Investigating Alternative Climate Data Sources for Hydrological

18 Pages·2016·5.22 MB·English

water
Article
Investigating Alternative Climate Data Sources for
Hydrological Simulations in the Upstream of the
Amu Darya River
AyetiguliSidike1,2,XiChen1,2,*,TieLiu1,2,KhaydarDurdiev1,2,3andYueHuang1,2
1 StateKeyLaboratoryofDesertandOasisEcology,XinjiangInstituteofEcologyandGeography,
ChineseAcademyofSciences,818SouthBeijingRoad,Urumqi830011,China;[email protected](A.S.);
[email protected](T.L.);[email protected](K.D.);[email protected](Y.H.)
2 UniversityofChineseAcademyofSciences,No.19A,YuquanRoad,ShijingshanDistrict,
Beijing100049,China
3 MinistryofAgricultureandWaterResourcesoftheRepublicofUzbekistan,ScientificResearchInstituteof
IrrigationandWaterProblemsundertheTashkentInstituteofIrrigationandMelioration,
Tashkent100187,Uzbekistan
* Correspondence:[email protected];Tel.:[phone]
AcademicEditor:Karl-ErichLindenschmidt
Received:13August2016;Accepted:27September2016;Published:11October2016
Abstract: The main objective of this study is to investigate alternative climate data sources for
long-termhydrologicalmodeling. Toaccomplishthisgoal,oneweatherstationdataset(WSD)and
threegrid-baseddatasetsincludingthreetypesofprecipitationdataandtwotypesoftemperaturedata
wereselectedaccordingtotheirspatialandtemporaldetails.Anaccuracyassessmentofthegrid-based
datasetswasperformedusingWSD. Then, the performances of corrected data combination and
non-correctedgrid-basedprecipitationandtemperaturedatacombinationsfrommultiplesourceson
simulatingriverflowintheupstreamportionoftheAmuDaryaRiverBasin(ADRB)wereanalyzed
using a Soil and Water Assessment Tool (SWAT) model. The results of the accuracy assessments
indicated that all the grid-based data sets underestimated precipitation. The Asian Precipitation
Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources
(APHRODITE)precipitationdataprovidedthehighestaccuracy(correlationcoefficients(CF)>0.89,
rootmeansquareerror(RMSE)<41.6mm),followedbytheCRUNCEPreanalysisdata(acombination
oftheCRUTS.3.2dataandtheNationalCentersforEnvironmentalPrediction(NCEP)reanalysis
data)(CF>0.5,RMSE<58.1mm)andPrinceton’sGlobalMeteorologicalForcingDataset(PGMFD)
precipitationdata(CF>0.46,RMSE<62.8mm).ThePGMFDtemperaturedataexhibitedahigher
accuracy(CF>0.98,RMSE<7.1◦C)thantheCRUNCEPtemperaturedata(CF>0.97,RMSE<4.9◦C).
In terms of the simulation performance, the corrected APHRODITE precipitation and PGMFD
temperaturedataprovidedthebestperformance.TheCFandNash-Sutcliffe(NSE)coefficientsin
thecalibrationandvalidationperiodswere0.96and0.92and0.93and0.83,respectively. Inaddition,
the combinations of PGMFD temperature data and APHRODITE, PGMFD and CRUNCEP
precipitation data produced good results, with NSE ≥ 0.70 and CF≥0.89. The combination
of CRUNCEP temperature data and APHRODITE precipitation produced a satisfactory result,
withNSE=0.58 and CF = 0.82. The combinations of CRUNCEP temperature data and PGMFD
andCRUNCEPprecipitationdataproducedpoorresults.
Keywords: climatedatasources;differentcombinationsofmultisourcedata;riverflowsimulation;
SWATmodel
Water2016,8,441;doi:10.3390/w8100441 www.mdpi.com/journal/water
Water2016,8,441 2of18
1. Introduction
One of the challenges in modeling watershed hydrology is obtaining accurate weather input
data[1,2],whicharegenerallyoneofthemostimportantdriversofwatershedmodels[3,4].Spatialand
temporalvariabilityarekeycharacteristicsofhydrologicalprocesses[5].Inmanyinstances,distributed
hydrologicalmodelsrequiredailydistributedmeteorologicaldatatosimulatethehydrologicalcycle.
However,somemodelingscenariosrequirehourlyormonthlydata. Lacksofdataandinaccuracies
indatahavethelargestimpactonmodelsimulations[6,7]. Distributedhydrologicalmodelsrequire
spatially distributed, long-term, continuous data to simulate the impact of climate change and
managementpracticesonhydrologicalprocesses. However,conventionalweatherstationsareoften
sparselydistributedandcannotfullyrepresenttheclimateconditionsacrossawatershed,particularly
iflargehydroclimaticgradientsexist[8–10]. Inaddition,weatherstationrecordsoftendonotcover
theproposedsimulationperiodorcontaingaps.
To solve this problem, some researchers have used grid-based data (e.g., atmospheric model
analysisorreanalysisoutputs, radardataandgriddedstationobservations, i.e., observationsthat
havebeeninterpolatedtoaregulargrid). Oneofthemostcommonwaysofdeterminingqualityisto
assesstheaccuracyofthedatasourceandtestitsperformanceinahydrologicmodel,oruncertainty
assessmentsofthepotentialimpactsofweatherinputsformodelpredictionusinglatentvariables[11],
simultaneousdataassimilationandparameterestimation[12]andusingprobabilistictechniquessuch
asBayesianModelAveraging(BMA)ortheIntegratedBayesianUncertaintyEstimator(IBUNE)[13,14].
Moststudieshavefocusedonevaluatingtheperformanceofgrid-basedprecipitationdatainsimulating
hydrologicprocesses[15–25],whileothershavefocusedonevaluatingtheperformancesofdifferent
parametersinonedatasetinsimulatinghydrologicprocesses[26–29]. Somestudieshaveevaluated
the respective performances of different variables associated with multisource grid-based data in
hydrologicmodeling[30,31]. However,nearly80%ofwaterresourcesinthecurrentregionofinterest
aregeneratedfromsnowandglaciermelt. Thus,theimpactoftheaccuracyoftemperaturedataon
runoff modeling in this region cannot be neglected. In addition, different types of data sets have
varyingaccuracylevelsacrossdifferentregionswithvariousweatherstationdistributions. Therefore,
alternativeclimatedatasourcesmustbeidentifiedindata-scarceregions[32].
The hydrologic regime of the Amu Darya River Basin (ADRB) is complex and vulnerable to
climatechange[8]. Waterdiversionforagricultural,industrialanddomesticusershassignificantly
reducedflowsindownstreamregions[33],resultinginsevereecologicaldamage[34]. Thescarcityof
meteorologicaldataremainsamajorhindranceinusinghydrologicmodelsinthisregion.Somestudies
havebeendesignedtoovercomethesedatalimitations. MonthlyreanalyzeddatafromtheClimate
ResearchUnit(CRUTS.3.2)havebeenusedinnumerousstudies[9,33,35,36]. Precipitationestimation
from remotely sensed information using artificial neural networks (PERSIANN) precipitation
products [8], the Willmott archived data set, the GSMaP satellite-driven data set [37], the global
climatologyprecipitationproduct(GPCP),theGlobalPrecipitationClimatologyCenter(GPCC)[38]
andERA-15data[39]havebeenusedtosimulatetheinfluenceofclimatechangeonwaterresourcesin
thisregion.
However, most of these studies have used data sets on monthly time scales [9,33,35,36],
useddailytimestepswithoutcorrectionforshort-termsimulations[8,16,20,22,37,40,41]orevaluated
the performances of different variables associated with multisource gridded data in hydrologic
modeling[23,24]. Inaddition,previousstudiesfocusedonevaluationoftheprecipitationdataand
neglectthetemperaturedataevaluation. Thus,itisessentialtoidentifyalternativeclimatedatasources
onadailytimestepandevaluatetheireffectivenessforlong-termhydrologicalmodeling. Toachieve
thisgoal,oneweatherstationdataset(WSD)andthreetypesofdatasetswithdailytimestepsover
long-termperiodsweretestedinthisstudytosimulateriverflowusingtheSoilandWaterAssessment
Tool(SWAT).Thus,themajorgoalofthisstudyistoinvestigatealternativeclimatedatasourcesfor
improving the performance of distributed hydrologic models and to provide a practical basis for
furtheranalysisonhydrologicalprocessesandothertopics. Therefore,wefocusedonadataaccuracy
Water2016,8,441 3of18
assessmentandperformanceevaluationsbutdidnotconsiderthesimulationuncertaintycausedby
weatherinputsduetothelengthofthepaper. Toachievethisgoal, theCentralAsiaTemperature
andPrecipitationData(CATPD),AsianPrecipitationHighlyResolvedObservationalDataIntegration
TowardstheEvaluationofWaterResources(APHRODITE)data,Princeton’sGlobalMeteorological
ForcingData(PGMFD)andCRUNCEPreanalysisdata(acombinationoftheCRUTS.3.2monthlydata
andtheNWaatteiro 2n01a6,l 8C, 4e41n tersforEnvironmentalPrediction(NCEP)reanalysisdata)werese3l eofc 1t8e ddueto
theirspatialandtemporalrepresentativenessoftheprocessesbeingmeasured. Ourfirstobjectivewas
simulation uncertainty caused by weather inputs due to the length of the paper. To achieve this 
toevaluatetheaccuracyofgrid-baseddata. Thesecondobjectivewastoinvestigatetheperformances
goal, the Central Asia Temperature and Precipitation Data (CATPD), Asian Precipitation Highly 
ofcorrectReedsodlvaetda Ocbosmerbvaintioantiaol nDaatan dIntnegornat-icoonr Treowctaerddsg trhied E-vbaaluseatdiodn aotf aWcaotemr Rbeinsoautricoens s(AoPfHpRrOecDiIpTiEt)a tionand
temperatduartea, dParitnaceftroonm’s Gmloubaltl iMpleetesooroulorgciecsal oFnorcsiinmg uDlaattai n(PgGrMivFDer) aflnodw CRinUNthCeEPu rpesantraelyasmis dpaotar t(iao n of the
combination of the CRU TS.3.2 monthly data and the National Centers for Environmental Prediction 
AmuDaryaRiverBasin(ADRB),andweselectedthesuitablecombinationsforsimulatingriverflow
(NCEP) reanalysis data) were selected due to their spatial and temporal representativeness of the 
inthisregion.
processes being measured. Our first objective was to evaluate the accuracy of grid‐based data. The 
second  objective  was  to  investigate  the  performances  of  corrected  data  combination  and 
2. Materials
non‐corrected grid‐based data combinations of precipitation and temperature data from multiple 
sources on simulating river flow in the upstream portion of the Amu Darya River Basin (ADRB), 
2.1. StudyArea
and we selected the suitable combinations for simulating river flow in this region. 
Thewatershedislocatedbetween38.66◦ N–39.86◦ Nand70.28◦ E–73.71◦ Eandcoversanarea
2. Materials 
of 19,638 km2. This area is a mountainous area, and the elevation ranges from 1294 m to 7198 m
(Figure 12)..1.T Shtuedyd Arraeian age area includes land cover types such as forest (3.64%), pasture (3.04%),
agricultural lTahned w(a0te.r1s7h%ed) ,is sloncoawteda bnetdweicene 3(81.566.5° 3N%–3)9,.8b6a° rNe alnadn 7d0.(2183° .E3–97%3.7)1°a En dansdp caovrseresl yanv aeregae otfa ted area
(64.23%)(1F9i,6g3u8r kem12).. TThhise amreaa iisn as moiolutnytpaiensouins athreias, raengdi tohne ealreevastaionnd ryansgoeisl f(r2o9m.3 122%94) ,mm too l7l1i9c8l emp (tFoisgoulrse (21.27%),
1). The drainage area includes land cover types such as forest (3.64%), pasture (3.04%), agricultural 
cumulicanthrosols(19.23%),haplickastanozems(17.03%)andcalcicchernozems(13.14%). Ingeneral,
land (0.17%), snow and ice (15.53%), bare land (13.39%) and sparsely vegetated area (64.23%) (Figure 
theclimateinthisregionexhibitscontinentalandsubtropicalfeatures.Theaverageannualtemperature
1). The main soil types in this region are sandy soil (29.32%), mollic leptosols (21.27%), cumulic 
ranges fraonmthr−os7o.l7s (◦1C9.23to%)8, .h3ap◦lCic, kaanstdantohzeemasn (n17u.0a3l%a)v aenrda gcaelcpicr echceiprniotazetimosn (1is3.1743%9). mInm ge(nienratlh, tehep eriod of
1965–2007cl)i.mWatei tihn itnhitsh reegmiono uexnhtiabiitns croanntgineesn,tatlh aencdl simubatrtoepdiciaflf feerastuarcesr.o Tshsed aivfefreargeen atnenlueavl atetmiopnerbaatunrde s. Inthis
study, theranegleesv afrtoimon −s7.o7 f°tCh eto L8y.3a i°rCu, na,ndD tahrea uantn-Kuaul ragvaerna,geS aprryectiapsithatiaonn dis F7e3d9 cmhmen (kino tGhel apceireiords toaf tions are
1965–2007). Within the mountain ranges, the climate differs across different elevation bands. In this 
2008m,2470m,3153m,and4169m,respectively. Thetemperaturedecreaseswithincreasingelevation,
study, the elevations of the Lyairun, Daraut‐Kurgan, Sarytash and Fedchenko Glacier stations are 2008 
whereasprecipitationpresentsdifferenttrendsindifferentelevationbandsandindifferentaspects.
m, 2470 m, 3153 m, and 4169 m, respectively. The temperature decreases with increasing elevation, 
More thawnh8er0e%as porfectihpeitaptiroenc pipreisteanttiso dniffoecrecnutr tsrefnrdosm in Odifcfteorebnet relteovaMtioany baonfdtsh aendf oinl ldoiwffeirnegnt yasepaerct(sF. igure 2).
ThemaximMourme thaannd 80m%i nofi mthue mprepcirpeictaiptioitna oticocnurtso frtoamls Oocctcoubreri ntoM Maayy oafn thdeA foullgowusint,gw yehaer r(eFaigsutrhe e2)p. Tehaek andlow
maximum and minimum precipitation totals occur in May and August, whereas the peak and low 
flowsoccurinAugustandMarch,respectively(Figure2). Themainwaterresourcesofthisregionare
flows occur in August and March, respectively (Figure 2). The main water resources of this region 
precipitation,snowmeltandglaciermelt.
are precipitation, snowmelt and glacier melt. 

Figure 1. Location of the study area in the Amu Darya River Basin and the land cover map. 
Figure1.LocationofthestudyareaintheAmuDaryaRiverBasinandthelandcovermap.
Water2016,8,441 4of18
Water 2016, 8, x FOR PEER REVIEW 4 of 18

Figure 2. Long-term monthly averages of precipitation and temperature at four stations (a)
Figure2.Long-termmonthlyaveragesofprecipitationandtemperatureatfourstations(a)long-term
lmonogn-tthelryma mveornagthelsyo afvperreacgipeist aotfi opnreacnipdittaetmiopne raantdu rteematpfeoruartustraet iaotn fso;u(rb )stlaotniogn-tse;r (mb)m loonngt-htleyrmav meroangethsloyf
asvtreeraamgefls oowf satnredapmr efcloipwit aatniodn p(rLeyc,iDpikta,tSiao,na n(LdyF, GDka,r eSaL,y aanirdu nF,GD aarrea uLty-Kaiururgna, nD,aSraaruytta-KshuargnadnF, eSdacrhyetanskho
aGnlda cFieerdcshtaetnioknos ,Grleascpieerc tsitvaetliyo)n.s, respectively).
2.2. Data
2.2. Data
22..22..11.. CClliimmaattee DDaattaa SSoouurrcceess
CClliimmaattee ddaattaa ssoouurrcceess aarree pprriimmaarriillyy ddiivviiddeedd iinnttoo ppooiinntt--bbaasseedd ((wweeaatthheerr ssttaattiioonnss)) aanndd ggrriidd--bbaasseedd
ssoouurrcceess,, ssuucchh aass aattmmoosspphheerriicc mmooddeell aannaallyyssiiss oorr rreeaannaallyyssiiss oouuttppuuttss,, rraaddaarr ddaattaa aanndd ggrriiddddeedd ssttaattiioonn
oobbsseerrvvaattiioonnss, ,ii..ee.,. ,oobbsseerrvvaattiioonnss tthhaatt hhaavvee bbeeeenn iinntteerrppoollaatteedd ttoo aa rreegguullaarr ggrriidd.. TToo iiddeennttiiffyy aalltteerrnnaattiivvee
ddaattaa ssoouurrcceess ffoorr uussee iinn lloonngg--tteerrmm hhyyddrroollooggiiccaall mmooddeelliinngg,, aavvaaiillaabbllee cclliimmaattee ddaattaa wweerree ccoolllleecctteedd ffrroomm
ddiiffffeerreenntt ssoouurrcceess ((TTaabbllee 11))..
ThreetypesofWSDcanbeusedintheADRB.TheseincludeCATPDatthemonthlytimescale,the
Three types of WSD can be used in the ADRB. These include CATPD at the monthly time scale,
GlobalSummaryOftheDay(GSOD)andtheGlobalHistoricalClimatologyNetwork-Daily(GHCND).
the Global Summary Of the Day (GSOD) and the Global Historical Climatology Network-Daily
ThelattertwodatasetscanbeobtainedfromTheNationalClimaticDataCenterforeverywhereinthe
(GHCND). The latter two data sets can be obtained from The National Climatic Data Center for
world. Althoughallofthesedatasetsprovidedprecipitation,maximumtemperatureandminimum
everywhere in the world. Although all of these data sets provided precipitation, maximum
temperaturedata,theGSODandGHCNDdonotprovidedatainourstudyareauntil1973,andno
temperature and minimum temperature data, the GSOD and GHCND do not provide data in our
dataexistsbetween1994and2005. Inaddition,thereisalotofmissingdatafortheavailableperiods.
study area until 1973, and no data exists between 1994 and 2005. In addition, there is a lot of
TheCATPDfrom1965to1990wasselectedforthisstudyduetoitscompleteness. Thesedataprovided
missing data for the available periods. The CATPD from 1965 to 1990 was selected for this study
datafromfourweatherstations(showninFigure1)inourstudyarea. TheCATPDcanonlybeusedto
due to its completeness. These data provided data from four weather stations (shown in Figure 1) in
evaluatetheaccuracyofgrid-baseddatabecausetheSWATmodelrequiresweatherdataatthedaily
our study area. The CATPD can only be used to evaluate the accuracy of grid-based data because
scaleformodelingriverflow.
the SWAT model requires weather data at the daily scale for modeling river flow.
Ameteorologicalreanalysisisameteorologicaldataassimilationproject,whichaimstoassimilate
A meteorological reanalysis is a meteorological data assimilation project, which aims to
historical observational data spanning an extended period using a single consistent assimilation
assimilate historical observational data spanning an extended period using a single consistent
(or“analysis”)schemethroughout.ThereanalysisdatasetslistedinTable1canbeusedinhydrological
assimilation (or “analysis”) scheme throughout. The reanalysis data sets listed in Table 1 can be
modeling. However,differenttypesofdatasetshavedifferentspatialandtemporalresolutionsand
used in hydrological modeling. However, different types of data sets have different spatial and
varyingaccuracylevelsacrossdifferentregionsbecauseofthevarietyofdatasourcesandassimilation
temporal resolutions and varying accuracy levels across different regions because of the variety of
methodsused. Inthispaper,accordingtothespatialandtemporalresolutionsofthedata,thePGMFD
data sources and assimilation methods used. In this paper, according to the spatial and temporal
andCRUNCEPreanalysisdatasetswerechosenforhydrologicalmodeling,andtheywereevaluated
resolutions of the data, the PGMFD and CRUNCEP reanalysis data sets were chosen for
andtestedfurtherinSections4.1and4.3. Bothofthemprovideddailyprecipitation,maximumand
hydrological modeling, and they were evaluated and tested further in Sections 4.1 and 4.3. Both of
minimumtemperature,dailytotalsolarradiation,dailyaveragerelativehumidityanddailyaverage
them provided daily precipitation, maximum and minimum temperature, daily total solar radiation,
windspeedforSWATmodelconstruction. However,onlydailyprecipitation,maximumtemperature
daily average relative humidity and daily average wind speed for SWAT model construction.
However, only daily precipitation, maximum temperature and minimum temperature were tested,
Water2016,8,441 5of18
andminimumtemperatureweretested,andotherparametersweresimulatedusingtheSWATweather
generatorduetothelackofmeasuredrelativehumidity,windspeedandsolarradiationdata.
Table1.Sourcesofclimatedata.
DataSet Period Resolution(◦) Temporal Region
Weatherstationdata
CATPD 1879–2003 - monthly CentralAsia
GSOD 1901–2016 - daily Global
GHCND 1763–2016 - daily Global
Reanalysisdata
ERA-15 1979–1993 2.5 6hourlyandmonthly Global
NCEP/NCAR 1948–present 2.5 6hourlyanddaily Global
JRA-25 1979–2004 1.125 6hourlyanddaily Global
MERRA 1979–present 1/2×2/3 hourly Global
CFSR 1979–present 0.5 hourly Global
CRUNCEP 1948–present 0.5 6hourlydata Global
ERA-Interim 1979–present 0.75 6hourlyanddaily Global
ERA-40 1957–2002 2.5 6hourlyandmonthly Global
PGMFD 1948–2010 0.5 daily Global
GLDAS 2000–present 0.25 3hourly Global
Wilmott 1900–2008 0.5 monthly Global
WFDEI 1979–2012 0.5 3hourlyanddaily Global
Griddeddata
APHRODITE 1951–2007 0.25 daily MonsoonAsia
TRMM 1998–present 0.25 3hourly Nearglobal
PERSIANN 2000–present 0.25 3hourly Nearglobal
GPCP 1997–present 1.00 daily Global
CMORPH 2002–present 0.25 3hourly Global
GSMaP 2002–present 0.1 hourly 60◦N–60◦S
WFD 1958–2001 0.5 3hourly Global
Thereareseventypesofgriddeddatasets,asshowninTable1. OnlytheAPHRODITEdatahas
thehighspatialandtemporalresolutionsneededinthisanalysis. Therefore,theAPHRODITEdataset
wasselectedasanalternativedatasourceforhydrologicalmodeling.
2.2.2. OtherDataforModelConstruction
ASWATmodelrequiresspatialdatasuchasadigitalelevationmodel(DEM),alanduse/cover
map and a soil map. The following were used to construct the SWAT model: a DEM with a 90 m
resolution [42]; land use/cover maps from the 1970s and 2005 with a 1000 m resolution, and the
Harmonized World Soil Database (HWSD) soil map with a scale of 1:5,000,000 [43]. The land
use/covermapswereobtainedfromtheCentralAsialandcoverchangedatasetofthe“973Program”,
describingtheresponseoflarge-scalelanduse/coverchangetoglobalclimatechange.
InadditiontothespatialdataanddailyweatherdatamentionedinSection2.2.1,aSWATmodel
alsorequiresphysicalandchemicalsoilpropertiessuchasmoistbulkdensity,depthfromthesoil
surfacetothebottomofthesoil,claycontent,siltcontentandsandcontent.Riverflowdataonacertain
timescalewererequiredformodelcalibrationandvalidation.
TheHWSDprovidessoilpropertiessuchasthedepthsofsoillayers,claycontent,siltcontent,
sand content and so on for each soil layer. Other properties, such as the available water capacity
andsaturatedhydrologicconductivity,werecalculatedusingSoil-Plant-Air-Water(SPAW)software
developedbytheU.S.DepartmentofAgriculture. Monthlyaverageriverflowdatafrom1965to1978
and1979–1985fromtheGlobalRunoffDataCenter(GRDC)wereusedforcalibrationandvalidation.
Water2016,8,441 6of18
3. Methodology
3.1. AccuracyAssessmentsoftheGrid-BasedDataSets
Precipitationandtemperaturedatafrom1965to2007wereextractedfromthegrid-baseddatasets
correspondingtothefourweatherstationsusingthenearestneighborinterpolationmethod. Thereare
otherinterpolationmethodssuchasbilinearinterpolation,inversedistance-weightedmethod[44,45].
Thenearestneighbormethodisthemostsimplemethodtoextractpointvaluesfromraster. Inorderto
savethecomputingpower,thenearestneighbormethodwasusedinthisstudy. Infurtherstudythe
impactofdifferentinterpolationmethodsontheextracteddataaccuracywillbediscussed.Anaccuracy
assessment was conducted by comparing the annual cycles and statistical box plots of grid-based
datasetswithWSDbasedonindicatorcriteria. Theannualcycleisusefulforevaluatingtheseasons
throughouttheyear,anditisnormallyestimatedfromobservationaldataormodeloutputbytaking
theaverageofeachmonthforagivennumberofyears[46]. Aboxplotwithmedian,upperquartile
(75thpercentile),lowerquartile(25thpercentile),minimumandmaximumvaluesisusedtodisplay
thequartiledistributionofthedata.
The following indicator criteria were applied to evaluate the grid-based data sets based on
WSD: the linear correlation coefficient (CF), root mean square error (RMSE), mean absolute error
(MAE),multiplicativebias(MBias)andNash-SutcliffeCoefficient(NSE)[32,45,47]. Themathematical
expressionsofthesecriteriaareasfollows:
∑n [(x −x)(y −y)]
CF= (cid:113) i=1 i i , (1)
∑n [(x −x)2]∑n [(y −y)2]
i=1 i i=1 i
(cid:114)
RMSE= 1 ∑n (x −y )2, (2)
n i=1 i i
1 ∑n
MAE= |(x −y )| (3)
i i
n
i=1
∑n x
MBias= i=1 i (4)
∑n y
i=1 i
∑n (x −y )2
NSE=1− i=1 i i (5)
∑n (x −y)2
i=1 i
where x and y are the gridded and stationary data sets (WSD), respectively. The CF is used to
assesstheagreementbetweenthegrid-baseddatasetandtheWSD.TherangeofCFvaluesisbetween
−1and+1.ACFvalueofexactly+1indicatesaperfectpositivefit,whileavalueofexactly−1indicates
aperfectnegativefit. TheMAEwasusedtorepresenttheaveragemagnitudeoftheerror. TheRMSE,
whichassignsalargerweighttolargererrorsrelativetotheMAE,wasusedtomeasuretheaverage
errormagnitude. TheoptimalvaluesoftheRMSEandMAEare0. TheMBiasistheratioofgrid-based
datatoWSD.AperfectestimationwouldresultinanMBiasvalueof1.Underestimationwilllead
to values less than 1 and overestimation to values greater than 1 [48]. NSE was used to describe
thegoodnessoffitofthegriddeddatasetsandtheobserveddataset. TherangeofNSEis −∞~1,
with1beingthebestvalue.
3.2. DataCorrectionandCombinations
Due to the lack of daily WSD, the APHRODITE precipitation data and the maximum and
minimumtemperaturesofthePGMFDwereselectedandcorrectedformodelconstruction. Thesimple
and widely used linear bias correction [49] method was used to correct the precipitation and
temperature data. The APHRODITE daily precipitation amounts P are transformed into P* such
thatP*=aP.ThevariableaisascalingparameterequaltoO/P,whereOand Paremonthlymean
Water2016,8,441 7of18
valuesofprecipitationbasedonWSDandAPHRODITEdata,respectively. Themonthlyscalingfactor
isappliedtoeachuncorrecteddailytimeseries. ThemaximumandminimumtemperaturesofPGMFD
werealsocorrectedusingthelinearbiascorrectionmethod. Thescalingparameterfortemperatureis
b =O −T,whereO andTaremonthlymeanWSDandPGMFDmaximumorminimumtemperatures.
t t
Themonthlyscalingfactorisappliedtoeachuncorrectedtimeseries. Fordailytimeseriesfrom1991
to2007,forwhichnoobserveddatawereusedforcorrection,thelong-termaveragemonthlymean
correctionfactorswereappliedtouncorrecteddailytimeseriesofeachmonth.
Toinvestigatetheperformancesofcorrecteddatacombinationandnon-correctedprecipitation
andtemperaturedatacombinationsfrommultiplesourcesonsimulatingriverflowinthestudyarea,
thefollowingcombinationswereused.CAPisthecombinationofcorrectedAPHRODITEprecipitation
and PGMFD temperature data. The six combinations include the combination of non-corrected
APHRODITEandPGMFDtemperaturedata(AP),thecombinationofprecipitationandtemperature
fromthePGMFDdataset(PP),thecombinationofCRUNCEPprecipitationandPGMFDtemperature
data(NP),thecombinationofAPHRODITEandCRUNCEPtemperaturedata(AN),thecombination
ofPGMFDprecipitationandCRUNCEPtemperaturedata(PN)andthecombinationofprecipitation
andtemperaturefromtheCRUNCEPdataset(NN).TheAP,PPandNPmodelswereusedtoevaluate
thesuitabilityoftheprecipitationdadatothemodelandthemodel’ssensitivitytotheaccuracyofthe
precipitationdata. TheAN,PNandNNmodelswereusedtoanalyzethesuitabilityoftemperature
dataandthesensitivityofthemodeltotheaccuracyoftemperaturedata.
3.3. TheSWATHydrologicalModel
TheSWATmodelisaphysicallybased,temporallycontinuous,semi-distributedhydrologymodel
thatcanoperateatadailytimestep. Itcansimulatecomplexhydrologicalprocessesandpredictthe
impactsofclimatechangeandlandmanagementpracticesonwater,sediment,andagriculturechemical
yieldsinlarge,complexwatershedswithvaryingsoils,landuses,andmanagementconditionsover
longperiods[50,51]. Itrunsonadailytimestepandrequiresspecificinformationregardingweather,
soilproperties,topography,vegetationandlandmanagementpractices[52]. Asasemi-distributed
hydrological model, SWAT possesses a simpler structure and requires less data than the fully
distributedMIKESHEmodel. However,themodelstructureuncertaintyinherentintheconceptual
lumpedmodelwillsignificantlyimpactthepredictionresults[53]. Inaddition,theconceptuallumped
modelcannotspecificallyanalyzehydrologicalprocessessuchasthespatialandtemporalvariability
associatedwithsnowandtheimpactsofsoilmoistureonirrigation. Thus,themainobjectiveofthis
paperistoinvestigateclimatedatasourcesfortheSWATmodelandprovideatheoreticalbasisfor
furtheranalysisofhydrologicalprocessesandothertopics.
The Soil Conservation Service (SCS) curve number procedure [54] and the Green and Ampt
infiltrationmethodareincludedintheSWATmodel.AlthoughtheGreenandAmptinfiltrationmodelis
morephysicallybasedthantheSCSmodel,theGreenandAmptinfiltrationmodelrequireslessreadily
availablesub-dailyprecipitationrecordsanddetailedsoilinformation.Thisisalargeobstacleforusing
thismodelindatascarceregions[55]. Thus, theSCSmethodisappliedinthisresearch.Thereare
two alternative functions of SCS method (antecedent soil moisture and plant evapotranspiration).
Inthisstudy,theantecedentsoilmoisturemethodwasusedbecauseofitssuitabilityinsemi-humid
and humid regions (this study area belong to the semi-humid region with annual precipitation
of 739 mm) [56–60]. The model offers three options for estimating potential evapotranspiration:
theHargreaves[61],Priestley-Taylor[62]andPenman-Monteithmethods[63]. ThethreePETmethods
included in SWAT vary in the number of required inputs. The Hargreaves method requires only
maximum,minimumandaverageairtemperature,whilethePriestley-Taylormethodrequiressolar
radiation,airtemperatureandrelativehumidity. TheinputsforPenman-Monteithmethodarethe
sameasthoseforthePriestley-Taylormethod,butitalsorequireswindspeed. TheHargreavesmethod
isappliedinthisstudybecauseofmeteorologicaldatalimitations.
Water2016,8,441 8of18
In SWAT, a watershed is divided into multiple sub-watersheds. Then, these watersheds are
divided into homogeneous spatial units with similar geomorphologic and hydrologic properties,
namely,hydrologicresponseunits(HRUs)[64]. Inthisstudy,thebasinwasdividedinto52sub-basins
and361HRUs.
3.4. ModelCalibrationandValidation
TheSWATmodelwasrunatamonthlyscaleinthisstudybecauseobserveddailyrunoffdata
were not available for model calibration and validation. The calibration period was from 1965 to
1978,andthefirsttwoyearswerethewarm-upperiod. Thevalidationperiodwasfrom1979to1985.
TheNSEandCFbetweensimulatedandobservedflows[65]wereusedtoevaluatetheresultsofthe
model.Therangesofthecriteriaforverygood, good, satisfactoryandunsatisfactoryresultswere
basedonthoseproposedbyBressianietal.[26]. Becauseofthelackofdailyweatherstationdata,
theAPHRODITEprecipitationdataandthemaximumandminimumtemperaturesofthePGMFD
wereusedtocalibrateandvalidatethemodelafterbiascorrection(selectedaccordingtotheaccuracy
assessmentinSection4.1).
Twentysensitiveparameterswereselectedaccordingtopreviousstudies[5,47,66,67]andtested
in the SWAT-CUP to perform a sensitivity analysis. Fourteen sensitive parameters were selected
accordingtotheirperformancesinthesensitivityanalysis,andmanualcalibrationandauto-calibration
were performed using a Sequential Uncertainty Fitting (SUFI-2) algorithm to achieve acceptable
performance[68–73].Thetopographiceffectswerealsoconsideredbydividingthewatershedinto
10elevationbandsandcorrectingthedatausingthetemperaturelapserate(TLAPS)andtheprecipitation
lapserate(PLAPS).
4. Results
4.1. EvaluationofDataAccuracy
Thepatternoftheannualcycleofprecipitationshowsthatthestudyareareceivedthemaximum
amountofrainfallinthespring(Figure3).TheAPHRODITEdatapatternwassimilartothedistribution
patternofWSD.BoththeCRUNCEPandPGMFDdatasetsoverestimatedprecipitationinthespring
andwinteratDaraut-Kurganstation,andthetrendatSarytashstationwasalmostthesame. However,
the CRUNCEP and PGMFD data sets underestimated precipitation in the spring and winter at
FedchenkoGlacier,andthetrendatLyairunstationwasalmostthesame.Theannualcycleofmaximum
andminimumtemperature(Figure3)indicatedthatthePGMFDunderestimatedthetemperature
andtheCRUNCEPdatasetoverestimatedthetemperatureatthreestations,excludingFedchenko
Glacierstation. ThePGMFDdatapatternmorecloselymimickedthedistributionpatternofweather
stationdata.
Theaveragemonthlyvaluesof25-yearprecipitationandmaximumandminimumtemperature
boxplotsatthefourstationsareplottedinFigures4and5basedontheWSDandgriddeddatasets.
Theinter-quartilerangeofthegriddeddatasetsillustratesthattheAPHRODITEdataprovidedthe
bestperformance. TheAPHRODITEdataspanalmostthesamerangeastheWSDatSarytashand
Daraut-Kurganstations. TherangesoftheAPHRODITEdataattheotherstationsareslightlynarrower
thantheweatherstationdata. However,thedifferencesareverysmall. TheCRUNCEPprecipitation
dataperformedbetterthanthePGMFDprecipitationdata. Intermsoftheannualaverageprecipitation
inthewatershed,allofthegriddeddatasetsunderestimatedprecipitation. TheAPHRODITEdataset
hadthehighestvalue,followedbythePGMFDandCRUNCEPdatasets.Themaximumandminimum
temperaturerangesofthePGMFDwereclosertotheWSDthanwerethoseoftheCRUNCEPdata
(Figure5).Therefore,weconcludethattheAPHRODITEdataandPGMFDdataareoptimalforforcing
the hydrologic model. In this study, this data was used to calibrate and validate the model after
correction. Theperformanceofcorrecteddatacombinationandnon-correcteddatacombinationswere
analyzedinSections4.2and4.3.
Water2016,8,441 9of18
Water 2016, 8, 441  9 of 18 
Water 2016, 8, 441  9 of 18 

FigureF i3g.u Aren3n.uaAl ncnyuclaels coyfc lteesmopfetreamtupreer aatnudr eparencdippitraetciiopnit abtaiosendb oanse dtheo nWtShDe WanSdD garnidddgedri dddaetda sdeattsa. (a)   
AFnignuursaeel  t3cs.y. Ac(alne)n Aoufna npl urceyaclcilcpeysitc aloetfi ootefnmp arpte ecDriapatirutaarutei toa‐nKndaut rpgDraaenrc aipsutitat-atKitoiuonrn;g  ab(bna)ss etAdatn iononnu ;ath(lb ec) yWAclnSeDn ou faa nlpdcry egccrliepidiodtafetpdior ednca iaptati  tsFaeteitdos.cn h(aaet)n ko 
GAlancniueFare l dsctcyahcteiloen nko;of  (Gpc)rl aeAcciinpernitsuattaailot inoc ynac;tl( ecDs) aAoranf unmtu‐aKalxuicrmygcualenms s ot(faTtmmioaanxx;i )m( bau)n mAd n(mTnmuinaailxm )cyuacnmlde  m(oTfi mnpiirmnec)u ipmteitm(aTtpimoenrina )atutt erFmee pdaetc hrSaeatnurkyroeta sh 
sGtaltaicoinear;t  S(sdatar)y tAitoannshn; u(scta)al t Aicoynncn;lu(edsa) lo Acfyn Tcnlmueasa lxoc fya cnmledas xToimfmTuimnm aa xt(T aDmnadarxTa)um tai‐nnKdau trmDgiaannriam sututa-mKtiou (nrTg;m a(nein)s )tA attneiomnnup;ae(elr )actAyucnrlene usa atol fSc yaTrcmyletasaxso hfa nd 
Tsmtaitnio Tanmt;  L(adyx)a aAinrundnnTu smatali ntciyoacntl;Le (syf a)o iAfr uTnnmnsuataaxtl i aconyndc;l (eTfs)m Aoifnn T namuta aDlxca yarcanludets ‐TKomfuTringm aaantx  FsateandtdicohTnem;n (ikeno) a AGtFnlaencduiceahrl  escntyakctolieoGsn lo.a fc iTemrsatxat iaonnd. 
Tmin at Lyairun station; (f) Annual cycles of Tmax and Tmin at Fedchenko Glacier station. 
(a)  (b) 
(a)  (b) 
Figure4.Cont.
Water2016,8,441 10of18
Water 2016, 8, 441  10 of 18 
Water 2016, 8, 441  10 of 18 
(c)  (d) 
(c)  (d) 
Figure 4. Box plots of precipitation based on WSD and gridded data sets at four stations. (a) box plot 
Figure4.BoxplotsofprecipitationbasedonWSDandgriddeddatasetsatfourstations.(a)boxplotof
Figure 4. Box plots of precipitation based on WSD and gridded data sets at four stations. (a) box plot 
of precipitation at Lyairun station; (b) box plot of precipitation at Fedchenko Glacier station; (c) box 
precipitationatLyairunstation;(b)boxplotofprecipitationatFedchenkoGlacierstation;(c)boxplot
of precipitation at Lyairun station; (b) box plot of precipitation at Fedchenko Glacier station; (c) box 
plot of precipitation at Sarytash station; (d) box plot of precipitation at Daraut‐Kurgan station. 
ofprecipitationatSarytashstation;(d)boxplotofprecipitationatDaraut-Kurganstation.
plot of precipitation at Sarytash station; (d) box plot of precipitation at Daraut‐Kurgan station. 
(a)  (b) 
(a)  (b) 
(c)  (d) 
(c)  (d) 
Figure 5. Box plots of temperature based on WSD and gridded data sets at four stations. (a) box plot 
Figure5.BoxplotsoftemperaturebasedonWSDandgriddeddatasetsatfourstations.(a)boxplotof
of temperature at Sarytash station; (b) box plot of temperature at Daraut‐Kurgan station; (c) box plot 
tFeimgupreer a5t.u Broexa tpSloatrsy otaf sthemstpaetiroantu;(rbe )bbaosxedp olont WofSteDm apnedr agtruidredaetdD daartaau ste-Ktsu artg faonurs tsattaitoino;n(sc.) (ba)o xbopxlo ptlooft 
of temperature at Lyairun station (d) box plot of temperature at Fedchenko Glacier station. 
toefm tepmerpaeturarteuarte Layt aSiaruryntasstaht isotanti(odn);b (obx) pblooxt polfotte mofp teermatpuerreatautrFee adtc DheanrkauotG‐Klaucrigearns tsattaiotino.n; (c) box plot 
of temperature at Lyairun station (d) box plot of temperature at Fedchenko Glacier station. 
Table 2 presents the comparative statistics at four stations on a monthly scale from 1965 to 1990. 
TThae bAlePH2RpOreDsIeTnEts ptrheecipcoitmatipoanr adtaivtae psrtoavtiisdteicds tahte fhoiugrhesstta taicocnusraocny, afomlloownethdl ybys ctahlee CfrRoUmNC19E6P5  to
Table 2 presents the comparative statistics at four stations on a monthly scale from 1965 to 1990. 
1990p.rTechipeiAtaPtiHonR dOaDtaI sTeEt. pTrheec CipFi toaft itohne AdaPtHaRpOroDvIiTdEe dprtehceiphiitgathioenst daactcau arta fcoyu,rf ostlalotiwonesd wbayst hhieghCeRr UthNanC EP
The APHRODITE precipitation data provided the highest accuracy, followed by the CRUNCEP 
prec0i.p89it,a wtihoenredaas ttahes eCt.F TvhaeluCesF oof fthteh eCRAUPNHCREOPD aInTdE PpGrMecFi ppirteactiipointadtiaotna daattaf owuerres thaitgiohners twhaans 0h.5ig0 her
precipitation data set. The CF of the APHRODITE precipitation data at four stations was higher than 
thanan0d.8 90.,4w6, hreersepaescttihveelyC,F evxcaelpute sato fDtahreauCt‐RKUuNrgCanE PstaatniodnP. GThMe FMpAreEc iopfi ttahtei oAnPdHaRtaOwDIeTrEe hmigohnethrlyth an
00..5809p,a rwencdhipe0ir.te4aa6tis,o rnteh sdepa CetacF tw ivvaeasll yul,oeewsx eocref  tpthhtaean t C3DR0a UmraNmuC,t -wEKPhu erargneaadns  PtshGtaeMt MioFnA p.ETrse hoceifp MtihteaA tCiEoRnoUf dNthaCteaEA PwP aeHnredR  hOPiGDghMITeFrED tmh daoannt at0 h.5ly0 
parnedcs iep0t.si4t aw6t,ei orrene s1dp6ae.9tca2t–iwv4e1a.ls5y6l, o mewxmecre aptnhtd aa n2t2 3.D604am–r4am7u.,2t‐5wK mhuemrrge,a arnes stpshteeacttMiiovAnel.Ey T.s Thohefe t MhCeFAC aERn dUo fNN tSChEeE  vPAaalPunHedsR PoOGf DtMhIeFT PDEG dmMaoFtanDts heltys 
wpreercaeinp1di6 t.Ca9tR2io–U4nN1 d.C5aE6tPam  wtmemasap nleodrwa2tue2rr.6 et4 hd–aa4nt7a . 32w05 emmremm h,,i grwehshepre ertcehtaaisnv  et0hl.y9e.  aTMnhdAe E0C.s5F 7oa,f n rtedhsepN eCSctREivUveaNlylu,C beEasPsoe afdnt hodne P PtGhGeMM WFFDSDD d,a antda 
setsa wnde rteh e1 6R.M92S–E4,1 M.56A mE amn da nMdB 2ia2s.6 o4f– t4h7e. 2P5G mMmFD, r edsaptae cwtievreel ylo.w Tehre t hCaFn  athnods eN oSfE t hvea lCuReUs NofC tEhPe  PdaGtaM. FD 
CRUNCEPtemperaturedatawerehigherthan0.9and0.57,respectively,basedontheWSD,andthe
andO CvRerUalNl, CthEe PP GteMmFpDe rtaemtupree rdatautrae  wdaetrae e hxhigibhieterd t ha ahnig 0h.e9r  aanccdu r0a.c5y7 ,t hreansp tehcet iCvReUlyN, bCaEsPe dte monp etrhaetu Wre SD, 
RMSE,MAEandMBiasofthePGMFDdatawerelowerthanthoseoftheCRUNCEPdata. Overall,
andd tahtae.  RMSE, MAE and MBias of the PGMFD data were lower than those of the CRUNCEP data. 
thePGMFDtemperaturedataexhibitedahigheraccuracythantheCRUNCEPtemperaturedata.
Ove rall, the PGMFD temperature data exhibited a higher accuracy than the CRUNCEP temperature 
data.