HYDROLOGICAL PROCESSES
Hydrol. Process. 26, 3510–3523 (2012)
Published online 24 January 2012 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/hyp.8427
Statistical downscaling of extreme daily precipitation,
evaporation, and temperature and construction of
future scenarios
Tao Yang,1*,† Huihui Li,1 Weiguang Wang,1 Chong-Yu Xu2 and Zhongbo Yu3
1
State Key Laboratory of Hydrology- Water Resources and Hydraulics Engineering, Hohai University, Nanjing 210098, China
2
Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
3
Department of Geoscience, University of Nevada Las Vegas, Las Vegas, NV89154, USA
Abstract:
Generally, the statistical downscaling approaches work less perfectly in reproducing precipitation than temperatures, particularly
for the extreme precipitation. This article aimed to testify the capability in downscaling the extreme temperature, evaporation,
and precipitation in South China using the statistical downscaling method. Meanwhile, the linkages between the underlying
driving forces and the incompetent skills in downscaling precipitation extremes over South China need to be extensively
addressed. Toward this end, a statistical downscaling model (SDSM) was built up to construct future scenarios of extreme daily
temperature, pan evaporation, and precipitation. The model was thereafter applied to project climate extremes in the Dongjiang
River basin in the 21st century from the HadCM3 (Hadley Centre Coupled Model version 3) model under A2 and B2 emission
scenarios. The results showed that: (1) The SDSM generally performed fairly well in reproducing the extreme temperature.
For the extreme precipitation, the performance of the model was less satisfactory than temperature and evaporation. (2) Both A2
and B2 scenarios projected increases in temperature extremes in all seasons; however, the projections of change in precipitation
and evaporation extremes were not consistent with temperature extremes. (3) Skills of SDSM to reproduce the extreme
precipitation were very limited. This was partly due to the high randomicity and nonlinearity dominated in extreme precipitation
process over the Dongjiang River basin. In pre-flood seasons (April to June), the mixing of the dry and cold air originated
from northern China and the moist warm air releases excessive rainstorms to this basin, while in post-flood seasons (July to
October), the intensive rainstorms are triggered by the tropical system dominated in South China. These unique characteristics
collectively account for the incompetent skills of SDSM in reproducing precipitation extremes in South China. Copyright © 2011
John Wiley & Sons, Ltd.
KEY WORDS
climate extremes; statistical downscaling; climate change; projection; scenarios
Received 16 August 2011; Accepted 10 November 2011
INTRODUCTION
The frequent occurrence of extreme weather events such
as heat waves and intense and persistent precipitation
associated with subsequent flooding have raised concerns
that human activity might have caused an alteration of the
climate system (Yang et al., 2008), which is believed to
be the culprit behind the severity of such events. There is
also a widespread belief that the climate system will
continue to change under the prevailing human activity
and that humanity will be faced with more of these
extreme events (Hundecha and Bardossy, 2008; Yang
et al., 2011). This leads to the growing concerns and
studies on changes in frequency, intensity, and/or
magnitude of such events in the past and for estimating
climate that will occur in the future.
*Correspondence to: Dr. Tao Yang, Professor, State Key Laboratory of
Hydrology-Water Resources and Hydraulics Engineering, Hohai University,
Nanjing 210098, The People’s Republic of China.
E-mail: yang.tao@ms.xjb.ac.cn
†
Present address: State Key Laboratory of Desert and Oasis Ecology,
Xinjiang Institute of Ecology and Geography, Chinese Academy of
Sciences, Urumqi, China.
Copyright © 2011 John Wiley & Sons, Ltd.
General circulation models (GCMs) and large-scale
circulation predictors are the most important and effective
tools and indicators for the climate impact study. These
numerical coupled models represent various earth systems
including the atmosphere, oceans, land surface, and seaice and offer considerable potential for the study of
climate change and variability. Over the past decade, the
sophistication of such models has increased, and their
ability to simulate present and past global and continental
scale climates has substantially improved. However, the
resolution of GCMs remains relatively coarse and does
not provide a direct estimation of hydrological responses
to climate change. For example, the Hadley Centre’s
Hadcm3 model is resolved at a spatial resolution of 2.5
latitude by 3.75 longitude, whereas a spatial resolution of
0.125 latitude and longitude is required by hydrologic
simulations of monthly flow in mountainous catchment
(Wilby et al., 2004). In other words, GCMs provide
output at nodes of grid-boxes, which are tens of thousands
of square kilometers in size, whereas the scale of interest
to hydrologists is of the order of a few hundred square
kilometers. Bridging the gap between the resolution of
climate models and regional- and local-scale processes
STATISTICAL DOWNSCALING OF HYDROMETEOROLOGICAL EXTREMES
represents a considerable problem for the climate change
studies including the application of climate change
scenarios to hydrological models. Thus, considerable
effort in the climate community has focused on the
development of techniques to bridge the gap, known
as ‘downscaling’.
More recently, downscaling has found wide application
in hydroclimatology for scenario construction and simulation of (1) regional precipitation (Kim et al., 2004;
Wang et al., 2011); (2) low-frequency rainfall events
(Wilby, 1998) (3) mean, minimum, and maximum air
temperature (Kettle and Thompson, 2004); (4) soil
moisture (Jasper et al., 2004); (5) runoff (Arnell et al.,
2003) and streamflows (Cannon and Whitfield, 2002);
(6) ground water levels (Bouraoui et al., 1999); (7)
transpiration (Misson et al., 2002), wind speed (Faucher
et al., 1999), and potential evaporation rates (Weisse and
Oestreicher, 2001); (8) soil erosion and crop yield (Zhang
et al., 2004); (9) landslide occurrence (Buma and Dehn,
2000), and (10) water quality (Hassan et al., 1998).
Downscaling methods could be broadly classified into
two categories (Xu, 1999): dynamic downscaling and
statistical downscaling. Both techniques have their
strengths and weaknesses. In dynamic downscaling, the
GCM outputs are used as boundary conditions to drive a
Regional Climate Model (RCM) or Limited Area Model
and produce regional-scale information up to 5–50 km.
This method has superior capability in complex terrain or
with changed land cover. However, this method entails
higher computation cost and relies strongly on the
boundary conditions provided by GCMs with considerable
uncertainties. In contrast, statistical downscaling gains local
or station-scale meteorological time series (predictands) by
appropriate statistical or empirical relationships with surface
or troposphere atmospheric features. Generally, statistical
downscaling methods can deliver ensembles of daily
climate that evolve in line with the large-scale, transient
changes of the host GCM. Moreover, given the advantages
of being computationally inexpensive, statistical downscaling method can access finer scales than dynamical methods
and relatively easily applied to different GCMs, parameters
and regions (Wilby et al., 2004). Therefore, it has been
widely employed in climate impact studies. However,
statistical downscaling approaches need much longer
historical time series to build the appropriate statistical
relationship. In addition, one of the assumptions of
statistical downscaling is still valid in the future. This
assumption cannot be testified at present. The conclusion
from the most recent studies is achieved in the statistical
and regional dynamical downscaling of extremes project
(STARDEX, http://www.cru.uea.ac.uk/projects/stardex)
that both statistical and dynamical downscaling techniques
are comparable for simulating current climate (Haylock
et al., 2006; Schmidli et al., 2006). The statistical
downscaling has been widely employed in climate change
impact assessments (Wilby et al., 1999; Huth, 2002;
Tripathi et al., 2006; Ghosh and Mujumdar, 2008), due to
its low expenditure on usage and the equivalent power
as dynamic downscaling.
Copyright © 2011 John Wiley & Sons, Ltd.
3511
In Wilby and Wigley’s study (2000), statistical
downscaling techniques are described as three categories,
namely: regression methods (e.g. Kim et al., 1984;
Wigley et al., 1990; Storch et al., 1993); weather patternbased approaches (e.g. Lamb, 1972; Hay et al., 1991;
Bardossy and Plate, 1992); and stochastic weather
generators (Katz, 1996). No matter whether the method
is simple or complex, it is always based on some kind of
a regression relationship. The statistical downscaling
model (SDSM) is best described as a hybrid of stochastic
weather generator and regression-based methods (Wilby
et al., 2002). Many comparative studies (Wilby et al.,
1998; Dibike and Coulibaly, 2005) have shown that it
has superior capability to capture local-scale climate
variability and is, therefore, widely applied (Wilby and
Harris, 2006).
General practices in downscaling of monthly outputs
from a full range of GCMs were presented as above in
past years. However, research in constructing reliable
scenarios of future climate extremes is still a challenge
and inadequate so far (e.g. Wilby and Harris, 2006).
Moreover, SDSM normally works worse in subtropical
and tropical regions than in inland regions for that
precipitation in subtropical and tropical regions always
presents more than one flood season due to the effect
of tropical cyclones, which are difficult to capture.
Therefore, the main objective of the present study is to
testify the capability of SDSM in downscaling extreme
events in temperature, evaporation, and precipitation in the
subtropical region in southern China and, if it is successful,
to project their future patterns for the study region. This
study strives to downscale extremes of temperature,
evaporation, and precipitation in the study region, more
importantly to identify the possible links between the
underlying driving forces and skills in downscaling
precipitation extremes in subtropical regions. It will
contribute to promote current downscaling knowledge in
similar subtropical regions of the world.
STUDY AREA AND DATA
Study area
Dongjiang River is located between 114.0 ~ 116.5 E
and 22.5 ~ 25.5 N (Figure 1). It has a 562 km long
mainstream to the Boluo station with a drainage area of
25,555 km2. The Dongjiang River is important not only
for the local region but also for Hong Kong because about
80% of Hong Kong’s water supply comes from Dongjiang
River through cross-basin water transfer. Three major
reservoirs (i.e. Xingfengjiang Reservoir since 1959,
Fengshuba Reservoir since 1973, and Baipenzhu Reservoir
since 1984) were built in the basin.
Annual average air temperature is about 20.4 C. The
precipitation of Dongjiang River demonstrates strong
seasonality due to a subtropical monsoon climate. Owing
to the influence of typhoons, precipitation exhibits strong
variability in both spatial and temporal perspective. The
annual precipitation varies between 1500 mm and 2400 mm.
Hydrol. Process. 26, 3510–3523 (2012)
3512
Xun
wu
Riv
er
25° N
T. YANG ET AL.
ng
Xingfengjiang
Reservior
R.
24° N
gR
fen
ia
gj
n
Do
Heyuan
R.
ng
xia
Qiu
hu
gz
en ior
p
i
Ba serv
Re
.
R
i
Xizh
Boluo
Streamflow gauges
Reservior
23° N
Xin
latitude° (N)
Fengshuba
ive Reservior
r
Li R
Shenzhen
114° E
115° E
116° E
longtitude° (E)
Figure 1. Map of Dongjiang river basin
More than 80% of the total annual precipitation falls in the
flood seasons from April to September.
Data
Observed data sets . Measured daily maximum
temperature, minimum temperature, pan evaporation, and
precipitation were provided by China Meteorological
Administration for 41-year period 1961–2001 at five weather
stations (Table I). The areal weights of five stations were
calculated using the Thiessen polygons method (Figure 2).
Reanalysis predictor sets used in calibration. Twentysix different large-scale atmospheric variables derived
from the daily reanalysis dataset of NCEP/NCAR in the
period of 1961–2001 were used to calibrate and validate
the SDSM model, which were downloaded freely from the
internet sites at a scale of 3.75 2.5 (http://www.cics.
uvic.ca/scenarios/sdsm/select.cgi). The geographical
extent (112.5–116.25 N, 22.5–25 E) was chosen to cover
the whole area with noticeable influence on the circulation
patterns that govern the weather pattern observed over
the Dongjiang River basin.
Table I. Basic information of the five meteorological stations in
the study region
No
1
2
3
4
5
ID
59096
59102
59293
59298
59493
Station
Lianping
Xunwu
Heyuan
Huiyang
Shenzhen
Latitude(N) Longitude(E) Areal weight
24 22’
24 57’
23 48’
23 05’
22 32’
114 29’
115 39’
114 44’
114 25’
114 00’
Copyright © 2011 John Wiley & Sons, Ltd.
0.215
0.201
0.332
0.047
0.205
Figure 2. The study area divided by the method of Thiessen polygons
GCM predictor sets used in hindcast and projection. The
validated SDSM was used to downscale the large-scale
predictor variables derived from A2 and B2 scenarios of
HadCM3 (Hadley Centre Coupled Model version 3) in
the period of 1961–2099. Both scenarios are characterized
by a continuously increasing global population with a
consequent increase in the emission of greenhouse gas
and with a higher rate in A2 than in B2. Maximum
temperature, minimum temperature, pan evaporation,
and precipitation were simulated during the following
periods: the current (1961–2001), 2020s (2010–2039),
2050s (2040–2069), and 2080s (2070–2099).
METHODOLOGY
Downscaling method
The SDSM, developed by Wilby et al. (2002), is
employed in this study to build statistical relationships
between GCM predictors and local climate variables. The
software tool for SDSM is available from the internet
site: http://www.cisc.uvic.ca/scenarios/index.cgi?More_
Info-Downscaling-Tool. The regional climate variables
conditioned by the large-scale state may be written as:
R ¼ F ðLÞ
(1)
in which R is the predictand (a local climate variable), L is
the predictor (a set of large-scale climate variables), and F a
deterministic/stochastic function conditioned by L and has
to be estimated empirically from historical observations.
Three implicit assumptions are made in order to use this
kind of downscaling methods for assessing regional
climate change: (1) the predictors are variables of relevance
and are realistically simulated by the GCM; (2) the
predictors employed fully represent the climate change
signal; and (3) the relationship is valid also under altered
climate condition.
Predictor selecting. The climate system is influenced by
the combined action of multiple atmospheric variables in the
wide tempo-spatial space. Therefore, any single circulation
Hydrol. Process. 26, 3510–3523 (2012)
3513
STATISTICAL DOWNSCALING OF HYDROMETEOROLOGICAL EXTREMES
predictor and/or small tempo-spatial space are unlikely to be
sufficient, as they fail to capture key precipitation mechanisms based on thermodynamics and vapor content (Wilby,
1998). Wilby and Wigley (2000) found that in many cases,
maximum correlations between precipitation and the
circulation predictors occurred away from the location of
the grid-box of the downscaled station and suggested that
selection of predictor domain was a critical factor affecting
the realisation and stability of downscaling model.
The climate in many zones of China is strongly controlled
by the East Asian monsoon, where the atmospheric
circulation feature is quite different between winter and
summer, and the scale of circulation pattern is large. Thus,
it is a big challenge to choose predictors in the wide tempospatial space (Samel et al., 1999). The procedure adopted in
the study for selecting suitable predictors for each predictand
is as follows: Table II
First, all of the 26 atmospheric variables in each one
of four grid-boxes (covering the whole study area and
surrounding) were taken as potential predictors. Second,
these variables were then screened by SDSM to determine
what amount explained variance is when the predictand
and predictor(s) were statistically compared. The user was
required to select predictors that produce the highest
explained variance (E) and lowest standard error (SE).
Finally, the predictors identified in this study were
summarized in Table III. It was shown that different
atmospheric predictors control different local variables: the
maximum and minimum temperature are more sensitive
to mean temperature at 2 m, and 850-hPa geopotential
height, mean sea level pressure, and 500-hPa geopotential
height are more sensitive predictors for the pan evaporation.
For the daily precipitation, the relative humidity at 500 hPa
and surface relative humidity are the most sensitive factors.
Calibration and validation of SDSM. Before downscaling of future climate with GCM predictors, the relationship between the selected predictors and precipitation in
Table III. Selected predictor variables for Dongjiang river basin
downscaling
Predictands
Predictors
Tmax
Tmin
Pcpn
Eva
1. Mslp
2. p__u
3. p__v
4. p__z
5. p500
6. p850
7. temp
8. p5zh
9. p5th
10. rhum
11. shum
12. r500
13. r850
14. p5_v
15. p5_z
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
Where:
mslp = mean sea level pressure; p__u = zonal velocity component @
surface; p__v = meridional velocity component @ surface; p__z = vorticity
@ surfacep500 = 500 hPa geopotential height p850 = 850 hPa geopotential
height p5th = 500 hPa wind direction; rhum = surface relative humidity;
shum = surface specific humidity; r500 = relative humidity at 500 hPa;
r850 = relative humidity at 850 hPa; p5_v = 500 hPa zonal wind; p5_z =
Vorticity at 500 hPa; Pcpn= daily precipitation; Eva = daily evaporation;
Tmax-= daily maximumoftemperature; Tmin- = daily minimum of temperature.
all stations need to be calibrated by using NCEP/NCAR
predictors. From the 41 years of data representing present-day
climate (1961–2001), the first 30 years (1961–1990) are used
for calibrating the regression model, while the rest 11 years of
data (1991–2001) are used to validate the model.
Measures of performance assessment
Four different measures were used to evaluate the
performance of the model: the coefficient of efficiency
(Ens), coefficient of determination (R2), ratio of simulated
and observed standard deviation (RS), and model biases.
Table II. Extreme indices for temperature, pan evaporation, and precipitation
Precipitation-related indices
Pav
Pnl90
Px1d
Px5d
Pxcdd
Pq90
Temperature-related indices
Txx
Txn
Txq90
Tnx
Tnn
Tnq10
Pan evaporation-related indices
Ex1d
Ex3d
Ex5d
Ex7d
Copyright © 2011 John Wiley & Sons, Ltd.
Mean of daily precipitation on all days [mm/day]
Number of events > long-term 90th percentile
The maximum of daily precipitation in given period [mm]
Maximum total precipitation from any consecutive 5 days [mm]
Maximum number of consecutive dry days [day]
Empirical 90% quantile of precipitation [mm]
The maximum of daily maximum temperature [ C]
The minimum of daily maximum temperature [ C]
Empirical 90% quantile of the daily maximum temperature [ C]
The maximum of daily minimum temperature [ C]
The minimum of daily minimum temperature [ C]
Empirical 10% quantile of the daily minimum temperature [ C]
The maximum of daily pan evaporation [mm]
Maximum total evaporation from any consecutive 3 days [mm]
Maximum total evaporation from any consecutive 5 days [mm]
Maximum total evaporation from any consecutive 7 days [mm]
Hydrol. Process. 26, 3510–3523 (2012)
3514
T. YANG ET AL.
The coefficient of efficiency (Ens) describes how well the
volume and timing of the calibrated predictand compares to
the observed predictand and is defined by
Pn
ðOi Si Þ2
Ens ¼ 1 Pi¼1
(2)
n
2
i¼1 ðOi OÞ
in which
¼1
O
n
Xn
O
i-1 i
(3)
Where n is the number of time steps, Oi is the observed
predictand at time step i, and Si is the simulated
predictand at time step i. Coefficient of determination
R2 measures the amount of variation of a dependent
variable that is explained by variation in the independent.
The closer the values of Ens and R2 equal to 1, the more
successful the model calibration/validation is.
The ratio of standard deviation of the modelled and
observed indices describes the degree of dispersion of
variables (Hundecha and Bardossy, 2008):
RS ¼
Table IV. Performance assessment for predictands in calibration
and validation
Items
Daily maximum
temperature
Daily minimum
temperature
Daily pan evaporation
Daily precipitation
Periods
Ens
R2
bias
RS
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
0.90
0.90
0.94
0.94
0.65
0.61
0.50
0.48
0.90
0.90
0.98
0.94
0.65
0.65
0.50
0.48
0
1.1
0
0.12
0
0.42
0.39
0.30
0.93
0.93
0.97
0.98
0.77
0.83
0.67
0.66
bias ¼
32
28
1 Xn
ð S Oi Þ
i¼1 i
n
(5)
obs
ncep
A2
B2
35
30
25
Txn( C)
Txx( C)
36
(4)
Where Ssim is the standard deviation of the modeled
indices and Sobs is the standard deviation of the observed
indices. Model bias describes the amount of system
deviation, which is defined by
obs
ncep
A2
B2
40
Ssim
Sobs
20
15
10
24
20
5
0
2
4
6
8
10
0
12
0
2
4
month
obs
ncep
A2
B2
30
8
10
12
20
obs
ncep
A2
B2
25
20
Tnn( C)
25
Tnx( C)
6
month
15
10
5
15
0
10
0
2
4
6
8
10
-5
12
0
2
4
month
obs
ncep
A2
B2
40
8
10
12
30
obs
ncep
A2
B2
30
25
Tnq10( C)
35
Txq90( C)
6
month
20
15
10
25
5
20
0
2
4
6
month
8
10
12
0
0
2
4
6
8
10
12
month
Figure 3. Comparison of the indices of extreme temperature from observed data and simulated by SDSM driven by NCEP and H3A2 and H3B2
scenarios in validation period
Copyright © 2011 John Wiley & Sons, Ltd.
Hydrol. Process. 26, 3510–3523 (2012)
3515
STATISTICAL DOWNSCALING OF HYDROMETEOROLOGICAL EXTREMES
Indices of extreme climate predictands
Changes in extremes of climate events have received
increased attention in the last years (IPCC, 2007). Since
the early 1990s, it has been known that the largest
changes in the climate under enhanced greenhouse
conditions were likely to be seen in changes of extremes
(Gordon et al., 1992). Kunkel et al. (1999) reported that
potential changes in extreme events can generate greater
impact on human activities and natural environment than
mean climate changes.
The select implementation of indices to describe extreme
climate events should have several characteristics: relevant,
easy to interpret, understandable for policy makers, and
covering both frequency and intensity description of
extreme processes comprehensively. The core indices of
climate extremes recommended by STARDEX Project
funded by the European Commission under the Fifth
Framework Programme (FP5) (STARDEX, 2001) were
used in this study. These core indices were shown in
Table II. It should be noted that index of mean precipitation
is also included in the list. The indices were used to examine
the skills of the downscaling method in constructing
scenarios for both climate extremes and means.
RESULTS
Model calibration and validation
The calibration (1961–1990) and validation results
(1991–2001) were shown in Table IV. It could be seen
that both the simulated maximum and minimum temperatures were closely consistent with observations. R2,
Ens, and RS between simulated and observed temperature
exceeded or equaled to 0.9 in calibration and validation.
The simulation of daily pan evaporation was less
satisfactory (Ens and R2 were between 0.61 and 0.65).
As for daily precipitation, Ens and R2 values for the
downscaled precipitation were about 0.5, much lower than
daily temperature and pan evaporation. The biases for the
maximum temperature, minimum temperature, pan evaporation, and precipitation were 1.1 C, 0.12 C, 0.42 mm/
day, and 0.39 mm/day in validation. In summary, those
biases were acceptable for practical uses. The statistical
model built using SDSM is capable of reproducing daily
climate variables.
Inter-comparison of extreme indices of downscaling for
the calibration and validation period
Temperature. Generally, the performance of a downscaling model in constructing temperature indices is better
than the performance of precipitation indices. It was
shown (Figure 3) that the pattern of seasonal variations of
temperature was well downscaled with all three datasets
(NCEP/NCAR, H3A2, H3B2). In simulating the maximum of daily maximum temperature (Txx) and empirical
90% quantile of the daily maximum temperature (Txq90),
the results from NCEP/NCAR were systematically
lower than observations in all seasons, while the simuCopyright © 2011 John Wiley & Sons, Ltd.
lations from the H3A2 and H3B2 were closer to
observations. For the other four indices (the minimum of
daily maximum temperature, Txn; maximum of daily
minimum temperature, Tnx; minimum of daily minimum
temperature, Tnn; and empirical 10% quantile of the daily
minimum temperature, Tnq10, Table II), the results from
NCEP/NCAR were relatively satisfactory. Tnx was underestimated in summer and winter; instead, the minimum of
daily maximum temperature (Txn) from the H3A2 and
H3B2 were 6 C overestimated in summer. As for Tnq10,
the results from all three datasets were consistent with the
observations, while H3B2 provided a worst performance
for Tnn. Table V summarized the coefficient of efficiency
(Ens), coefficient of determination (R2), ratio of standard
deviation (RS), and biases between the 16 downscaled
and observed indices.
Pan evaporation. The performance for pan evaporation
downscaling was less satisfactory than daily temperature.
The results for daily pan evaporation are provided by
Figure 4. It can be seen that in simulating these four indices
(Ex1d, Ex3d, Ex5d and Ex7d, 1 Table II), all the simulated
results were lower than observations in September. In
general, the seasonal patterns were well simulated, while
the simulated magnitude was less satisfactory.
Table V. Comparison of the extreme indices between observed
and simulated results during calibration (1961–1990) and
validation (1991–2001) periods based on NCEP predictors
Indices
Periods
Ens
R2
1. Txx
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
Calibration
Validation
0.81
0.82
0.88
0.92
0.91
0.87
0.86
0.85
0.96
0.97
0.97
0.97
0.40
0.69
0.57
0.76
0.66
0.79
0.73
0.81
0.82
0.81
0.49
0.55
0.2
0.06
0.62
0.57
0.35
0.12
0.62
0.67
0.93
0.93
0.94
0.95
0.96
0.95
0.95
0.93
0.97
0.98
0.98
0.98
0.67
0.74
0.77
0.77
0.80
0.79
0.83
0.81
0.83
0.82
0.71
0.71
0.47
0.40
0.67
0.69
0.73
0.61
0.67
0.75
2. Txn
3. txq90
4. Tnx
5. Tnn
6. tnq10
7. Ex1d
8. Ex3d
9. Ex5d
10. Ex7d
11. Pav
12. pnl90
13. px1d
14. px5d
15. Pxcdd
16. pq90
bias
1.25
2.40
1.70
1.17
0.98
2.12
1.03
1.04
0.77
0.45
0.64
0.52
0.97
0.23
2.08
0.29
2.86
0.18
3.35
0.24
0.39
0.29
0.03
0.03
13.45
14.87
12.85
16.6
4.29
3.54
2.28
2.51
RS
1.08
1.09
1.03
1.01
1.04
1.08
1.11
1.08
1.02
1.07
0.99
1.00
0.98
1.00
0.91
0.95
0.88
0.93
0.86
0.92
0.98
0.89
1.32
1.24
0.61
0.56
0.76
0.69
0.87
1.03
0.68
0.68
Hydrol. Process. 26, 3510–3523 (2012)
3516
T. YANG ET AL.
obs
ncep
A2
B2
12
45
Ex5d(mm)
Ex1d(mm)
10
8
obs
ncep
A2
B2
50
40
35
30
6
25
4
0
2
4
6
8
10
20
12
0
2
4
month
obs
ncep
A2
B2
35
Ex7d(mm)
Ex3d(mm)
30
25
20
15
10
0
2
4
6
6
8
10
12
month
8
10
12
65
60
55
50
45
40
35
30
25
20
obs
ncep
A2
B2
0
2
4
6
8
10
12
month
month
Figure 4. Comparison of the indices of extreme pan evaporation from observed data and simulated by SDSM driven by NCEP and H3A2 and H3B2
scenarios in validation period
obs
ncep
A2
B2
14
10
100
pnl90(day)
pav(mm/day)
12
8
6
4
80
60
40
20
2
0
obs
ncep
A2
B2
120
0
0
2
4
6
8
10
12
0
2
4
obs
ncep
A2
B2
150
8
10
12
100
50
obs
ncep
A2
B2
250
200
px5d(mm)
px1d(mm)
6
month
month
150
100
50
0
0
2
4
6
8
10
0
12
0
2
4
obs
ncep
A2
B2
35
30
8
10
12
25
20
15
10
obs
ncep
A2
B2
30
25
p90(mm)
pxcdd(day)
6
month
month
20
15
10
5
5
0
0
2
4
6
month
8
10
12
0
0
2
4
6
8
10
12
month
Figure 5. Comparison of the indices of extreme precipitation from observed data and simulated by SDSM driven by NCEP and H3A2 and H3B2
scenarios in validation period
Copyright © 2011 John Wiley & Sons, Ltd.
Hydrol. Process. 26, 3510–3523 (2012)
STATISTICAL DOWNSCALING OF HYDROMETEOROLOGICAL EXTREMES
A2 scenario
7
6
5
4
3
2
1
0
-1
-2
2050s
Winter
Spring
2020s
2020s
Summer
2050s
Autumn
2080s
5
5
4
4
3
2
2050s
Spring
2080s
Summer
2050s
Autumn
2080s
3
2
1
0
0
-1
Winter
2020s
6
1
Winter
Spring
2020s
Summer Autumn
2050s
-1
2080s
5
4
4
3
2
1
Winter
2020s
5
txq90 (°C)
txq90 (°C)
7
6
5
4
3
2
1
0
-1
-2
6
TXn (°C)
TXn (°C)
B2 scenario
2080s
TXx (°C)
TXx (°C)
2020s
3517
Spring
Summer
2050s
Autumn
2080s
3
2
1
0
Winter
Spring
Summer
Autumn
0
Winter
Spring
Summer
Autumn
Figure 6. Changes (%) in extreme temperature between the period (1961–1990) and the period (2011–2099) under the H3A2 and H3B2 scenarios
Precipitation. Among the six indices in simulating
precipitation extremes, four of them are associated with
extreme wet events: 90th percentile (pq90), maximum of
daily precipitation (px1d), maximum 5-day total (px5d), and
number of heavy events (pnl90). The maximum number of
consecutive dry days (pxcdd) describes very dry events, and
mean of daily precipitation on all days (pav) describes
changes of mean daily precipitation. The threshold of 1 mm
was used for a wet day (Hennessy et al., 1999). A dry day
was defined as having less than 1-mm precipitation.
The calibration and validation results from NCEP/
NCAR were shown in Table V. It indicated that the
indices were not equally well modeled. Pav has the
highest performance (Ens > 0.8), while px1d (Ens< 0.3)
and pxcdd (Ens < 0.4) were the worst reproduced
indices, implying that the model still cannot fully capture
the true persistence of the precipitation occurrence process.
Monthly precipitation can be better downscaled by SDSM
than the extreme precipitation. In general, the model could
simulate most indices well, but the capability in simulating
heavy rainfall under abnormal climate and the persistence
of the precipitation occurrence was still limited.
The inter-comparison between the simulated and
observed six indices in the validation period was shown
in Figure 5. As for p90, px5d, and px1d, the simulations
were generally underestimated, and the underestimation
was rather obvious in summer under H3A2 and H3B2
Copyright © 2011 John Wiley & Sons, Ltd.
scenarios. Underestimation of extremes to some extent
can be attributed to the short validation period which is
heavily influenced by some extreme events with very
high return period. For instance, the underestimation of
px1d and px5d in April was because Huiyang, Heyuan,
and Shenzhen stations had recorded rain as high as 146.7,
133.6, and 344 mm/day on 14 April 2000. The return
period of the rainfall total in April in Shenzhen was
estimated to be 100 years approximately. Since the
validation period only had 10 years, the simulation could
not accurately capture some abnormal and extreme
storms. Although the pxcdd was underestimated using
the NCEP/NCAR in most seasons, the trend and
variability were well simulated. It should be noted that
the results from H3A2 and H3B2 were less satisfactory
compared with the NCEP/NCAR data especially for px1d
and pxcdd. In summary, the simulation results from
NCEP/NCAR data were closer to the observations than
the results from H3A2 and H3B2.
Projected changes for future climate scenarios
1. Temperature
Changes in extreme temperature between the baseline
period (1961–1990) and the future period (2011–2099)
were shown in Figure 6. Under the H3A2 scenario, all six
Hydrol. Process. 26, 3510–3523 (2012)
3518
T. YANG ET AL.
2050s
2020s
2080s
5
4
4
TNx (°C)
TNx (°C)
2020s
5
3
2
Winter
Spring
2020s
Summer
2050s
2
5
4
4
3
2
5
Summer
2050s
Autumn
2080s
3
2
1
Winter
Spring
2020s
Summer
2050s
0
Autumn
2080s
5
Winter
2020s
Spring
Summer
2050s
Autumn
2080s
4
tnq10 (°C)
4
3
2
1
0
Spring
6
5
0
Winter
2020s
2080s
1
tnq10 (°C)
3
0
Autumn
TNn (°C)
TNn (°C)
6
2080s
1
1
0
2050s
3
2
1
Winter
Spring
Summer Autumn
0
Winter
Spring
Summer Autumn
Figure 6. (Continued )
temperature indices will increase in future 90 years. Txx
(6.2 C) and Tnx (4.8 C) showed the highest increase in
summer, while Txn (5.5 C) and Tnn (4.9 C) increase
most considerably in spring. Txq90 and tnq10 will increase
with similar magnitude during different seasons. Under
H3B2 scenario, the projected Txx (in 2020s and 2050s) and
Txn (in 2050s) will decrease slightly in spring, while the
other four indices (Txq90, Tnx, Tnn, and Tnq10) showed
upward trends. Therefore, the extreme temperature events
will be more frequent in the future.
2. Pan evaporation
Figure 7 showed that all the indices of pan evaporation in
H3A2 and H3B2 scenario would increase by 10% (in 2020s)
and 40% (in 2080s) in summer. However, the change trends
of H3A2 and H3B2 projections are opposite in winter: the
projections from H3A2 scenario are decreasing while a
slight increase was projected from H3B2 scenario. Ex3d,
Ex5d, and Ex7d would decrease in spring during 2020s, but
they would increase during 2050s and 2080s under H3A2
scenario. Under the H3B2 scenario, they will decrease by
5% during 2020s and 2050s and increase by 2% to 12% in
2080s.
3. Precipitation
The projected changes of precipitation extremes (Figure 8)
were inconsistent with temperature extremes. It can be
Copyright © 2011 John Wiley & Sons, Ltd.
seen that under H3A2 scenario, the pav and p90 would
decrease in winter and spring and increase in summer and
autumn, while in H3B2, they showed decreasing trend
only in winter. As for pnl90, the number of events higher
than long-term 90th percentile will decrease in winter and
spring and increase in summer and autumn, and this is
more obvious under H3A2 scenario. Projection of pxcdd
under H3A2 scenario showed considerable increases only
in winter. Under H3B2 scenario, pxcdd showed increases
in all seasons. For the px1d and px5d, the results of H3A2
had distinct change patterns in different seasons and
periods. In the future, the maximum daily precipitation
(px1d) and the cumulative 5-day total precipitation (px5d)
under H3B2 scenario will increase.
DISCUSSION
In this section, we attempt to identify the linkages
between the underlying driving forces and skill scores in
downscaling precipitation extremes over the Dongjiang
basin. During the calibration and validation of SDSM
with the NCEP/NCAR reanalysis data, the temperature
indices were downscaled rather perfectly, but SDSM was
not very effective in downscaling precipitation extremes.
This can be attributed to the reasons below.
Dongjiang River basin located in southern China
suffers frequent rainstorms, and the major driving
forces are more complicated than in other inland regions
Hydrol. Process. 26, 3510–3523 (2012)
STATISTICAL DOWNSCALING OF HYDROMETEOROLOGICAL EXTREMES
A2 scenario
Winter
2050s
Spring
2020s
Winter
Spring
2020s
Winter
50
40
30
20
10
0
-10
-20
-30
-40
60
50
40
30
20
10
0
-10
-20
-30
-40
Summer Autumn
60
50
40
30
20
10
0
-10
-20
-30
-40
Winter
2020s
2080s
Summer Autumn
Winter
2020s
2080s
2050s
Spring
50
40
30
20
10
0
-10
-20
-30
-40
2080s
Summer Autumn
2050s
Ex1d (%)
60
50
40
30
20
10
0
-10
-20
-30
-40
2020s
Summer Autumn
2020s
Ex3d (%)
Ex5d (%)
60
50
40
30
20
10
0
-10
-20
-30
-40
Spring
B2 scenario
2080s
Ex5d (%)
Ex3d (%)
50
40
30
20
10
0
-10
-20
-30
-40
Winter
2050s
Ex7d (%)
Ex1d (%)
50
40
30
20
10
0
-10
-20
-30
-40
Ex7d (%)
2020s
3519
Winter
2020s
Winter
2050s
Spring
Summer Autumn
2050s
Spring
2080s
Summer Autumn
2050s
Spring
2080s
Summer Autumn
2050s
Spring
2080s
2080s
Summer Autumn
Figure 7. Changes (%) in extreme evaporation between the period (1961–1990) and the period (2011–2099) under the H3A2 and H3B2 scenarios
(See Fig. 9). Hereby, the flood season (April to October)
was divided into pre-flood and post-flood seasons for sake
of discussion. The pre-flood season (April to June) in
South China is composed of the frontal precipitation
period and the summer monsoon precipitation period
(Qiao et al., 2010). In pre-flood season, the main
atmospheric general circulation system dominated in
middle high latitude of the Eurasia is two-trough and
one-ridge, which help cold air move toward the South
China. The Western Pacific Subtropical High was stable
at 18 N, which creates favorable conditions for the
prevailing of the southerly airstream in South China and
coastal areas. Meanwhile, the active cold air in the
southern Hemisphere and strengthening of the crossequatorial flow contributed to form and intensify low
tropospheric jet in China and northern South China Sea.
A large amount of moisture and unstable air-mass with
Copyright © 2011 John Wiley & Sons, Ltd.
high humidity and temperature is transported to the upper
level. In this favorable situation, along with the special
topography and underlying surface, difference of sea land
distribution, non-uniform heating, thermodynamic and
dynamical processes in atmosphere and the interaction in
different scales would release heavy rain to the South
China. Besides, unbalance force of atmospheric motion
and the coupling reaction among convective cloud cluster
and moisture frontal zone and low level jet lead to the
continuation of strong storm. In post-flood season (July to
October), the rainstorms are triggered by tropical system,
such as tropical cyclone, inter-tropical convergence zone,
and easterly wave. The tropical cyclone would not only
bring tremendous moisture; they form big rainstorm
directly due to the strong convergence and updraft. If
combined with outside system (cold air and westerly belt
system), it will bring more intense rainfall into the region.
Hydrol. Process. 26, 3510–3523 (2012)
3520
T. YANG ET AL.
A2 scenario
pnl90 (%)
2080s
2020s
30
20
20
pav (%)
30
10
0
Winter
Spring
Winter
-10
-20
-20
2080s
150
100
100
0
Winter
Spring
Summer
Autumn
-100
px1d (%)
2080s
Winter
Spring
Summer Autumn
Spring
Winter
-100
2050s
2050s
2080s
0
-50
2020s
Summer Autumn
50
-50
180
160
140
120
100
80
60
40
20
0
-20
Spring
2020s
150
50
2080s
0
Summer Autumn
2050s
2050s
10
-10
2020s
px1d (%)
B2 scenario
2050s
pnl90 (%)
pav (%)
2020s
180
160
140
120
100
80
60
40
20
0
-20
2020s
Winter
Summer
2050s
Spring
Autumn
2080s
Summer Autumn
Figure 8. Changes in extreme precipitation between the period (1961–1990) and the period (2011–2099) under the H3A2 and H3B2 scenarios
For example, under the influence of the hitting of typhoon
and the cold air traveling from the north and northwest
China, a heavy rainstorm occurred in southern
Guangdong providence on 24 September 1979. The
highest rainfall of Huiyang exceeded 400 mm. The
monsoon trough is another important driving force
compared with tropical cyclone. It brings persistent
rainfall to South China. As for the precipitation in winter
and spring, the anomalous vapor transport of the western
Pacific and the low level in the South China Sea were the
main impact factors, which was caused by the ENSO
teleconnection. The El Niño made the low-level anticyclone of the Philippine Sea abnormal, which offered
favorable water vapor condition for the rainstorm. In
addition, prevailing south wind contributed to the
continuous water vapor convergence in south China.
While in case of the La Niña, the opposite phenomenon
occurs. Therefore, the complex precipitation processes in
Dongjiang River basin increase the difficulty in precipitation simulation. This explains why the indices that
described very wet events (maximum of daily precipitation, maximum 5-day total, number of heavy events) were
not simulated well.
In addition, SDSM is not sufficiently powerful to capture
the features of extreme precipitation events similar with other
SDSMs (e.g. Srikanthan and McMahon, 2001). The defect
of stochastic precipitation models need to be improved
(Gregory et al., 1993). According to Wilby et al. (2004), this
Copyright © 2011 John Wiley & Sons, Ltd.
might attribute to the more stochastic nature of precipitation
occurrence and magnitude, and the regression-based
SDSMs often cannot explain entire variance of the downscaled variable. Additionally, while there is a strong
seasonal consistency between stations for a number of
predictors (e.g. geopotential heights and humidity), the
seasonal specific predictor also play an important role (e.g.
surface divergence during the summer months, Fealy and
Sweeney, 2007). Hence, it is recommended the selected
predictors at seasonal scale (or month scale) improve the
downscaling performance to a certain degree.
CONCLUDING REMARKS
In this study, the large-scale atmospheric variables from
GCMs output were downscaled to the regional scale in
order to investigate the spatial-temporal changes in
extreme precipitation, temperature, and pan evaporation
over the Dongjiang River basin during 2010–2099 under
H3A2 and H3B2 emission scenarios. It will improve
current understanding on hydrological impacts under
future climate change in the subtropical regions. The
results for downscaling temperature under scenarios
H3A2 and H3B2 showed that the temperature extreme
events would be more significant in the rest 21st century
(2010–2099). Despite the similar changes supplied by
both scenarios, the magnitudes of the changes projected
Hydrol. Process. 26, 3510–3523 (2012)
STATISTICAL DOWNSCALING OF HYDROMETEOROLOGICAL EXTREMES
2020s
2050s
2080s
80
80
60
60
40
20
0
-20
Spring
Summer Autumn
-20
2050s
Winter
2080s
Spring
2020s
175
125
125
pxcdd (%)
pxcdd (%)
20
Summer Autumn
-40
2020s
75
2050s
2080s
75
25
25
Winter
Spring
2020s
Summer Autumn
2050s
-25
30
30
20
20
10
0
Spring
Spring
2020s
40
Winter
Winter
2080s
40
p90 (%)
p90 (%)
2080s
40
175
-10
2050s
0
Winter
-40
-25
2020s
100
px5d (%)
px5d (%)
100
3521
Summer Autumn
2050s
2080s
10
0
-10
-20
Summer Autumn
Winter
Spring
Summer Autumn
-20
Figure 8. (Continued )
Westward extension and northward of
Western Pacific subtropical high
Two-trough and one-ridge circulation system
in middle high latitude of the Eurasia
Southward warm moist air
Southward cold dry air
Forming cold and stationary front
Meso-and small-scale system convergence,
shear, convective activity
The special topography
and underlying surface
Rainstorm
in south
China
Tropical system
Non-uniform heating,
difference of sea land
ENSO cycle
Figure 9. Conceptual diagram explained the heavy rain processes in South China
by the two scenarios are generally different. As to the pan
evaporation, the predicted value from H3A2 indicated
that the maximum 1, 3, 5, and 7 days evaporation will
decrease in winter while increase in other three seasons in
2010–2099. For H3B2, a general upward trend was
identified in future. However, the projected changes for
precipitation-related indices are uncertain.
Copyright © 2011 John Wiley & Sons, Ltd.
Although some preliminary results of changes in
downscaled extreme indices are obtained in the present
work, a number of uncertainties still exist in assessing the
changes of regional-scale extreme indices. More research
work in the future, particularly the ensemble projections
by higher resolution GCMs or especially RCMs, as well
as analyzing the uncertainties related to the model spread,
Hydrol. Process. 26, 3510–3523 (2012)
3522
T. YANG ET AL.
are needed for a more profound understanding of the
futures changes in climate extremes.
ACKNOWLEDGEMENTS
The work was jointly supported by grants from the
National Natural Science Foundation of China
(40901016, 40830639, 40830640), a grant from the State
Key Laboratory of Hydrology-Water Resources and
Hydraulic Engineering (2009586612, 2009585512), and
the Fundamental Research Funds for the Central
Universities (2010B00714), the Australian Endeavour
Fellowship Program, and CSIRO Computational and
Simulation Sciences Transformational Capability Platform. Finally, cordial thanks are also extended to the editor,
Professor Malcolm G. Anderson and two anonymous
referees for their valuable comments which greatly
improved the quality of this paper.
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