Abstract

In recent years, water crisis caused by human activities has attracted much more attention from the public, and the water security problem has become a hot spot in the world. In this paper, applying the theory of system dynamics, a system dynamic model for urban water cycle was developed to simulate the conversion and consumption processes of water resources between a regional social system and water system. To improve the guarantee capacity of water security, three types of industrial development patterns were designed. Moreover, an optimization model for water security regulation schemes was developed. Based on simulation of the key indexes on regional water security status, the value of water security degree was evaluated under different industrial development patterns, and the optimal scheme obtained according to these assessment results. Results from investigation and research in Zhengzhou City, China found that all seven regulation schemes could increase water security degree to different extents; and water security degree of the schemes with compound patterns was higher than the schemes with a single pattern. Scheme 7 was recommended as the ideal scheme for Zhengzhou City.

Introduction

As fundamental natural resources and strategic economic resources, water resources are the important guarantee for economic and social development. However, some water security problems, such as water shortage and water pollution, have been exacerbated due to unreasonable exploitation and utilization of water resources by humans since the 20th century. Thus, the water security problem has become an urgent challenge which is increasingly threating billions of people (Vörösmarty et al., 2010; Olivia & Wu, 2018). Therefore, study on effective measures to solve water security problems has become one of the hot spots in recent years; especially, in August 2000, an international seminar on water issues entitled ‘Water Security in the 21st Century’ was held in Stockholm, Sweden, bringing increasing attention to water security issues.

A great deal of work has been done on water security. Some researchers tried to elaborate the concept and connotation of water security from different aspects (Jia et al., 2002; Zhang et al., 2005). Many international organizations such as the Global Water Partnership and United Nations Environment Program have also proposed their own definitions of water security (Global Water Partnership, 2000; United Nations Environment Program (UNF), 2009). In order to accurately reflect the regional water security status, some researchers have defined some indicators. For example, the per capita water resource was defined as the Water Scarcity Index to measure the scarcity of regional water resources (Falkenmark, 1989), the Water Poverty Index which is similar to the Consumer Price Index was used to measure the water security status (Sullivan, 2001), and the water resources carrying capacity was considered as the basic measure of water security (Xia & Zhu, 2002). Furthermore, water security assessment systems and water security assessment methods also developed at the same time. An assessment system for sustainable development of water resources based on the five levels of resources, ecosystem health, infrastructure, human health, and capabilities was proposed by the Government of Canada, Policy Research Initiative (GC PRI, 2007). An evaluation index system was tentatively set up by the Asian Development Bank and used to make preliminary assessments of the state of water security in 47 countries in Asia and the Pacific (Asian Development Bank, 2013). Many regional studies of sustainable water use and water security have been carried out, and developed their own indicators according to their particular purposes (Kang & Lee, 2011; Zeng et al., 2013; Vollmer et al., 2018), and the multi-attribute decision-making (MADM) method (Jia et al., 2015) has been widely used in water security assessment.

Cities have assembled a large number of people, resources, and social wealth, thus urban water security directly affects human livelihood and economy, and so it becomes a more pressing issue. Urban expansion, population aggregation, increased water use and sewage have caused serious water crises. Faced with serious urban water security problems, many corresponding countermeasures were put forward in different regions. In response to urban water security emergencies, Yokoi et al. (2006) conducted research on risk analysis and control of water quality management in water supply systems. Fisherjeffes et al. (2017) proposed that storm water harvesting can improve water security in South Africa's urban areas. There are also some policy recommendations such as Uzbekistan promoting water management through public participation (Khasankhanova, 2005). The idea of developing an eco-based water policy to promote sustainable development was pointed out by Jonathan et al. (2001) after reviewing the evolution of American water policy. These studies provide reference for how to solve the regional water crisis.

Although the issue of water security has drawn extensive attention, current research has usually focused on the interpretation of the concept of water security or on the assessment of water security status. Few studies have explored the internal connection between the various constraints and regional water security status, and then put forward the appropriate management or controlling countermeasures. In this paper, using the theory of system dynamics and optimization decision-making, an effective way to solve the water security problem was found through the simulation-evaluation-regulation of the urban water cycle, and is a beneficial attempt at water security research.

Study area

Located in the hinterland of the Central Plains, Zhengzhou is the political, cultural, economic, and commercial center of Henan Province in China. In recent years, the water consumption of Zhengzhou has rapidly increased with the acceleration of urbanization as well as the expansion of the population. However, its water resource per capita is less than one-tenth of the national average under the restriction of natural conditions. Meanwhile, adjustment of the industrial structure and expansion of the economic scale have led to an ever-increasing amount of sewage. As a result, serious water quality deterioration and ecological degradation have been triggered. According to the survey in 2011, only 7% of the rivers in Zhengzhou City met the standard of water quality protection, which reflected an unsatisfactory water security status.

Methods

Water security regulation involves hydrology, ecology, and society, which requires the knowledge of environmental science, management science, and others, so developing a model is an effective method to combine these factors for reasonable regulation. The main models and methods in this paper include a system dynamic model for urban water cycle (SD-UWC), evaluation method for water security degree, and optimization model for water security regulation schemes (OM-WSRS) of different industrial development patterns. The SD-UWC based on the system dynamics theory can describe the interaction between the regional social system and water system, which was developed to simulate the various constraints influencing the regional water security and the chain reactions. The water security evaluation index system was used to quantitatively evaluate the water security degree under different development levels through fuzzy comprehensive evaluation method. The water security regulation scheme set consisted of the combinations of different industrial development patterns. The optimization model was developed to select the optimal regulation scheme. Water security status in the future under different schemes is evaluated, and the scheme which both meets the economic and social development needs and also maintains the higher water security degree will be the optimal scheme.

SD-UWC

Various behaviors about water affairs and the consequences caused by them must be accurately described before studying the issue of water security. The urban water cycle system, considering the impact of social system on the hydrologic cycle in urban areas, can be used to describe it. With the acceleration of urbanization, the water cycle in urban areas has been increasingly complex, which prompted formation as a sub-discipline (Fletcher et al., 2013). Some scholars have proposed some similar concepts that take both natural systems and artificial/social systems in the water cycle into consideration, such as socio-hydrology that treats people as an endogenous part of the water cycle (Sivapalan et al., 2012). This paper developed the SD-UWC, which reflects the interaction and feedback mechanism of the urban water cycle system, in order to describe the operation process of water resources in the whole system and its effect on the various elements of the system.

System dynamics is a mathematical method used to describe closed feedback loops between various subsystems or elements within a subsystem, which has clear advantages in simulating urban water cycle systems. All kinds of water affairs in the SD-UWC are the key to causing the whole system to work. The positive and negative impacts brought about by these water affairs constituted the feedback loop of the whole system. It was divided into three parts: economy-society system feedback loop, water resource system feedback loop, and ecosystem feedback loop (Figure 1).

Fig. 1.

Schematic diagram of feedback loop of urban water cycle. Note: ‘ + ’ means that the last factor produces positive feedback on the next factor; ‘ − ’ means that the last factor has negative feedback on the next factor.

Fig. 1.

Schematic diagram of feedback loop of urban water cycle. Note: ‘ + ’ means that the last factor produces positive feedback on the next factor; ‘ − ’ means that the last factor has negative feedback on the next factor.

The causality tree analysis method was used to describe the relationship between the key elements of the urban water cycle system and the internal environment, including the relationship among water utilization, water supply, ecological environment, investment flow, and total benefits, which formed the framework of the system flowchart for the entire system (Figure 2). In order to optimize the water security regulation scheme, the secondary industry was subdivided into traditional industry, advanced manufacturing industry, and high-tech industry accordingly; the tertiary industry was subdivided into logistics transportation, financial industry, and other service industry.

Fig. 2.

System flowchart of urban water cycle process.

Fig. 2.

System flowchart of urban water cycle process.

Some major equations which constitute the SD-UWC are given as follows.

Population and industry added value are two key basic parameters for calculating water consumption in the urban water cycle. The prediction formulas are as follows: 
formula
(1)
 
formula
(2)
where: H is the total population; V is the industry added value; HI, VI are the initial value of total population and the industry added value, respectively; and GRP, GRv are the growth rate of population and the industry added value, respectively.
Based on some basic data and parameters, the water consumption in the urban water cycle can be calculated as follows: 
formula
(3)
 
formula
(4)
 
formula
(5)
 
formula
(6)
 
formula
(7)
where: WD is the domestic water consumption; W1, W2, and W3 are the water consumption of primary industry, secondary industry, and tertiary industry, respectively; WE is the ecological water consumption; W is the total water resources; HU, HR are the urban population and rural population, respectively; QU, QR are the urban domestic water quota and rural domestic water quota, respectively; A is the agricultural land area; QI is the irrigation quota; V2, V3 are the added value of secondary industry and tertiary industry, respectively; and WV2, WV3 are the water consumption of 10,000 yuan added value of secondary industry and tertiary industry, respectively.
The available water supply for the urban water cycle system can be calculated according to the following formula: 
formula
(8)
where: WA is the available water supply; RW is the water available rate; WR is the reclaimed water; and WT is the transferred water from other regions.
The pollution generated from the urban water cycle system is as follows: 
formula
(9)
 
formula
(10)
 
formula
(11)
 
formula
(12)
where: S is the total sewage; RL1, RL2, RL3 are the water loss rate of domestic water consumption, secondary industry water consumption, and tertiary industry water consumption, respectively; P is the total pollutants; PR is the pollutant load into the river; PRT is the pollutant tolerance of the river; RPE is the ratio of water quality not meeting the protection target; CPH, CPV are the coefficient of pollutant product per capita and 10,000 yuan added value, respectively; and CPR is the proportion of pollutants discharged into the river.
The benefits and investments of the entire system can be calculated according to the following formulas: 
formula
(13)
 
formula
(14)
where: B, BW, BE, BS are the total benefits of total water, water supply, ecological, and the negative effect of sewage discharge, respectively; IW, IWI are the net investment for water conservation and the initial net investment for water conservation, respectively; and VN is the annual net value added.

Evaluation method for water security degree

The assessment of water security status includes evaluating various security factors in the area and making a quantitative description of the water security status. This paper uses a fuzzy comprehensive evaluation method to evaluate the water security status. We have proposed a set of urban water security evaluation index systems and used fuzzy comprehensive evaluation method to evaluate the water security degree (Zhang et al., 2010), which were adapted and used in this paper.

The indicators of urban water security evaluation index system were selected from four aspects of social security: economic security, ecological security, engineering, and water security degree was used as the final indicator to measure the overall status of the region (Table 1).

Table 1.

Index system for water security degree evaluation.

Target layer Criterion layer Index layer Explanation 
Water security degree (A) Social security degree (B1) Water shortage rate (C11) Total water shortage in the region ÷ Total water demand in the region × 100% 
Water recycling rate (C12) Repeated water consumption ÷ Total water consumption × 100% 
Economic security degree (B2) GDP per cubic meter water (C21) GDP ÷ Total water consumption 
Elasticity coefficient of total water use (C22) Change rate of total water consumption ÷ Change rate of GDP 
Ecological security degree (B3) The ratio of water quality not meeting the protection target (C31) (1 − Pollutant tolerance capacity ÷ pollutant load into the river) × 100% 
COD emissions per capita (C32) Total COD emissions ÷ Total population 
Engineering security degree (B4) The ratio of river runoff to flood control capacity (C41) (Regional surface runoff−Surface water supply) ÷ Flood control capacity 
Growth rate of water conservancy engineering investment (C42) (Engineering investment of planning year ÷ Engineering investment of current year)(1-Year) − 1 
Target layer Criterion layer Index layer Explanation 
Water security degree (A) Social security degree (B1) Water shortage rate (C11) Total water shortage in the region ÷ Total water demand in the region × 100% 
Water recycling rate (C12) Repeated water consumption ÷ Total water consumption × 100% 
Economic security degree (B2) GDP per cubic meter water (C21) GDP ÷ Total water consumption 
Elasticity coefficient of total water use (C22) Change rate of total water consumption ÷ Change rate of GDP 
Ecological security degree (B3) The ratio of water quality not meeting the protection target (C31) (1 − Pollutant tolerance capacity ÷ pollutant load into the river) × 100% 
COD emissions per capita (C32) Total COD emissions ÷ Total population 
Engineering security degree (B4) The ratio of river runoff to flood control capacity (C41) (Regional surface runoff−Surface water supply) ÷ Flood control capacity 
Growth rate of water conservancy engineering investment (C42) (Engineering investment of planning year ÷ Engineering investment of current year)(1-Year) − 1 

The weight of each index was determined by analytic hierarchy process (AHP). The weight coefficient values of criterion layer are ω = {0.40, 0.25, 0.20, 0.15}. The weight coefficient values of each index at index layer are ω1 = {0.60, 0.40}, ω2 = {0.45, 0.55}, ω3 = {0.50, 0.50}, ω4 = {0.50, 0.50}.

The water security class standard was established based on domestic and overseas research results of classification of water security indicators, standards, and plans promulgated by local government and river protection requirements, as shown in Table 2.

Table 2.

Water security class thresholds for evaluation index.

Index Unit Water security class
 
Class 1 (Dangerous) Class 2 (Unsafe) Class 3 (Sub-safe) Class 4 (Safe) Class 5 (Excellent) 
C11 ≥20 18~13 10~8 5~2 ≤1 
C12 ≤17 17~20 20~23 23~25 ≥25 
C21 m3/104 yuan ≤20 20~30 30~40 40~50 ≥50 
C22 – ≥30 30~26 26~22 22~18 ≤18 
C31 ≥90 90~85 85~80 80~75 ≤75 
C32 kg/person ≥55 55~50 50~45 45~35 ≤35 
C41 – ≥6 6~5 5~4 4~3 ≤3 
C42 ≥5 5~6 6~7 7~8 ≤8 
Index Unit Water security class
 
Class 1 (Dangerous) Class 2 (Unsafe) Class 3 (Sub-safe) Class 4 (Safe) Class 5 (Excellent) 
C11 ≥20 18~13 10~8 5~2 ≤1 
C12 ≤17 17~20 20~23 23~25 ≥25 
C21 m3/104 yuan ≤20 20~30 30~40 40~50 ≥50 
C22 – ≥30 30~26 26~22 22~18 ≤18 
C31 ≥90 90~85 85~80 80~75 ≤75 
C32 kg/person ≥55 55~50 50~45 45~35 ≤35 
C41 – ≥6 6~5 5~4 4~3 ≤3 
C42 ≥5 5~6 6~7 7~8 ≤8 
The specific method for calculating water security degree has been mentioned in the previous paper (Zhang et al., 2010). The main formula is as follows: 
formula
(15)
where: S is the water security degree; ω is the weight coefficient; C is the score of the indicator; and f is an algorithm, which represents multiplication here.

Regulation schemes of different industrial development patterns

Regional industrial development pattern refers to a characteristic development direction influenced by regional history, economy, and cultural backgrounds. It means the choice of ownership structure and economic system in the process of modernization. A previous paper showed the essential characteristics and characterization indicators of the different regional industrial development patterns (Dou et al., 2010). Combining the characteristics of the study area, three kinds of industrial development pattern, which include water saving agriculture pattern (WSAP), environmental protection industry pattern (EPIP), and advanced service industry pattern (ASIP) were further designed. The WSAP aimed at prioritizing water saving agriculture and the agriculture of high water use efficiency, as well as promoting the comprehensive development of regional agriculture by adjusting the planting structure and vigorously promoting agricultural water saving measures. The EPIP aimed at developing high-tech industry of ‘low input, high efficiency, and low emission’, advanced manufacturing industry, adjusting regional industrial structure and transforming production mode from extensive mode to sophisticated mode by relying on resource advantage, technical advantage and supporting industries in the region. The ASIP aimed at developing the industries of the logistics transportation, information transmission, and finance with the ‘low water consumption and emission’ characteristic, also, rapidly developing service industry with regional advantages to achieve the upgrade and extension of the industrial chain.

Based on these three industrial development patterns, the water security regulation schemes were further designed. The water security regulation scheme is a regulation and control plan designed in consideration of the effects of industrial structure adjustment, water saving, and pollution control under the leading role of the regional industrial development pattern. Taking the EPIP as an example, adjusting the proportion of traditional industry, advanced manufacturing industry, and high-tech industry will change the industrial structure, adjusting the industry water quota will change the water demand, and adjusting the pollution discharging coefficient and sewage treatment rate will change the pollutant emissions. This paper selected seven representative schemes for comparative analysis (Table 3). Schemes 1 to 3 (S1, S2, S3) only took single industrial development pattern into consideration; in contrast, schemes 4 to 7 (S4, S5, S6, S7) were the combinations of multiple industrial development patterns. The choice of different industrial development patterns will not only affect the industry added value, but also cause changes in industry water quota and pollutant discharging coefficient, thus triggering a series of changes in the urban water cycle process (Figure 1), eventually changing the regional water security status.

Table 3.

Water security regulation schemes based on industrial development pattern.

   Unit Scheme
 
Driving forces S1 S2 S3 S4 S5 S6 S7 
WSAP Irrigation water quota m3/Mu −   − −  − 
EPIP Advanced manufacturing industry Water consumption quota m3/104 yuan  −  −  − − 
Pollutant discharging coefficient kg/104 yuan  −  −  − − 
High-tech industry Added value proportion    
Water consumption quota m3/104 yuan  −  −  − − 
Pollutant discharging coefficient kg/104 yuan  −  −  − − 
Sewage treatment rate    
Reclaimed water reuse rate    
ASIP Logistics transportation Added value proportion    
Water consumption quota m3/104 yuan   −  − − − 
Financial industry Added value proportion    
Water consumption quota m3/104 yuan   −  − − − 
   Unit Scheme
 
Driving forces S1 S2 S3 S4 S5 S6 S7 
WSAP Irrigation water quota m3/Mu −   − −  − 
EPIP Advanced manufacturing industry Water consumption quota m3/104 yuan  −  −  − − 
Pollutant discharging coefficient kg/104 yuan  −  −  − − 
High-tech industry Added value proportion    
Water consumption quota m3/104 yuan  −  −  − − 
Pollutant discharging coefficient kg/104 yuan  −  −  − − 
Sewage treatment rate    
Reclaimed water reuse rate    
ASIP Logistics transportation Added value proportion    
Water consumption quota m3/104 yuan   −  − − − 
Financial industry Added value proportion    
Water consumption quota m3/104 yuan   −  − − − 

Note: ‘ + ’ means to increase 10% of the indicator during the design of the plan; ‘ − ’ means to reduce 10% of this indicator when designing the solution.

OM-WSRS

The OM-WSRS is a model for selecting the decision-making scheme, which takes ‘maximal water security degree’ as the objective, the ‘SD-UWC’ as a link, the ‘industrial structure adjustment’ as the driving factor, and takes various constraints condition into account. The model consists of objective functions and constraint conditions. The objective functions aim at optimizing the regional water security degree S, as the following formula shows: 
formula
(16)

Constraints focus on the following aspects:

  1. Constraint of SD-UWC. In order to find the optimal water security regulation scheme, the first thing is to identify the various driving factors which affect water security and then effectively describe the human–water interactions under the influence of these driving factors. Therefore, it is the necessary for the OM-WSRS to meet the simulation accuracy needs of the SD-UWC: 
    formula
    (17)
     
    formula
    (18)
     
    formula
    (19)
    where: SubMod [SSD (Xi)] is the SD-UWC, including formulas (1)–(14); SSD (Xi) is the index value calculated or predicted by the model; Xi is the different indicators, including population, gross domestic product (GDP), water consumption, sewage, and so on; Max [SSD (Xi)] is the maximum error of calculated or predicted indicator value; Ave [SSD (Xi)] is the average error of calculated or predicted indicator value; a is the maximum allowable error threshold and b is the average allowable error threshold (a= 40%, b= 20%).
  2. Constraint of water resource. The total amount of water consumption should be less than the amount of available water supply in the study area according to the given formula: 
    formula
    (20)
     
    formula
    (21)
    where: WP is the water consumption of production; WS is the amount of surface water resources; WG is the amount of groundwater resources; and cS and cG the available coefficients of surface water and groundwater resources, respectively. The meanings of ‘WD, WE, W1, W2, W3, WT, WR’ have been explained in the section ‘SD-UWC’.
  3. Constraint of environmental capacity of water function zone. The total amount of pollutant load into the river in the study area shall not exceed the pollutant tolerance of the water function zone in the area, according to the formula: 
    formula
    (22)
    where: SI and SD are the amount of pollutant load into the river of industrial pollution and domestic pollution, respectively; and SRN is environmental capacity of water function zone in the study area.
  4. Constraint of industry structure. In order to meet the needs of economic and social development, the adjustment of the three major industrial structures in different years is given by: 
    formula
    (23)
    where: is the proportion of industry added value to GDP; is the lower limit of the proportion; and is the upper limit of the proportion. The value of i can be 1, 2, or 3, which means primary industry, secondary industry, or tertiary industry, respectively.
  5. Constraint of economic benefit. The economic benefits brought by water in various industries (that is, the added value of the industry) must achieve the expected target. Industry added value is calculated as follows: 
    formula
    (24)
    where: E1, E2, E3 are the water benefit coefficient of primary industry, secondary industry, and tertiary industry, respectively. GDPP is the planning goal value of GDP and the other symbols have the same meanings as before.

Since OM-WSRS is a complex non-linear optimization model which is difficult to solve directly, computer simulation techniques are needed, as follows: first, to verify SD-UWC and make sure the simulation accuracy of it can meet the requirements; second, to substitute the corresponding calculation conditions and parameters under different water security regulation schemes into SD-UWC to simulate the economic and social development scale, water demand, available water supply, and sewage discharge under the circumstances; third, to analyze the water demand, available water supply, and river water quality, then regulate economic and social development speed and optimize the water resources allocation based on the analysis results; and finally, to select the scheme that meets all the constraints and provides the maximum water security degree as the final regulation scheme.

Results and discussion

Data collection

The model calculation requires a large number of data, such as water consumption quota, water resource availability, pollutant produce coefficient, water loss rate, and proportion of pollutant load into the river of different industries. The socio-economic data mainly come from the Zhengzhou City Statistical Yearbook. The relevant planning results, water supply, consumption, and discharge information mainly come from monitoring water quantity. Finally, the water quality data are provided by the monitoring departments.

Model verification and simulation results analysis

It is necessary to simulate the actual situation of the water security evaluation index by using SD-UWC before the selection of water security regulation schemes. In order to verify the rationality of the model, parameter calibration and validation need to be performed. The data for calibration are from 2005 to 2009, and the data for validation are from 2010 to 2011. Indicators such as water demand, water supply, sewage, and pollutant load into the river in various industries of Zhengzhou City from 2005 to 2011 were calculated. Compared with the statistical data of the same period, the results are as follows: in the calibration period, the maximum error among indicators is 16.2% and the average error is 8.5%. In the verification period, the maximum error among indicators is 26.9% and the average error is 14.8%. It can be seen that the accuracy of the model is in accordance with the requirements and can be used for water security regulation.

According to the future development goals of Zhengzhou, proposed in the planning report of Zhengzhou City Master Plan and Zhengzhou Water Resources Comprehensive Plan, the indicators of population growth rate, urbanization rate, industrial added value growth rate, agricultural land area change rate and investment growth rate, and other indicators describing scientific and technological progress such as water reclaim rate and sewage treatment rate of Zhengzhou City from 2012 to 2030 was obtained. The population and economic development level, water supply, water demand, and sewage discharge between 2012 and 2030 can be calculated at 50% incoming water frequency depending on SD-UWC. The results are shown in Table 4.

Table 4.

Prediction results of main indexes of the urban water cycle model.

Index Unit 2012 2015 2020 2025 2030 
Population Urban population 104 524.8 570.8 641.8 712.1 779.8 
Rural population 104 270 262.5 254.6 232.7 212.3 
Subtotal 104 794.9 833.3 896.4 944.8 992.1 
Economy Primary industry added value 108 yuan 128.1 153.1 209.7 265.2 310.4 
Secondary industry added value 108 yuan 2,445.1 3,034.3 4,543.3 6,022.5 7,387.8 
Including: Advanced manufacturing industry 108 yuan 357 479.4 767.8 1,072 1,374.1 
High-tech industry 108 yuan 618.4 772.4 1,220.8 1,633.1 2,044.9 
Tertiary industry added value 108 yuan 2,229.3 2,964 4,882.5 7,049.2 9,306.2 
Including: Logistics transportation industry 108 yuan 362.4 497.4 924.5 1,428.6 1,995.8 
Financial industry 108 yuan 232.6 306.3 513.9 754.5 1,029.9 
GDP 108 yuan 4,802.5 6,151.4 9,635.5 13,336.9 17,004.4 
Water supply and demand Domestic demand 104 m3 31,763 34,919 39,974 44,155 48,003 
Primary industry water demand 104 m3 56,783 52,138 49,600 47,669 46,328 
Secondary industry water demand 104 m3 75,193 86,837 112,064 123,168 128,860 
Tertiary industry water demand 104 m3 16,488 21,398 32,514 43,533 54,630 
Ecological water demand 104 m3 17,535 22,013 28,356 33,011 35,797 
Total water demand 104 m3 197,761 217,305 262,508 291,536 313,617 
Water supply 104 m3 162,265 204,592 222,122 236,854 246,321 
Water shortage rate 17.9 5.9 15.4 18.8 21.5 
Pollutant discharge Sewage discharge 104 m3 64,433 66,966 69,082 72,280 76,745 
COD emission 388,033 390,313 399,842 411,650 423,689 
COD load into river 151,529 156,125 166,980 182,432 194,897 
The ratio of water quality not meeting the protection target 37.5 38.8 39.6 40.7 41.2 
Index Unit 2012 2015 2020 2025 2030 
Population Urban population 104 524.8 570.8 641.8 712.1 779.8 
Rural population 104 270 262.5 254.6 232.7 212.3 
Subtotal 104 794.9 833.3 896.4 944.8 992.1 
Economy Primary industry added value 108 yuan 128.1 153.1 209.7 265.2 310.4 
Secondary industry added value 108 yuan 2,445.1 3,034.3 4,543.3 6,022.5 7,387.8 
Including: Advanced manufacturing industry 108 yuan 357 479.4 767.8 1,072 1,374.1 
High-tech industry 108 yuan 618.4 772.4 1,220.8 1,633.1 2,044.9 
Tertiary industry added value 108 yuan 2,229.3 2,964 4,882.5 7,049.2 9,306.2 
Including: Logistics transportation industry 108 yuan 362.4 497.4 924.5 1,428.6 1,995.8 
Financial industry 108 yuan 232.6 306.3 513.9 754.5 1,029.9 
GDP 108 yuan 4,802.5 6,151.4 9,635.5 13,336.9 17,004.4 
Water supply and demand Domestic demand 104 m3 31,763 34,919 39,974 44,155 48,003 
Primary industry water demand 104 m3 56,783 52,138 49,600 47,669 46,328 
Secondary industry water demand 104 m3 75,193 86,837 112,064 123,168 128,860 
Tertiary industry water demand 104 m3 16,488 21,398 32,514 43,533 54,630 
Ecological water demand 104 m3 17,535 22,013 28,356 33,011 35,797 
Total water demand 104 m3 197,761 217,305 262,508 291,536 313,617 
Water supply 104 m3 162,265 204,592 222,122 236,854 246,321 
Water shortage rate 17.9 5.9 15.4 18.8 21.5 
Pollutant discharge Sewage discharge 104 m3 64,433 66,966 69,082 72,280 76,745 
COD emission 388,033 390,313 399,842 411,650 423,689 
COD load into river 151,529 156,125 166,980 182,432 194,897 
The ratio of water quality not meeting the protection target 37.5 38.8 39.6 40.7 41.2 

Table 4 shows the economy and population of Zhengzhou City is developing rapidly from 2012 to 2030. The total population may increase by 24.8% and GDP may increase by 254%. Moreover, the urbanization rate and industrial structure have been adjusted constantly. By 2030, urban population will account for 78.6% of the total population, added value of advanced manufacturing and high-tech industry will account for 46.3% of the secondary industry added value, and the added value of the logistics, transport, and finance sectors will account for 32.5% of the tertiary industry added value. The expansion of urban scale also leads to rapid increase of water consumption. The total water demand may increase by 58.6% from 2012 to 2030, and the water shortage rate may increase from 17.9% to 21.5%. With worsening of water shortage, water pollution also intensifies. During the period from 2012 to 2030, the sewage discharge of the whole city may increase by 19.1%, chemical oxygen demand (COD) emission may increase by 9.2% and the ratio of water quality not meeting the protection target may increase from 37.5% to 41.2%.

Through the evaluation of the water security status (as shown in Figure 3), the water security of Zhengzhou in the future will be less than the scoring value of 3 in the natural trend, which belongs to the ‘sub-safe’ class. The main reasons for low water security degree are as follows: (1) a high proportion of high water consumption industries, while a relatively small proportion of high-tech industries with low water consumption, means the three major industrial structures are not reasonable; (2) small-scale enterprises lack water-saving and pollution control capacity, and various industries have a large water consumption quota and pollutant discharge coefficient; and (3) inadequate investment in infrastructure for sewage treatment and reclaimed water use makes the water reclaim rate extremely low.

Fig. 3.

Comparison of water security degrees under different schemes.

Fig. 3.

Comparison of water security degrees under different schemes.

Regulation effect of different industrial development pattern and scheme optimization

In order to reflect the effect of different industrial development patterns on the water security of Zhengzhou City, the schemes took into account the choice of the major industry, the difference of water demand and pollutant discharge in the region. The regulation and control schemes are listed in Table 3. S1, using WSAP, adopted agricultural water-saving measures such as increasing the utilization coefficient of farmland irrigation water and reducing the average irrigation quota per Mu to regulate regional water security. The agricultural water loss rate of Zhengzhou City has reduced from 0.71 in 2012 to 0.62 in 2015 by building an efficient agricultural demonstration zone, promoting water-saving reform in large- and medium-sized irrigation districts, and speeding up water saving reform in well irrigation areas. S2, using EPIP, adopted industrial water-saving measures and pollution control measures such as increasing the proportion of high-tech industries, reducing the water quota and emission coefficient per unit of added value to improve regional water security. By closing down technologies and equipment that have high water consumption, serious pollution, and poor economic returns, and guiding enterprises to adopt advanced production technology, encouraging industries and technologies that save water and have low pollution, Zhengzhou City uses electronic information and automobile equipment manufacturing as a strategic support industry to promote the development of high-tech industries and advanced manufacturing industries. Its industrial wastewater discharges decreased by 59% from 2012 to 2015. S3, using ASIP, adopted water-saving measures in the service industry, such as increasing the proportion of logistics transportation and the financial industry, reducing the water quota per unit of added value to adjust regional water security. With the continuous adjustment, the industrial structure of Zhengzhou City has changed from 2.5:56.9:40.1 in 2012 to 2.1:49.3:48.6 in 2015. The proportion of the primary industry showed a decreasing trend, and the proportion of the tertiary industry increased at the same time. From 2012 to 2015, the water consumption per 10,000 yuan GDP decreased by 47%. S4 combined the two industrial development patterns of WSAP and EPIP which is a composite of S1 and S2. S5 combined the two industrial development patterns of WSAP and ASIP which is a composite of S1 and S3. S6 combined the two industrial development patterns of EPIP and ASIP, which is a composite of S2 and S3. S7 combined S1, S2, and S3, considering the regulation effects of all the three industrial development patterns of WSAP, EPIP, and ASIP.

According to the designed parameters of different regulation schemes and the socio-economic development goals of Zhengzhou City, predictions of the water security evaluation indicators of Zhengzhou City in 2030 were simulated by SD-UWC (Table 5). In terms of water supply and demand, the water shortage rate is above zero in S1, S2, S3, S4, and S5, which means the water supply cannot meet the water demand. In terms of water use efficiency, S2, S4, S6, and S7 are better than other schemes regarding the improvement of industrial water efficiency. In terms of water quality, the ratio of water quality not meeting the protection target of all regulation schemes is less than that of natural trend (41.2%), with S6 and S7 being close to 23%. In terms of ecological protection, the ratio of river runoff to flood control capacity under S6 and S7 is larger, indicating that there is a high guarantee rate of ecological water in rivers.

Table 5.

Prediction results of water security evaluation indicators under different schemes in 2030.

Scheme Water shortage rate/% Water recycling rate/% GDP per cubic meter water/Yuan/m3 Elasticity coefficient of total water use/- The ratio of water quality not meeting the protection target/% COD emissions per capita/ kg The ratio of river runoff to flood control capacity/- Growth rate of water conservancy engineering investment/% 
S1 20.3 22.3 55.0 30.8 34.6 66.0 2.60 6.0 
S2 4.9 28.2 63.8 29.5 25.3 55.3 2.63 7.8 
S3 5.8 25.5 66.2 21.6 31.9 64.3 2.64 5.2 
S4 3.4 28.2 63.8 29.3 26.1 52.1 2.75 8.6 
S5 4.4 25.2 66.3 21.5 33.7 64.0 2.78 6.9 
S6 0.0 28.2 63.7 20.9 23.3 50.8 2.85 7.8 
S7 0.0 28.4 63.1 20.5 23.1 50.8 2.87 8.6 
Scheme Water shortage rate/% Water recycling rate/% GDP per cubic meter water/Yuan/m3 Elasticity coefficient of total water use/- The ratio of water quality not meeting the protection target/% COD emissions per capita/ kg The ratio of river runoff to flood control capacity/- Growth rate of water conservancy engineering investment/% 
S1 20.3 22.3 55.0 30.8 34.6 66.0 2.60 6.0 
S2 4.9 28.2 63.8 29.5 25.3 55.3 2.63 7.8 
S3 5.8 25.5 66.2 21.6 31.9 64.3 2.64 5.2 
S4 3.4 28.2 63.8 29.3 26.1 52.1 2.75 8.6 
S5 4.4 25.2 66.3 21.5 33.7 64.0 2.78 6.9 
S6 0.0 28.2 63.7 20.9 23.3 50.8 2.85 7.8 
S7 0.0 28.4 63.1 20.5 23.1 50.8 2.87 8.6 

Combined with the prediction results in Table 5, the water security degrees under different regulation schemes from 2012 to 2030 can be further evaluated (Figure 3). The seven schemes can all improve water security degree to some extent. However, S1 has a small impact on water security degree. It can be seen that its water shortage rate is still much higher than other schemes and the water recycling rate is low, therefore, its regulation effect is not obvious (and will not achieve ‘sub-safe’ class until 2025). The reason for this is because many urbanization lands and agricultural water-saving potential are limited in Zhengzhou. Agriculture is related to the social stability and sustainable development of a region, so it is still necessary to increase investment in agricultural development. S2 will reach the ‘safe’ class in 2026 and S3 will reach it in 2028. Both S2 and S3 are better than S1. The change trend of water security under S2 is faster in the early stage but smaller in the later stage which is conducive to the rapid improvement of water security. The water security fluctuation is slight under S3, which indicates a stable industrial development pattern that can be used as a regulation scheme in a steady development period. The main reason for different regulation effects between S2 and S3 is related to the industrial structure change of Zhengzhou, which is dominated by industry in the early 2020s but by the service industry in the late 2020s.

Comparing the regulation schemes under a single industrial development pattern (schemes 1–3) and compound industrial development pattern (schemes 4–7), it can be found that the water security of the four compound industrial development patterns is obviously higher than that of the three single industrial development patterns. Therefore, development priority should be given to the compound industrial development patterns in the future. In the process of development, the EPIP is the driving growth force for the composite patterns in the period 2012–2023, and the driving force is the ASIP for the composite pattern in the period 2024–2030. S7 is the most ideal regulation scheme. Its water recycling rate is the highest among all schemes, but the water shortage rate, the ratio of water quality not meeting the protection target, and COD emissions per capita are the lowest. According to the regulation result of S7, the water security degree reaches the ‘safe’ class by 2018 and will approach the ‘excellent’ class by 2030. Although the regulation effect of S6 is also significant, it is detrimental to long-term and sustainable development at the expense of neglecting agricultural development. S4 also highlights the impact of the secondary industry structure adjustment on water security. Although its regulation effect is better, there is also a problem of uncoordinated industrial development, which will result in an irrational structure of the service industry. Also, the ratio of water quality not meeting the protection target and COD emissions per capita is higher than S6 and S7, indicating that it is not good enough regarding water pollution control. Thus, both S6 and S4 can be adopted under limited investment, but a more balanced program for the three major industries, i.e., S7, should be considered when the economic and social development reaches a higher level.

Conclusion

The issue of urban water security is a problem that will continue to plague people. This paper proposed a set of water security regulation theories driven by industrial development patterns using system dynamics and optimization decision theory to improve urban water security. The SD-UWC constructed in this paper can clearly describe the operation mechanism and effect of water resources in regional human and water systems, and it can be a basis for the quantitative evaluation of regional water security. To improve the guarantee capability of regional water security, this paper proposed the three industrial development patterns of WSAP, EPIP, and ASIP. Taking the three development patterns as basic regulation schemes, seven kinds of water security regulation schemes can be further designed. Different scenarios can be used to analyze the future water security status and the main driving factors under different schemes, and OM-WSRS can provide the ideal regulation scheme through optimization. It can be seen from the application in Zhengzhou City that S7 will have the best effect on optimizing the industrial structure and improving water security. Cities should adopt a comprehensive development pattern for stable and sustainable development.

Acknowledgments

The research is supported by the National Natural Science Foundation of China (No. 51679218, 51779230, 51879239, 51779230), the Program for Science & Technology Innovation Talents in Universities of Henan Province (No. 17HASTIT031) and the Outstanding Young Talent Research Fund of Zhengzhou University (No. 1521323001). We declare that all the authors have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no actual and potential conflict of interest with others. The manuscript is entitled to be submitted to this journal to be reviewed and published by the authors.

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