Flood Simulations and Uncertainty Analysis for the Pearl River Basin Using the Coupled Land

13 Pages·2017·3.01 MB·English

water
Article
Flood Simulations and Uncertainty Analysis for the
Pearl River Basin Using the Coupled Land Surface
and Hydrological Model System
YongnanZhu1,2,ZhaohuiLin2,3,YongZhao1,*,HaihongLi1,FanHe1,JiaqiZhai1,
LizhenWang1andQingmingWang1
1 StateKeyLaboratoryofSimulationandRegulationofWaterCycleinRiverBasin,ChinaInstituteofWater
ResourcesandHydropowerResearch,Beijing100038,China;[email protected](Y.Z.);[email protected](H.L.);
[email protected](F.H.);[email protected](J.Z.);[email protected](L.W.);[email protected](Q.W.)
2 InternationalCenterforClimateandEnvironmentSciences(ICCES),InstituteofAtmosphericPhysics,
ChineseAcademyofSciences,Beijing100029,China;[email protected]
3 CollaborativeInnovationCenteronForecastandEvaluationofMeteorologicalDisasters,
NanjingUniversityofInformationScienceandTechnology,Nanjing210044,China
* Correspondence:[email protected];Tel.:[phone]
AcademicEditors:GordonHuangandYuruiFan
Received:23March2017;Accepted:28May2017;Published:1June2017
Abstract: The performances of hydrological simulations for the Pearl River Basin in China were
analysedusingtheCoupledLandSurfaceandHydrologicalModelSystem(CLHMS).Threedatasets,
includingEastAsia(EA),high-resolutiongaugesatellite-mergedChinaMergedPrecipitationAnalysis
(CMPA)-Daily,andtheAsianPrecipitationHighly-ResolvedObservationalDataIntegrationTowards
Evaluation(APHRODITE)dailyprecipitationwereusedtodrivetheCLHMSmodeltosimulatedaily
hydrologicalprocessesfrom1998to2006.Theresultsindicatethattheprecipitationdatawasthemost
importantsourceofuncertaintyinthehydrologicalsimulation. Thesimulatedstreamflowdrivenby
theCMPA-Dailyagreedwellwithobservations,withaPearsoncorrelationcoefficient(PMC)greater
than0.70andanindexofagreement(IOA)similaritycoefficientgreaterthan0.82atLiuzhou,Shijiao,
andWuzhouStations. ComparisonoftheNash-Sutcliffeefficiencycoefficient(NSE)showsthatthe
peakflowsimulationabilityofCLHMSdrivenwiththeCMPA-Dailyrainfallisrelativelysuperior
tothatwiththeEAandAPHRODITEdatasets. Thesimulationresultsforthehigh-flowperiodsin
1998and2005indicatethattheCLHMSispromisingforitsfutureapplicationinthefloodsimulation
andprediction.
Keywords: coupled land surface-hydrology model; flood simulation; uncertainty analysis;
PearlRiverBasin
1. Introduction
Climatechangecausedbynaturalfactorsandhumanactivitieshasbeencontinuouslyaggravated
in China [1,2], which has resulted in water shortages, drought and flood disasters, and other
socio-economicproblems. Morefrequentandsevereextremehydrologicaleventshavesignificant
impactsonsocialdevelopmentandhumanlivesandlivelihoods. Landsurfacehydrologicalmodels,
importanttoolsinstudyingtheterrestrialwatercycleandtherelatedhydrologicalextremes,hasbeen
widelyappliedforwatershedflooddisasterforecastingandwaterresourceprotection[3,4].
Abnormal precipitation is an important factor leading to flooding and drought. As the main
prognosticvariableoftheclimatemodellingandprediction,precipitationistheprimarysourceof
uncertaintyinlandsurfaceandhydrologicalsimulations[5–7]. Successfulsimulationofhydrological
extremesunderachangingclimatedependsstronglyonspatial-temporalresolutionandtheaccuracy
Water2017,9,391;doi:10.3390/w9060391 www.mdpi.com/journal/water
Water2017,9,391 2of13
of precipitation information. China has recently developed a variety of gridded precipitation
datasetswithhightemporal–spatialresolutionsbasedoninstrumentrecordsfromgroundstations
and satellite products. These include the 1◦ × 1◦ gridded daily precipitation data established by
Feng[8]with728stationsinChina;0.5◦ ×0.5◦ dailyprecipitationdataestablishedbyChen[9]using
753stationsinChina,and0.25◦ ×0.25◦ dailyprecipitationdatainChinapublishedbytheNational
MeteorologicalInformationCentre[10]basedon2425stations. Thereareanothertwowidely-used
precipitation datasets, one is the 0.5◦ × 0.5◦ daily precipitation data for East Asia established by
Xie[11],basedonobservationaldatafromEastAsianstations,andanotheristhe0.25◦ ×0.25◦ Asian
PrecipitationHighly-ResolvedObservationalDataIntegrationTowardsEvaluation(APHRODITE)
datasetdevelopedjointlywiththeJapanMeteorologicalAgency[12].
These data have been widely applied for studying the water cycle, climate change, and
related issues. However, significant uncertainties remain in the precipitation datasets from
meteorologicalstationsusedforgeneratingthefinalrainfallproduct,whichwillhenceleadtohigh
uncertaintiesinhydrologicalsimulations. ThispapertakesthePearlRiver—thethird-longestriver
in China—as an example and applies the Coupled Land Surface and Hydrological Model System
(CLHMS)tosystematicallyinvestigatetheimpactofdifferentprecipitationdatasetsonhydrological
processsimulations.
2. ModelandMethod
2.1. StudyArea
ThestudyregioninthispaperisthePearlRiverBasin,whichcontainsthethird-longestriver
in Southern China (Figure 1). The Pearl River system includes the West, East, and North Rivers
andthePearlRiverDelta. MeasuredfromtheheadwatersoftheWestRiver,thePearlRiversystem
hasalengthofmorethan2300kmandabasinareaof447,000km2,withamildandrainyclimate,
whichisaffectedbythesubtropicalmonsoonsystemandtropicalcyclones. Thebasinmeanannual
precipitationreaches1525mm,andrainfallvariesthroughouttheyearwithmorethan80%ofannual
totalprecipitationoccurringfromApriltoSeptember. Owingtothelargequantityofprecipitation
intherainyseason,withhighstrengthandlongduration,floodsrapidlyconvergeinthemountains
andhillsintheupperandmiddlereachesoftheriverwithonlyfewadjustableimpoundlakesinthe
middlereaches. Thisconditiondirectlyendangersthedevelopedandpopulouscitiesandtownsinthe
lowerreaches. SeveralseverefloodsoccurredalongthePearlRiverin1998,2000,2005,and2006that
directlythreatenedhumanlifeandproperty. Accordingtoincompletestatistics,flooddisastersinthe
PearlRiverthatoccurredbetween2000and2013causedmorethanRMB100billionindirecteconomic
losses[13].
TheWestRiveristhewesterntributaryofthePearlRiver. LiuzhouandNanningStationsare
locatedalongitstributaries,withcatchmentareasof45,413km2and72,656km2,respectively. Wuzhou
Station,themostimportantcontrolsiteonthemainstreamoftheWestRiver,hasacatchmentareaof
327,006km2,accountingfor94.6%oftheriver’scatchmentarea. Theothertwomaintributariesofthe
PearlRiveraretheEastandNorthRivers. BoluoStationislocatedonthelowerreachesoftheEast
River,whichistheeasterntributary. ThecatchmentareaaboveBoluoStationis25,325km2,accounting
forabout71.7%ofthetotalareaoftheEastRiverBasin. ThecatchmentareaaboveShijiaoStation,
thecontrolstationforthelowerreachesofthenortherntributaryofthePearlRiver, is38,363km2
andaccountsfor82.1%ofthetotalareaoftheNorthRiverBasin. Thethreemainriversconvergein
thePearlRiverDelta,wheretherivernetworkisstaggered,andthetributariesandSouthChinaSea
simultaneouslyaffectthewaterflowandwaterlevel.
Water2017,9,391 3of13
Water 2017, 9, 391 3 of 13

FFiigguurree 11.. TTooppooggrraapphhyy ooff tthhee PPeeaarrll RRiivveerr BBaassiinn aanndd llooccaattiioonn ooff tthhee mmaaiinn hhyyddrroollooggiiccaall ccoonnttrrooll ssttaattiioonnss..
2.2. Model Description and Simulation Design
2.2. ModelDescriptionandSimulationDesign
The Coupled Land Surface and Hydrology Model System (CLHMS) is applied in this study and
The Coupled Land Surface and Hydrology Model System (CLHMS) is applied in this study
was briefly introduced by Yang et al. [14], developed on the basis of the Land Surface Transfer Model
and was briefly introduced by Yang et al. [14], developed on the basis of the Land Surface Transfer
–Hydrologic Model System (LSX–HMS) model [15]. The CLHMS includes a large-scale LSX [16] and
Model–HydrologicModelSystem(LSX–HMS)model[15].TheCLHMSincludesalarge-scaleLSX[16]and
a fine-grid distributed HMS. The land surface model includes a two-layer vegetation module, a three-
afine-griddistributedHMS.Thelandsurfacemodelincludesatwo-layervegetationmodule,athree-layer
layer snow module, and a six-layer soil module. The LSX model calculates the surface energy balance
snowmodule,andasix-layersoilmodule. TheLSXmodelcalculatesthesurfaceenergybalanceand
and assigns the evaporation, runoff, infiltration, and soil moisture results to the HMS model. The
assigns the evaporation, runoff, infiltration, and soil moisture results to the HMS model. The latter
latter includes a terrestrial hydrologic module, a groundwater hydrologic module, and a channel-
includesaterrestrialhydrologicmodule,agroundwaterhydrologicmodule,andachannel-groundwater
groundwater interaction module. The terrestrial hydrologic module simulates overland flow and
interactionmodule. Theterrestrialhydrologicmodulesimulatesoverlandflowandriverrunoff; the
river runoff; the dynamic process of surface water flow is calculated using a two-dimensional
dynamicprocessofsurfacewaterflowiscalculatedusingatwo-dimensionaldiffusionwaveineight
diffusion wave in eight probable directions, and the channel flow velocity is described by the
probabledirections,andthechannelflowvelocityisdescribedbytheManningequation. Groundwateris
Manning equation. Groundwater is described as a single-layer aquifer and the combined water flux,
describedasasingle-layeraquiferandthecombinedwaterflux,includingsurfacewater,isrepresented
including surface water, is represented by Darcy’s Law. The spatial resolution of both the land surface
by Darcy’s Law. The spatial resolution of both the land surface and hydrological models is set to
2a0nd× h2y0dkrmolotgoiacavlo midocdhealnsg iisn sgest ctaol e2s0. T× h2e0i nktmer atoc taiovnoibde tcwheaenngitnhge LscSaXleasn. dThHeM inSteisrabcatsioedn obnetpwreeednic ttehde
LSX and HMS is based on predicted soil moisture and groundwater depth [15,17]. The CLHMS
soilmoistureandgroundwaterdepth[15,17]. TheCLHMSaccuratelyreproducesnaturalhydrological
accurately reproduces natural hydrological processes, water balance, and seasonal and inter-annual
processes,waterbalance,andseasonalandinter-annualvariationinstreamflow. Ithasbeenverified
variation in streamflow. It has been verified against historical data for the Yellow, Huai, Song-Liao,
againsthistoricaldatafortheYellow,Huai,Song-Liao,andPearlRiverBasins[18–20].
and Pearl River Basins [18–20].
Parameters in the CLHMS model include surface elevation, soil texture, vegetation type,
Parameters in the CLHMS model include surface elevation, soil texture, vegetation type, hydro-
hydro-geologicalparameters,andotherlandsurfaceandhydrologicalcomponents. Thelandsurface
geological parameters, and other land surface and hydrological components. The land surface soil in
soilintheCLHMSmodelhassixlayersintheupper4.35m;thethicknessesfromthesurfacetothe
the CLHMS model has six layers in the upper 4.35 m; the thicknesses from the surface to the lower
lowerlayerare0.05,0.10,0.20,0.40,1.0,and2.5m. Thesoiltexturecharacterincludessixlayersof
layer are 0.05, 0.10, 0.20, 0.40, 1.0, and 2.5 m. The soil texture character includes six layers of
sand/silt/claycontentfromtheglobalsoilprofiledatabasegeneratedbyWebbetal.[21].
sand/silt/clay content from the global soil profile database generated by Webb et al. [21].
Thehydrologicalparameters,suchasslope,basinboundary,andelevationdeviationarederived
The hydrological parameters, such as slope, basin boundary, and elevation deviation are derived
fromtheUnitedStatesGeologicalSurvey(USGS)HYDRO1kdatabasewiththeZBalgorithm[22].
from the United States Geological Survey (USGS) HYDRO1k database with the ZB algorithm [22].
Otherphysicalparametersincludingporosity,Manningroughness,andhydraulicconductivityare
Other physical parameters including porosity, Manning roughness, and hydraulic conductivity are
calibrated on the basis of the newest version of the Harmonized World Soil Database (HWSD)
calibrated on the basis of the newest version of the Harmonized World Soil Database (HWSD)
developedbytheFoodandAgriculturalOrganization(FAO)oftheUnitedNationsandInternational
developed by the Food and Agricultural Organization (FAO) of the United Nations and International
Institute for Applied System Analysis (IIASA). Launched in partnership with the Institute of Soil
Institute for Applied System Analysis (IIASA). Launched in partnership with the Institute of Soil
Science,ChineseAcademyofSciences,HWSDprovidesthemostrecent1:1,000,000-scalesubsoiland
Science, Chinese Academy of Sciences, HWSD provides the most recent 1:1,000,000-scale subsoil and
topsoilmapofChina[23,24].
topsoil map of China [23,24].
Three high-resolution precipitation datasets—the East Asia (EA), APHRODITE, and CMPA-
Daily precipitation data—were selected to investigate the impact of precipitation on the hydrological
Water2017,9,391 4of13
Threehigh-resolutionprecipitationdatasets—theEastAsia(EA),APHRODITE,andCMPA-Daily
precipitationdata—wereselectedtoinvestigatetheimpactofprecipitationonthehydrologicalprocess
simulationusinglandsurface–hydrologicalcoupledmodels. Abriefdescriptionoftherainfalland
othermeteorologicalforcingdatasetsisgivenbelow.
(1) EastAsiaDailyPrecipitationData
The East Asia precipitation data was provided by the National Oceanic and Atmospheric
Administration(NOAA)ClimatePredictionCenter(CPC)[11]. Ithasaspatialresolutionof0.5◦ ×0.5◦
andspatialextentof5◦N–60◦N,65◦E–155◦Eandcoverstheperiod1962–2006.Thedatawerecollected
byanalysingtheoptimaldifferencevalueswiththeobservedprecipitationdatafrom2200stationsin
EastAsia,including730meteorologicalstationsfromtheChinaMeteorologicalAdministration(CMA)
and1000hydrologicalobservationstationsfromtheYellowRiverConservancyCommissionofthe
MinistryofWaterResourcesofthePeople’sRepublicofChina.
(2) APHRODITEPrecipitationDataSet
The APHRODITE plan was implemented jointly by the Research Institute of Humanity and
Nature(RIHN)andtheMeteorologicalResearchInstituteofJapanMeteorologicalAgency(MRI/JMA).
TheseagenciesestablishedadatasetdescribingtheprecipitationfeaturesoftheAsianmonsoonregion
(MA), Middle Asia (ME), Russia (RU), and Japan (JR) by integrating the observational data in the
Asianregionsandprecipitationstationsfromdifferentcountries. Here,theAPHRODITEdatasetfor
monsoonAsia(APHRO-MA)wasselected,whichhasaspatialresolutionof0.25◦ ×0.25◦;aspatial
extentof15◦ S–55◦ N,60◦ E–150◦ E;anddurationof1951–2007. Thisdatasetiscomposedprimarilyof
stationdataprovidedbytheCMAandGlobalTelecommunicationSystem(GTS)datapreparedbythe
WorldMeteorologicalOrganization[12].
(3) High-ResolutionGauge–SatelliteMergedCMPA-DailyData
TheCMPA-Dailyprecipitationdatasethasaspatialresolutionof0.25◦ ×0.25◦,aspatialextentof
0◦ N–60◦ N,60◦ E–160◦ E,anddurationof1998–2013. Itusesdatafrom2425national-levelground
weatherstationsandtheCPCMorphingTechnique(CMORPH)datadevelopedbytheNOAAGlobal
Precipitation Climatology Project (GPCP). The errors in CMORPH satellite precipitation data are
correctedusingtheprobabilityanddensitymatchingmethodandaregeneratedwiththeoptimal
differencemethodonthebasisoftheclimatebackgroundfield[9].
To compare the uncertainty from precipitation data in land surface hydrological simulations,
ThreesetsofcontrastingnumericalexperimentsbasedonCLHMShavebeendesignedtosimulate
thedailywatercycleprocessforthePearlRiverduringanine-yearperiodof1998–2006. Thethree
setsofexperimentsusedtheEA,APHRO-MA,andCMPA-Dailydataasvaryingprecipitationfields,
whereastheothermeteorology-drivendataremainedthesame,asshowninTable1. Foursimulation
experimentsweredesignedtocomparetheinfluenceofdifferentprecipitationdatasetsandmodel
resolutiononwatercycleprocesssimulations. Intheexperiments,allofthemeteorologicaldatawere
interpolatedtothemodelresolution;thetimestepsforthehydrologicalandlandsurfacemodelswere
24hand15min,respectively.
Table1.Introductiontotheexperimentdesign.
No. TestName Resolution PrecipitationDataSources MeteorologicalParameters
1 EA 20km×20km Xieetal.[11]
2 APHRO-MA 20km×20km Yatagaietal.[12] CN05dailytemperaturedataset[25];
3 CMPA-Daily(20km) 20km×20km 6-hNCEP-NCARreanalysisdata[26]
Shenetal.[9]
4 CMPA-Daily(10km) 10km×10km
ThetemperaturedatawerebasedonthedailytemperaturedatasetoverChina(CN05)[25]from
the CMA. The sub-daily data for temperature was disaggregated using statistical downscaling of
Water2017,9,391 5of13
the global 3-h temperature dataset from Princeton University [27]. The near surface wind speed,
humidity,airpressure,radiationflux,andotherbasicmeteorologicalforcingdatawereobtainedfrom
the6-hNationalCentresforEnvironmentalPrediction–NationalCentreforAtmosphericResearch
(NCEP–NCAR)reanalysisdata[26].
The full model was integrated with three separate processes to obtain near equilibrium
groundwatertables. Thefirstphaseisthe“coldstartprocess”,inwhicha50-yearrunwasperformed
withthefullmodelandobservedmeteorologicaldatafor1998–2006,forthemodeltoreachinitial
surfacewaterbalance. Thesecondisthe“spin-upprocess”,inwhichonlythegroundwatermodel
wasspunupfor5000yearsusingthecoldstartresultastheforcing,inorderforthemodeltoreach
equilibriumforgroundwaterbalance. Usingthemodeloutputfromthe“spin-upprocess”asinitial
conditions,theCLHMSwasthenrunfrom1998to2006fortwocycles,for18yearsintotal,andthe
observeddailyprecipitationdata,CN05dailytemperaturedata,andNCEP/NCARsix-hourreanalysis
datawereusedasforcingdata. Toreducethemodelbiascausedbymodelinitialization, onlythe
modelsimulationresultsfromthesecondcycle(year10toyear18)wereusedtoanalysethewater
cyclefrom1998to2006.
To correctly compare the influence of different precipitation forcings on the simulation
performanceofthelandsurface–hydrologicalmodel,thewaterbalanceindex(WBI),Nash–Sutcliffe
efficiencycoefficient(NSE),Pearsonproduct-momentcorrelationcoefficient(PMC),IOAsimilarity
coefficient,andnormalizedrootmeansquareerror(NRSE)wereusedinthisstudy. Amongthese,
theWBIprimarilyreflectstheabilityofthemodeltosimulatethewaterquantitybalanceprocess;the
NSEreflectsthesimulationabilityforpeakflow; thePMCandIOArepresentthetimecorrelation
andsimilaritybetweentheobservedandsimulatedstreamflow, respectively; andtheNRSEisthe
rootmeansquareerroradjustedbytheaveragevalueoftheobservationsequenceateachsite,which
facilitatesthecomparisonofdifferentsites. Foreachofthesefiveindices,valuesclosertooneindicate
highercapabilitiesofthemodelsimulation. Theformulaeforthefiveindicesareasfollows:
∑N P
WBI= i=1 i; (1)
∑N O
i=1 i
∑N (P −O)2
NSE=1.0− i=1 i i ; (2)
∑N (cid:0)O −O(cid:1)2
i=1 i
∑N (P −P)(cid:0)O −O(cid:1)
PMC= i=1 i i ; (3)
(cid:104)∑N (P −P)2(cid:105)0.5(cid:104)∑N (O −O)2(cid:105)0.5
i=1 i i=1 i
∑N (P −O)2
IOA=1.0− i=1 i i ; (4)
∑iN=1(cid:0)(cid:12)(cid:12)Oi−O(cid:12)(cid:12)+(cid:12)(cid:12)Pi−O(cid:12)(cid:12)(cid:1)2
(cid:118)
NRSE=1.0−(cid:117)(cid:117)(cid:116) 1 ∑N (cid:18)Pi−Oi(cid:19)2 (5)
N O
i=1
among them, P and O are the values at the ith day simulated and observed daily streamflow,
i i
respectively;PandOaretheaveragevaluesofthesimulatedandobservedsequences;Nisthetotal
numberofsamples. Inthisstudy,weselectedLiuzhou,Nanning,andWuzhouStationsontheWest
River,BoluoStationontheEastRiver,andShijiaoStationontheNorthRiverasthemaincontrolsites
forthePearlRiverBasin. TherivernetworkinthePearlRiverDelta,wheretheWest,East,andNorth
Riversconverge,isstaggeredandisinfluencedbytheseawaterintrusion,sothehydrologicalstation
inthePearlRiverDeltawasnotselectedforthisstudy.
Water2017,9,391 6of13
3. AnalysisofSimulationResults
3.1. EvaluationofDailySimulationPerformance
Figure 2 shows the simulated daily streamflow under three different precipitation forcings
comparedwiththeobserveddailystreamflowatthemainstationsinthePearlRiverBasinduring
1998–2006. Table2presentstheperformanceoftheCLHMSmodelbasedontheWBI,NSE,PMC,IOA
sWimateirl a2r0i1t7y,, 9a, n39d1 NRSEforNanning,Liuzhou,Wuzhou,Shijiao,andBoluoStations. 6 of 13

FFiigguurree 22.. SSccaattteterprplolotst soof fa acocmompapraisroisno nbebtweteweene ntheth oebosebrsveerdv esdtresatrmeaflmowflo awnda nsdimsuimlatuiolant iofrnomfr othme
tChoeuCploeudp LleadndL SaunrdfacSeu arnfadc eHyadnrdolHogyidcarol Mlogoidceall SMysotedmel (CSyLsHteMmS)( mCLodHeMl fSo)r (ma)o Ldieulzhfooru;( (ab)) LWiuuzzhhoouu;;
((bc)) WShuijziahoo;u a;n(dc) (Sdh) iBjiaoolu;oa nSdta(tdio)nBso oluno thSeta Ptieoanrls Ronivtehr efrPoemar 1l9R9i8v etor f2r0o0m6 u19n9d8erto d2if0f0e6reunnt dperrecdipififtearteionnt
pforercciipngitsa.t ionforcings.
ATallbtlhe r2e. eEvtealsutastisohno owf ethde gpoerofdormsiamnucel aotfi othne aCboiuliptlyedin Latnedrm Susrofafcteh aendW HById.rFoolorgtihcael Msimoduella Styiostnemu sing
theC(MCLPHAM-DSa).i lyrainfallastheforcing,thePMCcorrelationcoefficientsfortheNanning,Liuzhou,
andBoluoStationswere0.63,0.70,and0.65,respectively,whicharethehighestamongthethreesets
Hydrologic Station Nanning Liuzhou Wuzhou Shijiao Boluo
ofsimulationswithdifferentEpAr ecipitatio0n.91fo rcings0..9In6 contra0s.9t,8t hePM0.C99c orre0la.8t3io ncoefficientsat
WuzhouandShijiWaoBSI tatiAoPnHsfRoOr-tMheAC MPA0.9-D3 ailywe1r.0e10 .92and1.000.8 2,resp0.e9c9t ively0,.w86h ichshowsbetter
simulationabilityinthemaCinMsPtAre ams. 0.94 1.00 1.1 1.13 0.84
EA 0.36 0.44 0.78 0.61 0.27
NSE APHRO-MA 0.36 0.42 0.76 0.62 0.27
CMPA 0.38 0.47 0.81 0.67 0.25
EA 0.62 0.68 0.89 0.78 0.63
PMC APHRO-MA 0.61 0.66 0.89 0.79 0.64
CMPA 0.63 0.70 0.92 0.82 0.65
EA 0.75 0.80 0.92 0.86 0.76
IOA APHRO-Ma 0.75 0.78 0.92 0.86 0.78
CMPA 0.75 0.82 0.95 0.90 0.77
EA −0.06 −0.21 0.49 0.20 0.14
NRSE APHRO-MA −0.06 −0.23 0.47 0.21 0.14
CMPA −0.06 −0.17 0.53 0.20 0.10
Water2017,9,391 7of13
Table 2. Evaluation of the performance of the Coupled Land Surface and Hydrological Model
System(CLHMS).
HydrologicStation Nanning Liuzhou Wuzhou Shijiao Boluo
EA 0.91 0.96 0.98 0.99 0.83
WBI APHRO-MA 0.93 1.01 1.00 0.99 0.86
CMPA 0.94 1.00 1.1 1.13 0.84
EA 0.36 0.44 0.78 0.61 0.27
NSE APHRO-MA 0.36 0.42 0.76 0.62 0.27
CMPA 0.38 0.47 0.81 0.67 0.25
EA 0.62 0.68 0.89 0.78 0.63
PMC APHRO-MA 0.61 0.66 0.89 0.79 0.64
CMPA 0.63 0.70 0.92 0.82 0.65
EA 0.75 0.80 0.92 0.86 0.76
IOA APHRO-Ma 0.75 0.78 0.92 0.86 0.78
CMPA 0.75 0.82 0.95 0.90 0.77
EA −0.06 −0.21 0.49 0.20 0.14
NRSE APHRO-MA −0.06 −0.23 0.47 0.21 0.14
CMPA −0.06 −0.17 0.53 0.20 0.10
TheNSEefficiencycoefficientshowsthatthepeakflowsimulationabilityoftheCMPA-Dailytest
isobviouslysuperiortothatoftheEAandAPHRO-MAdatasets. However,itsabilitytosimulatethe
peakflowinmainstreamsandtributariesshowsacleardifference. AccordingtotheCMPA-Daily
precipitation forcing, the simulated peak flow was larger than the EA- and APHRO-MA-driven
simulatedflowfor1998,2000,2001,2004,and2005. Takingthe1998catastrophicfloodintheWest
Riverasanexample,WuzhouStationonthemainstreamoftheWestRiverobservedthefirstpeak
on28Juneataflowof51,500m3/s; thearrivaltimeofthepeaksimulatedintheEAprecipitation
forcing test was 25 June, which is three days prior to observation. The simulated peak flow was
27,933 m3/s, which is 45.7% smaller than the observation; the peak arrival time simulated in the
APHRO-MAforcingtestwasthesameasthatintheEAtest. Thepeakflowwas26,423m3/s,whichis
48.6%smallerthantheobservation. ThearrivaltimeofthepeaksimulatedintheCMPA-Dailytestwas
21June,whichisonedayaheadoftheobservation. Thesimulationpeakflowwas46,136m3/s,which
is10.4%smallerthantheobservation. ThisfloodalsooccurredintheNorthRiverduringthesame
year. ShijiaoStationobservedthepeakarrivaltimeon26Junewithapeakflowof12,200m3/s;the
simulatedpeakarrivaltimeintheEArainfall-driventestwas25June,whichisonedayaheadofthe
observation. Thesimulationpeakflowwas6681m3/s,whichis45.2%smallerthantheobservation;
thepeakarrivaltimesimulatedinAPHRO-MAtestwasthesameasthatintheEAtest. Thepeakflow
was7621m3/s,whichis37.5%smallerthantheobservation. InthesimulationfromtheCMPA-Daily
test,thepeakarrivaltimewas24June,andthepeakflowwas9393m3/s,whichis23.0%smallerthan
theobservation.
Acomparisonoftheofflowsimulationresultsimilaritiesunderdifferentprecipitationdrivers
withtheobservationflowsequencerevealedthattheIOAsimilarityindicatorsforthethreetestsat
BoluoStationwerenear0.76.ThosefortheEAandAPHRO-MAtestsatNanning,Wuzhou,andShijiao
Stationswere0.75,0.92,and0.86,respectively. Thevalueswere0.75,0.95,and0.90,respectively,forthe
CMPAtest. ComparingthesimulationresultsfromLiuzhouStation,thesimilarityfortheAPHRO-MA
testwas0.78,whichwasthelowestofthethreetests. TheIOAsimilarityfortheCMPAtest,0.82,was
thehighest. TheIOAindicatorsforallstationsunderdifferentprecipitationdriverswerehigherthan
0.75,indicatingthatallsimulationresultsandobservationswerehighlysimilar. TheCMPAsimulation
resultsweresuperiorthoseofEAandAPHRO-MA.
Toanalysethecausesforthedifferencesinthesimulatedstreamflowresults,wecomparedthe
differencesinmonthlyanddailyprecipitationdistributionfromtheEA,APHRO-MA,andCMPA-Daily
datasets. Taking the rainstorm period in June 1998 as an example, as shown in Figure 3a–c, the
maximumrainfallareaappearedatthenorthernbranchoftheWestRiver. However,somedifferences
Water2017,9,391 8of13
inspatialdistributionofthethreeprecipitationdatasetswerenoted. Moreover,theprecipitationgrid
datashowedadivergenceintherainfallamount. ThemaximummonthlyrainfallofEA,APHRO-MA,
and CMPA-Daily datasets were 754.59, 681.22, and 967.43 mm, respectively. Figure 3d shows the
variationintheregionalaveragedailyprecipitationinJune1998. ThedailyrainfalleventsofEAwere
essentiallysimilartothoseofAPHRO-MA,exceptforarainstormon16–26June. Comparedwiththe
othertwodatasets,theCMPA-Dailyrainfalleventsshowedgreaterdifferencesatthebeginningof
themonth. For1998–2006,theCMPA-Dailyaverageannualprecipitationintheriverwas1759mm;
the values from the EA and APHRO-MA data were 1498 mm and 1525 mm in the same period,
respectively. TheEAandAPHRO-MAprecipitationdatavalueswere14.8%and13.3%smallerthan
theCMPA-Dailydata,respectively. The1998–2006summerprecipitationwasalsoclearlydifferent,
withtheCMPA-Daily,EA,andAPHRO-MAprecipitationdatashowingaveragesummerprecipitation
(JuWneat–erA 2u01g7,u 9s, t3)91o f9.64,8.12,and8.13mm/d,respectively. 8 of 13

FigFuigreur3e. 3C. oCmompapraisriosnono fofp prerecicpipitiatatitoionni inn JJuunnee 11999988 ffoorr mmoonntthhllyy rraaininfafalll lddisitsrtirbiubtuiotino nfrofrmom (a)( aE)aEsta AstsAias ia
(EA(E)A;()b; )(bA) sAiasnianP rPerceipciiptaittaitoinonH Higighhlyly-R-Reessoollvveedd OObbsseerrvvaattiioonnaal lDDaatata InIntetgegrartaiotino nToTwowaradrsd EsvEavluaalutiaotnio n
dadtaasteatsefto rfoMr MonosnosoononA Asisaia( A(APPHHRROO--MMAA));; ((cc)) hhiigghh--rreessoolluuttiioonn ggaauuggee ssaatetellliltiet-em-meregregde dChCihnain MaMergeerdge d
PrePcriepciitpaittiaotnionA Ananlaylsyissis( C(CMMPPAA)-)D-Daaililyy;; aanndd ((dd)) tthhee rreeggiioonnaall ddaailiyly aavveerargaeg eprperceicpiiptaittiaotnio nforf otrhrteher ee
prepcriepciitpaittiaotniofno rfocirncignsg.s.
3.2. Evaluation of Hydrological Extreme Simulation Performance
3.2. EvaluationofHydrologicalExtremeSimulationPerformance
To evaluate the CLHMS simulations of flood processes on the Pearl River, this study used the
ToevaluatetheCLHMSsimulationsoffloodprocessesonthePearlRiver,thisstudyusedthe
20 × 20 km and 10 × 10 km grid resolution CLHMS models and the CMPA-Daily precipitation data to
20×20kmand10×10kmgridresolutionCLHMSmodelsandtheCMPA-Dailyprecipitationdatato
simulate the streamflow on the Pearl River for 11 June to 1 August 1998 and 20 May to 20 July 2005.
simulatethestreamflowonthePearlRiverfor11Juneto1August1998and20Mayto20July2005.
In June 1998, a flood with a return period of 100 years occurred on the West and North River
InJune1998, afloodwithareturnperiodof100yearsoccurredontheWestandNorthRiver
tributaries of the Pearl River. The regional rainfall quantity exceeded 400 mm in the Liujiang and
tributaries of the Pearl River. The regional rainfall quantity exceeded 400 mm in the Liujiang and
Guijiang Rivers, at the rainstorm centre. At Wuzhou Station, the maximum observation flow was
GuijiangRivers, attherainstormcentre. AtWuzhouStation, themaximumobservationflowwas
52,900 m3/s with a recurrence interval of 100 years.
52,900m3/switharecurrenceintervalof100years.
From 9 to 25 June 2005, catastrophic flooding occurred on the Pearl River owing to continuous
From9to25June2005,catastrophicfloodingoccurredonthePearlRiverowingtocontinuous
rainstorms. At Longmen Station on the East River, the maximum precipitation reached 1442 mm. The
raipnrsetcoirpmitas.tioAnt aLt otnhge mraeinnsStotramtio cnenotnret hone Ethaes tWReisvte Rr,ivthere wmaasx 4im00u–m500p mremcip. iAtat tWionuzrheoauc hSetdati1o4n4, 2thme m.
Thpeeparke cfilpowita trieoanchaetdt h5e3,r9a0i0n smto3r/sm, wcehnictrhe roannkths eaWs ethste Rsievceorndw-hasig4h0e0st– 5f0lo0wm smin.cAe tthWe usztahtoiounS wtaatiso n,
theesptaebalkisflheodw. Trehaec lhaerdge5st3 ,f9lo0o0dm d3u/rsin,gw thhiec hparastn 2k0s yaesatrhse oscecucorrnedd- hoing hthees tEflaostw Risvinerc,e atfhfeectsitnagt io30n.3w2 as
estmabillliisohne dp.eTophlee laanrgde csatuflsoiondg ddiurreicntg ecthoenopmasitc 2lo0syseeas rosfo RcMcuBr r3e1d.4o5n btihlleioEna [s1t3R].i v er,affecting30.32million
peopleFaingdurcea 4u ssihnogwdsi trheec tseimcounlaotmedic alnods soebssoerfvReMd rBai3n1s.t4o5rmbi lpliroonce[s1s3e]s. at the main stations of the Pearl
River from 11 June to 1 August 1998. Table 3 presents the simulation performance based on WBI,
NSE, PMC, IOA similarity, and NRSE during the same period. The rainfall was concentrated mainly
on the West and North Rivers; flooding did not occur on the East River. CLHMS simulated the flood
process better at Liuzhou, Wuzhou, and Shijiao Stations, and the simulation results at Boluo Station
on the East River were also consistent with the observations. During the rainstorm, three peak
flooding events occurred continuously at Liuzhou Station on the West River. The third event, which
occurred on 24 June, had the largest peak of the flooding process with a streamflow of 20,000 m3/s.
The simulation results with 20 km resolution showed that the highest peak at Liuzhou Station
occurred during the second flood event on 21 June, with a streamflow of 14,838 m3/s. In addition, the
third flood event, occurring on 23 and 24 June, was lower than the previous event; this result
contradicts the observations. In contrast, the simulation result from the 10 km grid resolution showed
stronger similarity to the observed flood events at Liuzhou Station. The largest peak occurred on 23
Water2017,9,391 9of13
Figure4showsthesimulatedandobservedrainstormprocessesatthemainstationsofthePearl
Riverfrom11Juneto1August1998. Table3presentsthesimulationperformancebasedonWBI,NSE,
PMC,IOAsimilarity,andNRSEduringthesameperiod. Therainfallwasconcentratedmainlyon
theWestandNorthRivers; floodingdidnotoccurontheEastRiver. CLHMSsimulatedtheflood
processbetteratLiuzhou,Wuzhou,andShijiaoStations,andthesimulationresultsatBoluoStationon
theEastRiverwerealsoconsistentwiththeobservations. Duringtherainstorm,threepeakflooding
eventsoccurredcontinuouslyatLiuzhouStationontheWestRiver. Thethirdevent,whichoccurredon
24June,hadthelargestpeakofthefloodingprocesswithastreamflowof20,000m3/s. Thesimulation
resultswith20kmresolutionshowedthatthehighestpeakatLiuzhouStationoccurredduringthe
secondfloodeventon21June,withastreamflowof14,838m3/s. Inaddition,thethirdfloodevent,
occurringon23and24June,waslowerthanthepreviousevent;thisresultcontradictstheobservations.
Water 2017, 9, 391 9 of 13
Incontrast,thesimulationresultfromthe10kmgridresolutionshowedstrongersimilaritytothe
observJuende,fl wohoidche vise onntes datayL piurziohro tuo tShtea otibosne.rvTehde elvaerngte. sTthpee paekako cflcouwrr wedaso 1n6,26325J umn3e/s,, wanhdic thhei ssiomnueladtaioynp rior
totheeorrbosre wrvaes d16e.9v%en. t. Thepeakflowwas16,625m3/s,andthesimulationerrorwas16.9%.

Figure 4. Flood simulation results for (a) Liuzhou; (b) Wuzhou; (c) Shijiao; and (d) Boluo Stations on
Figure4.Floodsimulationresultsfor(a)Liuzhou;(b)Wuzhou;(c)Shijiao;and(d)BoluoStationson
the Pearl River in 1998.
thePearlRiverin1998.
Table 3. Evaluation of hydrological extremes simulation in 1998.
Table3.Evaluationofhydrologicalextremessimulationin1998.
Hydrologic Station Liuzhou Wuzhou Shijiao Boluo
20 km 0.70 1.02 0.87 0.78
HydrologicWSBtaIt ion Liuzhou Wuzhou Shijiao Boluo
10 km 0.82 1.00 0.90 0.87
20km 20 km 0.700.62 0.19.30 2 0.80 0.870.22 0.78
WBI NSE
10km 10 km 0.802.76 0.19.00 0 0.95 0.900.60 0.87
20km 20 km 0.602.90 0.09.89 3 0.91 0.800.88 0.22
NSE PMC
10km 10 km 0.706.90 0.09.59 0 0.98 0.950.88 0.60
20 km 0.77 0.96 0.90 0.74
IOA20 km 0.90 0.98 0.91 0.88
PMC 10km 10 km 0.900.90 0.09.59 5 0.98 0.980.84 0.88
20 km 0.52 0.96 0.62 0.67
NRS2E0 km 0.77 0.96 0.90 0.74
IOA 10 km 0.62 0.95 0.81 0.76
10km 0.90 0.95 0.98 0.84
20km 0.52 0.96 0.62 0.67
The meNasRuSrEed peak flow at Wuzhou Station occurred on 28 June, when the peak flow was 51,500
10km 0.62 0.95 0.81 0.76
m3/s. The peak occurred on 27 June for both the 10 km and 20 km grid resolution simulations one day
prior to the observed event. The simulated peak flow with 10 km resolution was 50,988 m3/s, and the
simulation error was only −0.4%. The simulation error with 20 km resolution was −10.4%. During the
flood events in 1998, the peak flow at Shijiao Station was 12,200 m3/s, and the peak arrival date was
26 June. The 20 km resolution simulation results showed the peak arrival date to be 24 June, which is
two days prior to the observed event. The peak flow was 9393 m3/s, which is 23.0% less than the
observed flow. The peak flow was 12,259 m3/s with 10 km resolution simulation, which is 0.5% larger
than the observation, and the peak arrival time was consistent with the observation. The 20 km grid
resolution simulation error in peak flow was larger. Generally, the 10 km resolution simulation of
flooding of the Pearl River in 1998 is more accurate.
Figure 5 and Table 4 present the results for the flood season from 20 May to 20 July 2005. We
compared the observed and simulated streamflow from the coupled land surface–hydrological
Water2017,9,391 10of13
The measured peak flow at Wuzhou Station occurred on 28 June, when the peak flow was
51,500m3/s. Thepeakoccurredon27Juneforboththe10kmand20kmgridresolutionsimulations
onedaypriortotheobservedevent. Thesimulatedpeakflowwith10kmresolutionwas50,988m3/s,
andthesimulationerrorwasonly−0.4%. Thesimulationerrorwith20kmresolutionwas−10.4%.
Duringthefloodeventsin1998,thepeakflowatShijiaoStationwas12,200m3/s,andthepeakarrival
datewas26June. The20kmresolutionsimulationresultsshowedthepeakarrivaldatetobe24June,
whichistwodayspriortotheobservedevent. Thepeakflowwas9393m3/s,whichis23.0%lessthan
theobservedflow. Thepeakflowwas12,259m3/swith10kmresolutionsimulation,whichis0.5%
largerthantheobservation,andthepeakarrivaltimewasconsistentwiththeobservation. The20km
gridresolutionsimulationerrorinpeakflowwaslarger. Generally,the10kmresolutionsimulationof
floodingofthePearlRiverin1998ismoreaccurate.
Figure 5 and Table 4 present the results for the flood season from 20 May to 20 July 2005.
Wecomparedtheobservedandsimulatedstreamflowfromthecoupledlandsurface–hydrological
model at different grid resolutions. The results showed that 10 km grid resolution provides more
accurateWpateera 2k017fl, o9, w391a ndtimingsimulationsandthatofthesimulatedpeakflow,whichwa1s0 olfo 1w3 erthan
theobservationwhenusingthe20kmresolutiongrid. Thesimulationsusingthe20kmresolution
model at different grid resolutions. The results showed that 10 km grid resolution provides more
showedacacupreaatek pfleoawk flaotwW aundz htiomuinSgt astiimounlaotifo3n5s ,0an2d5 tmha3t/ osf, twheh iscimhuisla3te4d. 3p%easkm floawlle, rwthhiachn wthaes lloawrgeer stpeak,
53,300mth3a/n st,hme eoabssuerrveadtioonn w2h2eJnu nuesi.nWg thheen 20u skimng rtehsoelu1t0ioknm grride.s oTlhuet isoimnuglraitdio,ntsh uessinimg uthlea t2e0d kpme akflow
atWuzhreosuoluSttiaotni oshnowweads a5 6p,e1a2k0 flmow3/ ast, Wwuhzihcohu iSst5at.i3o%n olfa r3g5,e0r25t hma3n/s,t hwehiocbh sies r3v4e.3d%e svmeanllte.rF toharnb tohteh 10km
largest peak, 53,300 m3/s, measured on 22 June. When using the 10 km resolution grid, the simulated
and20kmgridresolutions,thesimulatedarrivaldateofthelargestpeakflowatWuzhouStationwas
peak flow at Wuzhou Station was 56,120 m3/s, which is 5.3% larger than the observed event. For both
20June,whichistwodayspriortoobservation. Theobservedpeakarrivedon24JuneatBoluoStation,
10 km and 20 km grid resolutions, the simulated arrival date of the largest peak flow at Wuzhou
andthepeakflowwas7760m3/s. Whenusing20kmand10kmresolutiongrids,thesimulatedpeak
Station was 20 June, which is two days prior to observation. The observed peak arrived on 24 June at
arrivaldBoaltueos Swtaetiroen2, a1nJdu tnhee apneadk 2fl3owJu wnaes, r7e7s6p0 emc3t/isv. eWlyh.enT huseinsgim 20u klamte adndp 1e0a kkmfl orewsowlutaiosn1 g2r,i4d3s0, tmhe3 /sand
10,391msim3/usl,atoevd epreeaskt iamrraivtaeld dbatyes6 w0%erea 2n1d Ju3n3e. 9a%nd, 2re3 sJpunece,t irveseplye.ctTivheley.s Timheu sliamtiuolanterde spuealtks fwlower ewgase nerally
higher t1h2,a4n30 tmhe3/so abnsde r1v0,a3t9i1o nms3./s,A ovcecroersdtiimnagtetdo btyh 6e0%C hainnda 3w3.9a%te, rrersepseoctuivrecleys. Tbhuel lseimtiunl,at2io5nm reesdulitus m- and
were generally higher than the observations. According to the China water resources bulletin, 25
large-sizedreservoirswereestablishedonthePearlRiverfrom2001to2005,includingfivelarge-scale
medium- and large-sized reservoirs were established on the Pearl River from 2001 to 2005, including
reservoirs[13]. Therefore,theregulationandcontrolonthepeakarrivaltimeandpeakflowaremore
five large-scale reservoirs [13]. Therefore, the regulation and control on the peak arrival time and
importantindeterminingthestreamflow.
peak flow are more important in determining the streamflow.

Figure 5. Flood simulation results at (a) Liuzhou; (b) Wuzhou; (c) Shijiao; (d) and Boluo Stations in
Figure5.Floodsimulationresultsat(a)Liuzhou;(b)Wuzhou;(c)Shijiao;(d)andBoluoStationsinthe
the Pearl River in 2005.
PearlRiverin2005.
Table 4. Evaluation of hydrological extreme simulation in 2005.
Hydrologic Station Liuzhou Wuzhou Shijiao Boluo
20 km 0.77 1.01 0.96 1.09
WBI
10 km 0.66 1.09 0.88 1.04
20 km −0.08 0.61 0.74 −0.67
NSE
10 km −0.46 0.66 0.61 0.40
20 km 0.43 0.83 0.86 0.46
PMC
10 km 0.20 0.84 0.81 0.85
20 km 0.33 0.75 0.88 0.54
IOA
10 km 0.08 0.88 0.83 0.87
20 km 0.21 0.58 0.59 −0.31
NRSE
10 km 0.08 0.60 0.49 0.22