Modelling climate change effects on Atlantic salmon: Implications for mitigation in regulated rivers

2018·3 MB·English

by  L. E. Sundt-Hansen

ScienceoftheTotalEnvironment631–632(2018)1005–1017
ContentslistsavailableatScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Modelling climate change effects on Atlantic salmon: Implications for
mitigation in regulated rivers
L.E.Sundt-Hansena,⁎,R.D.Hedgera,O.Ugedala,O.H.Diseruda,A.G.Finstada,e,J.F.Sauterleuteb,d,L.Tøfteb,
K.Alfredsenc,T.Forsetha
aNorwegianInstituteforNatureResearch,P.O.Box5685,Sluppen7485,Trondheim,Norway
bSINTEFEnergyResearch,P.O.Box4761,Sluppen7465,Trondheim,Norway
cNorwegianDepartmentofHydraulicandEnvironmentalEngineering,NorwegianUniversityofScienceandTechnology,7491Trondheim,Norway
dSWECO,ProfessorBrochsgate2,7030Trondheim,Norway
eDepartmentofNaturalHistory,NorwegianUniversityofScienceandTechnology,7491Trondheim,Norway
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
• Futureclimatechangeislikelytoimpact
onAtlanticsalmonabundanceinrivers
• Abundancewasmodeledcombiningbi-
ological, hydrological and hydraulic
models
• Futurejuvenileabundancewasreduced
inthreeoffourfuturescenarios
• Reductioninabundancewascausedby
reducedwettedareainsummerperiods
• Reducedfuturejuvenileabundancecan
bemitigatedinriverswithreservoirca-
pacity by releasing water in critical
periods
a r t i c l e i n f o a b s t r a c t
Articlehistory: Climatechangeisexpectedtoalterfuturetemperatureanddischargeregimesofrivers.Theseregimeshaveastrong
Received8December2017 influenceonthelifehistoryofmostaquaticriverspecies,andarekeyvariablescontrollingthegrowthandsurvivalof
Receivedinrevisedform5March2018 Atlanticsalmon.ThisstudyexploreshowthefutureabundanceofAtlanticsalmonmaybeinfluencedbyclimate-
Accepted6March2018
inducedchangesinwatertemperatureanddischargeinaregulatedriver,andinvestigateshownegativeimpacts
Availableonlinexxxx
inthefuturecanbemitigatedbyapplyingdifferentregulateddischargeregimesduringcriticalperiodsforsalmon
survival.Aspatiallyexplicitindividual-basedmodelwasusedtopredictjuvenileAtlanticsalmonpopulationabun-
Editor:D.Barcelo
danceinaregulatedriverunderarangeoffuturewatertemperatureanddischargescenarios(derivedfromclimate
Keywords: datapredictedbytheHadleyCentre'sGlobalClimateModel(GCM)HadAm3HandtheMaxPlankInstitute'sGCM
Salmonids ECHAM4),whichwerethencomparedwithpopulationspredictedundercontrolscenariosrepresentingpastcondi-
Individual-basedmodelling tions.Parrabundancedecreasedinallfuturescenarioscomparedtothecontrolscenariosduetoreducedwetted
Populationabundance areas(withtheeffectdependingonclimatescenario,GCM,andGCMspatialdomain).Toexaminethepotential
Hydropowerregulation formitigationofclimatechange-inducedreductionsinwettedarea,simulationswererunwithspecificminimum
Mitigation dischargeregimes.Anincreaseinabundanceofbothparrandsmoltoccurredwithanincreaseinthelimitofmin-
Climatescenarios
imumpermitteddischargeforthreeofthefourGCM/GCMspatialdomainsexamined.Thisstudyshowsthat,inreg-
ulatedriverswithupstreamstoragecapacity,negativeeffectsofclimatechangeonAtlanticsalmonpopulationscan
potentiallybemitigatedbyreleaseofwaterfromreservoirsduringcriticalperiodsforjuvenilesalmon.
©2018PublishedbyElsevierB.V.
⁎ Correspondingauthor.
E-mailaddress:[email protected](L.E.Sundt-Hansen).
https://doi.org/10.1016/j.scitotenv.2018.03.058
0048-9697/©2018PublishedbyElsevierB.V.
1006 L.E.Sundt-Hansenetal./ScienceoftheTotalEnvironment631–632(2018)1005–1017
1.Introduction ThecurrentstudyexploreshowthefutureabundanceofAtlantic
salmonmaybeinfluencedbyclimate-inducedchangesinwatertem-
Climatechangeisexpectedtomodifythermalandhydrologicalre- peratureanddischargeinaregulatedriver,andhownegativeim-
gimes of rivers, with uncertain consequences for aquatic species pacts of climate change may be mitigated by implementing
(KnouftandFicklin,2017).IntheNorthernHemisphere,climatemodels different minimum discharge regimes during critical periods. A
havepredictedanincreaseinairtemperatureandwinterprecipitation, spatially-explicitindividual-basedmodelisusedtopredictpopula-
butadecreaseinsummerprecipitation(IPCC,2007;Schneider,Laize, tion abundance under future climate regimes for comparison to
Acreman,andFlorke,2013).ForSouthernNorway,run-offisexpected abundanceundercontrol(past)regimes,withafocusonhowthe
toincreaseinwinter,butdecreaseinsummer(Schneideretal.,2013). dischargeregimeaffectswettedareaandconsequentlycarryingca-
Episodesoflowsummerdischargesareexpectedduetolongerperiods pacity.Theeffectofclimatechangeonsalmonidfreshwaterabun-
withlowprecipitationandlowerlevelsofgroundwaterinsummer dancehasbeenexaminedinpreviousstudies(Battinetal.,2007;
(Hanssen-Bauer, Førland, Haddeland, et al., 2015). The projected Hedgeretal.,2013b;Leppi,Rinella,Wilson,andLoya,2014),but
changesintemperatureanddischarge(IPCC,2007),i.e.increased thisistotheauthors'knowledgethefirststudytoincludeminimum
temperatures and changed discharge patterns, may have dischargeregimescenarios,implementedasmitigationstrategiesfor
detrimentaleffectsonaquaticorganismsinhabitingriversbecause climatechange,intothemodelpathway.
bothtemperature(Angilletta,Niewiarowski,andNavas,2002)and
dischargeregimesinfluenceimportantlifehistorytraitsofmany
aquaticspecies,suchasgrowthandmortality(Heino,Virkkala,and 2.Materialandmethods
Toivonen,2009).
TheAtlanticsalmonlifecycleisdividedintotwophases:thejuvenile 2.1.Studyarea
phase,whichtakesplaceinfreshwater;andtheadultphase,which
largelytakesplaceintheocean.Forjuvenilesalmonintheriver,the Thestudyriver,theRiverMandalselva(58.2°N,7.5°E),isoneofthe
temperatureanddischargepatternarekeyparametersforsurvival largestriversinsouthernNorway.Theriveris115kmlongandisregu-
andgrowth.Thewatertemperaturewillaffectthespeedofphysiologi- latedwithsevenhydropowerstationsandseveralreservoirs(Ugedal,
calandbiochemicalreactionsofthispoikilothermic(cold-blooded)or- Larsen,Forseth,andJohnsen,2006).Atlanticsalmonandbrowntrout
ganism(Angillettaetal.,2002).Salmongrowthisstronglyinfluencedby (SalmotruttaL.)canmigratefromthesea47kmupstreamtothenatural
temperature(Forseth,Hurley,Jensen,andElliott,2001)andsizedeter- waterfallofKavfossen(Fig.1).Themeandischargeattheoutletofthe
minedbygrowthisanimportantfactorforjuvenilesurvival(Einumand mostdownstreamhydropowerstation(Laudal)is88m3s−1;average
Fleming,2007).Thestagewhenjuvenilesmigratefromtherivertothe lowestdailydischargesrangebetween18.6m3s−1duringsummer
oceanassmoltsisalsolargelydeterminedbysize(Metcalfe,Fraser,and (Jul–Sep) and 33.1 m3 s−1 during winter (Jan-Mar) (Ugedal et al.,
Burns,1998)andthereforestronglyinfluencedbywatertemperature. 2006).TheMandalsystemhasatotalstoragecapacityof358million
Thedischargedeterminesthewettedareaoftheriver,dependingon m3 (NVE Atlas, https://atlas.nve.no), providing the opportunity to
theriverprofile,withthewettedwidthofa“U”-shapedcrosssection storewaterfromextrawinterprecipitationandtoreleasethiswater
changing less with changing discharge than a “V”-shaped cross indrierperiodsoftheyear.
section.Thewettedareaoftherivercontrolstheriver'scarrying Inthebeginningofthe20thcentury,thesalmonfisheriesinthe
capacity,butcarryingcapacityisalsodependentonhabitatquality riverwerehighlyproductive,butacidificationduringthe1960s
forjuvenilesalmon,i.e.shelteravailabilityisofgreatimportance extirpatedtheoriginalsalmonpopulation.However,limingwas
(Finstadetal.,2009).Thewettedareastronglyinfluencesdensity- initiatedin1997andanewsalmonstock,resultingfromstrayers
dependentmortalityinearlylife-stages,acommonbottleneckfor fromotherriversandthereleaseofeggsandfryfromastockina
salmon abundance in rivers (Einum, Sundt-Hansen, and Nislow, nearby river, rapidly increased in size. The catch peaked at
2006).Thus,watertemperatureanddischargedrivefundamental 10 tonnes in 2001. The present salmon stock in the river is a
biologicalmechanisms,bothattheindividual-andpopulationlevel. geneticblend,withlikelyweakornolinkstotheoriginalstock
Atlanticsalmonhasbeeninalong-termdeclineinmostofitsdistri- (Hesthagen,2010).
butionarea,bothintermsofthenumberofpopulationsandintermsof
abundanceinfreshwateraswellasthemarineenvironment(Hindar,
Hutchings,Diserud,andFiske,2011;Windsor,Hutchinson,Hansen, 2.2.Modellingprocedure
andReddin,2012;ICES,2013).InNorway,approximately30%ofrivers
withsalmonstocksareaffectedbyhydropowerdevelopment,withef- Water temperature and discharge (and consequently wetted
fectsonsalmonstocksrangingfromextirpationtomodestreductions area)intheregulatedstretchdownstreamoftheLaudalhydropower
inabundanceorevenpositiveeffects(Hvidstenetal.,2015).Environ- station–stretchingdownstream20kmfromtheoutletofthestation
mentalflowpracticesinregulatedriversarecommonlydominatedby (upstreamdistance=20.5km)tothestartofthetidalzone(up-
adefinedconstantminimumdischargevalueforwinterandahigher streamdistance=700m–weregeneratedforselectedclimatesce-
constantvalueforsummer(Alfredsen,Harby,Linnansaari,andUgedal, nariosusingamodellinghierarchy(Fig.2)).Coarse-scalepredictions
2012).Thesevaluesareoftenexceeded,butcannotbelower.Inamajor- ofairtemperatureandprecipitationfromGlobalClimateModels
ityofregulatedriversystems,watermassesarecommonlystoredinres- (GCMs)weredownscaledusingaRegionalClimateModel(RCM)to
ervoirsduringperiodsofhighrun-offandreleasedinperiodswhen providefiner-scalepredictions(spatialresolution=1km2)ofair
electricityisrequired,usuallyinwinter.Thusdischargeregimesmay temperatureandprecipitationacrossthecatchmentencompassing
beadjusted,whichoftenleadstoincreasedwinterdischargesandre- theMandalselva.Thesedatawereusedtodeterminethehydrologi-
ducedspringfloodsinNorwegianrivers,comparedtounregulatedriv- calregimeofthecatchmentusingahydrologicalmodel.Giventhat
ers. In a few documented cases, hydropower developments have theMandalselvaisregulatedandthehydraulicpropertiesofthe
increasedsmoltabundanceinpartsoftheriver(Ugedaletal.,2008) riverareinfluencedbyhydropoweroperationaswellasthehydro-
ortotalsmoltabundance,inbothcasesduetoincreasedwaterdischarge logicalregime,outputsfromthehydrologicalmodelwereusedina
duringwinter(Hvidstenetal.,2015).Activemanagementofriverdis- hydropowerproductionmodel,toprovidewatertemperatureand
chargepatternsinregulatedwatercoursesmaythereforerepresenta discharge.Ahydraulicmodelwasthenusedtoderiveweeklywetted
rarecaseofeffectivemitigationofnegativeclimatechangeeffectson area,acriticalcomponentoftheindividual-basedpopulationmodel,
fishpopulations(PiouandPrevost,2013). fromthedischargedata.
L.E.Sundt-Hansenetal./ScienceoftheTotalEnvironment631–632(2018)1005–1017 1007
2.3.GlobalClimateModel(GCM)andRegionalClimateModel(RCM)
ClimatedatausedinthisstudywereprovidedbytheNorwegianMe-
teorologicalInstituteDNMI(Engen-Skaugen,Haugen,andHanssen-
Bauer,2008).ThesedatawerederivedfrompredictionsfromtheHadley
Centre'sHadAm3HGCMandtheMaxPlankInstitute'sECHAM4GCM.
Scenarios used were both for (1) future climates, the SRS A2 (high
CO )andB2(lowtomediumCO )emissionscenarios(2071–2100),
2 2
and(2)acontrolclimate,SRSCN(1960–1990)(Table1).Thesescenarios
areusedbytheDNMItoevaluatethefutureclimateinNorway.TheSRS
A2andB2emissionscenariosaredescribedintheIPCCFourthAssess-
mentReport(IPCC,2007)andrepresenttheclimateforcingthrough
CO emissionsforspecificscenariosoffuturedevelopmentoftheworld
2
(IPCCSRES2000),andarerespectivelysimilartotheRCP8.5andRCP
6.0scenariosusedintheIPCCFifthAssessmentReport(IPCC,2014)
(seeVanVuurenandCarter,2014).GCMdatawereata55×55kmre-
gional domain (spatial resolution) for the HadAm3H model, and at
boththisdomainandafinerdomain(25×25km)fortheECHAM4
model.GCMpredictionsoftemperatureandprecipitationwerethen
downscaled toafinerresolution (grid of1km2cells) by theDNMI
usingtheRegionalClimateModel(RGM)HIRHAM(Christensenetal.,
2007;May,2007)whichwasbiasadjustedbasedonobservationsof
localclimate(Engen-Skaugen,2007).Theuseofdatafromdifferent
GCMswithdifferentspatialdomainsallowedtheinvestigationof
how pronepredictionsofsalmonpopulationabundancewereto
theclimatemodeloutputsonwhichtheanalyseswerebased.
2.4.Hydrologicalmodel
DownscaledtemperatureandprecipitationpredictionsfromtheRCM
wereusedasinputtoahydrologicalmodeltopredictwaterinflowsalong
thecompletewatercourseoftheMandalselva.Hydrologicalmodelling
wasperformedintheopensourcemodelplatformENKI(Kolbergand
Bruland,2012).Thisgriddedmodelsimulatesinflowbasedonmeteoro-
logicalinputs(temperatureandprecipitation)andcatchmentcharacter-
istics(elevation,soilwaterstorage,distributionofsub-catchmentsand
rivernetwork).Themodelinterpolatestemperatureandprecipitation
acrossthecatchmentusingInverse-DistanceWeightingandKriging,re-
spectively.Soil-moistureandsurfacerunoff(fromexcesssoilmoisture)
are then calculated using the response function developed for the
HydrologiskaByrånsVattenbalanavdeling(HBV)model.Toachievea
bettermodelofsoilmoistureandsurfacerunoff,themodelincludesan
evaporationroutinebasedontheroutineinLandPinemodel(Rinde,
2000)andasnowaccumulation/snowmeltroutine(seeKolberg,Rue,
andGottschalk,2006).Theformerreducessoilmoistureandrunoff(par-
ticularlyinthesummer);thelatterintroducesalaginsoilmoistureand
runoffbystoringprecipitationassnowduringwinter,andreleasingthis
inspring.Themodeliscalibratedforindividualsub-catchmentswithin
theentirecatchmentbysettingthemodeltorunforoneindividual
sub-catchmentatatimeandvalidatingwiththeremainingcatchments
(iterating with the Shuffled Complex Evolution Method, with Nash
Sutcliffe'sefficiencycriteria(NashandSutcliffe,1970)).
2.5.Hydropowerproductionmodel
Thehydropowerproductiondischargeintheriverwassimulated
usingthenMAGmodel(KillingtveitandSælthun,1995;Killingtveit,
2004)basedoninputsfromthehydrologicalmodel.ThenMAGmodel
simulateshydropoweroperationsforwholesystemsconsistingofhy-
dropowerstations,reservoirsandtransfers,andpredictsriverdischarge
amongothervariables.Amodelofthehydropowersystemwithallres-
ervoirsandpowerstationsintheMandalselvawasused(Fjeldstad,
Alfredsen,andBoissy,2014)andtheoperationalstrategyofthepower
Fig.1.RiverMandalselvawiththemodelledstretchoftherivermarkedwithathickblue
systemwaskeptunchangedamongtheclimatescenarios.Watertem-
line,fromtheoutletofLaudalPowerstationanddownstreamtoKrossen(startofthetidal
zone).(Forinterpretationofthereferencestocolorinthisfigurelegend,thereaderis peraturedownstreamoftheLaudalhydropowersystemwasfound
referredtothewebversionofthisarticle.) fromregressionequationsbasedonobservedwatertemperature,air
1008 L.E.Sundt-Hansenetal./ScienceoftheTotalEnvironment631–632(2018)1005–1017
Fig.2.ThemodelpathwayfromdownscalingoftheGlobalClimateModel(GCM),viaaRegionalClimateModel(RCM),tohydrological-,hydropower-andhydraulic-models,andfinally
downtotheindividual-basedpopulationmodel,IB-salmon.
8
temperature,productiondischargeandsimulatedinflow.Watertem- < T ¼0:13 T b−6
res a
peratureinthemodelledwatercourse,T ,wascalculatedbyweighting T ¼0:0567T 2þ0:5117T þ1:5617 −6≥T ≤1 ð3Þ
w : res a a a
thetemperaturecontributionsfromnaturalinflowandthereservoir Tres¼1:0324Taþ1:1685 TaN1
immediatelyupstream(Eq.1):
Q Q
Tw¼QinTinþQresTres ð1Þ 2.6.Hydraulicmodel
tot tot
Inordertoestimatetherelationshipbetweendischargeandwetted
whereTinandTresarewatertemperaturesfromnaturalinflowandthe areainthemodelledstretch,theHEC-RAS®(2008)hydraulicmodel
reservoirrespectively,QinandQresaretherespectivedischarges,and was applied. This model can simulate discharges within rivers, for
Qtotisthetotaldischarge.Watertemperaturesinthenaturalinflow bothsteady-flowsurfaceprofiles,and1-Dand2-Dunsteadyflowcondi-
and in the reservoir were estimated from regression relationships tions.TheHEC-RASmodelwasusedtodeterminedischarge–wetted
establishedwithairtemperature,Ta(Eqs.2and3). widthrelationshipsforeachofthreestationswithdifferentchannel
8 profilecharacteristicsrepresentativeofthemodelledstretchofthe
< T ¼0:5 T b−3 river:channelprofilesweremore“U”-shaped,more“V”shaped,andin-
in a
:Tin¼0:0658Ta2þ0:5287Taþ1:5707 −3≥Ta≤1 ð2Þ termediatebetweenthesetwo.Themodelwascalibratedwithfieldob-
Tin¼0:9567Ta−0:8926 TaN1 servations of water level measurements in each station at a single
Table1
OverviewofGlobalClimateModels(GCM),emissionscenarios,spatialresolutionsofGCMoutputdata,andscenariossimulationsusedwiththeindividual-basedmodel(IBM).
GCM Emissionscenario Domain Spatialresolution(km) IBMscenarioname
HadAm3H A2+control RegClim 55×55 Had.Reg.A2
Had.Reg.CN
HadAm3H B2+control RegClim 55×55 Had.Reg.B2
Had.Reg.CN
ECHAM4 B2+control RegClim 55×55 ECHAM.Reg.B2
ECHAM.Reg.CN
ECHAM4 B2+control NorAcia/NorClim 25×25 ECHAM.Nor.B2
ECHAM.Nor.CN
L.E.Sundt-Hansenetal./ScienceoftheTotalEnvironment631–632(2018)1005–1017 1009
discharge by varying the Manning's roughness coefficient 2.7.1.Modelfunctions
(0.020–0.095)untilthemodelpredictionmatchedfieldobservations. The IBM used (IB-salmon) (Hedger et al., 2013a; Hedger et al.,
1-Dsteady-statesimulationswerethenperformedatvariousdischarges 2013b;Sauterleuteetal.,2016)isaspatially-explicitmodeldesigned
toestablishdischarge–wettedwidthcurves(Fig.3a).Curveswereex- forpredictingpopulationcharacteristicsofthefreshwaterstageofjuve-
trapolatedfordischargesoutsideofthevalidateddischargerangeofthe nileAtlanticsalmon,butalsomodelsseasurvivalandthereturnofsur-
HEC-RASsimulations.Thedischarge–wettedwidthcurvesofthethree vivingadultsfromthesea.Themodelhasatime-stepintervalofone
stationswerethentransferredtothe50mlongriversectionswithcor- week,withtheriverdividedintoaseriesof50msections.Individual
respondingchannelprofiles(determinedbyaerialphotography)and life-historytraits(growth,smoltificationtiming,fecundity,mortality)
scaledbytheratiobetweenthemaximumwettedwidthofthe50m andothercharacteristics(location,migration)aremodelledusingem-
sectioninquestion(again,determinedbyaerialphotography)andthe pirical functions (Hedger et al., 2013a; Hedger et al., 2013b). Life-
maximumwettedwidthoftherespectivestation.Thisenabledtheesti- stages modelled as individual elements are parr (juveniles in the
mationofthedischarge–wettedwidthrelationshipforeachsection river),smolts(parrthathavesmoltified,whichthenmigratetosea),
throughouttheentiremodelledstretchoftheriver.Thewettedarea searesidentadults,andreturningadults(adultsthathavereturnedto
of each section was calculated by multiplying the section wetted therivertospawn).Themaininputparametersofthemodelarewetted
widthbythesectionwettedlength(50m),givingahighlynon-linear area(dependentonchannelprofileanddischarge),watertemperature,
relationshipbetweendischargeandtotalwettedareainthemodelled spawninglocationandarea,andparrcarryingcapacity.Atthebeginning
watercourse(Fig.3b). ofasimulation,annualeggdepositionisreadfromafile,andbinned
into sections according to relative spawning habitat quality. Later
whenafullage-distributionofspawningadultshasreturned,eggsare
2.7.Atlanticsalmonpopulationmodel depositedinsectionsasafunctionofspawningfemaleabundanceand
bodymassinwhichthespawningfemalewasborn.Theweeklyparr
Anindividual-basedmodelling(IBM)approachwasusedtopredict growth is determined using a Ratkowsky-type model (Ratkowsky,
theimpactofclimatechangeandmitigationmeasuresonpopulation Lowry,Mcmeekin,Stokes,andChandler,1983)parameterizedwithex-
abundances(expressedinthisstudyastotalnumberofindividuals perimentaldataongrowth/temperaturerelationshipsforNorwegian
withinthemodelledstretch)andchangesinlifehistorycharacteristics Atlanticsalmonjuveniles(Jonsson,Forseth,Jensen,andNæsje,2001)
ofAtlanticsalmonwithinthemodelledwatercourse.Thisapproach (Eq.4).
wasusedbecauseseveralaspectsoftheprocessesaffectingthesalmon
populationwouldhavebeendifficulttoparameterizeusingpopulation 8
differentialequations(seeDeAngelisandGrimm,2014).Forexample, >>>< Mt¼Mt−1 TbTLorTNTU
coohpnaelnroagcteaelsacitanrarmyspionargttiaacllailtpyyalcoriectasyul,llleotevcdaell.frrLiovomecarlcpchrhoaafinnlgege,eslsoiicnnalwwheeattbtteeitddataaqrreueaaa,lditweypheainnchdd >>>:Mt¼ Mbt−1þb dðT−TLÞ(cid:2)110−0egðT−TUÞ(cid:3)!!ð1=bÞ T≥TU&T≤TU ð4Þ
local parr biomass, and also the spatial configuration of different
carrying capacities, channel widths and biomass. Running an IBM
allowed population characteristics to dynamically emerge from whereMistheindividualbodymassfortimet,Tistheweeklymean
heuristicfunctionsthatwerewellparameterizedatthespatiallylocal temperature,TLandTUareloweranduppertemperaturesforgrowth,
andindividuallevel. andb,dandgareparametersofthemodel.
Fig.3.Relationshipbetweendischargeand(a)wettedwidthintheHEC-RASstations,and(b)totalwettedareainthemodelledwatercourse.
1010 L.E.Sundt-Hansenetal./ScienceoftheTotalEnvironment631–632(2018)1005–1017
Bodylength,L,ispredictedfrombodymasseveryweek,usinga
powerfunctionrelationship(Eq.5).
(cid:4) (cid:5)
1=3
L¼ 105M=0:84 ð5Þ
Theannualsmoltificationprobability(appliedinweekofyear20),
SP,isestimatedforeachindividualasalogisticfunctionofbodylength,
L(Eq.6).
8
< eðp1þðp2(cid:2)LÞÞ
:SP¼1þeðp1þðp2(cid:2)LÞÞ if L≤250 ð6Þ
SP¼1 if LN250
wherep1andp2areparametersofthemodel.
Parrdensitydependentmortalityinanygivensectionisdependent
onthetotalbiomassofparrwithinthatsection(thesumofindividual
bodymassesofallparrinthesection)andthetotalcarryingcapacity
ofthesection(thetotalbiomassthatcanbesupported,whichisthe
productofthecarryingcapacityperunitareaandthetotalwetted
areaofthesection).Ifthebiomasswithinasectionexceedsthetotalcar-
ryingcapacity,surplusparrareforcedtomigrateoutofthesection:they
mayeithermigratetoanewsectionorexperiencemortality(density
dependentmortality)(Eq.7).
8>< Bs;t¼Bs;t−1 Bs;t−1bK Fdiegn.si4t.ieOsbcsoemrvpeadrepdatrorsaimbuunladtaendcpeaarrndabaugnedadniscteriabnudtiaognefdriosmtribeuleticotnroffirosmhintghejusavlemnoilne
>: BDs;st;t¼¼Ksþ(cid:2)BDs;ts−þ1K;t−(cid:3)1 Bs;t−1≥K ð7Þ abundancemodel(IBsalmon).
whereBisthetotalparrbiomass(gm−2)withinthesection,s,attime,t, GiventhattheIBMincludedprobabilisticfunctions,generatedpop-
Disthetotalparrbiomass(gm−2)ofindividualsthatdisperseoutofthe ulationsabundanceswouldvaryaccordingtosimulation,evenwiththe
sectionandsurvive,Kisparrcarryingcapacity(gm−2)ands istheparr samesetofparametervalues.However,apreliminaryanalysisofsimu-
survivalprobability.Densitydependentmortalitymayincreaseifthe lationsshowedthattheeffectofstochasticityingeneratedoutputwas
sectionbiomassincreases(duetobodymassgrowthoranincreasein small.Forexample,whenrunningtenseparatesimulationsforeachof
abundanceviarecruitmentorimmigration)orthetotalcarryingcapac- theclimatescenarios,thecoefficientofvariation(CV)forannualsmolt
ityofthesectiondecreasesduetoadecreaseinthewettedarea. abundancewasalwaysb0.2%foreachclimatescenario.ThisCVwas
negligibleincomparisontothedifferenceinsmoltabundancebetween
2.7.2.ParameterizingandrunningthemodelintheMandalselva differentclimatescenarios.
Themodelwasparameterizedtorunonapartoftheriverstretching
20kmdownstreamfromtheoutletofthemostdownstreamhydro-
powerstation(Fig.1).Dischargeintothisstretchoftheriverisregulated 2.8.Mitigationinfutureclimates
bythehydropoweroperator:withwaterenteringfromaturbineoutput
andfromanupstreambypass(minimumdischargesof3ms−1and Inordertoexplorepotentialmitigationmeasuresforclimatechange
1.5ms−1insummerandwinterrespectively).Theminimumdischarge andhowthesewouldaffectjuvenileabundance,simulationswererun
wasmanipulatedinfuturescenariostostudytheeffectofmitigation withtheimplementationofminimumdischargeregimesduringsum-
measures.Themodelwasparametrizedandvalidatedusingavailable merweeks(week20–40).Summerdischargesinmostfuturescenarios
dataonelectrofishingjuveniledensities(Fig.4)andjuvenilesizeatage were less than those of the corresponding control scenarios (see
(basedonelectrofishingatsevenstationsinOctober/November,yearly Table2).Therefore,regulatingdischargessothattheyweregreater
from2002to2010,NorwegianEnvironmentAgency),andsmoltabun- thanwhatwouldoccurnaturallyduringthisperiodallowedforcompar-
danceatage(seeUgedaletal.,2006).Habitatqualityofeachsectionin ingpotentialmitigationmeasuresforclimatechange.Fivesummertime
themodelledstretchwasbasedonfieldsurveysofsubstratetypeand minimumdischargeregimeswereexamined:2,4,6,8and10m3s−1.
size,spawninghabitatandsheltermeasurements(Finstadetal.,2007).
Spawninghabitatwasdeterminedfromsurveydata.Aspawninghabitat
Table2
qualityindex,varyingbetweenzero(nospawning)andone(maximum Thesummarystatistics(median,min,maxandrange)ofdischarge(m3s−1)incontrol
spawning),wasusedtoallocatetheinitialeggdepositionatstartofsim- (1961–1990)andfuturescenarios(2070–2100),forallyearsandforthesummerperiod
ulationstothedifferentsections. (week20–40).
Themodelwasrunwitha“burn-in”timeof10yearstoallowforthe
Allyear Summerperiod
simulationofasalmonpopulation,followedby40yearsofsimulationto (week20–40)
provideoutputdataonthepopulation.Theburn-in-timeisusedtocre-
Scenarios Median Min Max Range Median Min Max Range
atestablepopulationprocesses(seeWilliams,O'Brien,andYao,2017)–
inthecaseofIB-salmon,thisinvolvedgeneratinganage-andsize- Control 64.6 2.4 445.0 442.6 53.1 2.4 260.0 257.6
specificpopulationdistributionfromaninitialestimateofeggdeposi- (1961–1990) 67.7 1.8 490.0 488.2 52.4 1.8 311.3 309.6
66.4 2.4 462.5 460.1 55.0 2.4 267.2 264.8
tion.Predictionsfromtheburn-intimewereexcludedfromtheanalysis. Future 61.37 0.61 347.5 346.9 12.5 0.6 116.5 115.9
Foranalysisoftheeffectofchangesinclimatescenariosanddischarge (2070–2100) 60.33 1.5 521.83 520.33 18.5 1.5 197.6 196.1
regimesonpopulationabundance,onepopulationwassimulatedper 75.03 3.08 499.21 496.13 38.4 3.1 374.8 371.7
64.84 1.08 390.41 389.33 21.5 1.1 198.2 197.1
setofparametervalues.
L.E.Sundt-Hansenetal./ScienceoftheTotalEnvironment631–632(2018)1005–1017 1011
Fig.5.Modelledmeanweeklywatertemperature,dischargeandwettedareainthecontrol(1961–1991)andfuture(2071–2100)scenarios.Curvesshowmeanweeklyvaluesacrossall
outputyearsofthesimulation:controlscenarios(filledsquares);A2scenarios(circles);andB2scenarios(triangles).Hatchedareasshowthesummerseason.
Theseminimumdischargeregimesaresustainablefromthehighstor- reducedinallfuturescenarioscomparedtothecontrol,andoccurred
agecapacityoftheMandalselvasystem. afewweeksearlierintheyear.Dischargesinthesummermonthsof
June,JulyandAugustwerelowerinthefuturescenariosthaninthecon-
3.Results trol scenarios (Fig. 5). Of the future scenarios, the ECHAM.Reg.B2
3.1.Climatechangeinfuturescenarios
Table3
Meanwatertemperatures(°C)inthecontrol(1961–1991)andfutureclimatescenarios
3.1.1.Hydraulicpredictions (2071–2100).
Watertemperatureincreasedunderallfuturescenarios(Had.Reg.
Scenarios Globalcirculationmodels Temperature(°C)
A2,Had.Reg.B2,ECHAM.Reg.B2andECHAM.Nor.B2)comparedtothe
controlscenarios(Fig.5,Table3).TheHad.Reg.B2scenarioshoweda Control Had.Reg.CN 6.25(SD±5.16)
Control ECHAM.Reg.CN 6.27(SD±5.16)
slightlylowermeantemperatureduringsummerweeksthantheHad.
Control ECHAM.Nor.CN 6.27(SD±5.14)
Reg.A2scenario,butonethatwasstillseveraldegreeshigherthanthe Future Had.Reg.A2 9.05(SD±5.76)
controlscenario. Future Had.Reg.B2 8.48(SD±5.52)
Winterdischargesinallfuturescenariosweregreaterthaninthe Future ECHAM.Reg.B2 8.66(SD±5.76)
corresponding control scenarios. In contrast, the spring flood was Future ECHAM.Nor.B2 8.89(SD±5.16)
1012 L.E.Sundt-Hansenetal./ScienceoftheTotalEnvironment631–632(2018)1005–1017
Fig.6.Meanparrabundance(a)andsmoltabundance(b)accordingtoage(0+,1+,2+,3+,4+)inthecontrol(1961–1991)andfuture(2071–2100)scenarios.
scenariopredictedahigherdischargeinsummerthanthefutureHad. Themajorityofsmoltsinthefuturescenarioswere2+,comparedto3
Reg.A2,Had.Reg.B2andECHAM.Nor.B2scenarios. +and4+inthecontrolscenarios.
Insummer,wettedareawasconsiderablyreducedinthefuturesce- Theweeklydensitydependentmortalityofparr(asaproportionof
nariosHad.Reg.A2,Had.Reg.B2andECHAM.Nor.B2.Incontrast,wetted thetotalparrabundance)wasinverselycorrelatedwithwettedarea
areapredictedundertheECHAM.Reg.B2scenario,didnotchangecon- andwashighestduringthesummerperiodinallfutureclimatescenar-
siderablycomparedtothecontrolscenario.(Fig.5). ios,whenthewettedareawaspredictedtobesmall(Fig.7).Futuresce-
narios with greater summertime reduction in wetted area (HAD
scenarios)causedgreaterdensitydependentmortalitythanscenarios
3.1.2.Salmonpopulationpredictions. withsmallersummertimereductionsinwettedarea(ECHAMscenarios).
Parrabundancedecreasedinallfuturescenarios,comparedtothe
respectivecontrolscenarios(Fig.6a).Thisreductionwas,however, 3.2.Mitigationofclimatechange
small in the ECHAM.Reg.B2 scenario. Because of changes in age at
smoltificationandsubsequentemigrationtosea,theagecomposition 3.2.1.Dischargeandwettedarea
inallfuturescenariosshiftedtowardsyoungerparrincomparisonto Dischargesinthefuturescenarioswerereducedduringsummer
thecontrolscenarios.Inthefuturescenarios,the3+and4+juvenile weeks,particularlyinprojectionHad.Reg.A2,Had.Reg.B2andECHAM.
ageclass(inyears)disappearedandaverysmallproportionof2+ Nor.B2,andlesssointheECHAM.Reg.B2scenario.Implementationof
wasleft,comparedtothecontrolscenarios. minimumdischargeregimes(from2to10m3s−1)increasedsummer
Smoltabundanceinthreeofthefuturescenarios–Had.Reg.A2,Had. wettedareafortheHad.Reg.A2,Had.Reg.B2andECHAM.Nor.B2scenar-
Reg.B2andECHAM.Nor.B2–waslessthanintherespectivecontrolsce- ios(Fig.8).TheeffectwasstrongestfortheHad.Reg.A2wheresummer
narios(Fig.6b).However,smoltabundanceintheECHAM.Reg.B2sce- wettedareasincreasedfrom≈4.5×106m2underconditionsofno
nario was greater than in the respective control scenario. The age assignedminimumdischargeto≈7×106m2underaminimumdis-
compositionofsmoltschangedinallfuturescenarios,withageshifting chargeof10ms−1.Incontrast,implementationofminimumdischarge
towardsayearyoungercomparedtosmoltsinthecontrolscenarios. regimeshadlittleeffectonwettedareasfortheECHAM.Reg.B2scenario
Fig.7.Meanweeklydensitydependentmortalityofparr(proportionofthetotalweeklyparrabundance)andthecorrespondingweeklywettedareainthecontrol(1961–1990)andfuture
(2071–2100)scenarios.Barplotsandcurvesshowmeanweeklyvaluesacrossalloutputyearsofthesimulation.Hatchedareasshowthesummerseason.
L.E.Sundt-Hansenetal./ScienceoftheTotalEnvironment631–632(2018)1005–1017 1013
1014 L.E.Sundt-Hansenetal./ScienceoftheTotalEnvironment631–632(2018)1005–1017
Fig.8.Meanweeklywettedareainthecontrol(1961–1990)andfuture(2071–2100)scenarioswithaminimumdischargeinweekofyear20–40of2,4,6,8and10m3s−1inthefuture
scenarios.Curvesshowmeanweeklyvaluesacrossalloutputyearsofthesimulation.Hatchedareasshowthesummerseason.
duringsummermonthsduetosummerdischargesin thisscenario forsalmonabundanceinfutureclimateswereidentified.Thisstudysug-
beinggreaterthanthoseassignedintheminimumdischargeregimes. geststhattheabundanceofAtlanticsalmoninfutureclimateswillde-
creasefortheregionmodelled,andelucidatesmechanismsimportant
3.2.2.Parrandsmoltabundance forregulationofjuvenileAtlanticsalmonindividuals.Lowwaterdis-
WhenrunningtheIBMwitharangeofdifferentminimumdischarge chargeinsummerwasidentifiedasapossiblebottleneck,andsimula-
regimes,parrandsmoltabundanceincreasedwithincreasingminimum tionofdifferentminimumdischargeregimesshowedthatchangesin
dischargeinallscenariosexceptfortheECHAM.Reg.B2scenario(Fig.9). riverregulationmaybeapossiblemitigationmeasure.Theexactchange
Theincreaseinabundanceofbothparrandsmoltoccurredwhenmin- inAtlanticsalmonabundancedependedontheGCMandGCMdomain
imumdischargeinsummerwas4m3s−1orgreater.TheECHAM.Reg.B2 usedtosupplypredictionsofairtemperatureandprecipitation,sofur-
scenariowasnotstronglyinfluencedbythedifferentminimumdis- theradvancesinGCMmodellingarerequiredtoincreasetherobustness
chargeregimesbecausethepredicteddischargeinsummerwashigher ofthepredictionofhowanygivenAtlanticsalmonpopulationwillre-
thanthatassignedintheminimumregimes. spondtoclimatechange.
Reducedparrabundancewasfoundinallfutureclimatescenariosin
4.Discussion comparisontocontrolscenarios,althoughthereductionwassmallfor
theECHAM.Reg.B2scenario.Threeoutoffourclimatescenarios(Had.
4.1.Effectofclimatescenariosonfuturefreshwatersalmonabundance Reg.A2,Had.Reg.B2andECHAM.Nor.B2)predictedreducedsmoltabun-
dance,whereasthefourth(ECHAM.Reg.B2)predictedincreasedsmolt
In this study, the effect of future climate change on an Atlantic abundance.Climateoutputsderivedfromthethreeclimatescenarios–
salmonpopulationinaregulatedriverwasmodelled,andbottlenecks Had.Reg.A2, Had.Reg.B2 and ECHAM.Nor.B2 – resulted in a strong