Hydrologic Evaluation of Six High Resolution Satellite Precipitation Products in Capturing Extreme

17 Pages·2017·6.06 MB·English

water
Article
Hydrologic Evaluation of Six High Resolution
Satellite Precipitation Products in Capturing Extreme
Precipitation and Streamflow over a Medium-Sized
Basin in China
ShanhuJiang1,2,ShuyaLiu1,2,LiliangRen1,2,*,BinYong1,LinqiZhang2,MenghaoWang2,
YujieLu2andYingqingHe3
1 StateKeyLaboratoryofHydrology-WaterResourcesandHydraulicEngineering,HohaiUniversity,
Nanjing210098,China;[email protected](S.J.);[email protected](S.L.);
[email protected](B.Y.)
2 CollegeofHydrologyandWaterResources,HohaiUniversity,Nanjing210098,China;
[email protected](L.Z.);[email protected](M.W.);luyujiefi[email protected](Y.L.)
3 NanjingBranch,Huai’anSurveyingandDesignInstituteofWaterResourcesCo.,Ltd.,Nanjing210008,
China;butterfl[email protected]
* Correspondence:[email protected];Tel.:[phone]
Received:22November2017;Accepted:25December2017;Published:29December2017
Abstract:Satelliteprecipitationproducts(SPPs)arecriticaldatasourcesforhydrologicalprediction
andextremeeventmonitoring,especiallyforungaugedbasins.Thisstudyconductedacomprehensive
hydrologicalevaluationofsixmainstreamSPPs(i.e.,TMPA3B42RT,CMORPH-RT,PERSIANN-RT,
TMPA3B42V7,CMORPH-CRT,andPERSIANN-CDR)overhumidXixianbasinincentraleastern
Chinaforaperiodof14years(2000–2013). Theevaluationspecificallyfocusedontheperformance
of the six SSPs in capturing precipitation and streamflow extremes. Results show that the
two post-real-time research products of TMPA 3B42V7 and CMORPH-CRT exhibit much better
performancethanthatoftheircorrespondingreal-timeSPPsforprecipitationestimationatdailyand
monthlytimescales.Bycontrast,thenewlyreleasedpost-real-timeresearchproductPERSIANN-CDR
insignificantlyimprovesprecipitationestimatescomparedwiththereal-timePERSIANN-RTdoesat
daily time scale. The daily streamflow simulation of TMPA 3B42V7 fits best with the observed
streamflow series among those of the six SPPs. The three month-to-month gauge-adjusted
post-real-time research products can simulate acceptable monthly runoff series. TMPA 3B42V7
andCMORPH-CRTpresentgoodperformanceincapturingprecipitationandstreamflowextremes,
although they still exhibit non-ignorable deviation and occurrence time inconsistency problems
comparedwithgauge-basedresults. CautionshouldbeobservedwhenusingthecurrentTMPA,
CMORPH,andPERSIANNproductsformonitoringandpredictingextremeprecipitationandflood
at such medium-sized basin. This work will be valuable for the utilization of SPPs in extreme
precipitationmonitoring,streamflowforecasting,andwaterresourcemanagementinotherregions
withsimilarclimateandtopographycharacteristics.
Keywords: satelliteprecipitationproduct;streamflowsimulation;extremeprecipitation;extreme
streamflow;statisticalevaluation
1. Introduction
Extreme precipitation and flood often exert serious impacts on human society and
infrastructure[1]. AccordingtoGlobalAssessmentReportonDisasterRiskReduction,floodsaffect
peopleworldwidemorethananyothernaturalhazards,withanestimatedglobalannualaverageloss
Water2018,10,25;doi:10.3390/w10010025 www.mdpi.com/journal/water
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of$104billion[2]. Accurateprecipitationestimatesandreliablefloodmodelingarecriticaltonumbers
ofnationalandregionalagenciesfordisastermonitoring,mitigation,andmanagement[3–6]. Inrecent
decades,satelliteprecipitationproducts(SPPs)havebecomenewalternativeprecipitationdatasetsfor
globalhydrometeorologicalresearchandapplications[7–9]. Forexample,thePrecipitationEstimation
fromRemoteSensingInformationusingArtificialNeuralNetwork(PERSIANN)[10], theClimate
PrecipitationCenter(CPC)MorphingTechnique(CMORPH)byNationalOceanicandAtmospheric
Administration(NOAA)[11],theTropicalRainfallMeasuringMissionMulti-satellitePrecipitation
Analysis(TMPA)[12],andtheGlobalPrecipitationMeasurement(GPM)[13],haverecentlybeenmade
operationallyavailable. AmongsuchSPPs,TMPA,CMORPH,andPERSIANNplayimportantroles
owingtotheirlong-termseriesdatasetsandadequateretrievalexperiencesandtechniques. Beforeand
after the GPM mission implemented on 27 February 2014, the TMPA, CMORPH, and PERSIANN
systemshavebeencontinuouslyupgradingtheirretrievalalgorithmsandreleasingtheirlatestsatellite
precipitationproducts[14–16].
Inrecentyears,manyworkshavebeenmadetoanalyzetheaccuracyandhydrologicalutilityof
thelatestSSPsoverdifferenttopographicorclimaticregionsallovertheworld,andtheyfoundthat
theerrorsofdifferentSPPsdependsignificantlyontheprecipitationretrievalalgorithm,geographical
location, topography condition (plain or mountainous), and seasonality [17–24]. For example,
Yongetal. [17] compared the precipitation products of TMPA over two different latitude basins
withindependentgaugeobservationsandconcludedthatTMPAexhibitmuchbetterperformanceover
thelowlatitudeMishuibasinthanoverthehighlatitudeLaohahebasin. Zhuetal.[18]evaluatedthe
hydrologicalutilityofTRMM3B42V7,PERSIANN-CDR,andNCEP-CFSRovertwoChinesehumid
watershedsandfoundthatTMPA3B42V7andPERSIANN-CDRpresentgoodperformancefordaily
andmonthlystreamflowsimulations. Hussainetal.[22]assessedsixSPPs(real-timeandbias-adjusted
TMPA, CMORPH, and PERSIANN) over plain, mountainous, and glacial regions in Pakistan and
concludedthatTMPA3B42V7andCMORPH-CRTshowthebestagreementwithinsitudataoverthe
entirestudyarea.
Some efforts have also been made to evaluate the utility of these SPPs in capturing extreme
precipitation and streamflow over different spatial scales [25–34]. Huang et al. [26] evaluated the
utility of TMPA 3B42RT and 3B42V7 in capturing one typical extreme rainfall event in Beijing in
21 July 2012 and emphasized that both SPPs present large deviations from the temporal variation
in rainfall and underestimate the storm. Miao et al. [27] evaluated PERSIANN-CDR in capturing
the extreme precipitation over China and found that PERSIANN-CDR underestimates the values
of extreme heavy precipitation. Shah and Mishra [29] analyzed uncertainty and bias in five SPPs
overIndiansub-continentalbasinsandhighlightedthatbiasinSPPsaffectstheinitialconditionand
precipitationforcing,therebyaffectingfloodpeaktimingandmagnitudes. Meietal.[31]analyzed
error in SPPs-driven flood simulations in complex alpine terrain in Italy and found a dampening
effectinbothsystematicandrandomerrorcomponentsofthesatellite-drivenhydrographrelative
tothesatellite-retrievedhyetograph. Thesepreviousresearchesprovidedimportantreferencesfor
application on SPPs in flood hydrology. However, the further researches of uncertainties of these
SPPsincapturingextremeprecipitationintermsofrainfallamountdeviationandoccurrencetime
inconsistencyindifferentregionsandtheirerrorpropagationcharacteristicsinfloodsimulationare
stillneeded.
Thisstudyfocusesoncomprehensivehydrologicalevaluationofsixlong-termseriesSPPs(i.e.,TMPA
3B42RT,CMORPH-RT,PERSIANN-RT,TMPA3B42V7,CMORPH-CRT,andPERSIANN-CDR)inhumid
XixianbasinincentraleasternChina.Thisstudymainlyaimsto(1)evaluatetheaccuracyofsixmainstream
SPPs with rain gauge observations, (2) validate the hydrological simulation utility of the SSPs via
hydrologicalmodeling,and(3)analyzetheuncertaintiesoftheSSPsincapturingextremeprecipitation
and extreme streamflow. This work is valuable for the utilization of SPPs in extreme precipitation
monitoring,streamflowforecasting,andwaterresourcemanagementincentraleasternChinaandother
regionswithsimilarclimateandtopographycharacteristics.
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2. StudyAreaandData
2.1. StudyArea
XixianBasin,theupstreamcatchmentofHuaiRiver,wasselectedasourcasestudyarea(Figure1).
Extendedfromlongitudes113.25◦ Eto115◦ Eandlatitudes31.5◦ Nto32.75◦ N,theXixianbasinhas
adrainageareaof10,191km2 abovetheXixianhydrologicstation. Theelevationwithinthebasin
rangesfrom33mto1110m. ThelanduseandlandcoverageinXixianbasinis54.4%forestandshrubs,
33.5%grassland,11.8%cropland,and0.3%urbanandwater. Thebasinliesinthetransitionzoneof
themid-latitudehumidclimateandsemi-aridclimate. Thelongtermmeanannualprecipitationand
temperatureare1145mmand15.2◦C,respectively. Theseasonaldistributionoftheprecipitationin
thebasinismainlyaffectedbymonsoonclimateandthevastmajorityoftheannualprecipitationfalls
inthesummerseason. Duringthesemonths,particularlyinJuneandJuly,basin-wideheavyrains
continuouslyoccur,therebyresultinginfloods. Thelongtermmeanannualcumulativeflowrateis
3.58×109m3atXixianhydrologicstation.
Figure1.MapofXixianBasininupstreamofHuaiRiverBasin.
2.2. Data
2.2.1. SatellitePrecipitationProducts
TheSPPsusedinthisstudyarethreepurereal-timeSPPs,namely,TMPA3B42RT,CMORPH-RT,
andPERSIAN-RT,andthreepost-real-timeresearchSPPs,namely,TMPA3B42V7,CMORPH-CRT,
andPERSIANN-CDR.
The TMPA combines the low-orbit satellite microwave (MW) data where available and
the MW-calibrated geostationary infrared (IR) data elsewhere to produce quasi-global (50◦ N-S)
precipitationdataset[12].TheTMPA3B42RTandTMPA3B42V7precipitationproductswereproduced
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bythelatestTMPAVersion7algorithmsupgradedon28January2013[14]. Duringthepastyears,
hydrologistsandmeteorologistshavewidelyevaluatedandappliedtheTMPAproducts[17,18,20–26].
While, the utility of the latest version 7 TMPA products in capturing extreme precipitation and
streamflowhasnotbeenfullyevaluatedatriverbasinscale.
TheCMORPHisprimarilybasedonlow-orbitsatelliteMWobservations,andthegeostationary
IRdataareusedonlytoderivethemovementofprecipitationsystems[11]. AtNOAACPC,CMORPH
reprocessedanewdatasetnamedCMORPHVersion1.0in2014. CMORPHVersion1.0includesthe
raw,satellite-onlyprecipitationestimates(CMORPH-RT)andthenewbias-correctedprecipitation
products(CMORPH-CRT).CMORPH-CRTadjustsprecipitationestimatethroughmatchingthePDFof
dailyCMORPH-RTagainstthatfortheCPCunifieddailygaugeanalysisateachmonthoverland[15].
The PERSIANN derives the relationships between IR and MW data by a neural network
approachandthenapplytherelationshipstotheIRdatatogeneratereal-timeprecipitationproducts
(PERSIANN-RT) [10]. PERSIANN-CDR is a newly developed bias-adjusted satellite precipitation
productthatcoversfrom1January1983topresent[16]. InPERSIANN-CDR,theIRdataarefirstly
appliedinthePERSIANNmodeltoproducehistoricalrainfallestimates,thentheseestimatesarebias
adjustedbyusingtheGlobalPrecipitationClimatologyProject2.5◦ monthlydata[16]. Currently,the
availablePERSIANN-CDRisdailytemporalresolutionand0.25◦spatialresolutionforthe60◦ S–60◦ N
latitudeband.
2.2.2. GaugedPrecipitationandDischargeData
Daily precipitation data for the year of 2000–2013 were obtained from 22 rain gauge stations
in Xixian Basin (Figure 1). The same long-term series daily streamflow and meteorological data
wereobtainedfromtheXixianhydrologicstationandXinyangevaporationstation,respectively. The
Penman–Monteithequationwasusedtocalculatedailypotentialevapotranspirationasoneforcing
input data for the hydrological model [35]. The inverse distance weighting method was used to
interpolate the rain gauge data into the spatial distribution [36]. The 30 arc-second global digital
elevationmodeldatawascollectedfromtheU.S.GeologicalSurvey[37]. Thevegetation-typedata
werecollectedfromtheModerateResolutionImagingSpectroradiometerlandcoverdatausingthe
InternationalGeosphere–BiosphereProgramclassificationsystem[38].
3. Methodology
3.1. EvaluationStatistics
Inthepresentstudy,threestatisticalindiceswereadoptedtoqualitativelyevaluatetheprecision
of the six SPPs. The Person correlation coefficient (CC) assesses the correlation between satellite
precipitationproductsandraingaugeobservations. Theroot-mean-squareerror(RMSE)measuresthe
averageerrormagnitudeofsatelliteprecipitation. Inaddition,therelativebias(BIAS)demonstrates
the systematicbias ofsatellite precipitation. Twocategorical statisticalindices, namedprobability
ofdetection(POD)andfalse-alarmrate(FAR),representthecorrespondencebetweenSPPsandrain
gaugedata[39]. POD,alsonamedhitrate,showshowoftenrainoccurrencesarecorrectlydetectedby
satellite. FARdenotesthefractionofcasesinwhichsatelliterecordsprecipitationwhenraingauge
doesnot. Aprecipitationthresholdof1.0mmwasusedtodefinerainorno-raineventsinthisstudy.
TheCC,BIAS,andNash–Sutcliffecoefficient(NSE)comprehensivelyreflectthehydrologicmodel
performanceonthebasisoftheobservedandsimulatedstreamflow[40]. Formulastocalculatethe
indicesmentionedabovearelistedinTable1.
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Table1.StatisticalevaluationindicesforevaluatingtheSPPsandtheirhydrologicalutility.
EvaluationIndices Unit Formulas PerfectValue
∑n (Gi−G)(Si−S)
Correlationcoefficient(CC) NA CC= (cid:118)(cid:117)(cid:117)(cid:116)∑ni=(G1i−G)2(cid:115)∑n (Si−S)2 1
i=1 i=1
(cid:115)
n
Root-mean-squareerror(RMSE) mm RMSE= n1 ∑ (Si−Gi)2 0
i=1
∑n (Si−Gi)
Relativebias(BIAS) % BIAS= i=1 ×100% 0
∑n Gi
i=1
Probabilityofdetection(POD) NA POD= tH 1
tH+tM
False-alarmrate(FAR) NA FAR= tF 0
tH+tF
∑n (Qsim(i)−Qobs(i))2
Nash–Sutcliffecoefficient(NSE) NA NSCE=1−i=1 1
∑n (Qobs(i)−Q−obs)2
i=1
Notes:wherenisthetotalamountofsamples;SiandGiaretheithvaluesoftheevaluateddataandreferencedata,
respectively;SandGarethemeanvaluesoftheSiandGi,respectively.H,M,andFaredifferentcases:H,observed
raincorrectlydetected;M,observedrainnotdetected;F,raindetectedbutnotobserved. tH,tM,andtFarethe
timesofoccurrenceofthecorrespondingcase.ThedetailsoftheseparameterscanbefoundinEbertetal.[39].
3.2. HydrologicalModelandCalibration
Xinanjiangmodel(XAJmodel)isafamousphysically-basedconceptualrainfall-runoffmodel
developedbyZhao[41]. IthasbeenwidelyusedindifferenthumidriverbasinsoverChina[42,43].
Inthecurrentstudy,agrid-basedXAJmodelwasconstructed. Themodelcalculatedthetotalrunoff
withineachgridunitusingthesaturationexcessrunoffscheme. Thetotalrunoffwasdividedinto
surfacerunoff,soilrunoffandundergroundrunoffusinganoverflowreservoirwithbottomandside
holes. ThenthelinearreservoirwasusedtoperformthesloperunoffconvergenceandtheMuskingum
segmentationfloodroutingwasusedtoperformtherivernetworkrunoffconvergence. Themodel
contains15parameters(seeTable2), andtheywereautomaticallycalibratedthroughtheShuffled
Complex Evolution (SCE-UA) global optimization algorithm proposed by Duan et al. [44]. In the
currentstudy,theXAJmodelwascalibratedwithraingaugeprecipitationdataatdailytimescaleand
0.25◦ ×0.25◦ spatialresolution. Inaddition,themodelrunswerethenrepeatedwiththesixSPPsas
inputs. WedeterminedtheJanuary2000toDecember2006ascalibrationperiodandJanuary2007to
December2013asvalidationperiod.
Table2.ParametersoftheXAJmodelandtheirpriorranges.
Parameter PhysicalMeaning PriorRange
Kc ratioofpotentialevapotranspirationtopanevaporation 0.5–1.5
WUM watercapacityintheuppersoillayer 10–40
WLM watercapacityinthelowersoillayer 50–90
WDM watercapacityinthedeepersoillayer 10–70
B exponentoftensionwatercapacitycurve 0.1–0.5
C coefficientofdeepevapotranspiration 0.1–0.3
EX exponentoffreewatercapacitycurve 1–1.5
SM freewatercapacityofsurfacesoillayer 10–60
KI0 outflowcoefficientsoffreewaterstoragetointerflow KI+KG=0.7
KG0 outflowcoefficientsoffreewaterstoragetogroundwater 0.1–0.5
CI0 recessionconstantofthelowerinterflowstorage 0.1–0.9
CG0 dailyrecessionconstantofgroundwaterstorage 0.9–0.999
CS0 recessionconstantforchannelrouting 0.1–0.5
KE slotstoragecoefficient 20–24
XE flowproportionfactor 0.1–0.5
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4. ResultsandDiscussion
4.1. EvaluationofSatellitePrecipitationProducts
TheselectedsixSPPscoveringtheentireXixianBasinfrom1January2000to31December2013
(inwhichTMPA3B42RTandPERSIANN-RTwerefrom1March2000)wereevaluatedintermsof
errorsindailyprecipitation,extremeprecipitation,andmonthlyprecipitation.
4.1.1. DailyPrecipitationEvaluation
Scatterplots of the basin average daily precipitation estimates of six SPPs versus rain gauge
observationsareshowninFigure2, andthestatisticalmeasuresofsixSPPsforthedailyscaleare
summarizedinTable3. Amongthethreereal-timeSPPs,CMORPH-RTexhibitsthebestperformance
with the highest CC of 0.79 and CSI of 0.55 and lowest RMSE of 5.42 mm, BIAS of −12.60, and
FAR of 0.26. TMPA 3B42RT presents a performance comparable to that of CMORPH-RT, except
for a large overestimation of precipitation estimates, with BIAS of 23.75%. The performance of
PERSIANN-RT is the worst among those of the three real-time SPPs. For the three post-real-time
researchSPPs,TMPA3B42V7andCMORPH-CRTpresentbetterperformancethanthatofthelatest
PERSIANN-CDR. As documented in many prior studies, the two post-real-time research SPPs of
TMPA and CMORPH exhibit much better performance than that of their corresponding real-time
satelliteprecipitation[18,21]. WithregardtoPERSIANNproducts,thenewlyreleasedpost-real-time
researchversionPERSIANN-CDRinsignificantlyimprovesprecipitationestimatescomparedwith
thereal-timePERSIANN-RTdoesatdailytimescale. Therefore,cautionshouldbeexercisedwhen
applyingthenewlyreleasedPERSIANN-CDRfordailyquantitativeprecipitationestimationatsimilar
riverbasins. Ingeneral,TMPAandCMORPH(especiallythetwopost-real-timeresearchSPPs)show
goodperformancefordailyprecipitationestimatesoverXixianBasin.
Figure2.ScatterplotsofthebasinaveragedailyprecipitationbetweenSPPsandraingaugeobservations
for Xixian Basin: (a) TMPA 3B42RT; (b) CMORPH-RT; (c) PERSIANN-RT; (d) TMPA 3B42V7; (e)
CMORPH-CRT;(f)PERSIANN-CDR.
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Table3.StatisticalmeasuresoftheSPPsforthedailyprecipitationestimates.
Products CC RMSE(mm) BIAS(%) POD FAR
TMPA3B42RT 0.76 6.17 25.25 0.75 0.36
CMORPH-RT 0.76 5.65 −11.87 0.67 0.28
PERSIANN-RT 0.59 7.06 −17.20 0.62 0.47
TMPA3B42V7 0.78 5.71 15.10 0.76 0.30
CMORPH-CRT 0.77 5.84 9.20 0.73 0.31
PERSIANN-CDR 0.60 7.15 19.04 0.76 0.56
4.1.2. ExtremePrecipitationEvaluation
Threeextremeprecipitationindices,namely,annualmaximum1-dayprecipitation(R×1d),3-day
precipitation(R×3d),and5-dayprecipitation(R×5d)[27],wereadoptedtoevaluatetheperformance
ofthesixSPPsincapturingthebehaviorofprecipitationextremes.ThestatisticalmeasuresoftheSPPsfor
R×1d,R×3d,andR×5daresummarizedinTable4,andtheboxplotsoftheBIASvaluesofR×1d,R
×3d,andR×5dofthesixSPPsareshowninFigure3.Amongthethreereal-timeSPPs,TMPA3B42RT
exhibitsthebestperformanceincapturingthebehaviorofprecipitationextremeswithcomparableCCand
thelowestRMSE,andBIAS,whereasTMPA3B42RTstillshowsseriousoverestimationofR×1d,R×3d,
andR×5dforsomeyears(Figure3). Notably,theBIASvaluesofthethreenear-real-timeproducts,
asshownintheboxplotsofFigure3,aredistributedinawiderrangethanthatoftheircorresponding
post-real-timeproducts.Thisresultindicatesthegreatuncertaintyofthenear-real-timeSPPsincapturing
thebehaviorofprecipitationextremes. Forthethreepost-real-timeresearchSPPs,TMPA3B42V7and
CMORPH-CRTpresentmuchbetterperformancethanthatofthelatestPERSIANN-CDRwhichseriously
underestimatesprecipitationextremesprecipitationextremes. Withregardtothemultipleindicators,
TMPA3B42V7andCMORPH-CRTexhibitgoodperformanceincapturingthebehaviorofprecipitation
extremesoverXixianBasin,whereastheystillpresentcertainunderestimationsofprecipitationextremes.
Thisfindingisconsistentwiththatofafewpreviousworks[18,32].Othertimeconsistencyuncertainties
areobservedintheperformanceofSPPsincapturingprecipitationextremes.Table5showstheoccurrence
dateofR×1ddetectionbyraingaugeobservationsandsixSPPsfortheperiodof2000–2013.Amongthe
sixSPPs,TMPA3B42RTandCMORPH-RTshowthebestperformanceintimeconsistencyfordetecting
the occurrence date of R × 1 d. The two SPPs are followed by TMPA 3B42V7 and CMORPH-CRT.
ThePERSIANNproductsexhibittheworstperformance.Ingeneral,mostoccurrencedatesofR×1d
detectionbySPPsarestillinconsistentwiththedatedetectedbygaugeobservations. Thus,owingto
theunderestimationinprecipitationextremesandtheinconsistencyofoccurrencetimewithraingauge
observations,cautionshouldbeexercisedwhenapplyingthecurrentTMPA,CMORPH,andPERSIANN
productsinpredictingextremeprecipitationatsuchmedium-sizedhumidbasin.
Table4.StatisticalmeasuresoftheSPPsforannualmaximum1-,3-,and5-dayprecipitation.
ExtremePrecipitationIndices Products CC RMSE(mm) BIAS(%)
TMPA3B42RT 0.74 22.05 −5.90
CMORPH-RT 0.73 26.06 −15.36
PERSIANN-RT 0.76 29.34 −23.70
R×1d
TMPA3B42V7 0.88 19.99 −14.54
CMORPH-CRT 0.84 18.99 −3.64
PERSIANN-CDR 0.74 34.21 −30.29
TMPA3B42RT 0.67 44.76 4.82
CMORPH-RT 0.71 46.06 −16.55
PERSIANN-RT 0.67 59.76 −31.52
R×3d
TMPA3B42V7 0.87 30.41 −8.92
CMORPH-CRT 0.79 35.55 −1.42
PERSIANN-CDR 0.87 49.89 −27.91
TMPA3B42RT 0.71 47.31 4.91
CMORPH-RT 0.75 50.87 −20.35
PERSIANN-RT 0.56 67.00 −28.52
R×5d
TMPA3B42V7 0.88 31.24 −7.27
CMORPH-CRT 0.85 34.11 −7.53
PERSIANN-CDR 0.78 47.95 −18.27
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Figure 3. Box plots of the BIAS index of the annual maximum (a) 1-day precipitation; (b) 3-day
precipitationand(c)5-dayprecipitationforthesixSPPsversusraingaugeobservationsover14years.
Theupperandloweredgesoftheboxmarktheupperandlowerquartiles(75%and25%,respectively);
thesolidlineintheboxmarksthemedianvalue;theupperandlowerhorizontallinesoutofthebox
markthe90%and10%quartiles,respectively;thepointsmarkthemaximumandminimumvalues.
Table5.Occurrencedateofannualmaximum1-dayprecipitation(R×1d)detectionbyraingauge
observationsandsixSPPsfortheperiodof2000–2013.GrayboxesindicatethedateofR×1ddetected
bySSPsisconsistentwiththedatedetectedbyraingaugeobservations.
TMPA TMPA
Year Gauge CMORPH-RT PERSIANN-RT CMORPH-CRT PERSIANN-CDR
3B42RT 3B42V7
2000 8/18 6/1 6/1 6/1 6/1 6/1 6/1
2001 6/17 7/29 8/7 7/21 10/25 10/25 7/26
2002 6/22
2003 6/29 7/20 4/17 4/17 10/10 8/24 8/28
2004 8/4 7/16
2005 7/9 6/25 6/25 6/25 8/28 6/25
2006 9/4 6/29 5/4 7/21 5/4 5/4 7/21
2007 7/13 5/30 5/30 5/30 7/8 7/8 7/4
2008 7/22 8/13 8/13 7/17 8/13 8/13 7/17
2009 8/28 6/27 6/27 7/21 5/27 8/17 5/27
2010 7/16 5/3 9/6
2011 7/26 6/13
2012 9/7 8/26 7/7 8/26 8/26 8/18
2013 8/22
4.1.3. MonthlyPrecipitationEvaluation
The daily precipitation estimates from the six SPPs were aggregated to monthly time series
andwerethencomparedwiththeraingauge-observedmonthlyprecipitation. Figure4depictsthe
scatterplotsofthebasinaveragemonthlyprecipitationfromSPPsandraingaugeobservations. Similar
tothedailyresults,amongthethreereal-timeSPPs,CMORPH-RTpresentsthebestperformancewith
thehighestCCof0.91andthelowestRMSEof39.78mm,andBIASof−12.60%.TMPA3B42RTexhibits
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aperformancecomparabletothatofCMORPH-RTexceptforalargeoverestimationofprecipitation
estimates. PERSIANN-RTtakesthelastplacebecauseofthelowestCCof0.82andthelargestRMSE
of57.51mm. Forthethreepost-real-timeresearchSPPs, TMPA3B42V7andCMORPH-CRTshow
muchbetterperformancethanthatofthenewlyreleasedPERSIANN-CDR.Atmonthlytimescale,
allthreepost-real-timeresearchproductsofTMPA3B42V7,CMORPH-CRT,andPERSIANN-CDR
outperformtheircorrespondingreal-timeestimates(Figure4),suggestingthatthemonthlytimescale
gaugeadjustmentsappliedtotheTMPA,CMORPH,andPERSIANNsystemssubstantiallyimprove
themonthlydataaccuracyofretrievals. Themonth-to-monthgauge-adjustedpost-real-timeresearch
productsaresuggestedasidealSPPsformonthlywaterresourceconditionanalysis.
Figure 4. Scatterplots of the basin average monthly precipitation between SPPs and rain gauge
observationsforXixianBasin: (a)TMPA3B42RT;(b)CMORPH-RT;(c)PERSIANN-RT;(d)TMPA
3B42V7;(e)CMORPH-CRT;(f)PERSIANN-CDR.
4.2. EvaluationofStreamflowSimulations
Thestreamflowsimulationaccuracywhenthemodelwascalibratedonthebasisofraingauge
data was analyzed to evaluate the streamflow simulation utility of the six SPPs. The simulated
streamflowusingthesixSPPsasinputswerecomparedwiththeobservedstreamflowinthefollowing
threeaspects.
4.2.1. DailyStreamflowEvaluation
Figure 5a compares the rain gauge data-simulated daily hydrograph with that observed
streamflowandsummarizesthestatisticalmeasuresoftheraingaugedata-simulatedresults. Thereare
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goodagreementsbetweentheobservedandsimulateddailystreamflowseries. Thestatisticalindices,
withdailyCCof0.92and0.90,NSEof0.85and0.81,andBIASof−2.54%and9.71%forthecalibration
andvalidationperiods,respectively,demonstratethattheXAJmodelcanhavegoodsimulationsof
thestreamflow.
Figure5. ObservedandXAJmodel-simulateddailystreamflowsfromtheraingaugeobservations
andthethreereal-timeSPPsfor(a)raingaugeprecipitation;(b)TMPA3B42RT;(c)CMORPH-RTand
(d)PERSIANN-RT.
ThecalibratedXAJmodelwasthenforcedbythesixSPPsasinputsintheperiodof2000–2013.
Figure 5b–d show the XAJ model-simulated daily streamflows from the three real-time SPPs, and
Figure6a–cdepictstheXAJmodel-simulateddailystreamflowsfromthethreepost-real-timeresearch
SPPs. Forthethreereal-timeSPPs,thesimulationsofTMPA3B42RThavesignificantoverestimation
of most streamflow series for its systematic overestimation of precipitation. On the contrary, the
simulations of CMORPH-RT and PERSIANN-RT significant underestimation of most streamflow
series for their systematic underestimation of precipitation. The streamflow simulation of TMPA
3B42RTpresentsdailyCCof0.74, dailyNSEof0.44, andBIASof32.68%fortheentiresimulation
period. The streamflow simulation of CMORPH-RT exhibits daily CC of 0.70, daily NSE of 0.46,