoil-sandsmining(AWC,2013).Theriver
vironmentprocess,anintegratedmodellingframeworkthatconsiders receivespointsourcepollutionfromwastewatertreatmentplantsin
allterrestrialandaquaticecosystemsisneeded(Argentetal.,2006). the urban centres, effluent from industries, and leachate from oil-
Insuchintegratedmodellingframeworks,erosionandsoiltransport sandstailingponds(AWC,2014),aswellasnon-pointsourcepollution
modellingisessentialbecausesedimentsprovidesurfaceareaforcon- fromagriculture.
taminantstobeadsorbed,andsedimentscanbeapathwayfornutrient Forestisthedominantlandusetypeinthebasinwithalmost82%
transfer from the river-bed to the water column and vise-versa coverage. Of the different forest types, evergreen forest (FRSE,
(Ouattaraetal.,2011;Shresthaetal.,2014;Shresthaetal.,2013b).In TableS1)hasthehighestproportioninallregions(Fig.S1)oftheARB.
cold regions, such as the Athabasca River Basin (ARB) in Alberta, Relativelyflattopographyandahighercoverageofdifferenttypesof
Canada,higherwintertemperaturesmightconvertnon-erosivesnow- forests(~90%)intheborealplainregion(Fig.S1)makethisarealeast
fallintoerosiverainfall.Thiswouldmeltpermafrost,therebyincreasing pronetoerosion.However,theheadwaterregion,withsteeptopogra-
wintersurfacerunoff,andleadingtoincreasedsoilerosionfromhill phy(slopesN20%,Fig.S2)andthehighestpercentageofbarrenland,
slopesandsedimenttransportthroughthereaches(IPCC,2007;IPCC, makeitmostsusceptibletoerosion.Also,regionssuchasPrairieand
2014).Furthermore,explorationoftheoil-sandsistypicalintheARB. LesserSlave,whichhavethehighestpercentageofagriculturearea,
Thiscaninducechangesinthelandscapeofthebasin.Lesserodiblefor- (~45%and~19%respectively,TableS1),arepronetohigherratesofero-
estwouldbechangedintoeasilyerodible,barelandpatches.Onsuch sionduetolowornovegetationcoverinnon-growingseasons.
1032 N.K.Shrestha,J.Wang/ScienceoftheTotalEnvironment625(2018)1030–1045
Fig.1.LocationofthebasinwiththeLakeAthabasca,theoutletoftheAthabascaRiver,digitalelevationmodel(DEM)andurbancentres.
2.2.Thesoilandwaterassessmentmodel andmanagementfactor(−);P =supportpracticefactor(−);LS =
USLE USLE
topographicfactor(−);CFRG=coarsefragmentfactor(−).
TheSoilandWaterAssessmentTool(SWAT)isasemi-distributed SWATalsoaccountsforthesnowcoverageeffectonerosion(Neitsch
hydrologicalsimulatorusedforcontinuous,longtermsimulationofa etal.,2011):
varietyofprocesses(Arnoldetal.,1998).Thesimulatorusesspatial
datasetsofelevation,landuseandsoiltypesalongwithseveralhydro- sed0
meteorologicaldatasets,typicallyintegratedusinggeographicinforma- sed¼ (cid:4)3(cid:2)SNO(cid:5) ð2Þ
tionsystems(Winchelletal.,2010).SWATdividesawatershedinto exp 25:4
sub-basins, which are further divided into Hydrological Response
Units(HRUs),thelatterbeingauniquecombinationofsoil,landuse
where,sed′=sedimentyield(metrictons),asinEq.(1);SNO=water
and slope (Arnold et al., 2011). The hydrological compartment of
contentofthesnowcover(mm).
SWATconsidersprecipitation,infiltration,deepaquifer,channeltrans-
Sedimentcontributionfromlateralandgroundwaterflowscanalso
missionandevapotranspirationlosses,surfacerunoff,lateralandreturn
beconsidered.Once,sedimentsareinthestream,thesedimentcarrying
flowforwaterbalancecalculations.SWATusestheModifiedUniversal
capacityofthechannelisdeterminedas(Bagnold,1977):
SoilLossEquationtoestimatesoilerosionandsedimentyieldfrom
eachHRUs(WilliamsandBerndt,1977):
concsed;ch;mx¼csp:vscphe;pxkp ð3Þ
(cid:1) (cid:3)
sed0¼11:8 Q (cid:2)q (cid:2)area 0:56K (cid:2)C (cid:2)P (cid:2)LS
surf peak hru USLE USLE USLE USLE
where,conc =maximumsedimentconcentrationthatcanbe
(cid:2)CFRG ð1Þ sed,ch,mx
entertrainedinthechannel(tons/m3);c =acoefficient(−);spexp
sp
=anexponentparameter;v =peakchannelvelocity(m/s)=qch;pk
whah)e;rqes,usrfe=d′p=easkedruimnoefnft(myi3e/lsd);(mareeatrhircut=onHs)R;UQasurref=(has)u;rKfaUcSeLEr=unsooffil(emromd-/ ¼prAf(cid:3)cqhch;prf=peakrateadjuchs,tpmkentfactor(−);Ach=crosssectionAcahl
ibilityfactor(0.013metricton·m2·ha/(m3·metrictoncm));C =cover areaofthechannelflow(m2);q =averagechannelflow(m3/s).
USLE ch
N.K.Shrestha,J.Wang/ScienceoftheTotalEnvironment625(2018)1030–1045 1033
Themaximumcarryingcapacityisthencomparedagainsttheinitial (1983–1989 and 2006–2013) for model validation. These periods
sediment concentration at a reach (conc ). If conc werechosensystematicallyinordertoincludebothdrierandwetterpe-
sed,ch,i sed,ch,i
Nconc ,thendepositionprevails,otherwise,degradationpre- riods, as determined by the Standardized Precipitation Index (SPI)
sed,ch,mx
vails.Theamountdepositionordegradationis(Neitschetal.,2011): (McKeeetal.,1993).ReferShresthaetal.(2017)totheSPIplots.This
(cid:6) (cid:7) wouldensurethatthecalibratedandvalidatedmodelcouldsimulate
seddep¼(cid:6)concsed;ch;i−concsed;ch;mx(cid:7)(cid:3)Vch ð4Þ variablesofinterestsatisfactorilyinany(drierandwetter)futurecondi-
seddeg¼ concsed;ch;mx−concsed;ch;i (cid:3)Vch(cid:3)Kch(cid:3)Cch: tions(Daggupatietal.,2015).Aserosionandsedimenttransportdy-
namicsarehydrologicallydrivenprocesses,simultaneouscalibration
where, sed = amount of sediment deposited (tons); sed = ofstreamflowandsedimentrelatedparametersisrequired.Further,
dep deg
amountofsedimentdegraded(tons);V =volumeofwater(m3); someparameterssimultaneouslyaffectthestreamflow,erosionand
ch
K =channelerodibilityfactor(cm/ha/Pa);C =channelcoverfactor. sedimenttransportdynamics.Assuch,weselected30flowrelated
ch ch
Aftermaintainingthesedimentbalanceonthereach,thesediment (TableS2)and10sedimentrelatedparameters(Table1)forsensitivity
concentrationoutofthereachisgivenas(Neitschetal.,2011): analysis. The model was calibrated using streamflow data
(Environment-Canada,2016)recordedat35stations(Fig.S3),andsed-
V imentdata(AEP,2016)recordedat5stations(Fig.2).Apropercalibra-
sed ¼sed out ð5Þ
out chVch tionisassumedtobeachievedwhenreasonablevaluesoftheselected
goodness-of-fitstatistics,andagoodvalueofp-andr-statistics(ideal
where,sed =sedimenttransportedoutofthereach(tons);sed = valueis1,and0,respectively)isobtained(Table2).Thep-statisticsre-
out ch
sedimentinthereach(tons);V =volumeofwatertransportedoutof flectthepercentageofobservationsbracketedbythe95%predictionun-
out
thereach(m3);V =volumeofthewaterintherach(m3). certainty band, and the r-factor denotes the width of the band
ch
(Abbaspouretal.,2007).Forthemodelperformanceevaluation,we
2.3.Modelinputsandbuild-up chosePercentageofBias(PBIAS),theNSE,andratioofroot-mean-
squarederrortostandarddeviation(RSR).Basedontherangeofvalues
A90m×90mhole-filleddigitalelevationmodel(Jarvisetal.,2008) ofthesethreeselectedgoodness-of-fitstatistics,weassignedaqualita-
(Fig.1),1kmx1kmGlobalLandCoverCharacterizationbasedland-use tiveratingoutofthefour(VeryGood,Good,SatisfactoryandUnsatisfac-
map(Lovelandetal.,2000)(Fig.S1),and1:1millionscalesoilmap tory)suggestedbyMoriasietal.(2007)tothemodelresults.
(SLC,2010)werethespatialdatasetsusedinthemodelbuild-up.We
areawareofpotentialbenefitsoffinerresolutionspatialdatasetssuch 2.5.Climatechangedata
astheDEMasithasbeenshownthatfinerspatialdatasetstendtogen-
eratebetterhydrological(Teegavarapuetal.,2006)andsoilerosionre- Selectionofsuitableclimatemodelstouseforimpactstudiesisnota
sults(Zhangetal.,2008).However,somestudieshavealsoshownlittle straightforwardtask(Lutzetal.,2016).Differentsourcesofuncer-
effectoffinerresolutionDEMsupto90m(ReddyandReddy,2015). taintiessuchasinternalclimaticvariability,climaticmodeluncertainty
Owingtothespatialextentofthebasin,themodelused,andtheneed andscenariouncertainty(HawkinsandSutton,2009)inherentinfuture
oflong-termsimulationinclimatechangeimpactstudies,webelieve climateprojectionsnecessitatetheuseofmultipledatasources.With-
thattheresolutionofspatialdatasetsusedinthestudywouldbeappro- standing the obvious advantages of fine resolution climate data in
priate.However,thesespatialdatasetscould,preferablybereplacedby process-basedmodels,futureclimaticprojectionsofGlobalCirculation
finerones.Then,athresholddrainageareaof200km2wasusedtode- Models(GCMs)areoftendownscaledandbias-corrected(Bergetal.,
lineatesub-basins.Additionalsub-basinoutletsatmajorriverconflu- 2012). As for the Western North America (WNA) region, Murdock
ences, and at stream flow and sediment monitoring stations, were etal.(2013)assessedaccuracyofclimaticprojections(dailyprecipita-
defined.Theprocessresultedinatotalof131sub-basins.Todefine tion,maximumandminimumtemperature)ofall26GCMsinvolved
HRUs,aslopemapwasderivedanddividedinto4classes(Fig.S2). intheCoupledModelIntercomparisonProject(CMIP5)(Tayloretal.,
Usingathresholdof10%,5%and10%forlanduse,soilandslope,respec- 2011),andrankedthetop12GCMsfortheWNAregion.Usingvarious
tively,atotalof1370HRUswereobtained.Dailyprecipitation,maxi- downscalingmethods,includingtheBias-Correction/SpatialDisaggre-
mum and minimum temperatures data at 73 stations recorded by gation (BCSD), (Wood et al., 2004) using the ANUSPLIN data
(GoC,2016),relativehumidity,solarradiation,andwindspeeddataat (Hutchinsonetal.,2009),Murdocketal.(2013)preparedfutureclimate
230stationsrecordedby(CFSR,2016)werefedintothemodel.Toiden- dataat0.0833°×0.0833°(~10km×10km)spatialresolution.Assuch,
tifythepointandnon-pointsourcesofpollution,weuseda30m×30m wehaveusedthesame,biascorrected,spatiallydisaggregateddaily
cropinventorymap(AAFC,2013)andfound‘SpringWheat’and‘Barley’ precipitation,maximumandminimumtemperaturedatafromthetop
asthemajorcropsgrowninthebasin.Accordingly,weassignedappro- 3GCMs(CentreNationaldeRecherchesMeteorologiquesandCerfaces
priatemanagementoperations(seedlingandharvestingdates,andfer- –CNRM-CM5,CanadianCentreforClimateModellingandAnalysis–
tilizerrates)forthesecrops.Whiletheforestisassumedtobegrowing CanESM2,andCenterforAustralianWeatherandClimateResearch–
atthestartofthesimulation,appropriatemanagementoperationsfor ACCESS1–0)outofthe12recommendedby(Murdocketal.,2013).
pasture(seedlingandharvestingdates,andfertilizerrates),grassland Then,wedownloadedandprocessedthedatasetsat3306gridpoints
andrangeland(grazingoperation,dailybiomassconsumption,andma- coveringtheARB(Fig.S3)fromthePacificClimateImpactConsortium
nureproduction)weredefined.Finally,annualconstantpointsource (PCIC)dataportal.Asfortheothermeteorologicalvariablessuchasspe-
datafrom7industrialeffluentsand4wastewatertreatmentplants cifichumidity(convertedtorelativehumidity),solarradiationand
(AEP,2016)werefedintothemodel. windspeed,weuseddownscaleddata(ataspatialresolutionof0.22°
×0.22°–25km×25km)fromtheCanadianCenterforClimateModel-
2.4. Model sensitivity analysis, calibration, validationand uncertainty ling and Analysis (CCCMA), Regional Climate Model (RCM) - the
analysis CanRCM4(Scinoccaetal.,2015).Wedownloadedandprocessedthe
datasetsat273gridpoints(Fig.S3).Furthermore,datasetscorrespond-
TheSWAT-CUP(Abbaspouretal.,2007)wasusedforthesensitivity, ingtotwoIPCCAR5emissionscenarios-theRCP4.5andtheRCP8.5
calibrationandvalidation,anduncertaintyanalysis.TheNashSutcliffe (IPCC,2014)wereconsidered.Inordertoquantifytheclimatechange
Efficiency–NSE(NashandSutcliffe,1970)wasusedastheobjective impactsindifferenttimeperiods,amid-centuryperiod(2040's)with
function. We chose the years 1980–1982 as a warm-up period, atimeframeof2021–2060,andalate-centuryperiod(2080’s)witha
1990–2005 for model calibration, and two different periods timeframeof2061–2100,arealsoconsidered.Finally,wedividedthe
1034 N.K.Shrestha,J.Wang/ScienceoftheTotalEnvironment625(2018)1030–1045
Table1
Sedimentrelatedparametersconsideredforthesensitivityanalysis.
Name Description Unit Min. Max. Default
value value value
v__ADJ_PKR.bsn Peakrateadjustmentfactorforsedimentroutinginthesubbasin(tributarychannels)- – 0 2 1
prftributary
v__PRF_BSN.bsn Peakrateadjustmentfactorforsedimentroutinginthemainchannel(prfmain) – 0 2 1
v__SPCON.bsn Linearparameterforcalculatingthemaximumamountofsedimentthatcanbereentrained – 0.0001 0.01 0.0001
duringchannelsedimentrouting(csp)
v__SPEXP.bsn Exponentparameterforcalculatingsedimentreentrainedinchannelsedimentrouting – 1 2a 1
(spexp)
v__CH_COV1.rte Channelerodibilityfactor(Kch) – −0.05 0.6 0
v__CH_COV2.rte Channelcoverfactor(Cch) – −0.001 1 0
v__CH_ERODMO.rte Monthlychannelerodabilityfactor(Kch,monthly) – 0 1 0
v__USLE_C.plant.dat MinimumvalueofUSLECfactorapplicabletothelandcover/plant – 0.001 0.5 Plantb
v__USLE_P.mgt USLEequationsupportpractice – 0 1 HRUc
r__USLE_K.sol USLEequationsoilerodibility(K)factor 0.013metric −10% 10% Soil
ton·m2·ha/(m3·metrictoncm) layerd
v=parametervalueisreplacedbygivenvalue;r=parametervalueismultipliedby1±agivenvalue
a Maximumlimitwidened.
b Plantdependent.
c HRUdependent.
d Soillayerdependent(maximumvalue=0.65andminimumvalue0).
Fig.2.Thebasindivisions(Headwater,Foothill,Prairie,LesserSlaveandBoreal)withstreamflowrecording,sedimentmonitoring,andpointsourcesofpollution(industrialandwaste
watertreatmentplant)stations.
N.K.Shrestha,J.Wang/ScienceoftheTotalEnvironment625(2018)1030–1045 1035
Table2 3.Resultsanddiscussion
Valuesofchosengoodness-of-fitstatisticsofsedimentloadsimulationresultsinthecali-
brationperiodwithp-andr-statisticsatfiveselectedsedimentmonitoringstations. TheSWATmodelisabletosimulatethestreamflowatvariousloca-
Station Figure n PBIAS NSE RSR p-Stat r-Stat tionswithvaryingdegreesofaccuracy(Fig.S4)asconfirmedbythe
Headwater(Athabasca 7(a) 55 −6.5 0.42 0.76 0.47 1.15 valuesofgoodness-of-fitstatistics(TableS3).However,wedidnotre-
riveratHilton) (V) (U) (U) producestreamflowresultsinthispaperandreadersarethusreferred
Foothill(Athabascariver 7(b) 59 59.8 −0.2 1.10 0.15 1.16 to(Shresthaetal.,2017)forfurtherdetails.
atWindfall) (U) (U) (U)
LesserSlave(Athabasca 7(c) 115 8.90 0.72 0.53 0.57 0.93
3.1.Parametersensitivity
RiveratAthabasca) (V) (V) (V)
Boreal(Athabascariverat 7(d) 115 6.80 0.75 0.50 0.37 0.84
FortMcMurray) (V) (V) (V) WerefertoShresthaetal.(2017)fordetailsonsensitiveparameters
Boreal(Athabascariver 7(e) 136 0.10 0.74 0.51 0.73 1.01 relatedtostreamflows.Outofthesedimentrelatedparametersconsid-
atOldFort) (V) (V) (V)
eredforsensitivityanalysis,thechannelcoverfactor(CH_COV2)and
monthlychannelerodibilityfactor(CH_ERODMO)werefoundtobe
Statisticsarecalculatedaftertransformingthesedimentloadinlog(10-based)
scale themostsensitiveparameters.Theexponentparameterforcalculating
sedimentcarryingcapacityinthechannel(SPEXP)andpeakrateadjust-
n=numberofobservations,p-stat=percentageofobservationcapturedby95%
predictiveuncertaintyband. mentfactorforsedimentroutinginthetributarychannel(ADJ_PKR)
PBIAS=percentageofbias(%),r-stat=thicknessof95%predictiveuncertainty werefoundtobethethirdandfourthmostsensitiveparameters.Only
band. one parameter related to erosion from upland processes, the land
NSE=Nash-SutfliffeEfficiency(−).
coverfactor(USLE_C)forvariousplanttypes,wasinthetop5ranked
RSR=ratioofroot-mean-squared-errortostandarddeviationofobservations.
sensitiveparameters.Thisindicatesthatchannelerosionisthedomi-
Qualitativeratingforsediment,asperMoriasietal.(2007) nantprocessinsedimenttransportintheARBratherthanuplandero-
V:VeryGoodRating:PBIASb15%;0.70bNSE≤1.00;0.00bRSR≤0.55. sion or sediment yield from sub-basins. This is expected, as the
G:GoodRating:±15%≤PBIAS≤±30%;0.60bNSE≤0.70;0.55bRSR≤0.63. dominantland-usetypeinallregionsoftheARBisforest(TableS1),
S:SatisfactoryRating:±30%≤PBIAS≤±55%;0.45bNSE≤0.60;0.63bRSR≤0.71. andsedimentyieldfromforestland-useisgenerallylow.Furthermore,
U:Unsatisfactory:PBIAS≥±55%;NSE≤0.45;RSRN0.71. intensesummerrainfallandhighstreamflowduringspringdueto
snowmeltwouldgenerallycausesubstantialchannelerosion.Thesensi-
tivityoftheparametersobtainedinthisstudyissimilartootherstudies
(Betrieetal.,2015;Vigiaketal.,2016)insimilarwatersheds.
3.2.Sedimentyieldfromsub-basins
ARBintofiveregions,namely–headwaters,foothills,prairie,Lesser
Slaveandboreal(Fig.2)inordertoquantifyclimatechangeimpacts Fig.3showsspatialdistributionoftheannualaveragesediment
onaregionalscale. yield from the delineated sub-basins of theARB, and coefficient of
Fig.3.Yearlyaveraged(left)andcoefficientofvariation(CV)sedimentyieldfromsub-basinsofthebasininbaseperiod(1983–2013).
1036 N.K.Shrestha,J.Wang/ScienceoftheTotalEnvironment625(2018)1030–1045
slopeofthetrendline(Fig.5)indicatesthatsedimentyieldfromthe
foothillregionismostlystable.WhileintheprairieandLesserSlavere-
gions, the average sediment yield is fairly comparable (0.78 and
0.8t/ha/yr,respectively),somesub-basinsoftheregionshaveresulted
inhighersedimentyieldastheseregionshaveasubstantialpercentage
oftheirareaasagriculturalland.Themanagementoperationscarried
outintheagriculturalareas(e.g.plowing)wouldexposethemtoa
higherriskoferosion.Further,agriculturalareashavelowvegetation
coverduringthecoldseason.Asaresult,althoughthemajorityofthe
areaintheseregionshaveaslopeb5%,higherinter-annualvariation
insedimentyield(Fig.4)alsoindicateshighersusceptibilityofthere-
Fig.4.Annualaveragedsedimentyieldfromdifferentregionsofthebasininbaseperiod giontoerosion.Thisisalsoreflectedinthehigherslopeofthetrendline
(1983–2013). (Fig.5).Attheborealplains,asthetopographyisgenerallyflat(slope
b 5%), and N95% of its area is forest, the erosion potential is low,
variationduringthebase(1983–2005)period.Thesub-basinaverage whichisreflectedinthesedimentyieldplot(Fig.3).Thenarrowest
sedimentyieldoftheARBisfoundtobe0.4t/ha/yr,whichiswithin boxplotofinter-annualsedimentyield(Fig.4),andaveryflatslope
therangeofvaluesreportedintheliteratureforthisregion.Forin- ofthetrendlinealsoconfirmthis.Theaveragesedimentyieldofthere-
stance,Benderetal.(2000)reportederosionratesforAlbertaranging gionisverylow(average:0.02t/ha/yr).
from0.05–46.8t/ha/yr,and0.05–2.6t/ha/yrintheOil-Sandsregions Asexpected,theforestandrangelandhaveresultedinthelowest
of the basin. Similarly, Golder-Associates (1996) reported erosion sedimentyield,andtheagriculturallandhasthehighestyield(Fig.6).
ratesintheUSandCanadarangingfrom0.16–27t/ha/yr.AstheARB Thiscanbemainlyrelatedtolandcoverfactors.Therangelandsandfor-
hasflattopography(Fig.S2)andthemajorityofland-usetypeisforest estconsistofwoodyand/orherbaceousperennials,andtopsoillayers
(TableS1),itisquitenaturalthatouraverageestimatefallsintothe arebarelyexposedtotheerosiveeffectsofextremerainfall,thereby
lowerend of the reported ranges in theliterature. However, some leadingtolowererosion.Ontheotherhand,plantsgrownontheagri-
sub-basinsattheheadwaterregionhavesedimentyieldashighas culturallandsareannualones,meaningthatthetopsoilisexposedas
10t/ha/yr(Fig.3). bare(orlowvegetationcover)foralmosthalfoftheyear.Further,agri-
Asexpected,theheadwaterregionisthemainsourceofsediment culturalrelatedoperationssuchasplowingfurtherloosenthetopsoil.
withanaveragesedimentyieldof3.23t/ha/yr,asthisregionhassteep Hence,erosiveextremerainfallwouldeasilyerodethetopsoiland
topographyandahigherpercentageofbare-land.However,marked theseareaswouldyieldhigheramountsofsediment.Moreover,agricul-
spatial heterogeneity in the sediment yield is evident in the basin turalareasareoftensubjectedtoephemeralgullyerosion(Daggupati
(Fig.3).Higherinter-annualvariationsofsedimentyield,asindicated etal.,2014)albeitassistedbyrowcropping.Insuchacase,theconcen-
bythewiderinter-quartilerangeoftheboxplot(Fig.4)intheheadwa- trationofchannelizedflowwouldleadtoanevenhigheramountofero-
terregionindicatesthatthesedimentyieldfromthisregionoftheARB sionfromagriculturalareas.Aswehaveseen,thereiswiderinter-
issensitivetoclimaticforcing(e.g.precipitation)andland-usetype.The quartilerangeofsedimentyieldfromagricultureland,whichalsocon-
scatterplotofannualprecipitationandsedimentyield(Fig.5),andthe firmsthehighererosionpotentialofagriculturalland.Ourestimates
slopeofthelineartrendlinefittedbetweenthem,however,confirms ofsedimentyieldfromdifferentland-usetypesarefairlycomparable
thatclimaticforcingexertsgreatercontrolonerosionratesinthehead- tothevaluesreportedinliteratureforsimilarwatersheds.Forinstance,
waterregion.Itshouldbenotedthattheseverityofinstantaneous,ex- forforestland-use,Benderetal.(2000)reportedcitingGrayandLeiser
tremehighrainfallisofhighersignificancethanthetotalprecipitation (1982), US-EPA (1973), and US-EPA (1976), sediment yield of
amountforthesedimentyieldfromsub-basins. 0.078t/ha/yr;HuangandLo(2015)reportedarangesedimentyield
Atthefoothills,whiletheslopeisprimarilyb5%(Fig.S2),andforest value(0.02–0.07t/ha/yr);andRodríguez-Blancoetal.(2016)reported
isthedominantland-usetype(Fig.S1,TableS1),thesedimentyield(av- nearzero(0.002t/ha/yr)sedimentyield.Thesevaluesarefairlycompa-
erage:0.6t/ha/yr)reduceddrasticallyincomparisontotheheadwater rabletothemediansedimentyield(0.02t/ha/yr)fromtheforestobtain-
region.Anarrowerinter-annualvariation(Fig.4)andafairlystable ed in this study. Similarly, our estimate of mean sediment yield
Fig.5.Rateofsedimentyieldasfunctionoftotalannualprecipitationfromdifferentregionsofthebasininbaseperiod(1983–2013).
N.K.Shrestha,J.Wang/ScienceoftheTotalEnvironment625(2018)1030–1045 1037
(Stephensetal.,1977)forwoodland.However,ourmeansediment
yieldestimateof2.91t/ha/yrfromagriculturallandseemstobeabit
lowerthanmostofthereportedvalues;15.6t/ha/yrbyBenderetal.
(2000),18.67t/ha/yrbyStephensetal.(1977),21.6–30t/ha/yrby
Huang and Lo (2015), and 3.09 t/ha/yr by Rodríguez-Blanco et al.
(2016).Whilethesedimentyielddependsonvariousfactors,alower
yieldcouldbeduetotherelativelyflatslope(b5%)inagriculturaldom-
inatedareasoftheARB.Further,themeanannualprecipitationinthese
regions is quite low (~500 mm), of which a significant proportion
(~20%)issnowfall.However,wemadeanefforttoincreasethesedi-
mentyieldatHRUshavingagriculturalareasastheland-usetype,byin-
Fig.6.Annualaveragedsedimentyieldfromdifferentland-usetypesofthebasininbase creasingtherelatedparameterstotheupperendoftheallowablerange
period(1983–2013). (e.g.,C calibratedvalue=0.45,maximumvalue=0.5;andK
USLE USLE
fixedtoitsmaximumvalueof0.65,ADJ_PKRandPRF_BSN=2).Simi-
(0.78t/ha/yr)fromrangelandiscomparabletoavalueof0.78t/ha/yrby larly,themeansedimentyieldfrompastureis1.53t/ha/yr,whichis
Bender et al. (2000) for grassland, and to a value of 1.01 t/ha/yr also comparable to values reported in literature (0.78 t/ha/yr by
Fig.7.Observedandsimulatedsedimentloadtransportedthrough(a)Headwater(atHilton),(b)Foothill(atWindfall),(c)LesserSlave(atAthabasca),(d)Boreal(atFortMcMurray),
(e)Boreal(atOldFort)incalibrationperiod(1990–2005)with95%totalpredictiveuncertaintybands.
1038 N.K.Shrestha,J.Wang/ScienceoftheTotalEnvironment625(2018)1030–1045
(Benderetal.,2000)and4.33t/ha/yrbyStephensetal.(1977),and theuplandcatchmentswouldbeminimal,orevenzeroduringsuchpe-
higherthantheestimate(0.005t/ha/yr)of Rodríguez-Blancoetal. riods,theoverestimationshouldthusberelatedtothesedimentcarry-
(2016).TheoptimizedvaluesforC foragriculture,forest,pasture ingcapacityofthechannel.Therefore,wepurposefullyloweredthe
USLE
andrangelandwere0.475,0.009,0.4505and0.005respectively.Simi- valuesSPEXPandSPCONtotheirallowableminimumvaluesandset
larly,theoptimizedvalueforP wascloseto1.0,andtheoptimized CH_ERODMOvaluetozero.However,theloweredvaluesoftheparam-
USLE
value for K for agriculture, forest, pasture and rangeland were etersalsoloweredthehighersedimentvaluesandthemodelmissedthe
USLE
0.65, 0.05, 0.3and0.05respectively.Theseparametersweretuned high sediment loads. To overcome this problem, we increased the
basedontheinitialestimateprovidedbyAAFC(2002). SPEXPbeyondtheirallowablelimitof2.0,soastogetthedesiredeffect
ofloweringsedimentloadduringlowflowperiodsandincreasingsed-
3.3.Sedimentloadtransportedthroughreaches imentloadduring storm conditions.An illustration of the effect of
SPEXPonthesedimentcarryingcapacityofachannelfordifferentsets
Ingeneral,themodelisabletoreproducethetrendinobservations ofchannelvelocity(Vpk,ch)isshowninFig.S5.Itisevidentthatwhen
ofsedimentloadsatallstations.Qualitativeratingsofsedimentload Vpk,chislowerthan1.0(lowflowperiods),thesedimentcarryingcapac-
simulationsindicatethatthesedimentloadissimulatedwitha‘Very itydecreasesastheSPEXPincreases.Conversely,whenVpk,chisN1.0
Good’accuracy(PBIASb15%,NSEN0.7andRSRb0.55)atdownstream (stormconditions),thesedimentcarryingcapacityincreasesasSPEXP
stations(Fig.7c–e),whilethesedimentloadissimulatedwith‘Satisfac- increases. In the Bagnold's equation, which was mainly for non-
tory’ accuracy in upstream stations (Fig. 7 a–b). Graphical plotting cohesiveparticles,SPEXPensuresnonlinearitiesinthecarryingcapacity
showsthat,whilethemodeltendstocapturehighersedimentload ofachannelasvelocitiesincrease(forSPEXP≠1.0).Duringlowflowpe-
quitewell,thelowersedimentloadhasbeenslightlyoverestimated,es- riods,cohesivesedimentmightonlyslightlyerode,butwhenvelocity
peciallyatdownstreamstations.Suchoverestimationistypicallyob- increases,erosionofcohesivesedimentmightincreaseexponentially
servedinlowflowperiods(coldseason).Asthesedimentyieldfrom morethannon-cohesivesediments.Hence,theuseofSPEXPbeyond
Fig.8.(a)Percentagechangeinaveragemonthlyprecipitation,(b)absolutechangeinaveragemonthlyairtemperatureoverthebasinbetweenbaseandtwofutureperiodsfortwo
emissionscenarios(iandii).
N.K.Shrestha,J.Wang/ScienceoftheTotalEnvironment625(2018)1030–1045 1039
Fig.9.Annualaveragedchangesinsedimentyield(SYLD)fromsub-basinsofthebasinintwofutureperiodsfortwoemissionscenarios,relativetobaseperiod.
the maximum allowable limit might bejustified. This issue indeed 2017;Tothetal.,2006).Moreextremeseasonalvariationsareexpected
needsfurtherinvestigation.Theoptimizedvaluesfortheparameters; indifferentregionsoftheARB(TableS4).ExceptfortheCanESM2based
SPCON, SPEXP, CH_COV2, CH_ERODOMOJan → Mar & Nov → Dec, projections,summerprecipitation,whichisofparticularrelevanceto
CH_ERODOMOApr → Oct were 0.0002, 2.99, 0.975, 0.01 and 0.99, theerosionandsedimenttransport,isexpectedtodecreaseinallre-
respectively. gionsoftheARB.However,substantialincreasesinspringprecipitation
(upto53%)andearlyspringfreshetduetoincreasesinspringtemper-
3.4.Climatechangeimpact ature(upto4.4°C)wouldindicatethatbothsoilerosionandsediment
transportwouldincreaseinthefuture,especiallyinthespringseason.
Basin-wideaveragedmonthlypercentageandabsolutechangesof Further,frequencyanalysisofextremeprecipitation(dailyprecipitation
precipitationandtemperature,respectively,intwofutureperiodsrela- N25mm,Fig.S6)revealedthatthiseventswouldbecomeslightlymore
tivetothebaseperiodfortwoemissionscenarios(Fig.8)showedthat extreme,especiallythosecorrespondingtohigherreturnperiods(e.g.
weatherintheARBisprojectedtobewarmerandwetter(exceptin N100 years)andin thelate-centuryperiod.Reflectingthetrendof
Summerseason),especiallyinthelate-centuryperiodforthehigher changesinprecipitation,thestreamflow,onaverage,isprojectedtoin-
emissionscenario.However,therearenoticeabledifferencesbetween creaseinallseasonsexceptatthefoothills,prairieandLesserSlavere-
theprojectionsofdifferentclimatemodels.Ingeneral,CanESM2based gionsduringthesummerseason(TableS5).
projectionsarerelativelywetterandwarmerthanACCESS1–0and
CNRM-CM5basedprojections.Themid-centurymeanannualtempera- 3.4.1.Sedimentyieldfromdifferentregions
tureisprojectedtochangeby0.4–2.0°C,dependingontheclimatic Thechangesintheannualaveragesedimentyieldshowmarkedspa-
modelsandemissionscenarios.Furtherincreases(1.7–5.2°C)areex- tial(Fig.9)andtemporal(Table3,TableS6)heterogeneity.Thechanges
pected in the late-century period. These increments are consistent areregion,climaticmodel,futureperiod,andemissionscenariospecific.
withobservationsmadebyseveralstudies,e.g.(Eumetal.,2017;Toth Attheheadwaterregion,theincreaseinannualsedimentyield,on
etal.,2006).Similarly,increasesof3–15%and7–26%inannualaverage average,ishighest(+11%)inthemid-centuryperiodforthehigher
precipitationareexpectedinthemid-centuryandlate-centuryperiods, emissionscenario(Table3),dominatedbysignificantincreases(upto
respectivelyrelativetothebaseperiod.Theseincrementsareconsistent 70%)inautumnperiodseasonalsedimentyield(TableS6).Thismight
(orslightlyhigher)withmostofthereportedvalues,e.g.(Eumetal., bedrivenbyhigheroccurrencesofextremeprecipitationforthemid-
Table3
Percentagechangeinannualaverageinsedimentyieldintwofutureperiodsandfortwoemissionscenarios,relativetobaseperiod(t/ha)indifferentregionsofthebasin.
Periods/regions Climatemodel Baseperiod(1983–2013) RCP4.5(2021–2060) RCP4.5(2061–2100) RCP8.5(2021–2060) RCP8.5(2061–2100)
Headwater ACCESS1-0 3.23 3.17(−2%) 3.03(−6%) 2.92(−10%) 2.8(−13%)
CanESM2 3.18(−1%) 3.59(11%) 3.02(−6%) 3.39(5%)
CNRM-CM5 3.82(18%) 4.13(28%) 3.44(7%) 3.71(15%)
Mean(%) 3.39(5%) 3.59(11%) 3.13(−3%) 3.3(2%)
Foothill ACCESS1-0 0.60 0.49(−19%) 0.57(−6%) 0.41(−32%) 0.46(−24%)
CanESM2 0.56(−7%) 0.73(20%) 0.56(−8%) 0.65(7%)
CNRM-CM5 0.59(−3%) 0.56(−8%) 0.58(−4%) 0.69(14%)
Mean(%) 0.55(−10%) 0.62(2%) 0.51(−15%) 0.6(−1%)
Prairie ACCESS1-0 0.78 0.62(−20%) 0.73(−6%) 0.65(−16%) 0.64(−18%)
CanESM2 0.76(−3%) 0.75(−3%) 0.69(−12%) 0.78(0%)
CNRM-CM5 0.69(−12%) 0.61(−21%) 0.62(−20%) 0.82(6%)
Mean(%) 0.69(−11%) 0.7(−10%) 0.65(−16%) 0.75(−4%)
LesserSlave ACCESS1-0 0.80 0.73(−9%) 0.9(13%) 0.71(−12%) 0.78(−2%)
CanESM2 1(25%) 1.38(71%) 0.99(23%) 1.63(103%)
CNRM-CM5 0.83(3%) 0.75(−7%) 0.76(−6%) 1.12(39%)
Mean(%) 0.85(6%) 1.01(26%) 0.82(2%) 1.18(47%)
Boreal ACCESS1-0 0.02 0.01(−31%) 0.01(−14%) 0.01(−19%) 0.02(−2%)
CanESM2 0.02(−8%) 0.02(41%) 0.02(5%) 0.03(70%)
CNRM-CM5 0.01(−28%) 0.01(−21%) 0.01(−25%) 0.02(−5%)
Mean(%) 0.01(−22%) 0.02(2%) 0.02(−13%) 0.02(21%)