Research Article Climate Change Impact on the Hydrology of ...downloads.hindawi.com/journals/amete/2015/960471.pdf · Research Article Climate Change Impact on the Hydrology of a

Research ArticleClimate Change Impact on the Hydrology of a TypicalWatershed in the Tianshan Mountains

Gonghuan Fang123 Jing Yang14 Yaning Chen1 Shuhua Zhang12 Haijun Deng12

Haimeng Liu25 and Philippe De Maeyer3

1State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geography Chinese Academy of SciencesUrumqi 830011 China2University of Chinese Academy of Sciences Beijing 100049 China3Department of Geography Ghent University 9000 Ghent Belgium4National Institute of Water and Atmospheric Research Christchurch 8011 New Zealand5Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China

Correspondence should be addressed to Jing Yang yangjingmsxjbaccn

Received 28 November 2014 Revised 26 March 2015 Accepted 15 April 2015

Academic Editor Ming Pan

Copyright copy 2015 Gonghuan Fang et alThis is an open access article distributed under theCreative CommonsAttribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

To study the impact of future climatic changes on hydrology in the Kaidu River Basin in the TianshanMountains two sets of futureclimatic data were used to force a well-calibrated hydrologic model one is bias-corrected regional climate model (RCM) outputsfor RCP45 and RCP85 future emission scenarios and the other is simple climate change (SCC) with absolute temperature changeof minus1sim6∘C and relative precipitation change of minus20sim60 Results show the following (1) temperature is likely to increase by 22∘Cand 46∘C by the end of the 21st century under RCP45 and RCP85 respectively while precipitation will increase by 2sim24 witha significant rise in the dry season and small change in the wet season (2) flow will change by minus1sim20 while evapotranspirationwill increase by 2sim24 (3) flow increases almost linearly with precipitation while its response to temperature depends on themagnitude of temperature change and flow decrease is significant when temperature increase is greater than 2∘C (4) similar resultswere obtained for simulations with RCM outputs and with SCC for mild climate change conditions while results were significantlydifferent for intense climate change conditions

1 Introduction

The Tianshan Mountains regarded as the ldquowater tower ofCentral Asiardquo [1] are located in the innermost center of theEurasian continent The long distance to the surroundingoceans causes a dry climate especially for the surroundingbasins Rivers starting in the mountainous regions provideagricultural and domestic water for the surrounding basinsand oases With their distinctive topographic and landscapefeatures the Tianshan Mountains show a unique energy bal-ance and hydrological cycle and are expected to be sensitiveto climate change [2 3]

Many reports show a widespread climatic and hydro-logic change in the Tianshan Mountains during the pastfew decades [4] For example temperature demonstrated asignificant rising trend (significant level is smaller than 0001)

at a rate of 033sim034∘Cdecade during 1960sim2010 whichis higher than China (025∘Cdecade) and the entire globe(013∘Cdecade) [5 6] precipitation increased substantiallyin most regions especially for the middle and high latitudesglacier area decreased by 115 and the thickness of snowpackhas also decreased [5 7] Pan evaporation and wind speedhave also changed [3] The annual runoff increased as wellfor example for the Urumqi River the Kaidu River and theAksu River [8 9]

Future changes in the streamflow and watershed hydrol-ogy have become increasingly important to water resourcemanagement in the Tianshan Mountains However only alimited number of studies currently focus on impact of futureclimate change on hydrology for example Sorg et al [1]indicated that the total runoff is likely to remain stable oreven increase slightly in the near future but it will decrease

Hindawi Publishing CorporationAdvances in MeteorologyVolume 2015 Article ID 960471 10 pageshttpdxdoiorg1011552015960471

2 Advances in Meteorology

at the end of the 21st century for Central Asia There arealso researches demonstrating that the annual runoff willdecrease to some extent in the first half of the 21st century[2 10] Previous studies seldom address implications ofclimate change on the hydrological cycle and hydrologicalcomponents (eg ET surface flow and groundwater) Tocomplement these studies this paper aims at understandingthe future hydrological system and assessing the responses ofthe hydrologic system to climate change

In the present study two sets of climatic data that isRCM outputs and SCC data are used to force SWAT [11]and are applied to the Kaidu River Basin a typical watershedon the south slope of the Tianshan Mountains to assessfuture changes of the hydrologic cycle and the hydrologicaleffects of changes in climate variables Questions that areaddressed include the followings (1) How will the futureclimate and hydrology change in this region (2) What is theeffect of climate change on the hydrologic cycling (3) Whatis the difference between simulations with RCM outputsand SCC Understanding these issues will enable assessingthe future hydrological change and its unique hydromete-orological processes better The remaining is constructedas follows Section 2 introduces the study area and dataSection 3 describes the hydrological model bias correctionmethods and analysis procedures Section 4 provides theresults and discussion followed by conclusions in Section 5

2 Study Area and Data

The Kaidu River Basin with a drainage area of 18634 km2above the Dashankou hydrological station is consideredto be a very typical watershed in the Tianshan Mountains(Figure 1) Its altitude ranges from 1342m to 4796m abovesea level (asl) with an average elevation of 2995m Theclimate here is temperate continental with alpine climatecharacteristics and obvious seasonal variation This riverprovides water resources for agricultural activity and theecological environment of the oasis with an area of over70000 km2 and a population of over 115 million which ismainly stressed by water scarcity [12] Therefore projectingthe impact of future climate change on water resources isurgent for the sustainable development of this region andit also provides information on the implications of climatechange on the water tower in Central Asia

The daily observed meteorological data (including pre-cipitation maximumminimum temperature wind speedand relative humidity of two meteorological stations Bayan-bulak and Baluntai) from 1970 to 2005 are from the ChinaMeteorological Data Sharing Service System (httpwwwcmagovcn2011qxfw2011qsjgx) The annual mean temper-ature at the Bayanbulak meteorological station amountsto minus41∘C and the mean annual precipitation is 278mm(Figure 1) Generally precipitation falls as rain from May toSeptember each year and as snow from October to April

The observed streamflow data at the Dashankou hydro-logic station are from the Xinjiang Tarim River Basin Man-agement Bureau The average flow at the Dashankou hydro-logical station amounts to approximately 120m3s (equivalentto 202mm runoffyear) ranging from 15m3s to 973m3s

Bayanbulak

Baluntai

Dashankou

China

Kaidu River Basin

Sea area

Land area

Hydrological station

Meteorological stationRiver

DEM (m)High 4796

Low 1342

Tianshan Region

Average T = minus41∘C

Annual P = 278mm

Average Q = 120m3s

0100200300400

J F MAM J J A S OND

J F MAM J J A S OND

J F MAM J J A S OND

0

20

0

2

4

6

minus20

minus40

T(∘

C)P

(mm

)Q

(m3 s

)

42∘ 0

998400 N43∘ 0

998400 N44∘ 0

998400 N45∘ 0

998400 N

42∘ 0

998400 N43∘ 0

998400 N44∘ 0

998400 N45∘ 0

998400 N

83∘0998400E 84∘0998400E 85∘0998400E 86∘0998400E 87∘0998400E 88∘0998400E

83∘0998400E 84∘0998400E 85∘0998400E 86∘0998400E 87∘0998400E 88∘0998400E

Figure 1 Location (top left) topography and hydrometeorologicstations (bottom) of the Kaidu River Basin and the daily averagetemperature (119879) and precipitation (119875) at the Bayanbulak station andstreamflow (119876) at the Dashankou station (top right)

3 Methodology

31 Regional Climate Model and RCP Scenarios The outputsof a regional climate model (RegCM40) [13] forced bya global climate model (Beijing Climate Center ClimateSystem Model BCC CSM11) [14 15] at a horizontal reso-lution of 50 km are used as future climate data Firstly theRCM model was validated with the observational data setover China for the period from 1976 to 2005 and then itwas used to predict the future climate change under thenew emission scenarios of the Representative ConcentrationPathways RCP45 (lower emission scenario) and RCP85(higher emission scenario) RCP45 is a stabilization scenariowith the total radiative forcing rising until 2070 and thenremaining at a stable centration of 45Wm2 In contrastRCP85 is a continuously rising radiative forcing pathway (ata target of 85Wm2 in 2100) with a further enhanced residualcirculation and significant CH

4increases [16 17] The RCM

validation shows reasonable simulations of temperature andprecipitation were obtained over China and compared tothe BCC CSM11 model marked improvement of the RCMwas achieved in reproducing present day precipitation andtemperature (for more details refer to [18])

32 Bias Correction Methods Five precipitation and threetemperature correctionmethodswere selected to bias-correctthe raw RCM outputs Precipitation correction methodsinclude linear scaling local intensity scaling power transfor-mation distribution mapping and quantile mapping Tem-perature correction methods include linear scaling variancescaling and distribution mapping They are combined into

Advances in Meteorology 3

15 schemes to evaluate their performances in simulatingstreamflow It turns out that the precipitation correctionmethods have more significant influence than the tempera-ture correction methods on streamflow simulation and thepower transformation and quantile mapping perform best interms of frequency based statistics Thereafter the quantilemapping method (for precipitation) and the distributionmapping (for temperature) are selected to correct the rawRCM outputs for the future period (for more details see [19])

33 Hydrologic Model and Uncertainty Analysis MethodSWAT has been extensively used for the comprehensivemodeling of the impact of management practices and climatechange SWAT simulates the hydrologic and sedimentaryprocesses plant growth river routing and in-stream waterquality process among which the surface runoff is calculatedfrom daily rainfall and snowmelt with a modified Soil Con-servation Service (SCS) curve number method [20] waterrouting is simulated using variable storage or theMuskingumriver routing method [11]

The SWAT model input includes the digital elevationmodel (DEM) soil textural and physicochemical propertiesand land use data The meteorological variables includingdaily precipitationmaxmin temperature relative humiditysolar radiation and wind speed were used to force theSWAT model SWAT uses elevation bands to represent thetopographic effects on precipitation and temperatureWithineach elevation band the precipitation and temperature areestimated based on their lapse rates For more details referto the SWAT manual (httpwwwbrctamusedu)

The SWATmodel (forced by the observedmeteorologicaldata) was calibrated against the observed streamflowThe cal-ibration period is from 1986 to 1989 and the validation periodfrom 1990 to 2005 [21]The calibrated optimal parameters arethen kept fixed in the following simulations The evaluationindices for the hydrological model include NS PBIAS andthe determination coefficient 1198772 Consider

NS = 1 minussum

119899

119894=1

(119884

obs119894

minus 119884

sim119894

)

2

sum

119899

119894=1

(119884

obs119894

minus 119884

mean)

2

PBIAS =sum

119899

119894=1

(119884

obs119894

minus 119884

sim119894

)

sum

119899

119894=1

(119884

obs119894

)

(1)

where119884obs119894

and119884sim119894

are the 119894th observed and simulated flows119884

mean is themean of the observed data and 119899 is the number ofobservations Normally NS gt 050 |PBIAS| lt 25 and 1198772 gt06 are taken as the criteria for satisfactory modeling of theriver discharge and the model performance can be evaluatedas excellent if NS gt 075 and |PBIAS| lt 10 [22]

GLUE (generalized likelihood uncertainty estimation)[23] is an uncertainty analysis technique in which theparameter uncertainty accounts for all sources of uncertaintysuch as input uncertainty structure uncertainty parameteruncertainty and response uncertainty [24] In GLUE theparameter uncertainty is described as a set of discreteldquobehavioralrdquo parameter sets with corresponding ldquolikelihoodweightsrdquo

The procedure of a GLUE analysis consists of three stepsFirstly after the definition of the ldquogeneralized likelihoodmea-surerdquo 119871(120579) a large number of parameter sets are randomlysampled from the prior distribution and each parameter setis assessed as either ldquobehavioralrdquo or ldquononbehavioralrdquo by com-paring its value of 119871(120579) to the threshold value Secondly eachbehavioral parameter set is given a ldquolikelihood weightrdquo andwe gave them equal weights in this study Finally predictionuncertainty is represented by 5 and 95 quantiles of thecumulative distribution of the behavioral parameter sets

Two indices are used to quantify the quality of theuncertainty performance Those indices are the percentageof measurements bracketed by the 95 prediction uncer-tainty band (119875-factor) and width of band (119877-factor cal-culated by the average width of the band divided by thestandard deviation of the corresponding measured vari-able)

34 SCC Data Description and Analysis Procedures In thefollowing section temperature and precipitation are denotedas 119879 and 119875 and the absolute and relative changes are repre-sented by Δ and 120575 For example Δ119879 refers to an absolutetemperature change and 120575119875 a relative precipitation changeThe hydrological processes analyzed in this study includestreamflow surface runoff subsurface runoff and evapotran-spiration which are denoted as 119876 119877

119904 119877119892 and ET and their

relative changes are described as 120575119876 120575119877119904 120575119877119892 and 120575ET

respectivelyThe SCC was constructed to represent a wide range of

changes in climatic variables and how these changes mighttranslate in streamflow and other hydrological componentsand also to analyze the differences between simulationswith RCM outputs and SCC For SCC perturbations of thecorrected RCM simulated 119875 and 119879 from 1986sim2005 (controlperiod) are set that is for 119879 an additive change (Δ) isused Δ119879 = minus1 0 1 2 3 4 5 and 6∘C For 119875 a relativechange (120575) is used 120575119875 = minus20 minus10 0 10 20 3040 50 and 60 They are put into 81 SCC scenarioswith Δ119879 = 0 and 120575119875 = 0 being the climate for controlperiod

By investigating the transient evolution of climate changein the corrected RCM outputs on decadal scales five periods(each spanning 20 years) are defined 1986sim2005 (controlperiod) and 2020sim2039 2040sim2059 2060sim2079 and 2080sim2099 Due to the intra-annual characteristics of the hydrom-eteorology in the Kaidu River Basin (Figure 1) the wet season(from April to September) and dry season (from Octoberto March next year) are defined based on the intra-annualdistribution of 119875 and 119876 for example 119875 and 119876 in the wetseason account for 88 and 73 of their annual amountsThe climatic and hydrological changes are classified into threecategories that is a significant change small change andinsignificant change to clearly demonstrate the changingmagnitude according to the values of relative change forprecipitation and hydrological components and absolutechange of temperature These categories are presented inTable 1


Table 1 Classification of magnitude for climatic and hydrologicalchanges 120575 and Δ represent relative change and absolute change

Precipitation amphydrological

components ()

Temperature(∘C)

Significantchange |120575| ge 20 |Δ| ge 2

Small change 10 le |120575| lt 20 1 le |Δ| lt 2

Insignificantchange |120575| lt 10 |Δ| lt 1

4 Results and Discussion

41 Validation of the Hydrological Model and the Bias Correc-tion Methods Performance of the hydrological model forcedby observed meteorological data and the 95 predictionuncertainty bands are shown in Figure 2 The simulatedstreamflow agrees quite well with the observation for bothcalibration period (1986sim1989) and validation period (1990sim2002) For the uncertainty analysis NS is used as 119871(120579) and070 as threshold value with 10000 initial parameter sets288 sets were selected as behavioral points The results showthat most of the observations are bracketed by the 95prediction uncertainty band (119875-factor being 87 and 80for calibration and validation periods and 119877-factor being 118and 119 resp) The lower 119875-factor for the validation periodcan be partly attributed to operation of hydropower stationsince 1991 (Figure 2) which leads to great fluctuation inwinterstreamflow Statistics of model efficiency (Table 2) indicateexcellent performances for both calibration and validationperiods with ldquoNSrdquos and ldquo1198772rdquos over 080 which is highlyacceptable according to Moriasi et al [22] Concerning themonthly streamflow the ldquoNSrdquo is 089 during 1986sim2005 and itindicates that the SWATmodel captured the natural monthlystreamflow variability adequately

The performances of bias-corrected RCM outputs (com-pared to observed meteorological data) are listed in Table 2The ldquoNSrdquos are minus057 (057) and 077 (095) for daily (monthly)precipitation and temperature for 1990sim2005 respectivelyAnd the statistics of the streamflow simulated with the bias-corrected RCM outputs shows acceptable results with ldquoNSrdquosequal to 046 and 062 and PBIAS within 10 for daily andmonthly streamflows

42 RCM Projected Hydrometeorologic Changes

421 Changes in Temperature and Precipitation Tempera-ture is highly likely to increase in the future with a basinwarming of 10sim22∘C and 16sim46∘C under RCP45 andRCP85 in the 21st century (Table 3) Temperature increasescontinuously under both scenarios but the magnitude islarger under RCP85 (Figure 3)

Precipitation shows an overall increasing trend in the21st century with an annual increase of 2sim16 and 7sim24 under RCP45 and RCP85 which confirms the previousarguments of Sorg et al [1] However precipitation changevaries substantially among seasons (Figure 3) Normally

0

200

400

600

800

198611 198711 198811 198911 199011 199111

Q(m

3sminus

1)

(a)

0

200

400

600

800

199211 199411 199611 199811

Q(m

3sminus

1)

(b)

ObservationSimulation forced by observed meteorology

0199911 200111 200311 200511

500

1000

Q(m

3sminus

1)

(c)

Figure 2 Time series of daily observed streamflows (dots) andsimulated streamflows forced by observedmeteorological data (blueline) for calibration period (1986ndash1989) and validation period (1990ndash2005) with 95 prediction uncertainty bands (blue shaded area)

a small increase in the wet season (minus2sim16) and a signif-icant increase during dry season (18sim78) are projectedNote that the relative increase (not the absolute increment)of precipitation for the dry season is much bigger than for thewet season which is in line with the climate changes in otherregions for example the semiarid Colorado River Basin [25]and the wet Ganges-Brahmaputra-Meghna basin [26]

422 Changes in the Hydrological Cycle The changes inprecipitation and temperature cause changes in potentialstreamflow The average annual streamflow rises by minus1sim18 and 4sim20 under the RCP45 and RCP85 in the 21stcentury based on the average annual streamflow of 194mmfor the control period (1986sim2005) (Table 3) Note that thestreamflow stopped increasing in 2080sim2099 (end of 21stcentury) under RCP85 despite the rise in precipitationwhich confirms the finding of Sorg et al [1] andmay aggravatewater scarcity in this region

Figure 3 also shows the projected changes in surfacerunoff (119877

119904) subsurface runoff (119877

119892) and evapotranspiration

(ET) under RCP45 and RCP85 Overall changes of hydro-logic components are bigger for RCP85 than for RCP45Theannual change of 119877

119904is insignificant (lt5) but with obvious

seasonal variability for example changes of 119877119904range from

minus22 to 2 for the wet season and in 4sim78 for the dry


minus20

0

20

0

10

20

0

100

200

0

100

200

0

100

200

W D A

W D A

W D A

W D A

W D A

W D A

0

200

400

0

2

4

6RCP45

020406080

minus20

0

20

40

minus500

50100150

0

20

40

Wet Dry Annual0

20

40

0

2

4

6RCP85

020406080

minus20

0

20

40

minus500

50100150

0

20

40

Wet Dry Annual

Wet Dry Annual Wet Dry Annual





0

20

40

120575P

()

T(∘

C)

1986sim2005 2020sim2039

2040sim2059

2060sim2079

2080sim2099

2020sim2039

2040sim2059

2060sim2079

2080sim2099

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)P

(mm

)

120575Q

()

120575Rs

()

120575Rg

()

120575ET

()

ΔT

(∘C)

Figure 3 Summary of future climate inputs (119875 and 119879) and simulated hydrologic components (119876 119877119904

119877119892

and ET) under RCP45 and RCP85compared to their values in the control period (1986sim2005) All these hydrometeorologic factors are presented in terms of wet season dryseason and annual values

season under RCP85 The annual 119877119892changes by minus07sim17

and 4sim18 for RCP45 and RCP85 which is consistentwith the changes of 119876 ET increases continuously in the21st century with average increases of 2sim10 and 7sim24under RCP45 and RCP85

43 Response of Hydrological Cycle to Climate Change Theresponse of the hydrological cycle to climate change isestimated by running the hydrological model forced by SCCThe responses of 119876 119877

119904 119877119892 and ET to climate change are

demonstrated with response surfaces in Figure 4 119876 is posi-tively related to119875 and negatively related to119879The relationship

of 120575119876 and 120575119875 is almost linear with the streamflow elasticity(120575119876120575119875) being about 10 when Δ119879 lt 2∘C that is a 1 changein the mean annual precipitation results in a 1 change inthe mean annual streamflow 120575119876120575119875 is lower than that forother arid regions for example 20sim35 for Australia [27]Thepossible reasons arementioned as follows (1) theKaiduRiverBasin located in the south slope of the Tianshan Mountainswith a high average altitude (2995m) is characterized by acold climate (average annual temperature is minus41∘C for theBayanbulak station) and accordingly there is a low amountof energy available for ET which results in a relatively highrunoff coefficient (119876119875 = 051) and consequently a low


Table 2 Statistics of bias-corrected RCM outputs and SWAT simulated streamflows forced by the observed climate variables and bias-corrected RCM outputs

Statistics NS PBIAS 119877

2

ldquoRCM simulated precipitation with bias correctionrdquoa

Validation period 1990sim2005 (daily)b minus057 minus680 000

Validation period 1990sim2005 (monthly) 057 minus680 060

ldquoRCM simulated maximum temperature with bias correctionrdquoa

Validation period 1990sim2005 (daily) 077 380 080

Validation period 1990sim2005 (monthly) 095 400 090

ldquoStreamflow simulated with observed meteorological datardquoCalibration period 1986sim1989 (daily) 080 001 080

First validation period 1990sim2002 (daily) 081 294 081

Second validation period 1986sim2005 (monthly) 089 286 090

ldquoStreamflow simulated with bias-corrected RCM outputsrdquoValidation period 1990sim2002 (daily) 046 minus698 047


aBias correction methods used are quantile mapping for precipitation and distribution mapping for temperature [19]bldquoDailyrdquo or ldquomonthlyrdquo in the brackets means the time step used to calculate the statistics

Table 3 RCMprojected precipitation change (120575119875) temperature change (Δ119879) and streamflow change (120575119876) for the 21st century under RCP45and RCP85 compared to the control period (1986sim2005)

2020sim2039 2040sim2059 2060sim2079 2080sim2099

RCP45120575119875 () 40 20 110 160Δ119879 (∘C) 10 16 20 22120575119876 () 60 minus10 100 180

RCP85120575119875 () 70 150 190 240Δ119879 (∘C) 16 23 33 46120575119876 () 40 160 200 150

streamflow elasticity (2) the streamflow is also influencedby temperature dominated snowmelt (snowfall accounts forabout 17 of watershed precipitation) which reduces thedependence of streamflow on precipitation and thereforeresults in a low streamflow elasticity [27]

The response of119876 toΔ119879depends on themagnitude ofΔ119879119876 decreases slightly when 0 lt Δ119879 le 20∘C while it decreasesdramatically when Δ119879 gt 20∘C for both wet and dry seasonsFor example when Δ119879 = 20∘C a 40 precipitation increaseresults in an average value of 119876 being 240mm (23 increasecompared to the average streamflow of 194mm) but whenΔ119879 = 40

∘C the same precipitation increase only generatesan average 119876 of 180mm (about 7 decrease) (Figure 4)

The responses of119877119904119877119892 and ET to climate change are also

demonstrated in Figure 4 For 119877119904 the responses of 119877

119904to Δ119879

are quite different for the wet and dry seasons the higher Δ119879the lower 119877

119904for the wet season but the higher 119877

119904for the dry

season Since 119877119904in the dry season only accounts for 13 of

the annual 119877119904 the response of annual 119877

119904is consistent with

that of the wet season For 119877119892 the responses of 119877

119892to Δ119879 and

120575119875 are similar to the responses of119876 due to the dominant roleof groundwater recharge in water yield in the Kaidu RiverBasin For ET it is mainly influenced by Δ119879with temperaturesensitivity (120575ETΔ119879) being 73∘C To verify this result wefirstly investigated basin-scale energy and water budget using

the Budyko method [28 29] It is shown that ET is mainlyenergy limited rather than water limited (average ET119875 =067 and PET119875 = 088) Secondly the high determinatecoefficient 1198772 = 075 (significant level is smaller than 001)between themean annual119879 and ET also indicates that ET hasa strong correlation with 119879 This is consistent with previousstudies which have shown that a significant variation in ETis expected to follow changes in air temperature [30 31]

In addition simulations with RCM outputs are shownin Figure 4 to analyze the differences between simulations ofthese two data sets Two typical periods of RCM simulationsare selected that is 2020sim2039 under RCP45 and 2080sim2099under RCP85 to represent mild and intense climate changescenarios (shown as blue and red stars in Figure 4) It isindicated that the simulations of hydrological componentswith RCM outputs for 2020sim2039 under RCP45 (mildclimate change) are close to these of the nearby contourlines (simulations with SCC) which suggests that similarresults of 119876 119877

119904 119877119892 and ET are obtained for RCM outputs

and for SCC under mild climate change scenarios Howeverfor 2080sim2099 under RCP85 with 120575119875 = 24 and Δ119879 =46

∘C the simulated values of 119876 119877119904 119877119892 and ET deviate

from the simulations of SCCThere are two possible reasons(1) changes of other meteorological inputs that is solarradiation wind speed and humidity are slightly smaller for


60

80100

120140160

180

Wet season Dry season Annual

143

155

minus20 0 20 40 60

0246 20

3040

50607080

9061

67

minus20 0 20 40 60

0246

90120150

180210240

270205

222

minus20 0 20 40 60

0246

40

70

100130105

77

minus20 0 20 40 60

0246

1620

2428

32

25

42

minus20 0 20 40 60

0246

6090

120150131

119

minus20 0 20 40 60

0246

6080

100

120140

106

118

minus20 0 20 40 60

0246 20

40 60

8057

63

minus20 0 20 40 60

0246

80

120160200

163

180

minus20 0 20 40 60

0246

240280320

360

239

287

minus20 0 20 40 60

0246

30

35

40

30

39

minus20 0 20 40 60

0246

280

320360

400

269

329

minus20 0 20 40 60

0246

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)

Figure 4 Response surfaces of streamflow (119876) surface runoff (119877119904

) subsurface runoff (119877119892

) and evapotranspiration (ET) to climate changeThe simulations with RCM outputs for 2020sim2039 under RCP45 and for 2080sim2099 under RCP85 (their corresponding meteorologicalchanges are listed in Table 3) are indicated using blue and red stars with labels

2020sim2039 under RCP45 than those for 2080sim2099 underRCP85 (minus08 26 and 09 compared to minus22 41and 14) (2) for 2080sim2099 under RCP85 precipitationincreases by 24 with great seasonal variation which mayalter the hydrological regime for example precipitationincreases by 139 for March April and May while itdecreases by minus01 for June July and August Since changesof solar radiation wind speed and humidity are withinplusmn5 the second reason that is the shift of the precipitationtemporal distribution contributes a lot to the deviation ofsimulations with RCM outputs from simulations with SCC

Furthermore the exceedance probability curves of theannual runoff in response to climate change are demonstratedin Figure 5 The exceedance probability curves are almostparallel when Δ119879 ranges in 0sim6∘C However the responsesof 119876 to 120575119875 are not the same for each exceedance probabilityhigh sensitivity of 119876 with probabilities less than 01 andlow sensitivity of 119876 with probabilities larger than 09 Acomparison of the simulationswithRCMoutputs (four futureperiods under RCP45 and RCP85) and with SCC (the samechanges in 119879 or 119875 with the corresponding RCM outputs)indicates that differences between simulation with RCMoutputs and SCC are becoming greater as climate change

gets more intense for example the simulation with RCM for2080sim2099 under RCP85 overestimates the correspondingsimulations with SCC (Figure 5(h)) which collaborates theconclusion that under intense climate change scenarios thesimulated hydrology with RCM deviates from that simulatedwith SCC

The contributions of hydrologic components to wateryield are displayed by the De Finetti diagram in Figure 6 Forthe control period the averages of 119877

119904 119877119892 and ET are 022

028 and 050 For SCC as Δ119879 increases from 0 to 6∘C thecontribution of ET increases rapidly from 049 to 073 andthe contributions of 119877

119904and 119877

119892decrease from 022 to 011 and

from 029 to 016 Δ119879 has a more significant influence on theproportion change than 120575119875 As 120575119875 changes from minus20 to60 ET decreases from 071 to 058 and 119877

119904and 119877

119892increase

from 013 to 015 and from 016 to 027 For simulations withRCM outputs proportions of hydrological components donot change significantly under RCP45 while the proportionof ET shows a significant increase under RCP85

44 Sources of Uncertainty and Other Considerations Thereare uncertainties in estimating climate change impact onhydrology As indicated by previous studies [32] the sources


0 05 10

50

100

150

Exceedance

120575P = 4

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 10 ∘C

Q(m

3sminus

1)

(a)

0 05 10

50

100

150

Exceedance

120575P = 2

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 16 ∘C

Q(m

3sminus

1)

(b)

0 05 10

50

100

150

Exceedance

120575P = 11

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 20 ∘C

Q(m

3sminus

1)

(c)

0 05 10

50

100

150

Exceedance

120575P = 16

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 22 ∘C

Q(m

3sminus

1)

(d)

0 05 10

50

100

150

200

Exceedance

ΔT = 16 ∘C

120575P = 20

120575P = 20120575P = 0

120575P = 7

120575P = 40120575P = 60

Q(m

3sminus

1)

(e)

0 05 10

50

100

150

200

Exceedance

ΔT = 23 ∘C

120575P = 20

120575P = 20 120575P = 15120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(f)

0 05 10

50

100

150

200

Exceedance

ΔT = 33 ∘C

120575P = 20

120575P = 20 120575P = 19120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(g)

Exceedance0 05 1

0

50

100

150

200

ΔT = 46 ∘C

120575P = 20

120575P = 20 120575P = 24120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(h)

Figure 5 Exceedance probability curves of average annual streamflow (119876) in response to temperature change and precipitation changebased on SCC with each plot either with fixed precipitation change (asimd) or with fixed temperature change (esimh) Dotted blue line in eachplot denotes exceedance probability curves of average annual streamflow for the simulation with RCM outputs given fixed 120575119875 and Δ119879 assummarized in Table 3

of uncertainty may rise from climate models emissionscenarios downscaling and the hydrological model

For hydrological modeling itself the effect of futureclimate in any specific catchment is difficult to project due to

the possibility that the hydrological system may not be sta-tionary with complex feedbacks [33] For example the sameland cover and soil datawere used for both control period andfuture climate change period which may not well represent


Rg

RsET

00

0000

02

02

02

04

04

04

06

06

06

08

08

08

10

10

10

RCM outputsControl periodRCP45 2020sim2039RCP45 2040sim2059RCP45 2060sim2079

RCP85 2020sim2039RCP85 2040sim2059RCP85 2060sim2079

120575P = 20

120575P = 20120575P = 0

120575P = 40120575P = 60

SCC (ΔT = 46∘C)ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘CΔT = 6 ∘C

SCC (120575P = 4)

RCP45 2080sim2099

RCP85 2080sim2099

Figure 6 De Finetti diagram (ternary plot) of evapotranspiration(ET) surface runoff (119877

119904

) and subsurface runoff (119877119892

) for SCC(shown as dots) and RCM outputs (shown as stars for details of theprojected changes in RCM outputs refer to Table 3)

the land surface under the future climate changes Effects ofland cover change on streamflows and other components ofthe hydrological cycle are not considered

Though uncertainty in hydrologic modeling was quan-tified with the GLUE method it only accounts for partof the total uncertainty in climate change impact studies[34] Uncertainties associated with the climate model anddownscalingwere not considered here although two emissionscenarios were included Any uncertainty associated withthem may cause the results to deviate from reality Howeverwe are dedicated to pursuing a thorough investigation of theresponse of the hydrological cycle to future climate change forthis region and we believe this study is an important first stepin achieving this goal

5 Conclusions

This study assessed the implications of climate change onhydrology in a typical watershed in the Tianshan Mountainswith two sets of climatic data that is RCM outputs andSCC loosely coupled to a hydrological model (SWAT)Majorconclusions can be summarized as follows

(1) The hydrological model shows excellent performancewith ldquoNSrdquos over 08 for the daily streamflow for both

calibration and validation periods And the selectedbias correction methods were effective in downscal-ing RCM outputs with ldquoNSrdquos being 057 and 095regarding monthly precipitation and temperature

(2) 119879 increases by 10∘Csim22∘C and by 16∘Csim46∘C underRCP45 andRCP85 in the 21st century For119875 it showsan overall increasing trend (2sim24)with significantincrease for the dry season (18sim78) and relativelysmall change for the wet season (minus2sim16) Theprojected 119876 shows an overall increasing trend (minus1sim18 and 4sim20 for RCP45 and RCP85) in the 21stcentury

(3) 119876 increases almost linearly with 119875 while the responseof 119876 to 119879 depends on the magnitude of Δ119879 and 119876decreases significantly when Δ119879 is greater than 2∘C

(4) Similar responses of 119876 119877119904 119877119892 and ET to 119875 and 119879

are obtained for the RCM outputs and for SCC undermild climate change scenarios However for intenseclimate change scenarios simulations of 119876 119877

119904 119877119892

and ETwith RCMoutputs (eg for 2080sim2099 underRCP85) deviate from simulations with SCC

(5) Δ119879 has more significant influence on the proportionchange of each hydrologic component than 120575119875 doesAs Δ119879 increases from 0 to 6∘C the contribution ofET increases rapidly from 049 to 073 and 119877

119904and 119877

119892

decrease by 011 and 013 As 120575119875 changes from minus20to 60 ET 119877

119904 and 119877

119892change by minus013 002 and 011

as a result

It is valuable to quantify the future responses of hydrologyto climate change in the TianshanMountainsThis study willprovide useful information for water resource managementand will serve as a basis for further climate change impactstudies

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

Research was supported by the ldquoThousand Youth TalentsPlanrdquo (Xinjiang Project) the National Natural Science Foun-dation of China (41471030) and the Foundation of StateKey Laboratory of Desert and Oasis Ecology (Y371163) Theauthors wish to thank Professor Xuejie Gao at the NationalClimate Center (China) for providing the outputs of theregional climate model used in this paper

References

[1] A Sorg T Bolch M Stoffel O Solomina and M BenistonldquoClimate change impacts on glaciers and runoff in Tien Shan(Central Asia)rdquo Nature Climate Change vol 2 no 10 pp 725ndash731 2012

[2] Z Liu Z Xu J Huang S P Charles and G Fu ldquoImpacts ofclimate change on hydrological processes in the headwater


catchment of the Tarim River basin Chinardquo Hydrological Pro-cesses vol 24 no 2 pp 196ndash208 2010

[3] Z Li Y Chen Y Shen Y Liu and S Zhang ldquoAnalysis of chang-ing pan evaporation in the arid region of Northwest ChinardquoWater Resources Research vol 49 no 4 pp 2205ndash2212 2013

[4] Y Shi Y Shen E Kang et al ldquoRecent and future climate changein Northwest Chinardquo Climatic Change vol 80 no 3-4 pp 379ndash393 2007

[5] S Wang M Zhang Z Li et al ldquoGlacier area variation andclimate change in the Chinese TianshanMountains since 1960rdquoJournal of Geographical Sciences vol 21 no 2 pp 263ndash273 2011

[6] B Li Y Chen and X Shi ldquoWhy does the temperature rise fasterin the arid region of northwest Chinardquo Journal of GeophysicalResearchD Atmospheres vol 117 no 16 Article IDD16115 2012

[7] V B Aizen E M Aizen J M Melack and J Dozier ldquoClimaticand hydrologic changes in the Tien Shan central Asiardquo Journalof Climate vol 10 no 6 pp 1393ndash1404 1997

[8] B Ye D Yang K Jiao et al ldquoThe Urumqi River source GlacierNo 1 Tianshan China changes over the past 45 yearsrdquo Geo-physical Research Letters vol 32 no 21 Article ID L21504 2005

[9] H Wang Y Chen W Li and H Deng ldquoRunoff responses toclimate change in arid region of northwestern China during1960ndash2010rdquoChineseGeographical Science vol 23 no 3 pp 286ndash300 2013

[10] T Liu P Willems X L Pan et al ldquoClimate change impact onwater resource extremes in a headwater region of the Tarimbasin in Chinardquo Hydrology and Earth System Sciences vol 15no 11 pp 3511ndash3527 2011

[11] J G Arnold R Srinivasan R S Muttiah and J R WilliamsldquoLarge area hydrologic modeling and assessment part I modeldevelopmentrdquo Journal of the American Water Resources Associ-ation vol 34 no 1 pp 73ndash89 1998

[12] Y Chen and Q Du Sustainable Water Use in the Bosten LakeBasin Science Press Beijing China 2013

[13] F Giorgi and L O Mearns ldquoIntroduction to special sectionregional climate modeling revisitedrdquo Journal of GeophysicalResearch vol 104 no D6 pp 6335ndash6352 1999

[14] TWuW Li J Ji et al ldquoGlobal carbon budgets simulated by theBeijing climate center climate systemmodel for the last centuryrdquoJournal of Geophysical Research D Atmospheres vol 118 no 10pp 4326ndash4347 2013

[15] X Xin T Wu J Li et al ldquoHow well does BCC CSM1 1 repro-duce the 20th Century climate change over Chinardquo Atmo-spheric and Oceanic Science Letters vol 6 pp 21ndash26 2013

[16] D P van Vuuren J Edmonds M Kainuma et al ldquoThe repre-sentative concentration pathways an overviewrdquo ClimaticChange vol 109 no 1 pp 5ndash31 2011

[17] H Kawase T Nagashima K Sudo and T Nozawa ldquoFuturechanges in tropospheric ozone under Representative Concen-tration Pathways (RCPs)rdquo Geophysical Research Letters vol 38no 5 2011

[18] X Gao M Wang and F Giorgi ldquoClimate change over Chinain the 21st century as simu-lated by BCC CSM11-RegCM40rdquoAtmospheric and Oceanic Science Letters vol 6 pp 381ndash3862013

[19] G H Fang J Yang Y N Chen and C Zammit ldquoComparingbias correction methods in downscaling meteorological vari-ables for hydrologic impact study in an arid area in ChinardquoHydrology and Earth System Sciences Discussions vol 11 no 11pp 12659ndash12696 2014

[20] Soil Conservation Service ldquoUrban hydrology for small water-shedrdquo Technical Release 55 US Department of Agriculture1968

[21] G Fang J Yang Y Chen C Xu and P de Maeyer ldquoContri-bution of meteorological input in calibrating a distributedhydrologic model in a watershed in the Tianshan MountainsChinardquo Environmental Earth Sciences 2015

[22] D N Moriasi J G Arnold M W van Liew R L Bingner RD Harmel and T L Veith ldquoModel evaluation guidelines forsystematic quantification of accuracy inwatershed simulationsrdquoTransactions of the ASABE vol 50 no 3 pp 885ndash900 2007

[23] K Beven and A Binley ldquoThe future of distributed modelsmodel calibration and uncertainty predictionrdquo HydrologicalProcesses vol 6 no 3 pp 279ndash298 1992

[24] J Yang P Reichert K C Abbaspour J Xia andH Yang ldquoCom-paring uncertainty analysis techniques for a SWAT applicationto the Chaohe Basin in Chinardquo Journal of Hydrology vol 358no 1-2 pp 1ndash23 2008

[25] N S Christensen and D P Lettenmaier ldquoA multimodel ensem-ble approach to assessment of climate change impacts on thehydrology and water resources of the Colorado River BasinrdquoHydrology andEarth SystemSciences vol 11 no 4 pp 1417ndash14342007

[26] M Masood P J Yeh N Hanasaki and K Takeuchi ldquoModelstudy of the impacts of future climate change on the hydrologyof GangesndashBrahmaputrandashMeghna basinrdquo Hydrology and EarthSystem Sciences vol 19 no 2 pp 747ndash770 2015

[27] F H S Chiew ldquoEstimation of rainfall elasticity of streamflow inAustraliardquo Hydrological Sciences Journal vol 51 no 4 pp 613ndash625 2006

[28] J A Jones I F Creed K L Hatcher et al ldquoEcosystem processesand human influences regulate streamflow response to climatechange at long-term ecological research sitesrdquo BioScience vol62 no 4 pp 390ndash404 2012

[29] M I Budyko Climate and Life Academic Press New York NYUSA 1974

[30] K C Abbaspour M Faramarzi S S Ghasemi and H YangldquoAssessing the impact of climate change on water resources inIranrdquoWater Resources Research vol 45 no 10 2009

[31] S G Setegn D Rayner A M Melesse B Dargahi and RSrinivasan ldquoImpact of climate change on the hydroclimatologyof Lake Tana Basin EthiopiardquoWater Resources Research vol 47no 4 Article IDW04511 2011

[32] S Hagemann C Chen D B Clark et al ldquoClimate changeimpact on available water resources obtained using multipleglobal climate and hydrology modelsrdquo Earth System Dynamicsvol 4 no 1 pp 129ndash144 2013

[33] R P Silberstein S K Aryal J Durrant et al ldquoClimate changeand runoff in south-western Australiardquo Journal of Hydrologyvol 475 pp 441ndash455 2012

[34] D G Kingston and R G Taylor ldquoSources of uncertainty inclimate change impacts on river discharge and groundwaterin a headwater catchment of the Upper Nile Basin UgandardquoHydrology and Earth System Sciences vol 14 no 7 pp 1297ndash1308 2010

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of


EarthquakesJournal of


Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining


Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of


Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering


GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of


OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in


MineralogyInternational Journal of


MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Geological ResearchJournal of


Geology Advances in


at the end of the 21st century for Central Asia There arealso researches demonstrating that the annual runoff willdecrease to some extent in the first half of the 21st century[2 10] Previous studies seldom address implications ofclimate change on the hydrological cycle and hydrologicalcomponents (eg ET surface flow and groundwater) Tocomplement these studies this paper aims at understandingthe future hydrological system and assessing the responses ofthe hydrologic system to climate change

In the present study two sets of climatic data that isRCM outputs and SCC data are used to force SWAT [11]and are applied to the Kaidu River Basin a typical watershedon the south slope of the Tianshan Mountains to assessfuture changes of the hydrologic cycle and the hydrologicaleffects of changes in climate variables Questions that areaddressed include the followings (1) How will the futureclimate and hydrology change in this region (2) What is theeffect of climate change on the hydrologic cycling (3) Whatis the difference between simulations with RCM outputsand SCC Understanding these issues will enable assessingthe future hydrological change and its unique hydromete-orological processes better The remaining is constructedas follows Section 2 introduces the study area and dataSection 3 describes the hydrological model bias correctionmethods and analysis procedures Section 4 provides theresults and discussion followed by conclusions in Section 5

2 Study Area and Data

The Kaidu River Basin with a drainage area of 18634 km2above the Dashankou hydrological station is consideredto be a very typical watershed in the Tianshan Mountains(Figure 1) Its altitude ranges from 1342m to 4796m abovesea level (asl) with an average elevation of 2995m Theclimate here is temperate continental with alpine climatecharacteristics and obvious seasonal variation This riverprovides water resources for agricultural activity and theecological environment of the oasis with an area of over70000 km2 and a population of over 115 million which ismainly stressed by water scarcity [12] Therefore projectingthe impact of future climate change on water resources isurgent for the sustainable development of this region andit also provides information on the implications of climatechange on the water tower in Central Asia

The daily observed meteorological data (including pre-cipitation maximumminimum temperature wind speedand relative humidity of two meteorological stations Bayan-bulak and Baluntai) from 1970 to 2005 are from the ChinaMeteorological Data Sharing Service System (httpwwwcmagovcn2011qxfw2011qsjgx) The annual mean temper-ature at the Bayanbulak meteorological station amountsto minus41∘C and the mean annual precipitation is 278mm(Figure 1) Generally precipitation falls as rain from May toSeptember each year and as snow from October to April

The observed streamflow data at the Dashankou hydro-logic station are from the Xinjiang Tarim River Basin Man-agement Bureau The average flow at the Dashankou hydro-logical station amounts to approximately 120m3s (equivalentto 202mm runoffyear) ranging from 15m3s to 973m3s

Bayanbulak

Baluntai

Dashankou

China

Kaidu River Basin

Sea area

Land area

Hydrological station

Meteorological stationRiver

DEM (m)High 4796

Low 1342

Tianshan Region

Average T = minus41∘C

Annual P = 278mm

Average Q = 120m3s

0100200300400

J F MAM J J A S OND

J F MAM J J A S OND

J F MAM J J A S OND

0

20

0

2

4

6

minus20

minus40

T(∘

C)P

(mm

)Q

(m3 s

)

42∘ 0

998400 N43∘ 0

998400 N44∘ 0

998400 N45∘ 0

998400 N

42∘ 0

998400 N43∘ 0

998400 N44∘ 0

998400 N45∘ 0

998400 N

83∘0998400E 84∘0998400E 85∘0998400E 86∘0998400E 87∘0998400E 88∘0998400E

83∘0998400E 84∘0998400E 85∘0998400E 86∘0998400E 87∘0998400E 88∘0998400E

Figure 1 Location (top left) topography and hydrometeorologicstations (bottom) of the Kaidu River Basin and the daily averagetemperature (119879) and precipitation (119875) at the Bayanbulak station andstreamflow (119876) at the Dashankou station (top right)

3 Methodology

31 Regional Climate Model and RCP Scenarios The outputsof a regional climate model (RegCM40) [13] forced bya global climate model (Beijing Climate Center ClimateSystem Model BCC CSM11) [14 15] at a horizontal reso-lution of 50 km are used as future climate data Firstly theRCM model was validated with the observational data setover China for the period from 1976 to 2005 and then itwas used to predict the future climate change under thenew emission scenarios of the Representative ConcentrationPathways RCP45 (lower emission scenario) and RCP85(higher emission scenario) RCP45 is a stabilization scenariowith the total radiative forcing rising until 2070 and thenremaining at a stable centration of 45Wm2 In contrastRCP85 is a continuously rising radiative forcing pathway (ata target of 85Wm2 in 2100) with a further enhanced residualcirculation and significant CH

4increases [16 17] The RCM

validation shows reasonable simulations of temperature andprecipitation were obtained over China and compared tothe BCC CSM11 model marked improvement of the RCMwas achieved in reproducing present day precipitation andtemperature (for more details refer to [18])

32 Bias Correction Methods Five precipitation and threetemperature correctionmethodswere selected to bias-correctthe raw RCM outputs Precipitation correction methodsinclude linear scaling local intensity scaling power transfor-mation distribution mapping and quantile mapping Tem-perature correction methods include linear scaling variancescaling and distribution mapping They are combined into






NS = 1 minussum

119899

119894=1

(119884

obs119894

minus 119884

sim119894

)

2

sum

119899

119894=1

(119884

obs119894

minus 119884

mean)

2

PBIAS =sum

119899

119894=1

(119884

obs119894

minus 119884

sim119894

)

sum

119899

119894=1

(119884

obs119894

)

(1)


and119884sim119894







119904 119877119892 and ET and their








components ()

Temperature(∘C)










0

200

400

600

800

198611 198711 198811 198911 199011 199111

Q(m

3sminus

1)

(a)

0

200

400

600

800

199211 199411 199611 199811

Q(m

3sminus

1)

(b)


0199911 200111 200311 200511

500

1000

Q(m

3sminus

1)

(c)












minus20

0

20

0

10

20

0

100

200

0

100

200

0

100

200

W D A

W D A

W D A

W D A

W D A

W D A

0

200

400

0

2

4

6RCP45

020406080

minus20

0

20

40

minus500

50100150

0

20

40

Wet Dry Annual0

20

40

0

2

4

6RCP85

020406080

minus20

0

20

40

minus500

50100150

0

20

40

Wet Dry Annual






0

20

40

120575P

()

T(∘

C)

1986sim2005 2020sim2039

2040sim2059

2060sim2079

2080sim2099

2020sim2039

2040sim2059

2060sim2079

2080sim2099

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)P

(mm

)

120575Q

()

120575Rs

()

120575Rg

()

120575ET

()

ΔT

(∘C)


119877119892











2














2020sim2039 2040sim2059 2060sim2079 2080sim2099

RCP45120575119875 () 40 20 110 160Δ119879 (∘C) 10 16 20 22120575119876 () 60 minus10 100 180

RCP85120575119875 () 70 150 190 240Δ119879 (∘C) 16 23 33 46120575119876 () 40 160 200 150






119904to Δ119879



119904for the dry





119892to Δ119879 and









60

80100

120140160

180


143

155

minus20 0 20 40 60

0246 20

3040

50607080

9061

67

minus20 0 20 40 60

0246

90120150

180210240

270205

222

minus20 0 20 40 60

0246

40

70

100130105

77

minus20 0 20 40 60

0246

1620

2428

32

25

42

minus20 0 20 40 60

0246

6090

120150131

119

minus20 0 20 40 60

0246

6080

100

120140

106

118

minus20 0 20 40 60

0246 20

40 60

8057

63

minus20 0 20 40 60

0246

80

120160200

163

180

minus20 0 20 40 60

0246

240280320

360

239

287

minus20 0 20 40 60

0246

30

35

40

30

39

minus20 0 20 40 60

0246

280

320360

400

269

329

minus20 0 20 40 60

0246

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)








119904 119877119892 and ET are 022


119904and 119877



119904and 119877

119892increase




0 05 10

50

100

150

Exceedance

120575P = 4

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 10 ∘C

Q(m

3sminus

1)

(a)

0 05 10

50

100

150

Exceedance

120575P = 2

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 16 ∘C

Q(m

3sminus

1)

(b)

0 05 10

50

100

150

Exceedance

120575P = 11

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 20 ∘C

Q(m

3sminus

1)

(c)

0 05 10

50

100

150

Exceedance

120575P = 16

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 22 ∘C

Q(m

3sminus

1)

(d)

0 05 10

50

100

150

200

Exceedance

ΔT = 16 ∘C

120575P = 20

120575P = 20120575P = 0

120575P = 7

120575P = 40120575P = 60

Q(m

3sminus

1)

(e)

0 05 10

50

100

150

200

Exceedance

ΔT = 23 ∘C

120575P = 20

120575P = 20 120575P = 15120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(f)

0 05 10

50

100

150

200

Exceedance

ΔT = 33 ∘C

120575P = 20

120575P = 20 120575P = 19120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(g)

Exceedance0 05 1

0

50

100

150

200

ΔT = 46 ∘C

120575P = 20

120575P = 20 120575P = 24120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(h)






Rg

RsET

00

0000

02

02

02

04

04

04

06

06

06

08

08

08

10

10

10



120575P = 20

120575P = 20120575P = 0

120575P = 40120575P = 60


SCC (120575P = 4)

RCP45 2080sim2099

RCP85 2080sim2099


119904





5 Conclusions








119904 119877119892



119904and 119877

119892


119904 and 119877


as a result




Acknowledgments


References














































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in






NS = 1 minussum

119899

119894=1

(119884

obs119894

minus 119884

sim119894

)

2

sum

119899

119894=1

(119884

obs119894

minus 119884

mean)

2

PBIAS =sum

119899

119894=1

(119884

obs119894

minus 119884

sim119894

)

sum

119899

119894=1

(119884

obs119894

)

(1)


and119884sim119894







119904 119877119892 and ET and their








components ()

Temperature(∘C)










0

200

400

600

800

198611 198711 198811 198911 199011 199111

Q(m

3sminus

1)

(a)

0

200

400

600

800

199211 199411 199611 199811

Q(m

3sminus

1)

(b)


0199911 200111 200311 200511

500

1000

Q(m

3sminus

1)

(c)












minus20

0

20

0

10

20

0

100

200

0

100

200

0

100

200

W D A

W D A

W D A

W D A

W D A

W D A

0

200

400

0

2

4

6RCP45

020406080

minus20

0

20

40

minus500

50100150

0

20

40

Wet Dry Annual0

20

40

0

2

4

6RCP85

020406080

minus20

0

20

40

minus500

50100150

0

20

40

Wet Dry Annual






0

20

40

120575P

()

T(∘

C)

1986sim2005 2020sim2039

2040sim2059

2060sim2079

2080sim2099

2020sim2039

2040sim2059

2060sim2079

2080sim2099

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)P

(mm

)

120575Q

()

120575Rs

()

120575Rg

()

120575ET

()

ΔT

(∘C)


119877119892











2














2020sim2039 2040sim2059 2060sim2079 2080sim2099

RCP45120575119875 () 40 20 110 160Δ119879 (∘C) 10 16 20 22120575119876 () 60 minus10 100 180

RCP85120575119875 () 70 150 190 240Δ119879 (∘C) 16 23 33 46120575119876 () 40 160 200 150






119904to Δ119879



119904for the dry





119892to Δ119879 and









60

80100

120140160

180


143

155

minus20 0 20 40 60

0246 20

3040

50607080

9061

67

minus20 0 20 40 60

0246

90120150

180210240

270205

222

minus20 0 20 40 60

0246

40

70

100130105

77

minus20 0 20 40 60

0246

1620

2428

32

25

42

minus20 0 20 40 60

0246

6090

120150131

119

minus20 0 20 40 60

0246

6080

100

120140

106

118

minus20 0 20 40 60

0246 20

40 60

8057

63

minus20 0 20 40 60

0246

80

120160200

163

180

minus20 0 20 40 60

0246

240280320

360

239

287

minus20 0 20 40 60

0246

30

35

40

30

39

minus20 0 20 40 60

0246

280

320360

400

269

329

minus20 0 20 40 60

0246

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)








119904 119877119892 and ET are 022


119904and 119877



119904and 119877

119892increase




0 05 10

50

100

150

Exceedance

120575P = 4

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 10 ∘C

Q(m

3sminus

1)

(a)

0 05 10

50

100

150

Exceedance

120575P = 2

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 16 ∘C

Q(m

3sminus

1)

(b)

0 05 10

50

100

150

Exceedance

120575P = 11

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 20 ∘C

Q(m

3sminus

1)

(c)

0 05 10

50

100

150

Exceedance

120575P = 16

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 22 ∘C

Q(m

3sminus

1)

(d)

0 05 10

50

100

150

200

Exceedance

ΔT = 16 ∘C

120575P = 20

120575P = 20120575P = 0

120575P = 7

120575P = 40120575P = 60

Q(m

3sminus

1)

(e)

0 05 10

50

100

150

200

Exceedance

ΔT = 23 ∘C

120575P = 20

120575P = 20 120575P = 15120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(f)

0 05 10

50

100

150

200

Exceedance

ΔT = 33 ∘C

120575P = 20

120575P = 20 120575P = 19120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(g)

Exceedance0 05 1

0

50

100

150

200

ΔT = 46 ∘C

120575P = 20

120575P = 20 120575P = 24120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(h)






Rg

RsET

00

0000

02

02

02

04

04

04

06

06

06

08

08

08

10

10

10



120575P = 20

120575P = 20120575P = 0

120575P = 40120575P = 60


SCC (120575P = 4)

RCP45 2080sim2099

RCP85 2080sim2099


119904





5 Conclusions








119904 119877119892



119904and 119877

119892


119904 and 119877


as a result




Acknowledgments


References














































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in




components ()

Temperature(∘C)










0

200

400

600

800

198611 198711 198811 198911 199011 199111

Q(m

3sminus

1)

(a)

0

200

400

600

800

199211 199411 199611 199811

Q(m

3sminus

1)

(b)


0199911 200111 200311 200511

500

1000

Q(m

3sminus

1)

(c)












minus20

0

20

0

10

20

0

100

200

0

100

200

0

100

200

W D A

W D A

W D A

W D A

W D A

W D A

0

200

400

0

2

4

6RCP45

020406080

minus20

0

20

40

minus500

50100150

0

20

40

Wet Dry Annual0

20

40

0

2

4

6RCP85

020406080

minus20

0

20

40

minus500

50100150

0

20

40

Wet Dry Annual






0

20

40

120575P

()

T(∘

C)

1986sim2005 2020sim2039

2040sim2059

2060sim2079

2080sim2099

2020sim2039

2040sim2059

2060sim2079

2080sim2099

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)P

(mm

)

120575Q

()

120575Rs

()

120575Rg

()

120575ET

()

ΔT

(∘C)


119877119892











2














2020sim2039 2040sim2059 2060sim2079 2080sim2099

RCP45120575119875 () 40 20 110 160Δ119879 (∘C) 10 16 20 22120575119876 () 60 minus10 100 180

RCP85120575119875 () 70 150 190 240Δ119879 (∘C) 16 23 33 46120575119876 () 40 160 200 150






119904to Δ119879



119904for the dry





119892to Δ119879 and









60

80100

120140160

180


143

155

minus20 0 20 40 60

0246 20

3040

50607080

9061

67

minus20 0 20 40 60

0246

90120150

180210240

270205

222

minus20 0 20 40 60

0246

40

70

100130105

77

minus20 0 20 40 60

0246

1620

2428

32

25

42

minus20 0 20 40 60

0246

6090

120150131

119

minus20 0 20 40 60

0246

6080

100

120140

106

118

minus20 0 20 40 60

0246 20

40 60

8057

63

minus20 0 20 40 60

0246

80

120160200

163

180

minus20 0 20 40 60

0246

240280320

360

239

287

minus20 0 20 40 60

0246

30

35

40

30

39

minus20 0 20 40 60

0246

280

320360

400

269

329

minus20 0 20 40 60

0246

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)








119904 119877119892 and ET are 022


119904and 119877



119904and 119877

119892increase




0 05 10

50

100

150

Exceedance

120575P = 4

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 10 ∘C

Q(m

3sminus

1)

(a)

0 05 10

50

100

150

Exceedance

120575P = 2

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 16 ∘C

Q(m

3sminus

1)

(b)

0 05 10

50

100

150

Exceedance

120575P = 11

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 20 ∘C

Q(m

3sminus

1)

(c)

0 05 10

50

100

150

Exceedance

120575P = 16

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 22 ∘C

Q(m

3sminus

1)

(d)

0 05 10

50

100

150

200

Exceedance

ΔT = 16 ∘C

120575P = 20

120575P = 20120575P = 0

120575P = 7

120575P = 40120575P = 60

Q(m

3sminus

1)

(e)

0 05 10

50

100

150

200

Exceedance

ΔT = 23 ∘C

120575P = 20

120575P = 20 120575P = 15120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(f)

0 05 10

50

100

150

200

Exceedance

ΔT = 33 ∘C

120575P = 20

120575P = 20 120575P = 19120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(g)

Exceedance0 05 1

0

50

100

150

200

ΔT = 46 ∘C

120575P = 20

120575P = 20 120575P = 24120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(h)






Rg

RsET

00

0000

02

02

02

04

04

04

06

06

06

08

08

08

10

10

10



120575P = 20

120575P = 20120575P = 0

120575P = 40120575P = 60


SCC (120575P = 4)

RCP45 2080sim2099

RCP85 2080sim2099


119904





5 Conclusions








119904 119877119892



119904and 119877

119892


119904 and 119877


as a result




Acknowledgments


References














































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


minus20

0

20

0

10

20

0

100

200

0

100

200

0

100

200

W D A

W D A

W D A

W D A

W D A

W D A

0

200

400

0

2

4

6RCP45

020406080

minus20

0

20

40

minus500

50100150

0

20

40

Wet Dry Annual0

20

40

0

2

4

6RCP85

020406080

minus20

0

20

40

minus500

50100150

0

20

40

Wet Dry Annual






0

20

40

120575P

()

T(∘

C)

1986sim2005 2020sim2039

2040sim2059

2060sim2079

2080sim2099

2020sim2039

2040sim2059

2060sim2079

2080sim2099

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)P

(mm

)

120575Q

()

120575Rs

()

120575Rg

()

120575ET

()

ΔT

(∘C)


119877119892











2














2020sim2039 2040sim2059 2060sim2079 2080sim2099

RCP45120575119875 () 40 20 110 160Δ119879 (∘C) 10 16 20 22120575119876 () 60 minus10 100 180

RCP85120575119875 () 70 150 190 240Δ119879 (∘C) 16 23 33 46120575119876 () 40 160 200 150






119904to Δ119879



119904for the dry





119892to Δ119879 and









60

80100

120140160

180


143

155

minus20 0 20 40 60

0246 20

3040

50607080

9061

67

minus20 0 20 40 60

0246

90120150

180210240

270205

222

minus20 0 20 40 60

0246

40

70

100130105

77

minus20 0 20 40 60

0246

1620

2428

32

25

42

minus20 0 20 40 60

0246

6090

120150131

119

minus20 0 20 40 60

0246

6080

100

120140

106

118

minus20 0 20 40 60

0246 20

40 60

8057

63

minus20 0 20 40 60

0246

80

120160200

163

180

minus20 0 20 40 60

0246

240280320

360

239

287

minus20 0 20 40 60

0246

30

35

40

30

39

minus20 0 20 40 60

0246

280

320360

400

269

329

minus20 0 20 40 60

0246

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)








119904 119877119892 and ET are 022


119904and 119877



119904and 119877

119892increase




0 05 10

50

100

150

Exceedance

120575P = 4

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 10 ∘C

Q(m

3sminus

1)

(a)

0 05 10

50

100

150

Exceedance

120575P = 2

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 16 ∘C

Q(m

3sminus

1)

(b)

0 05 10

50

100

150

Exceedance

120575P = 11

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 20 ∘C

Q(m

3sminus

1)

(c)

0 05 10

50

100

150

Exceedance

120575P = 16

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 22 ∘C

Q(m

3sminus

1)

(d)

0 05 10

50

100

150

200

Exceedance

ΔT = 16 ∘C

120575P = 20

120575P = 20120575P = 0

120575P = 7

120575P = 40120575P = 60

Q(m

3sminus

1)

(e)

0 05 10

50

100

150

200

Exceedance

ΔT = 23 ∘C

120575P = 20

120575P = 20 120575P = 15120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(f)

0 05 10

50

100

150

200

Exceedance

ΔT = 33 ∘C

120575P = 20

120575P = 20 120575P = 19120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(g)

Exceedance0 05 1

0

50

100

150

200

ΔT = 46 ∘C

120575P = 20

120575P = 20 120575P = 24120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(h)






Rg

RsET

00

0000

02

02

02

04

04

04

06

06

06

08

08

08

10

10

10



120575P = 20

120575P = 20120575P = 0

120575P = 40120575P = 60


SCC (120575P = 4)

RCP45 2080sim2099

RCP85 2080sim2099


119904





5 Conclusions








119904 119877119892



119904and 119877

119892


119904 and 119877


as a result




Acknowledgments


References














































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in




2














2020sim2039 2040sim2059 2060sim2079 2080sim2099

RCP45120575119875 () 40 20 110 160Δ119879 (∘C) 10 16 20 22120575119876 () 60 minus10 100 180

RCP85120575119875 () 70 150 190 240Δ119879 (∘C) 16 23 33 46120575119876 () 40 160 200 150






119904to Δ119879



119904for the dry





119892to Δ119879 and









60

80100

120140160

180


143

155

minus20 0 20 40 60

0246 20

3040

50607080

9061

67

minus20 0 20 40 60

0246

90120150

180210240

270205

222

minus20 0 20 40 60

0246

40

70

100130105

77

minus20 0 20 40 60

0246

1620

2428

32

25

42

minus20 0 20 40 60

0246

6090

120150131

119

minus20 0 20 40 60

0246

6080

100

120140

106

118

minus20 0 20 40 60

0246 20

40 60

8057

63

minus20 0 20 40 60

0246

80

120160200

163

180

minus20 0 20 40 60

0246

240280320

360

239

287

minus20 0 20 40 60

0246

30

35

40

30

39

minus20 0 20 40 60

0246

280

320360

400

269

329

minus20 0 20 40 60

0246

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)








119904 119877119892 and ET are 022


119904and 119877



119904and 119877

119892increase




0 05 10

50

100

150

Exceedance

120575P = 4

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 10 ∘C

Q(m

3sminus

1)

(a)

0 05 10

50

100

150

Exceedance

120575P = 2

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 16 ∘C

Q(m

3sminus

1)

(b)

0 05 10

50

100

150

Exceedance

120575P = 11

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 20 ∘C

Q(m

3sminus

1)

(c)

0 05 10

50

100

150

Exceedance

120575P = 16

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 22 ∘C

Q(m

3sminus

1)

(d)

0 05 10

50

100

150

200

Exceedance

ΔT = 16 ∘C

120575P = 20

120575P = 20120575P = 0

120575P = 7

120575P = 40120575P = 60

Q(m

3sminus

1)

(e)

0 05 10

50

100

150

200

Exceedance

ΔT = 23 ∘C

120575P = 20

120575P = 20 120575P = 15120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(f)

0 05 10

50

100

150

200

Exceedance

ΔT = 33 ∘C

120575P = 20

120575P = 20 120575P = 19120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(g)

Exceedance0 05 1

0

50

100

150

200

ΔT = 46 ∘C

120575P = 20

120575P = 20 120575P = 24120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(h)






Rg

RsET

00

0000

02

02

02

04

04

04

06

06

06

08

08

08

10

10

10



120575P = 20

120575P = 20120575P = 0

120575P = 40120575P = 60


SCC (120575P = 4)

RCP45 2080sim2099

RCP85 2080sim2099


119904





5 Conclusions








119904 119877119892



119904and 119877

119892


119904 and 119877


as a result




Acknowledgments


References














































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


60

80100

120140160

180


143

155

minus20 0 20 40 60

0246 20

3040

50607080

9061

67

minus20 0 20 40 60

0246

90120150

180210240

270205

222

minus20 0 20 40 60

0246

40

70

100130105

77

minus20 0 20 40 60

0246

1620

2428

32

25

42

minus20 0 20 40 60

0246

6090

120150131

119

minus20 0 20 40 60

0246

6080

100

120140

106

118

minus20 0 20 40 60

0246 20

40 60

8057

63

minus20 0 20 40 60

0246

80

120160200

163

180

minus20 0 20 40 60

0246

240280320

360

239

287

minus20 0 20 40 60

0246

30

35

40

30

39

minus20 0 20 40 60

0246

280

320360

400

269

329

minus20 0 20 40 60

0246

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔP () ΔP () ΔP ()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

ΔT

()

Rs

(mm

)Rg

(mm

)E T

(mm

)Q

(mm

)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)

ΔT

(∘C)








119904 119877119892 and ET are 022


119904and 119877



119904and 119877

119892increase




0 05 10

50

100

150

Exceedance

120575P = 4

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 10 ∘C

Q(m

3sminus

1)

(a)

0 05 10

50

100

150

Exceedance

120575P = 2

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 16 ∘C

Q(m

3sminus

1)

(b)

0 05 10

50

100

150

Exceedance

120575P = 11

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 20 ∘C

Q(m

3sminus

1)

(c)

0 05 10

50

100

150

Exceedance

120575P = 16

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 22 ∘C

Q(m

3sminus

1)

(d)

0 05 10

50

100

150

200

Exceedance

ΔT = 16 ∘C

120575P = 20

120575P = 20120575P = 0

120575P = 7

120575P = 40120575P = 60

Q(m

3sminus

1)

(e)

0 05 10

50

100

150

200

Exceedance

ΔT = 23 ∘C

120575P = 20

120575P = 20 120575P = 15120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(f)

0 05 10

50

100

150

200

Exceedance

ΔT = 33 ∘C

120575P = 20

120575P = 20 120575P = 19120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(g)

Exceedance0 05 1

0

50

100

150

200

ΔT = 46 ∘C

120575P = 20

120575P = 20 120575P = 24120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(h)






Rg

RsET

00

0000

02

02

02

04

04

04

06

06

06

08

08

08

10

10

10



120575P = 20

120575P = 20120575P = 0

120575P = 40120575P = 60


SCC (120575P = 4)

RCP45 2080sim2099

RCP85 2080sim2099


119904





5 Conclusions








119904 119877119892



119904and 119877

119892


119904 and 119877


as a result




Acknowledgments


References














































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


0 05 10

50

100

150

Exceedance

120575P = 4

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 10 ∘C

Q(m

3sminus

1)

(a)

0 05 10

50

100

150

Exceedance

120575P = 2

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 16 ∘C

Q(m

3sminus

1)

(b)

0 05 10

50

100

150

Exceedance

120575P = 11

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 20 ∘C

Q(m

3sminus

1)

(c)

0 05 10

50

100

150

Exceedance

120575P = 16

ΔT = 0 ∘CΔT = 2 ∘CΔT = 4 ∘C

ΔT = 6 ∘CΔT = 22 ∘C

Q(m

3sminus

1)

(d)

0 05 10

50

100

150

200

Exceedance

ΔT = 16 ∘C

120575P = 20

120575P = 20120575P = 0

120575P = 7

120575P = 40120575P = 60

Q(m

3sminus

1)

(e)

0 05 10

50

100

150

200

Exceedance

ΔT = 23 ∘C

120575P = 20

120575P = 20 120575P = 15120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(f)

0 05 10

50

100

150

200

Exceedance

ΔT = 33 ∘C

120575P = 20

120575P = 20 120575P = 19120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(g)

Exceedance0 05 1

0

50

100

150

200

ΔT = 46 ∘C

120575P = 20

120575P = 20 120575P = 24120575P = 0

120575P = 40120575P = 60

Q(m

3sminus

1)

(h)






Rg

RsET

00

0000

02

02

02

04

04

04

06

06

06

08

08

08

10

10

10



120575P = 20

120575P = 20120575P = 0

120575P = 40120575P = 60


SCC (120575P = 4)

RCP45 2080sim2099

RCP85 2080sim2099


119904





5 Conclusions








119904 119877119892



119904and 119877

119892


119904 and 119877


as a result




Acknowledgments


References














































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


Rg

RsET

00

0000

02

02

02

04

04

04

06

06

06

08

08

08

10

10

10



120575P = 20

120575P = 20120575P = 0

120575P = 40120575P = 60


SCC (120575P = 4)

RCP45 2080sim2099

RCP85 2080sim2099


119904





5 Conclusions








119904 119877119892



119904and 119877

119892


119904 and 119877


as a result




Acknowledgments


References














































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in












































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in










Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

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