Journal of Hydrology 505 (2013) 188–201

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Journal of Hydrology

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The impact of climate change on runoff in the southeastern TibetanPlateau

0022-1694/$ - see front matter � 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.jhydrol.2013.09.052

⇑ Corresponding author. Tel.: +61 (2)62465761; fax: +61 (2)62465800.E-mail address: yongqiang.zhang@csiro.au (Y. Zhang).

Fapeng Li a,b,c, Yongqiang Zhang b,⇑, Zongxue Xu a, Jin Teng b, Changming Liu d, Wenfeng Liu a,Freddie Mpelasoka b

a Key Laboratory of Water and Sediment Sciences, Ministry of Education, College of Water Sciences, Beijing Normal University, Beijing 100875, Chinab CSIRO Land and Water, GPO BOX 1666, Canberra, ACT 2601, Australiac Development Research Center, Ministry of Water Resources, Beijing 100038, Chinad Key Laboratory of Water Cycle and Related Land Surface Process, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing100101, China

a r t i c l e i n f o

Article history:Received 29 October 2012Received in revised form 23 May 2013Accepted 30 September 2013Available online 9 October 2013This manuscript was handled byKonstantine P. Georgakakos, Editor-in-Chief,with the assistance of David J. Gochis,Associate Editor

Keywords:Climate changeImpactsPrecipitationRunoffYarlung Tsangpo River basinTibetan Plateau

s u m m a r y

The Tibetan Plateau (TP) is the ‘‘water tower of Asia’’ and strongly influences both the hydrology and cli-mate of southern and eastern Asia. Exploring the impact of climate change on the runoff of TP rivers iscritical to improve water resources management. However, thorough studies on the runoff response toclimate change are seldom conducted on large TP river systems. To complement the current body ofwork, this study uses two rainfall–runoff models (SIMHYD and GR4J) to simulate the monthly and annualrunoff across the Yarlung Tsangpo River (YTR) basin in the southeastern TP (i.e., upstream of the Brah-maputra) under historical (1962–2002) and future (up to approximately 2030) climate conditions. Thefuture climate series are obtained by using 20 Global Climate Models (GCMs) outputs from the Intergov-ernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to reflect a 1 �C increase inthe global average surface air temperature. The two rainfall–runoff models successfully simulate the his-torical runoff for the eight catchments in the YTR basins with median monthly runoff Nash–Sutcliffe Effi-ciencies (NSE) of 0.86 for SIMHYD and 0.83 for GR4J. The mean annual future precipitation and runoffacross the region are projected to increase by most of the modeling results. The mean annual precipita-tion changes obtained from the 20 GCMs are �15%, 7% and 16% for the 10th percentile, median and 90thpercentile of GCM outputs, respectively, and the corresponding changes in the simulated mean annualrunoffs are �24%, 13% and 29% for the SIMHYD model outputs and �22%, 11% and 26% for the GR4J modeloutputs. The projected increase in the runoff at the median percentile mainly occurs in the middlereaches of the YTR and its two tributaries, the Lhasa River and the Nyangqu River, with a 12% increasein annual runoff that mainly occurs in the wet season from May to September. The present work is thefirst comprehensive study on the hydrological response to climate change covering the entire upstreamarea of the Brahmaputra, and the results found in this study are not only helpful for local water resourcemanagement but also for the lower reaches of the Brahmaputra.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

The Tibetan Plateau (TP) is the highest plateau in the world andis known as the ‘‘water tower of Asia’’ and the ‘‘third pole of theearth’’. With its distinctive topographical and landscape features,the TP shows unique energy and hydrological cycles and stronglyinfluences on the Asian monsoon, the East Asian atmospheric cir-culation patterns and even global climate change.

Many reports show widespread climatic changes in the TP indi-cated by long-term changes in the air temperature, precipitation,

wind speed, solar radiation and land surface parameters. The airtemperature showed a significant increase from 1960 to 2007,the precipitation increased for most areas in the TP (Wang et al.,2008; Xu et al., 2008), and the wind speed decreased, which isassociated with the steady decline of the East Asian Monsoonwinds (Xu et al., 2006; Zhang et al., 2007). A pan evaporation par-adox is reported with a reference evapotranspiration decrease andpan evaporation increase (Liu et al., 2011; Xie and Zhu, in press;Zhang et al., 2007). As a result of aerosol and cloud changes, thesurface solar radiation transitioned from brightening to dimmingsince the 1980s over the TP (Lu et al., 2012; Tang et al., 2011). Someland surface parameters show certain trends over the last 19 years.For instance, the land surface temperature shows a clear warmingtrend, while the Normalized Difference Vegetation Index (NDVI)

F. Li et al. / Journal of Hydrology 505 (2013) 188–201 189

shows an upward trend and the albedo a downward trend from1982 to 2000 (Zhong et al., 2011). Similarly, the environmentalconditions on the TP show noticeable changes, indicated by perma-frost degradation (Cheng and Wu, 2007; Guo et al., 2012), glacierretreat (Kang et al., 2010), desertification (Yang et al., 2004), andland use changes (Li et al., 2013; Liu et al., 2012).

The major river systems of the TP are expected to be very vul-nerable to climate change and variations in the environmental con-ditions (Immerzeel et al., 2010). Future changes in the streamflowand watershed hydrology caused by climate change have becomeincreasingly important to water resources management on theTP. Moiwo et al. (2011) reported an overall negative water storagechange on the TP using the Gravity Recovery and Climate Experi-ment (GRACE) data. Future projections on a global scale show largeincreases in the runoff for the rivers originating on the TP, such asthe Yangtze, Mekong, Yellow, Indus and Brahmaputra, and in-creases in the maximum discharges for these rivers (Sperna Wei-land et al., 2012). Regional climate change impact studies of thelarge rivers originating on the TP also indicated certain short-termbeneficial effects on the downstream water availability corre-sponding to winter snow cover reduction and glacier retreat inthe headwater areas (Cao et al., 2011; Eastham et al., 2008; Gainet al., 2011; Immerzeel et al., 2009; Laghari et al., 2012; Singhet al., 2010; Xu et al., 2011; Zhang et al., 2012). Observed and pro-jected climate warming will accelerate the hydrological dynamicalongside the snow cover reduction and glacier retreat over theTP, and increased water availability downstream has already beenobserved. However, previous studies have mainly focused on thedownstream beneficial attributes in large river systems or in sev-eral small upstream sub-catchments, the studies have seldom ad-dressed the climate change impacts on the entire upstream areasof the large TP river systems, particularly the data-sparse upstreamarea of the Brahmaputra. To complement these studies, this studyapplies two lumped rainfall–runoff models, SIMHYD and GR4J, tosimulate the runoff for each grid cell at a 0.5� resolution in the Yar-lung Tsangpo River basin on the TP (the entire headwater area ofthe Brahmaputra) and investigates the climate change impactson the runoff given a 1 �C increase in the global average surfaceair temperature.

Global Climate Models (GCMs) are the primary tools availableused to simulate the present climate and projected future climatefor a given greenhouse emission scenario. However, the GCMs typ-ically have coarse resolutions (1–2.5�) and are difficult to apply tohydrology and water resource studies at sub-grid scales. Statisticaldownscaling approaches have emerged as a means of transformingfrom a coarse resolution to a fine resolution for catchment model-ing (Fowler et al., 2007; Xu, 1999). Large uncertainties are gener-ally expected in climate predictions from GCMs, and thedisagreements in regional climate changes between differentGCMs represent the largest source of uncertainty (Chen et al.,2011). Therefore, to investigate the uncertainties from severalGCMs, multiple GCMs are usually used simultaneously to illustratethe ensemble scenarios for impact studies.

In this study, the links between the anticipated climate changeand the watershed-scale hydrological processes are studied to bet-ter understand the climate change impacts on the upstream waterresources in the Brahmaputra in the southeast TP; the A1B scenar-ios from 20 GCMs in the Special Report on Emissions Scenarios(SRES) from the Fourth Assessment Report (AR4) of the Intergov-ernmental Panel on Climate Change (IPCC) were examined. Twoconceptual rainfall–runoff models incorporated with snowmeltprocesses are applied to each 0.5� grid cell to model the historicaland future monthly runoff. The main objectives of this study are (1)to construct future climate projections using one empirical scalingmethod informed by 20 GCMs’ output; and (2) to explore therunoff response to future climate projections over the study area.

Section 2 presents the study area and various data souces used inthis study; Section 3 describes the methodology for the hydrolog-ical modeling and construction of the climate projections; Section 4presents the modeled climate change impacts on runoff; and thediscussions and conclusions are summarized in Sections 5 and 6.

2. Study area and data

2.1. Study area

The TP is approximately 2.5 million km2 in area and coversmost of the Tibet Autonomous Region and Qinghai Province in Chi-na and part of Ladakh in Jammu and Kashmir. The TP is home to thelargest freshwater reservoir apart from the North and South Polesand feed water into Asia’s major rivers, which supply water to overa billion people. The Yarlung Tsangpo River (YTR) in China, up-stream of the Brahmaputra, originates from the Gyima YangzoinGlacier in south-central Tibet and is approximately 2229 km long.The YTR basin is approximately 0.2 million km2 and the basin’slocation is shown in Fig. 1. The YTR basin is one of the highest ma-jor river basins in the world and is approximately 4500 m abovesea level (Guan et al., 1984). The YTR basin is part of the IndianOcean water system and is characterized by intense solar radiation,low air temperature and large daily temperature variations but lowyearly temperature fluctuations with a clear division between thedry (from October to April) and wet (from May to September) sea-sons (Yang and Zheng, 2004; You et al., 2007). The precipitationand land cover exhibit clear climate variations from upstream todownstream caused by the topographical features of the TP. Theupstream region is located in the Frigid Zone with less than300 mm of annual precipitation and is mainly covered by alpinemeadow. The midstream YLN region is mainly covered by alpinegrassland with 300–600 mm annual precipitation. The down-stream region is located in the subtropical humid zone and ismainly covered by alpine forest with >2000 mm annualprecipitation.

Two regions in the YTR basin that have unique economic orhydrological features are also highlighted in Fig. 1. The first region,which includes the middle reaches of the YTR and its two tributar-ies (the Lhasa River and the Nyangqu River), noted as the YLN re-gion in the following sections, is the center of politics, economy,and culture for the Tibet Autonomous Region. There has been a ra-pid population growth in this region in recent years that has re-sulted in increased human activities in agriculture (crops andlivestock), industry, and transportation development (Dong et al.,1995; Shen et al., 2012). The second region is the Yarlung TsangpoGrand canyon, noted as the Grand canyon region in the followingsections, which is the deepest and largest canyon in the world. Af-fected by the Indian monsoon with its abundant moisture and hightemperature, this region’s climate ranges from subtropical to arcticand has a distinctive flora and fauna that have been barely ex-plored and affected by human influence (Guan et al., 1984).

2.2. Climate data

This study uses a 0.5� resolution gridded daily time series over41 years (1962–2002) as the observed historical climate dataset.The source of this dataset is the National Climate Center of ChinaMeteorological Administration (NCC-CMA) (http://ncc.cma.-gov.cn). The dataset provides surfaces of daily precipitation andmean and maximum air temperatures interpolated from meteoro-logical stations while considering orographic effects. A detaileddescription of the spatial interpolation can be found in Xie et al.(2007) and Xu et al. (2009). The potential evapotranspiration(ETp) is calculated from mean air temperature using the

Fig. 1. Study area and locations of the calibration catchments (YLN region: the middle reaches of the Yarlung Tsangpo River and its two tributaries (the Lhasa River and theNyangqu River)).

190 F. Li et al. / Journal of Hydrology 505 (2013) 188–201

Blaney–Criddle formula (Allen and Pruitt, 1986). The ETp used hereis conceptually the upper limit of the actual evaporation in thehydrological models.

The future climate data are obtained from 20 GCMs in theFourth Assessment Report (AR4) of the Intergovernmental Panelon Climate Change (IPCC). The GCM outputs are obtained fromthe World Climate Research Program’s (WCRP’s) phase 3 of theCoupled Model Intercomparison Project (CMIP3) archived at theProgram for Climate Model Diagnosis and Intercomparison(PCMDI) (http://www-pcmdi.llnl.gov). The detailed informationabout the 20 GCMs is listed in Table 1, and they are described fur-ther on the PCMDI Web site (http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php). The dailyGCM outputs for the twentieth and twenty-first centuries drivenby the Special Report on Emissions Scenarios (SRES) A1B scenarioare used to obtain the long-term trend for pattern scaling (Sec-tion 3.3). The daily precipitation series used for scaling the dailyprecipitation distribution are obtained from two 20-year periodGCM outputs: (1) 1981–2000 in the twentieth century (1980–1999 for BCCR-BCM and NCAR-PCM) and (2) 2046–2065 in thetwenty-first century under the A1B scenario (Section 3.3).

2.3. Streamflow data

The streamflow data used in the model calibration are obtainedfor eight catchments that together cover approximately 65% of thestudy area (Fig. 1). The streamflow data cover the period from the1960s to 2000/2002 and are sourced from the Hydrology andWater Resource Survey (HWRS) in the Tibetan Autonomous Region(TAR) of China (Table 2). Note that only the monthly streamflowdata for the eight catchments are provided. Six of the eight catch-ments are unregulated, while the Shigatse and Gyantse catchmentsare mainly affected by irrigation during the crop growing season.The streamflow data we used in the calibration for these twocatchments are considered to be natural flows that are estimatedby adding the irrigation volume into the actual flows by the HWRS,TAR.

3. Methodology

3.1. Hydrological models and model calibration

The hydrological models used in this study are SIMHYD andGR4J; both are used extensively in studies of the climate changeimpact on runoff and regionalization (Oudin et al., 2008; Zhang

and Chiew, 2009). The SIMHYD model used here contains nineparameters and contains three storages for interception loss, soilmoisture and groundwater and the routing process (Chiew et al.,2002). The GR4J model is a four-parameter parsimonious rain-fall–runoff model (Perrin et al., 2003). Considering the snowfalland snowmelt processes in the study area, a relatively simple tem-perature index-based snowfall/snowmelt module used in theSWAT (Soil and Water Assessment Tool) is incorporated into theSIMHYD and GR4J models. A detailed description of the snowmeltmodule can be found in Neitsch et al. (2011). The parameters forthe snowmelt module built into the modified rainfall–runoff mod-els were not calibrated. Fig. 2 illustrates the structure of the twomodels (the snowmelt module is not shown here). The modelparameters and algorithms that describe the water movement inand out of the storages are also shown in the figure.

The SIMHYD and GR4J models are first calibrated against theobserved streamflow data and are then driven by the historicaland future climate data with the same optimized parameter valuesto model the historical and future runoff. The simulated future andhistorical runoff are then compared to estimate the climate changeimpacts on the runoff (Section 4).

The simulated daily runoff values are first aggregated to amonthly scale, and then the model is calibrated and validatedagainst the monthly-observed streamflow data with first one yearfor model warm up. The calibration and validation periods areshown in Table 2. For each catchment, approximately two-thirdsof the observed streamflow data are used to calibrate the modeland the rest are used to validate the model. In the model calibra-tion, the hydrological model parameters are optimized using theParticle Swarm Optimization (PSO) toolbox in MATLAB to mini-mize the Nash–Sutcliffe Efficiency (NSE)-bias objective function,which is a weighted combination of the NSE and a logarithmic biasfunction given by

OBJ ¼ ð1� NSEÞ þ 5� j lnð1þ BÞj2:5 ð1Þ

where NSE is the Nash–Sutcliffe Efficiency of the monthly stream-flow (Nash and Sutcliffe, 1970), B is the bias (the sum of themonthly modeled error divided by the sum of the observed monthlystreamflow), and the coefficients of this equation control the sever-ity and shape of the bias constraint penalty, which help to bettercalibrate the model (Gupta et al., 2009; Viney et al., 2009). TheNSE is commonly used in hydrological modeling to measure theagreement between the modeled and observed streamflow values,with NSE = 1.0 indicating a perfect agreement between the modeledand observed streamflow. The bias, B, is included in the objective

Table 1List of the 20 GCMs used in this study.

Code Abbreviation Origination group(s) Country CMIP3 ID Resolution(�degree)

1 MPI-ECHAM5

Max Planck Institute for Meteorology (MPI) Germany ECHAM5/MPI Ocean Model (MPI-OM) 1.9 � 1.9

2 GISS-ER National Aeronautics and Space Administration (NASA)/Goddard Institutefor Space Studies (GISS)

UnitedStates

GISS ModelE-R (GISS-ER) 5 � 4

3 IPSL Institut Pierre Simon Laplace (IPSL) France IPSL Climate Model version 4.0 (IPSL-CM4)

3.8 � 2.5

4 IAP State Key Laboratory of Numerical Modeling for Atmospheric Sciences andGeophysical Fluid Dynamics (LASG)/Institute of Atmospheric Physics (IAP)

China Flexible Global Ocean–Atmosphere-Land System Model gridpoint version1.0 (FGOALS-g1.0)

2.8 � 2.8

5 CSIRO-MK3.0

CSIRO Atmospheric Research Australia CSIRO Mark version 3.0 (CSIRO-Mk3.0) 1.9 � 1.9

6 INGV Instituto Nazionale di Geofisica e Vulcanologia (INGV) Italy INGV SINTEX-G (INGV-SXG) 1.1 � 1.17 CSIRO-

MK3.5CSIRO Atmospheric Research Australia CSIRO-Mk3.5 1.9 � 1.9

8 CCCMA-T63 Canadian Centre for Climate Modelling and Analysis (CCCma) Canada CGCM3.1 (T63) 2.8 � 2.89 CCCMA-T47 Canadian Centre for Climate Modelling and Analysis (CCCma) Canada CCCma Coupled General Circulation

Model version 3.1 [CGCM3.1 (T47)]3.8 � 3.7

10 BCCR-BCM Bjerknes Centre for Climate Research (BCCR) Norway BCCR-BCM2.0 2.8 � 2.811 MIUB Meteorological Institute of the University of Bonn (MIUB), Meteorological

Research Institute of the Korean Meteorological Administration (KMA),and Model and Data group

Germany/Korea

ECHAM and the global Hamburg OceanPrimitive Equation (ECHO-G)

3.8 � 3.7

12 GISS-AOM National Aeronautics and Space Administration (NASA)/Goddard Institutefor Space Studies (GISS)

UnitedStates

GISS Atmosphere–Ocean Model (GISS-AOM)

4 � 3

13 NCAR-PCM National Center for Atmospheric Research (NCAR) UnitedStates

Parallel Climate Model (PCM) 2.8 � 2.8

14 CNRM Météo-France/Centre National de Recherches Météorologiques (CNRM) France CNRM Coupled Global Climate Modelversion 3 (CNRM-CM3)

2.8 � 2.8

15 GFDL 2.1 US Department of Commerce/National Oceanic and AtmosphericAdministration (NOAA)/Geophysical Fluid Dynamics Laboratory (GFDL)

UnitedStates

GFDL-CM2.1 2.5 � 2

16 MRI Meteorological Research Institute (MRI) Japan MRI-CGCM2.3.2 2.8 � 2.817 INMCM Institute for Numerical Mathematics (INM) Russia INM Climate Model version 3.0 (INM-

CM3.0)5 � 4

18 MIROC-M Center for Climate System Research (The University of Tokyo), NationalInstitute for Environmental Studies, and Frontier Research Center forGlobal Change [Japan Agency for Marine-Earth Science and Technology(JAMSTEC)]

Japan MIROC 3.2, medium-resolution version[MIROC 3.2 (medres)]

2.8 � 2.8

19 GFDL 2.0 US Department of Commerce/National Oceanic and AtmosphericAdministration (NOAA)/Geophysical Fluid Dynamics Laboratory (GFDL)

UnitedStates

GFDL Climate Model version 2.0 (GFDL-CM2.0)

2.5 � 2

20 MIROC-H Center for Climate System Research (The University of Tokyo), NationalInstitute for Environmental Studies, and Frontier Research Center forGlobal Change [Japan Agency for Marine-Earth Science and Technology(JAMSTEC)]

Japan Model for Interdisciplinary Research onClimate 3.2, high-resolution [MIROC 3.2(hires)]

1.1 � 1.1

F. Li et al. / Journal of Hydrology 505 (2013) 188–201 191

function to ensure that the total modeled streamflow is similar tothe total observed streamflow, with B = 0 indicating a perfect agree-ment or a canceling of the positive and negative errors.

3.2. Runoff modeling

For each catchment, the two rainfall–runoff models are cali-brated for all of the 0.5� grid cells within the catchment againstthe observed streamflow data at the catchment outlet (Fig. 1). Usingthe 0.5� grid cells allows better representations of the spatial pat-terns and gradients in the precipitation. The optimized parametervalues are identical for all grid cells within a calibration catchment.The runoff for grid cells that are not within a calibration catchmentis modeled using optimized parameter values from the geographi-cally closest grid cell that lies within a calibration catchment.

Many studies show that using spatial proximity to transfermodel parameters from a geographically nearest catchment to atarget ungauged catchment is easily applied and performs reason-ably well compared to other regionalization approaches, such asregression and physical similarity approaches (Oudin et al., 2008;Zhang and Chiew, 2009). A similar result is obtained from a region-alization study in the southeastern TP (Li et al., submitted forpublicaion).

3.3. Climate projections for future runoff simulations

Daily series of precipitation, ETp and mean and maximum airtemperatures are required to drive the adapted SIMHYD andGR4J rainfall–runoff models with the snowmelt module. The meth-odology used in this study to derive the future climate data, pat-tern scaling, is similar to the one used in southeast Australia(Chiew et al., 2009; Teng et al., 2012). The approach has beenwidely used in climate change scenario generation for hydrologicalimpact studies (Mitchell, 2003; Mpelasoka and Chiew, 2009; Tenget al., 2012), and the process involves standardizing climate changepatterns (changes per �C of mean global warming) for the variablesderived from the GCM(s) over a geographic region and conformingthese patterns to the observed climate time series. This method isrobust and relatively simple to implement and can be used forhydrological impact assessment studies over very large regions,particularly when the main targets are changes in the seasonaland annual catchment water yield (Chiew et al., 2009; Vaze andTeng, 2011; Vaze et al., 2011).

Twenty future climate variants, each with 42 years of daily pre-cipitation, ETp and mean and maximum air temperatures data, aredeveloped by scaling the observed historical daily precipitation,ETp and mean and maximum air temperatures sequences for the

Table 2Summary of the model calibration and validation.

Area km2 Calibration period Validation period NSE-calibration NSE-validation B-calibration B-validation

SIMHYD GR4J SIMHYD GR4J SIMHYD GR4J SIMHYD GR4J

1 Pondo 15,518 1976–1994 1995–2002 0.92 0.88 0.87 0.87 �0.03 �0.04 �0.12 �0.142 Tangya 3761 1991–1998 1999–2002 0.88 0.89 0.86 0.88 �0.05 �0.01 0.04 0.103 Lhasa 6322 1968–1989 1990–2002 0.87 0.83 0.86 0.84 �0.01 0.02 �0.09 �0.064 Nugesha 43,916 1991–1997 1998–2000 0.95 0.93 0.86 0.88 �0.02 �0.01 0.01 �0.045 Yangcun 20,004 1970–1990 1991–2000 0.75 0.77 0.79 0.81 �0.08 0.03 �0.25 �0.146 Lhatse 49,739 1983–1993 1994–2000 0.76 0.41 0.74 0.24 �0.04 �0.10 �0.15 �0.447 Shigatse 4779 1980–1994 1995–2001 0.73 0.50 0.86 0.58 �0.02 �0.12 �0.02 �0.098 Gyantse 9580 1972–1993 1994–2002 0.89 0.70 0.90 0.79 �0.01 �0.09 �0.08 �0.25

Median 12,594 – – 0.87 0.80 0.86 0.83 �0.03 �0.02 �0.09 �0.12

Fig. 2. Model structures of the SIMHYD and the GR4J.

192 F. Li et al. / Journal of Hydrology 505 (2013) 188–201

0.5� grid cells based on the changes simulated by the 20 GCMs for1 �C global warming. The dataset of 1 �C global warming is derivedfrom the median of the projected increase in the global averagesurface air temperature by 2030 relative to 1980–1999 under theSRES A1B scenario (IPCC, 2007). The SRES A1B scenario describes

a future world of very rapid economic growth, a global populationthat peaks mid-century and declines thereafter, and a rapid intro-duction of new and more efficient technologies (IPCC, 2007).

The seasonal pattern scaling is used to scale the historical cli-mate series (precipitation, ETp and mean and maximum air tem-

F. Li et al. / Journal of Hydrology 505 (2013) 188–201 193

peratures) to account for changes in each of the four seasons. Thefour seasons are defined based on the local climate: March–Mayas spring, June–August as summer, September–November as au-tumn, and December–February as winter. The pattern scalingdecouples the model’s response from the greenhouse gas emis-sions scenarios by fitting a linear regression through the localGCM variables against the global average temperature simulatedfrom the same GCM, e.g., the ‘‘change per degree global warming’’(Chiew et al., 2009; Whetton et al., 2000). The ‘‘change per �C glo-bal warming’’ can be rescaled by a given amount of global warmingto produce a pattern of change that would apply for a given futuredate and global warming scenario.

To reflect the changes in the daily precipitation distribution, adaily scaling is used in addition to the seasonal pattern scaling toobtain the future precipitation series. The daily scaling methodhas been described in detail in Chiew et al. (2009). This methodconsiders the changes in the daily distribution in addition to thechanges in the seasonal means. This characteristic is importantfor precipitation (and runoff) because many GCMs indicate thatthe future extreme precipitation is likely to be more intense, evenin some regions where projections indicate a decrease in the meanseasonal or annual precipitation. As the high precipitation eventsgenerate large runoff, using pattern scaling alone that assumesthe entire precipitation distribution will change in the same waywould lead to an underestimation of the extreme runoff as wellas the mean annual runoff.

As the future climate series are obtained by scaling the histori-cal daily climate series from 1962 to 2002, the daily climate seriesfor the historical and future climate have the same data duration(42 years) and the same sequence of daily climate. Therefore, thefuture climate scenario is not a climate forecast for 2030 but a42-year daily climate series based on the 1962–2002 data se-quence for a 1 �C increase in the global average surface airtemperature.

The 20 variants of the 42-year future daily precipitation, ETpand mean and maximum air temperatures series, each informedby a different GCM, are used to run the SIMHYD and the GR4J rain-fall–runoff models to estimate the future runoff for all of the 0.5�grid cells across the YTR basin. The 42 years of modeled future run-off is then compared with the 42 years of modeled historical runoffto assess the future climate impacts on runoff.

Table 3Percentage change in the future precipitation and runoff over the study area.

Historical(mm)

Future percentage change informed by20 GCMs (%)

10thPercentile

Median 90thPercentile

Precipitation 663 �15.1 7.0 15.9Runoff

(SIMHYD)439 �23.7 13.0 29.0

Runoff (GR4J) 429 �21.6 10.8 26.0

4. Results and analysis

4.1. Hydrological simulation

The SIMHYD and GR4J rainfall–runoff models are well cali-brated as indicated by a high NSE of the monthly runoff and alow bias (B). The median calibrated NSE and B values are 0.87and �0.03, respectively, for SIMHYD and are 0.80 and �0.02,respectively, for GR4J. Compared with the calibration results, thevalidation results for the two models are not noticeably degraded.The median validated NSE and B values are 0.86 and �0.09, respec-tively, for SIMHYD and are 0.83 and �0.12, respectively, for GR4J.In the eight catchments, the SIMHYD model slightly outperforms

Fig. 3. Spatial distributions of the historical annual precipit

the GR4J. In particular, the SIMHYD model clearly outperformsthe GR4J model in Lhatse, Shigatse and Gyantse for both the cali-bration and validation. A further analysis shows that the SIMHYDmodel performs better than the GR4J model for a continuous lowflow simulation, which explains the better performance of the SIM-HYD model. The GR4J model obtains a poor validation result inLhatse because severe drought occurred during the validation per-iod. Previous studies have suggested that obtaining satisfactoryvalidation results for a dry period using parameters optimized ina wet period is difficult (Vaze et al., 2010; Zhang and Chiew,2009). To overcome this difficulty, instead of using two-thirds ofthe observed streamflow data, we use all available streamflow datato re-calibrate the models and the optimized parameters are usedin the observed runoff dataset for the calibration and regionaliza-tion studies as shown in the following section.

Fig. 3 shows the spatial distribution of the mean annual andseasonal precipitation and the modeled runoff for the SIMHYDand the GR4J models from 1962 to 2002 for the YTR basin. Thereis a clear southeast–northwest precipitation gradient across the re-gion, where the highest mean annual precipitation is greater than1000 mm in the southeast and the lowest is less than 400 mm inthe northwest. The runoff gradient is much more pronounced thanthe precipitation gradient, with a mean annual runoff greater than800 mm in the southeast being much higher than elsewhere in theregion.

Fig. 3 also shows similar spatial distributions for the annualrunoff estimated by the SIMHYD and the GR4J models. Averagedacross the region, the mean annual precipitation for 1962–2002is 663 mm, and the mean annual runoff values estimated by theSIMHYD and GR4J models are 439 mm and 429 mm, respectively(Table 3). The difference in the annual runoff obtained from thetwo models is less than 3%, which indicates a strong agreement be-tween them. Over 60% of the precipitation occurs during summer,and the corresponding summer runoff ratio is approximately 57%for SIMHYD and approximately 54% for GR4J. The runoff amountssimulated using the two rainfall–runoff models are very similaras indicated by a coefficient of determination (R2) of 0.97 for themean annual runoff and 0.98, 0.91, 0.93 and 0.87 for the four sea-sons (Fig. 4). Considering the similarity of the two sets of modelingresults, only the results obtained from SIMHYD are shown in thefollowing sections.

ation and runoff for the SIMHYD and the GR4J models.

Fig. 4. Comparison of the historical runoff estimated by the SIMHYD and the GR4J models (red line is the 1:1 line). (For interpretation of the references to colour in this figurelegend, the reader is referred to the web version of this article.)

194 F. Li et al. / Journal of Hydrology 505 (2013) 188–201

4.2. Future precipitation

Fig. 5 shows the spatial distribution of the percentage change inthe mean annual precipitation based on the climate change projec-tions from the 20 GCMs for 1 �C global warming. The 20 GCMs areranked from left to right and from top to bottom based on the fu-ture mean annual precipitation (averaged across the region) withthe driest GCM in the top-left panel and the wettest GCM in thebottom-right panel. The same presentation is used for the runoffresults in Fig. 9.

A large difference is observed in the spatial patterns of the per-centage change in the future annual precipitation projected by the20 GCMs (Fig. 5). Approximately 11 GCMs project increases overthe entire study area, but several GCMs show different variationpatterns for different areas in the YTR basin. MPI-ECHAM5 and IPSLproject decreases over the entire YTR basin, while GISS-ER projectsdecreases only in the eastern and western parts. IAP projects aslight decrease in the future mean annual precipitation with a per-centage change of �5% to 2%. CSIRO-MK30 and INGV project smallvariations in most of the YTR basin with percentage changes of�2% to 2%. BCCR-BCM projects a considerable increase (>20%) inthe western part but small variations (�5% to 2%) in the centraland eastern parts of the study area. Interestingly, CNRM projectsan opposite tendency to that from BCCR-BCM. MIROC-H projectsthe largest precipitation increase over the YTR basin, but the in-crease mainly occurs in the western part with percentage changesof >30% and there is a >�5% precipitation decrease in the easternpart of the region.

The median, 10th and 90th percentiles of the percentage changein the mean annual precipitation are calculated for each grid cell,and the results are shown in Fig. 6. The median estimates show0–5% increases in the northwest, central and western parts of thestudy area and 5–10% increases in the Shigatse and Gyantse catch-ments and downstream of the Lhatse catchment. However, the10th and 90th percentile percentage changes in the annual precip-itation show different variation trends in the eastern and western

parts. In the western part, the 10th and 90th percentile percentagechanges are �5% to �10% and 20–35%, respectively, while they are<�15% and 5–15%, respectively, in the eastern part.

Considerable differences arise in the future mean annual pre-cipitation projections from the 20 GCMs with the values varyingfrom �24% to 43% when averaged across the whole region (Table 3and Fig. 7). Nevertheless, 80% of the projections show increases inthe mean annual precipitation (16 out of 20, Figs. 5 and 7). Percent-age changes in the mean seasonal precipitation also show similarpatterns to that of the annual precipitation. Averaged across the re-gion, the median, 10th and 90th percentile percentage change esti-mates in the future annual precipitation are �15%, 7% and 16%(Table 3), respectively. Twelve out of the 20 GCMs project increasesin the future mean annual precipitation in most grid cells (Fig. 8).

4.3. Future runoff

As with the simulated historical runoff, the future runoffamounts modeled by SIMHYD and GR4J are similar (Table 3 andFig. 7). Fig. 9 compares the future mean annual runoff percentagechanges in all grid cells modeled by SIMHYD and GR4J for the fu-ture precipitation projections from the 20 GCMs. The impacts ofclimate change on the runoff estimated by the two rainfall–runoffmodels are very similar (Fig. 9). The median differences in themodeled climate change impact on the mean annual and seasonal(spring, summer, autumn and winter) runoff estimated by thetwo models are 4.1% and 4.3%, 3.6%, 3.2% and 2.5%, respectively.For the precipitation projections from 8 GCMs (CSIRO-MK30,INGV, CCCMA-T47, MIUB, CNRM, GFDL 2.1, GISS-AOM and MIR-OC-M), the impact on the runoff estimated by SIMHYD is largerthan that estimated by the GR4J model (Fig. 9). The differencein the SIMHYD and GR4J modeling results is most likely due tothe different model structures in the actual evapotranspirationand soil moisture.

The differences between rainfall–runoff models in their mod-eled climate change impacts on the runoff can be significant, and

Fig. 5. Percentage change in the future mean annual precipitation for a 1 �C increase in global average surface air temperature.

F. Li et al. / Journal of Hydrology 505 (2013) 188–201 195

the SIMHYD model estimates a larger increase in the future meanannual runoff compared to GR4J by 2–10%. However, the futuremean annual runoff variability across the study area is much largeramong the 20 GCMs compared with the runoff variability betweenthe SIMHYD and the GR4J models (Fig. 7). The differences in themodeling results from the two rainfall–runoff models will becomemore important as GCM projections of the future precipitation be-come more accurate and consistent from advancements in climatechange science (Teng et al., 2012).

Fig. 10 shows the percentage change in the mean annual runofffor the future (for a 1 �C global warming) modeled by the SIMHYDmodel using climate change projections from the 20 GCMs relativeto the historical runoff (1962–2002). As with the future mean an-nual precipitation percentage change, considerable differences ex-ist in the runoff modeling results informed by the different GCMs.The 20 GCMs project similar percentage changes in the annual run-off as the annual precipitation, and approximately 11 GCMs projectrunoff increases over the study area, but several GCMs show

Fig. 6. Percentage change in the 10th, median and 90th percentile for the annual precipitation and runoff from the 20 GCMs.

Fig. 7. Percentage change in the future precipitation and runoff over the study area.

Fig. 8. Number of modeling results (out of 20 GCMs) showing an increase in future mean annual precipitation and runoff.

196 F. Li et al. / Journal of Hydrology 505 (2013) 188–201

decreases in some parts of the YTR basin (Fig. 10). However, thevariability in the future mean annual runoff is greater than thatin the future mean annual precipitation. Averaged across the studyarea, the percentage change in the future mean annual runoff var-ies from �36% to 63%. The median, 10th and 90th percentile per-centage changes for the future mean annual runoff are �24%,13% and 29% (Table 3 and Fig. 7), respectively. As with the annualprecipitation, 80% of the projections show increases in the meanannual runoff (16 out of 20, Figs. 10 and 7). Spatially, 70% of theprojections show increases in the future mean annual runoff inmost grid cells (14 out of 20, Fig. 8).

The median, 10th and 90th percentile percentage changes in themean annual runoff are shown for each grid cell in Fig. 6. The med-ian and 10th percentile estimates show similar percentage varia-tions, which are a 5–10% increase in the median and a <20%decrease in the 10th percentile for most grid cells. However, the90th percentile estimates show regional differences: an approxi-mately >30% runoff increase in the central and western parts whilejust a 15–25% increase in the northeast part.

4.4. Regional comparison

Fig. 11 compares the projected future mean annual precipita-tion and runoff over the entire study area and in sub-regions ofthe YLN and Grand canyon in the YTR basin. Most projections fromthe 20 GCMs show increases in the mean annual precipitation overthe YTR basin and in the two sub-regions (YLN and Grand canyon)with medians of 7.0%, 6.8% and 5.2%, respectively. The increase forthe entire area is larger than the YLN and Grand canyon regions.The Grand canyon region has the smallest increase in the futuremean annual precipitation. The percentage change in the futuremean annual runoff is similar to that of precipitation. The entirebasin has the largest increase in runoff followed by the YLN region;the Grand canyon region has the smallest increase in runoff. Themedian percentage change estimates the future mean annual run-off are 13.0%, 11.7% and 8.7% for the YTR basin, YLN and Grand can-yon region, respectively. However the uncertainty in the projectedrunoff change from the 20 GCMs is larger than that for precipita-tion in all three regions.

Fig. 9. Comparison of the percentage change in the future mean annual runoff estimated by the SIMHYD and the GR4J models using future precipitation projections from the20 GCMs (red line is the 1:1 line). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

F. Li et al. / Journal of Hydrology 505 (2013) 188–201 197

The historical and future mean monthly precipitation and mod-eled runoff in the YTR basin and the two selected regions (Fig. 1)and the extreme ranges of the projections are shown in Fig. 11.The lower and upper bounds are determined by the 10th percentileand 90th percentile monthly runoff estimates informed by the 20GCMs. For the YTR basin and the two regions, most of the precip-itation and runoff occur in the summer-half of the year. Largeuncertainty bands exist for both the future precipitation and therunoff in the results, especially for the Grand canyon region. Toexamine the percentage changes in the future and historicalmonthly precipitation and the modeled runoff in the three regions,the median estimates informed by the 20 GCMs and historicalobservations are compared here (Fig. 12). The YTR basin and YLNregion show similar percentage changes in the monthly precipita-tion with larger precipitation increases (approximately 5%) duringthe wet season (from May to September) than during the dry sea-son. However, the monthly precipitation in the Grand canyon re-gion shows a slight variation from the 0.1% decrease in Februaryto a 3.2% increase in April. The modeled monthly runoff shows lar-ger uncertainty bands than those of the precipitation. The monthlyrunoff in the YTR basin shows a considerable increase of 10–37% inthe dry season (from October to April), while the runoff in the wetseason (from May to September) shows a 5–9% increase. Themonthly runoff in the YLN region shows a 10–13% increase fromApril to August but a 7–9% increase in the other months. Themonthly runoff in the Grand canyon region shows a considerableincrease of 45% in March and 56% in April, and the runoffs in Octo-ber and November show 11% and 12% increases, respectively; how-ever, the runoff in the other months shows an approximately 0–6%increase. In the YTR basin, the dry season monthly runoff shows a

relatively larger percentage increase. However, in the YLN region,the wet season monthly runoff shows relatively larger percentageincrease.

5. Discussion

The impacts of climate change on the runoff in the YarlungTsangpo River basin are analyzed in this paper, and the resultsshow increased precipitation and runoff projected by a majorityof the modeling results informed by the 20 GCMs. Many previousstudies have reported insignificant increases in the annual and sea-sonal past precipitation in the YTR basin (You et al., 2007; Li et al.,2011; Tong et al., 2013) and a historical annual streamflow in-crease with climate warming (Zhang et al., 2011; Yang et al.,2012). The modeled increasing tendency of the precipitation andrunoff here indicates a wetter future with increasing water avail-ability that provides an important guarantee for agriculturalgrowth and economic and social development in this area, espe-cially for the YLN region, which is the economic center of Tibet.Furthermore, the increased water availability in the future willprovide a foundation for an ecological shelter zone maintainingsteady improvement. There are obvious benefits for the agricul-tural and industrial growth in the downstream area as well. How-ever, the frequency of flooding in the downstream area willincrease along with the increasing precipitation and runoffupstream.

This paper details the climate change impact studies carried outin the Tibetan Plateau. Several studies (Immerzeel et al., 2010;Singh et al., 2010) show that runoff will increase with climatechange in the upstream of Brahmaputra, Yellow, and Irrawaddy.

Fig. 10. Percentage change in the future mean annual runoff modeled by the SIMHYD for a 1 �C increase in global average surface air temperature.

198 F. Li et al. / Journal of Hydrology 505 (2013) 188–201

This paper complements and further supports these studies byusing 20 GCMs projections together with hydrological modeling.These findings will be very helpful for understanding future wateravailability and food security in this region. It is noted that hydro-logical response to climate change in the Tibetan Plateau is differ-ent from a catchment to another. Several studies indicate thatrunoff shows a decline trend in the upstream of Yangtze and Indus(Immerzeel et al., 2010; Cao et al., 2011).

The result found in this study is opposite to that in southeasternAustralia, but is similar to that in the Mississippi River basin. TheYTR basin is mainly influenced by the Indian monsoon and com-plex plateau topography. Compare to this, climate in southeasternAustralian is mainly influenced by the El Niño – Southern Oscilla-tion, the Indian Ocean Dipole, and the Southern Annular Mode.Majority of 15 GCMs project that climate there will become drierin approximately 2030 under A1B scenario, and as a result, runoff

Fig. 11. Comparison of the projected annual precipitation and runoff in the YTR basin, YLN and Grand canyon region.

Fig. 12. Mean monthly precipitation and runoff for the historical climate and the range of predictions for future climate.

F. Li et al. / Journal of Hydrology 505 (2013) 188–201 199

tends to decline in future (Chiew et al., 2009; Teng et al., 2012). TheMississippi River basin is mainly dominant by subtropics monsoonalongside frequently summertime mesoscale convective precipita-tion. Projection by a regional climate model coupled with a hydro-logical model (SWAT) showed that climate in this region willbecome wetter in the 2040s. Runoff in the Upper Mississippi Riverbasin will increase approximately 50%, which is much more signif-icant than that in the YTR basin (Jha et al., 2004).

This study focuses on the climate change impacts on runoff, andthe observational data are scarce in the study area. Therefore, twoconceptual hydrological models with a simple temperature index-based snowmelt module are applied here. Although the tempera-ture index-based snowmelt model has a solid physical basis usingcoarsely sampled data and showed good performance, the spatialvariability cannot be modeled accurately as melt rates may varysubstantially due to topographic effects (Ohmura, 2001; Hock,

200 F. Li et al. / Journal of Hydrology 505 (2013) 188–201

2003; Essery and Ethcevers, 2004). Furthermore, a high summerrunoff percentage may cover or hurt the performance of the snow-melt module. Conceptual models are appropriate powerful toolsfor data-scarce area; however, they cannot model the hydrologicalresponse spatial variability. We applied two distributed conceptualhydrological models in the YTR basin that show the runoff spatialvariability to a certain extent. However, the temporal fluctuationwithin a spatial variability is not considered during the model cal-ibration. Therefore, the modeled results here are stationary resultswithout changing effects. It is necessary to apply distributedhydrological models coupled with the newly available remotesensing datasets to explore the runoff response to climate changeboth temporally and spatially in future studies.

Due to the data scarcity in the YTR basin, the Blaney–Criddle(BC) formula is employed to obtain the ETp with calibration forseveral meteorological stations. The BC formula is simple to utilizewithout complex inputs such as wind speed, roughness andhumidity, which are difficult to obtain in the YTR basin. Althoughcalibrated with observational data, the BC formula shows somebias with overestimation in calm, humid areas and underestima-tion in windy, dry areas. Improvements in the BC ETp are possible,such as using air temperature interpolation considering elevationand other climate variables.

6. Conclusions

This study examines the potential impacts of climate change onrunoff in the YTR basin under 1 �C global warming. Two conceptualrainfall–runoff models are used to simulate the runoff for 0.5� gridcells across the study area for the historical (1962–2002) and fu-ture climate (up to approximately 2030) based on outputs from20 of the IPCC AR4 GCMs. The models are calibrated against themonthly streamflow data from eight catchments, and optimizedparameter values from the closest grid cell geographically in a cal-ibration catchment are used to model the runoff for each 0.5� gridcell in the region. The future climate series are obtained by scalingthe historical daily climate series, informed by the 20 GCMs for amidrange global warming scenario, using a scaling method thatconsiders changes in the future mean seasonal precipitation, ETpand mean and maximum air temperatures as well as changes inthe daily precipitation distribution.

A majority of the modeling results using the precipitation pro-jections informed by the 20 GCMs predict more runoff in the YTRbasin under 1 �C global warming. Averaged over the entire studyarea, the median estimate for a 1 �C increase in the global averagesurface air temperature shows a 13% increase in the mean annualrunoff. However, the estimates contain considerable uncertaintiesdue to the large range of future precipitation projections fromthe GCMs. The 10th percentile and 90th percentile estimates fromthe 20 GCMs range from a 24% decrease to a 29% increase in themean annual runoff over the study area. The YLN region, whichcontains most of the majority economic development of Tibet,shows a 12% runoff increase, while the runoff in the Grand canyonshows a 9% increase. A larger monthly runoff increase in the wetseason is anticipated in the YLN region as well.

The validation of the rainfall–runoff models indicates that themodeling approach is generally robust and can be used to estimatethe potential impacts of climate change on runoff. The difference inthe modeled climate change impact on the mean annual and sea-sonal runoff from the two parallel-applied rainfall–runoff modelsis generally less than 5%. This difference is relatively small com-pared to the difference in the modeled runoff informed by differentGCMs.

This work is the first comprehensive study on the hydrologicalimpacts of climate change covering the entire upstream area of

Brahmaputra, and the impact results show significant positive ef-fects on the water availability in the study area. This work also pro-vides a comparative case study for climate change impacts on theupstream runoff for large river systems on the TP.

Acknowledgments

This study was supported by the Fundamental Research Fundsfor the Central Universities (Grant No. 2009SC-5), the Natural Sci-ence Foundation of China (NSFC) No. 40971023 and the EuropeanUnion Tibetan Plateau Project (CEOP-AEGIS-WP8). We are thankfulto the China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/) for providing the meteorological data used inthe study. We are grateful to Dr. Fangfang Zhao and Dr. Lei Chengof CSIRO Land and Water for useful discussions. The first authoracknowledges the Chinese Scholarship Council for supporting hisPh.D. Study at the CSIRO Land and Water.

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