Stoch Environ Res Risk Assess (2011) 25:781–792
DOI 10.1007/s00477-010-0441-9
ORIGINAL PAPER
Estimation of future precipitation change in the Yangtze River
basin by using statistical downscaling method
Jin Huang • Jinchi Zhang • Zengxin Zhang
ChongYu Xu • Baoliang Wang • Jian Yao
•
Published online: 24 October 2010
Ó Springer-Verlag 2010
Abstract In this study, the applicability of the statistical
downscaling model (SDSM) in downscaling precipitation in
the Yangtze River basin, China was investigated. The
investigation includes the calibration of the SDSM model
by using large-scale atmospheric variables encompassing
NCEP/NCAR reanalysis data, the validation of the model
using independent period of the NCEP/NCAR reanalysis
data and the general circulation model (GCM) outputs of
scenarios A2 and B2 of the HadCM3 model, and the prediction of the future regional precipitation scenarios.
Selected as climate variables for downscaling were measured daily precipitation data (1961–2000) from 136
weather stations in the Yangtze River basin. The results
showed that: (1) there existed good relationship between the
observed and simulated precipitation during the calibration
period of 1961–1990 as well as the validation period of
1991–2000. And the results of simulated monthly and seasonal precipitation were better than that of daily. The
average R2 values between the simulated and observed
monthly and seasonal precipitation for the validation period
J. Huang J. Zhang (&) Z. Zhang J. Yao
Jiangsu Key Laboratory of Forestry Ecological Engineering,
Nanjing Forestry University, Long Pan Road 159, Nanjing
210037, China
e-mail: zjcforest@yahoo.com.cn
C. Xu
School of Geographic and Oceanographic Sciences,
Nanjing University, Nanjing 210093, China
B. Wang
Nanjing Institute of Soil Science, Chinese Academy of Science,
Nanjing 210008, China
C. Xu
Department of Geosciences, University of Oslo, Oslo, Norway
were 0.78 and 0.91 respectively for the whole basin, which
showed that the SDSM had a good applicability on simulating precipitation in the Yangtze River basin. (2) Under
both scenarios A2 and B2, during the prediction period of
2010–2099, the change of annual mean precipitation in the
Yangtze River basin would present a trend of deficit precipitation in 2020s; insignificant changes in the 2050s; and a
surplus of precipitation in the 2080s as compared to the
mean values of the base period. The annual mean precipitation would increase by about 15.29% under scenario A2
and increase by about 5.33% under scenario B2 in the
2080s. The winter and autumn might be the more distinct
seasons with more predicted changes of precipitation than
in other seasons. And (3) there would be distinctive spatial
distribution differences for the change of annual mean
precipitation in the river basin, but the most of Yangtze
River basin would be dominated by the increasing trend.
Keywords Statistical downscaling model SDSM Precipitation The Yangtze River basin China
1 Introduction
Human activities, especially the burning of fossil fuels and
changes in land cover and use, are nowadays considered to
increase the atmospheric concentrations of greenhouse
gases, which in turn change the climate of the Earth (Keller
2009). It is reported in the fourth report of the Intergovernmental Panel on Climate Change (IPCC 2007) that the
increasing concentration of CO2 and other greenhouse
gases are main cause of global warming. It has been
pointed out in the report that the global surface temperature
has increased by 0.74°C in the latest century (1906–2005),
and the increasing rate is about 0.13°C/10 years in the past
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50 years; this report also predicted that surface temperature
would continue to increase at a rate of 0.2°C/10 years in
the next 20 years and would increase about 1.1–6.4°C
during the next century (IPCC 2007). Global warming will
have significant impact on local and regional precipitation
and hydrological regimes, which in turn will affect ecological, social and economical systems of human, such as
health of ecosystems and fish resource management,
industrial and agricultural water supply, resident living
water supply, water energy exploitation, and human health,
etc. These potential changes request us to make some
qualitative and quantitative estimations on the impact of
climate change upon regional water resource.
At present, the General Circulation Models (GCMs) are
the important method of studying and predicting the future
climate change, which can reproduce important processes
about global- and continental scale atmosphere and predict
future climate under different emission scenarios (Chu
et al. 2010). Unfortunately, the current versions of GCMs
are restricted in their use for local impact studies by their
coarse spatial resolution (typically of the order
50,000 km2) and inability to resolve important sub-grid
scale features such as clouds and topography (Wilby et al.
2002). For many regional and local scale applications, the
limitations of the GCMs simulation results of climate have
long been dissatisfied. In order to deal with the disadvantage of spatial scale mismatch, downscaling methods have
emerged as means of connecting regional-scale atmospheric predictor variables to local-scale surface weather.
Downscaling methods can be broadly divided into two
classes: dynamical downscaling (DD) and statistical
(empirical) downscaling (SD). In DD, the GCM outputs are
used as boundary conditions to drive a Regional Climate
Model (RCM) or Limited Area Model (LAM) and produce
regional-scale information up to 5–50 km, this method
responds in physical consistent ways to different external
forcing. However DD requires higher computational cost
and depends strongly on the boundary conditions provided
by GCMs. While SD produces local or station-scale
meteorological time series by appropriate statistical or
empirical relationships with predictor variables, this
method is cheap, readily transferable and computationally
undemanding, and it has been widely used in climate
change risk or uncertainty assessments. However, disadvantage of SD is that building the appropriate statistical
relationship needs historical observed data having sufficient length (Wilby et al. 2002). To get statistically
meaningful and stable relationship a reasonable length of
30 years would be needed as suggested in user manual of
SDSM 4.2. At present, there are various SD models
available, in which the Statistical-Downscaling Model
(SDSM) is a promising one. SDSM is the first tool of its
type offered to the broader climate change impacts
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Stoch Environ Res Risk Assess (2011) 25:781–792
community (Wilby et al. 2002), and had been coded into
software that is freely available. Many comparative studies
have shown that this model is simple to handle and operate,
and its large and superior capability makes it had been
widely applied (Wilby et al. 1998; Harpham and Wilby
2005; Dibike and Coulibaly 2005; Khan et al. 2006; Wilby
and Harris 2006; Fowler et al. 2007).
Statistical downscaling methods have received increasing attention from many scholars in China, however, most
of the previous work was mainly focused on maximum and
minimum temperatures (Chen and Chen 2001; Liao et al.
2004; Yuan et al. 2005; Chen et al. 2006; Fan et al. 2007;
Chu et al. 2008; Liu et al. 2008a, b; Huang et al. 2008; Liu
and Xu 2009). Meanwhile, the downscaling methods have
not been often used in large river basins, such as Yangtze
River basin. The Yangtze River, being the longest river in
China and the third longest river in the world, plays a vital
important role in the social-economic development of
China (Zhang et al. 2010). Being affected by the monsoon
climate, flood disaster occurs almost every year in the
Yangtze River basin, which had brought life and property
loss to the people lived along river, and also seriously
affected social-economic development in the region. Previous studies (e.g. Zhang et al. 2005, 2008) showed that
both the mean and extreme precipitation had a significant
increasing trend in the middle and lower Yangtze River
basin during past half century which has potential to result
in higher variability of flood risk in the region. Therefore,
good knowledge of future precipitation scenarios in the
Yangtze River basin will be of great importance in better
evaluating the risk of floods and droughts in the future,
which would also provide reference for reasonable
exploitation and utilization of water resources. So there are
two objectives of this paper: (1) to investigate the adaptability of SDSM for downscaling precipitation in a large
river basin, such as the Yangtze River basin; (2) to generate
local-scale precipitation scenarios in the Yangtze River
basin under future emission scenarios and project the
spatial and temporal characteristics of precipitation. Such a
study has not been reported at least for such a large river
basin, and it may provide valuable database for future
climate change scenarios in the Yangtze River basin.
2 Study area, data and methods
2.1 Study region
The Yangtze River basin, located between 91°E and 122°E
and 25°N and 35°N, and has a drainage area of 1, 808,
500 km2. For the sake of analysis and comparison, the
whole basin is divided into three parts based on longitude
(Table 1): The upper region has a mean altitude of about
Stoch Environ Res Risk Assess (2011) 25:781–792
783
Table 1 The upper, middle and lower Yangtze River basin
Longitude
The upper Yangtze River basin
The middle and lower Yangtze River basin
The entire Yangtze River basin
Latitude
91–110°
25–35°
111–120°
25–35°
91–120°
25–35°
2551 m above sea level (m.a.s.l.), and the middle and lower
regions have a mean altitude of about 627 and 113 m.a.s.l.,
respectively. The climate of the Yangtze River basin is of
the subtropical monsoon type and the rain zone is closely
related to monsoon activities (Zhang et al. 2010).
2.2 Data
The observed daily precipitation data covering 1961–2000
from 136 National Meteorological Observatory (NMO)
stations were used in this study. The data were provided by
the National Climatic Centre (NCC) of the China Meteorological Administration (CMA). The missing data of one
day or two days were replaced by the average precipitation
values of the neighboring stations. If consecutive days had
the missing data, the missing values were replaced with
long term averages of the same days. The location of the
gauging stations can be referred to Fig. 1, which was more
or less uniformly distributed and could cover the whole
river basin well.
The reanalysis dataset of NCEP/NCAR derived from the
National Center for Environmental Prediction (NCEP/
NCAR) were used in this study, and this dataset is daily
series for 1961–2000 at a spatial scale of 2.5°
(long.) 9 2.5° (lat.), which includes 26 atmospheric variables such as mean sea level pressure, near surface relative
humidity, near surface specific humidity, relative humidity
at 500 hPa, 2-m air temperature, etc.
GCM outputs dataset of scenarios A2 (high greenhouse
gases emission scenarios) and B2 (low greenhouse gases
emission scenarios) derived from the Hadley Center’s
coupled ocean/atmosphere climate model (HadCM3) were
also used in this study, and this dataset is daily series for
1961–2099 at a resolution of 3.75° (long.) 9 2.5° (lat.),
which includes the same atmospheric variables as NCEP
data. The HadCM3 grid boxes selected in the Yangtze River
basin can be referred to Fig. 1. NCEP data should be
interpolated in order to adjust its resolution to the same as
under scenarios A2 and B2 of HadCM3 model. The transformed data can be directly downloaded from the internet
site: http://www.cics.uvic.ca/scenarios/sdsm/select.cgi.
2.3 Methodology
The Statistical-Downscaling Model (SDSM) is a multivariate linear regression method for generating future climate
scenarios to assess the impact of global climate change,
which is a combination of a transfer function model and a
stochastic weather generator approach by using two types of
daily data. The first type corresponds to local predictands of
interest (e.g. temperature, precipitation) and the second type
corresponds to the data of large-scale predictors (NCEP and
GCM) of a grid box closest to the study area (Hashmi et al.
2010). During downscaling with the SDSM, a multiple linear regression model is derived from a few selected largescale predictor variables and local scale predictands such as
temperature and precipitation. Large-scale relevant predictors are selected by the results of correlation analysis, partial
correlation analysis and scatter plots, and the physical sensitivity between selected predictors and predictand should
also be considered in study. SDSM provides two means of
optimizing the model—Dual Simplex and Ordinary Least
Squares (Wilby et al. 2007), and both approaches give
comparable results; Ordinary Least Squares is much faster.
The model is structured as monthly model for both daily
precipitation and temperatures downscaling, in which case,
12 regression equations are derived for 12 months using
different regression parameters for each month equation.
The output of SDSM is daily series, when the model is
Fig. 1 The location of climate
gauging stations and HadCM3
grid boxes in the Yangtze River
basin
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established, the daily data of NCEP and GCM is used to
construction of current and future daily weather series.
SDSM had been coded to software of current version 4.2,
which can make the work of user with an ease and quickness.
The SDSM 4.2 reduces the task of statistically downscaling
daily weather series into five discrete processes (Wilby et al.
2002): (1) screening of predictor variables; (2) model calibration; (3) synthesis of observed data; (4) generation of
climate change scenarios; (5) diagnostic testing and statistical analyses.
In this study, SDSM was applied in the Yangtze River
basin to downscale the daily precipitation with the following steps. Firstly, we screened appropriate atmospheric
variables for predictors, and constructed the statistical
regression models between predictors and predictand in
each station; secondly, the gridded data of NCEP and
HadCM3 were inputted into the models to generate the
current and future daily precipitation series of each station,
and the monthly, seasonal and annual precipitation series of
each station were calculated from the daily precipitation
series; finally, the monthly, seasonal and annual precipitation series under the scale of river basin could be constructed by the average values of all stations.
3 Results
3.1 Calibration and validation of SDSM
SDSM was first calibrated using large-scale predictor
variables of the current climate condition derived from the
NCEP reanalysis dataset as driving data, and then validated
in an independent time period using three sets of atmospheric data, i.e., NCEP and scenarios A2 and B2 from
HadCM3 model (Wilby et al. 2002; Dibike and Coulibaly
2005; Chu et al. 2010). The user manual of SDSM 4.2
suggests that the total data length for calibration and validation should exceed 30 years. Similar to most of applications of SDSM reported, the model was calibrated and
validated separately for precipitation using 30 years
(1961–1990) for calibration and 10 years (1991–2000) for
validation in this study (Hay et al. 2000; Dibike and
Coulibaly 2005; Nieto and Wilby 2005; Khan et al. 2006;
Liu et al. 2008a; Rong et al. 2010; Hashmi et al. 2010). The
use of 40 years of data is considered to be capable of
representing the true climatic condition for the site in question including less frequent climate events (Khan et al. 2006).
3.1.1 Selecting predictors
Identifying empirical relationships between gridded predictors (such as mean sea level pressure) and single-site
predictands (such as station precipitation) is central to all
statistical downscaling methods (Wilby et al. 2002). The
predictor selection process was consistent with that adopted
in similar studies (Wilby et al. 2002; Dibike and Coulibaly
2005; Khan et al. 2006), screening of the most relevant
predictors’ set was performed in SDSM 4.2 on the basis of
correlation and partial correlation analysis among the predictand and the individual predictors. Daily data of 26 largescale predictor variables (Table 2) representing the current
climate condition derived from the NCEP reanalysis data set
was used to investigate the percentage of variance explained
by each predictand–predictor pairs. The correlation coefficient (corr) and partial correlation coefficient (p_corr)
among the daily precipitation and the individual NCEP
atmospheric variables in different stations showed obvious
differences, and the maximum of their absolute values in this
study could be seen in Fig. 2. In general, the correlation
between the predictor variables and each predictand is not
Table 2 Name and description of all NCEP variables (the one in bold text was selected for the calibration of model)
Variable
Description
Variable
Description
1
ncepmslpas
Mean sea level pressure
14
ncepp5zhas
500 hPa divergence
2
ncepp_fas
Surface airflow strength
15
ncepp8_fas
850 hPa airflow strength
3
ncepp_uas
Surface zonal velocity
16
ncepp8_uas
850 hPa zonal velocity
4
ncepp_vas
Surface meridional velocity
17
ncepp8_vas
850 hPa meridional velocity
5
ncepp_zas
Surface vorticity
18
ncepp8_zas
850 hPa vorticity
6
ncepp_thas
Surface wind direction
19
ncepp850as
850 hPa geopotential height
7
ncepp_zhas
Surface divergence
20
ncepp8thas
850 hPa wind direction
8
9
ncepp5_fas
ncepp5_uas
500 hPa airflow strength
500 hPa zonal velocity
21
22
ncepp8zhas
ncepr500as
850 hPa divergence
Relative humidity at 500 hPa
10
ncepp5_vas
500 hPa meridional velocity
23
ncepr850as
Relative humidity at 850 hPa
11
ncepp5_zas
500 hPa vorticity
24
nceprhumas
Near surface relative humidity
12
ncepp500as
500 hPa geopotential height
25
ncepshumas
Surface specific humidity
13
ncepp5thas
500 hPa wind direction
26
nceptempas
Mean temperature at 2 m
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Fig. 2 The maximum of
absolute values of correlation
coefficient (corr) and partial
correlation coefficient (p_corr)
between the daily precipitation
and 26 NCEP atmospheric
variables
0. 5
785
cor r
p_cor r
0. 4
0. 3
0. 2
ncept empas
ncepshumas
ncepr humas
ncepr 500as
ncepr 850as
ncepp8t has
ncepp8zhas
ncepp850as
ncepp8_vas
ncepp8_zas
ncepp8_uas
ncepp8_f as
ncepp5zhas
ncepp5t has
ncepp500as
ncepp5_zas
ncepp5_vas
ncepp5_f as
ncepp5_uas
ncepp_zhas
ncepp_t has
ncepp__zas
ncepp__vas
ncepp__uas
ncepmsl pas
0
ncepp__f as
0. 1
Table 3 Overall correlation coefficient between predictor variables
mslpas
p_zas
mslpas
p_zas
p5_fas
1
-0.44
1
p5_fas
p5_uas
p500as
p5_uas
p500as
p8_zas
p850as
r500as
r850s
rhumas
shumas
tempas
0.46
0.49
-0.42
-0.26
0.92
-0.15
-0.13
-0.09
-0.79
-0.86
0.44
-0.51
-0.43
0.35
-0.49
-0.14
-0.22
-0.07
0.73
0.71
0.95
-0.74
0.02
0.39
-0.31
-0.15
-0.24
-0.81
-0.81
1
-0.76
1
1
0.01
0.40
-0.32
-0.16
-0.27
-0.82
-0.83
-0.03
-0.21
0.26
-0.08
0.23
0.81
0.82
1
-0.42
0.20
0.26
0.12
0.15
0.18
-0.12
-0.17
-0.15
-0.66
-0.61
0.22
0.26
0.34
0.32
1
0.67
0.12
0.09
p8_zas
p850as
r500as
r850s
rhumas
shumas
tempas
high in case of daily precipitation (Wilby et al. 2002; Dibike
and Coulibaly 2005; Hashmi et al. 2010). The predictor
variables identified for downscaling precipitation used in
this study were shown in bold text in Table 2. The correlation analysis among a set of 12 predictor variables selected
showed there was higher correlation between some predictor
variables (Table 3), and considering the collinearity among
different predicators in the multiple regression process, the
number of predictors in individual station was controlled 2
to 7. In the predicators selected, the usage frequency of
8_zas, r500as, r850as and shumas were amongst the highest
in this downscaling experiment.
3.1.2 Calibration of SDSM
In this study, the calibration period for daily precipitation
was 30 years from 1961 to 1990. Following the user
manual of SDSM 4.2, when using NCEP reanalysis data as
predictors, threshold of wet day was setting as 0 mm, a
1
1
1
0.41
0.35
1
0.95
1
fourth root transformation was applied to the original
precipitation series to convert it to a normal distribution
(Wilby et al. 2002), and the ordinary least squares was used
for optimization. SDSM 4.2 provided several statistical
indicators such as the percentage of explained variance
(E %) and the Standard Error (SE) to reflect calibration
results (Wilby et al. 2002). In this paper, the E % of
downscaling experiment in each site ranged from 18.5 to
32.4%, and the SE ranged from 0.24 to 0.55. The percentage of explained variance (E %) in some researches of
downscaling precipitation were all low (Table 4), and the
table showed that the calibration results of this study in
Yangtze River Basin were closer to the overall level. For
heterogeneous and random variables such as daily precipitation occurrence/amounts, percentage of explained variance (E %) is more likely \40% (Wilby et al. 2002). The
calibration is probably seriously biased by the large number of zero values entered in the multiple regressions, and
the underlying surface factors are not considered in SDSM.
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Table 4 Percentage of
explained variance for modeling
precipitation with SDSM in
different regions
Author
Region
Percentage of explained
variance (%)
Liu et al. (2008a, b)
Upper-middle reaches of the Yellow
River (China)
8.00–20.00
Zhao and Xu (2007)
Source of the Yellow River Basin (China)
14.78
Masoud et al. (2008)
East of Quebec
15.00–31.00
Chen (2008)
Yangtze-Huaihe River Basin (China)
8.8–20.6
Wilby et al. (2002)
Toronto
28.00
Rong et al. (2010)
Dongjiang River Basin (China)
23.30–28.30
This study
Yangtze River Basin (China)
18.5–32.4
3.1.3 Validation of SDSM
results of daily precipitation in the most of similar researches were worse than that of monthly and seasonal precipitation. (Wilby et al. 2002; Dibike and Coulibaly 2005;
Khan et al. 2006; Fealy and Sweeney 2007). When
downscaling the precipitation in case of monthly and seasonal time steps, the SDSM showed better applicability,
especially in the modeling the seasonal precipitation. It
could be seen that the seasonal precipitation series simulated from NCEP, H3A2 and H3B2 with the mean R2
values being higher than 0.8, and the mean RE between
observed and downscaled did not exceeded 20%. On the
whole, the precipitation series simulated by SDSM with
three sets of atmospheric data had good liner relationship
with that of observed, but there were obvious deviation of
amount between them. The simulation results derived from
NCEP were better than from H3A2 and H3B2; and H3B2
was a little worse than H3A2. Because SDSM was calibrated with NCEP data; therefore, the built parameters had
biases when the model was driven by the H3A2 and H3B2
data.
Besides the comparison based on station simulations, the
simulation results of monthly precipitation series and
To validate the SDSM model, three sets of atmospheric
data were used, i.e., from NCEP, as well as scenarios A2
and B2 from HadCM3 model (noted as H3A2 and H3B2,
respectively). Four common evaluation indices applied in
the climate simulation as correlation coefficient (corr),
determination coefficient (R2), relative error (RE) and root
mean standard error (RMSE) were used to qualify the
simulation results of daily precipitation series, monthly
precipitation series and seasonal precipitation series in each
station. In this study, the validation periods for precipitation were 10 years from 1991 to 2000, the results for the
validation period showed obviously difference in different
stations (Table 5). It is seen that the daily precipitation
series simulated from NCEP, H3A2 and H3B2 with the
mean R2 values being slightly high than 0.2, which is
comparable with literature values (Wilby et al. 2002;
Dibike and Coulibaly 2005; Khan et al. 2006; Fealy and
Sweeney 2007). This is because the amount of precipitation
is stochastic processes, the downscaling of daily precipitation is always a difficult subject, and the stimulation
Table 5 The corr, R2, RE and RMSE between observed and simulated results for each station in the validation period (1991–2000; a = 0.05)
R2
Corr
RE (%)
Range
Mean
Range
Mean
NCEP
0.32–0.56
0.45
0.13–0.32
H3A2
0.29–0.54
0.43
H3B2
0.28–0.52
0.43
NCEP
0.72–0.95
H3A2
H3B2
0.71–0.93
0.69–0.91
NCEP
H3A2
H3B2
Range
RMSE
Mean
Range
Mean
0.23
2.68–9.37
7.95
0.11–0.29
0.21
2.91–10.09
8.12
0.11–0.28
0.21
2.91–10.19
8.24
0.80
0.53–0.89
0.65
23.4–41.8
28.9
0.79
0.78
0.50–0.87
0.48–0.84
0.62
0.62
26.1–44.5
27.5–44.8
30.2
31.6
0.86–0.98
0.94
0.75–0.97
0.86
10.7–30.1
17.4
0.84–0.97
0.91
0.71–0.94
0.82
12.1–33.8
19.1
0.83–0.97
0.89
0.70–0.94
0.81
12.3–34.6
19.6
Daily
Monthly
Seasonal
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Table 6 The R2 and RE between observed and simulated results at river basin scale in the validation period (1991–2000; a = 0.05)
The upper Yangtze reaches
R
2
The mid-lower Yangtze reaches
RE (%)
R
2
The whole river basin
RE (%)
R2
RE (%)
Monthly
NCEP
0.91
20.3
0.78
29.8
0.80
27.1
H3A2
0.87
24.4
0.76
31.4
0.77
29.6
H3B2
0.86
25.3
0.76
31.8
0.76
30.4
Seasonal
NCEP
0.97
12.3
0.88
23.9
0.92
17.6
H3A2
H3B2
0.96
0.94
15.1
15.2
0.86
0.87
25.1
24.6
0.91
0.89
19.6
21.9
250
OBS
NCEP
H3A2
H3B2
a
200
150
100
50
0
1
2
3
4
5
6
7
8
9
10 11 12
precipitation mm/month
precipitation mm/month
250
b
OBS
NCEP
H3A2
H3B2
200
150
100
50
0
1
2
3
4
month
5
6
7
8
9
10 11 12
month
Fig. 3 Comparison of monthly mean precipitation among observed and downscaled derived from NCEP data and HadCM3 data during
validation periods (1991–2000) in the Yangtze River basin (a the upper Yangtze reaches; b the mid-lower Yangtze reaches)
seasonal precipitation series averaged over the three
reaches of the basin as well as the whole basin were also
discussed in this study (Table 6). It is seen that the monthly
precipitation and seasonal precipitation of the upper Yangtze reaches, the mid-lower Yangtze reaches and the whole
river basin could be simulated well by SDSM, the R2
values were all higher than 0.75 and the average values for
the monthly and seasonal simulations were 0.78 and 0.91
respectively for the whole basin. This indicated that SDSM
had good applicability in modeling the change process of
monthly precipitation and seasonal precipitation under the
scale of river basin.
3.1.4 Comparison of mean precipitation of downscaling
for the validation period
For illustrative purposes, the comparison between the
observed and downscaled mean monthly precipitation for
the river basin was discussed in details. The performance
on modeling the monthly mean precipitation of the river
basin during the validation periods were focused in many
researches of SDSM (Wilby et al. 2002; Dibike and
Coulibaly 2005; Zhao and Xu 2007; Fealy and Sweeney
2007; Chu et al. 2010), this was attributable to that the
performance of current GCMs and downscaling models
was not yet skillful enough to predict accurately the daily
precipitation. It could be seen that the simulation results
agreed reasonably well to observation results in the subbasins (Fig. 3). The mean value and seasonal variation of
precipitation could be accurately simulated by SDSM fed
by the data of NCEP, H3A2, or H3B2. Being similar to that
in the other region of China (e.g. Chu et al. 2010), the
simulation results were all slightly lower compared to the
observated values, especially in the summer.
In addition, the performance on modeling annual mean
precipitation was also analyzed in this study, the statistical
results of relative error (RE %) between observed and
modeled in each station were shown in Table 7. It could be
seen the RE of annual mean precipitation downscaled by
NCEP data and HadCM3 data ranged from -10 to 10%
roughly. The spatial distribution for the annual mean
Table 7 The statistical result of RE of simulated results
Range
(%)
Mean
(%)
Standard
deviation
Kurtosis
Skewness
NCEP
-10.7 to 8.1
-1.6
3.76
2.99
0.30
H3A2
-12.5 to 10.3
-2.6
5.14
2.50
0.25
H3B2
-14.2 to 11.6
-4.6
6.06
3.39
0.43
123
788
precipitation of observed and simulated during the validation periods in the Yangtze River basin were built by
Inverse Distance Weighted (IDW) interpolation technology
with Arcgis 9.2 software package. It could be seen in Fig. 4
there was a good spatial similarity between observed and
modeled. The simulation results could be a good reflection
of the spatial distribution features that the annual precipitation gradually alters from west to east, except that an
underestimation was noted especially under scenarios A2
and B2 in the southeast region where annual precipitation
was larger than 1700 mm.
On the basis of above analyses, SDSM had a certain
applicability on simulating precipitation in the Yangtze
River basin, and it also indicated that there was a certain
credibility on using calibrated and validated model with
HadCM3 data to predict future precipitation change of the
river basin.
3.2 Downscaling precipitation under future emission
scenarios
In this study, the period of 1961–1990 was taken as base
period as was used in most impact studies worldwide, and
the future period was divided into 2020s (2010–2039),
2050s (2040–2069), 2080s (2070–2099). The patterns of
change about future precipitation scenarios compared to
base period were then analyzed, using only H3A2 and
Stoch Environ Res Risk Assess (2011) 25:781–792
H3B2 data. Taking the simulation results of SDSM in the
modeling precipitation of current period (1991–2000) into
account, the change of seasonal and annual mean precipitation of Yangtze River basin under scenarios A2 and B2
were discussed in this paper for illustrative purposes.
3.2.1 The future changes of seasonal and annual mean
precipitation
The changes of seasonal and annual mean precipitation
(compared to base period 1961–1990) in Yangtze River
basin under scenarios A2 and B2 are shown in Fig. 5. It is
seen that under scenario A2, the changes of annual mean
precipitation of future periods (2020s, 2050s and 2080s) in
the upper Yangtze reaches would be -8.71, ?0.33 and
?13.10% respectively, as to the mid-lower Yangtze
reaches, the change would be -6.72, ?2.83 and ?16.72%
respectively; when it come to the whole river basin, the
changes would be -7.50, ?1.85 and ?15.29% respectively. Under scenario B2, the changes of annual mean
precipitation of future periods (2020s, 2050s and 2080s) in
the upper Yangtze reaches would be -4.67, -2.15 and
?4.01% respectively; as to the mid-lower Yangtze reaches,
the change would be -4.75, ?0.99 and ?6.18% respectively; when it came to the whole river basin, the changes
would be -4.72, -0.24 and ?5.33% respectively. The
change of annual mean precipitation in the Yangtze River
Fig. 4 Spatial distribution for annual mean precipitation of observed and downscaled using NCEP and HadCM3 data during the validation
periods (1991–2000) in the Yangtze River basin (a observed, b NCEP, c A2, d B2)
123
Stoch Environ Res Risk Assess (2011) 25:781–792
789
20
20
10
0
DJF
MAM
JJA
SON
Annual
-10
-20
2020s
2050s
b(B2)
Percentage %
Percentage %
a(A2)
2080s
10
0
DJF
MAM
JJA
SON
Annual
-20
2050s
2080s
SON
Annual
2020s
2050s
2080s
d(B2)
20
10
0
-10
-30
DJF
MAM
JJA
SON
Annual
2020s
2050s
2080s
40
e(A2)
20
10
0
DJF
MAM
2020s
2050s
JJA
SON
Annual
-20
f(B2)
30
Percentage %
30
Percentage %
JJA
-20
2020s
40
-30
MAM
30
Percentage %
Percentage %
20
-10
DJF
-10
40
c(A2)
30
-30
0
-20
40
-10
10
20
10
0
-10
DJF
MAM
JJA
SON
Annual
-20
2080s
-30
2020s
2050s
2080s
Fig. 5 The change percentage of seasonal and annual mean precipitation (compared to base period) in 2020s, 2050s, and 2080s under scenarios
A2 and B2 (a, b the upper Yangtze reaches; c, d the mid-lower Yangtze reaches; e, f the whole river basin)
basin would present the overall situation of increase under
both scenarios A2 and B2, and the change under scenario
A2 would be more distinct compared to that of scenario
B2.
The changes of seasonal mean precipitation in Yangtze
River basin under scenarios A2 and B2 would present
obvious differences in different seasons. Under scenario
A2, the seasons in which changes of seasonal mean precipitation would be most remarkable in the future periods
(2020s, 2050s and 2080s) in the upper Yangtze reaches
were autumn and summer respectively, which in the midlower Yangtze reaches were autumn and winter respectively; for the whole river basin, the ones were autumn and
winter respectively. Similar change trends are found for the
results under scenario B2 with the difference in changing
magnitude and percentage only.
3.2.2 Spatial distribution for the future change of annual
mean precipitation
The spatial distribution for the future changes of seasonal
and annual mean precipitation of the Yangtze River basin
(compared to base period) under scenarios A2 and B2 were
built by Inverse Distance Weighted (IDW) interpolation
technology with Arcgis 9.2 software package. For illustrative purposes, the results for annual changes are shown
in Fig. 6, from which we can see the remarkable spatial
difference of the change. In the 2020s (2010–2039), the
annual mean precipitation in most parts of the Yangtze
River basin would decrease under both scenarios A2 and
B2, especially in the Yangtze River headwaters area, the
decrease would be more remarkable; as for the 2050s
(2040–2069), the annual mean precipitation in the most
part of the mid-lower Yangtze reaches and some areas of
the upper Yangtze reaches would increase in the range of
0–10% under both of scenarios A2 and B2; for 2080s
(2070–2099), the annual mean precipitation in most parts
of the Yangtze River basin would increase more than 5%
under both scenarios A2 and B2, especially under scenarios
A2, the increase would be larger than 20% in more than
half of Yangtze River basin. The spatial distribution
characteristics for the changes of annual mean precipitation
under scenario B2 were comparable to that of A2, but the
changing magnitude and percentage were weaker.
123
790
Stoch Environ Res Risk Assess (2011) 25:781–792
Fig. 6 Spatial distribution for the change of annual mean precipitation (compared to base period) in 2020s, 2050s, and 2080s under scenarios A2
and B2 (a, b, c the change of 2020s, 2050s and 2080s under scenarios A2; d, e, f the change of 2020s, 2050s and 2080s under scenarios B2)
4 Conclusions and discussions
Statistical downscaling methods were effective measures to
construct the bridge between large-scale climate change
and local-scale hydrological response; among them, SDSM
was widely used due to its general applicability and free
availability of the software. In this study, SDSM was
applied to simulate and project the precipitation in the
Yangtze River basin of China, and some important conclusions could be obtained as follows.
1.
During calibrating and validating, the SDSM showed a
good applicability in simulation of monthly and
seasonal precipitation in the Yangtze River basin for
both individual stations and the river basin. The
precipitation downscaled by NCEP data and HadCM3
had good liner relation with the observed ones. As for
123
2.
the individual stations, although the simulation results
of daily precipitation was not so good in some stations,
the monthly and seasonal precipitation could be
simulated by SDSM with higher R2 values; especially
in modeling the seasonal precipitation, the mean R2
values were higher than 0.8, and the mean RE did not
exceed 20%. When come to the basin, the monthly and
seasonal precipitation could be well simulated with the
R2 values of 0.78 and 0.91 respectively. Above all, the
SDSM method was adaptable in the Yangtze River
basin.
The simulation results of future precipitation showed
that when compared to the base period, the annual mean
precipitation of the Yangtze River basin of three future
periods would show different change patterns under
scenarios A2 and B2. As for A2 scenario, in the 2020s,
Stoch Environ Res Risk Assess (2011) 25:781–792
3.
the change would present a situation of decease being
smaller than 10%; as for the 2050s, the change would be
not obvious; when it comes to 2080s, the change would
present a situation of increase being larger than 10%.
The change patterns of annual mean precipitation in the
future under scenario B2 would be comparable to that of
scenarios A2, but the change range would be smaller. In
the 2080s, the annual mean precipitation of the Yangtze
River basin under scenarios A2 and B2 would increase
by 15.29 and 5.33% respectively, which were smaller
than the simulation results developed by PRECIS model
(Xu et al. 2005) for the same region. When compared to
the base period, there would be remarkable seasonal
variations in the change of precipitation, the precipitation of the Yangtze River basin would increase more
significantly in the winter, and decrease more significantly in the autumn.
There would be distinctive spatial distribution features
for the change of annual mean precipitation in the
three future periods under scenarios A2 and B2, the
annual mean precipitation of 2020s in the most parts of
the Yangtze River basin would decrease; in the 2050s,
the annual mean precipitation in the most parts of the
mid-lower Yangtze reaches and some areas of the
upper Yangtze reaches would increase slightly; while
in the 2080s, the annual mean precipitation in the most
parts of the Yangtze River basin would increase more
than 5%. During the total future period, the annual
mean precipitation in the most parts of the Yangtze
River basin would be dominated by an increasing trend
under both scenarios A2 and B2, this change feature
was in agreement with other analyses of future climate
change in China (Xu et al. 2005; Xu 2005; Mo et al.
2007). These results would provide important scientific
base and practical information for water resources
planning and management in the basin.
Acknowledgments This paper was financially supported by Ministry of Water Resources’ special funds for scientific research on
public causes, (No. 200901042), fully supported by Key Project of
National Science and Technology during the 11th Five-Year Plan
(No. 2006BAD03A16), National Nature Science Foundation of China
(Grant No: 40801015), State Key Laboratory of Hydrology-Water
Resources and Hydraulic Engineering fund from Hohai University
(Project No. 2008zd07). We would like to thank the National Climate
Centre (NCC) in Beijing for providing valuable climate datasets.
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