Stoch Environ Res Risk Assess (2013) 27:1359–1370
DOI 10.1007/s00477-012-0672-z
ORIGINAL PAPER
Urban water consumption in a rapidly developing flagship
megacity of South China: prospective scenarios and implications
Pengfei Shi • Tao Yang • Xi Chen • Zhongbo Yu
Kumud Acharyad • Chongyu Xu
•
Published online: 5 December 2012
Springer-Verlag Berlin Heidelberg 2012
Abstract With a booming expansion of urbanization,
urban water consumption (WC) attracts increasing concerns in developing countries worldwide, particularly for
megacities. In this study, an urban WC model for Shenzhen, a rapidly developing flagship megacity in South China
from a small agrarian fishery village since 1979, was built
up to simulate the WC changes (1994–2009) with aim to
formulate local water resources management strategies.
Basically, the model was constructed using a variety of
methods including a back-propagation artificial neuron
network (BP-ANN), a quadratic polynomial model, a
regression and auto-regressive moving average combination model, and a Grey Verhulst model. Simulation of the
WC was conducted using a multiple regression forecasting
model and a BP-ANN model. The results from these two
models showed that the BP-ANN model is outperformed.
Subsequently, a series of social–economic and demographic scenarios were formulated to project WC
(2011–2020) with uncertainty analysis. The results suggest
that the total WC will increase slower and slower over the
decade. It might approach a saturated threshold soon after
2020. Scenarios of WC incorporating uncertainty analysis
aiming to provide reliable prediction results constitute the
highlight of this study. This study will be beneficial to
formulate appropriate sustainable development strategies
of water resources for similar megacities in South China.
P. Shi T. Yang (&) Z. Yu
State Key Laboratory of Hydrology-Water Resources
and Hydraulic Engineering, Hohai University,
Nanjing 210098, China
e-mail: yang.tao@ms.xjb.ac.cn
1 Introduction
T. Yang X. Chen
State Key Laboratory of Desert and Oasis Ecology,
Xinjiang Institute of Ecology and Geography,
Chinese Academy of Sciences, Ürümqi, China
Z. Yu
Department of Geoscience, University of Nevada Las Vegas,
Las Vegas, NV 89154, USA
K. Acharyad
Division of Hydrologic Sciences, Desert Research Institute,
Las Vegas, NV 89119, USA
C. Xu
Department of Geosciences, University of Oslo, P.O. Box 1047,
Blindern, 0316 Oslo, Norway
Keywords Water consumption Back-propagation
artificial neural network (BP-ANN) Grey Verhulst model ARMA model Projection Uncertainty South China
Predictions of urban water consumption (WC) are useful
for urban planning and management of water resources
(Wong et al. 2010). To improve existing infrastructure with
minimizing costs, it is important to have accurate predictions of WC during the planning phase. These predictions
can be obtained using the WC quota method, regressions,
and time series analysis (Maidment et al. 1985). However,
WC in urban areas is dynamic, fluctuates, differs between
high and low-income consumers, and tends to increase over
time (e.g. Dube and van der Zaag 2003). Therefore, models
based on WC quotas and time series analysis may fail to
precisely characterize the nonlinear relationships between
urban WC and related influencing factors. This will lead to
overestimates or underestimates of future WC.
Models that take various influencing factors into account
have been developed to reduce prediction uncertainties.
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Jia and Zhang (2003) studied the influence of water price
increments on industrial water. Viswanathan (1985) investigated the effect of restrictions on WC levels in Newcastle,
New South Wales, using a linear model with the following
independent variables: rainfall depth, and the concurrent and
previous day’s daily maximum air temperature. Liu et al.
(2003) developed a three-layer ANN to process an input
vector consisting of water price, household income, and
household size to produce the output vector of water demand.
Jorgensen et al. (2009) proposed a model for understanding
household WC and argued that trust plays a role in household
WC because people will not save water if they feel others are
not minimizing their water use. Wei et al. (2009) reported a
simulation analysis of domestic water demand and its future
uncertainty through a case study in Beijing. In their study,
urban per capita discretionary income, consumer price index
(CPI), and rural population were used to construct a linear
regression model. Artificial neural networks (ANNs) techniques have also been applied in WC prediction in recent
years (Jain et al. 2001). For example, Bougadis et al. (2005)
developed some regression, time series analysis and ANN
models to forecast short-term municipal water demand and
found that ANN models consistently outperformed regression and time series models. Liu et al. (2003) developed a
water demand forecasting model using an ANN model with
water price, household income, and household size as input
vectors. Model evaluation showed correlation coefficients
higher than 90 % for both the training and testing data.
Yurdusev et al. (2010) used an approach of generalized
regression neural networks to predict the monthly use of
water from several socio-economic and climate factors that
affect water use. Wang et al. (2012) developed a scenariobased water conservation planning support system by using
the monthly average maximum daily temperature, monthly
aggregate WC, property parcel polygons with land use
classification to help the decision makers manage the water
resource more efficiently.
Although many studies have reported various factors
affecting water demand and constructed various models,
few studies have paid adequate attention to industrialization levels and social–economic factors. While those
impact factors have considerable effects on WC, especially
for a modern megacity developed from an agrarian fishery
village since 1979 to a flagship megacity in South China.
As a result of its unique geographical setting and economic
structure, urban WC in Shenzhen differs significantly from
those in many northern Chinese regions (e.g. Beijing and
Jinan; Zhang et al. 2010). Therefore, this study aims to: (1)
identify correlations between climate conditions, population, industrialization levels and urban WC in Shenzhen;
(2) build up an appropriate model to predict WC incorporating uncertainties; and (3) construct prospective scenarios
of future WC in Shenzhen (2011–2020).
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Stoch Environ Res Risk Assess (2013) 27:1359–1370
2 Study region and data
2.1 Study region
Shenzhen (22270 –22520 N and 113460 –114370 E) is situated in the subtropical region of China, located near the
Tropic of Cancer. Under Koppen’s climate classification,
Shenzhen has a humid subtropical climate (Municipal Water
Affairs Bureau of Shenzhen [MWABS] 2011). Annual
average precipitation is 1,830 mm (MWABS 2011), some of
which comes from typhoons. The municipality covers an
area of 2,050 km2 including urban and rural areas, with a
total population of 8,912,300 at the end of 2009 (Statistics
Bureau of Shenzhen 2011). Owing to China’s economic
liberalization under the ‘‘reform and open policy’’, this area
became China’s first—and one of the most successful—
Special Economic Zones. It is now one of the fastest growing
cities in the world. Shenzhen ranked fourth in Gross
Domestic Product (GDP) among mainland Chinese cities in
2001, while it ranked number one in GDP per capita during
the same period (Statistics Bureau of Shenzhen 2011).
Currently, the annual per capita water availability of
Shenzhen is less than 200 m3, is only 1/12 of the annual per
capita water availability of China (MWABS 2011). The per
capita water availability for Shenzhen is far less than the
world critical threshold for water shortage (1,000 m3).
With a fast population growth, booming economic development and accelerated urbanization processes, the WC in
Shenzhen is increasing dramatically.
2.2 Data
The study data includes climate data, social–economic data
and water resources data of Shenzhen during 1980–2009 as
below:
•
•
•
•
•
•
•
•
•
Resident population at year-end (RP);
Gross Domestic Product (GDP);
The primary sector output (PSO);
The secondary sector output (SSO);
The tertiary sector output (TSO);
Consumer price index (CPI);
Water consumption (WC)
Annual mean temperature (T)
Annual precipitation (P)
Due to the limited data about changes of industrial
structure and stages of industrialization, GDP, PSO, SSO,
TSO are used to collectively show the process of industrialization of Shenzhen to a certain degree. Meanwhile,
due to unavailability of water prices, CPI was used instead
of domestic water price. The WC data of Shenzhen were
compiled from the Statistical Yearbook of Shenzhen
(2010), water resources bulletin for Shenzhen. The climate
Stoch Environ Res Risk Assess (2013) 27:1359–1370
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data, including annual mean temperature (T) and annual
precipitation (P), were retrieved from China Meteorological Data Sharing Service System.
2002). The new sequence goes as S-curve or partial Scurve. The GVM can be defined as:
2
xð0Þ ðkÞ þ azð1Þ ðkÞ ¼ b zð1Þ ðkÞ
ð2Þ
3 Methodology
The whitening equation of GVM is therefore, as follows:
2
dxð1Þ ðkÞ
þ axð1Þ ðkÞ ¼ b xð1Þ ðkÞ
ð3Þ
dt
In this paper, a three-layer back-propagation (BP) neural
network model incorporating uncertainty analysis was
designed to process an input vector consisting of some
influencing factors to produce the output vector of WC. A
range of prediction models for the impact factors were
constructed collectively, including the quadratic polynomial for GDP, regression-auto-regressive moving average
(ARMA) combination model for CPI and Grey Verhulst
model (GVM) for RP. The study framework (Fig. 1) can be
summarized as follows: (1) building up models of GDP,
RP, CPI respectively through quadratic polynomial, Grey
Verhulst and ARMA techniques by using the observed data
of GDP, RP and CPI. (2) Developing three economic
development scenarios to generate a number of scenarios
of future WC in 2011–2020. (3) Constructing two models
of WC using the observed GDP, RP, CPI and WC. One is
BP-ANN model, and the other one is multiple linear
regression (MLR) model. Compare the two models thereby
to forecast WC in future by using the outperformed one. (4)
Forcing the BP-ANN model to run with the 27 scenarios of
WC as inputs to predict WC of Shenzhen in 2011–2020.
Assume that x(0) is the sequence of raw data values, x(1) is the
sequence obtained through accumulating generation. Similar to
the GM (1, 1) model (‘‘Grey Model First Order One Variable’’,
is a time series forecasting model. Deng 2002), the least square
estimation of the parameter ^u ¼ ½a; bT is shown as below:
1
ð4Þ
½a; bT ¼ BT B BT Y
where
h
iT
Y ¼ xð0Þ ð2Þ; xð0Þ ð3Þ; . . .; xð0Þ ðnÞ
h
i
zð1Þ ðkÞ ¼ 0:5 xð1Þ ðkÞ þ xð1Þ ðk 1Þ ;
2
3
zð1Þ ð2Þ ðzð1Þ ð2ÞÞ2
6 zð1Þ ð3Þ ðzð1Þ ð3ÞÞ2 7
7
B¼6
4 ...
... 5
ð1Þ
z ðnÞ
ð1Þ
ðz ðnÞÞ
^xð1Þ ðk þ 1Þ ¼
In mathematics, a quadratic polynomial or quadratic is a
polynomial of degree two, also called second-order polynomial. Generally, a polynomial may be defined over any
ring. A quadratic polynomial may involve a single variable
x, or multiple variables such as x, y, and z.
In this study, a single-variable quadratic polynomial
model is used to model GDP. The general form of any
single-variable quadratic polynomial can be expressed as:
y ¼ f ðxÞ ¼ ax2 þ bx þ c
ð1Þ
3.1.2 The GVM
The Verhulst model, a biological growth model, was firstly
proposed by a German biologist Verhulst (1838). It is
effective in describing a few processes, such as an S-curve
or partial S-curve that has a saturation region (Kayacan
et al. 2010). The GVM is to build a Verhulst model based
on a new sequence obtained through accumulating generation of the initial data that goes as single-peak type (Deng
k ¼ 2; . . .; n
ð6Þ
ð7Þ
2
According to formula (4), the parameter ^u ¼ ½a; bT can be
calculated. Therefore, the time response formula is shown
as below:
3.1 Models for impact factors
3.1.1 The quadratic polynomial model
ð5Þ
bxð1Þ ð0Þ
axð1Þ ð0Þ
þ ½a bxð1Þ ð0Þeak
ð8Þ
The response formula can be used for simulation and
prediction. Normally, performances of Grey models are
evaluated by the method of posterior variance examination.
The measures of model efficiency (C and P) are as follows:
C ¼ S2=S1
P ¼ PfjeðiÞ ej\0:6745S1g
ð9Þ
ð10Þ
where S2 and S1 are standard deviation of residual
sequence and raw data sequence respectively, e(i) is the
residual between raw data values and simulated sequence,
e is the mean value of e(i). Small value of C and large
value of P stands for good skill scores.
In our study, a GVM technique was used to capture the
nonlinear information and likely tendency to saturation of
population, thereby to predict the future RP.
3.1.3 ARMA time series model
The ARMA model (Wang and Hu 2007), a kind of stochastic time series model, is developed by Box and Jenkins
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Water consumption
Model calibration and verification
Observed data
Impact factors of water consumption
Models for impact factors
GDP
Quadratic
Polynomial Model
RP
BP-ANN Model
MLR Model
Grey Verhulst Model
CPI
ARMA Model
Scenarios Construct
and Analysis
Model
Intercomparison
1. 5% higher than business as usual
2. Business as usual
3. 5% lower than business as usual
Combination
Projection
Input
27 scenarios of GDP, RP, and CPI
Water consumption scenarios
( 2011-2020)
Fig. 1 Overview of model framework
(1970). The assumption behind this model is that the series
must be stationary random sequence. After the difference
of time series, the series may become stationary random
sequence. The ARMA model structure or format of a time
series such as Xt is as follows:
Xt ¼ u1 Xt1 þ u2 Xt2 þ þ up Xtp þ et h1 et1
h2 e2 hq etq
ð11Þ
where Xt = u1Xt-1 ? u2Xt-2 ? ? upXt-p ? et is pth
order autoregression series, i.e. AR(P), u1, u2, …, up are
autoregressive coefficients; Xt = et - h1et-1 - h2e2 - hqet - q is qth order moving average series, i.e.
MA(q), h1, h2,…,hq are moving average coefficients. In
this paper, the ARMA time series model was used to
construct the forecast model of CPI.
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3.2 Models for WC
3.2.1 BP artificial neural network (BP-ANN)
An ANN, inspired by biological neural system, is composed of processing elements called neurons or nodes. The
feed-forward neural network (FFNN) consists of at least
three layers: input, output and hidden layers. There are
many nodes in each layer. The input signals presented to
the system in the input layer are processed forward through
to the hidden layer. The summation of weighted input
signals is transferred by a nonlinear activation function.
The response of the network is compared with the actual
observation results and the network error is calculated. The
BP neural network, which is a kind of a FFNN, is based on
the back-propagation arithmetic. The BP-ANN is mostly
Stoch Environ Res Risk Assess (2013) 27:1359–1370
1363
commonly used neural network and has been widely
applied in predicting WC (Firat et al. 2010) and hydrologic
prediction.
The input vector is X ¼ ðX1 ; X2 ; . . .; Xi ; . . .; Xn ÞT ; i = 1,
2,…,n, of which Xi is an element; the output vector is
O = (o1, o2,…,ok,…,ol)T, k = 1, 2,…,l, of which ok is an
element; the desired output is d = (d1, d2,…,dk,…,nl)T,
k = 1, 2,…,l, of which dk is an element. V and W are both
the weight matrixes, V connects the input and the hidden
layer, and W connects the hidden and the output layer.
When the output value differs from the desired values, the
error (E) therefore emerges, the equation is as follows:
E ¼ ðd OÞ2 =2
ð12Þ
The BP-ANN model can reduce the error continuously
by adjusting the weights to minimize the error. In this paper,
a BP-ANN model is examined to simulate WC of Shenzhen.
3.2.2 MLR model
In statistics, linear regression is an approach to model the
relationship between a scalar dependent variable y and one or
more explanatory variable devoted X. The linear regression
model can be simply presented by the following equations:
yi ¼ b1 X1i þ b2 X2i þ þ bp Xpi þ b0 þ ui ;
p ¼ 1; 2; 3; . . .n;
i ¼ 1; 2; 3; . . .; n
Fig. 2 Matrix scatter plot of WC and some influencing factors
ð13Þ
where yi are the values of the dependent variables under
observation, Xpi are independent (or explanatory) variables,
bp are parameters of the equation, ui is the unobserved
random term. After developing such a model, if an additional value of X is given without its accompanying value
of yi, the fitted model can be used to make a prediction of
the value of y. In this paper, a MLR model is used to
simulate WC of Shenzhen.
3.3 Measures of performance assessment
Five different measures are collectively used to evaluate
the performance of the prediction models of WC: the
coefficient of efficiency (Ens), coefficient of determination
(R2), ratio of simulated and observed standard deviation
(RS), root mean square error (RMSE), and model bias.
4 Results
4.1 Model calibration and verification
4.1.1 Models of the impact factors
It was highly important to choose independent variables to
simulate WC. Figure 2 indicated that there are almost no
Fig. 3 Time series of GDP, SSO and TSO
statistical correlations between climate factors (i.e. the
annual mean temperature, annual precipitation) and WC
identified in the region. Therefore, only social–economic
factors were used to build a WC model for Shenzhen city.
Secondly, PSO was excluded owing to unsatisfied correlation between PSO and WC (Fig. 2). Meanwhile, although
SSO and TSO have significant statistical correlations with
WC, they were excluded in modeling the WC due to their
autocorrelations among SSO, TSO and GDP (Figs. 2, 3).
Considering SSO and TSO were both components of GDP,
GDP was used to build the model. Therefore, only GDP,
CPI and RP were included as independent impact factors in
the WC model.
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Stoch Environ Res Risk Assess (2013) 27:1359–1370
(a)
GDP for Shenzhen was split into two periods: 1994–2006
for calibration and 2007–2009 for validation. The model
for GDP can be calculated using the following equation:
LnGDP ¼ 0:003 x2 þ 0:219 x þ 3:990
ð0:001Þ
ð0:010Þ
ð14Þ
ð0:032Þ
where x = t - 1994, t is the time (year), 1994 is the first
year. The numbers in the parentheses stand for the standard
errors of coefficient estimate (F statistic = 2,802, Prob(F
statistic) = 0.000, R2 = 0.998, adjusted R2 = 0.998).
Standard error of the estimate is 0.032. And the forecasting
results and relative errors in validation periods are shown in
Table 1. This quadratic polynomial model of GDP is only
appropriate to cities where economy development is fast and
stably increasing.
(b)
4.1.1.2 Model of RP The simulation and projection results
of RP model during 1994–2020 are shown in Fig. 4b. Due to
rapid development of the economy, population in Shenzhen
increased dramatically in the last decades. However, the
population increase is a nonlinear process, which is influenced by the resource carrying capacity and local population
policy. In this study, a GVM was used to simulate population growth. Here, in calibration period, we take the population (1994–2006) as x(1), and its 1-IAGO as x(0). According
to formula (4), the parameter ^u ¼ ½a; bT can be calculated
as a = -0.1109, b = -0.000198. Therefore, the time
response formula is shown as below:
(c)
^xð1Þ ðk þ 1Þ ¼
0:1109xð1Þ ð0Þ
0:000198xð1Þ ð0Þ þ ½0:1109 þ 0:000198xð1Þ ð0Þe0:1109k
ð15Þ
(1)
(1)
where we use x (1) instead of x (0). The Eq. 15 can now
be used to simulate and forecast population in Shenzhen.
Fig. 4 Comparisons in observation and simulation and projections of
GDP (a), RP (b), CPI (c)
4.1.1.1 Model of GDP The simulation results of the GDP
model during 1994–2020 are shown in Fig. 4a. The model
was built based on a polynomial curve fitting. The observed
C ¼ S2=S1 ¼ 0:11165=1:61575 ¼ 0:069\0:35;
ð16Þ
P ¼ PfjeðiÞ ej\0:6745S1g ¼ 1 [ 0:95
ð17Þ
C and P were calculated using the whole sample
(including the calibration and validation data). According
Table 1 Observation of GDP, RP and CPI, the forecast results, and the relative errors in validation periods
Years
Observed
GDP
Predicted
GDP
Relative errors
(%)
Observed
RP
Predictied
RP
Relative errors
(%)
2005
2006
Observed
CPI
Predicted
CPI
Relative errors
(%)
675.7
695.0
2.86
690.6
729.1
5.57
2007
680.16
644.19
-5.29
8.62
8.67
0.64
718.9
762.1
6.01
2008
778.68
735.10
-5.60
8.77
8.83
0.65
761.3
794.3
4.34
2009
820.13
833.81
1.67
8.91
8.96
0.50
751.4
825.8
9.90
Note Score skills of Grey prediction models can be classified by Excellent (P [ 0.95, C \ 0.35), Good (0.80 \ P \ 0.95, 0.35 \ C \ 0.5),
Qualified (0.70 \ P \ 0.80, 0.50 \ C \ 0.65), and Moderate (P \ 0.70, C [ 0.65)
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Stoch Environ Res Risk Assess (2013) 27:1359–1370
(a)
1365
4.1.1.3 Model of CPI The results of the CPI model during 1980–2020 are shown in Fig. 4c. This model was built
based on the ARMA time series and regression method.
In this approach, the ARMA method was aimed to model
the random component of CPI. Meanwhile, the regression
method was used to model the deterministic component of
CPI. CPI is affected by a series of different factors, such as
service price, wage level, commodity price, investment
conditions and so forth. The CPI change process is therefore too complicated to model with general means. In a
long-term time series, CPI can be simulated and predicted
to some extent by time series analysis. 1980–2004 is calibration period and 2005–2009 is validation period.
CPI ¼ 27:52 Y 54; 444:01 þ½ARð1Þ; MAð1Þ
(b)
ð6:18Þ
ð18Þ
ð12;338:20Þ
where Y is the year, the numbers in the parentheses under the
modeling equation are standard errors of the coefficient estimate (AR(1) = 0.84, MA(1) = 0.59, F statistic = 798.61,
Prob(F statistic) = 0, R2 = 0.991, adjusted R2 = 0.990).
The results in validation periods (Table 1) are satisfied.
4.1.2 Construction of WC models
(c)
Fig. 5 Comparison in observed and simulated WC using the BPANN model (1994–2009) and MLR model (1994–2009) (a), WC of
Shenzhen in 1994–2020 by BP-ANN model (b), projected 27
scenarios of WC of Shenzhen in 2011–2020 by BP-ANN model (c)
to the skill scores shown under Table 1 and the results of
validation in Table 1, the model performs perfect in
modelling the population growth. The GVM model is
effective in describing a few processes, such as an S-curve
or partial S-curve that has a saturation region, or a curve
presented as single-peak type as well.
4.1.2.1 The BP-ANN predicting model The influencing
factors (GDP, CPI, and RP) were non-autoregressive
independent variables. These three variables were the input
vectors, WC was the output vector, and one node was set in
the output. The network was trained with a different
number of hidden layer nodes. Finally, six nodes were used
in the hidden layer based on the trial and error method. In
this study, the training epoch was 3,000, the performance
function was MSE (mean squared error), and the training
goal was 0.0001. In order to reduce uncertainty, a ‘‘trainbr’’
function was considered in training stage. It was shown that
‘‘trainbr’’ can improve the ability of generalization in the
neural network.
The data during 1994–2006 was used in calibration and
2007–2009 in verification. The simulation results and
errors were shown in Fig. 5a and summarized in Table 2.
Figure 5a and Table 2 shows simulation errors were generally less than 2 % in training stage and the results in
validation (2007–2009) were closely matched with observations (below 4.68 %). This suggested that the BP-ANN
model performed well in modeling the WC of Shenzhen.
4.1.2.2 Multiple linear regression model (MLR) A MLR
model was constructed for sake of an intercomparison with
the BP-ANN model. The parameters of the MLR model
were given in Eq. 19. The stepwise regression method was
used in the establishment of this model. Finally, SSO and
TSO were removed from the model according to their
statistical significance.
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Table 2 Performance assessment for BP-ANN model and MLR model
Years
Observed urban
WC (million m3)
Simulated urban WC of
BP-ANN (million m3)
Relative errors (%)
of BP-ANN model
-0.24
Simulated urban WC
of MLR (million m3)
521.29
Relative errors (%)
of MLR model
Purpose
-0.34
Training
1994
523.08
521.81
1995
574.08
580.12
1.05
582.99
1.55
1996
647.43
637.38
-1.55
639.16
-1.28
1997
701.94
712.33
1.48
714.31
1.76
1998
795.96
792.60
-0.42
784.16
-1.48
1999
865.6
860.50
-0.59
854.80
-1.25
-1.17
2000
920.68
920.19
-0.05
909.95
2001
973.34
978.01
0.48
1000.89
2.83
1084.34
0.34
1105.12
2.26
2002
1080.7
2003
1227.95
1223.21
-0.39
1212.83
-1.23
2004
2005
1350.26
1394.87
1344.00
1405.71
-0.46
0.78
1315.94
1389.68
-2.54
-0.37
2006
1452.27
1447.98
-0.30
1477.04
1.71
2007
1542.3
1474.41
-4.40
1549.66
0.48
2008
1569.56
1496.043
-4.68
1592.537
1.46
2009
1500.94
1505.812
0.32
1631.221
8.68
Bias
RS
Measures
Ens
R
2
Validation
RMSE
BP-ANN model
0.9948
0.9994
-8.5327
0.9684
25.6837
MLR model
0.9893
0.9966
10.0388
1.0374
36.7705
Table 3 Water related information of Shenzhen from 2007 to 2009
Years
WC
2007
?6.7 %
1542.30 million m3
?1.77 %
?5.5 %
-10 %
1569.56 million m3
6.70 million m3/day
24 m3
-4.37 %
0%
-5 %
2008
2009
Ability of water
production and distribution
3
1500.94 million m
WC for per
10,000 yuan GDP
Ratio of domestic
sewage treatment (%)
?8 %
-7.7 %
88
6.38 million m3/day
27.7 m3
3
6.70 million m /day
WC ¼ 741:7129 LnGDP 808:4892 LnCPI
ð47:66Þ
ð152:95Þ
636:4770 LnRP þ 3400:719
ð136:06Þ
ð917:63Þ
ð19Þ
R2 = 0.9966, F statistic = 888.47, Prob(F statistic) = 0.
The comparison of observed and simulated date for WC
was shown in Table 2. Simulation errors are less than 3 % in
calibration period. The forecasting errors of 2007–2009 are
below 8.68 %. Further performance assessment was listed in
Table 2 as well. Table 2 suggested that the MLR model’s
skill in simulating WC of Shenzhen was acceptable.
4.1.2.3 Intercomparison of two WC models In general,
Table 2 showed that both the BP-ANN model and MLR
model performed well in modelling WC. However, there
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22.3 m
75
80
3
were slight differences between the two models. R2 of the
two models almost equals, and the other four performance
coefficients of ANN are consistently better than those of
MLR model. Hence, BP-ANN model is chosen for the
scenario projection of WC for Shenzhen.
While, it is necessary to point out that both models have
discrepancy of 2007–2009s observed data and the simulated
results (Fig. 5a). As can be seen in Fig. 5a, there exist spike
of WC in 2007–2008 and a drop in 2009. It probably attribute to the policy, administrative means as well as the
consciousness of water utilization. Table 3 shows that WC
increased 6.7 % in 2007 and 1.77 % in 2008, while
decreased 4.37 % in 2009. The spike of WC in 2007–2008
may probably owe to the continuous increased ability of
water production and distribution. The ability of water
Stoch Environ Res Risk Assess (2013) 27:1359–1370
1367
Table 4 Projections of GDP (billion RMB dollars), CPI and RP (million) under the three scenarios
Years
GDP1
GDP2
GDP3
CPI1
CPI2
CPI3
RP1
RP2
RP3
2011
1106.32
1053.63
1000.95
931.28
886.94
842.59
2012
1232.49
1173.80
1115.11
962.66
916.82
870.98
9.62
9.16
8.70
9.70
9.24
2013
1364.84
1299.84
1234.85
993.64
946.33
8.78
899.01
9.77
9.31
8.84
2014
1502.36
1430.82
1359.27
1024.30
2015
1643.84
1565.56
1487.28
1054.68
975.52
926.75
9.83
9.36
8.90
1004.46
954.24
9.88
9.41
2016
1787.89
1702.75
1617.61
1084.83
8.94
1033.17
981.51
9.92
9.45
2017
1932.93
1840.88
1748.84
8.98
1114.78
1061.70
1008.61
9.96
9.48
2018
2077.23
1978.31
9.01
1879.40
1144.57
1090.07
1035.56
9.99
9.51
2019
2218.95
9.03
2113.29
2007.62
1174.22
1118.30
1062.39
10.01
9.53
9.06
2020
2356.16
2243.97
2131.77
1203.75
1146.43
1089.11
10.03
9.55
9.07
Table 5 Projected scenarios for WC in Shenzhen from 2011 to 2020
Scenarios
identifier
Scenario
schemes
Scenarios
identifier
Scenario
schemes
Scenarios
identifier
Scenarios
schemes
S1
GDP1 ? CPI1 ? RP1
S10
GDP2 ? CPI1 ? RP1
S19
GDP3 ? CPI1 ? RP1
S2
GDP1 ? CPI1 ? RP2
S11
GDP2 ? CPI1 ? RP2
S20
GDP3 ? CPI1 ? RP2
S3
GDP1 ? CPI1 ? RP3
S12
GDP2 ? CPI1 ? RP3
S21
GDP3 ? CPI1 ? RP3
S4
GDP1 ? CPI2 ? RP1
S13
GDP2 ? CPI2 ? RP1
S22
GDP3 ? CPI2 ? RP1
S5
GDP1 ? CPI2 ? RP2
S14
GDP2 ? CPI2 ? RP2
S23
GDP3 ? CPI2 ? RP2
S6
GDP1 ? CPI2 ? RP3
S15
GDP2 ? CPI2 ? RP3
S24
GDP3 ? CPI2 ? RP3
S7
S8
GDP1 ? CPI3 ? RP1
GDP1 ? CPI3 ? RP2
S16
S17
GDP2 ? CPI3 ? RP1
GDP2 ? CPI3 ? RP2
S25
S26
GDP3 ? CPI3 ? RP1
GDP3 ? CPI3 ? RP2
S9
GDP1 ? CPI3 ? RP3
S18
GDP2 ? CPI3 ? RP3
S27
GDP3 ? CPI3 ? RP3
Note Si (i = 1, 2, 3,…,27), combination of three impact factors of WC in three assumed development modes, refers to a likely background or
scenario of WC in Shenzhen. For example, S1(GDP1 ? CPI1 ? RP1) means the development of GDP, CPI and RP are all higher than that of
‘‘business as usual’’
production and distribution has increased 0.47 million m3/
day after the further construction and operation of water
sources in MWABS (2007). While, the growth of WC in
2008 is smaller than that in 2007. This may mainly owing to
the decreased growth ratio of ability of water production and
distribution and enormous decreased WC per ten thousand
yuan GDP. Additionally, according to the development plan
for Shenzhen (MWABS 2009), high-resource industries
such as chemicals, rubber and plastics are strictly restricted.
Whereas, high value-added and low-resource industries,
logistic and financial sectors were encouraged with utmost
priority. Hence, Fig. 3 indicated that SSO dropped in 2009
while TSO still keeping continuous growing. This could
account for the drop of WC in 2009 (Fig. 5a).
4.2 Scenario development
Future WC is full of uncertainties and risks due to unexpected changes in different driving forces (Wei et al. 2009).
To address these issues, a series of scenarios were
formulated to quantify associated impacts of future risks
and uncertainties. Three different scenarios (‘‘1’’, ‘‘2’’,
‘‘3’’) of GDP, CPI and RP were produced for the time
period during 2011–2020. The first ones are ‘‘business as
usual’’ (GDP2, RP2, CPI2), which are modeling results of
Eqs. 14, 15 and 18. The other two scenarios, designed
based on ‘‘business as usual’’, were 5 % higher (GDP1,
RP1, CPI1) and 5 % lower (GDP3, RP3, CPI3) than the
‘‘business as usual’’ scenario. Table 4, quantitatively
demonstrated the three assumed development modes of
each impact factor. Based on the combination of 3 impact
factors presented as 3 diverse designed scenarios, 27 different scenarios of WC were constructed (Table 5).
As a flagship megacity of Chinese Special Economic Zone,
Shenzhen has a remarkable achievement in economic development. Both of the GDP, RP and CPI grew fast in the past
years. However, as can be seen in Fig. 4, the growth tendencies of GDP, RP and CPI are different from each other. GDP
may keep the growth tendency in supporting of policies and
the advantages of Special Economic Zone. With the further
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Stoch Environ Res Risk Assess (2013) 27:1359–1370
Table 6 Projection of WC under the five selected scenarios
Years
Scenarios of WC
S3
S14
S15
S18
S25
2011
1582.27
1563.72
1568.54
1568.09
1542.95
2012
1610.49
1592.64
1596.65
1595.65
1572.39
2013
1636.25
1619.64
1622.95
1621.71
1600.47
2014
1658.86
1643.85
1646.54
1645.26
1626.21
2015
1678.00
1664.80
1666.95
1665.74
1648.99
2016
1693.72
1682.37
1684.06
1682.97
1668.50
2017
2018
1706.29
1716.12
1696.69
1708.11
1698.00
1709.11
1697.06
1708.31
1684.75
1697.96
2019
1723.68
1717.05
1717.80
1717.13
1708.48
2020
1729.41
1723.93
1724.49
1723.93
1716.73
development of economic and improvement of structure,
GDP2 and GDP3 may probably appear in the future, while
unreasonable booming of GDP (GDP1—5 % higher than
‘‘business as usual’’) will not happen to a great degree.
However, the probability of appearance of GDP1 cannot be
eliminated yet. On the other hand, Shenzhen has suffered a
population explosion before 2000, and the growth slowed
down thereafter. Hence, the RP2 are mostly likely scenario in
the future 2011–2020. RP3 may also emerge in the future due
to the limited carrying capacity, while the probability of RP1
is smaller than RP2 and 3 to a great degree. CPI has experienced both the high growth and the low growth, even the
decrease as well. Each of the three scenarios of CPI seems to
appear in the future. CPI2 and CPI3 are expected to happen
under the reasonable and powerful government’s macrocontrol and market adjustment.
Consequently, the scenarios including GDP2 or GDP3,
RP2 or RP3, and CPI2 or CPI3 are more likely to appear,
while the likelihood of the others are relatively small.
Nevertheless, each of the 27 scenarios in Table 5 stands for
a likely scenario of social–economic progress which may
happen in the unknown future, and the whole 27 scenarios
formulated were expected to be the background of WC.
4.3 Projected scenarios of WC
Table 5 showed that 27 different scenarios of WC were constructed, and those scenarios were to be set as the input variables of the BP-ANN predicting model, where WC was the
output variables. Figure 5c showed the results of 27 projected
scenarios of WC from 2011 to 2020. Figure 5b illustrates the
observed and projected WC from 1994 to 2020.
Figure 5b, c showed the WC of Shenzhen under 27 scenarios. Some representative scenarios were selected for
analysis. As can be seen in Fig. 5c and Table 6, in the S14
scenario (‘‘business as usual’’), WC in Shenzhen will increase
from 1,563.72 million m3 in 2011 to 1,723.93 million m3 in
123
2020, neither high nor efficient. S14, a ‘‘business as usual’’
scenario, was selected due to that it can be a reference to the
other likely scenarios. S3 is the scenario where GDP and CPI
grow faster than the S14 scenario, and RP growth is lower.
Opposite to the S3, S25 is a pessimistic scenario for social–
economic development, in this scenario GDP and CPI growth
are lower while RP increases fast. The S3 and S25 are selected
because they are the extremes among all the 27 scenarios and
they formulate the boundary of our projections of WC of
Shenzhen in 2011–2020. If GDP and CPI were not controlled
at reasonable levels, S3 and S25 may appear. S3 is an unreasonable great booming of GDP and CPI, which means a strong
currency inflation and great life pressure, and thereby lead to a
decrease of RP. S3 is the highest scenario of WC, in which WC
increase from 1,582.27 to 1,729.41 million m3 from 2011 to
2020. On the contrary, S25 is a scenario which usual exists in
the underdeveloped region, where GDP and CPI are lower and
the amount of RP is large. It means an underdevelopment
mode of our society and economy, just like some regions of
China 30 years ago. S25 is the most efficient scenario, with a
WC increasing from 1,542.95 to 1,716.73 million m3 during
2011–2020. However, it is not a sustainable strategy for
regional social–economic development, due to that the high
efficiency of WC is based on lower GDP and a sharp population explosion. Consequently, we suggest S14, S15, and S18.
S14 and S15 are scenarios that GDP and CPI grow as usual,
together with a normal or lower population growth. What’s
more, S18 is a most sustainable and healthy scenario, where
GDP grow as usual, CPI and population grow slowly, and it
means a high quality life of people and a sustainable development of social–economic and water resources. In summary,
what the three suggested scenarios bring is a sustainable
development of social–economic activities and water resource
though they are not the most saving utilization of water.
The results showed that annual WC of Shenzhen will
increase during the years 2011–2020. The two boundary
scenarios (S3 and S25) form a range of variation of WC for
Shenzhen during 2011–2020. The WC increase will vary from
1,542.95 to 1,729.41 million m3 in the upcoming 10 years.
However, the WC scenarios in the future (2011–2020) in
Shenzhen differs from earlier years, the growth speed will
slow down, and WC will tend to be stable in the near future.
This is distinct from the linear growth of WC in Beijing
identified by Wei et al. (2009). Slowdown of increasing WC is
likely attributed to adjustment of economic structure, slower
population growth, further improvement of water-saving
technologies, and increased consciousness of water-saving.
5 Conclusions and discussion
This article presents an effort in developing an urban WC
model for Shenzhen in South China using a variety of
Stoch Environ Res Risk Assess (2013) 27:1359–1370
approaches to generate possible scenarios of WC in future.
A variety of social–economic and demographic scenarios
of impact factors to WC was generated to constitute projections of WC over the forthcoming years (2011–2020).
Projections indicate that the total WC will increase from
1,542.95 to 1,729.41 million m3 in the forthcoming 10
years. However, the growth rate will slow down and tend to
be stable, differs from Beijing with a linear growth trend of
WC modeled by a MLR technique (Wei et al. 2009).
Preferential scenarios of WC have been discussed based
on the projected scenarios. Scenario generation of WC
incorporating uncertainty to address the social–economic
and policy issues constitute highlights of this model.
Currently, the most economically developed regions in
China are experiencing the industrialization stage, characterized by upgrade of economy’s focus from agricultural productivity to industrial productivity. There are three periods in
this stage. In the early stage of industrialization, the industrial
structure is labor-intensive with a large quantity of WC (Zang
2005). The middle stage of industrialization is characterized
by capital-intensive industries, which has an increasing
demand for water, and the water resources crisis emerges. In
the late industrialized period, the tertiary industry and hightech industries increase rapidly, and industries that consume
large amounts of water gradually are replaced (Jia 2001). As a
flagship megacity of Chinese Special Economic Zone,
Shenzhen has entered the late-industrial period after more
than 30 years of ‘‘reform and open’’ policy and rapid development. Though the demand for water will increase with
increasing production and population, the WC will not
increase any more due to the wide-spread water-saving consciousness and the development of better policies and technologies, as well as the rise of high-tech industries instead of
high-cost industries. Therefore, with further development in
the post-industrial period, there will be a saturation point in
Shenzhen (Zhang 2010). WC may increase very slowly after
this. Although some preliminary results are obtained in the
present work, some uncertainties still exist in our current
model. More research work in the future is needed for a more
profound understanding of the influence of changing industrial structure on urban WC.
Acknowledgments The work was jointly supported by grants the
National Basic Research Program of China (2010CB951101) and
grants from the National Natural Science Foundation of China
(40901016, 40830639, 40830640, 41071020).
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