Effect of baseline meteorological data selection on hydrological modelling of climate change ...

12 Pages·2015·3.72 MB·English

by  Renji Remesan

JournalofHydrology528(2015)631–642
ContentslistsavailableatScienceDirect
Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol
Effect of baseline meteorological data selection on hydrological
modelling of climate change scenarios
⇑
Renji Remesan, Ian P. Holman
CranfieldWaterScienceInstitute,CranfieldUniversity,UnitedKingdom
a r t i c l e i n f o s u m m a r y
Articlehistory: Thisstudyevaluateshowdifferencesinhydrologicalmodelparameterisationresultingfromthechoiceof
Received5December2014 griddedglobalprecipitationdatasetsandreferenceevapotranspiration(ETo)equationsaffectssimulated
Receivedinrevisedform27May2015 climatechangeimpacts,usingthenorthwesternHimalayanBeasrivercatchmentasacasestudy.Six
Accepted14June2015
combinationsofbaselineprecipitationdata(theTropicalRainfallMeasuringMission(TRMM)andthe
Availableonline9July2015
Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water
Thismanuscriptwashandledby
Resources(APHRODITE))andReferenceEvapotranspirationequationsofdifferingcomplexityanddata
KonstantineP.Georgakakos,Editor-in-Chief,
withtheassistanceofMatthewMcCabe, requirements(Penman–Monteith,Hargreaves–SamaniandPriestley–Taylor)wereusedinthecalibration
AssociateEditor oftheHySimmodel.Althoughthesixvalidatedhydrologicalmodelshadsimilarhistoricalmodelperfor-
mance(Nash–Sutcliffemodelefficiencycoefficient(NSE)from0.64to0.70),impactresponsesurfaces
Keywords: derivedusingascenarioneutralapproachdemonstratedsignificantdeviationsinthemodels’responses
Uncertainty to changes in future annual precipitation and temperature. For example, the change in Q10 varies
TRMM3B42V7 between(cid:2)6.5%and(cid:2)11.5%inthedriestandcoolestclimatechangesimulationand+79%to+118%in
Aphrodite thewettestandhottestclimatechangesimulationamongthesixmodels.Theresultsdemonstratethat
Impactresponsesurface thebaselinemeteorologicaldatachoicesmadeinmodelconstructionsignificantlyconditionthemagni-
Evapotranspiration tudeofsimulatedhydrologicalimpactsofclimatechange,withimportantimplicationsforimpactstudy
Climatechange
design.
(cid:2)2015TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense
(http://creativecommons.org/licenses/by/4.0/).
1.Introduction However,quantificationofthehydrologicalimpactsofclimate
change requires quality baseline data to enable meaningful com-
Understandingthecurrentandfuturetemporaldynamicsofthe parisonbetweenpresentandfuture,butthereisapaucityorlack
hydrological behaviour of rivers is vital for management of of coverage of land based measurements of meteorological vari-
hydro-power generation, irrigation systems, public water supply ablesinmanypartsoftheworld.Datasparsitytendstobeexacer-
and flood control structures (Jain et al., 2010). However, there is batedinmountainousregionswhereverysteeptemperatureand
a widely recognised cascade of uncertainty in top-down climate precipitationgradientsarepoorlycharacterisedbythelimitedspa-
change impact studies (Wilby and Dessai, 2010) that affects the tial and temporal extents of raingauge and weather station net-
certainty in future water resource assessments (Refsgaard et al., works (Legates and Willmott, 1990), leading to significant
2007). Many elements of the uncertainty cascade within climate uncertaintyinprecipitationandevapotranspiration.
impactsmodellinghavebeenquantitativelyassessed,highlighting Recently,manyglobal/regionaldatasetshavebeendevelopedas
the uncertainty associated with climate model (RCMs or GCMs) an alternative or supplement to ground-based data over basins
choice(FowlerandKilsby,2007;Woldemeskeletal.,2012),emis- with severe climate data scarcity (Meng et al., 2014) for use in
sionsscenario(Maurer,2007),downscalingmethod,modelchoice, hydrological modelling studies (Andermann et al., 2012; Meng
etc. (Pappenberger and Beven, 2006; Buytaert et al., 2009; Kay et al., 2014). These data sets include the Asian Precipitation –
et al., 2009; Chen et al., 2011; Xu, 1999; Wilby et al., 2004; Highly Resolved Observational Data Integration Towards
Woodetal.,2004). Evaluation of Water Resources (APHRODITE) data and
satellite-based precipitation products such as the Tropical
RainfallMeasuringMission(TRMM).However,thereareconsider-
⇑ abletemporalandspatialdifferencesbetweensuchdataproducts
Correspondingauthorat:CranfieldWaterScienceInstitute,CranfieldUniver-
in comparisonto weatherstation precipitation(Tian etal., 2007;
sity,Cranfield,BedfordMK430AL,UnitedKingdom.
Habib et al., 2009; Andermann et al., 2011; Li et al., 2012; Lu
E-mailaddress:i.holman@cranfield.ac.uk(I.P.Holman).
http://dx.doi.org/10.1016/j.jhydrol.2015.06.026
0022-1694/(cid:2)2015TheAuthors.PublishedbyElsevierB.V.
ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/4.0/).
632 R.Remesan,I.P.Holman/JournalofHydrology528(2015)631–642
et al., 2013; Jamandre and Narisma, 2013), although these may demonstrated that the choice of ETo method affected the simu-
partlybeduetothedifficultiesingauge-radarassimilationorcom- latedhydrologyoftheMekongandAndermannetal.(2011)high-
parison (Vasiloff et al., 2009). A number of studies have used lightedthesignificantinconsistenciesthatexistbetweendifferent
TRMM and APHRODITE for hydrological simulation in large river precipitation data products, including APHRODITE with
catchmentswhilstalsodetailingtheiruncertainties–forexample, TRMM-3B42(and 3B43 data), no studies have assessed the com-
Collischonn et al. (2007, 2008) used TRMM rainfall data in mod- binedeffectsofthesetwouncertaintiesforfutureclimatechange
ellingtheTapajósriverbasininBrazil;andAPHRODITEprecipita- simulations.Thereisthereforealackofunderstandingconcerning
tion data were used in hydrological modelling of the Aksu River theeffectthatthemodeller’ssubjectivechoiceofhistoricalmete-
basin,North-WesternChina(Zhaoetal.,2013)andHimalayanriv- orological data, as determinedby their selectionofboth baseline
ers in Nepal (Andermann et al., 2012). Meng et al. (2014) high- weatherdataproductsandthemethodstoderivemeteorological
lighted that TRMM (3B42V6) daily data sets were more variablessuchasETo,haveontheuncertaintyinfuturehydrolog-
appropriate for monthly hydrological modelling in hilly regions icalimpacts.
of the Tibetan Plateau within the Yellow river basin, but studies Thisstudyevaluateshowthechoiceofgriddedglobalprecipita-
byXueetal.(2013)haveshownaclearimprovementof3B42V7 tiondatasetsandreferenceevapotranspiration(ETo)methodaffects
data sets over 3B42V6 in hydrological applications in the baseline hydrological model parameterisation and thereby the
Wangchu Basin in Bhutan. Their study demonstrated that uncertainty in simulated future climate change impacts using
3B42V7 provided better basin-scale agreement with observed scenario-neutralimpactresponsesurfaces.Sixcombinationsofbase-
(2001–2010) monthly and daily rain gauge data and improved linedaily precipitationdatasets (TRMMand APHRODITE)and ETo
rainfall intensity distribution than 3B42V6. Andermann et al. methods (Penman–Monteith, Hargreaves–Samani and Priestley–
(2011) compared APHRODITE with TRMM-3B42 (and 3B43 data) Taylor)wereusedinthecalibration/validationoftheHySimmodel
alongwithotherremotesensingbasedprecipitationdatasetsalong (ManleyandWaterResourceAssociatesLtd.,2006),usingthenorth
theHimalayanfrontandsuggestthatestimationofprecipitationin westernHimalayanBeasrivercatchmentasacasestudy.
high elevations regions such as the Himalaya is challenging and
that significant inconsistencies exist between different remote
sensingdataproducts. 2.Studyareaandmethods
ThequantificationofReferenceEvapotranspiration(ETo)isalso
associatedwithsignificantuncertaintiesthatcanaffectwaterand TheBeasRiverisoneofthefivemajorriversoftheIndusbasin
energy budgeting. Recent PET based studies including FLUXNET inIndiaandoriginatesintheHimalayas,flowingforapproximately
(Baldocchi et al., 2001) and LandFlux-EVAL (Mueller et al., 2011, 470km before joining the Sutlej River. The catchment area,
2013) have addressed evapotranspiration uncertainty quantifica- upstreamofthePong reservoir, isaround12,560km2, and varies
tion and evaluation. These studies principally aimed to compare in elevation from 245 to 6617 metres above sea level (m asl).
satellite-based estimates, IPCC AR4 simulations, land surface The catchment is bounded by Latitude 31(cid:3)280–32(cid:3)260N and
model(LSM)simulationsandreanalysisdataproductstoproduce Longitude75(cid:3)560–77(cid:3)480E(Fig.1).Soilsinthecatchmentareyoung
an ensemble of global benchmark PET datasets. Kay and Davies and relatively thin, with their thickness increasing in the valleys
(2008) found importantdifferences between ETo estimates using andareaswithgentleslopes(Pandey,2002).Themajorlandcover
Penman–Monteith,asimplertemperature-basedpotentialevapo- classesincludeforest,snowandbarerock,withabout65%ofthe
transpiration (PET) method and the UK Meteorological Office area covered with snow during winter (Singh and Bengtsson,
Rainfall and Evapotranspiration Calculation System (MORECS) 2003).TheBeascatchmentisundertheinfluenceofwesterndis-
when applied to data from five global and eight regional climate turbancesthatbringsnowfalltotheuppersub-catchmentduring
models. However, whilst Thompson et al. (2014) have winter (December–April), whilst the monsoon provides around
Fig.1. ThestudyareaoftheBeasriverbasininnorthernIndia.
R.Remesan,I.P.Holman/JournalofHydrology528(2015)631–642 633
70%oftheannualrainfallduringJune–September.Thecatchment (cid:3) Asian Precipitation – Highly Resolved Observational Data
ischaracterisedbymoderate–lowtemperatureswithmeanmin- Integration Towards Evaluation of Water Resources
imum and mean maximum winter temperatures of (cid:2)1.6(cid:3)C and (APHRODITE)– the APHRODITE Water Resources project
7.7(cid:3)C, respectively (Singh and Ganju, 2008). The Pong reservoir (http://www.chikyu.ac.jp/precip/) has created long-term daily
providesanactivestoragecapacityof7051Mm3tosupportflood gridded precipitation datasets over Asia from the year 1951.
protection,hydropowergenerationandalmost9000Mm3/yearof Inthisstudywehaveused0.25(cid:3)(cid:4)0.25(cid:3)griddeddailydatasets
irrigationwaterdemands(Jainetal.,2007).Dailygaugedreservoir over Monsoon Asia (APHRO_MA_V1101), available between
inflowsfromJanuary1998toDecember2008(11years)wereused 60(cid:3)E–150(cid:3)Eand15(cid:3)S–55(cid:3)N,whicharecalculatedbyinterpola-
inthisstudy. tion of rain-gauge data from meteorological stations in the
The methodology adopted in this study for analysis of uncer- region(Yatagaietal.,2012).
taintypropagationisshowninFig.2.SixHySimmodelsemploying (cid:3) NCEPClimateForecastSystemReanalysis(CFSR)data–meteoro-
differentcombinationsoftwogriddedprecipitationdatasetsand logicalvariables(e.g.:dailymaximumtemperature,dailymini-
three evapotranspiration equations (Sections 2.1 and 2.2) have mum temperature, daily wind velocity, daily average relative
been calibrated and validated against observed daily river dis- humidity,dailyaveragesolarradiation,etc.)ataspatialresolu-
charge data (Section 2.4). Then a scenario-neutral approach has tion of 0.5(cid:3)(cid:4)0.5(cid:3) are available from the National Centres for
been adopted to study the propagation of uncertainties in the Environmental Protection (NCEP) Climate Forecast System
HySim models. The methodology departs from conventional Reanalysis (CFSR) for the 31-yr period from 1979 to 2009 for
GCM/RCM based scenario-led impact studies because it is based thecalculationofETo.
onsensitivityanalysesofcatchmentresponsestoaplausiblerange
of climate changes, avoiding the application of time varying
2.2.Potentialevapotranspirationequations
GCM/RCM scenario outcomes simulated under certain assump-
tions of social/economic/environmental policies, thus making it
There are numerous methods available for the calculation of
scenario-neutral (Prudhomme et al., 2010). Within the
evapotranspiration, which differ in their data requirements and
Scenario-neutralapproach,arangeofannualtemperaturechanges
accuracy (see the reviews by Kumar et al. (2011) and Kumar
and annual precipitation changes (Section 2.5) are applied to the
et al. (2012)). In this study we have used three reference evapo-
baseline(2000–2008)weather.EachofthesixHySimmodelwere
transpirationequationsofdifferingcomplexityininputdatausage
thenrunwitheachofthemodifiedweathertimeseries,andtheir
and process representation for the calculation of ETo viz. FAO
changesinhydrologicalindicatorsplottedasimpactresponsesur-
Penman–Monteith (Allen et al., 1998), Hargreaves–Samani
faces [a surface plot showing the variation of a certain quantity
(Hargreaves and Samani, 1985a,b) and Priestley–Taylor (Priestley
(e.g.: surface runoff) across the ranges of plausible changes in
andTaylor,1972).
futuretemperatureandprecipitation].
2.2.1.TheFAOPenman–Monteithmethod
2.1.Rainfallandmeteorologicaldata
TheFAOPenman–Monteithmethodisconsideredasoneofthe
bestmethodsforETocalculation(Ngongondoetal.,inpress),but
Thisstudyhasusedthreedatasets–twogriddedprecipitation
alsohasthegreatestdatademands.Itconsidersbothaerodynamic
datasetsandagriddedmeteorologicaldataset:
phenomena and surface resistance factors (resistance of vapour
flowthroughthetranspiringvegetationandevaporatingsoilsur-
(cid:3) Tropical Rainfall Measuring Mission (TRMM) Data product 3B42
face)(Allenetal.,1998).Theequationisgivenas
V7 of daily precipitation data has been used in this study.
TRMM is a joint space programme between NASA and the 0:408DðR (cid:2)GÞþc 900 u ðe (cid:2)e
Japanese space agency to monitor precipitation in the tropics ET0¼ Dnþcð1þ0T:þ3247u3 Þ2 s aÞ ð1Þ
and subtropics and its associated latent heat (launched on 2
November27,1997)whichprovidesanimportantprecipitation whereET isreferenceevapotranspiration(mmday(cid:2)1),R netradi-
0 n
database for environmental and hydrological research around ation at the crop surface (MJm(cid:2)2day(cid:2)1), G soil heat flux density
the globe. The gridded TRMM data at a spatial resolution of (MJm(cid:2)2day(cid:2)1), T mean daily air temperature at 2m height ((cid:3)C),
0.25(cid:3)(cid:4)0.25(cid:3)overthelatitudinalbandof50(cid:3)N–Sareavailable u windspeedat2mheight(ms(cid:2)1),e saturationvapourpressure
2 s
from1998. (kPa), e actual vapour pressure (kPa), (e (cid:2)e ) saturation vapour
a s a
Fig.2. Methodologyadoptedforanalysisofuncertaintypropagationinthisstudy.
634 R.Remesan,I.P.Holman/JournalofHydrology528(2015)631–642
pressuredeficit(kPa),Dslopevapourpressurecurve(kPa(cid:3)C(cid:2)1),(cid:2) Lower (no snow) sub-catchment areas of 5720, 3440 and
psychrometricconstant(kPa(cid:3)C(cid:2)1)(Mallikarjunaetal.,2014). 3350km2, respectively. Areal averaging was used to change the
precipitationandevapotranspirationdatafromthedifferentreso-
2.2.2.TheHargreaves–Samanimethod lutiongrids.HySimsoilhydraulicparameterswereestimatedfrom
The Hargreaves–Samani method (Hargreaves and Samani, thelandusesandsoiltypesoftheregionandfromtheliterature
1985a,b) is a well-established approach that has been shown to (Jainetal.,2010;FAO/IIASA/ISRIC/ISSCAS/JRC,2012).Initialvalues
give similar performance to Penman–Monteith (Heydari and oftheHySimsoilparameterswerebasedonthespatially-weighted
Heydari, 2014), but without the need for solar radiation, relative physical attributes given in the Harmonized World Soil Database
humidityandwindspeeddata: (HWSD) (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012) [resolution of about
1km(30arcsecondsby30arcseconds)]althoughmodeldefault
ET0¼0:0135KRsRkapffiðffiTffiffiffimffiffiffiaffiffixffiffi(cid:2)ffiffiffiffiffiTffiffiffimffiffiffiiffinffiffiÞðTþ17:8Þ ð2Þ values for the soil hydraulic parameters were calibrated
(Schwarz, 2013). Initial rooting depths were in the range of
whereRaistheextra-terrestrialradiation(MJm(cid:2)2day(cid:2)1),andkis 800mm (grassland)–5000mm (forest) (Manley and Water
thelatentheatofvaporisation(MJkg(cid:2)1)forthemeanairtempera- Resource Associates Ltd., 2006). Table 1 shows the minimum to
ture T ((cid:3)C), which is equal to 2.45MJkg(cid:2)1, KRs is the radiation maximum parameter ranges across the three sub-catchments
adjustment coefficient (the numerical value is 0.17) (De Sousa andsixHySimmodels,whichindicatescalibratedparametervari-
Lima et al., 2013; Samani, 2004). Pandey et al. (2014) considers ability.TocharacterisepermanentHimalayansnowandicecover
Hargreaves–Samani method as one of the promising approaches within the simulations, the upper sub-catchment in the model
for estimation of reference evapotranspiration under data-scarce was initialised with an arbitrary ice/snow depth of around 25m
mountainous conditions based on experiments in East Sikkim, informed by past research (Kulkarni et al., 2005; Kulkarni and
India. Karyakarte,2014;Linsbaueretal.,2014).
2.2.3.ThePriestley–Taylorequation
2.4.Modelcalibrationandvalidation
ThePriestley–Taylorequationreplacestheaerodynamictermin
the Penman–Monteith equation with a dimensionless constant
HySim was independently calibrated and validated for each
(Priestley–Taylorcoefficient)
combination of the two global gridded data sets and the three
ET ¼a(cid:5) D (cid:5)Rn(cid:2)G ð3Þ potentialevapotranspirationequations(Fig.2),producingsixvali-
0 Dþc HE dated models. After two years of model warm-up (1998–1999),
each model build was calibrated using flow data for 2000–2004,
where HE is specific heat of evaporation (MJm(cid:2)2mm(cid:2)1), a
and validated for 2005–2008. Seven parameters relating to land
Priestley–Taylorparameter(a=1.26)andspecificheatofevapora-
cover(rootingdepth,mm(RD));snowmelt(snowmeltthreshold,
tion can be calculated using HE=0.0864 (28.9(cid:2)0.028T)
(cid:3)C(ST); snowmelt rate, mm(cid:3)C(cid:2)1day(cid:2)1 (SM))and soil hydrology
(EidgenössischeTechnischeHochschule(ETH),2014).
(Permeabilityofhorizonboundary,mm/hour(PHB);Permeability
ofbaselowerhorizon,mm/hour(PBLH);Interflowinupperlayer,
2.3.TheHySimhydrologicalmodel
mm/hour (IU); and Interflow in lower layer, mm/hour (IL)) were
calibrated using the commonly used Nash–Sutcliffe Efficiency
HySim is a continuous, daily conceptual rainfall–runoff model
Criterion (NSE – Eq. (4)) and the Percent Bias (PBIAS – Eq. (5))
(ManleyandWaterResourceAssociatesLtd.,2006)withseparate
goodness-of-fit measures (as recommended by Moriasi et al.
sub-routines to simulate catchment hydrology and channel
2007):
hydraulics.Thecatchmenthydrologyissimulatedbysevensurface
andsubsurfacestores(snowstorage,upperandlowersoilhorizon, PNðM (cid:2)OÞ2
transitionalgroundwater,groundwaterstorageandminorchannel NSE¼1(cid:2) 1 i i ð4Þ
storage)whilstthehydraulicsub-routineuseskinematicroutingof PN1(cid:3)Oi(cid:2)O(cid:3)(cid:3)(cid:4)2
theriverflowstotheoutlet.HySimhasbeenextensivelyusedin "PNðO (cid:2)MÞ(cid:4)100#
uplandandmountainouscatchmentsundercurrentandfuturecli- PBIAS¼ 1 i i ð5Þ
mateconditions(e.g.Wilby,2005;Murphyetal.,2006). PN1Oi
TheHySimmodelusesinputsofprecipitation,potentialevapo-
transpirationandtemperature-basedsnowmelttosimulatestream whereOiandMiaretheobservedandsimulatedvaluesfortheith
flow– see Pillingand Jones (1999)for details ofthe main model streamflowvalue,respectively,O(cid:3)(cid:3) isthemeanobservedvalue,and
parameters.Theselectionofthenumberofsub-catchmentsissub- Nisthenumberofdays.
jective,buttheriverbasinhasbeensub-dividedonaltitude,given NSEisselectedasthebestobjectivefunctionforreflectingthe
the importance of the snow dynamics to hydrological behaviour, overallfitofahydrograph(Moriasietal.,2007)whilstPBIASmea-
into Upper (permanent snow/ice), Middle (seasonal snow) and sures the average tendency of the simulated data to be larger or
Table1
UncertaintyrangesintheHySimparameterboundswithdifferentinputspaceacrossthreesub-catchments.
Parameters TRMMPriestley– TRMMPenman– TRMM Aphrodite AphroditePenman– Aphrodite
Taylor Monteith Hargreaves Priestley–Taylor Monteith Hargreaves
Rootingdepth(mm)(RD) 1218–2746 1121–2752 166–4285 811–1146 864–3211 2195–2766.
Permeability–horizonboundary 764–7663 3.8–26 221–658 346–686 101–550 96–524
(mm/h)(PHB)
Permeability–baselowerhorizon 526–529 17–163 482–711 646–777 305–569 202–377
(mm/hour)(PBLH)
Interflow–upper(mm/h))IU) 3.7–600 5.7–7.6 282–500 70–307 10–606 15–814
Interflow–lower(mm/h)(IL) 3015–774 82–330 78–224 103–612 18–466 24–639
SnowThreshold((cid:3)C)(ST) 0.3–2.0 0.5–0.6 1.1–1.2 2.3–2.4 1.4–2.0 1.8–2.5
Snowmeltrate(mm/(cid:3)C/day)(SM) 2.5–2.5 0.8–1.2 1.7–1.8 2.0–2.5 1.8–2.3 2.2–2.5
R.Remesan,I.P.Holman/JournalofHydrology528(2015)631–642 635
Table2
StatisticalsummaryofdailyTRMMandAPHORDITEprecipitationdata(1998–2007).
Indices(mm/day) TRMMprecipitationdata APHRODITEdata
Uppersub-catchment Middlesub-catchment Lowersub-catchment Uppersub-catchment Middlesub-catchment Lowersub-catchment
Mean 2.77 3.76 4.12 2.00 3.10 4.47
Maximum 96.44 119.44 136.83 53.74 74.07 121.18
Minimum 0.00 0.00 0.00 0.00 0.00 0.00
SD 6.69 9.00 10.04 4.50 6.85 10.66
5thpercentile 0.38 0.22 0.20 0.06 0.01 0.02
95thpercentile 13.83 19.28 23.19 10.02 16.14 24.38
N.B:theindiceswereappliedongriddedaveragecorrespondingtoeachsub-catchment.
smallerthantheobserveddataandhencequantifiesoverallwater
balanceerrors(Guptaetal.,1999).Wehavebasedtheevaluation
of model performance using these metric on the NSE and PBIAS
limits specified by Moriasi et al. (2007) and Henriksen et al.
(2003).Forexample,Moriasietal.(2007)recommendsthat‘satis-
factory’performanceformonthlytimestepstreamflowisgivenby
0.50<NSE60.65and±156PBIAS(%)6±25.
2.5.‘Scenarioneutralmethod’ofclimatechangeimpactassessment
Plausible ranges of future changes in annual temperature and
annual precipitation were informed by the regional summary
results from 25 to 39 GCMs given in Christensen et al. (2013).
This study used the projections for the Tibetan Plateau area
(bounded by lat. 30(cid:3)–75(cid:3)N and long. 50(cid:3)–100(cid:3)E) and the Central
Asiaarea(lat.30(cid:3)–50(cid:3)Nandlong.40(cid:3)–75(cid:3)E),astheBeascatchment
islocatedclosetotheboundaryofthetwomodelledregions.Six
temperature change factors between DT=0(cid:3)C and DT=5(cid:3)C (in
steps of 1(cid:3)C) and seven precipitation change factors from
DP=(cid:2)10% to DP=+20% (in steps of 5%) were used, which span
the range of GCM projections for the Representative
Concentration Profiles (RCP) across the two areas for 2065, and
capture the median temperature increase under RCP8.5 (Riahi
Fig.3. Comparisonofannual(a)APHRODITEand(b)TRMMprecipitationdataat et al., 2011) and the 25th to 75th range in precipitation change
thesub-catchmentscale. acrosstheRCPsto2100.Theabovementionedchangefactorswere
appliedtothedailyvaluesofthewholebaselinetimeseries(2000–
2008)toconstructthefuturescenario-neutralclimatevariables.In
Fig.4. Cumulativeandprobabilitydensityfunctionsshowingtheuncertaintyinthegriddedprecipitationdatasets[TRMMandAPHRODITE]inthethreesub-catchmentsfor
1998–2007data.
636 R.Remesan,I.P.Holman/JournalofHydrology528(2015)631–642
the results section, all modelresults are comparedto those from 3.Resultsanddiscussions
2000to2008(i.e.withzerotemperature/precipitationchange).
Each temperature change factor was added to the historical The presentation of results is laid out as follows. Sections
NCEP data to provide modified temperature and subsequently 3.1 and 3.2 discuss, compare and contrast the different gridded
ETo time series. All other weather variables were assumed precipitation data and ETo series in three sub-catchments of
unchangedinthederivationofthemodifiedETotimeseries.The the river Beas. Section 3.3 presents the HySim based model cal-
relative changes in precipitation were applied to each of the ibration and validation under the six data input combination;
TRMM and APHRODITE historical time series (Prudhomme et al., whilst Section 3.4 examines how the resultant model parameter
2010). Each of the six calibrated/validated HySim models was uncertainty affects impact response surfaces of future Q10
run for the thirty combinations of changed temperature (5) and and Q90 daily discharge to changes in temperature and
precipitation(6).Allcalibratedmodelparameterswereunchanged. precipitation.
Table3
StatisticalsummaryofdailycalculatedETo(1998–2007).
Indices Uppersubcatchment Middlesubcatchment Lowersubcatchment
(mm/day)
PenmanETo Hargreaves Priestley– PenmanETo Hargreaves Priestley– PenmanETo Hargreaves Priestley–
(mm) ETo(mm) TaylorETo (mm) ETo(mm) TaylorETo (mm) ETo(mm) TaylorETo
Mean 1.97 1.58 2.49 4.62 3.22 3.36 6.58 4.43 3.51
Maximum 6.31 4.47 6.43 9.70 6.79 6.75 15.92 8.84 7.21
Minimum 0.27 0.00 0.48 0.53 0.50 0.61 0.73 0.67 0.50
Standard 1.03 0.96 1.38 1.80 1.44 1.85 2.65 1.91 1.99
deviation
5th 1.79 1.55 2.26 4.40 3.22 3.30 6.23 4.39 3.41
percentile
95th 3.87 3.19 5.05 7.94 5.50 6.16 11.36 7.50 6.42
percentile
Fig.5. CumulativeandProbabilityDensityFunctionsshowingtheuncertaintyinthedifferentdailyreferenceevapotranspirationtimeseries[Penman–Monteith,Priestley–
Taylor,Hargreaves–Samani]inthethreesub-catchmentsfor1998–2007data.
R.Remesan,I.P.Holman/JournalofHydrology528(2015)631–642 637
3.1.Comparisonofprecipitationdatasets value (cid:6)50%). In the Middle sub-catchment, the differences were
intherangeof(cid:6)6%(October)to(cid:6)29%(April)withthelowerand
Giventhepaucityoflandbasedmeasurementsofprecipitation higher values just after and before the South west summer
intheHimalayanregionandthechallengesofassessingtherela- Monsoon. The lowest monthly differences between the datasets
tive performance of raingauge and radar datasets (Vasiloff et al., inallthreesub-catchmentsof2–6%occurredinthesummersea-
2009), the differences between TRMM and APHRODITE are com- son. The differences between the Probability Density Function
pared (Fig. 3), but assessing their accuracy and reliability is not (PDF) and Cumulative Density Function (CDF) plots for the
withinthescopeofthispaper. TRMM and APHRODITE gridded precipitation data sets in Fig. 4
Therearesignificantdifferencesinthespatialandtemporaldis- show the uncertainty in daily precipitation in the upper, middle
tribution of precipitation between the two datasets (Table 2 and andlowersub-catchments.
Fig. 3). Differences in averageprecipitationbetweenTRMM3B42
V7andAPHRODITEincreasewithincreasingsub-catchmenteleva-
3.2.Comparisonofreferenceevapotranspirationmethods
tion.Althoughthereisan11%differenceinannualaverageprecip-
itationforthewholecatchment,thereisan(cid:6)39%differenceinthe
As would be expected, all three ETo methods give decreasing
Uppersub-catchmentand(cid:6)13%intheLowersub-catchment.The
ETowithincreasingsub-catchmentelevation (Table3). However,
datasets have greatest monthly differences in winter (maximum
there are distinct differences between the methods with
differenceof(cid:6)46%)andspring(maximumof(cid:6)47%)intheUpper
Penman–Monteithgivingannualaveragevaluesthatare47%and
sub-catchment; whereas the highest differences in the Lower
30% higher than the methods giving the lowest values in the
sub-catchment are in the low flow autumn season (maximum
Lower (Priestley Taylor) and Middle (Hargreaves–Samani)
sub-catchments. In contrast, the Priestley–Taylor method gave
thehighestannualaveragevaluein theUpperglacierdominated
sub-catchment, which was 36% higher than the lowest method
(Hargreaves).ThePDFsandCDFsofdailyreferenceevapotranspira-
tionfor2000–2008usedtodrivethesixhydrologicalmodels,given
inFig.5,demonstratethesignificantuncertaintyintroducedbythe
choice of ETo method (Priestly–Taylor, Penman–Monteith, and
Hargreaves–Samani).
3.3.Hydrologicalmodelling–calibrationandvalidation
Fig.6showsthenarrowuncertaintyboundsinsimulateddaily
flowacrossthesixmodelsforboththecalibrationandvalidation
periods.Thestrongseasonalityinobservedriverresponseassoci-
atedwiththemonsoonandsnowmeltisreproduced.Thereisgen-
erallygoodagreementacrosstheflowdurationcurve(Fig.1ofthe
Supplementary material), with the six models spanning the
observedflowsthroughtheflowrange,withtheexceptionofabout
theupper5%exceedanceprobabilitywhichmaypartlyreflectthe
difficultyofaccuratelymeasuringsuchhighdischarges.
Similarlevelsofmodelperformancewereachievedacrossthe6
model combinations in the calibration period, with NSE varying
between 0.64 and 0.70. The three TRMM models having slightly
Fig. 6. The uncertainty bounds in simulated daily discharge during (upper) higher NSEs and lower PBIAS (Table 4). The performance metrics
calibrationand(lower)validationperiodsduetothevariabilityininputparameter
forthethreeTRMMmodelsareverysimilarinthevalidationphase
spaceacrossthesixHySimmodels.
(0.66–0.71),althoughthereisanincreaseinthePBIAS.Incontrast,
Table4
StatisticalindicesofHySimmodelperformancefordifferentprecipitationandevapotranspirationcombinations.
Calibration(2000–2004) Validation(2005–2008)
Year Penman–Monteith Priestley–Taylor Hargreaves Year Penman–Monteith Priestley–Taylor Hargreaves
PBIAS(%) NSE PBIAS(%) NSE PBIAS(%) NSE PBIAS(%) NSE PBIAS(%) NSE PBIAS(%) NSE(%)
TRMMprecipitationdatawithdifferentETocombinations
2000 16.8 0.75 4.3 0.75 10.9 0.74 2005 39.9 0.50 31.7 0.57 31.8 0.66
2001 20.9 0.69 17.3 0.73 16.5 0.70 2006 31.2 0.70 21.5 0.75 26.9 0.70
2002 11.9 0.59 4.6 0.55 (cid:2)3.3 0.53 2007 14.6 0.72 2.3 0.69 7.2 0.71
2003 1.5 0.77 (cid:2)9.9 0.75 (cid:2)10.4 0.71 2008 (cid:2)2.4 0.74 (cid:2)14.9 0.71 (cid:2)7.1 0.77
2004 0.5 0.69 (cid:2)11.8 0.68 (cid:2)13.3 0.65 Average 20.8 0.66 10.2 0.68 14.7 0.71
Average 7.54 0.70 4.1 0.69 3.5 0.67
APHRODITEprecipitationdatawithdifferentETocombinations
2000 28.2 0.60 34.4 0.54 36.8 0.57 2005 25.1 0.56 24.8 0.51 30.8 0.54
2001 18.1 0.69 26.4 0.67 21.6 0.68 2006 (cid:2)0.6 0.44 (cid:2)1.5 0.46 7.1 0.39
2002 0.5 0.69 14.9 0.74 5.3 0.66 2007 0.4 0.44 1.5 0.45 8.5 0.46
2003 (cid:2)19.7 0.69 (cid:2)14.0 0.75 (cid:2)15.5 0.64 2008 (cid:2)14.6 0.72 (cid:2)14.0 0.77 (cid:2)17.5 0.73
2004 (cid:2)36.7 0.62 (cid:2)29.1 0.70 (cid:2)27.1 0.62 Average 2.58 0.54 2.69 0.55 7.24 0.53
Average 6.80 0.66 6.51 0.68 12.02 0.64
638 R.Remesan,I.P.Holman/JournalofHydrology528(2015)631–642
Fig. 7. Q–Q plot evaluation of the modelled daily discharge in the six models
againstobserveddischargedatafor2000–2008.
the NSE for the APHOPRITE runs within the validation period
reduce (0.53–0.55), albeit still within the satisfactory range as
defined by Moriasi et al. (2007) and Henriksen et al. (2003), but
isaccompaniedbyanimprovementinthePBIASto2.6–7.2%.The
reliability of the predictive output distribution in the different
models is demonstrated using the quantile–quantile (Q–Q) plots
inFig.8.Althoughtheoverallperformancesofthemodels(asgiven
by the NSE and PBIAS metrics for the calibration and validation
periods) are similar, it is apparent from Fig. 7 that there are
parameter-related uncertainties in simulated discharge. Fig. 8(a Fig.8. (a–b)Modeluncertainty(asgivenbythepercentagedifferencebetween
simulated and observed discharge) along the flow exceedance curves showing
andb)expressthisuncertaintybyshowingthemodeluncertainty
associateduncertaintiesinsixhydrologicalmodellingsetupsunderthecalibration
(as given by the percentage difference between simulated and
(2000–2004)andvalidation(2005–2008)phases.
observed discharge) associated with flow exceedance probability
foreachofthemodelsduringthecalibration(2000–2004)andval- performance metrics and calibrated parameter values across the
idation (2005–2008) periods. The discharge uncertainty arising sixmodelssolelyreflecttheeffectsofthedifferencesintheinput
fromthemodelparameterisationsarefrom+8%to(cid:2)13%forQ10 precipitation and ETo data, given that all other input data and
and+15%to(cid:2)45%forQ90inthevalidationperiod. theobserveddischargedataareconsistentacrossthesixmodels.
Table 1 shows that the variability in the observational input This demonstrates how the calibration process can effectively
data space has led to quite different optimal parameter vectors modify hydrological parameters to compensate for uncertainties
with similar model performance The differences in the ininputdata.
Fig.9. Impactresponsesurfacesofsimulatedchanges(%)in(upper)Q10and(lower)Q90tochangesinannualprecipitationandtemperatureforthesixHySimmodels.
R.Remesan,I.P.Holman/JournalofHydrology528(2015)631–642 639
Fig.10. Comparisonofthemodelvalidationuncertainty(greyshadedarea)withthefutureimpactuncertainty(asgivenbythespreadofthepercentagechangeinfuture
dailydischargerelativetothebaselineforthe6HySimmodels)acrosstheflowexceedanceprobabilityforfourchangefactorscenariosshowinghowthespreadoffuture
hydrologicaluncertaintyexpandsfromtheuncertaintyduetotheinputdataselectionsubjectivityimpactsvalidationperiodasthechangeinclimateincreases.
640 R.Remesan,I.P.Holman/JournalofHydrology528(2015)631–642
3.4.Effectofparameteruncertaintyonclimatechangeimpact catchment,streamflowuncertaintyissmallerwithchangesinprecip-
responsesurfaces itation than that of temperature. For the [Aphrodite+Penman–
Monteith]model,ahydrologicaluncertaintyrangeof(cid:2)6.4%to16%
The dailyflowthat isequalledor exceeded10and 90% ofthe wasassociatedwithchangesinprecipitationfrom(cid:2)10%to+20%at
time(annualhighflow(Q10)andannuallowflow(Q90)),respec- DT=0,comparedtoanuncertaintyrangeof0to+84%fortempera-
tively,arecommonlyusedindesigninghydropowerprojects(IITR, tureschangeof0to+5(cid:3)CatDP=0%.
2011).Fig.9showstheimpactresponsesurfacesforeachofthe6
validated models for Q10 and Q90, expressed as a percentage of
thatmodel’sbaseline(2000–2008)value,acrosstherangeofplau- 4.Discussionandconclusion
siblechangesinfutureannualtemperatureandprecipitation.The
uncertaintyrangeinQ10andQ90acrossthesixdifferentmodels The need to understand uncertainty within water resource
isgiveninFig.2oftheSupplementarymaterial.Bothflowindices assessments is widely recognised (Refsgaard et al., 2007), and
generallychangeinproportiontothechangesinbothprecipitation manyelementsoftheuncertaintycascadewithinclimateimpacts
andtemperature,indicatingthattheincreasesintemperaturepro- modellinghavebeenquantitativelyassessed,highlightingtherel-
duce greater modelled increases in snowmelt than actual evapo- ativeimportanceofGCMchoice,emissionsscenario,downscaling
transpiration. The simulated median (Q50), annual high flow method, model choice, etc. (Pappenberger and Beven, 2006;
index (Q10) and annual low flow index (Q90) characterising the Buytaert et al., 2009; Kay et al., 2009; Chen et al., 2011).
hydrologicalimpactsofplausibleclimatechangeassimulatedby However, the role of modeller subjectivity is generally ignored,
the six HySim models are given in Table 1 of the Supplementary despiteitsrecognitioninothermodellingfields,suchaspesticide
material.Thisindicatesthatthedifferencesinhydrologicalparam- fate (Dubus et al., 2003; Beulke et al., 2006) and hydraulic mod-
eters arising from input uncertainty have greater impact on the elling(Garcia-SalasandChocat,2006).
uncertaintiesinlowflows(Q90)thanonhighflows(Q10). Inthispaper,wehaveinvestigatedthecombinedconsequence
The Q90 is more sensitive to precipitation and temperature of modeller choice on two key elements of a hydrological model
changesthantheQ10inrelativeterms,asagivenabsolutechange build in data-sparse areas which, to-date, have received little
inflowrepresentsalargerproportionforlowflowsthanhighflows. consideration in the context of their overall contribution to the
The greatest decrease in both metrics occurs under the (DT= uncertaintyinclimateimpactassessment–thechoiceofbaseline
0(cid:3)C(cid:2)DP=(cid:2)10%)scenario,rangingfrom(cid:2)25%(TRMM-Priestley– precipitationdatasetandthechoiceofmethodforderivingrefer-
Taylor) to (cid:2)2% (TRMM+Penman–Monteith) for Q90; and from enceevapo-transpiration.
(cid:2)11%(TRMM+Penman–Monteith)to(cid:2)6%,(APHRODITE+Penman– The consequence of the differing model parameterisations
Monteith)forQ10. resultingfromthedataset/methodchoiceinthisstudy,throughcal-
Similarly, the greatest increases occurred under the ibrating the hydrological model with different precipitation and
(DT=+5(cid:3)C(cid:2)DP=+20%) scenario due to the increased precipita- ETo time series, do not manifest themselves in significant differ-
tionandsnowmeltoutweighingtheeffectsofincreasedevaporation/ ences in model behaviour and performance during the historical
evapotranspiration – they ranged from +133% (TRMM+Penman– baseline period as given by conventional performance metrics.
Monteith)to238%(APHRODITE–Hargreaves)forQ90,representing The HySim model was demonstrated to have similar NSE and
an absolute increase in the Q90 flow of between 37–51m3/s; and PBIASvaluesandflowdurationcurvestotheobservedriverflows
from +79% (TRMM+Penman–Monteith) to +118% (APHRODITE – across the 6 model builds, despite the significant uncertainty in
Priestley–Taylor)forQ10equivalenttoamodelledincreaseof470– dailyprecipitationbetweenAPHRODITEandTRMMandnotwith-
771m3/s.Itisapparentthat,whilstthegeneraltrendsintheresponse standingissuesaroundtheuseofsuchdataproductsindailyhydro-
surfacesforthe6differentmodelsrespondsimilarlyacrosstempera- logicalmodelling(Zhaoetal.,2013;Mengetal.,2014).Thesimilar
tureandprecipitationchanges,thechoiceofprecipitationdatasetand modelperformancereflectsthe‘success’ofeachofthecalibrations
ETo method differentially affects the magnitude of changes in Q90 in modifying the parameter values which control the rates and
andQ10.Fig.10showshowtheparameterdifferencesassociatedwith thresholdsofhydrologicalprocessestocompensatefordifferences
the choice of baseline data impact on the flow duration curves for in precipitation and ETo amount (through changing snow melt
selectedfutureclimatescenarios,superimposedwiththeuncertainty characteristicsandtheactualETviatherootingdepth)andtiming
rangeforthe6modelsforthevalidationperiod(fromFig.8b).The (throughchangingtheratesofverticalandhorizontalflowthrough
selected scenarios are [DP=(cid:2)10%, DT=0(cid:3)C] (highest reduction in thesub-catchments)toenablethesimulatedriverflowstosatisfac-
precipitationandnochangeintemperature),[DP=(cid:2)10%,DT=5(cid:3)C] torily match the observed. The results demonstrate that uncer-
(highest reduction in precipitationand highest change intempera- tainty in any set of hydrologic inputs (here precipitation and
ture), [DP=+20%, DT=0(cid:3)C] (highest increase in precipitation and evapotranspiration)canbeconserved,withthetranslationofthis
nochangeintemperature),[DP=+20%,DT=5(cid:3)C](highestincrease uncertainty into simulated stream flow and performance metrics
inprecipitationandtemperaturefrombaselineclimate).Theseshow concealedorreducedthroughthecalibrationofmodelparameters
that large precipitation changes in the absence of temperature (SinghandBárdossy,2012,Andreassianetal.,2004andOudinetal.
increasestendtoleadtothelargestpercentagechangesinthemedian 2006). However, parameter-related uncertainties do exist in the
to low flow end of the flow duration curve; whereas temperature baselinesimulateddischarge,asshownbytherangeofdischarge
increasescausethelargestincreasearoundthe25thto50thexcee- errorwithinthe6modelsalongtheflowexceedancecurves(Fig.8).
dance probability reflecting large increases in pre- and Itisusualformodellerstoassumethattherangeofuncertainty
post-monsoonsnowmelt.However,whenthefuturedischargeuncer- andmodelperformancestatisticsassessedduringcalibrationand
taintyforthe6modelsacrosstherangeofflowexceedanceiscom- validationremainlargelyinvariant,regardlessofthetypeorsource
pared with the model uncertainty from the validation period, it is of input data sets used for modelling (McMillan et al., 2011).
apparentthatthefuturechangesindischargetendtoexceedtheval- However, our analysis has shown that the baseline uncertainty
idationuncertaintyacrosstheflowexceedancerange,withtheexcep- duetoparameterbias(Fig.8)isgenerallyexceededbytheuncer-
tionofsmallclimatechanges(e.g.[DP=(cid:2)10%,DT=0(cid:3)C]).Decreasing taintyassociatedwithapplyingthosemodelswithalteredclimate
precipitation decreases the uncertainty range but temperature (indicating that model uncertainty is not invariant) and that the
increaseshaveamuchgreaterimpactontheuncertainty.Itisappar- impactuncertainty(arisingfrommodelparameterisation)magni-
ent that, in the case of such a highly responsive snow-dominated fiesasthedifferencefromthecurrentclimateincreases(Fig.10).