Development tendency analysis and evaluation of the water ecological carrying capacity in the Siping area of Jilin Province in China based on system dynamics and analytic hierarchy process

Ecological Modelling 275 (2014) 9–21

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Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel

Development tendency analysis and evaluation of the water ecological carrying capacity in the Siping area of Jilin Province in China based on system dynamics and analytic hierarchy process Z. Zhang, W.X. Lu ∗ , Y. Zhao, W.B. Song Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, PR China

a r t i c l e

i n f o

Article history: Received 28 August 2013 Received in revised form 26 November 2013 Accepted 30 November 2013 Available online 4 January 2014 Keywords: Analytic hierarchy process Sustainable development System dynamics Water ecological carrying capacity

a b s t r a c t In this study, system dynamics (SD) and analytic hierarchy process (AHP) were combined to establish an evaluation index system and a system dynamics simulation model for the regional water ecological carrying capacity (WECC). Six proposed planning schemes were used to address the existing water ecological environment problems in the Siping area of Jilin Province, China. The development trends in the WECC during the years 2008–2020 were simulated and evaluated under different planning schemes. The results showed that the water ecological environment in the Siping area would develop into a “poor carrying” state in 2020 with a WECC index of 0.1819 if current social development modes remain unchanged. However, a “good carrying” state with a corresponding WECC index of 0.6652 could be achieved in 2020 if the 5th scheme is applied, which is an integrated planning scheme that combines changes in watersaving, pollution control, water resource exploitation and economic development. The results of this study could provide a scientific basis for the coordinated development of the social economy and the water eco-environment in the Siping area. © 2013 Elsevier B.V. All rights reserved.

1. Introduction The pursuit of rapid economic growth under the current development policies in China has caused water resource shortages and water quality deterioration in many regions, but it has also accelerated the degradation of regional ecological environment. Furthermore, it has restricted the sustainable development of the regional social economy. Thus, there is an urgent need to assess the maximum population and socio-economic scale that can be sustained by the local water eco-environment (Mei et al., 2010). The water ecological carrying capacity (WECC) is a recently proposed management concept for water resources and the environment (Li et al., 2011), which aims to support sustainable socio-economic development in a region or basin. It is based on the carrying capacity theory and the exploration of response mechanisms between human activities and water eco-environment. It rationally evaluates the socio-economic scale threshold that can be sustained by the local water eco-environment. The water resource carrying capacity and the water environment carrying capacity have the same origin, but different emphases (Feng et al., 2008; Fan et al., 2009; Li, 2011; Xu et al., 2011). The water resource

∗ Corresponding author. Tel.: +86 431 88502610. E-mail address: [email protected] (W.X. Lu). 0304-3800/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ecolmodel.2013.11.031

carrying capacity focuses on water usage and access to water (Gong and Jin, 2009), whereas the water environment carrying capacity is concerned mainly with the pollution capacity of the water environment (Chen et al., 2000; Yan et al., 2011a,b). However, it is not possible to meet the requirements for sustainable development if only the water resource capacity or water environment capacity are simply used to assess the regional carrying capacity. It is necessary to combine these two capacities and evaluate the capacity of an ecological system to meet the demands from the society. Thus, the carrying capacities of water resource and water environment are considered, and the eco-environmental changes caused by human activity are incorporated into the study. Due to the limitations of available data, the present study mainly considers water resource factors, water environment factors, and some ecological factors, which correlate with the ecological environment changes caused by human activities. In the current study, six different planning schemes were designed to investigate the effects of human socio-economic activities on the WECC development trends in the Siping area, to comprehensively evaluate the effect of the carrying capacity of the local water eco-environment on socio-economic development, and also to provide a basis for the coordinated development of the regional social economy and water eco-environment. Based on related studies (Nakayama et al., 2010; Zhang et al., 2010), the present study defines WECC as the maximum population and social economy scale that can be supported

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by regional water resources and water environmental capacity to achieve the sustainable development of the regional water ecological environment and social economy. Currently, there are many methods for evaluating WECC, such as the background analysis method (Cole and Sinclair, 2002), an artificial neural network method (Haraldsson and Olafsdottir, 2006), and gray relation analysis (Liu, 2013). The background analysis method compares the natural environmental and social background of the study area with that of other similar regions, and uses the results of the comparison to deduce the carrying capacity of the study area. This method is often limited to a static historical background analysis without taking into account the mutual interaction between resources, society, and the ecological environment (Lane, 2010). The artificial neural network method provides excellent performance in terms of high-dimensional and non-linear mode recognition, but it has obvious limitations in actual practice. For example, it empirically determines the initial weight of the hidden layer, so the modeling results are affected by the training samples and it is difficult to quantify the evaluation results (Singh et al., 2009). The application of the gray relation analysis method in related research is also constrained by the variable weights affected by human factors and the evaluation results have low precision. However, combining the analytic hierarchy process (AHP) and system dynamics (SD) for evaluations to predict WECC can overcome the limitations of the methods mentioned above. The integration of these two methods can consider the interactions among the internal factors of a system during dynamic quantitative evaluations of WECC. This integrated method also allows the analysis of different development plans, according to the regional future direction of social development, as well as simulations and analyses of the development trend in the future regional WECC in various situations, thereby providing support for regional social development strategies. To evaluate the maximum population and socio-economic scale that the aquatic ecosystem of the Siping area can support, in the present study, SD and AHP were combined for establishment of WECC evaluation index system and SD model. The carrying capacity was also quantitatively assessed by calculating the WECC index for the Siping area, Jilin Province, Northeast China. In addition, six different planning schemes were designed to improve the WECC. The SD model was used to forecast the development trend of the WECC in the Siping area until 2020 under different schemes, and an optimal scheme for the coordinated development of the regional social economy and water ecological environment was formulated by comparing the simulation results.

2. Materials and methods 2.1. Study area The Siping area (123◦ 08 –125◦ 46 E, 42◦ 49 –44◦ 09 N) is located in the southwest of Jilin Province (Fig. 1), where it covers an area of 1.4037 × 104 km2 . Siping is a major industrial city in Jilin Province, which is known as the “Northeast Granary,” and it also acts as one of the major national commodity grain production bases to ensure national food safety. The average precipitation in recent years is 525 mm and the average evaporation amount is 900 mm. The regional water resource per capita is only 502 m3 , which is less than one-quarter of the national water resources per capita, i.e., 2100 m3 . The water resources occupied by 1 ha of arable land is 2730 m3 , which is less than one-sixth of the national water resources occupied by 1 ha of arable land, i.e., 16,800 m3 . Thus, Siping is a city with a severe water shortage. Siping’s economy has developed rapidly in recent years, with dramatic effects on the eco-environment. During agricultural production in the Siping area, 260,000 t of fertilizer is

used each year with 1860 t of pesticide. However, only 35% of the pesticides and fertilizers applied are utilized in the study area, and large volumes of residual pesticide and fertilizer pass into water bodies via runoff. Moreover, the agriculture fields in the region are mainly terraced, which contribute large amounts of non-point source pollution. The treatment rate of point source pollution is also low. According to statistical reports, about 1.384 × 107 t of industrial wastewater and 6.857 × 107 t of untreated domestic sewage are discharged directly into water bodies each year, and wastewater emissions are increasing year by year. This has resulted in continual water quality deterioration. This area is suffering from a lack of resources and low water quality (Li et al., 2012; Zhang et al., 2012). The shortage of water resources and the worsening of the water environment have severely restricted the sustainable development of the industrial and agricultural economy in the Siping area. Thus, understanding how to ensure the coordinated development of the social economy and the water ecological environment is a crucial task in the Siping area. 2.2. WECC index system and index weight calculation with AHP The establishment of a scientific and reasonable index system would help to provide a basis for the accurate assessment of the regional WECC (Yan et al., 2011a,b). WECC is a large complex system with many factors. First, WECC is based on the carrying capacity of the water resources and the water environment, so it involves the properties of both. Therefore, WECC assessment needs to include evaluation indexes for water resources and the water environment. Second, the health of the ecosystem affects the carrying capacity because it may reflect the carrying condition quality in some aspects. Thus, it is also necessary to consider indicators of the ecological health status. In ideal conditions, indicators of biological diversity directly reflect indices of the ecosystem health status. However, we do not propose an evaluation index system in the current study because there is a lack of suitable data for these indices. In the present study, some indicators were selected to reflect the ecosystem health status using available data. Finally, after considering the actual conditions in the study area and consulting with experts, the evaluation index system for WECC in the Siping area was established (Table 1), and the system was classified into four categories, i.e., water resources, water environment, water ecology, and society, using 18 indicators in total. Various indices are in the system, which have different effects on WECC, thereby resulting in ambiguity and uncertainty. AHP is often used to solve complex decision problems with multiple targets and multiple criteria, while it is also used widely to determine evaluation index weights (Kamal, 2001; Solnes, 2003; Dong et al., 2010; Xie and Tang, 2010; Vidal et al., 2011). Therefore, in the present study, the AHP was used to determine the weight of each evaluation index in the WECC system. One of the key steps in the quantification of WECC is the calculation of the weight values for each index, which can be used subsequently to calculate the WECC index. In this study, the WECC evaluation indexes were divided into three hierarchies, i.e., the object hierarchy, the rule hierarchy, and the index hierarchy. When using AHP to analyze the relative importance on system factors, the two key steps are judgment matrix construction and consistency testing. The steps used to apply AHP to obtain the relative importance of each evaluation index in this study are shown in Fig. 2. Finally, the relative weight of each evaluation index in the system was obtained (Table 1), and the weight of the index hierarchy relative to the object hierarchy is given by Eq. (1) (Tian et al., 2013): Ci = Bj × Cj,i

(1)

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Fig. 1. Location of the study area and the distributions of the main rivers and reservoirs in the area.

where Ci is the weight of the index hierarchy relative to the object hierarchy, Bj is the weight of the rule hierarchy relative to the object hierarchy, Cji is the weight of the index hierarchy relative to the rule hierarchy. In addition, i = 1, 2,. . .,18, while j = 1, 2, 3, 4. (Note. when j = 1, i = 1–4; when j = 2, i = 5–9; when j = 3, i = 10–13; when j = 4, i = 14–18.)

2.3. The SD method For SD, the basic structure unit of the system is a feedback loop that couples the status, the speed (or “decision”), and information in the system (Vincenot et al., 2011). The working rules of the internal factors of a system are similar to the rules

Table 1 Weight values of the evaluation indices. Object hierarchy

Rule hierarchy

System index for water resources B1

System index for the water environment B2 WECC in the Siping area

System index for water ecology B3

System index for human society B4

Index hierarchy

Weight value

Water consumption per 10,000 RMB of industrial production (C1,1 ) Available water resources per capita (C1,2 ) Irrigation water consumption per unit area (C1,3 ) Ratio of water supply to water demand (C1,4 ) COD emissions per 10,000 RMB of industrial production (C2,5 ) Ammonia and nitrogen emissions per 10,000 RMB of industrial production (C2,6 ) Wastewater discharge per 10,000 RMB industrial production (C2,7 ) Chemical fertilizer consumption per hectare (C2,8 ) Dilution ratio (C2,9 ) Vegetation coverage rate (C3,10 ) Surface water mining rate (C3,11 ) Water demand rate of the river eco-environment (C3,12 ) Water and soil loss rate (C3,13 ) Total population (C4,14 ) Recycling rate of industrial wastewater (C4,15 ) GDP (C4,16 ) Treatment rate for sewage (C4,17 ) Standard-reaching rate for industrial wastewater discharge (C4,18 )

0.06423 (C1 ) 0.02316 (C2 ) 0.07615 (C3 ) 0.15108 (C4 ) 0.02675 (C5 ) 0.01435 (C6 ) 0.02373 (C7 ) 0.05491 (C8 ) 0.01630 (C9 )  0.03488 (C10 )  0.01048 (C11 )  0.07683 (C12 )  0.03357 (C13 )  0.05131 (C14 )  0.08189 (C15 )  0.08905 (C16 )  0.08174 (C17 )  0.08905 (C18 )

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Fig. 2. The basic concepts and steps of AHP (analytic hierarchy process).

exhibited by a fluid moving in a loop. Fluid moving in loops inevitably results in accumulation. The gradual accumulation of materials produces pressure, which affects decision-makers via “information” transfer. As a result, decision-makers are forced to use the information received to make the necessary “decision” to change the flow rate according to a control strategy, thereby changing the “accumulated” material. SD includes basic methods with a flowchart, equations, and a simulation platform. Flowcharts use visual symbols to depict the logical relationship between system factors and to clarify the feedback flow in the system. The variables in flowcharts mainly include stock (state variable), flow (decision variable), and auxiliary variables. SD equations are a group of mathematical formulae that quantitatively describe the relationships between system factors based on flowcharts. SD equations begin from a known initial status to determine the recursive relationship of the next status, so they are in fact differential equations. The stock (status variable) equation is shown in Eq. (2) and the flow (decision variable) equation is given in Eq. (3). The simulation platform inputs the SD model into a computer, which simulates and debugs the environment (Guneralp and Barlas, 2003).

 L(t) = L0 +

t



(

Rin (t) −



Rout (t)) dt

(2)

0

R(ti ) =

L(ti+1 ) − L(ti ) DT

(3)

where L(t) in Eq. (2) is the value of status variable L at time t, L0 is the initial value of L, Rin is the input flow  of status variable, Rout is the output flow of status variable, and Rin (t) − Rout (t) is the

net inflow of status variable. The equation suggests that the value of the status variable at time t equals its initial value plus the accumulation of the net inflow over the time period [0,t]. In Eq. (3), R(ti ) is the flow value at time ti , L(ti+1 ) is the value of the status variable at time ti+1 , L(ti ) is the value of status variable at time ti , and DT is the time interval between time ti+1 and ti . There are three key steps in the SD method: (I) determine the dynamics flowchart for the system being studied and ascertain the logical relationships between the factors within the system; (II) establish mathematical equation groups to quantitatively describe the relationships between the internal factors in the system; (III) input the established flowchart and equation group into the simulation platform, then use the platform to design different policy plans and to simulate the development trend in the system in various situations. 3. Establishment of a WECC model for the Siping area The WECC system is affected by many factors, which interconnect and restrict each other. Most of the factors are dynamic variables, which change together with time (Li and Zhou, 2009). SD is based on systems theory and feedback theory, where computer simulation technology is used as the platform. SD has advantages in complex system studies and it is useful for solving problems with highly non-linear features, high orders, multiple variables, and multiple feedbacks. SD is also an effective method for studying the dynamics changes in complex systems (Yim et al., 2004). In this study, the SD method was used to establish a SD model of WECC in the Siping area and to simulate the development trend of WECC.

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Fig. 3. System dynamics flowchart of population subsystem.

3.1. Establishment of the WECC model Most current ecological studies use a watershed boundary as the system border and this boundary division is highly reasonable. However, the current management system for domestic basin resources and the environment aims to manage the resources and environment within the watershed boundary as a unit, which has some disadvantages, such as unclear management tasks for each unit and mutual shuffling questions. When the administrative boundary is set as the system border, clearer responsibilities are given to the management units and buck-passing problems may be avoided. If the administrative boundary is set as the system border, the following development plan will be easily enforced. Therefore, the administrative boundary of Siping City was used as the system border in this study. The simulated period was from 2008 to 2020 with a time step of one year. Based on the WECC evaluation index system described above (Table 1) and the actual socio-development state in the Siping area, the WECC system was divided into six subsystems: population subsystem, economy subsystem, animal husbandry subsystem, water resource subsystem, land resource subsystem, and water environment subsystem. These subsystems were connected by factors that affected WECC, which together guided the behavior of the overall system and determined its changing trend via mutual interactions. In the present study, Vensim Software was used to establish the SD model of WECC for Siping and to perform the simulation. The WECC system was divided into six subsystems and the SD flowchart of each subsystem is shown in Figs. 3–9. The main variables and expressions in the model are shown in Table 2. 3.1.1. Population subsystem Population is an important factor in the system (the flowchart of the population subsystem is shown in Fig. 3), which affects the “quality” and “quantity” of socio-economic development, as well as having profound effects on the water eco-environment. The functions of population subsystem in the overall system have the following three main aspects: reflecting the effects of the population in the Siping area on the water resource quantity in the region; reflecting the effects of population on the water environmental quality in the region; and reflecting the effects of population on the socio-economic development of the region. 3.1.2. Economy subsystem Economic construction is the core of social development in a region, which represents regional socio-economic development (the flowchart of the economic subsystem is shown in Fig. 4). Similar to the population subsystem, the functions of economy

subsystem in the overall system have the following three main aspects: reflecting the level of economic development in the study area; reflecting the effects of economic development on the water resource quantity in the study area; and reflecting the effects of economic development on the water environmental quality in the study area. 3.1.3. Animal husbandry subsystem Livestock breeding occurs on a fairly large scale in the Siping area, so its effect on water resources and the water environment in the region cannot be overlooked. In this study, the animal husbandry subsystem was based on the actual situation in the Siping area (the flowchart of the animal husbandry subsystem is shown in Fig. 5). We divided livestock and poultry breeding industry into two categories: intensive and non-intensive culture. Intensive culture contributes to point source pollution, whereas non-intensive culture contributes to non-point source pollution. 3.1.4. Water resource subsystem The water resource subsystem is one of the core components of the WECC system, which focuses on the response features of water resource supply and demand in different development models (the flowchart of the water resource subsystem is shown in Fig. 6). In recent years, because of the rapid growth of the population, agriculture, and the industrial economy, the imbalance between water supply and demand has intensified in the Siping area. Thus, ecological and environmental water cannot be guaranteed, and the utilization efficiency of surface water resources is low, while the development and utilization of groundwater resources is unreasonable. The Siping area has 16 large and medium-sized reservoirs (five are large reservoirs), as well as 43 small reservoirs, and the total design capacity is 22.18 × 108 m3 with an utilizable capacity of 10.08 × 108 m3 . However, over the past years, the surface water supply in the Siping area only accounted for 19.8% of the total water supply, while the available supply of groundwater in the area was 7.27 × 108 m3 per year. The recent exploitation level (6.98 × 108 m3 per year) is close to the maximum available supply. At present, there is still a gradually increasing trend toward groundwater exploitation, and drawdown funnels have begun to emerge in the study area because of local overexploitation. In the present study, the water resources demand was divided mainly into four classifications: urban and rural domestic water demand, industrial production water demand, agricultural irrigation demand, and livestock consumption demand. For industrial production water use, the impacts of water-saving measures, regulation of industrial water recycling rate, and other factors on the total water requirement are taken into account. The water

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Fig. 4. System dynamics flowchart of the economy subsystem.

supply in the study area mainly comprises the surface water supply and groundwater supply. Under conditions with low supply and limited surface water and groundwater resources, the development and utilization of surface water and groundwater are the major factors that affect the regional water resources supply. The regulation of the life water cycle and irrigation return water in the water supply has been considered for both domestic water and agricultural water use. The ratio of water supply to water

demand and the available index of water resources per capita were used as index to evaluate the water resources in the study area. 3.1.5. Land resource subsystem The land resource subsystem was classified into five types, i.e., farmland, woodland, grassland, urban land, and other land (the flowchart of the land resource subsystem is shown in Fig. 7). The

Fig. 5. System dynamics flowchart of the animal husbandry subsystem.

Z. Zhang et al. / Ecological Modelling 275 (2014) 9–21

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Fig. 6. System dynamics flowchart of the water resource subsystem.

functions of land resource subsystem in the overall system have the following two main aspects: reflecting the effects of land irrigation on water resource, and reflecting the effects of land fertilization on the water environmental quality.

population, land structure, economic situation, and the science and technology level.

3.1.6. Water environment subsystem The water environment subsystem is also a core component of the WECC system. It reflects the output level of the pollution load in the Siping area, which is a major factor when characterizing water environmental quality in the study area. In the water environment subsystem (Figs. 8 and 9), the main water quality evaluation indexes are the COD and NH3 N. Pollution comes mainly from point sources and non-point sources. Point source pollution includes industrial wastewater, urban domestic sewage, and wastewater discharged by intensive livestock and poultry breeding. Non-point source pollution includes rural domestic sewage, wastewater discharged from non-intensive agriculture, and agricultural production. The pollution load is affected mainly by the

The WECC SD model contains many parameters and each parameter exerts different degrees of influence on the model output, so the model output is sensitive to some parameters but insensitive to others (Perz et al., 2013). Therefore, to simplify parameter calibration, in this study, the Monte Carlo method was used to perform the model parameter sensitivity analysis. Each parameter was sampled randomly and uniformly within its value range 1000 times. Those parameters sensitive to the model output were selected for calibration based on the parameter sensitivity analysis results. In the present study, the total population, rural population, industrial output, agricultural output, and other factors were selected as the major variables, and the relative error between

3.2. Testing the effectiveness of the model

Fig. 7. System dynamics flowchart of the land resource subsystem.

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Z. Zhang et al. / Ecological Modelling 275 (2014) 9–21

Fig. 8. System dynamics flowchart of the water environment subsystem (1).

the simulated and actual values was calculated to calibrate and verify the model. The data used in the present study were limited, so the data from 1999 to 2005 were used to calibrate the model parameters, while the data from 2006 to 2007 were used to verify the validity of simulation. The results indicated that the average relative error was 3.6% in the calibration period of the model, while the average relative error was 5.6% in the validation period of the model. The increasing trend in the error was probably due to the calibration of parameters with limited data for 7 years, which affected the calibration accuracy required by the model. However, the simulation time was from 2008 to 2020 so the simulation cycle

was short with less than 10% model error, which was within the allowable range. Thus, the model can represent the actual system and can be used to simulate the real system. The model testing results are shown in Figs. 10 and 11. 3.3. WECC quantification 3.3.1. Data standardization The units, dimensions and magnitudes of the selected evaluation indices were different, which is inconvenient for WECC quantization. Thus, all of the evaluation indices needed to be standardized

Fig. 9. System dynamics flowchart of the water environment subsystem (2).

Z. Zhang et al. / Ecological Modelling 275 (2014) 9–21

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Table 2 Main variables and expressions in the model. Name of variables

Expression

Unit

Total population Urban population Urban natural population growth Industrial labor Urbanization level Agricultural production Agricultural production growth GDP growth GDP per capita Second industry production growth Number of nonintensive large livestock Intensive proportion of large livestock

=U.P+R.P =U.P+U.N.P.G =U.P+N.P.G.R =L.N×I.L.P =U.P/R.P+U.P. =A.P.+A.P.G =108×A.L0.5796 ×A.I0.6931 ×A.T.F =A.P.G+T.S.P.G+T.T.I.P.G =GDP/T.P =I.P.G/P.o.I.P.t.t.S.I =T.N.o.L.L.o.H×(1-I.P.o.L.L) = WITHLOOKUP {Time, [(2003, 0)–(2035, 1)], (2004, 0.09), (2005, 0.11), (2006, 0.12), (2007, 0.14), (2010, 0.16), (2015, 0.21), (2025, 0.3)} =W.Q.o.L.L×T.N.o.L.L.o.H =S.W.S+G.W.S+I.R.W+T.A.o.R.W =A.S.W.S×T.E.L.o.S.W = WITHLOOKUP {Time, [(2003, 0.23)–2035, 1], (2004, 0.25), (2005, 0.26), (2006, 0.28), (2007, 0.29), (2010, 0.3), (2015, 0.4), (2025, 0.5)} =D.W.C+I.W.C+W.D.o.L+W.D.o.E+A.W.C =W.C.P.I.P×(I-RU.R)

10,000 people 10,000 people 10,000 people 10,000 people – One hundred million yuan One hundred million yuan One hundred million yuan yuan One hundred million yuan 10,000 –

=A.W.C×I.R.C =T.A.o.I.W-T.A.o.T.I.W. =W.D.o.F.I+W.D.o.W.I+W.D.o.G.I =F.A.W.A+G.A+UL.A+OL.A = IF THEN ELSE(O . L . A ≤300, − A . V . V . o . U . L . A − A . V . V . o . W . A, F . A × A . V . R . o . F . A) =E.A.o.COD-R.A.o.COD =P.S.COD.E×R.R.o.P.S

m3 m3 m3 km2 km2

Water demand of large livestock Water supply Surface water supply Exploration level of surface water Water resource demand Amount of industrial wastewater per 10,000 Industrial production Irrigation regression water Amount of industrial wastewater discharged Agricultural water consumption Land area Annual variation value of farmland area COD load Reduced amount of COD

m3 m3 m3 – m3 m3 /10,000 yuan

Kg Kg

Note. 1. The variables in the expressions are the abbreviations of names such as “Water Demand of Farmland Irrigation” is expressed as “W.D.o.F.I”; 2. WITHLOOKUP{TIME (year, value)}: expresses the values of variables in different years; 3. IF ELSE ( ) is a logic function, such as: according to the logical function relationship of variable a; IF THEN ELSE (b < 1,0,0.5): this function means that in the case of b < 1, a = 0; if not, a = 0.5; 4. “¥” represents “RMB”.

(the determined index scores) to eliminate dimensional effects. The interval ratio method was used to determine the scores of the various WECC evaluation indices used in this study. For the optimized maximal index, i.e., “the greater, the better,” the following Eq. (4) was applied. m

maxgij − gij Sij =

j=1

m

m

j=1

j

(4)

maxgij − mingij For the optimized minimal index, i.e., “the smaller, the better,” the following Eq. (5) was applied. m

maxgij − gij Fig. 10. Error testing based on the total population.

Sij =

j=1

m

m

j=1

j

(5)

maxgij − mingij For the optimized interval index, i.e., “the closer a certain value is, the better,” the following Eq. (6) was applied. Sij =

1 − |gij − gij∗ | m

m

j=1

j=1

(6)

maxgij − mingij

Fig. 11. Error testing based on industrial production.

In the equations, Sij is the score of i-index in j-scheme; gij is the value of i-index in j-scheme; and gij∗ is the optimized value of interval index, which is the average of gij∗ in the study. Six different planning schemes were designed in the present study, which involved a total of 18 indicators. Thus, j = 1, 2, 3, 4, 5, 6; i = 1, 2,. . ., 17, 18. For the “optimized maximal index,” these were: the ratio of water supply to water demand, recycling rate of industrial wastewater, standardreaching rate of industrial wastewater discharge. The “optimized minimal index” included: COD emissions per 10,000 RMB of industrial production, and the wastewater discharge per 10,000 RMB of

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Z. Zhang et al. / Ecological Modelling 275 (2014) 9–21

Fig. 12. Development trend in WECC with different schemes.

industrial production. The “optimized interval index” included: the surface water mining rate and the total population. 3.3.2. Calculation of the WECC index The established SD model (Figs. 3–9) was used to simulate various WECC evaluation indexes. The WECC was calculated and assessed based on the simulation values of evaluation indexes during different periods. It is known that judgment of the good or bad carrying state of a system is relatively ambiguous. To describe the carrying state of a system clearly, this study quantified the carrying state of the system by calculating the WECC index and judged the carrying status of the system as good or bad based on the WECC index. The WECC index was calculated as follows: Ej =

n 

Ci Sij

(7)

i=1

where Sij is the standardized score of i-index in j-scheme; Ci is the weight of the i-index determined by AHP (Table 1); and Ej is the WECC index. At present, much WECC research is limited to qualitative description, but a few quantitative analyses have been performed. There is also no unified classification and calculation method for the carrying capacity. Thus, based on the membership degree in fuzzy mathematics and the water resource carrying capacity state classification method (Yang et al., 2010, 2011a,b), the carrying types were classified into five groups according to the WECC index values. The carrying status from “good” to “bad” were: “excellent carrying” (WECC index: 0.8–1.0), “good carrying” (WECC index: 0.6–0.8), “general carrying” (WECC index: 0.4–0.6), “poor carrying” (WECC index: 0.2–0.4), “very poor carrying” (WECC index: 0–0.2). 4. Results and discussion To facilitate the harmonious development of the water ecological environment and social economy in the Siping area, six planning schemes (Table 3) were proposed to improve the regional WECC. Using the SD model, various WECC evaluation indices were simulated to explore their changes between 2008 and 2020 with different planning schemes. Using the available water resources per capita as an example: A.W.R.P.Ci = U.W.Ri /T.Pi , A.W.R.P.Ci are the water resources that people can use in year i, U.W.Ri is the amount of available water resources for year i, and T.Pi is the total population for year i. The development trends in the regional carrying capacity were assessed using Eq. (7) and the WECC index. As shown in Fig. 12, the reference year (2008) for the WECC in the Siping area was “relatively poor.” If the current mode of social development is maintained, WECC will exhibit a continuous decline year by year in the study area. Up to 2016, the overall carrying

capacity in the area will move from a “poor carrying” to a “very bad carrying” state. In the study area, the contradiction between water resources supply and demand will appear to be more intense than before and the ecological environment situation will deteriorate continuously. Thus, the existing social development mode is unable to meet the needs of the sustainable development of the social economy and ecological environment in the Siping area. Scheme 1, which is different from the original scheme, aims to strengthen industrial development to ensure the steady growth of the regional economy in the Siping area, and to enhance industrial economic growth by increasing industrial investment. The annual rate of industrial investment will increase by 15%. Up to 2020, the city’s GDP growth will reach 18.9% compared with the original scheme. In addition, this scheme implements a family planning policy to control the population growth rate, reduce water consumption, and alleviate the conflict between the supply and demand of water resources. Agricultural water-saving is the focus in the implementation of the scheme, mainly by reducing water irrigation to farmland, woodland, and grassland. The irrigation volumes for farmland, woodland, and grassland decline from 9800 m3 /km2 /a, 3500 m3 /km2 /a, and 1500 m3 /km2 /a in 2008 down to 6000 m3 /km2 /a, 2200 m3 /km2 /a, and 800 m3 /km2 /a in 2020, respectively (the values during this period were obtained based on the average decline). At present, the irrigation mode in the Siping area is mainly flood irrigation, which is unreasonable and wastes water resources. The canal system needs repair in many areas and many irrigation water losses occur in the transportation process. Therefore, irrigation mode change and canal system repair can be used as water-saving measures in agriculture. As shown in Fig. 12, the WECC with the conditions of Scheme 1 was higher than that with the primary scheme. However, the overall improvement was not significant. Thus, the carrying situation might still be “poor carrying” in 2020. First, industrial water-saving is ignored and the rapid industrial development causes an increase in the industrial water demand and wastewater discharge. Thus, the relationship between water resources supply and demand is still fairly intense. Second, this scheme does not consider the procedures required to manage the eco-environment. Scheme 2 is a strategy based on Scheme 1, which tries to increase the ratio of agricultural investment (increasing agricultural investment by an annual rate of 12%), and strengthen the common economic growth of industry and agriculture to drive the overall economic growth in the Siping area. At present, due to the outdated production technology, the water consumption in the Siping area is 2.6 times higher than the national industrial output value, which is calculated as 10,000 RMB, and it is 10–20 times higher than that in developed countries. Thus, the industrial water-saving potential is huge in the Siping area. This scheme is based on agricultural watersaving measures, but it also implements industrial water-saving measures. From the perspective of water demand, this scheme alleviates the conflict between water supply and demand by reducing water consumption. Industrial water-saving during actual production can be achieved mainly by improving science and technology in the production process and by reducing water consumption relative to the industrial output value calculated as 10,000 RMB. In the model, the “water-saving coefficient” (the value is 1 in the original scheme) can be reduced to 0.4 in 2020 (the values during this period were obtained based on the average decline). Based on agricultural water-saving, industrial water-saving was also implemented. Due to the reduced demand for industrial water, the wastewater discharge also exhibited an overall decreasing trend. In 2020, the carrying capacity in Scheme 2 will obviously be better than that in Scheme 1, although it will still be somewhere between “poor carrying” and “general carrying”. The development strategy of Scheme 3 has two main aspects. First, in terms of economic development, the scheme emphasizes

Z. Zhang et al. / Ecological Modelling 275 (2014) 9–21

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Table 3 Improvement schemes for the water ecological carrying capacity in the Siping area. Scheme

Key strategies

Detailed procedures

Primary scheme

Maintaining the current situation (business as usual)

Scheme 1

Agricultural water-saving

Scheme 2

Agricultural water-saving and industrial water-saving

Scheme 3

Agricultural water-saving, industrial water-saving, and water resources exploitation

Scheme 4

Agricultural water-saving, industrial water-saving, and the control of point source and non-point source pollution

Scheme 5

Agricultural water-saving, industrial water-saving, water resources development, pollution control

Maintain the current socio-economic development pattern; do not adjust any policies related to water-saving and water environment protection; and use this as the reference project for the other five schemes Focus on industrial development to drive the overall growth of Siping’s economy; implement a prudent family planning policy to control the natural population growth rate withing 0.006; emphasize agricultural water-saving; adjust the irrigation regression coefficient to 0.35; and reduce the irrigation quota for farmland, woodland, and grassland gradually to 6000 m3 /km2 /a, 2200 m3 /km2 /a, and 800 m3 /km2 /a, respectively, from the base year to the target year Implement policies to coordinate the development of industry and agriculture to lead the overall economic increase; enhance the industrial and agricultural investment rate at speeds of 12% and 10%, respectively, from the existing baseline; consider a comprehensive method to save industrial and agricultural water based on the premise of saving agricultural water and adjust the industrial water-saving coefficient to 1.35 Speed up the development of tertiary industry and allow the tertiary industry output value to increase at a speed of 15% each year based on the existing baseline; implement exploratory policies based on the procedures for saving industrial and agricultural water to improve the regional water supply capacity Comprehensively coordinate the development speed and scale of industry, agriculture, and tertiary industry; strengthen eco-environment construction; increase investment in pollution control; increase the rate of point source treatment to 0.72; accelerate the pace of intensive livestock and poultry breeding; improve the methods used for fertilization and land cultivation; and decrease non-point source pollution in an effective manner Combine the advantages of the four schemes above to upgrade the industrial structure and divide the economic layout of three industries in a reasonable manner; based on this, consider eco-environment protection and implement a scheme that combines exploring, saving, pollution treatment, and reducing discharges

the development of tertiary industry (economic growth in tertiary industry increases by 15% per year) to achieve a balanced development trend in all three industries. Second, given the water-saving measures in industry and agriculture, the scheme strengthens the construction of water conservation projects and improves the utilization efficiency of water resources to increase water supply and alleviate the conflict between water supply and demand. In recent years, the over-exploitation of groundwater in the study area has damaged groundwater resources, so the scheme is aimed mainly at improving the utilization rate of surface water resources and reducing the exploitation and utilization of groundwater. As described earlier, the Siping area has 16 large and medium-sized reservoirs, and 43 small reservoirs. The total designed capacity is 22.18 × 108 m3 with an utilizable capacity of 10.08 × 108 m3 , which indicates that the utilization rate of surface water resources is low and that the surface water resources are not fully exploited by the water supply. Therefore, this scheme focuses mainly on conservation engineering for rainwater and floodwater in the study area, while it also considers the ongoing construction of the “Songhua River water supply project” to increase the water supplied from surface water resources. This engineering project was designed to use water in the Songhua River Fengman reservoir for production and living in the Siping area. The project has provided an average of 1.7 × 108 m3 water to the Siping area for many years. The maximum water supply is designed as 2.6 × 108 m3 and the scheme is aimed to ultimately improve the surface water supply in the Siping area. In the model, “surface water utilization degree” (the value in the original scheme was 0.20) rises to 0.5 in 2020 (the values during this period were obtained based on average increase), and the “utilization rate of groundwater development” (the original proposed value was 0.78) declines to 0.45 in 2020 (the values during this period were obtained based on the average decline). As shown in Fig. 12, the WECC in Scheme 3 is improved, but it is not significantly better than that in Scheme 2. This also indicates that the shortage of water resources is not the only cause of eco-environment problems in Siping. The ineffective treatment of regional water environment pollution may be another major cause of the deterioration of the eco-environment in Siping.

The inferences from Scheme 3 suggested the specific implementation measures in Scheme 4. In terms of economic development, Scheme 4 emphasizes the equitable division of the industrial structure and vigorously promotes tertiary industry with low water consumption and wastewater emissions (the industrial investment increased by an annual rate of 12%, agricultural investment increased by an annual rate of 10%, and economic growth in the tertiary industry increased by an annual rate of 20%). In addition, based on Scheme 2 (industrial and agricultural water-saving), Scheme 4 includes a series of effective environmental protection measures, which are aimed at the effective control and reduction of point and non-point source pollution. To control point source pollution, there are five sewage treatment facilities in the Siping area, but the pipe network construction required for sewage collection is lagging behind. In the Siping area, the treatment rate of point source pollution is only 38%, which is far lower than that the national average level (76%). Therefore, in Scheme 4, “the treatment rate of point source pollution” is adjusted from 0.38 in 2008 to 0.7 in 2020 (interval value calculated based on the average increase) and “the rate of reutilization” is adjusted from “0.3” in 2008 to “0.5” in 2020 (interval value calculated based on the average increase) to reduce the point source pollution output. It is known that a large amount of chemical fertilizers and pesticides are applied during agricultural production in the Siping area, but the utilization efficiency is very low and the cultivation mode is not feasible. First, the control of non-point source pollution mainly aims to improve the methods of fertilization and tillage, such as promoting organic fertilizer use and changing “farmland” to “high farming” or “terrace cultivation” to reduce the pollution output per unit area of farmland. Second, it accelerates the speed of livestock intensification, where the ratio of livestock increases from 0.22 in 2008 to 0.45 in 2020 (interval value calculated based on the average increase) and “the ratio of small livestock intensification” increases from 0.27 in 2008 to 0.5 in 2020 to reduce the pollutant output from livestock breeding. In 2020, WECC in the study area will be between the middle and good state. This further confirms the conclusions for Scheme 3. Based on Scheme 4, Scheme 5 incorporates revenue-raising measures and a comprehensive development plan, i.e., watersaving and emissions reductions, water resources exploitation, and

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Z. Zhang et al. / Ecological Modelling 275 (2014) 9–21

pollution control. Scheme 5 also aims to improve the ecological environment in the area by “returning farmland to forest and grassland” and taking other measures to increase the woodland and grassland area in the study area. WECC will achieve a “good carrying” status in 2020 in the study area. This also demonstrates that: a single water resource or water environment management measure cannot fundamentally solve the problems of the water ecological environment in the Siping area. The water shortage and water pollution problems need to be considered with ecological environment improvements in this region to facilitate the coordinated development of the social economy and the ecological environment in the Siping area. 5. Conclusions The present study combined SD and AHP to establish a WECC evaluation index system and a SD simulation model for the Siping area. Based on the existing eco-environment problems, six planning schemes were designed to improve the regional WECC. The development trends in Siping WECC were predicted with different schemes. The main conclusions are as follows. 1) The WECC system contains many factors and it exhibits complex behavior. Combining SD and AHP to study the regional WECC can be used to determine the importance of each factor in the system and to implements dynamic simulations of WECC. This provides an effective solution to the fuzzy definition problem for the carrying state and facilitates quantitative evaluations of WECC. Therefore, the combination of the two methods is a good approach for studying the regional WECC. The present study design has some weaknesses, but WECC research has only just begun and most previous studies have focused on qualitative descriptions with few quantitative analyses. Therefore, the present study may support further quantitative analyses of WECC. 2) Because of the limited data available, the evaluation indices of the state of the water ecological health selected in this study were not perfect and they need to be further improved. First, the water environmental quality is a major focus of WECC research. Current studies only use the pollution output load level to represent the water environment status in study area. However, the pollution output amounts cannot demonstrate the water quality in a region directly. Therefore, to visually depict the water environmental quality in a study area, it is suggested that the water quality is included in the WECC evaluation index system in future studies. Second, evaluation indexes, such as bio-diversity and species richness indices, which can reflect the regional water ecological status, should be included in future studies to make WECC research more appropriate and significant. 3) The simulation results for the WECC development trend with the six planning schemes in the Siping area showed that if the current development mode remains unchanged, the future WECC in the Siping area will have a declining trend, with a regional WECC index of 0.181 and a carrying type of “poor carrying” in 2020. Compared with the original scheme, Schemes 1–4 will improve the WECC in the study area to various degrees. However, as the improvements are limited, the goal of the coordinated development of the water ecological environment and social economy in the Siping area remains to be not realized. However, based on the development model in Scheme 5, the WECC in the study area can be improved significantly with a WECC index of 0.665 and a carrying type of “good carrying”. 4) The findings of this study indicate that in areas like Siping with water resource shortage and relatively poor water environmental quality, single water resource management or

water environment treatment measures cannot address the eco-environmental problems in an effective manner. Only a comprehensive development plan that incorporates watersaving, emission reduction, water resource exploitation, and pollution management will be effective in ensuring the coordinated development of the water ecological environment and social economy. The results of this study are expected to provide the basis for the sustainable development of the social economy in the Siping area.

Acknowledgments The present research is supported by Major Projects on Control and Rectification of Water Body Pollution (No. 2012ZX07201-001) and China Geological Survey Project (No. 1212011140027).

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