Estimation of future water resources of Xiangjiang River Basin with VIC model under multiple climate scenarios

Accepted Manuscript Estimation of future water resources of Xiangjiang River Basin with VIC model under multiple climate scenarios Guo-qing Wang, Jian-yun Zhang, Yue-ping Xu, Zhen-xin Bao, Xin-yue Yang PII:

S1674-2370(17)30058-3

DOI:

10.1016/j.wse.2017.06.003

Reference:

WSE 102

To appear in:

Water Science and Engineering

Received Date: 7 March 2017

Please cite this article as: Wang, G.-q., Zhang, J.-y., Xu, Y.-p., Bao, Z.-x., Yang, X.-y., Estimation of future water resources of Xiangjiang River Basin with VIC model under multiple climate scenarios, Water Science and Engineering (2017), doi: 10.1016/j.wse.2017.06.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Estimation of future water resources of Xiangjiang River Basin with VIC model under multiple climate scenarios Guo-qing Wang a,b, Jian-yun Zhang a,b,*, Yue-ping Xu c, Zhen-xin Bao a,b, Xin-yue Yang d State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China b Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China c Institute of Hydrology and Water Resources, Civil Engineering, Zhejiang University, Hangzhou 310058, China d College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China Received 7 March 2017; accepted 2 April 2016 Available online

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Abstract

1. Introduction

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Variation trends of water resources in the Xiangjiang River Basin over the coming decades have been investigated using the variable infiltration capacity (VIC) model and 14 general circulation models’ (GCMs’) projections under the representative concentration pathway (RCP4.5) scenario. Results show that the Xiangjiang River Basin will probably experience temperature rises during the period from 2021 to 2050, with precipitation decrease in the 2020s and increase in the 2030s. The VIC model performs well for monthly discharge simulations with better performance for hydrometric stations on the main stream of the Xiangjiang River than for tributary catchments. The simulated annual discharges are significantly correlated to the recorded annual discharges for all the eight selected target stations. The Xiangjiang River Basin may experience water shortages induced by climate change. Annual water resources of the Xiangjiang River Basin over the period from 2021 to 2050 are projected to decrease by 2.76% on average within the range from -7.81% to 7.40%. It is essential to consider the potential impact of climate change on water resources in future planning for sustainable utilization of water resources. Keywords: Water resources; Climate change; VIC model; Xiangjiang River Basin

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Water resources play a crucial role in ecological, social, and economic contexts (Zhang et al., 2013b). Global warming will alter the spatial and temporal distribution of water resources through accelerating hydrological cycle (IPCC, 2008; Zhang and Wang, 2007). A broad consensus indicates that global mean air temperature has risen by 0.85°C over the period from 1880 to 2012 with a higher rate of warming occurring in recent decades (IPCC, 2013). About 20% of the world’s large rivers have also experienced significant decreases in discharge (Dai et al., 2009); these decreases in low- and middle-latitude areas could be linked to the recent drying and warming in West Africa, South Europe, and East and South Asia (Dai, 2013). The recorded runoff of the major rivers in northern China have been decreasing significantly over the past 50 years and this is consistent with the rising temperature, decreasing precipitation, and increasing water demands due to rapid agriculture and industry development (Wang et al., 2013a). Climate change has reduced renewable water resources in most semi-arid and arid regions, and increased water stress to some extent (IPCC, 2014). Understanding the role of climate change in water availability is essential for sustainable utilization of water resources. Impacts of climate change on water resources have been widely investigated in recent years. In terms of climate scenarios, numerous studies of hydrological responses have been divided into two types, hypothetical scenarios and projections from general circulation models (GCMs) (Yu et al., 1999; Jones et al., 2006; Tavakoli and Smedt, 2012; Xu et al., 2013). Hypothetical scenarios are used to analyze the sensitivity of hydrological variables to changes in climatic conditions, such as changes in temperature, precipitation, etc. Bao et al. (2012) investigated the hydrological responses ————————————— This work was supported by the National Natural Science Foundation of China (Grants No. 41330854 and 41371063) and the National Key Research and Development Programs of China (Grants No. 2016YFA0601601 and 2016YFA0601501). * Corresponding author. E-mail address: [email protected] (Jian-yun Zhang).

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(streamflow, soil moisture, and actual evaporation) to climate change for the Haihe River Basin of China, and found that streamflow is much more sensitive than evapotranspiration and soil moisture to climate change. Fu et al. (2007) indicated that a 30% increase in precipitation could result in a 50% increase in streamflow for a watershed in the Pacific Northwest, the United States; conversely, a 20% decrease in precipitation could lead to a 25% 30% of reduction in streamflow. Wang et al. (2013b) found that an increase in precipitation has greater impact on runoff than a decrease in precipitation does. In addition to the hydrologic impacts from changes in climatic variables, direct environmental responses due to increased carbon dioxide have been modeled, e.g., increasing carbon dioxide and raising temperature by 6.4 in a highly agricultural watershed decreased evapotranspiration by 37.5% and increased streamflow by 23.5% compared to present-day climate, while increased precipitation resulted in proportionally increased runoff (Ficklin et al., 2009). Long-term average monthly streamflow is most sensitive to percentage change in precipitation, followed by the change in carbon dioxide concentration, and the change in temperature, for a forested watershed in the Mississippi River Basin in the United States (Parajuli, 2010). The Intergovernmental Panel on Climate Change (IPCC) summarized the current studies of runoff response to changes in precipitation, showing that changes in runoff are normally 1 3 times greater than equivalent percentage changes in precipitation (IPCC, 2014). GCMs are arguably the best available tools for modeling future climate (IPCC, 2014; Wang et al., 2012; Zhang and Wang, 2007). Projections from GCMs are mainly used to investigate future scenarios of regional water resources (Wang et al., 2012; Meng and Mo, 2012; Zhang et al., 2013a). However, the magnitude of anthropogenic climatic change on water resources will depend on the emissions scenario, global and local climatic response, and the geographical characteristics of each watershed (Rasilla et al., 2013; Jung and Chang, 2011; Zhang and Wang, 2015). Moreover, seasonal distribution patterns could be altered by climate change. For example, a reduction in annual runoff of the Iberian Basin was projected under the Special Report for Emissions Scenarios (SRES) of SRES-A2 and SRES-B2, with decreases in runoff principally occurring in spring and summer (IPCC, 2007; Rasilla et al., 2013). Using projections from five GCMs under SRES, runoff of the Songhua River Basin was projected to decrease in the next decades with notable differences ranging from 5.8% to 11.5% decreases of mean annual runoff (Meng and Mo, 2012). McFarlane et al. (2012) simulated water yields in southwestern Australia over future decades and found that surface water yields may decrease by about 24% but with a large range of decrease (4% to 49%) due to the uncertainty of projected climate scenarios. In the study of McFarlane et al. (2012), 15 GCMs and three emission scenarios used in the IPCC-AR4 (IPCC, 2007) have been employed. Currently, many of the challenges in assessing the impacts of climate change on water resources are associated with uncertain emission scenarios, imperfect GCMs, downscaling methods, and hydrological models (Khan and Coulibaly, 2010), among which, the largest sources of uncertainty are climate scenarios produced by GCMs for various emission scenarios (Kay et al., 2009). Relatively, the choice of hydrological models and methods for their parameterization are less important than the other factors when estimating long-term annual runoff (Gadeke et al., 2013). As uncertainty is an unavoidable issue in climate scenarios, using multiple climate scenarios is considered a practical and effective approach to quantifying the uncertainty. SRES have been adopted in most of the current studies. However, the updated representative concentration pathway (RCP) scenarios issued in the IPCC-AR5 (IPCC, 2013) and multiple GCMs projections are encouraged to be applied to the future climate change assessment (Yu et al., 2015). Issues of water resources shortage in dry regions have always attracted attention not only from the central government but also from local communities (Li, 2012). However, humid regions also experience significant challenges due to higher inter-annual variability of water resources, particularly in the context of climate change. The Xiangjiang River, located in the central region of China, is the largest river in Hunan Province. The river has abundant water resources and therefore is regarded as the “Mother River” by the local people in Hunan Province. In recent years, low flows in Xiangjiang River have frequently occurred due to less precipitation and higher temperatures, which has had significant impacts on communities throughout the region (Zhu, 2009). It is critically important to understand the variability and trends of water resources for effective water resources management. Therefore, the major objectives of this study were to: (1) analyze the expected climate change of the Xiangjiang River Basin based on multiple GCMs’ 2

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projections; (2) calibrate and validate the variable infiltration capacity (VIC) model and simulate hydrological processes for the whole basin; and (3) investigate the resulting trends in water resources.

2. Data and methodology 2.1. Study area and data sources

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The Xiangjiang River Basin is located in the area with longitudes from 109.5°E to 111.1°E and latitudes from 38.2°N to 39.8°N in the central region of China. It originates from the Haiyang Mountain in the Guangxi Autonomous Region and empties into Dongting Lake after running northward across five administrative regions of Yongzhou, Hengyang, Zhuzhou, Xiangtan, and Changsha in Hunan Province. The Xiangjiang River covers 85383 km2 of drainage area with a mainstream length of 842 km. The Xiangjiang River Basin is a highly populated area with a total population of 37.74 million by 2010. The GDP and industrial value added in 2010 within the basin were approximately 1220.5 billion RMB and 484.2 billion RMB, respectively, accounting for about 76.7% and 82.2% of the total GDP and industrial value added in Hunan Province. Located in the East Asia monsoon region, the Xiangjiang River Basin receives abundant precipitation (1500 mm per year), particularly in the flood season from April to August. Most of the basin area is covered by evergreen forest and deciduous broad-leaf and mixed evergreen trees, in which runoff is easily yielded (i.e., more than 50% of precipitation becomes runoff). The mean annual runoff depth within the basin is approximately 900 mm with the highest monthly runoff often occurring in May. The Xiangjiang River system consists of more than 70 rivers and streams, of which five first-level tributaries including the Chunlingshui, Leishui, Mishui, Lushui, and Liuyang rivers are located on its right bank and three major first-level tributaries including the Xiangguang, Zhengshui, and Lianshui rivers are located on its left bank (Fig.1).

Fig. 1. Xiangjiang River system and locations of hydrometric stations.

According to data availability, catchment size, and locations of hydrometric stations, six hydrometric stations on the tributaries and two key hydrometric stations, the Hengyang Station and Xiangtan Station on the main stream of the Xiangjiang River with a data length of over 20 years were selected to calibrate the hydrological model and test the model’s performance. Recorded daily discharge data series from 1983 to 2010 at the eight hydrometric stations were collected from the Hydrology and Water Resources Bureau of Hunan Province, China. Meteorological data including daily precipitation and temperatures (minimum, maximum, and mean) at 20 national meteorological stations, within or nearby the Xiangjiang River Basin, were obtained from the China Meteorological Administration (CMA). The basic information of the eight hydrometric stations is given in Table 1. 3

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Table 1 Basic information of eight hydrometric stations. Hydrometric station

Drainage area (km2)

Annual precipitation (mm)

Annual mean temperature ( )

Annual runoff depth (mm)

Annual pan-evaporation (mm)

Xiaoshui

Laobutou

21341

1543.8

17.4

954.2

980.0

Leishui

Leiyang

9902

1514.7

17.2

852.8

934.9

Mishui

Ganxi

9972

1635.1

17.7

827.7

915.3

Lushui

Daxitan

3132

1555.4

16.9

873.2

841.5

Lianshui

Xiangxiang

6053

1427.7

16.8

623.6

879.8

Liuyang

Langli

3815

1518.4

17.1

821.8

854.2

Hengyang

52150

1534.6

17.3

955.2

841.1

Xiangtan

81638

1528.9

17.3

971.6

805.2

Xiangjiang main stream Xiangjiang main stream

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Table 1 shows the following: (1) The annual precipitation for each catchment ranges from 1427.7 to 1635.1 mm with more precipitation occurring in the Mishui sub-basin located on the right bank of the middle reaches of the Xiangjiang River and relatively less precipitation occurring in the Lianshui sub-basin on the left bank of the lower reaches. (2) The runoff production efficiency is high, generating runoff ranging from 623.6 to 954.2 mm. The Xiaoshui sub-basin in the upper Xiangjiang River Basin has the highest runoff efficiency, probably due to the extensive forest coverage in this area. (3) Annual mean temperature and annual pan evaporation range from 16.8 to 17.4 and 841.5 to 980.0 mm, respectively. Higher temperature and potential evaporation could affect water yield indirectly through increasing evapotranspiration.

2.2. GCMs and climate scenarios

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IPCC-AR5 defines a number of climate scenarios from various GCMs. Based on these models’ performance over China (Chen et al., 2014), 14 GCMs’ projections (Table 2) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used to project future climate change for this study. The more details of these GCMs could be found in Taylor et al. (2012). RCPs are greenhouse gas concentration (not emissions) trajectories adopted by the IPCC-AR5. The scenario numbers indicate the expected radiative forcing (e.g., the lowest and highest scenarios, RCP2.6 and RCP8.5, will possibly have radiative forcing values of 2.6 and 8.5 W/m2 by 2100). RCP4.5 is a moderate scenario which considers both economic development and mitigation actions; it is considered the most likely scenario and was therefore selected as the most appropriate scenario for this study. The downscaled RCP4.5 projections of the 14 GCMs with a resolution of 0.5° × 0.5° and 1901—2100 data series were collected from CMA. Wang et al. (2015) selected the multi-year mean, Mann-Kendall coefficient, linear trend rate, as well as seasonal and spatial distributions of precipitation and temperature series as indicators, and evaluated the performance of the 14 GCMs in simulations over the period from 1961 to 2005 for the whole Xiangjiang River Basin by comparing indicators of the observed precipitation and temperature and the GCMs’ simulations. Results show that the BNU-ESM model ranks first among the 14 GCMs for historical simulation while no GCM could simulate historical variations of precipitation and temperature perfectly (Wang et al., 2015). Therefore, it is very essential to project future climate change by using multiple GCMs’ projections. Table 2 CMIP5 GCMs used in this study (resolution is the number of latitude/longitude grid points, globally) No.

Model

Resolution

Institution

1

BNU-ESM

128 × 64

College of Global Change and Earth System Science, Beijing Normal University

China

2

BCC-CSM1-1

128 × 64

Beijing Climate Center, China Meteorological Administration

China

3

CNRM-CM5

256 × 128

Centre National de Recherches Météorologiques

France

4

GISS-E2-R

144 × 90

Goddard Institute for Space Studies

USA

5

MRI-CGCM3

320 × 160

Meteorological Research Institute

Japan Japan Germany

6

MIROC-ESM

128 × 64

Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies

7

MPI-ESM-LR

192 × 96

Max Planck Institute

4

Country

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CanESM2

128 × 64

Canadian Centre for Climate Modelling & Analysis

Canada

9

CCSM4

288 × 192

National Center for Atmospheric Research

USA

10

CSIRO-Mk3-6-0

192 × 96

Commonwealth Scientific and Industrial Research Organisation (CSIRO) of Australia)

Australia China

11

FGOALS-g2

128 × 60

State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences

12

FIO-ESM

128 × 64

The First Institute of Oceanography, State Oceanic Administration (SOA)

China USA France

13

GFDL-CM3

144 × 90

Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration (NOAA), US Department of Commerce

14

IPSL-CM5A-LR

96 × 96

Institute Pierre Simon Laplace

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2.3. Description of VIC model

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The VIC model is a physically-based hydrological model, which was developed by Liang et al. (1994) and later improved by Lohmann et al. (1998). The model considers two types of runoff yield mechanisms, infiltration excess and saturation excess. The total runoff estimates of the VIC model consist of surface flow and base flow (Habets et al., 1999). The VIC model has been widely applied to a wide variety of sub-basins (Bao et al., 2012; Wang et al., 2012). In comparison with other hydrological models, the VIC model has advantages of physically-based interpretation, wide suitability for different climatic zones, and good performance for discharge simulation (Zhang and Wang, 2014). Estimates of surface flow and base flow are mathematically described as follows: I0 + P ≥ Im  P + W0 − W0max    I + P 1+ B  Qd =  (1) max I0 + P ≤ Im  1 − 1 − 0 P + W − W   0 0  Im      

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 Ds Dm −  W2 0 ≤ W2− ≤ WsW2c WsW2c  Qb =  (2) c 2 − c −  Ds Dm W − +  D − Ds Dm  W2 − WsW2  W2 > WsW2   W W c 2  m Ws  W2c − WsW2c  s 2   where Qd is the surface flow, Qb is the base flow, P is precipitation, W0 is the initial soil moisture of the upper soil layer, W0max is the maximum soil moisture of the upper soil layer, I 0 is the initial infiltration rate, I m is the maximum infiltration rate, B is a variable infiltration curve parameter, Dm is the maximum daily base flow, Ds is the fraction of Dm in which non-linear base flow occurs, W2c is the maximum soil moisture of the lower soil layer, W2− is the initial soil moisture of the lower soil layer, and Ws is the fraction of W2c in which non-linear base flow occurs. The maximum soil moisture of each soil layer is estimated based on soil texture and soil layer thickness. The VIC model needs three types of forcing input data, i.e., soil data, vegetation data, and hydro-meteorological data. Vegetation parameters include architectural resistance, the minimum stomata resistance, the leaf-area index, albedo, roughness length, and zero-plane displacement. The values of vegetation parameters in the VIC model are given in the Table 3, and the values of soil parameters are listed in Table 4. Table 3 Vegetation parameters in VIC model Vegetation classification Evergreen needle leaf forest Evergreen broad leaf forest Deciduous needle leaf forest Deciduous broad leaf forest Mixed forest Woodland Wooded

Albedo

Minimum stoma Leaf-area resistance index (s·m-1)

ZeroRoughness plane length (m) displacement (m)

0.12

250

3.40—4.40

1.476

8.040

0.12

250

3.40—4.40

1.476

8.040

0.18

150

1.52—5.00

1.230

6.700

0.18

150

1.52—5.00

1.230

6.700

0.18 0.18 0.19

200 200 125

1.52—5.00 1.52—5.00 2.20—3.85

1.230 1.230 0.495

6.700 6.700 1.000

5

grassland Closed shrub land Open shrub land Grassland Crop land (corn)

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135

2.20—3.85

0.495

1.000

0.19

135

2.20—3.85

0.495

1.000

0.20

120

2.20—3.85

0.074

0.402

0.10

120

0.02—5.00

0.006

1.005

Sand 4.10 Loamy sand 3.99 Sandy loam 4.84 Silt loam 3.79 Silt 3.05 Loam 5.30 Sandy clay loam 8.66 Silty clay cloam 7.48 Clay loam 8.02 Sandy clay 13.00 Silty clay 9.76 Clay 12.28

Bulk Porosity density (m3·m-3) -3 (kg·m ) 1490 1520 1570 1420 1280 1490 1600 1380 1430 1570 1350 1390

0.445 0.434 0.415 0.471 0.523 0.445 0.404 0.486 0.467 0.415 0.497 0.482

Saturated soil potential (m) 0.069 0.036 0.141 0.759 0.759 0.355 0.135 0.617 0.263 0.098 0.324 0.468

Saturated hydraulic conductivity (mm·d-1) 92.45 1218.24 451.87 242.78 242.78 292.03 384.48 176.26 211.68 623.81 115.78 84.15

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Table 4 Soil parameters in VIC model Soil texture

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The VIC model divides a target catchment into many grid cells with a flexible cell size (normally 0.25° × 0.25° or 0.5° × 0.5° for climate change studies), and runs at each grid cell (Wang et al., 2012; Zhang and Wang, 2014). In this study, we divided the whole Xiangjiang River Basin into 54 cells with a resolution of 0.5° × 0.5° (Fig. 1). The station meteorological data was interpolated to each grid cell using the linear distance weighted interpolation method. Daily discharges at the Hengyang and Xiangtan stations on the main stream and the outlet stations of the six tributary sub-basins were used to calibrate and validate the VIC model.

3. Results and discussion

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3.1. Future climate change in Xiangjiang River Basin

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Based on projections with the 14 GCMs, trends of precipitation and temperature over the period from 2021 to 2050 were analyzed through quartile analysis. Fig. 2 shows the box-and-whisker plots for changes in annual precipitation and temperature under 14 scenarios relative to the reference (from 1961 to 2010).

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Fig. 2. Box-and-whisker plots for changes in precipitation and temperature over Xiangjiang River Basin during 2021 2050 relative to 1961 2010.

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Fig. 2 shows the following results: (1) Uncertainty is still a major challenge in climate scenarios, with various GCMs producing different climatic changes. For example, scenario mean precipitation in the 2020s is expected to decrease by 1.73%, although eight GCMs’ projections decrease and six projections increase. (2) Precipitation over the period from 2021 to 2050 seems to be roughly equivalent to the baseline (from 1961 to 2010), with a mean decrease of 1.73% (within a range from -10.96% to 7.17%) in the 2020s and a mean increase of 2.65% (within a range from -7.59% to 14.88%) in the 2030s. The range of precipitation change for the period from 2021 to 2050 (-4.64% to 7.68%) is much smaller than those for other decades. (3) In contrast, the temperature will be consistently warming. The annual temperature over the period from 2021 to 2050 will likely increase by 1.36 (ranging from 0.84 to 2.14 ), with 1.0 (ranging from 0.61 to 1.65 ), 1.39 (ranging from 0.79 to 2.05 ), and 1.69 (ranging from 0.86 to 2.73 ) of increase in the 2020s, 2030s, and 2040s, respectively. In general, the Xiangjiang River Basin will likely become warmer in the next decades, with the possibility of drier conditions in the 2020s and wetter conditions in the 2030s relative to the period from 1961 to 2010.

3.2. Model calibration and hydrological modeling for Xiangjiang River Basin In this study, we used the Nash and Sutcliffe efficiency criterion (NSE) and the relative error of volumetric fit (RE) as objective functions to calibrate the hydrological model (Nash and Sutcliffe, 1970). A good simulation result will have NSE close to 1 and RE approaching 0. 7

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In order to calibrate and validate the VIC model, the data series from 1983 to 2010 were divided into two periods: a calibration period from 1983 to 2000, and a verification period from 2001 to 2010. The VIC model was run at a daily time step and the results were aggregated to monthly discharge volumes at the hydrometric stations. The performance of the VIC model for discharge simulation is summarized in Table 5. The monthly recorded and simulated discharges from 1983 to 2010 at the Laobutou Station on the tributary and the Xiangtan Station on the main stream of the Xiangjiang River are taken as two typical examples and shown in Fig. 3. Simulated annual runoff against recorded annual runoff for the eight stations are plotted in Fig. 4 Calibration period

Verification period

NSE (%)

NSE (%)

RE (%)

Laobutou

96.0

0.47

89.1

-3.50

Leiyang

89.7

8.80

79.5

-5.10

Ganxi

91.1

-0.32

81.6

0.12

Daxitan

92.9

6.60

78.0

-1.70

RE (%)

89.4

9.20

73.2

-3.30

Langli

93.3

6.60

75.1

-1.90

Hengyang

98.6

1.58

90.9

-0.52

Xiangtan

95.9

0.08

93.0

0.30

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Xiangxiang

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Hydrometric station

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Table 5 Performance of VIC model for discharge simulation for eight hydrometric stations.

Fig. 3. Recorded and simulated monthly discharge from 1983 to 2010 at Laobutou and Xiangtan stations.

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Fig. 4. Recorded and simulated annual runoff for eight hydrometric stations in Xiangjiang River Basin.

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Table 5 shows the following results: (1) The VIC model performs well for each sub-basin in general. The NSE value varies from 89.4% to 98.6% and 73.2% to 93.0% with the RE value ranging from -0.32% to 9.20% and -5.10% to 0.30% in the calibration and verification period, respectively, indicating that the model has high skill and small bias for discharge simulation. (2) Relatively, the VIC model performs better for monthly discharge simulation at the hydrometric stations on the main stream of the Xiangjiang River. The NSE values in both calibration and verification periods at the Hengyang and Xiangtan stations are higher than 90.0% with the RE values being very small. (3) There is a slight negative bias in runoff during the verification period (with the exception of the Ganxi and Xiangtan stations), probably due to the rapid urbanization in the region, thus increasing the impervious area and streamflow. Results in Figs. 3 and 4 are in accordance with those in Table 5. The observed and simulated monthly discharges match well in general with a slight overestimates for peak discharges in early years and underestimates in more recent years. The simulated annual runoff is highly correlated to the observed annual runoff for all the eight hydrometric stations with correlation coefficients exceeding 0.85 (Fig. 4). The simulated annual runoff approaches the recorded annual runoff in general. Higher values of runoff at the Laobutou, Xiangxiang, and Hengyang stations approach but are located below the 1:1 line, indicating good simulation and underestimation for higher runoff at these stations. In general, the VIC model performs well not only for tributaries, but also for the main stream of the Xiangjiang River.

3.3. Hydrological responses to climate change

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Various runoff simulations from 1960 to 2050 were performed with the calibrated VIC model and 14 climate projections under the emission scenario of RCP4.5. For the period from 1961 to 2010, the spatial distributions of the runoff simulation driven by the recorded meteorological forcing and the ensemble mean of the 14 GCMs’ projections are compared in Fig. 5. The spatial distributions of runoff simulations based on recorded meteorological forcing and multiple GCMs’ projections are highly in agreement. This implies that the ensemble mean of multiple GCMs’ scenarios has good performance in simulating the spatial pattern of historical runoff. Both simulations of runoff tend to decrease from the upper reaches to the lower reaches in general, with an exception of a high runoff region occurring in the three grid cells (in light blue in Fig. 5) in the lower Xiangjiang River Basin, which is in accordance with the spatial patterns of precipitation and water resources over the Xiangjiang River Basin (Xiao et al., 2013; Zhu, 2009).

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Fig. 5. Spatial distributions of simulated runoff obtained from recorded meteorological forcing and ensemble mean of 14 GCMs’ projections.

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Fig. 6. Box-and-whisker plot for change in runoff of the Xiangjiang River Basin relative to 1961 2010.

A box-and-whisker plot for changes in future annual runoff under 14 scenarios relative to the reference (from 1961 to 2010) is shown in Fig. 6. The figure shows the following: (1) The Xiangjiang River will probably experience decreased runoff relative to the baseline for the period from 1961 to 2010. The ensemble mean of annual runoff over the period from 2021 to 2050 will probably decrease by 2.76% (within a range from -7.81% to 7.40%). The 75th percentile is -1.94%, indicating that most of the GCMs’ projections decrease in runoff for the Xiangjiang River Basin except MPI-ESM-LR and CCSM4. (2) The ensemble mean runoff of the 2020s and 2040s will probably decrease by 4.85% (within a range from -17.63% to 8.75%) and 3.63% (within a range from -14.26% to 8.11%) relative to the period from 1961 to 2010, which implies that the Xiangjiang River may have higher water resource stress due to climate change. (3) In comparison to the baseline, there is no significant change in water resources over the period from 2031 to 2040, although all GCMs projected relatively a more significant change in runoff ranging from -11.11% to 16.43%. With runoff simulation based on the GCMs’ projections for the period from 1961 to 2010 as a baseline, the ensemble mean based spatial distributions of change in water resources for each decade over the next 20 50 years are 10

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presented in Fig. 7.

Fig. 7. Ensemble mean based spatial distribution of percentage change in water resources over Xiangjiang River Basin for 2021 relative to 1961 2010 (%)

2050

Fig. 7 indicates the following: (1) In the next 20 40 years, the Xiangjiang River will probably present a general decrease in annual runoff for the whole basin, particularly for the periods from 2021 to 2030 and from 2041 to 2050. (2) From 2021 to 2030, the annual runoff will probably decrease by 3.57% to 6.36% for the whole basin with less significant decrease occurring in the upper reaches and more significant decrease in the other areas. (3) The annual runoff for the period from 2031 to 2040 varies within a narrow range from -0.97% to 1.10% with a decrease occurring in the middle part of the Xiangjiang River Basin and an increase probably occurring in the other areas. (4) The spatial pattern of change in water resources for the period from 2041 to 2050 is similar to that for the period from 2021 to 2030, with more significant decrease occurring in the left part of the lower reaches.

4. Conclusions 11

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Variation trends in water resources of the Xiangjiang River Basin for the next decades were simulated using the VIC model and 14 GCMs’ projections under the RCP4.5 scenario. The results show that, in the next decades, temperature over the Xiangjiang River Basin will probably continue to rise while precipitation is highly uncertain with indications of possible decreases in the 2020s and 2040s and increases in the 2030s relative to the reference period from 1961 to 2010. By the middle of the 21st century, temperature will probably rise by 1.69 , which varies within a range from 0.86 to 2.73 . Over the period from 2021 to 2050, the annual precipitation is roughly equivalent to the baseline for the period from 1961 to 2010, with a large range of change from -4.64% to 7.68% relative to that of the reference period from 1961 to 2010. The VIC model performs well for monthly discharge simulation of the eight typical hydrometric stations with the NSE values exceeding 70% and the RE values falling within the range between -5.1% and 9.2%, for both calibration and validation periods. The simulated annual runoff is highly correlated to the observed annual runoff for all the eight hydrometric stations. Relatively, the VIC model presents better simulation of discharge for the hydrometric stations on the main stream of the Xiangjiang River than it does for the stations on tributaries. The VIC model not only can simulate the inter-annual variability of runoff, but also can simulate the spatial distribution of water resources over the Xiangjiang River Basin reasonably. The annual runoff depth over the Xiangjiang River Basin tends to decrease northward with a high value occurring in the lower reaches. The Xiangjiang River Basin may undergo climate-induced decreases in runoff in the next decades, particularly for the periods from 2021 to 2030 and from 2041 to 2050. Over the period from 2021 to 2050, the ensemble mean of annual runoff is expected to decrease by 2.76%, within a range from -7.81% to 7.40%. Although the projected water resources are still very uncertain, the Xiangjiang River Basin will probably confront a new stress in water shortages induced by climate change. It is therefore of significance to consider the potential effects of climate change in the plan of water resources utilization.

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