Hydrological modeling in glacierized catchments of central Asia

16 Pages·2017·1.88 MB·English

Hydrol.EarthSyst.Sci.,21,669–684,2017
www.hydrol-earth-syst-sci.net/21/669/2017/
doi:10.5194/hess-21-669-2017
©Author(s)2017.CCAttribution3.0License.
Review article: Hydrological modeling in glacierized catchments of
central Asia – status and challenges
YaningChen,WeihongLi,GonghuanFang,andZhiLi
StateKeyLaboratoryofDesertandOasisEcology,XinjiangInstituteofEcologyandGeography,
ChineseAcademyofSciences,Urumqi830011,China
Correspondenceto:YaningChen([email protected])
Received:27June2016–PublishedinHydrol.EarthSyst.Sci.Discuss.:25July2016
Revised:17December2016–Accepted:22December2016–Published:2February2017
Abstract. Meltwater from glacierized catchments is one of et al., 2014). In fact, in the alpine river basins of the north-
themostimportantwatersuppliesincentralAsia.Therefore, ernTienshans,glaciermeltwatercontributes10%ofannual
theeffectsofclimatechangeonglaciersandsnowcoverwill runoffand20%ofrunoffduringthedroughtyears(Aizenet
have increasingly significant consequences for runoff. Hy- al.,1997);therefore,climate-drivenchangesinglacier/snow-
drological modeling has become an indispensable research fedrunoffregimeshavesignificanteffectsonwatersupplies
approach to water resources management in large glacier- (Immerzeeletal.,2010;Kaseretal.,2010).
ized river basins, but there is a lack of focus in the mod- According to a study conducted by the Eurasian Devel-
eling of glacial discharge. This paper reviews the status of opment Bank, changes in temperature and precipitation in
hydrological modeling in glacierized catchments of central central Asia have led to rapid regression in glaciers (Ibat-
Asia, discussing the limitations of the available models and ullin et al., 2009). The overall decrease in total glacier area
extrapolatingthesetofuturechallengesanddirections.After andmassfrom1961to2012tobe18±6%and27±15%,
reviewing recent efforts, we conclude that the main sources respectively. These values correspond to a total area loss of
ofuncertaintyinassessingtheregionalhydrologicalimpacts 2960±1030km2, and an average glacier mass change rate
ofclimatechangearetheunreliableandincompletedatasets of −5.4±2.8Gtyr−1 (Farinotti et al., 2015). If the warm-
and the lack of understanding of the hydrological regimes ingprojectionsdevelopedbytheIntergovernmentalPanelon
of glacierized catchments of central Asia. Runoff trends in- ClimateChange(IPCC)provetobetrue,theglacierizedriver
dicateacomplexresponsetochangesinclimate.Forfuture systems in central Asia will undergo unfavorable hydrolog-
variationofwaterresources,itisessentialtoquantifythere- ical changes, e.g., altered seasonality, increased flood risk,
sponsesofhydrologicprocessestobothclimatechangeand higherandintensespringdischargeandwaterdeficiency,in
shrinking glaciers in glacierized catchments, and scientific hotanddrysummerperiods,especiallygiventhesharprise
focusshouldbeonreducinguncertaintieslinkedtothesepro- in water demand (Hagg et al., 2006; Siegfried et al., 2012).
cesses. The development of hydrological models on accounting for
changes in current and future runoff is therefore crucial for
water resources allocation in river basins, and includes un-
derstandingclimatic variabilityas wellas theimpact ofhu-
1 Introduction manactivitiesonclimate(Bierkens,2015).
Hydrological modeling is an indispensable approach to
Climate change is widely anticipated to exacerbate water water resources research and management in large river
stress in central Asia in the near future (Siegfried et al., basins. Such models help researchers understand past and
2012),asthevastmajorityofthearidlowlandsintheregion current changes and provide a way to explore the implica-
are highly dependent on glacier meltwater supplied by the tions of management decisions and imposed changes. The
TienshanMountains,whichareknownasthe“watertower” purposeofhydrologicalmodelingonbasinscaleisprimarily
of central Asia (Hagg et al., 2007; Sorg et al., 2012; Lutz
PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
670 Y.Chenetal.:HydrologicalmodelinginglacierizedcatchmentsofcentralAsia
tosupportdecision-makingforwaterresourcesmanagement, thetotalrunoffgeneratedfromthemeltingofglaciers(snow
which can be summarized as resource assessment, vulnera- and glacier), but can also include liquid precipitation on
bilityassessment,impactassessment,floodriskassessment, glacierized areas (Unger-Shayesteh et al., 2013). A large
prediction and early warning (World Meteorological Orga- numberofhydrologicalmodelsappliedinglacierizedcatch-
nization, 2009). It is important to choose the most suitable mentsofcentralAsiaarebasin-scalemodels,whichcontain
hydrological model for a particular watershed based on the empiricalhydrologicalmodelsaswellasphysicalhydrolog-
area’sclimate,hydrologyandunderlyingsurfaceconditions. icalmodels(Table2).Theseglacio-hydrologicalmodelsare
The Tienshan Mountains span several countries and sub- usefultoolsforanticipatingandevaluatingtheimpactsofcli-
regions, creating a decentralized political entity of complex matechangesintheheadwatercatchmentsofthemainAsian
multi-nationalandmulti-ethnicforms.Therearethreelarge rivers(Milleretal.,2012).
transboundary international rivers that originate in the high
mountains of central Asia. In an international river, hydro- 2.1 Currentandfuturerunoffchanges
logicalchangesarerelatedtotheinterestsoftheabuttingri-
pariancountries(StarodubtsevandTruskavetskiy,2011;Xie Riverrunoffrespondsinacomplexwaytovariationsincli-
etal.,2011;Guoetal.,2015).However,asconflictsbetween mate and the cryosphere. At the same time, runoff changes
political states may arise for any number of reasons (politi- alsodependondominantrunoffcomponents.Table2shows
cal, cultural, etc.), transboundary issues may result in frag- that annual runoff anomalies have increased to some ex-
mentedresearchandthuslimitthedevelopmentofhydrolog- tent (except in the western Tienshan Mountains) and in-
icalmodeling. consistencies between changes in precipitation and runoff
Amid this potential hindrance to robust research efforts, have occurred in heavily glacierized catchments. In rivers
theeffectofclimatechangeonglaciers,permafrostandsnow fed by snow and glaciers, runoff has increased (e.g., in
cover is having increasing impacts on runoff in glacierized northernTienshanMountains)andrisingtemperaturesdom-
central Asian catchments. However, solid water is seldom inate the runoff changes by, for instance, increasing the
explicitly considered within hydrological models due to the snowmelt/glacier melt and decreasing snowfall fraction (ra-
lackofcompleteglacierdata.Ourknowledgeofsnow/glacier tio of solid precipitation to liquid precipitation) (Chen,
changesandtheirresponsestoclimateforcingisstillmostly 2014). Khan and Holko (2009) compared runoff changes
incomplete. Analysis of current and future water resources withvariationsinsnowcoverareaandsnowdepth.Theysug-
variationsincentralAsiamaypromoteadaptationstrategies gestedthatthemismatchbetweendecreasingtrendsinsnow
toalleviatethenegativeimpactsofexpectedincreasedvari- indicatorsandtheincreasingriverrunoffcouldbetheresult
abilityinrunoffchangesresultingfromclimatechange. ofenhancedglaciermelting.Heavilyglacierizedriverbasins
In this paper, we review hydrological modeling efforts in showedmainlypositiverunofftrendsinthepastfewdecades
fivemajorriverbasinsoriginatingfromtheTienshanMoun- (simulatedunderdifferentscenariosintheheadriversofthe
tainsincentralAsia,namely,theTarimRiverbasin,thewa- TarimRiverbasin),whilethosewithlessornoglacierization
tersheds in the northern slope of the Tienshan Mountains exhibitedwidevariationsinrunoff(Duethmannetal.,2015;
(which includes several small river basins), the Issyk Lake Kaldybayevetal.,2016).
basin,theIliRiverbasin,andtheAmuDaryaandSyrDarya With further warming and the resulted acceleration of
basins (Fig. 1). Their topographical characteristics, climate glacier retreat, glacier inflection points will or have already
and vegetation together with the glacierized area are listed appeared.Theamountofsurfacewaterwillprobablydecline
inTable1.Weexaminethetypes,purposeanduseofexist- orkeephighvolatilityduetoglacialretreatandreducedstor-
ingmodelsandassesstheconstraintsandgapsinknowledge. age capacity of glaciers (Chen et al., 2015). For instance,
Thecurrentlackofunderstandingofhigh-altitudehydrolog- near-futurerunoffsareprojectedtoincreasetosomeextent,
icalregimesiscausinguncertaintyinassessingtheregional withincrementsof13–35%during2011–2050comparedto
hydrologicalimpactsofclimatechange(Milleretal.,2012). 1960–2006fortheYarkandRiver,−1–18%inthe21stcen-
Snowandglacialmeltassuppliesofsolidwaterisakeyel- tury compared to 1986–2020 under RCP4.5 for the Kaidu
ementinstreamflowregimes(Lutzetal.,2014);therefore,it River, and 23% in 2020 for the Hotan River (Table 2). For
isnecessarytoincludeglaciermassbalanceestimatesinthe thelong-term,however,totalrunoffisprojectedtobesmaller
model calibration procedure (Schaefli et al., 2005; Stahl et than today. The hydrological responses to climate change
al.,2008;KonzandSeibert,2010;Mayretal.,2013). aroundtheworldwerediscussedinSect.2.3.
2.2 Contributionofglaciermelt/snowmeltwaterin
2 Modelinghydrologicalresponsestoclimatechange riverrunoff
Changes in the amount and seasonal distribution of river Kemmerikh(1972)estimatedthecontributionofgroundwa-
runoff may have severe implications for water resources ter,snowmeltandglaciermelttothetotalrunoffofthealpine
management in central Asia. “Glacier runoff” is defined as rivers in central Asia. Based on the hydrograph separation
Hydrol.EarthSyst.Sci.,21,669–684,2017 www.hydrol-earth-syst-sci.net/21/669/2017/
Y.Chenetal.:HydrologicalmodelinginglacierizedcatchmentsofcentralAsia 671
Figure1.MapofcentralAsianheadwaterswithmainriverbasinsorhydrologicalregions,namely,theTarimRiverbasin,thewatershedsin
thenorthernslopeoftheTienshanMountains,theIssykLakebasin,theIliRiverbasin,andtheAmuDaryaandtheSyrDaryabasins.Lake
outlinesarefromNaturalEarth(http://www.naturalearthdata.com/).TheriversystemisderivedbasedonelevationsoftheSRTM(Shuttle
RadarTopographyMission)90mdata.GlacierinformationwasobtainedfromRGI(RandolphGlacierInventory).
Table1.Summaryofclimaticandunderlyingconditionsofthebasins.ThetopographyisbasedonSRTMdata,glacierdataarefromRGI
(RandolphGlacierInventory)andclimateisbasedontheworldmapoftheKöppen–Geigerclimateclassification.Vegetationisfromthe
landusedatafromXinjianginstituteofEcologyandGeography.
Catchment Tarim Catchments Issyk Ili Amu Syr
River innorthern Lake River Darya Darya
basin TienshanMountains, basin basin Basin Basin
China
Location Surroundedbythe Northern Western Western Western Western
TienshanMountainsand Tienshan Tienshan Tienshan Tienshanand Tienshan
theKunlunMountains Valley Pamir
Topography
Basinarea(km2) 868811 126463 102396 429183 674848 442476
Percentageofelevation 28.00 13.80 14.50 4.60 20.50 9.50
>3000m(%)
Glaciationarea(km2) 15789 1795 994 2170 9080 1850
Climate
Dominantclimate arid arid arid arid arid arid
cold cold cold; cold; cold; cold
continental continental snow
Vegetation
Forestpercent(%) 0.7 10.4 6.4 4.1 10.9 2.5
Pasturepercent(%) 16.7 14.5 31.2 28.6 19.4 17.3
Percentofwater,snow,ice(%) 5.4 3.9 7.8 5.3 5.3 2.8
methodology,theglaciermeltcontributionrangedbetween5 catchments in the Tienshan Mountains based on hydrologi-
and40%intheplainsandaround70%inupstreambasins. calmodeling(Y.Zhangetal.,2016).
The ratio of glacier melt contribution to runoff varies be- Distributed hydrological models provide a more useful
tween 3.5 and 67.5% with a mean of 24.0% for the 24 toolfortheinvestigationofchangesindifferentrunoffcom-
ponents.Forexample,thevariableinfiltrationcapacity(VIC)
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672 Y.Chenetal.:HydrologicalmodelinginglacierizedcatchmentsofcentralAsia
Table2.SummaryofhydrologicalmodelinginglacierizedcentralAsiancatchments.
Catchments Models Majorconclusions Innovationsandlimitations References
TarimRiver
TarimRiverbasin Modified two-parameter Improvedtheoriginaltwo-parametermonthlywaterbal- Lessinputdataarerequired;Lackof PengandXu(2010);
semi-distributed water ancemodelbyincorporatingthetopographicindexesand glacierandsnowmeltprocesses. Chenetal.(2006)
balancemodel couldgetcomparableresultstotheTOPMODELmodeland
Xinanjiangmodel.
TOPMODELmodel IntheAksuRiver,runoffwasmorecloselyrelatedtopre-
cipitation,whereasintheHotanRiver,itwasmoreclosely
relatedtotemperature.
Xinanjiangmodel RunoffsoftheAksu,YarkandandHotanriversexhibited
increasingtendenciesin2010and2020underdifferentsce-
nariosgeneratedfromthereferenceyears,e.g.,23%in-
creasefortheHotanRiver.
PPR(Projectionpursuitre- Iftemperaturerises0.5–2.0◦C,runoffwillincreasewith Lackofphysicalbasis. Wuetal.(2003)
gression)model temperaturefortheAksu,YarkandandHotanrivers.
VIC(variableinfiltrationca- FortheTarimRiver,runoffwilldecreaseslightlyin2020– Lackofglaciermodule. Liuetal.(2010)
pacity) 2025basedonVICforcedbyHadCM3underA2andB2
whennotconsideringglaciermelt.
TailanRiver Modifieddegree-daymodel Glacier runoff increases linearly with temperature over Consideredtheeffectofsolarradiation Y.Zhangetal.(2007)
includingpotentialclearsky theserangeswhetherornotthedebrislayeristakeninto andquantifiedthedebriseffect.
direct solar radiation cou- consideration.Theglacierrunoffislesssensitivetotemper-
pledwithalinearreservoir aturechangeinthedebris-coveredareathanthedebris-free
model area.
Aksu River includ- Xinanjiangmodel PrecipitationhasaweakrelationshipwithrunoffintheKu- Joinedthesnowmeltmodule. P.Wangetal.(2012)
ing Kumalike and malikeRiver.
Toxkanrivers
Themodelcouldnotwellcapturethe
snowmelt-/precipitation-induced peak
streamflow.
VIC-3Lmodel Glaciermelt,snowmeltandrainfallaccountedfor43.8,27.7 Themodelperformancewasobviously Zhaoetal.(2013,2015)
and28.5%ofthedischargefortheKumalikeRiverand improved through coupling a degree-
23.0,26.1and50.9%fortheToxkanRiver. dayglaciermeltscheme,butaccurately
estimatingarealprecipitationinalpine
regionsstillremains.
FortheKumalikeRiverandtheToxkanRiver,therunoff
hasincreased13.6and44.9%during1970–2007,and94.5
and100%oftheincreaseswereattributedbyprecipitation
increase.
FortheKumalikeRiver,glacierareawillreduceby>30%
resultingindecreasedmeltwaterinsummerandannualdis-
charge(about2.8–19.4%inthe2050s).
SWIMmodel Themodeliscapabletoreproducethemonthlydischargeat Investigated the glacier lake outburst Huangetal.(2015);
thedownstreamgaugewell,usingthelocalirrigationinfor- floodsusingamodelingtool.Inclusion Wortmannetal.(2014)
mationandtheobservedupstreaminflowdischarges. ofanirrigationmoduleandarivertrans-
mission losses module of the SWIM
model.
About18%oftheincomingheadwaterresourcesconsumed Modeluncertaintiesarethelargestinthe
uptothegaugeinXidaqiao,andabout30%additionalwa- snowmeltandglaciermeltperiods.
terisconsumedbetweenXidaqiaoandAlar.
Differentirrigationscenariosweredevelopedandshowed
thattheimprovementofirrigationefficiencywasthemost
effectivemeasureforreducingirrigationwaterconsumption
andincreasingriverdischargedownstream.
WASAmodel Glaciermeltcontributesto35–48and9–24%fortheKu- The model considered changes in Duethmannetal.(2015)
malikeRiverandtheToxkanRiver. glaciergeometry(e.g.,glacierareaand
surfaceelevation).
FortheKumalikeRiver,glaciergeometrychangesleadto It used a multi-objective calibration
areductionof14–23%ofstreamflowincreasecomparedto basedonglaciermassbalanceanddis-
constantglaciergeometry. charge.
Thetemperatureandprecip- TheAR(p)modeliscapableofpredictingthestreamflowin AR(p)needslesshydrologicalandme- Ouyangetal.(2007)
itationrevisedAR(p)model; theAksuRiverbasinwhiletheNAMmodelisnotideal. teorological data. Both model fails to
modelsuddenfloodssuchasicedam
collapsefloods.
NAM (NedborAfstromn-
ingsModel)rainfallrunoff
model
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Y.Chenetal.:HydrologicalmodelinginglacierizedcatchmentsofcentralAsia 673
Table2.Continued.
Catchments Models Majorconclusions Innovationsandlimitations References
TarimRiver
KaiduRiver MIKE-SHEmodel Comparedremotesensingdataandstation-baseddatain Missingglaciermelt;Lackofobserva- T.Liuetal.(2012);
simulatingthehydrologicalprocesses.Remotesensingdata tiontoverifythemeteorologicalcondi- Liuetal.(2013)
arecomparabletoconventionaldata.Remotesensingdata tioninthemountainousregions.
couldpartlyovercomethelackofnecessaryhydrological
modelinputdataindevelopingorremoteregions.
HBV(HydrologiskaByrans Whenthebaserunoffis100m3s−1,thecriticalrainfallfor Itunderestimatedthepeakstreamflow Fanetal.(2014)
Vattenbalansavdelning) primaryandsecondarywarningfloodsare50and30mm, whileoverestimatedthebaseflow.
model respectively,fortheKaiduRiver.
SRM (snowmelt runoff Spring streamflow is projected to increase in the future Limited observations resulted in low Y.C.Zhangetal.(2007);
model) including potential basedonHadCM3. modelingprecision.TheAPHRODITE Ma et al. (2013); Li et
clearskydirectsolarradia- precipitationperformedwellinhydro- al.(2014).
tionandtheeffectiveactive logicalmodelingintheKaiduRiver.
temperature.
SWAT Precipitationandtemperaturelapseratesaccountfor64.0% Quantifieduncertaintyresultedfromthe Fangetal.(2015a,b)
ofmodeluncertainty. meteorologicalinputs.
Runoffincreases(−1)–18and4–20%inthe21stcentury
underRCP4.5andRCP8.5comparedto1986–2005based
onacascadeofregionalclimatemodelRegCM,biascor-
rectionandSWATmodel.
Modified system dynamics Simulationsoflowflowandnormalflowaremuchbetter Applied the effective cumulative tem- F.Y.Zhangetal.(2016)
model thanthehighflow,andspringpeakflowisbetterthanthe peraturetocalculatesnowmeltprocess
summerpecksintheKaiduRiver. andsoiltemperatureforeachlayerto
describewatermovementinsoil.
YarkandRiver MIKE-SHEmodel Simulatedsnowpackusingstationdatadifferssignificantly Lackofglaciermodule Liuetal.(2016b)
fromthatusingremotesensingdata.
Integrating Wavelet Anal- Runoffpresentedanincreasingtrendsimilarwithtempera- Interpreted the nonlinear characteris- Xuetal.(2014)
ysis (WA) and back- tureandprecipitationatthetimescaleof32years.Butatthe ticsofthehydro-climaticprocessusing
propagationartificialneural 2-,4-,8-,and16-yeartimescale,runoffpresentednonlinear statisticmethod.
network(BPANN) variation.
Degree-daymodel Decreasingrateofglaciermasswas4.39mma−1result- Theglacierdynamicsisconsideredand Xieetal.(2006);Zhang
inginarunoffincreasingtrendof0.23×108m3a−1dur- thearea–volumescalingfactoriscali- etal.(2012a,b)
ing1961–2006.Sensitivityofmassbalancetotemperature bratedusingremotesensingdata.
is0.16mma−1◦C−1.
Glacier runoff will increase 13–35% during 2011–2050
comparedto1960–2006withobviousincreaseinsummer.
Tizinapu SRMincludingsnowalbedo ItcouldwellsimulatetherunoffoftheTizinapuRiver. Lackofglaciermodule. LiandWilliams(2008)
Runoffisdominatedbyprecipitationandtemperaturelapse
rates,andsnowalbedo.
HotanRiver Integrating Wavelet Anal- Runoffcorrelateswellwiththe0◦Clevelheightinsummer Interpretedthenonlinearcharacteristics Xuetal.(2011)
ysis (WA) and back- forthenorthernslopeofKunlunMountains. ofthehydro-climaticprocess.
propagationartificialneural
network(BPANN)
CatchmentsinnorthernslopeofTienshanMountains,China
ManasRiver (1)SWATmodel Glacier area decreased by 11% during 1961–1999 and Boththeglaciermeltmoduleandtwo- Yu et al. (2011); Luo
glaciermeltcontributes25%ofdischarge. reservoirmethodwereincludedinthe et al. (2012); Luo et
hydrologicalsimulations. al. (2013); Gan and
Luo(2013)
(2)SRMmodel Bettersimulationofsnowmeltrunoffthanrainfall–runoff Snow cover calculation algorithm is Yuetal.(2013)
bytheSRM. addedtovalidatemodelperformance.
(3)EasyDHMmodel EasyDHMmodelcouldreproducethestreamflow. Thevalidationisbasedonstreamflow
alone. Xingetal.(2014)
UrumqiRiver (1)Isotopehydrographsep- Glaciermeltwatercontributesto9%ofrunoff. TheIHSmethodhasoverwhelmingpo- KongandPang(2012)
aration(IHS) tentialinanalyzinghydrologicalcom-
ponentsforungaugedwatersheds.
(2)Waterbalancemodel Thecumulativemassbalanceoftheglacierwas-13.69m Foused on runoff generation on the Sunetal.(2013)
during1959–2008;proportionofglacierrunoffincreased glacierizedandablationarea.
from62.8to72.1%.
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674 Y.Chenetal.:HydrologicalmodelinginglacierizedcatchmentsofcentralAsia
Table2.Continued.
Catchments Models Majorconclusions Innovationsandlimitations References
CatchmentsinnorthernslopeofTienshanMountains,China
(3)HBVmodel Foraglacierizedcatchment(glacierizationratiois18%), Consideringfuturerunoffunderdiffer- Sunetal.(2015)
thedischargewillincreaseby66±35%ordecreaseby entglacierchangescenarios.
40±13%iftheglaciersizekeepsunchangedorglacierdis-
appearsin2041–2060.
(4)Exponentialregression Glacierrunoffiscriticallyaffectedbythegroundtempera- Thisstudyshedlightonglacierrunoff Chenetal.(2012)
ture. estimationbasedongroundtemperature
fordata-scarceregions.
(5)SRMmodel Thedegreedayfactorisnotconstantfordifferentelevation Calculated the curve of snow cover Huaietal.(2013)
bands. shrinkagebasedonMODISdata.
(6) THModel (Thermody- THModel can indeed simulate runoff processes in the Anenergybalancemodelisproposedto Mouetal.(2008)
namicWatershedHydrolog- glacierandsnow-dominatedcatchmentreasonablywell. closethebalanceequationofsoilfreez-
icalModel) ingandthawing.
Ebinur Lake catch- (1)SWATmodelandthese- For the Jinghe River, 85.7% of the runoff reduction is Identifiedtheeffectsofhumanactivities Dongetal.(2014);
mentincluding quentialclustermethod causedbyhumanactivityand14.3%byclimatechange. andclimatechangeonrunoff. Yaoetal.(2014)
Jinghe River, Kuy-
tun River and Bor-
talaRiver
(2)RunoffCAR(Controlled TheJingheRiverandKuytunRiverexhibitedaslightlyin- TheCARisbasedonpastandpresent
AutoRegressive)model creasingtrend,butanadversetrendintheBortalaRiver. valueswithoutphysicalbasis.
Inawarmhumidscenario,runoffintheJingheRiverand
BortalaRiverwillincreasewhileitwilldecreaseinthe
KuytunRiver.
JuntanghuBasin DHSVM (Distributed Hy- The coupled WRF (Weather Research and Forecasting) MODIS snow cover and the calcu- Zhaoetal.(2009)
drology Soil Vegetation modellingsystemandDHSVMmodelcouldpredict24h latedsnowdepthdataareusedinthe
Model) snowmeltrunoffwithrelativeerrorwithin15%. snowmeltrunoffmodeling.
IssykLakeBasin
Small rivers around Degree-dayapproach Runoffcontributionisvaryinginabroadrangedepend- Theglaciermeltrunofffractionatthe DikichandHagg(2003)
theIssykLake ingonthedegreeofglacierizationintheparticularsub- catchment outlet can be considerably
catchment.Allriversshowedarelativeincreaseinannual overestimated.
riverrunoffrangingbetween3.2and36%.
ChuRiver SWAT-RSG (RSG: rain, Generaldecreasewasexpectedinglacierrunoff(−26.6 Usetheglacierdynamicsandassessed Maetal.(2015)
snowandglacier)model to−1.0%),snowmelt(−21.4to+1.1%)andstreamflow themodelperformancebasedonboth
(−27.7to−6.6%);Peakstreamflowwillbeputforward streamflowandglacierarea.
for1month.
IliRiverbasin
GongnaisiRiver SRMmodel Runoffissensitivetosnowcoverareaandtemperature. SRMiscapabletomodelthesnowmelt MaandCheng(2003)
runoff.
Iftemperatureincreases4◦C,therunoffwilldecreaseby
9.7%withsnowcoverageandrunoffshiftingforward.
TekesRiver SWATmodel Glaciershaveretreatedabout22%since1970s,whichwas Using two land use data and two Xuetal.(2015)
considerablyhigherthantheTienshanaverage(4.7%)and Chineseglacierinventories,themodel
Chinaaverage(11.5%),resultinginadecreaseofpropor- couldwellreproducestreamflow.
tionofprecipitationrechargedrunofffrom9.8%in1966–
1975to7.8%in2000–2008.
IliRiver DTVGM (multi-spatial Dailyrunoffcorrelatedcloselywithsnowmelt,suggesting This method has less dependence on Caietal.(2014)
data-based Distributed asnowmeltmoduleisindispensable. conventionalobservation.
Time-Variant Gain Model)
model
Waterbalancemodel Water decrease in 1911–1986 in the middle and lower – KezerandMatsuyama
reachesoftheLakeBalkhashisduetodecreasedrainfall (2006);Guoetal.(2011)
andreservoirsstorage.
AmuDaryaandSyrDaryabasins
AmuDaryaandSyr STREAM TherunoffoftheSyrDaryadeclinedconsiderablyoverthe Simulatedlong-termdischargeforthe Aertsetal.(2006)
Darya last9000years,butshowmuchsmallerresponsestofuture Holoceneandfutureperiod.
warming.
For the Amu Darya and Syr Darya basins, the glacier- Themodelincludesthecalculationof Savoskul et al. (2003);
coveredareashavedecrease15and22%in2001–2010 rainwater,snowmeltwaterandglacier Savoskul et al. (2004);
comparedtothebaseline(1960–1990). runoff(basedontheglacieraltitudeand SavoskulandSmakhtin
equilibriumlimealtitude). (2013)
FortheAmuRiverBasin,20–25%oftheglacierswillre-
tainunderatemperatureincrementbeing4–5◦Candpre-
cipitationincreaseratebeing3%/◦C.
FortheSyrDarya,runoffundertheA2andB2scenarios
willincrease3–8%in2010–2039,withsharpenedspring
peakandaslightloweredrunofffromlateJunetoAugust.
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Y.Chenetal.:HydrologicalmodelinginglacierizedcatchmentsofcentralAsia 675
Table2.Continued.
Catchments Models Majorconclusions Innovationsandlimitations References
AmuDaryaandSyrDaryabasins
AralMountainmodel FortheAmuDarya,glaciermeltandsnowmeltcontribute Fully simulated the hydrological pro- Immerzeeletal.(2012a)
to38and26.9%ofrunoff,whilefortheSyrDaryathepro- cesses.
portionsare10.7and35.2%.
Glacierwillretreatby46.4–59.5%by2050dependingon
selectedGCM(GeneralCirculationModel).FortheSyr
Darya,averagewatersupplytothedownstreamwillde-
creaseby15%for2021–2030and25%for2041–2050.
FortheAmuDaryatheexpecteddecreasesare13%(2021–
2030)and31%(2041–2050).
Testsites HBV-ETH(HBVmodelex- Overallgoodmodelperformanceswereachievedwiththe Itconsideredgeographical,topographi- Haggetal.(2007)
“Abramov”in pandedattheSwissFederal maximumdiscrepancyofsimulatedandobservedmonthly calandhydrometeorologicalfeaturesof
SyrDaryaand InstituteofTechnology)and runoffwithin20mm. testsites,andreducedmodelinguncer-
“Oigaing”in OEZ(awaterbalanceequa- tainties.
AmuDarya tionmodel)
General enhanced snowmelt during spring and a higher Thisprocedurerequiresalotmeteoro-
floodriskinsummerarepredictedunderadoublingatmo- logicalandlandsurfacedataandknowl-
sphericCO2concentrationwithgreatestrunoffincreases edgeofthehydrologicalprocesses.
occurringinAugustforthehighlyglaciatedcatchmentsand
inJuneforthenivalcatchment.
PanjRiver HBV-ETH FortheupperPanjcatchment,thecurrentglacierextentwill Applicationofglacierparameterization Haggetal.(2013)
decreaseby36and45%,respectively,assumingtempera- scheme.
tureincrementbeing2.2and3.1◦C.
NarynRiver SWAT-RSGmodel Glacierareahasdecreased7.3%during1973–2002. Incorporatedglacierdynamicsandvali- Ganetal.(2015)
datedthemodelusingtwoglacierinven-
tories.
Glacierswillrecedewithonly8%ofthesmallglaciersre-
tainby2100underRCP8.5andnetglaciermeltrunoffwill
reachpeakinabout2040anddecreaselater.
SyrDarya NAMmodelwithaseparate Glaciervolumewilllose31%±4%underSRESA2until TheNAMmodelwasimprovedtobero- Siegfriedetal.(2012)
land-icemodel 2050s,andtherunoffpeakwillshiftforwardby30–60days bustusingonlyfivefreelycalibratedpa-
fromthecurrentspring/earlysummertowardsalatewin- rameters.
ter/earlyspringrunoffregime.
modelwasusedtocalculatethecomponentsofrunoffinthe tributed to about 11.4–28.7% of the lake level rise in the
sourceriverfortheTarimRiver.Theresultsshowedthat,in threeglacier-fedlakes,namely,SilingCo,NamCoandPung
termsofrunoff,glaciermeltwater,snowmeltwaterandrain- Co(Leietal.,2013).Analysisfromgroundwaterstoragein-
fall accounted for 43.8, 27.7 and 28.5% of the Kumalike dicated that the groundwater for the major basins in the Ti-
River, and 23.0, 26.1 and 50.9% of the Toxkan River, re- betan Plateauincreasedduring 2003–2009 with a trend rate
spectively(Zhaoetal.,2013);thisresultiscomparabletothe of +1.86±1.69Gtyr−1 for the Yangtze River Source Re-
conclusionthat glaciermelt accountsfor31–36% basedon gion and +1.14±1.39Gtyr−1 for the Yellow River Source
isotopetracer(Sunetal.,2016).However,accuratelyquan- Region(Xiangetal.,2016).
tifyingthecontributionsofglaciermelt,snowmeltandrain- FortheSouthAmericanAndes,meltingattheglaciersum-
falltorunoffincentralAsianstreamsischallenging(Unger- mit has occurred. With the continually increase in tempera-
Shayestehetal.,2013). ture, although glacier melt was dominated by maybe other
processes in some regions, the probability seems high that
2.3 Glacio-hydrologicalresponsestoclimatechange:a the current glacier melting will continue. With the loss of
comparison glacierwater,thecurrentdry-seasonwaterresourceswillbe
heavilydepletedoncetheglaciershavedisappeared(Barnett
To analyze the hydrological responses to climate change of etal.,2005).
theglacierizedTienshanMountains,theresponsesofseveral For the Alps, many investigations have been imple-
majorglacierizedmountainousregionsarediscussed.Forthe mented,rangingfromglacier-scalemodelingtolargebasin-
Himalaya–HinduKushregion,investigationssuggestedthat scale or region-scale modeling (Finger et al., 2015; Ab-
aregressionofthemaximumspringstreamflowperiodinthe baspour et al., 2015). Glacier meltwater provided about
annual cycle of about 30 days, and annual runoff decreased 5.28±0.48km3a−1offreshwaterduring1980–2009.About
by about 18% for the snow-fed basin, whereas it increased 75% of this volume occurred during July–September, pro-
byabout33%fortheglacier-fedbasinusingtheSatlujBasin vidingwaterforlargelow-lyingriversincludingthePo,the
asatypicalregion(SinghandBengtsson,2005).FortheTi- RhineandtheRhône(Farinottietal.,2016).Underthecon-
betanPlateau,theglacierretreatcouldleadtoanexpansion textofclimatechange,decreasesofglaciermeltwaterinboth
oflakes;e.g.,glaciermasslossbetween1999and2010con- annualandsummerrunoffcontributionsareanticipated.For
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676 Y.Chenetal.:HydrologicalmodelinginglacierizedcatchmentsofcentralAsia
example,annualrunoffcontributionsfrompresentlyglacier- cipitation data set (APPRODITE) to force the SRM model.
izedsurfacesareexpectedtodecreaseby13%by2070–2099 Applyinginsituobservationalmeteorologicaldataisalsoas-
comparedto1980–2009,despiteofnearlyunchangedcontri- sociatedwithotherchallenges,asdetailedbelow.
butions from precipitation under RCP 4.5 (Farinotti et al.,
2016). Lackofstations
Thehydrologicalprocessesintheglacierizedregionshave
Oneofthegreatestchallengesinherentinstation-scalemete-
somethingincommon;i.e.,theannualrunoffislikelytore-
orologicaldataisthelowdensityofmeteorologicalstations.
duce in a warming climate with high spatial–temporal vari-
AsthemountainousregionsofcentralAsiaarecharacterized
ation at the middle or end of the 21st century. Seasonally,
bycomplexterrain,itisinaccuratetorepresenttheclimatic
increased snowmelt runoff and water shortage of summer
conditions of basins using data from limited stations. Some
runoff with the disappearing glaciers are expected. How-
researchers (Liu et al., 2016b; Fang et al., 2015a) have ad-
ever, there are also differences in the responses of hydro-
dressed this challenge by attempting to interpolate temper-
logical processes to climate change. For example, the con-
ature/precipitation into a basin scale using elevation bands,
trasting climate change impact on river flows from glacier-
based on the assumption that climate variables increase or
izedcatchmentsintheHimalayasandAndes(Ragettlietal.,
decrease with elevation. Temperature lapse rates could also
2016).IntheLangtangcatchmentinNepal,increasedrunoff
bevalidatedusingtheIntegratedGlobalRadiosondeArchive
isexpectedwithlimitedshiftsbetweenseasons,whereasfor
(IGRA)dataset(LiandWilliams,2008).However,thismod-
the Juncal catchment in Chile, the runoff has already been
ificationcouldnottakeaccountofthesourceofwatervapor
decreasing. These qualitative or quantitative differences are
andmountainaspectforbasinswithcomplexlandform.Due
mainly caused by glaciation ratio, regional weather pattern
tothefactthatuniformprecipitationgradientscannotbede-
andglacierproperty(HaggandBraun,2005).
rivedandtemperaturelapseratesarenotconstantthroughout
However,formanyglacierizedcatchmentintheTienshan
theyear(Immerzeeletal.,2014),itisachallengetouseel-
Mountains, currently or for the next several decades, the
evationbandstointerpolatestation-scaleclimateintobasin-
runoffappearstobenormalorevenanincreasingtrend,giv-
scaleclimate.
ing an illusion of better prospects. It is particularly worth
mentioningthat,oncetheglacierstorage(fossilwater)melts
Lackofhomogeneitytest
away, the water system is likely to go from plenty to want,
exacerbatingwaterstressgiventheincreasingwaterdemand.
Many hydrological modeling studies do not factor in errors
in observations, even though homogeneous climate records
arerequiredinhydrologicaldesign.IncentralAsia,changes
3 Limitationsoftheavailablehydrologicalmodels
in regulation protocols or relocation of stations also lead to
3.1 Meteorologicalinputsinhydrologicalmodelingand observational errors. Checking the input data should be the
prediction firststepinhydrologicalmodelingduetotheruleof“garbage
inyieldsgarbageout”.
InmountainousregionsofcentralAsia,meteorologicalinput
uncertaintycouldaccountforover60%ofmodeluncertainty 3.1.2 Remotesensingdataandreanalysisdata
(Fang et al., 2015a). The greatest challenge in hydrological
Remote sensing and reanalysis data are increasingly being
modelinghasbeenlackofrobustandreliablecompleteme-
usedinhydrologicalmodeling.T.Liuetal.(2012)andLiuet
teorologicaldata,especiallysincethecollapseoftheSoviet
al.(2016b)evaluatedremotesensingprecipitationdataofthe
Union in the late 1980s. In this section, the value and limi-
TropicalRainfallMeasuringMission(TRMM)andtempera-
tations of different data sets used in hydrological modeling
turedataofModerateResolutionImagingSpectroradiometer
(e.g., station data, remote sensing data) and future predic-
(MODIS).Theresultsindicatedthatsnowstorageandsnow-
tions (e.g., outputs of GCMs (General Circulation Models)
pack that were modeled using the remote sensing climate
andRCMs(RegionalClimateModels)arediscussed.
are different from those modeled using station-scale obser-
3.1.1 Observationaldata vational data. The model forced by the remote sensing data
showedbetterperformanceinspringsnowmelt(T.Liuetal.,
Traditionally, hydrological models are forced by station- 2012). Huang et al. (2010a) analyzed the input uncertainty
scale meteorological data in or near the studied watershed of remote sensing precipitation data interpreted from FY-2.
(e.g., Fang et al., 2015a; Peng and Xu, 2010). However, Inadditiontometeorologicaldata,surfaceinformationinter-
station-scaledatacanonlydescribetheclimateataspecific pretedfromsatelliteimages,e.g.,soilmoisture,landuseand
pointinspace,andmostofthemlocatedatthefootofmoun- snowcover,canalsobeusedinhydrologicmodeling(Caiet
tains. This limitation needs to be taken into consideration al.,2014).
wheninterpolatingstationdataintobasin-scaleoverrugged As demonstrated in numerous research studies, data as-
terrain.Lietal.(2014)appliedtheinterpolatedgriddedpre- similationholdsconsiderablepotentialforimprovinghydro-
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Y.Chenetal.:HydrologicalmodelinginglacierizedcatchmentsofcentralAsia 677
logical predictions (Y. Liu et al., 2012). Cai et al. (2014) glacier module in Xinanjiang and TOPMODEL; and Fang
used Global Land Data Assimilation System (GLDAS) 3h etal.(2015a)failedtoaccountforglacierprocesses,though
airtemperaturedatatoforcetheMS-DTVGMmodel,while theglaciermeltcouldcontributeupto10%ofdischargeof
Duethmannetal.(2015)usedtheWatchForcingDatabased the Kaidu River basin. Similarly, in their research on the
onERA-40(WFD-E40)toforcethehydrologicalmodel. Yarkand River basin, Liu et al. (2016a) neglected the in-
Remote sensing and reanalysis data are supposed for use fluence of glacier melt in the SWAT and MIKE-SHE mod-
in large-scale hydrological modeling due to their low spa- els, even though the glacier covered an area of 5574km2.
tial resolution. Another limitation in using remote sensing Themostwidelyusedhydrologicalmodels,suchasthedis-
andreanalysisdataisthatthesedataarebiasedtosomeex- tributed SWAT, the MIKE-SHE model and the conceptual
tent.Forexample,theTRMMdataaremostlyvaluableonly SRMmodel,asaruledonotcalculateglaciermeltprocesses,
fortropicalregions,andreanalysisdata,includingERA-40, despite the fact that excluding the glacier processes could
NCEP/NCARandGPCC(GlobalPrecipitationClimatology induce large errors in glacierized catchments. Glacier pro-
Centre),failtorevealanysignificantcorrelationwithstation cesses are complex in that glacier melt will at first increase
data(Sorgetal.,2012). duetotheriseinablationandloweringofglacierelevation,
Given the advantages and disadvantages of observation and then, after reaching its peak, will decrease due to the
data, remote sensing data and reanalysis data, a better ap- shrinkinginglacierarea(Xieetal.,2006).Moreover,simu-
proachwouldbetocombineobservationsandotherdatasets lationerrorscanbere-categorizedasprecipitationorglacier
inhydrologicalmodeling. meltwaterandconsequentlyresultinagreateruncertaintyin
thewaterbalanceinhighmountainareas(Mayretal.,2013).
3.1.3 GCMorRCMoutputs Duringthelastfewdecades,alargevarietyofmeltmod-
elshavebeendeveloped(Hock,2005).Previousstudieshave
GCMs or RCMs provide climate variables for evaluating investigated glacier dynamics for the mountainous regions.
future hydrological processes. However, the greatest chal- Among these studies, Hock (2005) reviewed glacier melt-
lengesinapplyingthesedatasetsaretheirlowspatialresolu- related processes at the surface–atmosphere interface rang-
tions(e.g.,thespatialresolutionofGCMsinCMIP5ranges ing from a simple temperature-index model to a sophisti-
from0.75to3.25◦)andconsiderablebiases.Inaddition,dif- cated energy-balance model. Glacier models that are phys-
ferentGCMsorRCMsgenerallygivedifferentclimatepro- ically based (e.g., mass-energy fluxes and glacier flow dy-
jections. Therefore, when forcing a hydrological model us- namics) depend heavily on detailed knowledge of local to-
ingtheoutputsofclimatemodels,theevaluationresultsde- pographyandhydrometeorologicaldata,whicharegenerally
pendheavilyontheselectionofGCMsandconsequentlyre- limited in high mountain regions (Michlmayr et al., 2008).
sultinhigheruncertaintyinGCMsthanthatinothersources Hence,theymostlyappliedtowell-documentedglaciersand
(emissionscenarios,hydrologicalmodels,downscaling,etc.) havefewapplicationsinbasin-scalehydrologicalmodels.
(Bosshardetal.,2013). The temperature-index method (or its variants), which
Manydownscalingmethodshavebeendevelopedtoover- onlyrequirestemperatureformeteorologicalinput,iswidely
comethesedrawbacks.Althoughsomestatisticaldownscal- usedtocalculateglaciermelt(KonzandSeibert,2010).Asis
ingmethods,suchasSDSM(Wilbyetal.,2002),arewidely illustratedbyOerlemansandReichert(2000),glacierscanbe
usedinclimatechangeimpactstudies,theiruseinthemoun- reconstructed from long-term meteorological records, e.g.,
tainous regions of central Asia is limited due to the lack of summer temperature is the dominant factor for glaciers in
fineobservationaldatatodownscaleGCMoutputs.Toover- a dry climate (e.g., Abramov glacier). In recent years, hy-
comethedatascarcityforthisregion,G.H.Fangetal.(2015) drologistshavebeentryingtoaddothermeteorologicalvari-
evaluated different bias correction methods in downscaling ables into the calculations of glacier melt; e.g., Y. Zhang et
the outputs of one RCM model and used the bias-corrected al. (2007) included potential clear sky direct solar radiation
climate to force a hydrological model in the data-scarce inthedegree-daymodel,andYuetal.(2013)statedthatac-
KaiduRiverbasin.Liuetal.(2011)usedperturbationfactors cumulated temperature is more effective than daily average
to downscale the GCM outputs and force the hydrological temperatureforcalculatingthesnowmeltrunoffmodel.Us-
model. ingdegree-daycalculationismuchsimplerthanusingenergy
balance approaches and could actually produce comparable
3.2 Glaciermeltmodeling or better model performance when applied in mountainous
basins(Ohmura,2001).
Glaciermeltaccountsforalargepartofthedischargeforthe Morerecently,themeltmodulehasbeenincorporatedinto
alpine basins in central Asia as discussed above. However, different kinds of hydrological models. Zhao et al. (2015)
most hydrological modeling does not include glacier melt integrated a degree-day glacier melt algorithm into a
and accumulation processes. For example, Liu et al. (2010) macroscale hydrologic model (VIC) and indicated that an-
failed to account for the glacier processes in the VIC nualandsummerrunoffwoulddecreaseby9.3and10.4%,
model in the Tarim River; Peng and Xu (2010) missed the respectively, for reductions in glacier areas of 13.2% in the
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678 Y.Chenetal.:HydrologicalmodelinginglacierizedcatchmentsofcentralAsia
Kumalike River basin. Hagg et al. (2013) analyzed antici- 3.3 Modelcalibrationandvalidation
patedglacierandrunoffchangesintheRukhkcatchmentof
theupperAmuDaryaBasin,usingtheHBV-ETHmodelby
includingglaciermeltandsnowmeltprocesses.Theirresults
showedthatwithtemperatureincreasesof2.2and3.1◦C,the
currentglacierextentof431km2willreduceby36and45%, For model calibration, two important issues are discussed
respectively.Luoetal.(2013),takingtheManasRiverbasin here:thelengthofthecalibrationperiodandobjectivefunc-
asacasestudy,investigatedglaciermeltprocessesbyinclud- tions.
ing the algorithm of glacier melt, sublimation/evaporation, Generally, hydrological modeling requires several years’
accumulation, mass balance and retreat in a SWAT model. calibration.Forexample,Yangetal.(2012)indicatedthata
The results showed that glacier melt contributed 25% to 5-yearwarm-upissufficientbeforehydrologicalmodelcal-
streamflow, although the glacier area makes up only 14% ibration and a 4-year calibration could obtain satisfactory
ofthecatchmentdrainagearea. modelperformance.Moreventuresomely,a6-monthcalibra-
tioncouldleadtogoodmodelperformanceforanaridwater-
3.2.1 Paucityofglaciervariationdata shed (Sun et al., 2016). Konz and Seibert (2010) stated that
one year’s calibration of using glacier mass balances could
The existing glacier data set, which includes the World effectivelyimprovethehydrologicalmodel.Selectingtheap-
Glacier Inventory (WGI), the Randolph Glacier Inven- propriate calibration period is significant, as model perfor-
tory (RGI) and global land-ice measurements from space mance could depend on calibration data. Refsgaard (1997)
(GLIMS),hasbeendevelopedrapidly.Thesedata,however, usedasplit-sampleproceduretoobtainbettermodelcalibra-
generally focus on glaciers in the present time or those ex- tionandvalidationeffectivelyandefficiently.
isting in the former Soviet Union. For example, the source Moststudiesoncalibrationproceduresinhydrologyhave
data of WGI were derived during 1940s–1960s, and the examined goodness-of-fit measures based on simulated and
GLIMS for the Amu Darya Basin is from 1960 to 2004 observed runoff. However, as the hydrological sciences de-
(Donald et al., 2015). These data can depict the charac- velop further, multi-objective calibration is emerging as the
teristics of the glacier status, but fail to reproduce glacier preferredapproach.Itnotonlyincludesmulti-sitestreamflow
variation. Only a few glaciers (Abramov, Tuyuksu, Urumqi (which has proved to be advantageous compared to single-
no. 1 Glacier, etc.) have long-term variation measurements site calibration (S. Wang et al., 2012), and multi-metrics of
(Savoskul and Smakhtin, 2013). CAWa (Central Asian Wa- streamflow(Yangetal.,2014),butalsoinvolvesmultipleex-
ter; www.cawa-project.net/) are intended to contribute to a aminedhydrologicalcomponents(e.g.,soilmoisture).Most
reliable regional data basis of central Asia from the moni- of the studies reviewed here use the discharge to calibrate
toring stations, sampling and remote sensing. The missing andvalidatethehydrologicalmodel,yetGuptaetal.(1998)
glaciervariationinformationleadstoamisrepresentationof arguedthatastrong“equifinalityeffect”mayexistduetothe
glacierdynamics. compensation effect, where an underestimation of precipi-
tation may be compensated by an overestimation of glacier
3.2.2 Lackofglaciermassbalancedata melt, and vice versa. Stahl et al. (2008) suggested that ob-
servations on mass balances should be used for model cali-
Glacier measurements reproduced by remote sensing data bration,aslargeuncertaintiesexistinthedata-scarcealpine
usually give glacier area instead of glacier water equiva- regions. Therefore, multi-criteria calibration and validation
lent; therefore, errors will occur when converting glacier isnecessary,especiallyforglacier/snowrechargedregions.
area to glacier mass. Glaciologists normally use a speci- Many recent studies have attempted to include mass bal-
fied relation (e.g., empirical) between glacier volume and ance data into model calibration (Stahl et al., 2008; Huss
glacier area to estimate glacier mass balance (Stahl et al., et al., 2008; Konz and Seibert, 2010; Parajuli et al., 2009).
2008; Luo et al., 2013). Aizen et al. (2007) applied the Duethmannetal.(2015)usedamulti-objectiveoptimization
radio-echo sounding approach to obtain glacier ice volume. algorithm that included objective functions of glacier mass
Recently, ICESat (Ice, Cloud and land Elevation Satellite; balance and discharge to calibrate the hydrological model
http://icesat.gsfc.nasa.gov/) could provide multi-year eleva- WASA(ModelofWaterAvailabilityinSemi-AridEnviron-
tiondataneededtodetermineice-sheetmassbalance. ments).Anotherapproachforimprovingmodelefficiencyis
This paper focuses primarily on glacier melt modules. It to calibrate the glacier melt processes and the precipitation
does not discuss snowmelt processes, as hydrological mod- dominated processes separately (Immerzeel et al., 2012b).
els generally include them either in a degree-day approach Further,inadditiontothemassbalancedatausedtocalibrate
orenergybalancebasis.Furthermore,thispaperdoesnotan- thehydrologicalmodel,theglacier-area/glacier-volumescal-
alyzewaterroutingprocessesorevapotranspirationbecause ing factor can also be calibrated with the observed glacier
thereareseveralwaystosimulatesoilwaterstoragechange areachangemonitoredbyremotesensingdata(Zhangetal.,
andmodelevapotranspiration(Bierkens,2015). 2012b).
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