Rationalization of Altitudinal Precipitation Profiles in a Data-Scarce Glacierized Watershed ...

20 Pages·2016·2.29 MB·English

water
Article
Rationalization of Altitudinal Precipitation Profiles
in a Data-Scarce Glacierized Watershed Simulation
in the Karakoram
XiaoleiWang1,2,LinSun1,YiqingZhang1,2andYiLuo1,2,3,*
1 KeyLabofEcosystemNetworkObservationandModeling,InstituteofGeographicSciencesand
NaturalResourcesResearch,ChineseAcademyofSciences(CAS),11A,DatunRoad,ChaoyangDistrict,
Beijing100101,China;[email protected](X.W.);[email protected](L.S.);
[email protected](Y.Z.)
2 UniversityofChineseAcademyofSciences,No.19A,YuquanRoad,ShijingshanDistrict,
Beijing100049,China
3 XinjiangInstituteofEcologyandGeography,CAS,Urumqi830011,China
* Correspondence:[email protected];Tel.:[phone]
AcademicEditor:Y.JunXu
Received:25February2016;Accepted:29April2016;Published:5May2016
Abstract: Due to the scarcity of field observations and geodetic measurements in catchments in
theKarakoramMountainsinWesternChina,obtainingprecipitationdataforthehighmountains
involveslargeuncertaintiesanddifficulties. Inthisstudy,weusedafunctionalrelationshipbetween
the annual glacier accumulation and summer temperature at the equilibrium line altitude (ELA)
to derive precipitation lapse rates (PLAPSs) in a data-scarce watershed. These data were used
in a modified Soil and Water Assessment Tool (SWAT) model with a glacier module to simulate
glacio-hydrologicalprocessesintheYarkantRiverbasinintheKarakoram. ThePLAPSbasedon
thewidely-usedgriddatasetsconsiderablyunderestimatedprecipitation,yieldinganunreasonable
watershedwaterbalanceandinaccurateglacierchanges. However,theELA-basedPLAPSimproved
thesimulationsignificantly. IntheYarkantRiverbasin,theannualprecipitationreachedapeakof
800–1000mmatapproximately5300ma.s.l. Themodelsimulationsindicatedthatthecontributions
ofglaciermeltandicemelttototalrunoffwere52%and31%,respectively. Moreover,asignificant
precipitationincreaseandanon-significanttemperatureincreaseduringthemeltseasonmaybethe
majorreasonsforthedecreasedicemeltandslowerglaciershrinkageonthenorthernslopeofthe
Karakoramduringtheperiodof1968–2007.
Keywords: precipitationlapserates;high-elevationprecipitation;ELA;SWAT;Karakoram
1. Introduction
TheKarakoramMountainsarenaturalwatertowersforaridregionsinPakistanandWestern
China. Inrecentyears,thisregionhasbeenofinteresttoresearchersduetoitssignificanceandthe
“Karakoram anomaly” [1]. This phenomenon deserves further investigation to clarify the relation
betweenclimateforcingandglacierresponsesintheregion[2]. However, becauseofthecomplex
terrainandhighelevationsofthisarea,sufficienthydrologicalandmeteorologicaldataarelackingin
thehigh-elevationregions[3]. Thelackofobserveddataisamajorconstraintforresearchstudies.
Duetothescarcityofobservedhydrologicaldata,distributedhydrologicalmodelsareconsidered
usefultoolsforinvestigatingtheintra-andinter-annualdynamicsofthehydrologicalprocessdue
to the high temporal resolutions over long time periods and the different spatial scales of the
models [4]. Some hydrological models have been upgraded with glacial process modules and
are widely used in snow- and glacier-dominated regions [5–8]. These models provide a solid
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foundation for studies in the Karakoram, in which a large area is covered by glaciers. However,
astheavailabilityofhydrometeorologicaldataathighelevationsisextremelylimited,hydrological
modelingisachallengingtaskinglacierizedmountainousregions. Toalargedegree,theperformance
of a glacio-hydrological model depends on the forcing data for these catchments [9]. At high
elevations, air temperature influences snow and glacier melting [9,10], and precipitation controls
mass accumulation [3,11–13]. Therefore, the temperature and precipitation at high elevations
have a large impact on the outputs of glacio-hydrological models. The temperature lapse rate
(TLAPS) is one of the most important variables for modeling meltwater runoff using the T-index
method [9,14]. Immerzeeletal. [9] proposed that larger TLAPS values in specific months led to
anincreaseinglaciermeltof400%. However, relativetoprecipitation, temperatureismorestable
andlessspatiallyvariable[15]. Temperatureandelevationexhibitahighlylinearcorrelationwith
seasonalgradients[9,16,17]. Precipitationisgenerallymoreuncertainduetoorographiceffectsin
catchmentsandthefactthatmostraingaugesarelocatedatlowerelevations[18]. Bookhagenand
Burbank[19]confirmedastrongrelationshipbetweentopographyandmonsoonrainfall,illustrating
twotopography-relatedzonesofhighrainfallandanapproximatelyexponentially-decreasingtrendat
higherelevationsbasedonTropicalRainfallMeasuringMission(TRMM)dataintheHimalayas.
Currently, gridded precipitation products are generally used as forcing data in hydrological
assessments[6,20],suchastheAsianPrecipitationHighlyResolvedObservationalDataIntegration
Towards Evaluation (APHRODITE) and National Centers for Environmental Prediction-National
Center for Atmospheric Research (NCEP-NCAR) re-analyses. However, gridded data products
alwaysunderestimatehigh-elevationprecipitationinmountainousareas[4,20]. Anunderestimation
ofprecipitationcanbeoffsetinamodelbyanoverestimationofglaciermelt[21,22]. Inhydrological
modeling, model performance is typically assessed using observed runoff data, and vertical
precipitationgradientsaregenerallyderivedbasedontheobservedrunoff[23]. Iftheprecipitationis
underestimated,goodmodelperformancecanbeachievedbyincreasingglaciermelting. However,
thisprocessdistortstheactualrolesofglaciersandresultsinunrealisticlossesofglacierarea. The
magnitudeofprecipitationrequiredtosustainlargeglaciersystemsismuchhigherthanthatimpliedby
griddedprecipitationproducts[20]. Therefore,thesimilaritybetweenobservedandsimulatedglacier
changesmaybeanotherpotentialwaytocorrectmountainprecipitationinadditiontothetraditional
methodbasedonobservedstreamflow.Long-termchangesinglaciersindicatetherelationshipbetween
glaciermassaccumulationandablation. Inrecentyears,observedglaciermassbalancechangeswere
usedtoeffectivelyquantifyprecipitationathighelevationsintheHunzaandIndusRiverbasins[13,20].
However, in glacierized regions with no observed glacier mass balance data, other quantification
methodsrelatedtoglacierchangearenecessary.
In our study, the Yarkant River basin on the northern slope of the Karakoram was chosen as
thestudyarea. Weusedafunctionalrelationshipbetweenannualglacieraccumulationandsummer
temperatureatequilibriumlinealtitudes(ELAs)toderivetheinitialprecipitationathighelevations.
Theprecipitationlapserates(PLAPSs)werecalibratedbasedonbothobservedstreamflowandglacier
areachangesusingtheglacier-enhancedSoilandWaterAssessmentTool(SWAT).Oncetheobserved
streamflowandglacierareachangesmatchedthesimulatedvalues,thePLAPSsweredetermined,and
thehigh-elevationprecipitationwasinverselycalculatedbasedonthecorrectedPLAPSs. Finally,we
explainthereasonforthesmallerlossofglacierareainthisregionbasedonthesimulationresults.
2. StudyArea
The Yarkant River originates at the Karakoram Pass on the northern slope of the Karakoram
Mountains. The source region of the Yarkant River above the Kaqun Hydrologic Station (KQHS)
is4.67ˆ104km2 (Figure1). TheKuluklanganHydrologicStation(KLKHS)islocatedupstreamof
KQHSandhasacatchmentareaof3.29ˆ104 km2. Thereare3064glaciersinthebasin,and12%of
theglaciercoverageisestimatedtobeaboveKQHS.Amongtheglaciers,theInsukatiGlacieristhe
largestinChina,withanareaof359.09km2. TheK2Glacierisextremelyfamousandislocatedonthe
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mountainK2,thesecondhighestmountainintheworld. Thecatchmentelevationrangesfrom1446to
8611ma.s.l.,withanaverageelevationof4423ma.s.l. Approximately88%ofthebasinareaislocated
1446 to 8611 m a.s.l., with an average elevation of 4423 m a.s.l. Approximately 88% of the basin area
between3000and6000ma.s.l. Duetothespecificgeographicconditions,theaccumulationoficeand
is located between 3000 and 6000 m a.s.l. Due to the specific geographic conditions, the accumulation
snowinhighmountainsistheprimarysourceofrunoff[24–26].
of ice and snow in high mountains is the primary source of runoff [24–26].

Figure 1. Map of the source region of the Yarkant River basin in the Karakoram Mountains.
Figure1.MapofthesourceregionoftheYarkantRiverbasinintheKarakoramMountains.
The climate in the source region is influenced primarily by mid-latitude westerlies and the
suTmhmeecrl ismoautteheinasttheerns omuorcnesoreogni ofrnoimsi nthfleu Ienndcieadn pOricmeaanr.i lTyhbey hmigihde-slat tmituodnethwlye stetemrlpieesraatnudret hoef 7su.5m °Cm er
souinth tehaes tseorunrcme orengsiooonn offr othme tYhaerkIanndti aRnivOerc eoacncu.rTs hine hJuiglyh, easntdm tohne thlolwyetsetm mpoenrathtulyr eteomfp7e.5ra˝tuCrein otfh e
sou−r2c0e °Cre ogciocunrso fint hJaenYuaarryk.a TnhteR sievaesronoaclc duirsstriinbuJutiolyn, oafn pdrethciepiltoawtioens tism hoignhtlhyl yunteemvepne, rwaittuhr 6e7o%f o´f 2th0e˝ C
occaunrnsuianl Jparneuciapriyt.atTiohne csoenascoenntarladteidst rbiebtuwteioenn oMfapyr eacnidp iSteaptitoenmibsehr.i ghlyuneven,with67%oftheannual
precipitationconcentratedbetweenMayandSeptember.
3. Data and Methods
3. DataandMethods
3.1. Description of the Data
3.1. DescriptionoftheData
3.1.1. Topography, Soil, Land Cover and Glacier Maps
3.1.1. Topography,Soil,LandCoverandGlacierMaps
The Shuttle Radar Topography Mission (SRTM3) digital elevation model (DEM), with a spatial
resTohluetiSohnu ottfl e90R mad, awraTso upsoedg rtaop dheylinMeiastsei othne( sSoRuTrMce 3r)egdiiognit oafl ethleev Yaatiroknanmt Rodiveelr( iDntEoM su),bw-biathsinass apnadti al
restool ugteinoenraotfe9 t0hem t,riwbuastauryse adndto cdhealninneeal tseysthteemsso.u rceregionoftheYarkantRiverintosub-basinsandto
generatTehteh eFtirrisbt uCtahriynaa nGdlcahciaenr nIenlvseynsttoermys .(1960s) [27] and the Second China Glacier Inventory
(20T0h9e) [F2i8r]s twCerhei nuaseGdl ainc itehrisI nstvuednyto. Tryhe(1 d9i6g0itsa)l [g2l7a]ciaenrd vethcteoSr edcaotan dfroCmhi tnhae G19la6c0ise hraIvnev einnhtoerreyn(t2 i0s0su9)es[2, 8]
wesruecuhs aesd gineotrheifsersetnucdiyn.gT phreobdliegmitsa l[2g9la].c Tiehrevreefcotroer, dthaet agflraocmier tohuet1li9n6e0s sfrhoamve thineh Feirresnt tCihssinuae sG,lsaucciehr as
geoInrevfeenrteonrcyi nwgerper aodbjluesmtesd[ m29a]n.uTahlelyr ebfaosreed, tohne tgolpaocgierarpohuitcl imneaspsf raonmd athereiaFli prshtoCtohgirnaapGhsl.a BciaesredIn ovne nthteo ry
wepreeraioddju osft ethdem Fiarnstu Calhliynab aGsleadcieorn Intovpenotgorrayp, hthice msimapuslaatinodn abeergiaanl pinh o19to68g.r aTphhe so.bBsearsveedd ognlatchieer paereraio d
ofothf ethFei rYstarCkhanint aRGivlearc ibearsiInn vheans troertyr,eathteeds bimy u9%la tfiroonmb ethgea n19i6n0s1–926080.9T bhaeseodb soenr vtheed Fgilrasct iaenrda rSeeacoonfdth e
China Glacier Inventories. Land use/cover maps extracted from the European Space Agency
YarkantRiverbasinhasretreatedby9%fromthe1960s–2009basedontheFirstandSecondChina
GlobCover land cover maps (GlobCover 2009), which have a resolution of 300 m × 300 m, were used
GlacierInventories. Landuse/covermapsextractedfromtheEuropeanSpaceAgencyGlobCover
in this study. Soil data were obtained from the Harmonized World Soil Database of the Food and
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land cover maps (GlobCover 2009), which have a resolution of 300 m ˆ 300 m, were used in this
study. SoildatawereobtainedfromtheHarmonizedWorldSoilDatabaseoftheFoodandAgriculture
OrganizationoftheUnitedNations(HWSDVersion1.2),whichwasreleasedin2012. Thedatainclude
somesoilproperties,suchasthefractionsofsand,siltandclayandtheorganiccarboncontent.
3.1.2. PrecipitationandTemperature
OnlytheTaxkorganMeteorologicalStationprovideslong-termclimatemonitoringdatawithinthe
studyarea(seeFigure1). Therefore,theAsianPrecipitation–High-ResolvedObservedDataIntegration
TowardsEvaluationversion1101(APHRODITE)[30]andPrinceton’sGlobalMeteorologicalForcing
Dataset(PGMFD)[31]wereusedastheprecipitationandtemperatureinputs,respectively. Thedata
fromAPHRODITEarelong-termcontinental-scaledailygriddeddatafrom1951to2007basedon
a rain-gauge observation network with a spatial resolution of 0.25˝. PGMFD was constructed by
combiningasuiteofglobalobservation-baseddatasetswiththeNationalCentersforEnvironmental
Prediction-NationalCenterforAtmosphericResearch(NCEP-NCAR)reanalysisdataset.Thedatahave
adailyresolutionandaspatialscaleof0.5˝,includingmean,minimumandmaximumairtemperatures
from1948to2010. Thegriddeddailyairtemperaturedataataspatialresolutionof0.25˝wereobtained
viabilinearinterpolationfromthegridcellcentersinthePGMFDgriddedtemperaturedataset. Each
gridwasregardedasabasestation.
3.1.3. ObservedStreamflowData
Themonthlydischargedataspanning1968–1989fromKQHSandKLKHSwereusedtocalibrate
andvalidatethemodel. TheywereobtainedfromtheChinaHydrologicalAlmanac. Thefirsttwo
yearsweretreatedasthewarm-upperiodforthesimulation. Thedatasegmentfrom1970to1979was
usedformodelcalibration,andthedatasegmentfrom1980to1989wasusedformodelvalidation.
3.2. TheGlacier-EnhancedSWATModel
SWAT[32]isapublic-domain,open-source,physically-based,long-term,basin-scalecontinuous
modelthatenablesuserstomodifythemodelcodefortheirspecificuses. Ithasbeenusedwidelyin
hydrologicalcycleandwaterresourcemanagementsimulations[33,34].
The officially released versions of SWAT do not simulate glacier hydrology. Luo et al. [35]
developed a glacier module that was incorporated into the SWAT model to simulate watershed
glacio-hydrologicalprocesses. Themoduletreatseachindividualglacierasahydrologicalresponse
unit(GHRU).EachGHRUisdividedintotenelevationbands. Theglaciermassbalanceissimulatedin
eachband. Thesimulatedglaciermassbalancecomponentsincludemassaccumulation,sublimation,
meltingoficeandsnowandrefreezingofthesemeltwaters. Theiceandsnowmeltsaresimulated
usingthedegree-dayfactorapproach[14,36].Theglaciervolumeiscalculatedbasedonthetotalglacier
massbalance,andtheglacierareaisupdatedthroughtheglaciervolume-areascalingrelation[37].
Thisrelationisasfollows: ˆ ˙
V 1{n
gla
A “ (1)
gla m
whereA (km2)andV (m¨km2)arethesurfaceareaandvolumeoftheglacier,respectively. Based
gla gla
onLiuetal.[38],missetat40andnat1.35inthisstudy. Thesevalueswerederivedfrommeasured
glacierdatainnorthwestChina. Thisrelationcanalsobeusedtocalculateglacierwaterstorage.
Inthismodel,theSpatialProcessesinHydrology(SPHY)classification[6]wasusedtocalculate
therunoffineachhydrologicresponseunit(HRU/GHRU).Thiscalculationconsistsoffourpossible
contributingfactors: rainfallrunoff,snowmelt,glaciermeltandbaseflow. Precipitation-basedrunoff
consistsofsurfacerunoffandlateralflowreleasedfromsoilwaterstorageduetorainfall. Snowmelt
consistsofsnowmeltwaterreleasedfromsnowstorage. Therunofffromglaciermeltencompassesall
ofthemeltgeneratedintheglacierizedarea,includingsupraglacialsnowmelt,icemeltandrainfall
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runoffontheicesurface. Baseflowisreleasedfromshallowanddeepgroundwaterstorage. Thetotal
runoffinallHRUsissummedtodeterminetherunofffortheentirewatershed. Theriverdischargeis
routedtodeterminethelossviariverchannels. Theglacier-enhancedSWATmodelhasbeenapplied
toseveralalpinewatershedsintheTianshanMountains[35,39–41]andhasproventobearobusttool
forglacio-hydrologicalsimulationswithadailytimestep.
3.3. MethodforRationalizingHigh-ElevationPrecipitation
Inthisstudy,APHRODITEprecipitationatlowelevations(usuallyincludingthenon-glacierized
area)canbeassessedbasedontheobserveddata. However,athighelevations,therearenoweather
stations. Therefore,weassesstheAPHRODITEprecipitationathighelevationsbasedonthewater
balanceandpreviousinvestigations. IfAPHRODITEprecipitationisunderestimatedbasedonour
assessment,weusetheobservedstreamflowandglacierchangestoestimatetheprecipitationathigh
elevations. Thedetailedproceduresareasfollows.
3.3.1. TheGlacierMassAccumulation-SummerTemperatureRelationship
Becausenoobservedglaciermassbalancedataareavailableforthestudyarea,arelationship
between glacier mass accumulation and summer temperature was used to obtain the initial
high-elevationprecipitation. Severalauthorshaverelatedannualaccumulation(orablation)atthe
ELAtothesummermeantemperatureusingexponentialorpower-lawfunctions[42–44]. Bookhagen
andBurbank[19]andSalernoetal.[17]alsoshowedanapproximateexponentially-decreasingtrend
at higher elevations based on TRMM data and observed data in the Himalayas. Kotlyakov and
Krenke[45]developedanempiricalformulanamedthe“globalformula”basedon57observation
stationsaroundtheworldandusedittocalculatetheannualablationattheELA.Therelationshipis
formulatedasfollows:
A“1.33ˆp9.66`T q2.85 (2)
s
whereAistheaccumulation(orablation)attheELAperyear(mmw.e.,wherew.e. =waterequivalent)
andT isthemeantemperatureinthesummer(June–August)attheELA(˝C).
s
Thesummerseasontemperature-indexmethodhasbeenwidelyusedtocalculateELAclimates
and glacial melt [46–49]. Here, T was calculated from the observed summer temperature at the
s
TaxkorganMeteorologicalStationviaTLAPS.BecauseglacialaccumulationattheELAisequalto
ablation,theamountofaccumulationcanbeconfirmed. Accountingforsnowdrift,theaccumulation
is1.3-timesgreaterthantheprecipitation[19]. Thus,theannualprecipitationattheELA(P ,inmm)
ELA
canbeexpressedasfollows:
P “ A{1.3“1.02ˆp9.66`T q2.85 (3)
ELA s
Foreachglacier,theannualprecipitationattheELAwascalculatedusingEquation(3)from1968
to1970,whichisconsideredtohavebeenastableperiodforglaciersintheKarakoramMountains[2].
Therefore,annualprecipitationwasdeterminedatthesedifferentelevationsintheglacieredzones.
TheAPHRODITEprecipitationatlowelevationsandsimulatedprecipitationinglacierizedregions
wereusedtocorrecttheannualPLAPSsinthehigh-elevationregions. Eventually,ELA-basedPLAPS
valueswithmultiplerateswereobtainedovertheentirebasin.
3.3.2. CalibrationandValidation
IntheSWATmodel,themonthlydischargesobservedatKQHSandKLKHSandtheobserved
glacier change data were used as the primary data for calibrating PLAPSs and other parameters.
Duringcalibration,theinternalprocessesofthemodelweremonitoredtoguaranteetherationality
of the simulation. First, the model was calibrated and validated using the stream discharge data.
The simulated monthly discharges at KQHS and KLKHS were compared to the observed values.
ThecomparisonwasevaluatedusingtheNash–Sutcliffeefficiency(NSE)andthepercentbias(PBIAS)
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indices.Theperformanceofthemodelduringthecalibrationandvalidationstageswasratedaccording
tothesystemproposedbyMoriasietal.[50].
Next,themodelwasrunfrom1968to1970. PLAPSvaluesandotherparametersrelatedtoglacier
meltwereadjustedtomaintainglaciermassbalance,especiallyforlargerglaciers. Then,themodel
wasrunfrom1968to2007. Thesimulatedglacierareachangeduringthisperiodwascomparedtothe
changederivedfromthetwophasesoftheChinaGlacierInventory.
Werepeatedthisprocessmanytimes. Finally,theNSE,PBIASandthecomparisonoftheglacier
areachangeswereevaluatedcomprehensively. Whenallofthemwerewithinanacceptablelimitbased
onMoriasietal.[50],thePLAPSsandotherparameterswereaccepted.
Additionally,thewaterbalancefortheentirecatchmentwasexamined. Thecatchmentwater
balanceoveraperiodcanbeexpressedasfollows:
R“ P`∆S´ET (4)
a
whereR,PandET arethemeanrunoff,precipitationandevapotranspiration,respectively,foragiven
a
period. ∆Sisthecatchmentwaterstoragechange,whichincludeschangesinglacierwaterstorage,
snowwaterstorage,soilwaterstorageandaquiferwaterstorage. Asthesnow,soilwaterandaquifer
waterstoragevaluesremainedstableduringthelongperiod,thechangesinwaterstorageweremainly
attributed to changes in the glacier reserves. The observed R can be acquired based on observed
streamflowatKQHS.Thechangesinglacierwaterstoragecanbecalculatedbasedontheobserved
glacierareachangesfromtheFirstandSecondChineseGlacierInventoriesandEquation(1).
After the determination of the PLAPS, the precipitation at high elevations was acquired and
rationalized,althoughsomeuncertaintiesexisted.
3.4. TrendAnalysisMethod
Trend analyses of the temperature, precipitation and runoff were conducted using the
Mann–Kendallteststatistic[51–53],whichisanon-parametrictest. TheMann–Kendalltestrejects
thenullhypothesisifZě|Zcr|,whereZcristhecriticalvalueofthenormaldistributionatthe5%
confidencelevel(Zcr=˘1.96). ApositivevalueofZindicatesanincreasingtrend,whileanegative
valueofZindicatesadecreasingtrend. ArobustestimateoftheslopecanbecalculatedusingSen’s
non-parametricme`thod[˘53,54]. Inthiscase, Sen’sslopeestimatoristhemedianofthen(n´1)/2
slopesofthepairs x : x ,wherej>k.
j k
4. Results
4.1. ParameterSetting
4.1.1. TLAPS
TheTLAPSisgenerallylinearandvariesseasonally[9,16,17]. Therefore,monthlyvalueswere
adoptedbasedontheobservedandPGMFDtemperaturedatainthisstudy. TheinitialTLAPSwas
determined using the gridded PGMFD temperatures. According to the linear regression analysis,
theTLAPSfromtheannualPGMFDtemperaturewas´6.1˝C¨km´1. Aftercalibration,theTLAPS
basedontheannualaveragetemperaturewasdeterminedtobe´5.8˝C¨km´1(Figure2a),andthe
TLAPSvaluesforthespring,summer,autumnandwinterwere´5.3,´6.5,´5.8and´5.3˝C¨km´1,
respectively. Moreover,mostoftheobservedtemperaturesatthemeteorologicalsitesneartheYarkant
Riverbasin[55]weredistributedaroundtheTLAPSline.Onlytheobservedtemperaturesofsomesites
below3000ma.s.l. werehigherthanthesimulatedvalues. Becauselessthan10%ofthecatchmentarea
islocatedbelow3000ma.s.l.,theunderestimationmayonlyslightlyaffectthemodelingresults.Similar
TLAPSvalueswerereportedinpreviousstudiesinnearbyglacierizedcatchments(e.g.,´6.4˝C¨km´1
intheHunzaRiverbasininKarakoram[56],´5.4˝C¨km´1intheupperLangtangcatchmentinthe
centralHimalayasinNepal[9],´6.0and´6.5˝C¨km´1intheBaltoroandLangtangwatershedsin
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Water 2016, 8, 186 7 of 20
tHheimHailmayaalasy [a5s] [a5n]da n−d5.´5 5°.C5·k˝Cm¨−k1 min´ t1hien LthanegLtaannggt avnagllevya l[l3ey]).[ 3H])e.nHcee,n tchee, tThLeATLPAS PuSseuds eind itnhitsh sistusdtuyd iys
irserliealbialbe.l e.
8 8
a. determination of TLAPS b. determination of PLAPS
7 7
6 6
5 5
km) m)
Altitude ( 34 Altitude (k34
APHRODITE
2 2 Observed
PGMFD Simulated at ELA
1 Observed 1 Calculated by corrected PLAPS
Calculated by TLAPS linear(APHRODITE)
0 0
-18 -12 -6 0 6 12 18 0 300 600 900 1200 1500
Temperature (◦Ｃ) Annual precipitation (mm)
Figure 2. Comparison between the altitudinal distributions of the average annual temperatures based
Figure 2. Comparison between the altitudinal distributions of the average annual temperatures
on observed values, Princeton’s Global Meteorological Forcing Dataset (PGMFD) values and
basedonobservedvalues,Princeton’sGlobalMeteorologicalForcingDataset(PGMFD)valuesand
temperature lapse rate (TLAPS) (a); and the annual precipitation based on the observed values, Asian
temperaturelapserate(TLAPS)(a);andtheannualprecipitationbasedontheobservedvalues,Asian
Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE)
PrecipitationHighlyResolvedObservationalDataIntegrationTowardsEvaluation(APHRODITE)
values, Equation (3) results and corrected precipitation lapse rate (PLAPS) (b).
values,Equation(3)resultsandcorrectedprecipitationlapserate(PLAPS)(b).
4.1.2. Other Key Model Parameters
4.1.2. OtherKeyModelParameters
Except for TLAPS and PLAPS, the most sensitive parameters were adopted from similar
Except for TLAPS and PLAPS, the most sensitive parameters were adopted from similar
studies [5,6,35,57,58]. The initial values were based on the default lower and upper bounds in the
studies[5,6,35,57,58]. Theinitialvalueswerebasedonthedefaultlowerandupperboundsinthe
SWAT database and from the aforementioned studies. Calibration of these parameters was also
SWAT database and from the aforementioned studies. Calibration of these parameters was also
performed based on the trial and error method with respect to the observed and simulated monthly
performedbasedonthetrialanderrormethodwithrespecttotheobservedandsimulatedmonthly
streamflow at KQHS and KLKHS and the glacier area changes from 1968 to 2007. The
streamflowatKQHSandKLKHSandtheglacierareachangesfrom1968to2007. Theparameterization
parameterization results are listed in Table 1.
resultsarelistedinTable1.
TTaabbllee 11.. TThhee mmoosstt sseennssiittiivvee ppaarraammeetteerrss aanndd tthhee fifinnaall ccaalliibbrraatteedd vvaalluueess oorr rraannggeess ooff vvaalluueess ffoorr tthhee
ggllaacciieerr--eennhhaanncceedd SSWWAATTm mooddeelli nint htheeY YaarkrkaannttR Rivivererb baasisnin
Final
Sensitive ParaPmaertaemr eter F inalParameter
Sensitive ParameterDefinition Parameter
Parameters Parameter Definition ChangeCRhanangege ValueRange
Parameters Value
ALPHA_BF Baseflowalphafactor 0–1Range 0.02–0.8
Range
RCHRG_DP Deepaquiferpercolationfactor 0–1 0.4
ALPHA_BF Baseflow alpha factor 0–1 0.02–0.8
CH_K2 Channeleffectivehydraulicconductivity(mm/h) 0–300 10–50
RgCmHfRmGx_DP DDegereepe- adqayuiffaecrt oprefrocroilcaetmioenl tfaocnt2o1r June(mm¨˝C´1¨day´1) 1.4–16.00–1 3–104.4.5
CH_K2 DCeghraene-ndealy effafecctotirvfeo rhiycdermauelltico nco2n1dDueccteimvibtye r(mm/h) 0–300 10–50
gmfmn 1.4–16.0 1.0–8.8
gmfmx (mDmeg¨˝reCe´-d1¨adya yfa´c1t)or for ice melt on 21 June (mm·°C−1·day−1) 1.4–16.0 3–14.5
Degree-dayfactorforsnowmelton21June
SMFMX Degree-day factor for ice melt on 21 December 1.4–6.7 1.8–4.5
gmfmn (mm¨˝C´1¨day´1) 1.4–16.0 1.0–8.8
(mm·°C−1·day−1)
Degree-dayfactorforsnowmelton21December
SMSMFMFMNX (mDmeg¨˝reCe´-d1¨adya yfa´c1t)or for snow melt on 21 June (mm·°C−1·day−1) 1.4–61.7.4–6.7 01.5.8––34.5.5
Degree-day factor for snow melt on 21 December
SMFMN 1.4–6.7 0.5–3.5
(mm·°C−1·day−1)
4.2. ReconcilingHigh-ElevationPrecipitation
44..22..1 R.eAcossnecsilsimnge nHtigohf-AElPeHvaRtiOonD PITreEciPpirteactiiponit ation
ThewaterbalanceinthecatchmentwasunbalancedbasedontheAPHRODITEprecipitation.
4.2.1. Assessment of APHRODITE Precipitation
The average annual observed streamflow (R) at KQHS from 1968 to 1989 was 137 mm, but the
The water balance in the catchment was unbalanced based on the APHRODITE precipitation.
averageannualAPHRODITEprecipitation(P)duringthesameperiodwasonly98mm. Thedecline
The average annual observed streamflow (R) at KQHS from 1968 to 1989 was 137 mm, but the average
annual APHRODITE precipitation (P) during the same period was only 98 mm. The decline in glacier
Water2016,8, 186 8of20
in glacier water storage (∆S) was approximately 25 mm based on Equation (1) and the First and
SecondChinaGlacierInventories. ThesumofAPHRODITEprecipitationandglacierwaterstorage
changeswasstillsmallerthantheriverdischarge. Moreover,evapotranspirationinthearidregionof
northwesternChinaisintense. BasedonEquation(4),APHRODITEsubstantiallyunderestimatesthe
actualprecipitationintheYarkantRiverbasin.
Inaddition,whenalinearly-fittedPLAPSbasedonAPHRODITE(dottedlineinFigure2band
approximately22mm¨km´1)wasusedtoforcethecalibratedmodel,thesimulatedmonthlyaverage
streamflowwasunderestimatedby60%atKQHSand59%atKLKHScomparedtotheobservedvalues.
Additionally,theNSEvalueswereonly0.42and0.51atKQHSandKLKHS,respectively(Table2).
ThesimulatedglacierareashrinkageratebasedonthelinearPLAPSwas15.6%from1968–2007. This
valueislargerthantheobservedchanges,highlightingthefactthatunderestimatedprecipitationmay
beoffsetinthemodelbyoverestimatingmelt[21,22]. Ifthemodelattemptstoachieveanobserved
streamflow,alargerglacierareawillretreat,disagreeingwiththeactualsituation.
Table2.NSEandPBIASresultsforthesimulatedmonthlystreamflowattheKaqunandKuluklangan
hydrologicalstations(KQHSandKLKHS)basedoncorrectedandlinearPLAPSsfrom1968to1989in
theYarkantRiverbasinusingtheglacier-enhancedSWATmodel.
KQHS KLKHS
Scenarios Analysis
NSE PBIAS(%) NSE PBIAS(%)
Calibration 0.87 9.3 0.85 ´7.1
SimulatedusingcorrectedPLAPS
Validation 0.87 ´0.9 0.83 ´10.9
SimulatedusinglinearPLAPS 1968–1989 0.42 60 0.51 59
Overall,APHRODITEprecipitationforthecatchmentisunderestimatedandcannotbedirectly
usedinthehydrologicalmodeling. Figure2bshowsthatAPHRODITEprecipitationbelow4500mis
closetotheobservedvalues. Therefore,precipitationathighelevationsmustbecorrected.
4.2.2. DeterminationofPLAPS
At high elevations, precipitation was calculated via Equation (3) and calibrated based on the
observedmonthlydischargesatKQHSandKLKHSandglacierchangedata. Foreachglacier, the
annualprecipitationattheELAwascalculatedusingEquation(3)from1968to1970, asshownin
Figure2b(thegreydotsrepresenttheaverageprecipitationatthecorrespondingelevationandwere
derived from Equation (3) for each glacier). The precipitation exhibited a decreasing trend with
increasingelevation. BookhagenandBurbank[19]andSalernoetal.[17]usedTRMMandobserved
precipitationdatatoconfirmthatprecipitationathigherelevationsexhibitsadecreasingtrendwith
increasingelevationintheHimalayas. UsingAPHRODITEprecipitationdataatlowelevations,the
PLAPSforthecatchmentisnon-linearandcanbedividedintomultiplerates. However,theturning
pointsforthePLAPSaredifficulttodetermine. Asmentionedbefore,theYarkantRiverbasincontains
3064glaciers. Inthe1960s,theELAsof90%oftheglaciersrangedfrom5000to5800ma.s.l.,andthe
highestfrequencyoccurredatapproximately5300ma.s.l. basedondatafromtheFirstChinaGlacier
Inventory. Aftercalibration,weselected4800mand5300mastheturningpointsforthemultiple
PLAPSvalues. ThepolylineinFigure2bwasthusdeterminedbasedonthevaluesatthesepoints.
Aftercalibration,multiplePLAPSswereobtainedfortheentirebasin.
ThisvariablePLAPSwascalibratedbasedontheobservedandsimulatedmonthlystreamflow
atKQHSandKLKHSandtheglacierareachanges. Theresultsofthecalibrationandvalidationare
asfollows.
TheperformanceoftheSWATmodelforcedbythecorrectedPLAPSwasassessedbycomparing
simulatedandobservedstreamflowsusingcalibrationandvalidationanalyses. Duringthecalibration
period,NSEvaluesatKQHSandKLKHSwere0.87and0.85,respectively,andPBIASvalueswere9.3%
Water2016,8, 186 9of20
and´7.1%(Table2). Themodelperformancewasratedas‘verygood’basedonMoriasietal.[50]. For
thevalidationanalysis,NSEvalueswere0.87and0.83forKQHSandKLKHS,respectively,andPBIAS
Water 2016, 8, 186 9 of 20
valueswere´0.9%and´10.9%. Themodelperformancewasratedas‘good’and‘verygood’based
onNSEresapnedctiPvBelIyA, Sanmd ePaBsIuArSe vsa[l5u0e]s. were −0.9% and −10.9%. The model performance was rated as ‘good’
and ‘very good’ based on NSE and PBIAS measures [50].
Anassessmentofthegoodness-of-fitbetweenthevaluessimulatedusingcorrectedPLAPSand
An assessment of the goodness-of-fit between the values simulated using corrected PLAPS and
observedmonthlystreamflowatKQHSandKLKHSwasperformedbasedonlinearregressionanalysis
observed monthly streamflow at KQHS and KLKHS was performed based on linear regression
(Figure 3). It showed that the regression lines were very close to the 1:1 line, with a minor y-axis
analWysaitesr 2(F01i6g, u8,r 1e8 63 ). It showed that the regression lines were very close to the 1:1 line, with9 oaf 2m0 inor
intercept,suggestingthatthedischargeatKQHSwasslightlyunderestimatedandthatthedischarge
y-axis intercept, suggesting that the discharge at KQHS was slightly underestimated and that the
atKLKdHiscShreawsrpgaeesc taisvtl eKilgyLh,K atnHlydS Po wBvIaeAsr Sse lvsigatilhumtelsya wtoevedree.r e−Ts0th.9ime%a catoneded f.−fi T1c0hi.9ee% nco.t Tesfhofeicf mideonedttsee lo rpfme drifeontreamrtmiaonincnea (wtRioa2ns) r(awRte2e)d rw aese 0r‘ge.8o 0o8.d8a8’ n adnd0 .9for
and ‘very good’ based on NSE and PBIAS measures [50].
KQHS0.a9n fdorK KLQKHHS Sa,nrde sKpLeKctHivSe, lrye,spsuecgtigveeslyti, nsgugtgheasttitnhge thsiamt tuhlea tseimdurliavteedr driivsecrh dairsgcheaerxghe iebxihteibditeadg ao odfit
An assessment of the goodness-of-fit between the values simulated using corrected PLAPS and
withthgeooodob bfsisete rrwvvieetdhd mtvhoaen louthbelsyse risvntreetdah mveaflYlouawer ska iatn nK tthQReH iYSva earrkndabn aKts LRinKiv.HeSr bwaassi np. erformed based on linear regression
analysis (Figure 3). It showed that the regression lines were very close to the 1:1 line, with a minor
y-axi1s5 0i0ntercept, suggesting that the discharge at KQH1S20 w0 as slightly underestimated and that the
a. Kaqun b. Kuluklangan
discharge at KLKHS was slightly overestimated. The coefficients of determination (R2) were 0.88 and
0g.o93m/s)o fdo1 2rf0i 0tK wQiHthS t haen do bKseLy r=Kv 0He.9d8S x,v -ar4e.l9us1pese cinti vtheley Y, saurkgagnets tRinivge 3m/s)trh baa9t0s 0tihne. simulated river discharge exhibited a
ntly discharge ( 9001500 a. Kaqun R² = 0.88 ntly discharge (1206000 b. Kuluklangan y = 1.14x -8.99
Simulated mo 36003montly discharge (m/s)001692000000 y = R0². 9=8 0x. -884.91 1VR:ea1gl ulrieenssesion 3montly discharge (m/s)Simulated mo690030000 y = R1². 1=4 0xR. -9²08 =.9 091VR.9:ea10gl ulrieenssesion
Simulated 0 3000 O30b0served m60o0nthly di9s0ch0arge (m12310/:1s0) line 1500 Simulated 3000 0 Ob3se0r0ved month60ly0 discha1rg:1e 9 l(i0nm0e3/s) 1200
Values Values
FigureF3ig.uCreo m3. pCaormispoanrissobnest wbeetweneeonb osbesrevrRevedgerdeas sainonndd ssiimmuullaatteedd mmonotnhtlhy lsytrsetarmeaflmowflso awt sthaeRte KgtrheassqeiounKna (qa)u ann(da )and
0 0
Kuluklangan (b) hydrological stations in the Yarkant River Basin from 1968 to 1989.
Kuluklangan(b)0hydro30l0ogic6a0l0stat9i0o0nsi1n20t0heY15a00rkantRiver0Basin3f0r0om1960608to1990809. 1200
Observed monthly discharge (m3/s) Observed monthly discharge (m3/s)
However, using the change in the glacier area based on the different phases of glacier data as an
Figure 3. Comparisons between observed and simulated monthly streamflows at the Kaqun (a) and
Haodwdietivoenra,l ucrsiitnergiotnh emcahy ahneglpe cionntshtreaign ltahcei eurnacerretaaibntays eodf tohen htihgeh-delieffvearteionnt PpLhAaPsSe.s Tohfeg olbasceirevredd ataas
Kuluklangan (b) hydrological stations in the Yarkant River Basin from 1968 to 1989.
anaddgiltaicoinera larcerait reertiroenatmeda byyh 9e%lp frcoomn sthtrea 1in96t0hse tou 2n0c0e9r.t aTihnet ysimofutlhateedh igglahc-ieelre avraetai osnhrPinLkAagPeS r.aTteh beaosebds erved
glacierona rtehae rceoHtrrroeewcateteveded rP, buLysAin9Pg%S t hwfera cosh m8a.n9gt%he eifnr 1oth9me6 g01l9sa6ct8ioe rto 2a 0r2e00a90 b7.a,T swehdhe oicsnhi tm hiseu edlxiaftfrteeermdenegtl pylah ccalisoeessre o atfor ge tlahacesi ehorb rdisanetrkav aaesgd ae nvr aalutee.b ased
Moraedodvietrio, nfoalr cdriitfeferiroenn tm galya chieelrp s cizoen sctlraasins etsh,e t huen cseirmtauinlatyte odf athree ah isghhr-ienlekvaagteio rna PteLsA wPSe.r eT haels oob csleorsvee dto the
onthecorrectedPLAPSwas8.9%from1968to2007,whichisextremelyclosetotheobservedvalue.
obsegrlvaecdie rv aarleuae rse (tFreiagtuerde b 4y) ,9 a%n fdr otmhe t hmeo 1s9t6 o0sb vtoi o2u00s9 r.e Ttrheea sti mocucluatrerde dg lianc igerla acrieear ss hwriinthka agree raast eo bf alesesds than
Moreover,fordifferentglaciersizeclasses,thesimulatedareashrinkagerateswerealsoclosetothe
5 kmon2. thTeh ceo rrreecstueldt sP LmAaPyS wreavs e8a.9l %t hfraotm t h19e6 8p troe c2i0p0i7t,a wtihoinc hi ins ehxitgrehm-eelleyv calotisoen t o rtehgei oobnsse rcvaeldc uvlaaluteed. via
observedvMaolrueoevse(rF, fiogru drifefe4re),nta gnldacitehr esizme oclsatssoebs,v thioe usismruelatrteeda taroeca csuhrrirnekdagien ragtelas cwieerres awlsoit chloaser etoa sthoe flessthan
ELA-based PLAPS effectively mimics the actual precipitation, thus providing sufficient accumulation
5km2. Theobrseesruveltds vmaluaeys r(eFvigeuarel t4h),a atntdh tehep mreocsitp oibtavtioiouns rientrehaitg ohc-ceulrerveda tinio gnlarceiegriso wnisthc aarlecaus loaft eledssv thiaanE L A-based
for glaciers with low area shrinkage rates.
5 km2. The results may reveal that the precipitation in high-elevation regions calculated via
PLAPSeffectivelymimicstheactualprecipitation,thusprovidingsufficientaccumulationforglaciers
ELA-based PLAPS effectively mimics the actual precipitation, thus providing sufficient accumulation
withlowareashrinkage2r5ates.
for glaciers with low area shrinkage rates.
Calculated values by CGI
%)2025
e ( Simulated values (1968-2007)
eat rat1e (%)520 CSiamlcuulalatteedd v vaalulueess ( b19y6 C8G-2I007)
cier retr1etreat rat015
Glaer r510
ci
a
Gl 5
0
0-0.1 0.1-0.5 0.5-1 1.0-5.05.0-10.010.0-50 50-100 >100
0
0-0.1 0.1-0.5 0.5-1Gl1a.c0i-e5r.0 s5iz.0e-s1 0(k.0m120).0-50 50-100 >100
Glacier sizes (km2)
Figure 4. Comparison between the observed and simulated glacier shrinkage rates for different glacier
size Fcilgaussrees 4 .i nC othmep Yarairskona nbte tRwiveeenr tbhaes oinbs (eCrvGeId s atnadn dsism fuolra ttehde gClahciinera sGhrlianckiearg eI nravteens tfoorr yd)i.f ferent glacier
Figure4.Comparisonbetweentheobservedandsimulatedglaciershrinkageratesfordifferentglacier
size classes in the Yarkant River basin (CGI stands for the China Glacier Inventory).
si zeclassesintheYarkantRiverbasin(CGIstandsfortheChinaGlacierInventory).
Water2016,8, 186 10of20
Inaddition,theprecipitationcalculatedusingthecorrectedPLAPSmaintainedthewaterbalance.
From1968to1989,thesimulatedannualprecipitationandevapotranspirationfortheentirecatchment
were207mmand98mm,respectively. Thechangesinglacierwaterstorageandotherwaterstorages
(snow,soilwaterandaquiferstorage)were´26.9mmand0.6mm,respectively. Thesimulatedannual
averagerunoffforthesameperiodwas135mm,whichwasclosetotheobservedstreamflow. Based
onEquation(4),thewaterbalanceerrorwasalmostzero,andthecorrectedPLAPSproducedaclose
waterbalancefortheglacieredwatershedsimulation.
Therefore, the model performance based on the corrected PLAPS, in terms of the simulated
streamflowandglacierareachange,wasacceptable. Additionally,thePLAPSwasabletosimulatethe
precipitation,especiallythehigh-elevationprecipitationoftheYarkantcatchment,witharelatively
highaccuracy.
4.2.3. InverselyCalculatingPrecipitationUsingtheCorrectedPLAPS
ThecalibratedPLAPScontrolledbystreamflowandglacierareachangewasusedtoinversely
calculate the high-elevation precipitation in the Yarkant River basin. Figure 2b shows that the
precipitation simulated by the corrected PLAPS increased at elevations below 5300 m a.s.l. and
decreased at elevations above this level. It peaked at 5300 m a.s.l., and the precipitation between
5100 and 5500 m a.s.l. was approximately 800–1000 mm, which was approximately five-times
larger than the APHRODITE precipitation estimate. This result makes sense based on previous
observations and studies. Field data indicate that the annual precipitation increases significantly
between 4600 and 5150 m a.s.l. on the K2 glacier [11] and is 100 mm below 3000 m a.s.l. and
1300–1500 mm above 5000 m a.s.l. on the Batura glacier [11,59]. Some studies have reported
a precipitation peak between 5000 and 6000 m a.s.l. in the Karakoram [13,60]. Combining time
seriesanalysesofthevalley-basedstationswithshort-termcampaigns,Hijmansetal.[61]proposed
thattheannualprecipitationinnorthwesternKarakoramincreasedfrom150to500mmatelevations
of 1500–3000ma.s.l. to greater than 1700 mm at 5500 m a.s.l. due to winter and occasional spring
andsummerrainfall. Basedonconservativeassumptions,Winigeretal.[62]suggestedthatrainfall
inwestKarakorumcouldreach1500–1800mm¨y´1at5000ma.s.l. Moreover,Shi[63]revealedthat
thisregionlikelyreceivesconsiderableprecipitation,whichleadstoconsiderableaccumulation,in
turnsupportingthedevelopmentoffamouslylargeglaciers. Immerzeeletal.[5]alsosuggestedthat
theobservedglacierextentsintheKarakoramcanbeexplainedbystrongverticalprecipitationlapse
rates. Immerzeeletal.[20]unambiguouslyshowedthathigh-elevationprecipitationinthisregionwas
underestimatedusinggriddeddataandthatthelargeglaciersherecanonlybesustainedbymuch
higherratesofaccumulation. Therefore,thehighprecipitationratesathighelevationscalculatedusing
thecorrectedPLAPSarereasonableaccordingtotheaforementionedobservationsandinvestigations.
5. Discussion
5.1. UncertaintiesofthePLAPS
With respect to data-scarce, high-alpine catchments, glacio-hydrological simulations are
particularly limited by a lack of sufficient observation data. Despite the good simulation results
inthisstudy,uncertaintiesstillexist. ThePLAPSathighelevationswascorrectedbyprecipitation
that was calculated via Equation (3). On the one hand, Equation (3) is an experimental formula
basedonobservedglaciersfromaroundtheglobe,andthecalculatedprecipitationmayrepresent
large-scalevalues. Ontheotherhand,themultiplePLAPSinthisstudyusedfourpointstodetermine
the lapse rates in the three sections (Figure 2b). Among the points, the precipitation values and
elevationsofthesecondandthirdpoints(P_2,P_3andEL_2,EL_3;numberedfromlowtohigh)were
moredifficulttodetermineandweremoreuncertain. Figure5showsthatstreamflowissensitiveto
theprecipitationandelevationvaluesofthetwopoints,whereastheglacierareachangesaremore
sensitivetoprecipitationatthethirdpoint. BasedonMoriasietal.[50],toreacha“satisfactory”rate,
theelevationsatthetwopointscanrangefrom4600to5050ma.s.l. and5000to5800ma.s.l.,and