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Highlighting the Role of Groundwater in Lake– Aquifer Interaction to Reduce Vulnerability and Enhance Resilience to Climate Change

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Dr-Yohannes Yihdego Woldeyohannes at La Trobe University
Dr-Yohannes Yihdego Woldeyohannes
John A. Webb at La Trobe University
John A. Webb
Babak Vaheddoost at Bursa Technical University
Babak Vaheddoost
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A method is presented to analyze the interaction between groundwater and Lake Linlithgow (Australia) as a case study. A simplistic approach based on a “node” representing the groundwater component is employed in a spreadsheet of water balance modeling to analyze and highlight the effect of groundwater on the lake level over time. A comparison is made between the simulated and observed lake levels over a period of time by switching the groundwater “node “on and off. A bucket model is assumed to represent the lake behavior. Although this study demonstrates the understanding of Lake Linlithgow’s groundwater system, the current model reflects the contemporary understanding of the local groundwater system, illustrates how to go about modeling in data‐scarce environments, and provides a means to assess focal areas for future data collection and model improvements. Results show that this approach is convenient for getting first‐hand information on the effect of groundwater on wetland or lake levels through lake water budget computation via a node representing the groundwater component. The method can be used anywhere and the applicability of such a method is useful to put in place relevant adaptation mechanisms for future water resources management, reducing vulnerability and enhancing resilience to climate change within the lake basin.
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Hydrology2017,4,10;doi:10.3390/hydrology4010010
www.mdpi.com/journal/hydrology
Article
HighlightingtheRoleofGroundwaterinLake–
AquiferInteractiontoReduceVulnerabilityand
EnhanceResiliencetoClimateChange
YohannesYihdego
1,2,
*,JohnAWebb
2
,andBabakVaheddoost
3
1
SnowyMountainsEngineeringCorporation(SMEC),Sydney,NewSouthWales2060,Australia
2
EnvironmentalGeoscience,LaTrobeUniversity,Melbourne,Victoria3086,Australia;
john.webb@latrobe.edu.au
3
HydraulicLab.,IstanbulTechnicalUniversity,Istanbul34467,Turkey;babakwa@gmail.com
*Correspondence:yohannesyihdego@gmail.com
AcademicEditor:AbdonAtangana
Received:22December2016;Accepted:8February2017;Published:13February2017
Abstract:AmethodispresentedtoanalyzetheinteractionbetweengroundwaterandLake
Linlithgow(Australia)asacasestudy.Asimplisticapproachbasedona“node”representingthe
groundwatercomponentisemployedinaspreadsheetofwaterbalancemodelingtoanalyzeand
highlighttheeffectofgroundwateronthelakelevelovertime.Acomparisonismadebetweenthe
simulatedandobservedlakelevelsoveraperiodoftimebyswitchingthegroundwater“node“on
andoff.Abucketmodelisassumedtorepresentthelakebehavior.Althoughthisstudy
demonstratestheunderstandingofLakeLinlithgow’sgroundwatersystem,thecurrentmodel
reflectsthecontemporaryunderstandingofthelocalgroundwatersystem,illustrateshowtogo
aboutmodelingindata‐scarceenvironments,andprovidesameanstoassessfocalareasforfuture
datacollectionandmodelimprovements.Resultsshowthatthisapproachisconvenientforgetting
first‐handinformationontheeffectofgroundwateronwetlandorlakelevelsthroughlakewater
budgetcomputationviaanoderepresentingthegroundwatercomponent.Themethodcanbeused
anywhereandtheapplicabilityofsuchamethodisusefultoputinplacerelevantadaptation
mechanismsforfuturewaterresourcesmanagement,reducingvulnerabilityandenhancing
resiliencetoclimatechangewithinthelakebasin.
Keywords:lake–groundwaterinteraction;waterbalance;wetland;ecosystem;hydrology;climate
change;adaptation
1.Introduction
Whenwaterdemandexceedswateravailability,waterscarcityisinevitable.Climatechange,
populationgrowth,andeconomicdevelopmentaddtowaterscarcityrisksmainlyinaridregions[1–6].
Concerteddatacollectioneffortsareusuallylackingindevelopingcountries,especiallywhenit
comestogroundwatersystems.Manylakestudiesencountereddifficultiesinestimatinggroundwater
ordefiningaplausible,appropriateconceptualmodeloftheaquifersystemathand[7–11].Prudent
understandingofthe(ground)watersystemformsamajorinhibitingfactorforeffectivewater
management.Watermanagementbasedonsuchmodelsmayhaveunintendedorevendetrimental
consequences.
Wetland’simportancehasbeenrecognizedbytheRamsarconventionduetothepossible
impactsitmayhaveonpeopleinthenextdecadesandhowitsconservationcanamelioratepoverty
conditions.Recentresearchhasbeendonetoassesstherelationshipbetweengroundwaterand
surfacewater,duetothedependabilityofecosystemsongroundwatercontributions[12–16].The
pressureongroundwaterresourcesbytheseactivitieshasalertedtheinterestofenvironmental
Hydrology2017,4,10 2of18
authorities,whoneedtoassessthehydrodynamicbetweenwetlands,adjacentgroundwater,and
surfacewater[17–64].Manyactivitieshavebeencarriedouttovalidatethehydrogeological
conceptualmodelinthewetlandvicinity.Agroupofobservationwellsinstalledaroundthewetland
hasenabledtheassessmentthroughnon‐linearDarcy’sexpressiontoapproximatevolumesof
rechargeanddischargefromtheaquifertothewetland.
Groundwaterisimportantforunderstandinglakesystemsduetoitsinfluenceonalake’swater
budget,nutrientbudget,andacidbufferingcapacity[21,22].Asaresult,groundwaterflowstoand
fromlakeshaveoftenbeenestimatedusingsimpleflowgridsandone‐dimensionalDarcian
calculations.Groundwaterinteractionwithlakescanbespatiallyandtemporallyvariable,however.
Otherwaterbalanceapproacheshavealsobeenusedthatemployedarepresentativegroundwater
headunderneaththelakeforcalculatingthefluxovertheentirelakearea.Themostsophisticated
wayofinvestigatinglake–groundwaterinteractionsisbyexplicitlyincludinglakesingroundwater
flowmodels[10,28–30].Waterresourcesmanagersrelyontoolsthatassistwithstreamliningsupply
anddemand[25–27,31,49].
Modelinglake–aquiferinteractionisanessentialmilestoneinlimnologicalstudies[32].
Groundwaterisoneofthemostimportanthydrologicalvariablesofthewaterbudgetinlakes;
however,thisvariable,duetoitsnature,cannotbeaddressedwithoutuncertainties[32,33].Many
scientists,however,havetriedtomodeltheinteractionbetweengroundwaterandsurfacewater
usingvariousstatistical,conceptual,orempiricalmodels(e.g.,Lohman[34];Edelman[2];Lewiset
al.[35];Postetal.[36];Jakovovicetal.[37];Yihdegoetal.[38]).
Theroleofgroundwaterinwetlandwaterbudgetisofgreatconcerntoecologists,water
managers,andenvironmentalscientists[39].Groundwateriscriticalforunderstandingmostlake
systemsbecauseitinfluencesalake’swaterbudgetandnutrientbudget[40].Severalstudieshave
reportedontheuseofamassbalanceapproachtosimulatelakelevelsfromhydrologicaland
meteorologicaldata[41,42].Therehavebeenmanydifferentempirical,analytical,andnumerical
approachesforsimulatinglake–groundwaterinteractions(e.g.,fixedlakestages,High‐Knodes,and
LAK3packagethroughnumericalmodeling).Theadvantagesanddisadvantagesoftheseapproaches
havebeendocumentedbymanyresearchers.ManyhighlysophisticatedmodelslikeLAKPackage
arenotwidelyusedduetodataandresourceconstraints[17,35,43].ThelimiteduseofLAKPackage
isattributabletothelackofstandardizationandassociatedgraphicaluserinterfaces,complexthree‐
dimensionaldiscretizationanddataneeds,andspatialandtemporalcomplexityinherentin
includingsurfacewaterfeaturesinagroundwatermodel.Whilesophisticatedapproachesoffermore
detail,theiradvantagesmaybeoffsetbyassociatedcomplexityandeveninstabilityofthesolution
procedure[44].Consequently,afull‐featuredLAKpackageisnotanautomaticchoicefor
practitioners,regulators,andthewidercommunity;rather,thechosenmethodshoulddependon
boththehydrogeologicalconditionsandthemodelingobjectives[39,45–48].
WithintheGlenelgHopkinsCatchmentManagementAuthorityarea,LakeLinlithgowandthe
nearbyshallowlakesareconsideredtobeofenvironmentalimportanceduetothedrainageand/or
degradationofmanyotherwetlandsthroughoutthisregion[32,52,53].However,inrecentyearsLake
Linlithgowhasbeenaffectedbyalgalbloomsandhighlakesalinities,andhasbecomedryoverthe
summerduringmostyearssince2000.ThenormalseasonalfluctuationforLakeLinlithgowis10,000
μS/cminwinterandspring,risingto16,000μS/cminlateautumn[60].However,during1999,the
lowestsalinitylevelsrecordedwere27,000μS/cmandtheypeakedat58,000μS/cm(seawater)inthe
autumn,beforesharplyincreasingto63,000μS/cmpriortodryingoutinFebruary2000.Extensive
researchwascarriedouttocharacterizefuturechangesingroundwatersalinizationwithinthebasalt
aquifersintheHamiltonarea,includingLakeLinlithgow,usinghydrogeological,chemical,and
isotopictechniques[14,32,50].Theaimofthisstudyistoimproveourunderstandingoflake–aquifer
interactionthroughanalyzingthegroundwatercomponentofLakeLinlithgowasacasestudy.
Hydrology2017,4,10 3of18
2.StudyArea
LakeLinlithgowcoversanareaof9.65km2andisthelargestinaseriesofhighlytomoderately
salinewetlands,locatedapproximately16kmeastofHamilton,westernVictoria(Figure1).Thelake
isfedbyBoonawahCreekandthereisnosurfaceoutlet.ThecatchmentareaforLakeLinlithgowis
85km2.ThevolcanicplainssurroundingLakeLinlithgowaretopographicallysubdued,comprising
aflattoundulatingplainwithaverageelevationof~200mAustralianHeightDatum(AHD),gently
increasinginelevationfromsouthtonorth(Figure1).Theplainisdottedwithseveralprominent
eruptionpointsandanumberofsmallerlow‐reliefvolcaniccones.Thevolcanicplainisdeeply
dissected(upto80m)bystreamsonitswesternandsouthwesternmargins.
Figure1.DigitalelevationmodelshowingtherelativeelevationoftheterrainsurroundingLake
Linlithgow.
Thepre‐EuropeanvegetationcomprisedRiverRedGum,SwampGum,MannaGum,
Blackwood,andLightwoodalongmoderatelyinciseddrainagelines,andTea‐tree,SilverBanksia,
andLightwoodontheblackself‐mulchingclaysassociatedwiththemarginsofswampsandlakes
(i.e.,BuckleySwamp)[55].Thepoorlydevelopeddrainagelinesassociatedwiththeheadwatersof
GrangeBurn,VioletCreek,andMuddyCreekwereoftendominatedbygrasslands.LakeLinlithgow
andLakeKennedy(Figure2)werepresumablyvegetatedwithsalt‐tolerantspeciessuchasplantain,
Australiansaltgrass,andstreakedarrowgrass,andarerelativelyunalteredsinceEuropean
settlement.
Themostsignificantlandusechangeoccurredbetween1900and1920,withtheconversionto
introducedpasture[55].
Hydrology2017,4,10 4of18
Figure2.Landsatimagery(takeninFebruary2004)showingtheLakeLinlithgowarea.
2.1.Hydrology
Theaveragerainfalloverthecatchmentisabout689mm,recordedatHamiltonResearchCentre.
Rainfallrecordsoverthelast45yearsclearlyshowthatrainfallishighlyvariable,butoverthelast
decadetherehasbeenasubstantialdrop,withannualrainfallbelowthelong‐termaverage.
Maximumrainfallisreceivedoverwinter(thewettestmonthsareJulyandAugust)andexceedsor
equalsevaporationforMaytoSeptember,whengroundwaterrechargeismostlikelytooccur.June
toSeptemberrainfallcontributes45%tothemeanannualprecipitationinthecatchment;pan
evaporationishighestfromOctobertoAprilandtotals1053mm.
2.2.Geology
ThebasementgeologyoftheareaconsistsofCambrianvolcanics,EarlyPaleozoicturbidites,and
Siluriansandstone,intrudedbyDevoniangranites(Figure3).Thesebasementrocksdonotoutcrop
aroundLakeLinlithgow.
Thedisruptionofthedrainagesystembythebasaltflowsformedthelakesinthecenterofthe
catchment.LakeLinlithgowsitsonfirstphase(~4Ma)basalts,andisencircledbysecondphase(~2
Ma)basaltflows,formingarelativelyflatlakebedwithsteepbanks.Lunettesoccurontheeastern
andnortheasternmarginofthelake,~4mabovethecurrentmaximumlakelevel[50].Thereisalocal
groundwaterflowsystemaroundLakeLinlithgow[32,55],andaregionaltointermediate
groundwaterflowsysteminthesurroundingvolcanicplain(Figure4).
PotentiometricsurfacecontoursfortheNewerVolcanicsaquiferindicatethatLakeLinlithgow
isagroundwaterthrough‐flowlake,withgroundwaterflowenteringthelakefromtheeastand
leavingtowardsthewest(Figure4).Groundwaterentersthroughaseriesofspringsandseepsalong
theeasternlakemargin,mostlyfromthesecondphasebasaltaquifer,whichterminateshere[50,54–
56].HydrographsfromboresinthebasaltaquifersurroundingLakeLinlithgow(Figure5)showa
strongcorrelationwiththelakelevel.Thedecliningtrendof20cm/yearfrom1997to2001isclearly
aresponsetobelow‐averagerainfall[14].Thepalaeosolslyingbetweensuccessivebasaltphases
hinderoutflowfromthelakeandactasbarrierstogroundwaterflow(Figure6).
Hydrology2017,4,10 5of18
Figure3.GeologicalmapoftheareasurroundingLakeLinlithgow.
Figure4.LakeLinlithgowandassociatedwetlands(gray)andbasaltaquiferpotentiometricsurface
contours(mAHD).
Hydrology2017,4,10 6of18
Figure5.Comparisonofborehydrographswiththelakelevel.Thepositionsoftheboresareshown
inFigure4;screeneddepthofB57685isfrom82.5to88.5m.
Figure6.West–EastandSouth–NorthhydrogeologicalcrosssectionsthroughLakeLinlithgow[50,55].
2.3.LakeHydrology
MonthlylakelevelandsalinitydataareavailableforLakeLinlithgowfrom1964to2007,when
thelakedriedout,althoughthesalinitymeasurementsareavailableonlysporadicallyfrom1964to
1974(Figure7).LakeLinlithgowhasamediandepthof1.45m,buttypicallyvariesseasonallybyup
to1m(Figure7).AgraphofcumulativedeviationofrainfallfromthemeanforHamiltonResearch
Stationshowsastrongcorrelationwithlakelevels;peaksinthelakelevelgenerallycorrespondto
periodsofabove‐averagerainfall(Figure7).Inyearsofbelow‐averagerainfall,thelakedriesout
duringsummer,asoccurredinJanuary–Aprilof1983,2000,and2001,butafterasuccessionofhigh
rainfallyears,lakelevelsrisesubstantially(Figure7).
Hydrology2017,4,10 7of18
Figure7.Lakelevelwithvariationinrainfall.
LakeLinlithgowistypicallysaline(median12400μS/cm).Thelargeseasonalvariationinlake
leveliscorrelatedwithasubstantialrangeinlakesalinity(3300–98,900μS/cm;Figure8).Thereisa
generaltendencyforLakeLinlithgowtobecomemoresalineuntilthelakedriesout(Figure8).
Figure8.Relationshipbetweenmeasuredlakedepthandsalinityfortheperiod1964–2007.
3.Methodology
Aspreadsheetmodelwasemployedtoanalyzeforthecurrentstudy[32].Anexplanationofthe
modelisgivenbelow.
3.1.WaterBalanceModel
Monthlytime‐stepmodelingofthelakewaterlevelwascarriedoutusingExcelspreadsheets,
andtheresultingwaterbudgetwasusedasinputforthesaltbudget.Thelakewaterbalanceis
calculatedbyestimatingallthelake’swatergainsandlosses,andthecorrespondingchangein
volumeisexpressedas:
Volumechange=Surfacewaterinflow+Rainfall+Q
in
−Evaporation−Surface
wateroutflow−Q
out
,(1)
whereQ
in
isthegroundwaterinflowandQ
out
thegroundwateroutflow.
Thenetfluxofthegroundwaterflow(Q
in
−Q
out)
canbecalculatedas:
Q=C(H
lake
−H
aquifer
)inm
3
∙month
−1
,(2)
Hydrology2017,4,10 8of18
whereCistheconductanceofthelakebedsediments(m2∙month−1)andHisthewaterlevelinthelake
andsurroundingaquifer(m).Hydraulicconductivitycanbeexpressedas
C=K×A/Linm2∙month−1,(3)
whereKisthehydraulicconductivityofthelakebedsediments(m∙month−1)andAandLarethelake
area(m2)andlakebedsedimentthickness(m),respectively.Theformulationissimilartothatusedin
theriver(RIV)andlakepackages,whichspecifiesthefluxthroughtheriverbedorlakebedasa
functionofstage,potentiometricheadintheconnectedcells,andtheriverbedorlakebedconductance
inwhichthelakebedconductance,COND,ateachcelliseitherspecifiedbytheuserinthelake
packageinputfile,orcalculatedfromthelakebedgeometryandhydraulicconductivity.Aswiththe
riverpackage,flowfromthelaketothegroundwaterintheLAKpackageislimitedwhenthehead
inacellfallsbelowthelakebedbottom.Also,ifthestageofthelakeisbelowthetopofthelakebed,
thelakecellisdryandseepageintothegroundwateriscutoffforthatcell.Inthisspreadsheetmodel,
asimilarprocedurewasappliedtoformulatetheboundaryconditionsthatcontrolthesolutionof
potentiometrichead.
Thetemporalareaisestimatedfromthelakestage–area–volumerelationshipbuiltin[42],while
thelakebedsedimentisestimatedusingthesoilerosionmodelofthecatchment.Thewaterlevelin
thesurroundingaquiferisupdated(Haquifer‐new)usingtheinflowandoutflowcalculatedforthe
previousmonth(Haquifer‐pre)is
Haquifer‐pre=Q/A×Sy(m)(4)
Haquifer‐new=Hin‐old+Hin(m),(5)
whereAisthesurfaceareaoftheinteractingaquiferandSyisthespecificyieldoftheaquifer.The
modelrequiresknownhydrometeorologicaldata(inflowfromtherivers,rainfallonthelakesurface,
aquiferarea,andevaporationfromthelake)andestimatestheunknownnetgroundwaterfluxdue
tointeractionofthelakewiththesurroundingaquiferbycomparingthesimulatedandrecordedlake
levelsandcalculatingaresidual.Themodelwascalibratedusingsolveranditeration.Thenet
groundwatercomponentisrepresentedasanodeintheequationtoswitchonandoffandassessthe
significanceofthegroundwaterintheoveralllakewaterbudget,asexplainedthroughthefluctuation
andlakeleveltrendovertime.
3.2.LakeWaterBudgetandModelParameters
3.2.1.LakeStorage
Bathymetrydataarenotavailableforthelake;however,thearea–depthrelationshipwas
estimated.Thelakeareawasmeasuredfrom12Landsatimagestakenbetween1972and2004and
correlatedwiththemeasuredlakedepthinthemonthwhentheimagesweretaken(Figure9).The
lakeareawasestimatedusingENVIsoftware(withthresholdmethodandgrowbutton);thisismore
accuratethantheGIS(vector)methodusedby[50]becauseitfindssimilarpixelsselectedforthe
waterbodyandtherebybetteridentifiesthenaturalboundary.Thelineofbestfit(withr2=0.99)to
thearea/depthdatagivestherelationshipforLakeLinlithgowas:
A(t)=1.545(D(t))5−23.09(D(t))4+135.9(D(t))3−393.7(D(t)2+561.1(D(t)−305.8.(6)
Thepolynomialrelationshipbetweenlakearea(A(t))andlakedepth(D(t))isduetothelake’s
relativelyflatlakebedandsteepbanks,andgivesabetterfitforthedepth–arearelationshipthanthe
logarithmfunctionusedby[50],whichhadasmallerr2value.
Thelakevolumeatthebeginningofagivenmonthcanthereforebecalculatedfromthedepth
andareaattheendoftheprecedingmonth.Thelakedepthmustbefirstadjusted,becausethe
minimumreadingonthebaseofthelakelevelgaugeis~1.3m.Thus,thisvalueisdeductedfromthe
recordedlakeleveltogetthetruelakedepth.Forthemodeling,theinitialvolumeinSeptember1964
wascalculatedfromthemeasuredwaterdepthinthatmonth(2.79m).
Hydrology2017,4,10 9of18
Figure9.RelationshipbetweenlakeareaderivedfromLandsatimageryandmeasuredlakedepth.
3.2.2.PrecipitationonLake
Monthlyprecipitationistakenfromthenearestrainfallstation(HamiltonResearchCentre),
whichliesapproximately13kmsouthwestofLakeLinlithgow,andismultipliedbythelakeareato
givethevolumeofdirectrainfallintothelake.Theprecipitationdataareonlyavailableupuntil2001;
valuesforthesubsequenttimeperiodhavebeenextrapolatedfromtherainfallatCarinyastation(~65
kmfromHamiltonResearchCentre)usingthecorrelationbetweenrainfallatHamiltonResearch
CentreandCarinya.
3.2.3.SurfaceFlowtotheLake
SurfaceinflowtoLakeLinlithgowisreceivedthroughBoonawahCreek,forwhichthereareno
gaugingdataavailable.Surfaceinflowscan,however,beestimatedbythetanhcumulativesurplus
rainfallapproach[42]usingtheGrangeBurnflow,whichisgaugedatMorgiana(gaugeno.238219;
Figure1),becausetheGrangeBurnandLakeLinlithgowcatchmentsareadjacentandhavesimilar
topography,soiltype,vegetation,landuse,andrainfall.Thetanhcumulativesurplusrainfall
approachprovidesatoolwithwhichrunoffinGrangeBurncanbepredictedforanygivenmonthly
precipitation/evaporation(Figure10).Usingareascaling,thiscanbeconvertedtoaflowinBoonawah
Creek;thecatchmentareasforBoonawahCreek(atLakeLinlithgow)andGrangeBurnatMorgiana
are85km
2
and997km
2
,respectively.
Figure10.StreamflowmodelingfortheGrangeBurnatMorgiana(gaugeno.238219).Thelocation
ofthegaugestationisshowninFigure1.
Hydrology2017,4,10 10of18
3.2.4.OutflowfromtheLake
TheevaporationfromLakeLinlithgowisestimatedusingmonthlyevaporationdataatHamilton
ResearchStation,wherepanevaporationwasmeasuredfrom1968toJune2000.Theevaporationdata
fromJuly2000hasbeenextrapolatedfromWhiteSwanReservoirstationbyestablishingacorrelation
betweenpanevaporationatHamiltonResearchCentreandWhiteSwanReservoir.Evaporationdata
from1964to1968arelackingatWhiteSwanReservoir,soevaporationforthisperiodwasestimated
fromthecorrelationofevaporationattheHamiltonResearchCentrewiththatattheMelbourne
regionaloffice.Alocalcalibrationcoefficientwasusedtoadjusttheseasonalpanevaporationdata
fromHamiltonResearchCentreforthebestfitofthemodel.Theoptimizedlocalcalibration
coefficientis1.18;thistakesintoaccountthespatialvariationinposition,elevation,andstorageeffect
betweenHamiltonResearchCentreandLakeLinlithgow.
3.2.5.GroundwaterInflowandOutflowEstimation
Groundwaterinflow/outflowwasinitiallyestimatedusingDarcy’sLaw.Thewidthsofthe
groundwaterinflowandoutflowzonesalongthelakeperimeterare~6.1kmand3.6kmrespectively.
Thecross‐sectionalareaiscalculatedbymultiplyingthewidthofthegroundwaterinflow/outflow
zonebythesaturatedthickness(8m)ofthefirstphasebasaltaquiferinhydrauliccontactwiththe
lake.Theaveragehydraulicconductivityofthebasaltaquifer(0.09m/day)isderivedfrom
groundwaterflowratescalculatedusinggroundwaterradiocarbonagesbyBennetts[50].The
hydraulicgradienteithersideofthelake,deriveddirectlyfromthepotentiometriccontours,is3.7×
10−3(Figure4).SimilartoLakeBurrumbeet,anaveragevalueof0.000864m/dhasbeenchosenforthe
permeabilityofthelakefloorinordertoestimatethegroundwateroutflowthroughthelakebed.
Usingthesefigures,themonthlygroundwateroutflowandinflowfrom/tothelakewereestimated
as~0.29MLand~0.48ML,respectively.
Modelcalibration,carriedoutbyadjustinginputparameterssothatthesimulatedlakelevelsfit
theobservedlakelevel,givesthenetgroundwaterflowestimation.Inthiscasetheoptimizedlake–
aquiferconductance(C)valueis2.71×103m2/day,whichisequivalenttoahydraulicconductivity
valueoflakeshoresedimentsofabout7m/day;thisfallswithintherangeofprevioushydraulic
conductivityestimatesforthesesandbeaches.Theoptimizedspecificyieldandaquiferareaare0.1
and45km2,respectively[32].
Theoptimizedspecificyieldcomparesreasonablywithpreviousspecificyieldestimatesofthe
basaltaquifer,whichrangeupto0.18[51].Theaquiferareainhydrauliccontactwiththelake(45
km2)issignificantcomparedwiththecatchmentareaofLakeLinlithgow(85km2),indicatingthat
lakelevelfluctuationsdirectlyimpactabouthalfofthebasaltaquiferinthecatchmentarea.
Thebestpossiblefitofthewaterbudgetmodelwasattainedat~0.37×106Land3.1×106L
monthlyaveragegroundwateroutflowandinflow,withtheexceptionofdryperiods.Thevalueof
optimizedgroundwateroutflowcomparescloselywiththeinitialestimate.
4.ResultsandDiscussion
4.1.LakeWaterLevels
Thepredictedlakelevelsshowgoodagreementwiththemeasuredlakeleveldata(Figure11),
withanr2valueof~0.85(Figure12).Thesumofthesquareddifferencesdidnotexceed0.4m,except
forafewoutliers(Figure13).
Thewaterbalanceshowsthatthemajorinfluenceonlakelevelsisevaporation(Table1),
accountingforanaverageof54%ofthetotalwaterbudget.Ithasthegreatestinfluenceinsummer,
reachingupto99%ofthetotalbudget,butdecreasedduringdryperiods,becauseitisproportional
tothelakearea[32].Groundwaterinflowcontributesabout1%ofthelakewaterbudget.Eventhough
groundwateroutflowisminor(0.1%),itdominatesthelakewaterlossesduringearlywinter(June),
atimewhenevaporationisverysmall(Figure14).
Hydrology2017,4,10 11of18
Figure11.LakeLinlithgowmeasuredwaterlevelsandacomparisontomodeledresultsfortheperiod
1964–2007.
Figure12.CorrelationbetweenobservedandcalculatedlakeelevationinAHDm.
Table1.Averagelong‐term(1964–2007)monthlycontribution(inpercent)ofeachLakeLinlithgow
waterbudgetcomponenttotheoveralllakewaterbudget.
Evaporation
(%)
Groundwater
Outflow(%)
Precipitation
(%)
Surface
Inflow(%)
Groundwater
Inflow(%)
540.13781
198
198
199
199
200
200
201
201
Oct-63 Apr-69 Sep-74 Mar-80 Sep-85 Mar-91 Aug-96 Feb-02 Aug-07
Lake level (m AHD)
observed lake level predicted lake level
R2 = 0.8446
197.0
197.5
198.0
198.5
199.0
199.5
200.0
200.5
201.0
197.0 197.5 198.0 198.5 199.0 199.5 200.0 200.5 201.0
Actual lake leve l (m AHD)
Predicted lake level (m AHD)
Hydrology2017,4,10 12of18
Figure13.Temporaldistributionofsquaredifferencebetweenobservedandcalculatedlakelevels.
Figure14.AnnualwaterbalanceofLakeLinlithgow(1965–2006).
Thus,LakeLinlithgowisagroundwaterthrough‐flowlake,andthelake–groundwater
interactionisimportantsinceitaffectstheenvironmentalhealthofthismajorwetland.
4.2.WaterBudgetErrorsandSensitivityAnalysis
Asensitivityanalysisshowsthatthemodelismostsensitivetoevaporationandprecipitation
(Figure15),andsurfaceinflowtoalesserextent.However,themostlikelysourceoferrorwithinthe
modelisestimatingtheungaugedsurfaceinflow(i.e.,BoonawahCreek)bythetanhrelationship
fromGrangeBurn;thiscouldbeinerrorifthethresholdvalueatwhichcumulativesurplusrainfall
becomesrunoffinGrangeBurnatMorgianaisdifferenttothatofthesmallercatchmentofBoonawah
Creek.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Dec-62 Jun-68 Dec-73 May -79 Nov-84 May-90 Oc t-95 A pr-01 Oct -06
Lake level difference square (m)
Hydrology2017,4,10 13of18
Figure15.RelativesensitivityofLakeLinlithgowwaterbalancemodeltochangesinthewaterbudget
components.
4.3.Interpretation
Toassesstheeffectofgroundwatercomponent,whichisthemainobjectiveofthispaper,further
analysiswascarriedoutthroughasecondmodelrunwithoutagroundwatercomponent.The
calculatedwaterlevelsfollowedthesametrendasobservedlakelevels,butwereonaveragelower
thantheobservedvalues.Thelowercalculatedlakelevelimpliesthatthetotalobservedlakestorage
islowerthanwouldbeexpectedifthelakedidnothavegroundwaterinflow(Figure16).
Figure16.Observedandsimulatedlakelevel(deactivatingthegroundwaternodefromthemodel).
Theeffectofgroundwaterisevidentfromthegraph.Thesimulatedvolumetriccomponentof
thegroundwaterofthelakewaterbudgetisshowninFigure17.Thebaseflowisestimated(G=260)
(Figure10)usingthetanhcumulativesurplusrainfallapproach.Thissimplisticmodelseemsa
197.5
198.0
198.5
199.0
199.5
200.0
200.5
201.0
Oct-63 Apr-69 Sep-74 Mar-80 Sep-85 Mar-91 Aug-96 Feb-02 Aug-07
Lake level (m AHD)
observed lake level predicted lake level
Hydrology2017,4,10 14of18
reliableandmodesttooltoseethelake–groundwaterrelationshipataglanceandwillbeusefulif
constrainedbythemassbalanceandreliableestimateofparameters[9,32,51].Thisapproachcould
beadoptedformuchwetlandmanagement.Thismodelwillgiveinsightintothegroundwater–
surfacewaterrelationshipandguideourfuturedatagatheringfromsensitiveparameters.The
parametersusedaresmallandconvenientformanagementpractice.
Figure17.VolumetricgroundwatercomponentintheLakeLinlithgowwaterbudget.
Theneedforstudyoftheinteractionoflakesandgroundwaterstemsfromthefactthat
groundwateriscommonlyignoredoristheresidualterminlakewater.Becauseallvariablesina
lakewaterbudgetarerarelymeasured,itisimpossibletoadequatelyevaluatetheerrorsorthe
residual.
Evenifgroundwaterisincludedaspartofalakewaterbalancestudy,improperplacementof
wellscanleadtoamisunderstandingoftheinteractionbetweenlakesandgroundwater.Nomatter
howmanywellsareusedtodefinethewatertableinthesettings,themapsshowsagradienttowards
thelake,andtherewouldbenowaytodetecttheout‐seepagethatoccurs.Ifonlyoneorafewwells
areplacednearalake,thegroundwaterflowsystemwouldnotbeadequatelydefinedandwouldbe
subjecttomisinterpretation[16,55,58].
Overalongperiodoffallinglakelevelsfrom1968uptothedroughtperiod1982/3,thesimulated
lakelevelsaremorethanonemeterlowerthantheobservedlevelsduetothelackofgroundwater
inflow.However,thereisagoodmatchbetweenthesimulatedandactuallakelevelsduringaperiod
ofrisinglakelevels,indicatingthatthegroundwateroutflowfromLakeLinlithgowrechargingthe
aquiferisverysmall.Whenthegroundwatercomponentisaddedtothemodel,itaccuratelyfollows
theobservedlevels(Figure11).
GroundwaterisanimportantcomponentofthewaterbalanceofLakeLinlithgow,anditsflow
isinfluencedbythelakelevel.Ifthelakelevelrises,groundwateroutflowandthereforelakerecharge
intothesurroundingaquiferwillincrease.Ifthelakeleveldecreases,groundwaterdischargefrom
theaquiferintothelakewillincrease.Thisinteractioncausesinertiainthelake–groundwatersystem,
delayingreactionstoexternal(meteorological)stresses(Figure17).Thisphenomenoncanbeshown
usingthelakewaterbalancemodel.Ifthemodelisrunwithnogroundwatercomponents,it
overshootsafterperiodsofriseorrecession(Figure16).
-150
-100
-50
0
50
100
150
Dec-62 Jun-68 Dec-73 May-79 Nov-84 May-90 Oct-95 Apr-01 Oct-06
Volume (ML )
Hydrology2017,4,10 15of18
5.Conclusions
Thispaperaimsatanunderstandingofthegroundwatersysteminwetlands,viaacasestudyat
LakeLinlithgow,Australia.
Whenthemodelwasrunbasedonsurfacewaterbalancecomponents(viaswitchingthe“node”
off,i.e.,intheabsenceofgroundwater),therewasaprogressiveseparationbetweenobservedand
calculatedlakelevels.Thecalculatedlevelsimplythatthelakeshouldaccumulatemorestoragethan
isactuallyobserved.Thisseparationcouldnotbeattributedtosystematicerrorsinsurfacerunoff,
precipitation,andevaporationmeasurements.Rather,itisanindicationofsubterraneanwaterfluxes
(i.e.,groundwater).
Theresultsofsuchanalysisleadtoabetterunderstandingofthegroundwatersystemwithina
lake/wetlandecosystem.
Uncertaintiesassociatedwiththegroundwatercomponentofthelakewaterbudgetincrease
unpredictabilityandunreliabilityandfurthercomplicatethefuturemanagementofwetlands.No
matterhowmanywellsareusedtodefinethewatertableinthesettings,thegroundwaterflow
systemwouldnotbeadequatelydefinedandwouldbesubjecttomisinterpretation.Thepresent
methodgivesabetterwayofstudyingsurfacewater–groundwaterinteraction,whichisingreat
demandinthecurrentstrategyforbetterwetlandandintegratedwaterresourcesmanagement.The
resultunderscorestheneedtoputinplacerelevantadaptationmechanismstoreducevulnerability
andenhanceresiliencetoclimatechangewithinthelakebasin.
Acknowledgments:Wewouldliketothanktheanonymousreviewers.Themanuscripthasbenefittedfromthe
reviewers’andeditors’comments.
AuthorContributions:Y.Y.andJ.W.conceivedanddesignedtheresearch;Y.Y.performedtheresearch;Y.Y.
andJ.W.analyzedthedata;B.V.contributedanalysistools;Y.Y.wrotethepaper.
ConflictsofInterest:Theauthorsdeclarenoconflictofinterest.
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... So far, no universally acceptable definition for drought has been suggested, mostly because of the diverse aspects and outcomes regarding the phenomenon (Esfahanian et al., 2017;Hayes et al., 2011;Lloyd-Hughes, 2014). Droughts have been studied mainly using five categories: meteorologic, hydrologic, agricultural, socioeconomic, and ecologic drawbacks (Mersin et al., 2022a;Yihdego et al., 2017). These drought types can be interpreted as decrease in rainfall (meteorologic drought), lack of moisture in soil (agricultural drought), decrease in streamflow (hydrologic drought), water deficit in the habitat or environment to maintain its integrity (ecologic drought), and lack of water to meet anthropogenic activities (socioeconomic drought) (Yihdego et al., 2017). ...
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Full-text available
  • Dec 2024
Com base em revisão sistemática, são apresentados os impactos potenciais das mudanças climáticas através de alterações nos regimes de chuva, temperatura, evaporação e outros, e seus efeitos sobre as águas subterrâneas. São analisadas as pressões sobre as águas subterrâneas, a relação entre as mudanças climáticas e os sistemas aquíferos, e os eventos de intensificação de secas e estiagens, com base nas tendências da variabilidade climática em Santa Catarina. Existem importantes incertezas e lacunas de informação, que dificultam a compreensão da resposta dos aquíferos às alterações climáticas. Esta informação pode ajudar os gestores e responsáveis pela tomada de decisão na gestão e planeamento adequados dos recursos hídricos, bem como na compreensão das incertezas associadas.
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... The biases will cause errors in the water balance of IFS, which will affect the soil moisture, evaporation, temperature, and snowpack at the surface (Lavers et al. 2021). Closed lakes are highly influenced by surface runoff if the catchment area is much larger than the lake surface area and it can significantly influence the lake's water balance (Cao et al. 2021;Klimaszyk et al. 2015), particularly under conditions of extreme precipitation (Yihdego et al. 2017). Indeed, it is likely that in cases of large biases in precipitation, it can be assumed that the same happens for runoff. ...
A Water Mass Balance–Based Procedure Using ERA5 Land Reanalysis and Level Observation to Reconstruct the Past Level Changes of Closed Lakes toward Future Management
Article
Full-text available
This paper aims to refine an effective procedure to reconstruct the past level changes of closed lakes, as a tool for future management. In most cases, such water bodies are overlooked (particularly when they are small), even if they play a significant role in local communities from the economic and social points of view. In this perspective, this study explores the potential of using reanalysis data, as a single source of data to compute the lake water mass balance, and water level observations. It focuses on some closed lakes with minimal anthropogenic influence in Europe, Africa, and the United States. The number of cases studied is limited by the fact that a significant time series of water levels is difficult to obtain for small lakes. The research leverages ERA5 Land (ERA5L), the land component of ERA5 reanalysis, incorporating the Freshwater Lake (FLake) model. FLake allows simulation of the interaction of inland water bodies with the atmosphere. The use of ERA5L is preceded by data validation by considering satellite observation. To simulate the water mass balance of the selected lakes, the proposed procedure combines precipitation, runoff, and evaporation from the lake, provided by ERA5L, and the lake water level observations. The results indicate that even with a short time series of water level observations, a good agreement (Pearson coefficient ranging from 50% to 90%) between the observed monthly variation of the lake level and the corresponding values of the water storage, computed by using ERA5L, is achieved. The procedure exhibits adaptability and robustness, with an accurate representation of the lake water level trends. However, occasional discrepancies are noted during specific periods, primarily attributed to ERA5L precipitation biases. The proposed procedure can be used only when lake water level observations are available. This limits the number of lakes that it can be applied to. However, the procedure can be applied even with a short time series of data, without the need for additional ground-based or remotely sensed data to compute the water mass balance. Significance Statement This study employs ERA5L reanalysis data and lake level observations to effectively reconstruct the natural variability of closed lake water levels in some monitored lakes in Europe, Africa, and the United States. Results demonstrate the adaptability and robustness of the proposed procedure in capturing the past level changes. Despite occasional discrepancies attributed to precipitation biases, the procedure proves reliable for assessing lake water level trends. Future studies could refine the procedure and extend its applicability, enhancing the ability to monitor environmental changes.
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... Droughts are a hazard phenomenon associated with water deficit, and can be classified into several major types: (i) meteorological, as a reduction in precipitation; (ii) agricultural, as a lack of moisture in the soil; (iii) hydrological, with a decline in stream flows; (iv) ecological, as a water deficit in the environment (Yihdego et al., 2017); and (vi) anthropogenic, broadly defined as drought events caused or intensified by human activities . Flash droughts have recently also come to attention due to their sudden emergence and severe impacts on ecosystems and water resources . ...
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... Selection of local levels, such as plant diversity, irrigation time, and market responses, including the type of income sources, resources, and credit programs of enterprises, can help to adapt to climate change (Heller et al. 2015). Government responses like eliminating subsidies and maintaining and improving agricultural markets, a technological development that includes the development and upgrading of new products, various advances in water management, and encouraging and educating people to adapt to these changes can enhance adaptation to climate change (Yihdego et al. 2017;Becker et al. 2015). Vulnerability assessment and resilience mechanisms and adaptation to global climate change are critical . ...
Relationship Between Climate Change, Natural Disaster, and Resilience in Rural and Urban Societies
Chapter
  • Dec 2018
The government established several strategies to decline the hazards of climate alteration. The most significant of these strategies which usually neglected is recognizing the variances between several communities with regard to resilience indicators against these impacts and improving proper strategy for each of them. This research aims to examine the alteration between urban and rural communities with regard to resilience in the extreme event of climate change. The present research is applied in a practical way, and all possible methods (such as numerical/analytical modeling and statistical analysis) which are necessary for estimating the value of resilience or monitoring spatial-temporal characteristics of resilience have been used. The results displayed that there is an important difference between the resilience elements in urban and rural districts. Resilience in urban regions usually is influenced by economic parameters. However, social parameters are the most significant element in resilience in rural regions. Also, indicators which depend on spatial variations are critical in both rural and urban areas under the effect of climate change. Therefore, this research analyzed all the individual factors (in rural and urban areas) which can have an effect on resilience under the climate change conditions, so it can help managers to plan a better strategy for resilience during extreme events of climate change.
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A Geographical Appraisal of Hydrological Drought—A Case Study
Chapter
  • Mar 2025
Drought arises from periods of insufficient precipitation, leading to deficits in both surface and subsurface water resources. In this chapter, various factors affecting hydrological drought have been evaluated. Also, hydrological drought has been investigated using the Standardized Streamflow Index in a case study (Karun 4 basin in Iran). For this purpose, the monthly discharge data of Armand station during the years 1987–2020 have been used. First, these data were examined in terms of homogeneity, outliers, and trends. Then the 3-month SSI was calculated. Our case study reveals a trend of increasing drought severity from 2000 to 2018, with extreme droughts in 2008 and 2018, indicating an urgent need for intervention. The use of GIS and spatial data has improved real-time monitoring and assessment of droughts, providing valuable data on their severity and duration. Understanding the spatial distribution and dynamics of droughts, influenced by local catchment characteristics, is essential for effective management, especially in regions with variable drought conditions. The study emphasizes the threat of climate change and land use changes, which can alter and disrupt the hydrological cycle, increasing vulnerability to drought. To address these challenges, the study suggests a focus on emerging technologies like remote sensing, the development of early warning systems, drought management plans, and water conservation strategies. It advocates for transboundary cooperation and continuous research to enhance resilience against droughts, ensuring sustainable water management. This serves as a call to action for coordinated efforts to mitigate the impacts of hydrological droughts.
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Sustainability Nexus AID: groundwater
Article
Full-text available
  • Nov 2024
The Groundwater Module within the Sustainability Nexus Analytics, Informatics, and Data (AID) Programme of the United Nations University (UNU) addresses critical challenges in sustainable groundwater management. Groundwater resources around the world are under increasing stress from overextraction and pollution, threatening water and food security for billions. Groundwater governance is not one-dimensional but multi-faceted, and central to the management of environmental, social, and economic systems worldwide. In line with the Nexus Approach, the goal of the UNU Sustainability Nexus AID Programme's Groundwater Module is improving access to data and information tools that help scientists and decision-makers address interdisciplinary groundwater problems that affect humans and nature. Here, we describe the critical need for a Nexus Approach to groundwater-related issues and highlight current challenges involving data and information gaps and data-model operability. The Groundwater Module can help address these challenges by offering a central hub for data, analytics, and informatics for addressing groundwater-related issues. By integrating dispersed datasets and modeling tools, this module aims to enable analysis and new insights. We showcase some of the tools in the Groundwater Module and discuss future opportunities in the global pursuit to fulfill the UN Sustainable Development Goals (SDGs).
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Engineering and enviro-management value of radius of influence estimate from mining excavation
Article
Full-text available
  • Feb 2018
In mining operations carried out below the water table, mine area could potentially affect the surrounding. With further deepening of the mine and quarry, the drawdown can impact on water supply wells and base flow. The variation in radius of influence under confined and unconfined aquifer condition is assessed using a hypothetical case study with three operational quarry sites. Average pit-wise radius of influence (R0) for the pit mine, under unconfined and confined aquifer conditions is 963 m and 61 km, respectively, whereas effective radius (Re) for total (cumulative) excavated mine area is 1.7 and 146 km, respectively. It means that the maximum and minimum value of overall impact/influence for the unconfined aquifer lies in-between 963 m and 1.7 km. Similarly that the maximum and minimum value of overall impact/influence for the confined aquifer lies in-between 61 and 146 km. Meanwhile, Re or effective radius for total (cumulative) excavated area seems more appropriate from evaluation viewpoint due to the overlapping (superposition) concentric mining quarries. Thus, assessment of quarry-wise and total radius of influence is of importance to fulfil targeted production, economically with minimum interruptions as part of the mine planning. Moreover, it helps us to assess, monitor and regulate the impact of a mine-dewatering program in the area. Also, the pumping test is often used to estimate radius of influence; it can trigger the inflow or radius of influence. Estimate of the radius of influence using analytical equation mainly independent of discharge/abstraction from the aquifer stands as a preferred choice for the prediction of radius of influence at the quarry/mine pit. There is a strong rationale that policies should be informed by such analytical assessment because it helps us to prevent or minimize negative impacts as part of regulation and pit-mining management and are useful tools for practitioners to design economically efficient and cost-effective in situ groundwater remediation systems, to contain contaminant plumes, to evaluate the surface–subsurface water interaction and to verify numerical models. http://dx.doi.org/10.1080/23249676.2017.1287022
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Modelling bore and stream hydrograph and lake level in relation to climate and land use change in Southwestern Victoria, Australia. Ph.D. Thesis. Faculty of Science, Technology and Engineering, Melbourne, La Trobe University, Melbourne, Australia
Thesis
Full-text available
  • May 2010
In western Victoria, the water table has been declining for the last 10-15 years, and this is attributed to either the low rainfall over this time and/or a substantial change in land use, with grazing land replaced by cropping and tree plantations. Part of this study aimed to increase the understanding of salinity on the basalt plains by modelling the seasonal and decadal fluctuations in water table across the basalt plains. Two manifestations of groundwater were considered: water table and lake level. To determine the relative impact of climate and land use on the water table, hydrographs from 82 observation bores in three aquifers were subjected to time series modelling using two programs, PIRFICT and HARTT. Most of the groundwater level fluctuations are explained by climatic variables (r2 = 90%, on average), but there is a small downward non-climatic trend in the groundwater levels (averaging -0.04 m/yr). There is no evidence of any rise in groundwater level due to clearing of native forests during European settlement in the mid to late nineteenth century, when increased groundwater recharge is believed to have resulted from replacement of deep-rooted trees by shallow-rooted pasture and crops. This is as expected, given that the PIRFICT modelling calculated that impacts of recharge on groundwater level stay in the system only 4.85 years on average. The average downward non-climate trend is most likely due to the widespread replacement of dry land pasture by wheat and canola, as these crops use more water than pasture. However, the much more substantial non-climatic trend (-0.30 m/yr) for bores in the Condah irrigation area is largely related to groundwater extraction. In the study area, a large number of lakes are scattered across the volcanic plains, many of which have problems with increasing salinity. To identify the hydrologic components behind this problem, four lakes have been examined here through the construction of detailed water and salt budgets. The models predict the recession in lake level accurately, and modelled and measured lake levels compare favourably (r2 ranges from 0.78 to 0.94). The modelling results show that the lake level fluctuations are primarily driven by the surface water inflows and direct rainfall during wetter months and evaporation during drier periods, while groundwater inflow/outflow accounts for only < 2% on average, except for Lakes Burrumbeet and Purrumbete. The course of lake level is predominantly explained by climate fluctuations and show little evidence of land use change. These lakes provide no opportunity to discriminate human impacts from climate variables. Stream flow modelling was carried out on 37 stream gauges to assess whether or not the impact of land use change could be detected by a change in the magnitude of the resulting runoff. There were substantial decreases in stream flow in the 1970s-1980s, probably related to increasing livestock densities in the region. Smaller decreases in stream flow in the 1990s match spatially and temporally the negative non-climatic trend in groundwater level identified in bore hydrograph modelling, and are probably due to the widespread replacement of grazing land by wheat and canola. This demonstrates that although changes in land use can have significant impacts on groundwater and surface water resources, the impacts of climate variability on lake systems often overprint any effects of land use change. This study provides a valuable series of models that will assist the Catchment Management Authorities with management of the water resources of the region. Overall, the results of the modelling allow the impacts of climate variables and land management change on groundwater/lake resources and dry land salinity to be more reliably predicted and therefore better managed. https://primo-direct-apac.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_gale_ofa346162306&context=PC&vid=LATROBE&search_scope=All&tab=default_tab&lang=en_US https://primo-direct-apac.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=Almalu21177879990002146&context=L&vid=LATROBE&lang=en_US&search_scope=All&adaptor=Local%20Search%20Engine&tab=default_tab&query=any,contains,Yohannes%20Yihdego&sortby=rank
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Engineering and environmental remediation scenarios due to leakage from the Gulf War oil spill using 3-D numerical contaminant modellings
Article
Full-text available
  • Nov 2017
The transport groundwater modelling has been undertaken to assess potential remediation scenarios and provide an optimal remediation options for consideration. The purpose of the study was to allow 50 years of predictive remediation simulation time. The results depict the likely total petroleum hydrocarbon migration pattern in the area under the worst-case scenario. The remediation scenario simulations indicate that do nothing approach will likely not achieve the target water quality within 50 years. Similarly, complete source removal approach will also likely not achieve the target water quality within 50 years. Partial source removal could be expected to remove a significant portion of the contaminant mass, but would increase the rate of contaminant recharge in the short to medium term. The pump–treat–reinject simulation indicates that the option appears feasible and could achieve a reduction in the area of the 0.01 mg/L TPH contour area for both Raudhatain and Umm Al-Aish by 35 and 30%, respectively, within 50 years. The rate of improvement and the completion date would depend on a range of factors such as bore field arrangements, pumping rates, reinjection water quality and additional volumes being introduced and require further optimisation and field pilot trials. URL: https://link.springer.com/article/10.1007/s13201-016-0517-x
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Modelling of lake level and salinity for Lake Purrumbete in western Victoria, Australia.
Technical Report
Full-text available
  • May 2010
http://www.epa.vic.gov.au/~/media/Publications/Yihdego%202008%20Lake%20Purrumbete%20report%20updated%202010.pdf
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Hydrogeological constraints on the hydrology of Lake Burrumbeet, southwestern Victoria, Australia
Conference Paper
Full-text available
  • Sep 2007
itle-VUEESC La Trobe 2007 : 25th September 2007, La Trobe University / [editors, Sarah Hegarty, Dale McKenzie, Yohannes Yihdego]. Creator-Victorian -Universities Earth and Environmental Sciences Conference (21th : 2007 : Wodonga, Victoria) Other Creators Hegarty, Sarah. McKenzie, Dale. Yihdego, Yohannes. La Trobe University Geological Society of Australia Published Sydney : Geological Society of Australia, 2007. Physical Description 43 p. ; 30 cm. Series Abstracts (Geological Society of Australia), 0729-011X ; no. 88 Abstracts (Geological Society of Australia) ; no. 88 Language English Identifiers Libraries Australia ID 42377513 Contributed by Libraries Australia https://trove.nla.gov.au/work/35072836
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Asessment of Wetland Hydrological Dynamics in a Modified Catchment Basin: Case of Lake Buninjon, Victoria, Australia
Article
Full-text available
  • Dec 2016
The common method to estimate lake levels is the water balance equation, where water input and output result in lake storage and water level changes. However, all water balance components cannot always be quickly assessed, such as due to significant modification of the catchment area. A method that assesses general changes in lake level can be a useful tool in examining why lakes have different lake level variation patterns. Assessment of wetlands using the dynamics of the historical hydrological and hydrogeological data set can provide important insights into variations in wetland levels in different parts of the world. A case study from a saline landscape, Lake Buninjon, Australia, is presented. The aim of the present study was to determine how climate, river regime, and lake hydrological properties independently influence lake water levels and salinity, leaving the discrepancy, for the effect of the non-climatic/ catchment modification in the past and the model shows that surface inflow is most sensitive variable. The method, together with the analysis and interpretation, might be of interest to wider community to assess its response to natural/anthropogenic stress and decision choices for its ecological, social, scientific value, and mitigation measures to safe guard the wetland biodiversity in a catchment basin. https://onlinelibrary.wiley.com/doi/abs/10.2175/106143016X14798353399331 Publisher: ohn Wiley & Sons, Inc.
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Human impact assessment through a transient numerical modeling on the UNESCO World Heritage Site, Lake Naivasha, Kenya
Article
Full-text available
  • Dec 2016
  • ENVIRON EARTH SCI
Lake Naivasha was designated as a RAMSAR site in 1995. The lake environment is fragile and critically threatened by human-induced factors. The study presented a steady and transient numerical modeling. The long-term and system flux over time interaction between the lake and the surficial aquifer is represented in the Lake Package LAK3 with in the advanced 3-D simulation sofware (GMS). The model covers an area of 1817 km2. Model calibration was constrained by the observed groundwater and lake levels using PEST. The effect of excessive abstraction was rigorously analyzed via scenario analysis. The simulation was evaluated “with abstraction” and “without abstraction” scenarios. The abstraction scenario was simulated using range of combination assuming that all the abstraction was from the lake or the groundwater and in the ratio of groundwater and lake water. The effect of the stress was evaluated based on the observed aquifer heads and lake stage at the end of the simulation time. The development of low groundwater-level anomalies in the well field is explained. The result indicates that the one of the well fields is not in direct hydraulic connection to the main recharging water body. Apparently, similar development of cone of depression was not generated in the other two well fields, and this could have several reasons including due to the fact that these well fields are located relatively close by to the main recharging zones and concluded to have additional source of recharge, and this was supported by previous studies, whereby the isotopic composition of the boreholes has their source of recharge from precipitation and river and was also confirmed from the isotopic composition of unsaturated zone, which is a mixture of river and rain. The study reveals that seasonal variability of groundwater–surface water exchange fluxes and its spatially and temporally variable impact substantially on the water resource availability. Such analysis can be used as a basis to quantify the linkages between the surface water and groundwater regime and impacts in the basin. The model output is expected to serve as a basis via linking/coupling with others to incorporate the ecology and biodiversity of the lake to safeguard this high-value world heritage water feature.
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Characterizing groundwater dynamics in Western Victoria, Australia using Menyanthes software. Perth, Australia, 1–3 December 2009.
Conference Paper
  • Dec 2009
In western Victoria, southeast Australia, the watertable has been declining for the last 10-15 years, and this is attributed to either the low rainfall over this time and/or a substantial change in land use, with grazing land replaced by cropping and tree plantations. To determine the relative impact of climate and land use on the watertable, groundwater level fluctuations were modelled using two different approaches: Predefined Impulse Response Function In Continuous Time (PIRFICT) model (a transfer function-noise model), and an auto-regressive model, Hydrograph Analysis: Rainfall and Time Trends (HARTT). HARTT does not take evapotranspiration into account, and this is a serious drawback in areas with shallow groundwaters, because PIRFICT modelling showed that these areas have significant seasonal evaporation. However, the average trends calculated using both models differ only slightly. Most of the groundwater level fluctuations are explained by climatic variables (90%). The average non-climate trend is statistically insignificant, indicating that groundwater levels are not rising/falling due to changes in landuse, at least not during the observation period. However, bores screened in one aquifer, the Port Campbell Limestone, show a substantial negative trend (-0.3 m/yr) due to groundwater pumping from irrigation bores. The HARTT analysis showed that the impact of recharge on groundwater level occurred with less than a month’s lag time, and the PIRFICT modeling calculated that the impact stays in the system 5.7 years on average. This is an indication of the time needed for the groundwater storage to move to a new state of hydrologic (physical, pressure-related) equilibrium. The short groundwater response times mean that the effect of massive clearing more than 50 years ago on the water table could not be detected. These response times are much more rapid than those derived from groundwater flow system concepts. Overall, the results of the modelling allow the impacts of land management change on groundwater resources and dry land salinity to be more reliably predicted and therefore better managed.
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Reservoir operation during drought
Chapter
  • Jan 2017
The increasing occurrence of drought events along with lack of proper drought management practices in various regions of the world has caused several socioeconomic problems for natural-resource-dependent communities. The operation of surface water resources that are quickly affected by drought, in most regions, is performed using the reservoir. Therefore, reservoir operation during drought is one of the most important drought management policies. During wet and normal periods, when inflow is sufficient, achieving reservoir operating targets does not pose a problem. However, during drought or when forecasting a drought, it may not be possible to reach the target because of an inflow shortage. To minimize the impact of drought on reservoir performance, rule curves are coupled with hedging rules to balance the shortage in water supply with the storage target. This chapter discusses reservoir operation during drought.
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