Water Resour Manage (2013) 27:2871–2888
DOI 10.1007/s11269-013-0321-4
Assessment of Climate Change Impacts on River High
Flows through Comparative Use of GR4J, HBV
and Xinanjiang Models
Ye Tian & Yue-Ping Xu & Xu-Jie Zhang
Received: 3 January 2012 / Accepted: 26 February 2013 /
Published online: 15 March 2013
# Springer Science+Business Media Dordrecht 2013
Abstract This study analyses the extreme high flows in Jinhua River basin under the impact
of climate change for the near future 2011–2040. The objective of this study is to investigate
the effect of using the bias corrected RCM outputs as input on the extreme flows by
hydrological models. The future projections are obtained through the PRECIS model with
resolution of 50 km×50 km under climate scenario A1B. The daily precipitation from the
PRECIS is bias corrected by distribution based scaling method. Afterwards, three hydrological models (GR4J, HBV and Xinanjiang) are calibrated and applied to simulate the daily
discharge in the future. The hydrological models are driven with both bias corrected
precipitation and raw precipitation from the PRECIS model for 2011–2040. It is found that
after bias correction, the amount, frequency, intensity and variance of the precipitation from
the regional climate model resemble the observation better. For the three hydrological
models, the simulated annual maximum discharges are higher by using the raw precipitation
from PRECIS than by bias corrected precipitation at any return period. Meanwhile, the
uncertainties from different models cannot be neglected. The largest difference between
three models is about 2,100 m3/s.
Keywords Climate change . Hydrological models . Uncertainty . Extreme flows . East China
1 Introduction
In the past decades, the world has witnessed an obvious changing climate, particularly
temperature increased. Other effects on hydrological systems are also occurring, namely,
increased runoff and earlier spring peak discharge in many glacier and snow fed rivers
(IPCC, 2007). According to Zhang et al. (2011), north China was dominated by decreasing
precipitation and significant decreasing precipitation was found in the Yellow River basin
and Huaihe River basin from 1960 to 2000. In southwest and east China, the annual mean
Y. Tian : Y.-P. Xu (*) : X.-J. Zhang
Department of Civil Engineering, Institute of Hydrology and Water Resources,
Zhejiang University, Hangzhou, Zhejiang, China 310058
e-mail: yuepingxu@zju.edu.cn
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Y. Tian et al.
precipitation increased significantly. In Yangtze River Basin, the extreme precipitation
events increased by 10 %–20 % per decades in summer (Wang and Zhou 2005). The
Poyang Lake basin in south China, the annual and seasonal streamflows have increased in
the past 50 years (Zhao et al. 2010). Incorporated with climate change, human activities have
also exerted a huge impact on the water cycles. For example, in Huifa River Basin in
northeast China, human activities like regulation and storage of the water projects made a
larger contribution to the runoff decrease in wet years than the climate change itself (Zhang
et al. 2012a).
Global climate models (GCMs) are the most common tools to estimate the climate change
at a global scale. However, the resolution of GCMs is too coarse. They can provide
predictions of changes in climate of a few kilometers or so at best. In the mountainous
and coastal areas the GCM will fail to capture the local detail. However, hydrologic
modeling at basin scale requires more detailed information on smaller scales. To solve the
spatial resolution problem, there are mainly two kinds of methods commonly used to add
details to the coarse information of GCMs at present. One way is through statistical
downscaling methods that establish empirical relationships between circulation indices at
large scale and predictive variables at local scale (Wilby et al. 1999). Another way is through
dynamical downscaling by using a regional climate model (RCMs) for a particular area with
the boundary condition from a GCM. Statistical downscaling methods use a ‘weather
generator’ to generate weather time series based on an appropriate and fixed unknown
model coefficient (Wilby et al. 1998). Ghosh and Katkar (2012) has studied the uncertainties
of several downscaling methods including Linear Regression, Artificial Neural Network and
Support Vector Machine in climate change impacts assessment. The statistical downscaling
methods are cheap, flexible, computationally undemanding and widely used in hydrological
studies (Chu et al. 2010; Huang et al. 2011; Willems and Vrac 2011; Xu et al. 2012). But
there are also limitations. For example, the predictor–predictand relationships are often nonstationary and the weather series may not always be meteorologically consistent (Wilby and
Wigley 1997). RCMs are able to resolve the atmospheric processes and are consistent with
GCMs (Wilby et al. 2002). Though some parameterization in RCMs may have an empirical
basis, RCMs are more physically based than statistical downscaling and are more acceptably
transferable from current to future (Hay et al. 2002). There are many applications of
applying different RCMs to assess the future climate. For example, heavy rainfall events
over Vietnam are expected to decrease in most areas in 2001–2050 based on the IPCC
scenario A1B and A2 by using the Theoretical Physics regional climate model version 3
(RegCM3) (Ho et al. 2011). In the Crati River Basin, average annual temperature will
increase between 3.5 and 3.9 °C and cumulative annual precipitation will decrease between
9 % and 21 % in 2070–2099 under scenario A1B and A2 by applying the outputs of RegCM,
HIRHAM and COSMO-CLM (Senatore et al. 2011).
However, using the outputs of RCM results without bias correction in hydrologic models,
produced unacceptably biased hydrological results (Wood et al. 2004). The biases commonly
come from boundary conditions and parameterization. Kotlarski et al. (2005) found out that the
simulation bias range from −1.1 o C to +0.9 o C for temperature and −31 mm/year to
108 mm/year for precipitation over Germany. After the precipitation and temperature biases
are corrected, the accuracy of daily runoff simulation improved (Hay et al. 2002). So it is
necessary to carry out bias correction to assure meaningful results before applying it to the
hydrological models. There are many bias correction methods to adjust the results from RCM.
Linear scaling method is a convenient statistical method to correct the mean value of climate
variables (Lenderink et al. 2007). Correction factors are obtained by comparing the mean value
of RCM output with historical data sets and then applied to future daily precipitation value and
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temperatures. The major limitation of this method is that it is only sensible for mean values. For
the reason that the biases of extreme value of precipitation are larger or smaller than those of
mean value, non-linear bias correction methods are developed for simulations of high
flows(Leander and Buishand 2007). Non-linear bias correction method not only considers the
mean value but also the standard deviation. However such method fails to consider the
frequency of the precipitation. Distribution based scaling (DBS) adjusts the RCM precipitation
to approximate the long-term observed frequency and intensity distribution by mapping the
RCM data onto distribution of observed data. Such methods have been used by many researchers when the output from RCM are used to do some assessment (Wood et al. 2002; Payne
et al. 2004; Ines and Hansen 2006; Li et al. 2010; Ueyama et al. 2010).
Since climate change is interactive with the hydrological cycle, to investigate the impact
of climate change on the water resources, the hydrological model is an effective and widely
used tool to simulate the hydrological processes. Based on different recognition and
expression of the rainfall-runoff processes, there are a number of hydrological models.
They are different in concept, physical equations and therefore parameters and structures.
To assess the impact of projected climate change on the discharges, there are uncertainties
originated from green house gas emission scenarios, GCMs, downscaling methods, hydrological model structures and parameters (Wilby and Harris 2006). The model structure is one
of the most important sources of uncertainty (Chen et al. 2012b). Like the example of
finding out the aquifer’s vulnerability towards pollution in Refsgaard’s study (Refsgaard et
al. 2006), five consultants have five different results by using same input but five different
models and the model structure is a major source of uncertainty in model prediction. Until
now, some researchers have used several models in studying the impact of climate change.
Najafi et al. (2011) used four different hydrological models to analyze the runoff under two
emission scenarios in Tualatin River Basin in the United States, and the uncertainty in the
dry season are higher than that in the wet season. Kay et al. (2009) investigated the
uncertainty in the impact of climate change on flood frequency in England by using two
hydrological models and the results show the same pattern in flood frequency change but
different ranges for two models. Jiang et al. (2007) used six rainfall runoff models to
compare the impact of climate change on monthly runoff and shows that greater differences
in the model results occur when the models are used to simulate the winter flows. Fung et al.
(2012) used large ensembles of climate scenarios to explore the adaption of the hydrologic
impacts on the fresh water environment in south east England.
Much work above has been done related to bias correction of RCM data and the
uncertainty of the climate change impact on the water resources by using different methods.
The results vary with the models applied in the research and the study areas. As we know,
the floods are closely related to the safety of human’s daily life and properties. Especially in
recent years, the extreme climate events happened with increasing frequency and intensity.
Thus, this study mainly focused on the extreme high flows in Jinhua River basin in the east
China. We are curious about the extent of effect in extreme high flows by using bias
corrected and raw RCM data as input, and under the projected changing climate how much
differences are in the extreme high flows by using different models and different input. The
major objectives of this paper are 1) to assess the effect of the bias correction on the
precipitation by comparison of the amount, frequency, intensity and variance of the bias
corrected precipitation and raw precipitation from RCM; 2) to investigate the indirect effect
of the bias correction method on the extreme flows, because the bias in the precipitation may
be propagated into the discharges, therefore affects the extreme flows; 3) to find out the
differences in extreme runoff under scenarios A1B in the future by using three hydrological
models.
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2 Data and Methods
2.1 Study Area and Observed Data
The study area is Jinhua River basin, which is the upper catchment of Qiantang River
Basin, located in Zhejiang Province in the east China (see Fig. 1). The river basin
stretches from latitude of 28.25o–29.75oN and longitude of 119.25o–120.75oE and the
size is 5,996 km2. It is surrounded by mountains and hills and the elevation is higher in
the southern and northern parts than in the middle part. It has a typical sub-tropical
monsoon climate with hot rainy summer and cold dry winter. The annual temperature is
about 17.5 °C. The highest temperature reaches around 40 °C and the lowest temperature
seldom drops to below 0 °C. The annual precipitation for the period of 1981–1995 is
1,630 mm. However the precipitation is not evenly distributed in the whole year. About
50 % precipitation occurs during May to July. The flood occurred almost every year and
the water resources cannot be utilized properly. For example, one of the most serious
floods occurred in the June of 1995. The cumulative precipitation of 7 days reached
328.7 mm. The population in Jinhua River Basin is more than five million and is in the
growth. The frequent floods have caused a great damage and loss of lives. Therefore,
studying the extreme high flows under the changing climate in Jinhua River basin is a
meaningful and important issue.
Daily observed precipitation from five precipitation stations, daily potential
evaportranspiration (PET) from a precipitation station (see Table 1) and daily discharge
from a discharge station for the period 1981–1995 are used for model calibration and
verification. The daily discharges for the first 10 years, namely 1981–1990, are used for
the model calibration and those of the years 1991–1995 are for the model verification. The
observed discharge used in the calibration and verification are offered by the Hydrological
Bureau of Zhejiang Province, China. The discharge data are sampled at the Jinhua station
each day at 8 am and the quality of the data has been checked. The historical observed
discharge of 1961–1990 (as baseline) is used for the comparison with the future discharge.
The average areal precipitation for the whole basin is calculated by Thiessen pologon
method. The PET was calculated by Hamon’s equation (Hamon 1961; Haith and
Shoemaker 1987).
Fig. 1 Location of the Jinhua River basin and the distribution of the hydrological and meteorological stations.
The crosses represent the grid point in the PRECIS model
Assessment of Climate Change Impacts on High Flows
Table 1 Locations of the hydrological and meteorological stations
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Latitude (oN)
Longitude (oE)
Jinhua
Precipitation stations:
29.08
119.62
Jinhua
29.08
119.62
Bada
29.20
120.50
Yiwu
29.30
120.07
Yongkang
28.90
120.02
Zhengzhai
28.90
119.63
Discharge stations:
2.2 Regional Climate Model
PRECIS (Providing REgional Climates for Impacts Studies) regional climate model is
an atmospheric and land surface model that is able to be applied to any part of the
world with the resolution of 50 km×50 km and 19 vertical layers. The model was
developed by Hadley Centre of the UK Meteorological Office based on the modified
atmospheric component of HadCM3 (Gordon et al. 2000). For this study, the output
of HadCM3 was used as the initial and lateral boundary conditions to drive the
PRECIS model. The PRECIS model has been used for the study related to the climate
change worldwide, for example, China (Yuan et al. 2005; Xu et al. 2006b), India
(Gosain et al. 2006; Gosain et al. 2011), South America (Marengo et al. 2009), Africa
(Mileham et al. 2009) and Europe (Bartholy et al. 2009; Chenoweth et al. 2011). Xu
et al. (2006b) has applied the PRECIS model to the whole China and found that
under scenario B2 the precipitation over east China would increase largely in summer
but not so much in winter, and in the southern China the precipitation would
obviously decrease during 2071–2100. Under the scenario A2 the temperature would
increase about 4 °C and precipitation would increase with 12.9 % in 2071–2080 (Xu
et al. 2006a). The domain of the grid points in Xu’s study covers whole China by
145 grid points in longitude and 112 grid points in latitude. For our study, we choose
the area of latitude 28.25o–29.75oN and longitude 119.25o–120.75oE. Simulated daily
temperature and daily precipitation from the PRECIS model for the A1B emission
scenario (It describes a future world of very rapid economic growth, global population
that peaks in the mid-century and declines thereafter, and the rapid introduction of
new and more efficient technologies with a balance across all energy sources.) from
baseline 1961–1990 and near term 2011–2040 have been used. The potential impacts
of climate change on discharges are assessed by hydrological models.
2.3 Bias Correction
Climate data downscaled by dynamical downscaling includes model bias error from the
RCM and such error introduces uncertainty when climate data are used in some
assessment models (Ueyama et al. 2010). Therefore a bias correction is necessary to
match the RCM data with observed data. In our study, distribution based scaling (DBS)
is applied to adjust the future daily precipitation of PRECIS (Wood et al. 2002). We do
it separately for 12 months considering that the precipitation intensity is different for
each month. It involves two steps:
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to correct the precipitation frequency. Daily precipitation from the PRECIS model was
truncated at a threshold based on its distribution. A threshold value is calculated for
each subbasin based on the empirical observed and RCM cumulative precipitation
distribution as formula (1),
1
ðFobs ðPobs ÞÞ
Pthr ¼ FRCM
ð1Þ
Pthr is the threshold value; Pobs is the minimum observed precipitation amount
considered as a wet day and here we use 0.1 mm; Fobs (…) is empirical cumulative
distribution function of observed daily precipitation and F1
RCM ð. . .Þ is inverse cumulative distribution function of RCM.
(2) to correct the precipitation intensity. There are many theoretical distributions
available to describe the cumulative distribution function of precipitation intensities. One of the most commonly used distributions is two-parameter gamma
distribution. Here we use gamma distribution to represent both observed and
simulated precipitation intensity. Firstly RCM data are fitted to two-parameter
gamma distribution. And then daily precipitation intensity are corrected by
mapping it onto observed intensity distribution, which means taking inverse
gamma cumulative distribution function of observed data to get the corrected
value. The formula is as follow:
1
ðFRCM ðPRCM ÞÞ
Pcor ¼ Fobs
ð2Þ
Pcor is corrected daily precipitation; PRCM is truncated RCM precipitation;
FRCM (…) is two-parameter cumulative distribution of truncated RCM precipitation and F1
obs ð. . .Þ is inverse gamma cumulative distribution of observed data.
2.4 Hydrological Models
There are hundreds of hydrological models in the world. Here three hydrological models,
namely GR4J, HBV and Xinanjiang, are used to simulate the river discharge at Jinhua
hydrological station under the impact of potential climate change for the period from 2011 to
2040. All the three models are widely used for various purposes (Oudin et al. 2004; Akhtar
et al. 2008; Li et al. 2009b; Engeland et al. 2010; Peng and Xu 2010; Wu et al. 2010). Three
models have different number of parameters, different model structures and different
physical meaning in simulating the real rainfall-runoff processes. Therefore they are applied
in this study.
The GR4J is a four-parameter lumped rainfall-runoff model developed by Perrin in
2003 based on the GR3J model (Edijatno et al. 1999; Perrin et al. 2003). The GR4J
model has significant improvement in simulating the low flow compared with the
GR3J. The GR4J model firstly calculates the net rainfall and PET by subtracting the
PET from precipitation. Then through interception a portion of the precipitation goes
into the production store, in which the actual evaporation is calculated and percolation
occurs. The percolated water reaches the flow routing through leakage. Another
portion of precipitation goes directly to the flow routing. The two flow components
together are split into 90 % runoff routed by a unit hydrograph and then a non-linear
routing store and 10 % runoff routed by a single unit hydrograph. The total runoff is
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finally obtained by adding these two parts together. With those two hydrographs, the
time lag between the precipitation event and the corresponding peak discharge can be
simulated.
The HBV model is a semi-distributed conceptual rainfall-runoff model originally
developed by Swedish Meteorological and Hydrological Institute (SMHI) (Bergström
1976, 1992; Lindström et al. 1997). It has been applied widely in climate change,
uncertainty analysis and extreme flows estimation (Engeland and Hisdal 2009;
Deckers et al. 2010; Chen et al. 2012a; Dakhlaoui et al. 2012; Kriauciuniene et al.
2012). The HBV model is composed of a precipitation and snow accumulation
routine, a soil moisture routine, a quick runoff routine and a baseflow routine and a
transform function. The HBV model takes into account the effect of snow melting and
accumulation. But in the study area it seldom snows and therefore the snow accumulation and melting is not included in the HBV model used for our study. The
actual evaporation is obtained by a linear function which decreases as the soil
moisture drops. There are two kinds of runoff reservoirs in HBV. One is the upper
reservoir which generates the quick flow expressed by a non-linear function and
another one is the lower reservoir which generates the baseflow expressed by a linear
function. At last the runoff generated from these two reservoirs is routed through a
transformation function.
The Xinanjiang model is also a semi-distributed rainfall runoff model especially for
humid and semi-humid regions developed by Zhao (1992). Many studies have been
carried out using this model (Shi et al. 2011; Jiang et al. 2012; Li et al. 2012; Zhang
et al. 2012b). The input of three models is daily precipitation and PET. The output is
daily discharge. The Xinanjiang model consists of an evapotranspiration component
represented by a model of three soil layers, a runoff generation component, a runoff
production component, separation of runoff component separating the runoff into
surface water, interflow and ground water and flow routing. This model was initially
developed for Qiantang River Basin.
To assess the performance of the models, the Nash-Sutcliffe efficiency coefficient (NS) is
chosen as objective function (Nash and Sutcliffe 1970). The parameters of the models are
calibrated by the GLUE method using the observed daily precipitation and PET from 1981
to 1990. The GLUE method is a Bayesian analysis based Monte Carlo method for model
calibration and uncertainty analysis (Beven 2006). The verification of three models is carried
out for the period from 1991 to 1995. Both the bias corrected precipitation of PRECIS and
the one without bias correction are used to drive the hydrological models to calculate the
future discharges at Jinhua Station from 2011 to 2040. The corresponding two sets of results
are compared.
The extreme index, annual maximum discharge, is selected to analyze the potential
impact of the climate change on the extreme discharges.
3 Results
3.1 Bias Corrected Precipitation
Figure 2 shows the monthly mean amount, frequency, intensity and variance of the precipitation. The results of bias corrected precipitation from the PRECIS model are compared
with precipitation data of the PRECIS without bias correction and observation precipitation
from 1981 to 1995.
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Fig. 2 Monthly mean precipitation a amount, b frequency and c intensity d variance for Jinhua River basin.
Solid with squares represents observations; Dashes with circles represent bias corrected data from PRECIS
simulation; Dots with diamond represent raw data from the PRECIS simulation without bias correction
Without bias correction, there is continuous underestimation of the precipitation
amount of the observed one by the PRECIS climate model for the beginning of a year
from January to April. For the months from October to December the PRECIS climate
model overestimates the observed monthly precipitation. When the precipitation
amount is high in June, the PRECIS underestimates the high precipitation amount.
Fig. 2(b) shows that the PRECIS simulates less precipitation events than the real
situation in 7 months out of a year obviously. As to the precipitation intensity, the
PRECIS climate model does not catch the changing regularity of the observed data
very well. The observed precipitation intensity goes up from January to the end of
May, followed by a sudden decrease in June. A sudden increase can be observed in
September, and drops down to the end of year with fluctuation. However, large
overestimation of precipitation intensity is found in May and from September to
November. The variance of the simulated monthly precipitation is obviously larger
than observation in September to November which is similar to the intensity.
The four figures indicate that the PRECIS have a good simulation for the amount, but it
doesn’t simulate the extreme high precipitation in July very well as it underestimates the
intensity in July which is shown in Fig. 2(c). Zhang et al. (2006) found that the simulated
bias in the extreme precipitation in the south China was linked to the changes of the
Subtropical High in the West Pacific. Besides, it simulates less rainy days and more
precipitation amount in September and October, which caused the obvious overestimation
in the intensity and variance.
After bias correction, the monthly mean precipitation amount from PRECIS is
closer to the observed monthly mean precipitation amount for most of the time
although there are small differences. Besides, the frequency of precipitation is
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improved obviously compared to that of PRECIS climate model without bias correction. The most significant change is in the precipitation intensity and the variance, and
the bias correction makes the trend of precipitation intensity close to the observations.
The overestimation from PRECIS in May, September, October and November are
corrected and the results match the observed precipitation intensity much better than
the one without bias correction.
There is still some deviation from the observed data even after bias correction,
such as the underestimation of the precipitation frequency from January to April and
August. Relatively low frequency is probably because of the difference in the shape
of distribution of RCM data and that of observation data. The threshold value is
obtained by mapping the position of 1 mm in observed precipitation distribution onto
the distribution of daily precipitation of PRECIS. For PRECIS simulations, there are
more days with daily precipitation amounts under the threshold values than observed
data. The under-threshold RCM data are rejected, so less portion of the PRECIS data
are retained. When there is high intense precipitation, bias corrected PRECIS precipitation intensity is still lower than the observed one in July.
3.2 Calibration and Verification of the Hydrological Models
Using the observed daily precipitation and PET as the input, three models are
calibrated for the period 1981–1990 and verified for the period 1991–1995. Table 2
presents the NS (Nash-Sutcliffe) value during the calibration and verification periods
for GR4J, HBV and Xinanjiang models. The maximum NS value is one. If the NS
value equals to one, it means the simulation totally matches observations. So the
closer the NS value is to one, the better performance of the model simulation is. The
results show that the NS values for both calibration and verification periods for all
three models are above 0.8. Therefore, the performance of all three models is
satisfactory. The NS value show that during the calibration and verification period,
the performance of the GR4J model and the HBV model is somewhat better than the
Xinanjiang model. Besides, the performance for the verification period is a little bit
better than for the calibration period.
Figure 3 compares the simulated and observed daily discharges for 1981–1995. The
horizontal axis represents the observation and the vertical axis represents the simulation. The black solid line represents the perfect situation when the simulation is the
same as the observation. The squares represent the simulated discharge for the GR4J
model, the HBV model and the Xinanjiang model in Fig. 3a, b and c respectively.
Although there are biases from the daily observed discharge by the simulation, the
symbols are around the solid line. For the high discharges, most symbols are below
the solid line which indicates the underestimation of the high discharge from the
hydrological models.
Table 2 NS value for the GR4J model, the HBV model and the Xinanjiang model during the calibration and
verification periods
GR4J
HBV
Xinanjiang
Calibration
0.91
0.91
0.88
Verification
0.93
0.91
0.89
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Fig. 3 Daily discharge simulated by a GR4J, b HBV and c Xinanjiang for 1981–1995
3.3 Impact of Bias Correction on High Flows
For the simulation of the future high flows, a comparison is carried out to investigate the
discrepancy of using bias corrected data and raw data from PRECIS. In this paper, high
flows are represented by annual maximum discharge. Fig. 4 shows the annual maximum
discharge by using bias corrected data and raw data for 2011–2040 for the GR4J model,
HBV model and the Xinanjiang model. The line with circles represents the annual maximum
discharges from bias corrected data and the squares represent the results from raw PRECIS
data. For all three models, the largest annual maximum discharge reaches around
10,000 m3/s and the smallest value is less than 1,000 m3/s by using the raw PRECIS data.
After bias correction, the annual maximum discharges are lower for most of the time from
2011 to 2040 and the largest annual maximum discharge ranges between 4,000 m3/s and
around 6,000 m3/s due to different models. Such difference is caused by the different
precipitation input for the models. Bias correction changes the temporal distribution of
original PRECIS precipitation intensity, which causes the decrease in the annual maximum
discharge. Therefore, attention should be paid when applying the RCM data, for without bias
correction, the overestimation of the precipitation intensity may result in the overestimation
of the discharge. Besides, the differences of the discharge by using raw and bias corrected
input are dependent on the hydrological models. It’s shown that such difference is smallest in
the Xinanjiang model, and largest in the GR4J model, which indicates the sensitivity of the
models to the input data.
Figure 5 shows the annual maximum discharge simulated by GR4J, HBV and
Xiannjiang. Generally, there is no obvious trend for the annual maximum discharge,
but a three-to-five year interannual discharge cycle with peaks of more than 2,000 m3/s
and valleys of less than 1,000 m3/s is shown for the future climate. Though each model
simulates different value of annual maximum discharge under the same scenario and by
the same input, the fluctuation of the discharge is in consistency. When the value of
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Fig. 4 comparison of the annual maximum discharge from 2011 to 2040 by using bias corrected data and raw
data from PRECIS as two sets of input for a the GR4J model, b the HBV model and c the Xinanjiang model
annual maximum discharge is below 1,000 m3/s, the difference of simulated discharge
from three models is within 100 m3/s. When the value of the annual maximum
discharge is large, the differences from the three models tend to be more obvious.
For the year 2019, the annual maximum discharge ranges from around 400 m3/s to
5,000 m3/s due to different models. In most of the time, the HBV model predicts the
highest annual maximum discharge, while the Xinanjiang model predicts the lowest
annual maximum discharge. The HBV is in between. It is likely to be related to the
different model structures, such as the division of the soil layers for reserving water and
the mechanism of the runoff generation, etc.
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Fig. 5 simulated annual maximum discharge by GR4J, HBV and Xinanjiang for the period of 2011–2040
under scenario A1B for the Jinhua River basin
3.4 Extreme Value Analysis of High Flows
To compare the impact of bias correction on high flows, the design discharge versus return
periods based on simulated high flows by GR4J, HBV and Xinanjiang respectively for
2011–2040 with bias corrected input and raw PRECIS input are presented in Fig. 6. Here the
GEV (Generalized Extreme Distribution) distribution is used for extreme value analysis.
It is clear in the Fig. 6 that for all the three models the design discharges from raw
PRECIS input is larger than those from bias corrected input at any return period. And the
differences increase as the return periods increase. For 2 years return period, the difference
of the design discharge with bias correction and without bias correction is in the range of
1,400–2,500 m3/s. While for 100 years return period, the difference is 4,000–7,000 m3/s.
Such difference is due to the combination of many factors. First of all, the systematic error of
the RCM may contribute to it. Though the parameters in the RCM are calibrated, it is
impossible to completely remove bias using parameterization for the difficulty in parameterizing all factors that affect the RCM bias (Ueyama et al. 2010). Meanwhile, the bias
correction method is based on the assumption that the correction function is constant in time,
which means it is the same for the present and the future. And the bias correction method is
not a result of impeccable physically based theory and it just represents a curve fitting
exercise of convenience (Kundzewicz and Stakhiv 2010).
Furthermore, the design discharge of different return periods simulated by three
models using bias corrected input for 2011–2040 are compared with those of the
observations for the baseline 1961–1990. The results are shown in Fig. 7. The design
discharge with 5 years return period is about 2,600 m3/s for observation, and with
100 years return period it increases into almost 5,000 m3/s. Although the NS value from
calibration and verification shows that the performances of three models are all good, the
simulated design discharge from three models are very different in the changing climate.
Under the scenario A1B, the GR4J model shows a decrease of the extreme flows when it
is below 3,500 m3/s and an increase when it is above 3,500 m3/s, which indicates a
polarization over the extreme flows. The large floods would be even larger in the future
and the small ones would become smaller. The Xinanjiang model displays a lower
parallel line with the observation. It predicts a decrease in the extreme high flows for
all the return periods. As to the HBV model, it remains petty much similar as the current
situation within the return period of 3 years. The extreme flows are predicted to increase
in the future when it is larger than 3 years return period. That is, the flood risk in the
Assessment of Climate Change Impacts on High Flows
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Fig. 6 Design discharge versus return periods with bias corrected input and raw input from PRECIS, based
on annual maximum discharge from a the GR4J model, b the HBV model and c the Xinanjiang model
future under scenario A1B tends to be larger estimated by GR4J and HBV model but smaller by
Xinanjiang model. The range of such change between the future projection and historical data is
closely related with the return period. When the return period is 2 years, the change of the
design discharge ranges from −500 m3/s to −100 m3/s. As the return period increases, the range
is −900 m3/s to +1,200 m3/s with 100 years return period. In another word, the range for
100 years return period is five times as much as that for 2 years return period due to different
models, which indicates the uncertainty of the high flows from hydrological models becomes
large as the increase of the volume of the discharge. For this study, the uncertainty in the
extreme high flows in the future is due to (1) the parameters of the hydrological models. Even
the parameters of equally good performance may produce different results. Such phenomenon
of the equifinality of the parameters in calibration has been illustrated by Beven (2006); (2) the
different models applied for simulation. Different models have different interpretation and
expression of the real physical processes and therefore may cause varied results; (3) the
probability model used for extreme-value analysis of the high flows.
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Fig. 7 Design discharge versus return periods from the observation, the GR4J model, the HBV model and the
Xinanjiang model for the Jinhua River Basin
4 Discussion
This study was initially to assess the direct effect of the bias correction on the precipitation
from the PRECIS model and the indirect effect on the future extreme flows, and meanwhile
investigate the uncertainty of the extreme high flows using different models. In the bias
correction process, although the amount of the monthly precipitation gave good results but
the intensity and the variance of the precipitation from the PRECIS had a large bias from the
observation in July and from September to November. Zhang et al. (2006) pointed out that
the simulated precipitation of the PRECIS was better in the north China than that in
southwest coastal area. It was related to the position and intensity of the Subtropical High
in the West Pacific which caused the simulated bias in extreme precipitation events. If the
bias of the precipitation is not corrected in the projection of the future discharge, it would be
propagated to the discharge resulting in the overestimation in the extreme discharge in this
study.
Meanwhile, the changes of the future extreme discharges simulation by three models
were different. The GR4J model and the HBV model predicted the increase in the extreme
flows in the future but the Xinanjiang model predicted a decrease. The GR4J model
outperformed the other two models. The GR4J model had two soil layers, four parameters.
The HBV model has two soil layers and eight parameters. The Xinanjiang model had three
soil layers and 14 parameters. It seemed that the more complex and specific hydrological
model does not improve the simulation results. The results were different from Li’s study
(2009a), which showed that runoff prediction in ungauged catchments are improved by
using more complex, specific model structures defined by vegetation and other information.
It was likely due to the different study areas and the soil layers in this area were not the key
factors of the formation of the discharge. Besides, fewer parameters may be easier to identify
while more redundant parameters would add more uncertainty and reduce the chance of get a
better result.
The ‘best’ model in this study area was the GR4J model in terms of the highest NS value
in the calibration and verification. But it only illustrated that the results of the GR4J model
fitted the observation best when using the NS as the objective function. However, only using
the model with the highest NS value was too risky and not recommended, because the
simulation results were affected by many other factors like the objective function and the
time interval of the observed data. The uncertainty of the models cannot be neglected, as it
was stressed in other researches as well (Beven 2006). Therefore, attention should be paid in
Assessment of Climate Change Impacts on High Flows
2885
dealing with the simulated discharges. A way to deal with the uncertainty was the BMA
method which gave the weight to the models according to their performance (Madigan et al.
1996; Rojas et al. 2008; Parrish et al. 2012).
5 Conclusion
This study analyzed the impact of climate change on the extreme high flows in Jinhua
River basin for the near future 2011–2040. Under scenario A1B, the PRECIS RCM
was applied to downscale the GCM grids so as to get more detailed information for
the catchment scale. The daily precipitation from the PRECIS RCM was bias
corrected by distribution based scaling method. Annual maximum discharge was
chosen as the index to assess the impact on extreme high flows with three different
models, namely GR4J, HBV and Xinanjiang. The results showed that compared to the
observed precipitation, the precipitation amount from RCM is higher in the end of the
year but lower in the beginning of the year, the precipitation frequency is less and the
intensity of annual distribution is not in accordance with the observation. After bias
correction, though there are differences between observation and RCM simulation, the
bias of precipitation amount is reduced. Besides, there is obvious improvement for the
precipitation frequency and intensity with the annual distribution closer to the observation. However the precipitation intensity is a bit lower than observation. It is related
to the different shape of distribution between observation and precipitation from
PRECIS and the threshold we chose to truncate the precipitation from PRECIS.
Comparing the results using raw precipitation from PRECIS and bias corrected precipitation,
we found that there is a large difference for the annual maximum discharge for the period of
2011 to 2040. For all three models, the simulated high flows are higher by using the raw
precipitation from PRECIS than those by using bias corrected precipitation. The largest
difference of the extreme high flows reaches about 7,000 m3/s. It indicates the importance of
carrying out the bias correction for the RCM data when using them to assess the effect of
climate change on discharge.
Though each model presents different annual maximum discharge, they show similar
three-to-five year interannual discharge cycle and the fluctuation of the high flows is in
consistency. The difference of the design discharge between three models increases with the
value of return periods. The largest difference between three models reaches about
2,100 m3/s. In most of the time, the HBV model predicts the highest annual maximum
discharge; the lowest is for Xinanjiang and GR4J is in between. For all the three models the
annual maximum discharges from raw PRECIS input is larger than those from bias corrected
input at any return period. And the difference is increasing as the return periods increase.
Compared to the design discharge calculated based on the observations, three models show
different changing trends. The HBV model shows an increase in the extreme discharges for the
return periods larger than 3 years while the Xinanjiang model displays a decrease in extreme
discharges for all the return periods. As to the GR4J model, the extreme floods will become
more extreme. The flood risk in the future under scenario A1B tends to be larger estimated by
GR4J and HBV, but smaller by Xinanjiang. The magnitude of such change is related to the
models and return period.
Acknowledgments This study is financially supported by the International Science and Technology Cooperation
Program of China (Project No. 2010DFA24320) and the Nature Science Foundation of China (Project No.
50809058). Other supports from Met Office Hadley Centre, UK, Bureau of Hydrology, Zhejiang Province, and
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Y. Tian et al.
Nanjing Hydraulic Research Institute are highly acknowledged. Finally, many thanks are given to two anonymous
reviewers for their valuable comments.
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