Linking water, society and ecology in a system dynamics framework: case study of Huainan city

Recently, the impacts of human activities on the water cycle have been growing. These are mainly caused by population growth and accelerated urbanization (Koop & van Leeuwen 2017). The subsequent problems, such as water resource use dispute, water environment pollution and water ecosystem degradation, have also become progressively prominent, forcing decision-makers to adjust their strategies to reduce the negative effects of human activities and maintain the sustainability of water resources (Savenije 2015).

Therefore, recent studies have focused on the connection between hydrological processes and human society (Kreibich et al. 2017). Since developing an interdisciplinary paradigm is necessary to deal with water-related problems, it is urgent to consider synthetically the nexus and development mode of water resources with the natural environment, organisms' habits, society, economy and politics. In this context, the Earth System Science Partnership (ESSP) proposed the water system theory (Alcamo et al. 2005), which focuses on the comprehensive issues of water and society, water and environment, water and ecology, including the connotation and composition of the water system, internal complicated connections and overall evolutionary trends.

The water system is defined as a whole consisting of the three categories of components (physical, human, and biological and biogeochemical components) with water cycles acting as a link. In studying a water system, it is necessary to apply an appropriate method to model the interconnections of coupled systems. As one of the tools to simulate the temporal variations of a complex system, the theory of system dynamics (Forrester 1987) is very consistent with that of water system theory, which emphasizes the concept of multi-state, multi-factor, and multi-variable interconnection, conversion, feedback and coevolution. In fact, the theory of system dynamics has become an important method for water resources research (Chang et al. 2015; Huang et al. 2019). Since its internal multi-factor interaction and feedback mode of operation could well reflect the concept of ‘nexus’ emphasized in the water system theory, the system dynamics method is applied to establish a complex water system model including water, society, and ecology interconnected in this study.

This paper aims to construct a conceptual framework to explore the coupled dynamics of the physical, human, and biological and biogeochemical components in a water system. A conceptual description of the three categories of components and their associations is presented. Moreover, a system dynamics model consisting of governing equations and constitutive relations is set up to simulate the coupling and coevolution of the water system in Huainan, Anhui Province, China. Finally, the process of coevolution and its dynamics in the Huainan City water system are analyzed and discussed, and the corresponding conclusions are ultimately reached.

Huainan City, a prefecture-level city, is located in the central part of Anhui Province of China with a total area of 2,596 km² (Figure A2 in Appendix A, available online). The city consists of six counties with a total population of 2.44 million, containing 1.12 million urban population and 1.32 million rural population in 2014. The average annual precipitation is 886.5 mm and the average annual temperature is 16.6 °C. It belongs to the Huaihe River basin, located in the middle reaches of the Huaihe River, which is its main source of water supply.

With population growth, the accelerating pace of urbanization and the rapid development of social economy, the human components are gradually becoming the main driving force for the evolution of the water system of Huainan City. The contradiction among the departments of water consumption is becoming increasingly sharp, thus the remaining water in the river is gradually reduced. Meanwhile, the increases of domestic sewage and agricultural and industrial waste water aggravate the deterioration of the water environment and the pressure on the aquatic ecosystem, threatening the virtuous circle of the overall water system. Although decision-makers have developed a series of policies to address these issues, the effectiveness of these policies remains to be evaluated. Therefore, in order to maintain the sustainability of the water system, it is important to establish a model to analyze and evaluate the synergistic process of the water system nexus.

It is noteworthy that technology (T), policy (P), and productive forces (Pf) (Liu et al. 2014) are closely interrelated, for example, productivity and policy orientation can promote the development of technology, and the development of technology and productivity can provide more choices for policy makers.

In summary, the conceptual framework of the water system is shown in Figure 1.

The basic data of water resources are mainly from the Water Resources Bulletin of Anhui Province (2006–2015). The series of measured data of hydrological and water quality monitoring stations are provided by Huaihe River Basin Water Resources Protection Bureau. Population, economy, sewage and other data are mainly from the Statistical Yearbook of Anhui Province (2006–2015). In addition, planning data such as water use indicators mainly come from the report of Comprehensive Planning of Water Resources of Huaihe River Basin and Shandong Peninsula, which takes 2030 as the long-term planning year, compiled by Huaihe River Water Resources Commission in 2008.

The simulation period of the model is from 2006 to 2030. Specifically, 2006 to 2014 are the historical statistics years, while 2015 to 2030 are the simulated prediction years; 2030 is the long-term planning year which will be taken as the comparison year with the basin planning data set in the report. The time step (dt) is set to 1 year. Additionally, the detailed data can be obtained through the platform of the Statistical Database of Anhui Province's Economic and Social Development.

According to the scope of application of the statistical model for calculation of technological change and the availability of statistical data, the calculation method of technological change in the production function improved by Tinbergen (1942) is selected in this case. In this production function, the technology (T) is only determined by time (t), and therefore the variable which is only determined by technology (T) in the calculation is also only determined by time (t).

The specific calculation process is as shown in Equations (5)–(18). According to the observed sequence, all dependent variables and independent variables are functionally fitted by using MATLAB. The variables determined by technology (T), such as industrial value added (V_ia) and off-stream ecological water requirement (E_u^off), are functionally fitted with time (t). The model was calibrated using the basic variable data series from 2006 to 2014 (the constant variables and the calibrated constitutive relations are given in Appendix A), while the data in 2015 was used for validation purposes.

Since the model involves many variables, the representative part of the variables is selected for the historical test. These variables (Figure A3 in Appendix A) include (a) population, (b) domestic water consumption per capita, (c) water consumption per unit industrial value added, (d) irrigation water consumption per unit area, (e) COD emissions, (f) NH₃-N emissions, (g) GDP and (h) total water consumption. The mean absolute relative errors (MARE) of variables (from (a) to (h)) during the calibration period are 0.85%, 2.40%, 6.73%, 5.43%, 4.48%, 7.43%, 6.48% and 1.95% respectively, and those during the validation period are 1.72%, 1.75%, 9.11%, 3.69%, 2.06%, 5.66%, 4.17% and 0.04% respectively. Taking all the variables into account, the overall effect of the simulation can be accepted.

The planning report mentioned in the subsection ‘Data’ considered 2006 as the base year to predict the main indicators of the Huaihe River Basin in 2030. These indicators include water resource utilization efficiency, socio-economic development, and pollutant emission. The comparison of the target indicators can be seen in Tables 1–3. As shown in these tables, the trends of all target indicators are consistent with the planning.

The indicators of water resource utilization efficiency can reflect the level of water use, water saving, economy and science and technology development during a certain period. In 2006, the irrigation water consumption per unit area in Huainan City was much higher than the average level in the Huaihe River Basin. Although the gap would be smaller in 2030, the simulated value for Huainan City is still higher than the average level of the river basin planning. This indicates that the improvement of agricultural water-saving technologies in Huainan City needs to be accelerated. The same would be true for water-saving technologies of domestic water use through the analysis of domestic water consumption per capita. In contrast, the effectiveness of the policies for the improvement of industrial water-saving technologies is encouraging. Moreover, the comprehensive effectiveness of the policies for the improvement of water-saving technologies would be acceptable through the analysis of water consumption per unit GDP, which would be reduced to being nearly identical in the planning year (2030) from more than three times in 2006.

Socio-economic development indicators include the total population, GDP, industrial value added, and effective irrigated area of farmland. Since the planning values were set for the entire Huaihe River Basin, the simulated values and the planning values would be compared in the form of the increase or decrease of socio-economic indicators from 2006 to 2030. As shown in Table 2, the variation of GDP in Huainan City from 2006 to 2030 is roughly identical to the planning in the Huaihe River Basin. This indicates that the integrated effectiveness of the policies for supporting social and economic development is sufficiently satisfactory. However, the effectiveness of the policies for supporting industrial development might need to be improved. Moreover, although the increase of the effective irrigation area of Huainan City is smaller than that of the Huaihe River Basin, the same is true for population growth. In the river basin planning, it was planned to use a 5.0% increase in the effective irrigated area to ensure the food supply for a 17.7% population growth. The corresponding ratio in Huainan City ranges from 3.8% to 14.8%, which means that the effectiveness of the policies ensuring food supply could be acceptable.

The main pollutant emissions targets are the key indicators reflecting the pressure on the water environment. According to Table 3, emissions of both pollutants show a drastic downward trend. Based on the comparison, it can be inferred that stricter controls of both pollutant emissions are needed.

The coevolution of water consumption, output value, water consumption per unit output value, and pollutant emissions reduction rate are shown in Figure 2(a)–2(d).

In the study area, the current policy guidance is to support various industries to transform their mode of economic growth and adopt advanced techniques to improve the efficiency of resource utilization. As the overall water consumption per unit output value decreases, water use efficiency gradually increases. The most critical influence on the total water consumption is the industrial water consumption, which declines significantly with technological advance. This is mainly due to industrial structure changing significantly during the simulation period. In the 1980s, water resources management in Huainan City focused on meeting the water demand for production. Many industrial enterprises had no metering facilities, no water quotas, and no water-saving measures, and industrial circulating water discharged once, causing serious waste of water resources. In 2001, the industrial water reuse rate of Huainan City was only 35%.

At the beginning of the 21st century, various industrial water-saving measures were implemented and industrial water use efficiency improved. On the basis of the planning report, these measures included developing industries with high water use efficiency, transforming water use processes and promotion, strengthening industrial cooling water saving, strengthening enterprise water management, and upgrading equipment and water distribution networks. The annual water plan prepared by Huainan City has clearly stipulated that the quota of water use plans for enterprises with low industrial water repetition rate should not be increased, and the quotas of water use plans for those severely wasting water and violating water-saving regulations should be deducted. In addition, according to the planning report, major water-consuming industries, such as thermal power generation, petroleum and petrochemical, steel, textile, chemical and food, are the key objects for water-saving transformation in the production process. The water-saving measures include reducing water withdrawal, improving wastewater treatment and reuse, and utilizing seawater reclaimed water and mine water if available. The principal water-consuming industries in Huainan City are thermal power and mining. Due to the lack of detailed water utilization information for thermal power and mining in Huainan City, its evolution could not be simulated in this study. However, the availability of this detailed information might contribute to further research into the evolution process of industrial water consumption.

The agricultural water consumption also shows a downward trend under the influence of both technology and irrigated area, while the domestic water consumption gradually increases under the synergy of technological improvement and population growth. This is mainly due to the relatively stable agricultural industrial structure and the gradually maturing water-saving irrigation technology in the study area. The rate of decrease of water consumption per unit area has gradually stabilized. As the population grows, the demand for grain increases and the irrigated area increases accordingly. Consequently, the agricultural water consumption gradually tends to decline steadily. Overall, the speed of industrial water-saving technological innovation is faster than that of agriculture. This could be considered as a positive feedback loop, in which resources of water, land and human are combined to generate wealth which can promote technological advance. Technological innovation can further improve the efficiency of resource utilization, and is also conducive to environmental pollution control with policy guidance. Liu et al. (2014) also explained such a positive feedback cycle with the concept of productive force.

Over the past decade, guided by the environment-friendly mode for social development, the city has vigorously promoted resource conservation and pollution control to restore the various functions of ecosystems. In order to ensure the sustainable development of water resources and ecosystems, relevant policies for the discharge control indicators of the main sections of the mainstream of the Huaihe River have been formulated. Considering that the prefecture-level administrative region is an important unit of social–economic development and water resources management, it is significant to further formulate policies and regulations to restrict the discharge of the export sections of the prefecture-level cities, which would be conducive to the further development of the model. As seen in Figure 2(e), under the guidance of the relevant policies and depending on economic development and technological progress, the utilization rate of water resources has been decreasing year by year. Figure 2(f) indicates the degree of assurance for the instream ecological water requirement under the condition of the dry year, that is, the ratio of ecological water requirement to remaining instream water resources for different descriptions of flows. During the simulation period, the degree of assurance gradually increases with the decrease of water resource utilization rate, but the overall improvement degree is relatively small. This indicates that the recovery of various functions of ecosystems in the water system of Huainan City is relatively slow under the negative feedback loop dominated by a restorative force, which is consistent with the observation of Feng et al. (2016) on water supply, power generation and environment systems. They suggested that the degradation of ecosystems takes years and the recovery process could take decades. Furthermore, the current monitoring of ecosystems in various regions of China is still in its initial stage, and the basic data related to ecosystem function is relatively scarce, but its progress might be gradually accelerated. In order to more accurately formulate policies for the restoration of ecosystem functions, it is necessary to explore in future studies the linkages and feedback between water quantity, water quality and ecological indicators describing ecosystem state, such as biomass, density and diversity index.

This paper presents a water system model framework that describes the physical, human, and biological and biogeochemical components and their nexus. The coevolution of the nexus for Huainan City was simulated with a system dynamics model. By comparing the simulation results with the river basin planning, it is possible to test whether the development of the water system is consistent with the development direction and rate of government planning. The main driving factors for the evolution of the water system could be regarded as the sum of the positive feedback loop aimed at improving social productivity and the negative one aimed at ensuring the sustainability of natural resources. Among them, the technology and policy in the human components determine the utilization efficiency of resources and the speed of recovery of ecosystem functions, so both of them dominate the coevolution under the synergies of the two cycles.

Finally, the proposed water system model here possesses the ability to simulate the population growth, technological innovation and wealth accumulation mode. Therefore, the authors suppose this model framework can be used to simulate the coevolution of various coupled water systems. Against the background of different climates, political forms and economic developments, the model framework could be expanded according to specific research needs and used to study the interaction and feedback of the three categories of components in the water system. It is worth noting that with the increasing attention paid to the water demand contradiction between social–economic development and the ecosystems, the process of water resources management reform would continue to accelerate. More detailed regulations on water quantity and water quality in administrative districts at all levels would be gradually promulgated, and the collection and transmission of information on water resources and ecosystems would gradually be automated. Therefore, future studies should take these evolutions into account and adjust the model according to more detailed relevant data to improve the applicability of the model, which makes the collection and availability of data more critical.

This work was supported by the National Key Research and Development Program (during the 13th Five-year Plan) under Grant No. 2016YFC0401301, National Natural Science Foundation of China (Grant No. 41890823 and Grant No. 51809239), Ministry of Science and Technology, PRC, and the Major Science and Technology Program for Water Pollution Control and Treatment (No. 2017ZX07602-003).

The Supplementary Data for this paper are available online at http://dx.doi.org/10.2166/ws.2019.121.