Stoch Environ Res Risk Assess (2012) 26:847–858
DOI 10.1007/s00477-011-0524-2
ORIGINAL PAPER
Impacts of climate change on the Qingjiang Watershed’s runoff
change trend in China
Hua Chen • Tiantian Xiang • Xing Zhou
Chong-Yu Xu
•
Published online: 23 September 2011
Ó Springer-Verlag 2011
Abstract Qingjiang River, the second largest tributary of
the Yangtze River in Hubei Province, has taken on the
important tasks for power generation and flood control in
Hubei Province. The Qingjiang River watershed has a subtropical monsoon climate and, as a result, has dramatic
diversity in its water resources. Recently, global warming
and climate change have seriously affected the Qingjiang
watershed’s integrated water resources management. In this
article, general circulation model (GCM) and watershed
hydrological models were applied to analyze the impacts of
climate change on future runoff of Qingjiang Watershed. To
couple the scale difference between GCM and watershed
hydrological models, a statistical downscaling method based
on the smooth support vector machine was used to downscale
the GCM’s large-scale output. With the downscaled precipitation and evaporation, the Xin-anjiang hydrological
model and HBV model were applied to predict the future
runoff of Qingjiang Watershed under A2 and B2 scenarios.
The preformance of the one-way coupling approach in
simulating the hydrological impact of climate change in the
Qingjiang watershed is evaluated, and the change trend of the
future runoff of Qingjiang Watershed under the impacts of
climate change is presented and discussed.
H. Chen (&) C.-Y. Xu
State Key Laboratory of Water Resources and Hydropower
Engineering Science, Wuhan University, Wuhan 430072, China
e-mail: chua@whu.edu.cn
T. Xiang X. Zhou
School of Water Resources and Hydropower Engineering,
Wuhan University, Wuhan 430072, China
C.-Y. Xu
Department of Geosciences, University of Oslo, P.O. Box 1047,
Blindern, 0316 Oslo, Norway
Keywords Climate change Qingjiang watershed Statistical downscaling Xin-anjiang model HBV model
1 Introduction
Global climate change, due to the increase of greenhouse
gases concentration in atmosphere, has caused environmental crisis in many areas around the world. Since the
distribution of water resources is very sensitive to climate
change, global warming is likely to have significant effects
on hydrological cycle (Solomon et al. 2007). In China, water
resources are unevenly distributed between North and South
China, the same is true between seasons. Moreover, both
droughts and floods are becoming more frequent in different
areas under climate change, which makes it an important
and urgent task to conduct research about potential impacts
of climate change on water resources.
To investigate the impacts of climate change on surface
water resources, the most useful tool is the hydrological
model driven by the output from general circulation model
(GCM) (Gleick 1986; Obeysekera et al. 2011; Sahoo et al.
2011; Schulze 1997). In other words, the climate change
impacts on a watershed’s hydrological processes are mostly
investigated by analyzing the river flows which are simulated
from the hydrological models forced by the precipitation and
evaporation data derived from GCMs outputs corresponding
to a specific climate change scenarios. In this one-way connection, the climate change scenarios projected by GCMs
and the hydrological model are independent research
objects. GCMs are the most essential and feasible tools for
prediction of future global climate change at large scale.
However, because of the low resolution of the output from
large scale GCMs, the model can hardly provide detailed
climate features and dynamics of small regions (Wigley et al.
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848
1990; Xu et al. 2005). To cope with this challenge, it is of
vital importance to transform the changes of large scale
atmospheric predictors of GCMs to the changes of regionalscale climate variables, such as precipitation and temperature which could be used as input to hydrological model. The
methods used to convert GCMs outputs into local meteorological variables required for reliable hydrological modeling
are usually referred to ‘‘downscaling’’ techniques (Grotch
and Maccracken 1991; Vonstorch et al. 1993; Wilby et al.
2002; Wilby and Wigley 1997). There are various downscaling methods available, which are mainly classified into
two categories: dynamic and statistical downscaling methods. However, it is not clear which one can provide the most
reliable estimates of daily rainfall time series in a given
region. Over the past few years, numerous studies have been
conducted on the modeling of climate change impacts on
runoff by using downscaling methods (Chiew et al. 2010;
Dibike and Coulibaly 2005; Dibike and Coulibaly 2007; Kim
et al. 2007; Prudhomme and Davies 2009; Quilbe et al. 2008;
Segui et al. 2010; Wilby et al. 1999). Chiew et al. (2010)
assessed the runoff simulated by the SIMHYD rainfall-runoff model with daily rainfall which was downscaled from
three GCMs using five downscaling models. Segui et al.
(2010) evaluated the uncertainty related to climate change
impacts on water resources by applying a distributed
hydrological model and three different downscaling
techniques. Prudhomme and Davies (2009) used a lumped
conceptual rainfall-runoff model, three GCMs and two
downscaling techniques to investigate the climate change
impacts on river flows. Dibike and Coulibaly (2005) applied
two types of statistical (a stochastic and a regression based)
downscaling techniques and two different hydrologic models to simulate the corresponding future flow regime in the
catchment. Literature survey reveals that various methods
have been used to obtain catchment-scale climate series,
informed by GCMs simulations for the future and current
climates, to drive hydrological models. There is no single
‘‘best’’ scenario or hydrological model that was found to be
significantly better or to have a systematic bias smaller than
the others. Hence, more than one climatic scenarios and
hydrological models have been used to obtain more comprehensive and less uncertain results.
The aims of this study are (1) to evaluate the performance
of the one-way coupling approach in the Qingjiang River
region that links GCM’s scenarios with two hydrological
models through statistical downscaling method, and (2) to
analyze the future change trend of precipitation, evaporation
and runoff, and investigate the potential of the two hydrological models and compare the difference between them for
climate impact study. To achieve the primary goals, a statistical downscaling method, named smooth support vector
machine (SSVM) was used together with two well-known
hydrological models, i.e., the xin-anjiang model (Zhao
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Stoch Environ Res Risk Assess (2012) 26:847–858
1992) and HBV-96 model (Bergstrom 1975). In the study,
the NCEP reanalysis data was used to validate the statistical
downscaling model and the A2 and B2 emissions scenarios
of CGCM2 were used to predict future scenarios of precipitation, temperature and evaporation, which were used as
inputs to the hydrological models.
2 Study area and data
2.1 Study area
Qingjiang River watershed, the second largest tributary of
the Yangtze River in Hubei Province in China, flows circuitously from west to east for 423 km, has a drainage area
of about 17000 km2 (as shown in Fig. 1). Located in a
subtropical monsoon climate zone, this area is subject to
both the warm/wet airflows from southwest and the
southeast flow from the Pacific Ocean, which bring abundant precipitation to Qingjiang Watershed. The mean
annual precipitation measured in different stations varies
from 1000 to 2000 mm, and the area mean precipitation is
1460 mm from the period of 1960–1991. Tropical weather
system such as typhoon is also active in this area; hence,
rainstorm is most likely to happen, which leads to uneven
annual distribution of precipitation. By contrast, the interannual variation of precipitation is not very pronounced.
The ratio of the maximum annual precipitation to the
minimum value is generally about 1.5–2.0.
The cascade hydroelectric power stations in Qingjiang
Watershed regulate peak load and frequency in both central
China region and Hubei Province. After the construction is
completed in 2008, the peak load regulation capacity of those
power stations constitute about one-eighth to one-seventh of
that of the central China region. Meanwhile, since Qingjiang
Watershed is the nearest watershed to the Jingjiang reach
among all the main tributaries affluxing to Yangtze River,
flood regulation in Qingjiang Watershed plays significant role
to flood control in Yangtze River. Therefore, the knowledge
of the future water production is indispensable for water
resources planning and management. This study mainly analyzes the potential influence of global climate change on water
resources of Qingjiang Watershed. The results could contribute to the rational and efficient exploration of water
resources in the region and provide theoretical reference for
sustainable development of water resources.
2.2 Study data
The upper stream of Geheyan Reservoir is set as the
research area in this study. The drainage area above this
reservoir is about 14430 km2. In order to establish statistical relationship between large scale climate factors and
Stoch Environ Res Risk Assess (2012) 26:847–858
849
Fig. 1 Location of hydroclimate stations in the Qingjiang
watershed
observed data of precipitation and evaporation, longer time
series data was needed. As listed in Table 1, the daily
precipitation, evaporation and runoff data recorded at 23
hydro-climate stations were selected to calibrate and verify
the hydrological models in Qingjiang Watershed in this
study. However, only 6 stations (marked with bold) which
have long series of data from 1962 to 1999 were chosen to
establish the statistical downscaling method.
The NCEP reanalysis data are used to validate the statistical model and the A2 and B2 emissions scenarios of
CGCM2 are used to predict future precipitation in the
region. The spatial resolution of NCEP grid is 2.5° 9 2.5°
and Qingjiang Watershed is covered by four grids. As a
result of subtropical monsoon climate, the precipitation in
the Qingjiang Watershed, causing by sea level pressure
(MSLP), geopotential height (GH) and humidity, is concentrated mostly in summer and autumn. Considering the
physical correlation, the present study select 6 factors as
the input of downscaling model in the first place, i.e., sea
level pressure, surface air temperature, 500 hpa geopotential height (GH) and specific humidity (SH), 850 hPa GH
and SH.
3 The statistical downscaling method and hydrological
models
3.1 Statistical downscaling method
Statistical downscaling methods seek to draw empirical
relationships that transform large-scale features of GCM
(predictors) to regional-scale variables (predictands), such
as precipitation and temperature. In order to integrate GCM
with the watershed hydrological models, a statistical
downscaling method, named SSVM, is used to establish the
relationships between climate factors and hydrological
variables, which has been proved to be an effective statistical downscaling method in the analysis of climate
impact on the water resource in Hanjiang basin (Chen et al.
2010; Guo et al. 2009).
One of the most crucial steps in downscaling process is
to select the most relevant predictors from GCM’s largescale output. Since some of these factors may contribute
little to precipitation projection, second screening is needed. The correlation coefficients of each predictors and
local precipitation are shown in Table 2. Comparing to the
other factors, 500 and 850 hpa SH show the strongest
correlation, with correlation coefficients of over 0.3. For
precipitation projection, those factors with correlation
coefficients of over 0.3 are selected to establish the statistical relationship with local precipitation.
Since evaporation and precipitation are affected by different climate factors, all the predictors should be screened
again to select the most suitable ones. With the same
method, the correlation coefficients of local evaporation and
each predictor are calculated and showed in Table 2. The
results show that local evaporation has more distinct and
stronger relationship with climate predictors than the local
precipitation. To project evaporation, those factors with
correlation coefficients of over 0.5 are selected to establish
the statistical relationship with local evaporation.
3.2 Hydrological models
In order to investigate the difference resulted from using
between different hydrological models in calculating
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Stoch Environ Res Risk Assess (2012) 26:847–858
Table 1 Information of the meteoritical stations used in the study
No.
Station
Station type
Longitude
1
Xinbanqiao
P
109°180 2200
0
Altitude (m)
Length of data
30°280 4000
1840
1971–1984
00
30°480 0300
1250
1971–1984
30°200 0700
1000
1971–1984
00
2
Maotian
P
109°52 58
3
Tuanbao
P
109°080 2000
0
Latitude
00
0
4
Gaoping
P
110°04 48
30°39 42
770
1971–1984
5
Hongtuxi
P
109°540 3000
30°150 5200
1340
1971–1984
6
Huaguoping
P
109°590 4800
30°260 5900
1280
1971–1984
7
Yeshanguan
P
110°190 1500
30°370 0300
1100
1971–1984
8
Wufeng
P
110°400 2400
30°120 1500
640
1971–1984
9
Taoshan
P
110°400 4000
30°260 1000
0
00
0
330
1971–1984
00
10
Yazikou
P
110°07 04
30°26 07
116
1971–1984
11
12
Caihua
Baozi
P
P
110°260 5400
110°440 1000
30°120 3000
30°370 1600
480
1200
1971–1984
1971–1984
13
Dayan
P
111°050 0100
30°200 1600
600
1971–1984
14
Langping
P
110°300 0400
30°360 5200
480
1971–1984
00
0
00
0
15
Gaojiayan
P
111°03 00
30°35 58
133
1971–1984
16
Jinguoping
P
110°130 4200
30°170 3300
590
1971–1984
17
Enshi
P
109°270 5500
30°180 2800
520
1962–1999
18
Nanlidu
P
109°420 5800
30°270 1500
880
1962–1999
19
Xuanen
P
109°270 4400
29°590 0700
760
1962–1999
0
00
0
00
20
Lichuan
P
108°54 50
30°17 31
1080
1962–1999
21
Jianshi
P
109°430 4500
30°360 1500
540
1962–1999
22
Yuxiakou
P/E
109°430 4500
30°250 0800
216
1962–1999/1971–1999
30°270 4200
180
1962–1999
23
Geheyan
R
0
111°08 33
00
P precipitation station; P/E precipitation/evaporation station; R runoff station. Stations marked in bold letters are used to calibrate and verify
statistical downscaling model
hydrological impact of climate change, two widely-used
hydrological models, i.e., Xin-anjiang model (Zhao 1992)
and HBV model (Bergstrom 1975) are utilized in this
study.
3.2.1 Xin-anjiang model
Xin-anjiang model was initially developed by (Zhao
1992). It was first used in prediction of Xin-anjiang
Reservoir inflow, and since then was widely used for
flood forecasting, streamflow simulation and hydrological
impact studies in China and in many countries in the
world (Jiang et al. 2007; Li et al. 2009; Liu and Zheng
2004; Yang et al. 2010; Zhang and Lindstrom 1996;
Zhang and Chiew 2009). Its major feature is the concept
of runoff formation as a dependent variable of repletion
of storage, i.e., runoff is not produced until the soil
moisture content of the aeration zone researched field
capacity, and thereafter, runoff is equal to the rainfall
excess without further loss. The detailed description of
the model is widely available in the literature including
the above cited ones.
123
3.2.2 HBV model
The HBV model is a conceptual hydrological model and it was
originally developed at the Swedish Meteorological and
Hydrological Institute (SMHI) for runoff simulation and
hydrological forecasting in the early 1970s (Bergstrom 1975).
It consists of routines for snow accumulation and melt, soil
moisture accounting, runoff response, and finally a routing
procedure. The model is based on a sound scientific foundation and can meet its data demands in most areas, which has
the scope of applications in more than 40 countries (Ashagrie
et al. 2006; Bergstrom et al. 2001; Boggild et al. 1999; Hagg
et al. 2004; Love et al. 2010; Sorman et al. 2009; van den Hurk
et al. 2002; Wohling et al. 2006; Yu and Wang 2009).
4 Results and analysis
4.1 The development of the statistical downscaling
method
To test the performance of the statistical downscaling
model, SSVM, the NCEP/NCAR Reanalysis data are
Stoch Environ Res Risk Assess (2012) 26:847–858
851
Table 2 Correlation coefficients between climate factors and precipitation/evaporation
Precipitation
Hydrological
stations
Grid center lat/lon
SLP
SAT (2 m)
500 hpa GH
500 hpa SH
850 hpa GH
850 hpa SH
Enshi
28.75_108.75
-0.25
0.24
0.19
-0.22
0.3
0.32
28.75_111.25
-0.24
0.24
0.21
-0.2
0.29
0.31
31.25_108.75
-0.22
0.2
0.19
-0.19
0.37
0.28
31.25_111.25
-0.22
0.2
0.2
-0.19
0.39
0.3
28.75_108.75
-0.26
0.25
0.2
-0.22
0.31
0.33
28.75_111.25
-0.25
0.25
0.22
-0.21
0.3
0.32
31.25_108.75
31.25_111.25
-0.23
-0.23
0.21
0.22
0.2
0.21
-0.2
-0.2
0.38
0.4
0.29
0.31
Jianshi
Lichuan
Nanlilu
Xuanen
Yuxiakou
Area precipitation
Evaporation
28.75_108.75
-0.25
0.24
0.2
-0.21
0.32
0.32
28.75_111.25
-0.24
0.24
0.22
-0.2
0.31
0.31
31.25_108.75
-0.22
0.2
0.19
-0.19
0.39
0.28
31.25_111.25
-0.22
0.21
0.21
-0.19
0.4
0.3
28.75_108.75
-0.27
0.26
0.21
-0.24
0.34
0.35
28.75_111.25
-0.27
0.26
0.23
-0.23
0.34
0.35
31.25_108.75
-0.24
0.22
0.21
-0.21
0.41
0.31
31.25_111.25
-0.24
0.22
0.22
-0.21
0.43
0.33
28.75_108.75
-0.24
0.22
0.18
-0.22
0.31
0.31
28.75_111.25
-0.24
0.22
0.19
-0.21
0.3
0.31
31.25_108.75
-0.21
0.18
0.17
-0.19
0.36
0.27
31.25_111.25
-0.21
0.19
0.19
-0.19
0.39
0.29
28.75_108.75
-0.25
0.22
0.17
-0.22
0.3
0.31
28.75_111.25
31.25_108.75
-0.24
-0.22
0.22
0.19
0.19
0.17
-0.21
-0.2
0.3
0.34
0.31
0.27
31.25_111.25
-0.22
0.19
0.18
-0.2
0.38
0.29
28.75_108.75
-0.32
0.3
0.24
-0.28
0.39
0.4
28.75_111.25
-0.31
0.3
0.26
-0.26
0.38
0.4
31.25_108.75
-0.28
0.25
0.23
-0.25
0.46
0.36
31.25_111.25
-0.28
0.26
0.25
-0.25
0.49
0.38
28.75_108.75
-0.4658
0.5827
0.5167
-0.2965
0.2445
0.4251
28.75_111.25
-0.4883
0.5675
0.4933
-0.3279
0.213
0.4121
31.25_108.75
-0.4952
0.6172
0.5635
-0.305
0.2345
0.4953
31.25_111.25
-0.4986
0.5967
0.5462
-0.3127
0.1672
0.4733
SLP sea level pressure; SAT surface air temperature; GH geopotential hight; SH specific humidity
utilized as large scale predictors to calibrate and verify the
model. In the process of building SSVM model to predict
the precipitation, the 30 years’ data from 1962 to 1991 is
used for calibration, and the data from 1992 to 1999 is used
for validation; while to the evaporation, the data from 1971
to 1995 is used for calibration and from 1996 to 1999 for
validation. Since the main purpose of the statistical
downscaling in this study is to predict daily precipitation
and evaporation scenarios that are to be used as input to the
hydrological models to simulate future water resources
scenarios in the Qingjiang watershed, the differences in the
mean and standard deviation between observed and simulated daily precipitation and evaporation are considered to
be most important and therefore are used as the criteria in
evaluating the downscaling model. The comparison results
are shown in Table 3.
It can be seen from Table 3 that in simulating the
monthly mean precipitation of 6 hydrological stations and
the area precipitation of Qingjiang Watershed which was
calculated by using the arithmetic average method, the
relative errors of projected and observed monthly mean
precipitation are generally less than 5% in the calibration
and validation periods, and the relative errors of standard
deviation are around 10% for the area values and are
generally less than 15% for individual stations, in both
calibration and validation periods. These results demonstrate SSVM’s good performance in simulating monthly
precipitation. Evaporation, another important hydrological
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852
Stoch Environ Res Risk Assess (2012) 26:847–858
Table 3 Monthly precipitation and evaporation simulations of SSVM in the calibration and validation period
Hydrological stations
Time period
Observed
Mean
(mm/d*30.4)
Precipitation
Enshi
Mean
(mm/d*30.4)
RE (%)
SD
(mm/d*30.4)
RE (%)
123.92
103.15
128.39
3.68
98.85
-4.79
Validation
121.14
101.85
115.38
-3.11
90.22
-12.09
Jianshi
Calibration
Validation
118.66
118.02
100.59
108.95
122.31
110.46
2.86
-0.26
97.15
92.39
-6.98
-13.61
Lichuan
Calibration
110.98
88.07
113.56
4.68
84.34
-0.83
Validation
111.48
98.37
107.84
-2.10
86.62
-15.12
Calibration
117.57
93.93
121.31
3.42
89.67
-2.84
Validation
111.48
98.37
106.54
-5.67
88.20
-9.89
Calibration
125.69
102.64
130.90
5.19
94.76
2.79
Validation
118.68
93.30
115.03
-5.25
81.34
-15.38
Calibration
89.15
76.96
94.07
4.03
72.00
-8.50
Validation
80.61
69.98
78.37
-1.66
61.95
-13.97
Calibration
117.63
89.93
120.44
5.02
89.14
-8.18
Validation
114.39
90.44
107.25
-0.10
81.62
-11.84
Calibration
1.86
1.41
1.88
0.99
1.14
19.33
Validation
1.80
1.33
1.93
7.29
1.16
12.77
Xuanen
Yuxiakou
Area precipitation
input to the hydrological models, is also downscaled with
SSVM. Table 3 also shows statistics of evaporation simulation. In both periods, the relative errors between the
projected and observed evaporation are under 10%, and
the relative error of standard deviation is lower than 20%.
It is notable that SSVM is also a useful tool for building
relationship between large-scale GCM predictors and
evaporation.
To investigate the performance of SSVM in simulating
the precipitation and evaporation from GCM’s predictions,
the results of downscaled precipitation and evaporation
using A2 and B2 scenarios of CGCM2 as predictors are
compared with those using NCEP reanalysis data as predictors in the validation period (1992–1999), and comparison was shown in Fig. 2. For precipitation, there is a
better agreement between the observed precipitation and
the simulations from the NCEP/NCAR reanalysis data than
the simulations from A2 and B2 scenarios. In general, the
performance in downscaling evaporation was better than
that of precipitation except in spring. The comparison also
reveals that although there are some errors in downscaling
precipitation and evaporation from GCM predictions, the
results are considered to be acceptable in providing inputs
into the hydrological models to predict the runoff in future.
4.2 The results of the hydrological models
Observed daily precipitation, evaporation and runoff data
from 1971 to 1979 are used for model calibration, and data
123
SD
(mm/d*30.4)
Calibration
Nanlilu
Evaporation
SSVM
from 1980 to 1984 are served for evaluating the model
performance. The relative error of total runoff volume and
the Nash–Sutcliffe efficiency are used to evaluate the
predictive accuracy of the hydrological models. The
parameters of the hydrological models are optimized with
three algorithms, namely Rosenbrock (Rosenbrock 1960),
simplex (Nelder and Mead 1965; Spendley et al. 1962) and
genetic (Wang 1991). Efficiency of the hydrological
models is shown in Table 4. The both models demonstrate
good simulation performance, with NS of over 85% in both
calibration and validation periods, hence the two models
could be used in practical prediction. For illustrative purpose, the simulated and observed runoff hydrographs in
flood season of 1983 were shown in Fig. 3, which showed
both models provide satisfactory simulation results.
The responses of the two hydrological models to the
downscaled scenarios in the period of 1992–1999 are
presented in Fig. 4, which shows that the simulated runoff
driven by A2 and B2 scenarios of CGCM2 had bigger error
than that of NCEP/NCAR reanalysis data. When comparing Fig. 2 and 4, it can be deduced that the errors of runoff
simulations are mainly from their inputs, such as the
downscaled precipitation and evaporation, rather than from
the hydrological models themselves. Similar conclusions
are found in the literatures (Chiew et al. 2010; Prudhomme
and Davies 2009). The comparison annual runoff simulations were drawn in Fig. 5, which illustrated that there was
little difference between the performances of the two
hydrological models, however, significant difference
Stoch Environ Res Risk Assess (2012) 26:847–858
Obs.
350
NCEP
853
A2
B2
NCEP
A2
B2
120
Evap.(mm/month)
300
Prec.(mm/month)
Obs.
140
250
200
150
100
50
100
0
1
2
3
4
5
6
7
8
80
60
40
20
0
9 10 11 12
1
2
3
4
5
6
7
8
9 10 11 12
Month
Month
Fig. 2 Comparison of monthly precipitation and evaporation simulations and their observations respectively in the validation period
(1992–1999)
Table 4 Efficiency of hydrological models optimized on the Qingjiang catchment
Hydrological model
Calibration
NS (%)
Validation
RE (%)
NS (%)
RE (%)
Xin-anjiang model
89.57
3.70
93.01
5.40
HBV model
90.61
1.00
92.4
0.94
NS Nash–Sutcliffe efficiency; RE error of total runoff volume
0
10000
20
9000
7000
6000
40
Precipitation
60
Observed runoff
80
Xin-anjiang model result
100
5000
4000
HBV model result
120
3000
140
2000
160
1000
180
0
200
Precipitation(mm/d)
8000
Runoff(m 3 /s)
Fig. 3 Comparison of the
simulated and observed
hydrographs in the flood season
in 1983
Date
between the performances of the A2 and B2 scenarios of
CGCM2 are seen. This also implies that more climatic
scenarios should be considered in the study of climate
change impact on runoff.
4.3 Prediction and analysis of future precipitation
and evaporation in Qingjiang Watershed
To predict the future trend of precipitation, the observed
precipitation in the period of 1962–1990 is considered as
base period. The area precipitation of Qingjiang Watershed
is obtained from the 6 rainfall stations by using the arithmetic average method. The future time period was divided
into three parts: 2011–2040 (2020s), 2041–2070 (2050s),
and 2071–2100 (2080s). Statistics of the annual precipitation in the three time periods are shown in Table 5. In
2020s and 2050s, the annual precipitation under A2 scenario will be 13 and 0.13% less than the mean value of base
period, while the precipitation in 2080 s will be 13.68%
more than the mean value of base period. The evolution
trend of precipitation under B2 scenario is similar to that
under A2 scenario, except that the decreasing extent in the
first period and the increasing extent in the last period are
reduced. It is evident that the future precipitation under A2
scenario is more fluctuated. When the three periods are
considered as a whole, the average precipitation in the
123
854
Stoch Environ Res Risk Assess (2012) 26:847–858
Fig. 4 Comparison of monthly
mean runoff simulations by
Xinanjiang model (a) and HBV
model (b) in the validation
period (1992–1999)
(a)
Q_Obs
Q_A2
1400
Q_NCEP
Q_B2
Q(m3 /s/Month)
Q(m3 /s/Month)
800
600
400
600
400
3
4
5
6
7
8
9 10 11 12
1
2
3
4
5
6 7
8
9 10 11 12
Month
scenarios, which are 5.37 and 3.77%, respectively. Figure 7 presents the annual evaporation of future periods
under A2 and B2 scenarios. Generally, the future annual
evaporation exceeds that of base period, which is resulted
from rising temperature. According to Fig. 7, future
evaporation of Qingjiang Watershed under the two scenarios would maintain a significant linear trend of increase,
with the P-values less than 0.05.
qH_b2
qX_ncep
qH_ncep
1999
1998
1997
1996
1995
1994
1993
4.4 Prediction and analysis of future runoff
in Qingjiang Watershed
1992
8
3
Runoff(10 m )
800
0
2
Month
Year
Fig. 5 Comparison of annual runoff simulations driven by various
scenarios in the period of 1989–1999
future indicates a slight decrease with a change rate of
-0.29% under A2 scenario and -0.16% under B2 scenario.
Detailed variations of the projected annual precipitation
of future periods are shown in Fig. 6. The projected total
precipitation under B2 scenario is more than that under A2
scenario during the next 31 years (2011–2051), while the
precipitation under B2 is less than that under A2 scenario
after 2052. Figure 6 also shows that the annual precipitation during 2011–2052 is less than that of base period,
while after that the precipitation increases and exceeds the
values in base period until the end of the century. To
analyze the future evolution trend of precipitation in
Qingjiang Watershed, linear regression method is applied
in this study. The results, presented in Fig. 6, show that the
future precipitation of Qingjiang Watershed under the two
scenarios would maintain a significant linear trend of
increase, with the P-values much less than 0.05.
With the same method the future evaporation is downscaled. Table 5 shows the projected annual evaporation
under the two scenarios. It can be concluded from the table
that the future evaporation under A2 scenario would
increase at a faster rate than that under B2 scenario. This
can also be verified by the mean change rates under the two
123
1000
200
1
200
180
160
140
120
100
80
60
40
20
0
Q_NCEP
Q_B2
1200
1000
0
qX_a2
qH_a2
qX_b2
Q_Obs
Q_A2
1400
1200
200
qobs.
(b)
By inputting the precipitation and evaporation downscaled
by SSVM to the Xin-anjiang model and the HBV model,
future runoff under A2 and B2 scenarios could be projected. To analyze the future trend of runoff, the observed
data from 1962 to 1990 are considered as reference.
Table 6 shows the change rate of future runoff under two
scenarios. Under A2 scenario, the projected runoff of Xinanjiang model in the first future time period is 16.46% less
than the mean value in the base period, while in the last two
periods, the runoff will be 13.86 and 20.71% higher that the
mean values in the base period respectively. The result
obtained under B2 scenario is similar, but with less extent
of change in the three periods. In other words, the future
runoff projected under A2 scenario is generally more
fluctuated than that under B2 scenario. The projected
runoff of HBV model shows the similar trend.
To further analyze the change trend of runoff in the
upcoming 100 years, the future annual runoffs projected by
the two hydrological models under the two scenarios are
respectively presented in Fig. 8 and the linear regression
method is used in this part. The results presented in Fig. 8
show that the projected runoff of the two models in
Qingjiang Watershed would maintain an increasing trend
under A2 scenario. The increasing rate projected by the
two models would be 6.04 and 4.38% respectively. The
results under B2 scenario demonstrate the similar changing
trend. The increasing rates of the two models are 4.81 and
2.78%, respectively. According to Fig. 8, the future annual
runoff during 2011–2051 would be less than the annual
Stoch Environ Res Risk Assess (2012) 26:847–858
855
Table 5 Projected annual precipitation (mm) and evaporation (mm) under A2 and B2 scenarios in future
Scenario
Precipitation
A2
B2
Evaporation
A2
B2
Base period
2020s
2050s
2080s
1595.34
1403.33
1200.79
1401.56
Change rate (%)
-14.43
-0.13
13.68
1403.33
1273.29
1401.56
1528.26
Change rate (%)
-9.27
-0.13
8.9
671.08
670.62
688.63
762.06
Change rate (%)
-0.07
2.61
13.56
671.08
677.81
688.25
702.85
1.00
2.56
4.73
Change rate (%)
Mean
-0.29
-0.16
5.37
2.77
Base period is 1962–1990
2100
Annual precipitaion(mm/y)
Fig. 6 Projected annual
precipitation under A2 and B2
scenarios in future
1900
A2
B2
Linearity (A2)
1700
Lineartiy (B2)
1500
1300
1100
Annual precipitation of base period
900
2011
2021
2031
2041
2051
2061
2071
2081
2091
Year
Fig. 7 Projected annual
evaporation under A2 and B2
scenarios in future
runoff of base period. The result of HBV model demonstrates the same trend.
5 Summary and discussion
The one-way coupling approach that links GCM’s predictions with hydrological models by using a statistical
downscaling method evaluated in the Qingjiang watershed. The validated approach was used to investigate the
impacts of climate change on future runoff in Qingjiang
Watershed. The study also demonstrated the application
of Xin-anjiang hydrological model and HBV model in
this research area. After optimizing the parameters of the
models with observed data, their results showed satisfactory simulation performance, with NS efficiency value
123
856
Stoch Environ Res Risk Assess (2012) 26:847–858
Table 6 Projected annual runoff (108m3) in future
Model
Scenario
Xin-anjiang
A2
HBV
Base period
2020s
2050s
2080s
Mean
138.5
146.9
129.0
121.7
101.6
Change rate (%)
-16.46
B2
121.7
111.3
Change rate (%)
-8.54
A2
121.7
101.4
Change rate (%)
-16.68
121.7
110.2
Change rate (%)
-9.44
B2
13.86
20.71
127.2
6.04
144.1
4.52
127.5
18.45
134.9
4.81
144.7
10.91
127.0
18.9
124.4
4.38
140.6
2.26
125.0
15.53
2.78
Base period is 1962–1990
(a)
8
3
Annual runoff (10 m )
Fig. 8 Projected annual runoff
under A2 (a) and B2 scenario
(b) in future
210
190
170
150
Xin-anjiang
HBV
Linearity (Xin-anjiang)
Linearity (HBV)
130
110
90
Annual runoff of base period
70
50
2011
2021
2031
2041
2051
2061
2071
2081
2091
2061
2071
2081
2091
Year
Annual runoff (108 m3 )
(b) 190
170
150
130
110
90
70
50
2011
2021
2031
2041
2051
Year
of over 85% in both the calibration and validation
periods.
The simulations of precipitation and evaporation using
SSVM downscaling model showed that this model could be
used in future prediction of those hydrological variables in
the study region. The precipitation and evaporation
obtained from the downscaling model under A2 and B2
scenarios were used to analyze the future trend. Moreover,
the downscaled precipitation and evaporation were used as
inputs to Xin-anjiang hydrological model and HBV model
to predict the runoff in the future. Analysis of change trend
of precipitation, evaporation and runoff during 2011 to
2100 was accomplished with linear regression. The results
indicated that the average annual precipitation during
2011–2052 is less than that of base period (1962–1990),
while after that the precipitation increases and exceeds the
123
values in base period. In general, the future precipitation of
Qingjiang Watershed under the two scenarios would
maintain a significant increasing trend from below the base
period in the first half of the century to above the base
period in the second half of the century.
The future annual evaporation would increase by 5.37%
under A2 scenario, while the increase rate would be 3.77%
under B2 scenario. With the same method, the change trend
of the projected runoff was evaluated. The output from
Xin-anjiang model demonstrated that the future runoff of
Qingjiang Watershed under A2 and B2 scenarios would be
less than the base period during 2011–2040 and higher than
the base period thereafter. The results of HBV model show
the similar changing pattern.
From the above discussion, it can be seen that there is
slightly difference between the two hydrological models in
Stoch Environ Res Risk Assess (2012) 26:847–858
predicting future runoff in Qingjiang watershed and the
change trends of runoff driven by the two hydrological
models are consistent. It is also found that the runoff
simulation driven by A2 and B2 scenarios had a poorer
performance compared to the simulation driven by the
observed precipitation and even to NCEP/NCAR reanalysis
data in the period of 1992–1999. It can be concluded that in
the process of studying climate change impact on water
resources, the main uncertainties are from the GCMs and
the downscaling sector.
Acknowledgements NCEP–NCAR daily reanalysis data and
CGCM2 daily data were downloaded freely from the Internet:
http://dss.ucar.edu/pub/reanalysis/ and www.cccma.ec.gc.ca respectively. The study was supported financially by the National Key
Technologies R&D Program of China (2009BAC56B01) and the
National Natural Science Fund of China (50809049). The fourth
author was also supported by the Programme of Introducing Talents
of Discipline to Universities—the 111 Project of Hohai University.
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