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Article

A New Socio-Hydrology System Based on System Dynamics and a SWAT-MODFLOW Coupling Model for Solving Water Resource Management in Nanchang City, China

by
Zhihui Deng
1,
Qingshan Ma
2,*,
Jia Zhang
3,*,
Qingda Feng
1,
Zhaoxuan Niu
1,
Guilin Zhu
1,
Xianpeng Jin
1,
Meijing Chen
4 and
Honghan Chen
3
1
Center for Hydrogeology and Environmental Geology Survey, China Geological Survey, Tianjin 300309, China
2
Nanjing Center, China Geological Survey, Nanjing 210016, China
3
School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, China
4
Hebei Provincial Academy of Water Resources, Shijiazhuang 050011, China
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(22), 16079; https://0-doi-org.brum.beds.ac.uk/10.3390/su152216079
Submission received: 19 September 2023 / Revised: 6 November 2023 / Accepted: 13 November 2023 / Published: 18 November 2023
(This article belongs to the Special Issue Sustainable Groundwater Management Adapted to the Global Challenges)
Browse Figures
Versions Notes

Abstract

:
To address the issue of seasonal water resource shortages in Nanchang City, a multi-system coupling socio-hydrology simulation method was proposed. This approach involves dynamically integrating a centralized socio-economic model with a distributed surface water groundwater numerical model to explore the intricate relationships between the socio-economic system, the surface water–groundwater integrated system, and the outcomes related to seasonal water resource shortages. Taking Nanchang City as an example, this study conducted research on the water resource supply and demand balance, as well as the groundwater emergency supply, using the multi-system coupling model. Three scenarios were established: status quo, developing, and water-saving. The results show that with the increasing total water demand of social and economic development, the severity of the water resource shortage will be most pronounced in 2030. The minimum water resources supply and demand ratios for the status quo, developing, and water-saving scenarios are projected to be 0.68, 0.52, and 0.77, respectively. To meet residents’ water needs during drought conditions, emergency groundwater supply efforts are investigated. According to the simulation results, groundwater emergency supply would increase the total population by 24.0 thousand, 49.4 thousand, and 11.2 thousand people, respectively, in the status quo, developing, and water-saving scenarios. In the water-saving scenario, the Youkou and Xiebu water sources can serve as suitable emergency water sources. In the status quo scenario, the Youkou water source is the most viable emergency water source. However, in the developing scenario, relying solely on any single water source for emergency supply could have an irreversible impact on the aquifer. Therefore, considering the simultaneous use of multiple water sources is recommended, as it can fulfill water demands while ensuring the sustainable utilization of groundwater resources.

1. Introduction

With the increasing water consumption in social and economic development, many countries are already experiencing water scarcity conditions [1]. A large number of water resources are needed to support rapid social and economic development in China, so the contradiction between the supply and demand of water resources is particularly acute [2]. The extensive and uncontrolled exploitation and utilization of surface water and groundwater resources have given rise to challenges related to the sustainable development of water resources in most regions of China [3,4]. Affected by factors such as human activities and climate change, seasonal water shortages have become prevalent in typically humid areas of China [5]. Consequently, conducting a systematic study of social hydrology, which comprehensively considers the interplay between the social economy and water resources, holds great significance in addressing the issues surrounding water resource management.
System dynamics (SD) have the advantage of dynamic simulation. In research on water resources management, the constraints of the water resources system on social and economic development can be realized by constructing subsystems including society, economy, and water resources. In an analysis of the supply and demand of water resources, the ratio of supply to demand of water resources is used as a feedback variable, facilitating a more comprehensive examination of the causal feedback relationships within the complex system.
By comprehensively considering hydrological, environmental, economic, political, and social factors and their interactions, a dynamic model of water resources management system can be built to provide a powerful tool for decision-makers to solve the problems of water resources shortage in basins and the ecological environment of swamps [6]. In semi-arid areas, a system dynamics model, which incorporates water balance, population dynamics, groundwater, surface water cost-effectiveness indices, and water reuse, can be used to simulate water resource shortages [7]. Another system dynamics model can be created, encompassing population, water balance, groundwater, and surface runoff, to evaluate water resource carrying capacity by examining water resource gaps [8]. Water resource shortage crises and the sustainability of water resources can be simulated using system dynamics [9,10]. Therefore, system dynamics methods are widely used in socio-economic and water resources system studies, such as the water resource supply–demand ratio [11,12], water resource carrying capacity [13], water ecological carrying capacity [14,15], water environment [16], and water resource management [17,18,19]. System dynamics models with predictive power have been validated for scenario analysis and forecasting [11,12,20,21]. The system dynamics method has made remarkable progress in the study of the dynamic feedback of socio-economic and water resources systems, but it does not have the ability to represent spatial distribution.
Spatial system dynamics methods, which can simulate dynamic processes both spatially and temporally, have emerged [22]. The zoned spatial system dynamics model developed on the basis of spatial system dynamics can improve the ability of spatial distribution and interaction analysis [23]. The highly generalized surface water system and groundwater system in the zoned spatial system dynamics model are not capable of representing the physical process of surface hydrology and groundwater flow [24,25].
Surface water and groundwater are integral components of the hydrological cycle and serve as vital water sources for social and economic development. Numerical models based on physical significance can predict the spatio-temporal evolution trends in surface water [26] and groundwater [27] and can be used for visual analysis to help water resource managers make better decisions [28,29]. Notably, the SWAT-MODFLOW model has demonstrated its effectiveness not only in analyzing interactions between surface water and groundwater [30,31,32,33,34,35,36,37] but also in evaluating water resources [38,39,40] and managing them [41]. Compared with a single surface water model or groundwater model, the SWAT-MODFLOW model can improve the accuracy and capability of a model and is more convincing in the analysis and decision-making on surface water and groundwater [42,43]. Despite their utility in characterizing water systems, hydrological models have limitations in capturing the dynamic developmental processes inherent in complex social and economic systems. Therefore, it is imperative to fully consider the dynamic feedback relationship between human activities and the water system when engaging in water resources management [44,45,46,47].
In the coupling simulation of surface water and groundwater, the social economic model can be used to calculate and analyze the water quantity so as to realize the loose coupling of the social economic model and the hydrological model [48,49]. However, there is a complex causal feedback relationship between the social economy and the water resources system, which is characterized by high-order nonlinearity [44]. Therefore, in order to solve water resource management problems, such as seasonal water shortage, it is very necessary to couple the socio-economic model with the hydrological model based on physical significance. The development of industrialization and urbanization has intensified the coupling of human activities and hydrological processes and promoted the emergence of socio-hydrology [50]. The socio-hydrology model can help us understand and manage complex human–water systems [51] and has become an effective method that can be applied to solving water management problems [52,53].
This paper constructs an SD-SWAT-MODFLOW tightly coupled multi-system in Nanchang City, which can diagnostically analyze the water management plan for the rapidly expanding City of Nanchang in China, evaluate the dynamic changes in the coupling system of the social economy and water resources in different scenarios, and deploy dynamically the surface water–groundwater water supply structure in time to solve the problem of seasonal water resources shortage. In this paper, Nanchang City, Jiangxi Province, a typical humid area in China, was taken as an example to apply the method, but the proposed method is also suitable for seasonal water resource shortage problems in other cities. The method provides technical support for water resources managers to make reasonable water resources management plans and is conducive to realizing the sustainable development of the social economy and water resources.

2. Methodology

In order to evaluate the interaction between social and economic development and the surface water–groundwater system, an SD-SWAT-MODFLOW dynamic coupling model was established based on the SD model and the improved SWAT-MODFLOW model. The model can simulate the dynamic response process of the surface water–groundwater system in time and space in different socio-economic development scenarios and propose a comprehensive deployment scheme for surface water resources and groundwater resources to ensure the sustainable development of the social economy and water resources, which can help managers formulate and adjust water resources management plans to meet water demand.

2.1. System Dynamics Approach

System dynamics (SD) is a method of modeling system structure with computer simulation technology, mainly based on feedback control theory and nonlinear dynamics. The core of system dynamics is a causal loop diagram (CLD). Analyzing the causal loop relationship can help us to understand the real behavior of various systems more deeply. The elements constituting the system structure mainly include “state variables” (level), “rate variables” (flow), “Auxiliary variables” (auxiliary variables), etc. Stock and flow diagrams (CFD) are the elements, and the relationship between the elements expressed by certain rules.

2.2. Improved Surface Water–Groundwater Coupling

On the basis of the full reference of the SWAT 2012 and MODFLOW-NWT source code, the lake–groundwater interaction module was included according to the analysis method and calculation method of river–groundwater interactions. The specific principle is to use the reservoir module of the SWAT model to simulate surface water bodies such as lakes, transfer the lake water level calculated according to the lake storage capacity to the MODFLOW model, use the GHB module of the MODFLOW model to calculate the lake–groundwater exchange capacity of the SWAT model, and then use it to calculate the lake water level in the next moment. Thus, the lake–groundwater dynamic coupling calculation is realized. Notably, in the SWAT model, the lake is situated within the main river, which establishes an inherent and direct relationship between the inflow and outflow of water for both the river and the lake itself. To summarize, there exist rigorous mathematical formulas grounded in physical principles for river–groundwater, lake–groundwater, and lake–river interactions. Therefore, this enhanced coupling method enables real-time and spatially dynamic calculations for the intricate interactions among lakes, rivers, and groundwater systems.

2.3. SD-SWAT-MODFLOW Coupling Model Framework

The bidirectional tight coupling of the system dynamics model and the SWAT-MODFLOW model can be realized by establishing the feedback relationship between the social economy and the water resources system and linking relevant variables.
Figure 1 shows the specific process of constructing the SD-SWAT-MODFLOW dynamic coupling model. Initially, it was necessary to construct an SD model, SWAT model, and MODFLOW model, calibrate and verify them, respectively, and prepare the link files for the SWAT model and MODFLOW model. Subsequently, an analysis of the socio-economic development status within the study area was conducted. Using the system dynamics model, the surface water requirements in various socio-economic development scenarios were computed. These calculated requirements were then integrated into the improved SWAT-MODFLOW model, serving as the “sink” for the surface water system and groundwater system, respectively. The runoff simulation function of the SWAT model was used to calculate the amount of available surface water resources in the study area, and the groundwater withdrawal and water resource supply and demand were determined using a comprehensive comparison with the surface water demand. If surface water resources prove inadequate to meet the water demand, groundwater emergency water supply becomes necessary. Finally, the surface water flow and groundwater emergency supply obtained after the SWAT-MODFLOW model were used as the main factors of the system dynamics model to realize the dynamic feedback between socio-economic development and the water resources system. However, the actual intake information was not considered in this study. Based on the concept of a watershed, it is believed that the water intake in the sub-area comes from within the scope of the sub-area, and there is no cross-regional water transfer.
The dynamic coupling model developed in this paper was written in the FORTRAN language, and the executable program was packaged using Microsoft Visual Studio 2008 and Intel Visual Fortran 2013 platforms. The specification file and linking procedures of the SWAT-MODFLOW model can be downloaded from https://swat.tamu.edu/software/swat-modflow/ (accessed on 10 March 2023).
The key problem in realizing the SD-SWAT-MODFLOW dynamic coupling model is the dynamic link in time and space between the socio-economic model and the interaction variables between the surface water and groundwater systems. The dynamic coupling model developed in this paper is written in the FORTRAN language, and the executable program is packaged based on Microsoft Visual Studio 2008 and Intel Visual Fortran 2013 platforms.

3. Case Study

3.1. Study Area

Nanchang City (115°27′–116°35′ E, 28°09′–29°11′ N) is located in the north-central part of Jiangxi Province, China, within the middle and lower reaches of the Yangtze River Basin. The total area of Nanchang is 7402 km2, and the water area is roughly 2204 km2, accounting for 29.8% of the total area. The city boasts a well-developed network of water systems, prominently featuring the Ganjiang River, Fuhe River, and Xiushui River, which ultimately flow downstream into Poyang Lake (refer to Figure 2). Poyang Lake receives water from the Xiushui River, Ganjiang River, Fuhe River, Xinjiang River, and Raohe River and flows into the Yangtze River at the mouth of the lake. The Ganjiang River is the river with the largest discharge in this region, accounting for 55% of the total drainage of Poyang Lake [54]. The terrain of Nanchang is generally high in the north and low in the south, with low hills, hills, and plains successively developing, showing stratified geomorphic features. With Ganjiang River as the boundary, the northwest of Ganjiang River comprises low mountain hills and hilly land by structural denudation, and east of Ganjiang River lies a plain of river erosion and accumulation, where rivers, lakes, ports, and branches are distributed, and a braided water system has developed. The groundwater types mainly include loose rock pore water, red-layer dissolved fissure water, and bedrock fissure water. The main mining strata are quaternary loose rock strata.
Nanchang City is located in the typical humid zone of China. The mean annual precipitation is about 1596 mm, the average annual evaporation is about 1272 mm, the precipitation varies greatly across years and is distributed unevenly within years, and the rainfall from April to June accounts for 51% of the total yearly rainfall. Nanchang City is very rich in water resources with an annual average water resource of 6.60 × 109 m3; the annual average water supply accounts for only 47% of the annual average water resources, and surface water as the main water supply source accounts for about 96% of the total water supply. Being the capital of Jiangxi Province, Nanchang has a high population density and is currently undergoing rapid urbanization and industrialization.
From 2006 to 2022, the population, industrial added value, and urban green area of Nanchang City grew by about 2 million, CNY 220 billion, and 8500 hectares, respectively, with a multi-year average population growth rate, increase rate of industrial added value, and increase rate of urban green area of about 2.32%, 14.06%, and 5.35%. Affected by water resources and climate conditions, the crops in Nanchang are extremely vulnerable to drought, and the crop area affected by drought was 33.6 thousand hectares in 2011. Consequently, the city relies heavily on water resources to support its social and economic development. Escalating water demands, a predominant reliance on surface water supply, and recurrent seasonal droughts have collectively contributed to water resource shortages in Nanchang, despite its abundance of water resources [5].

3.2. System Dynamics Model

3.2.1. Causal Loop Diagram

In order to explore the feedback relationship between the social economy and the water resources system, the system dynamics model is divided into a “Water resources supply and demand balance” subsystem and an “Emergency groundwater supply” subsystem. The water resource supply and demand balance subsystem mainly includes the calculation and simulation of water demand and water supply. The emergency groundwater supply subsystem plays a role in the case of insufficient surface water supply and seasonal water shortage and uses groundwater for emergency water supply to ensure water resource security.
Water demand and water supply are the boundary elements of socio-economic and water resources systems, respectively, and a causal loop diagram (CLD) is established by combining socio-economic and water resources systems through the contradiction between water demand and water supply. Figure 3 shows the CLD developed based on the system problem, where each arrow represents a causal relationship, with “+” representing an increase (decrease) in one variable as the other increases (decreases) and “−” representing a decrease (increase) in one variable as the other increases (decreases). The feedback relationship between socio-economic water demand and the water supply system is realized with the supply–demand ratio of water resources, which is defined as the ratio of total water supply to total water demand, and its magnitude will affect industrial added value, total population, and the number of green areas.

3.2.2. Stock and Flow Diagram

Based on the causal cycle diagram, the flow diagram of the socio-economic water resources integrated system was developed, which mainly included the water resources supply and demand balance subsystems and the emergency groundwater supply subsystem (Figure 4).
The “Water Resources Supply and Demand Balance” subsystem serves the purpose of computing both the total socio-economic water demand and the overall water supply. The total water demand encompasses various components, namely, industrial water demand, domestic water demand, agricultural water demand, and ecological water demand. On the other hand, the total water supply is sourced from surface water, groundwater, and other available water sources. It is worth noting that, in the current conditions of Nanchang City, groundwater and other water sources provide relatively stable water supplies. However, during dry months, when the available amount of surface water falls short of the surface water demand, the water resources supply–demand ratio drops below 1. In such instances, water resources become a limiting factor that hampers economic and social development. Conversely, the “Emergency Groundwater Supply” subsystem primarily focuses on the analysis of emergency water provisioning in cases of seasonal water resource scarcity. Its fundamental purpose is to address water shortages during these periods by implementing emergency groundwater extraction. This intervention is aimed at alleviating water crises among residents. Subsequent to emergency groundwater extraction, residents are no longer constrained by water resource limitations in their water usage.

3.2.3. Data Sources and Primary Formulas

The data required by the model are primarily sourced from various authoritative references, including the Water Resources Bulletin of Nanchang City, the Water Resources Bulletin of Jiangxi Province, the Statistical Bulletin of National Economic and Social Development of Nanchang City, the Outline of the 13th Five-Year Plan for National Economic and Social Development of Nanchang City, the Prediction of Groundwater Resources and Environmental Geological Problems of Nanchang City in 2000, the investigation and evaluation of urban environmental geological problems of Nanchang City, Jiangxi Province Price Report, etc. These data can be categorized into two categories, namely, social and economic data, encompassing key variables such as total population, industrial added value, green area, effective irrigation area of farmlands, and water quotas for various industries. Water resources data mainly include surface water, groundwater resources, and water resources development and utilization.
The spatial boundary of the model is the administrative boundary of Nanchang City; the time boundary is from 2006 to 2030, in which the identification validation period is from 2006 to 2018; and the prediction period is from 2019 to 2030. The base forecast year is 2018, and the time step is 1 month.

3.3. SWAT Model

3.3.1. Surface Hydrology

The terrain of Nanchang is relatively gentle, and Ganjiang River, Fuhe River, and Xiushui River run through the city, which is not a complete natural basin, so it was necessary to use DEM data to determine the basin boundary. In accordance with the DEM data for Nanchang City and its vicinity, the watershed was extracted using spatial analysis with ArcGIS 10.2 software. The upstream watershed of Nanchang City was simulated using water intake data. Precipitation is the main driving force of the surface hydrological system; the upper boundary of the model is atmospheric precipitation and the lower boundary is the internal boundary of the system where surface water and groundwater meet. The lateral boundary mainly encompasses three types: upstream inflow section, watershed section, and downstream outflow section. The simulation time of the SWAT model is consistent with that of the system dynamics model. The data and sources collected are shown in Table 1.

3.3.2. Model Discretization and Hydrologic Response Units

The average surface elevation of Nanchang City is about 40 m, with little difference in topographic elevation except for Meiling Town, a newly built area in the northwest (Figure 5a). Based on DEM data, the method of extracting the river network water system of the basin and sub-watershed division using the DEM-based method is more effective in areas with large elevation differences, such as mountains and hills. This method uses the principle of steepest slope and minimum catchment area threshold to analyze DEM, extracts the water system according to the valley line, and takes the watershed as the boundary of the sub-watershed. However, the braided river system of Nanchang City is developed, so the river system directly extracted using the above method is quite different from the actual one, and it is difficult for the quantity and accuracy of the data required for the artificial division of the watershed to meet the requirements. Consequently, in this study, a method involving generalized interruption was used to transform the intertwined and looped river network into a simplified branched river network, better aligning it with the real-world river system. The resulting river network for the study area after the generalized interruption is depicted in Figure 5b. Utilizing the DEM data, which boasts a resolution of 30 × 30 m from the geospatial data cloud, in conjunction with the modified river network, the locations of sub-basin intakes, sub-basin outtakes, total basin exits, and reservoirs were determined. The outcomes of the watershed and water system division are presented in Figure 5c, revealing a total of 49 distinct sub-basins that were successfully delineated.
Based on the collected data on land use type, soil and slope subdivision, land use type, soil data and slope map were tailored and reclassified in ArcGIS to provide basic data for hydrological response unit (HRUs) analysis. Figure 5d shows the land use types after clipping and reclassification, which are mainly cultivated land (45.38%), forest land (17.59%), pastureland (2.13%), water area (28.40%), residential area (3.05%), residential area (low density) (2.16%), and public land (1.29%). There are a total of seven land use types within the study area. As illustrated in Figure 5e, the distribution map of soil types was generated after clipping and reclassification, resulting in a total of 18 distinct soil types. These primarily include saturated alluvial soil (0.45%), calcareous alluvial soil (0.28%), saturated gleysols (0.08%), calcic gleysols (1.30%), simple low activity strong acid soil (3.55%), low humic activity strong acid soil (39.72%), simple high activity strong acid soil (2.26%), and ferric high activity strong acid soil (28.08%), anthropogenic accumulated soil (0.67%), bright rubric soil (0.55%), ferric aluminum rubric soil (0.14%), simple and highly active luvic soil (0.27%), bleached low active luvic soil (0.25%), saturated planosol (0.04%), calcareous loose lithologic soil (0.15%), unsaturated loose lithologic soil (1.74%), built-up area (0.24%), and water bodies (20.23%). Figure 5f shows the spatial distribution map of slope. The overall terrain in the study area is gentle. Slopes of less than 10% account for 91.15% of the total area, while slopes of more than 10% account for only 8.85%.
The hydrological response unit (HRU) is a basic calculation unit in the SWAT model. Each calculation unit corresponds to a unique combination of land use type, soil type, and slope type, and each hydrological response unit has its own unique physical and chemical parameters. In order to simplify the calculation, the proportions of land use type, soil type, and slope type are less than 2%, 2%, and 10%, respectively, and the remaining sections comprise other HRUs combinations. In total, 617 HRUs can be identified.

3.3.3. Basic Database Construction

The basic database mainly includes the soil database, the land use type database, and the meteorological database. The basic database in the SWAT model is based on the data from the United States. Because the land use type attributes of China are similar to those of the United States, the native land use type database is not newly built. The soil data in this study were obtained from the World Coordinated Soil Database (HWSD), where the regional soil data of China were the results of the second national soil survey provided by Nanjing Soil. Since the soil data was converted to the FAO-90 soil classification system, which is consistent with the soil particle size classification standard in the SWAT model, no particle size conversion was needed. Some of the soil physical and chemical parameters were obtained from the HWSD database, and the remaining parameters were calculated using SPAW 6.02.75 (Soil Plant Air Water) software.

3.4. MODFLOW Model

3.4.1. Geology and Hydrogeology

Pre-Sinian, Upper Cretaceous, Paleogene, and Quaternary strata, as well as Jinning and Alpine magmatic rocks, are exposed in and around Nanchang City. The main outcrop strata are scattered in the Middle Pleistocene, Upper Pleistocene, Holocene, and Lower Pleistocene of the quaternary. According to the type of water-bearing medium, the groundwater in the study area can be divided into three categories: loose rock pore water, red-layer dissolved fissure water, and bedrock fissure water. The Quaternary aquifer is composed of an upper Middle Pleistocene sand gravel aquifer, Upper Pleistocene sand gravel aquifer, and Holocene sand gravel aquifer. The three aquifers form a unified aquifer and are widely distributed in the alluvial plain areas of the Ganjiang River and Fuhe River due to the small elevation difference between the top and bottom and the close hydraulic relationship. The water richness of aquifers is different due to varied lithology conditions. The water inflow per well is 1016~4916 m3/d in the vast areas along and to the east of the Ganjiang River. The north, middle, and south branches of the Ganjiang River are very rich in water, and the water inflow per well is 5486–9776 m3/d. The intervalley and residual slope deposits in the west of Ganjiang River are weak in water richness, and the water inflow per well is 100~1000 m3/d. Red fissure aquifers and bedrock fissure aquifers have poor water richness, so they are not suitable for large-scale groundwater emergency exploitation.
Affected by geological conditions and topography, the characteristics of groundwater recharge, runoff, and discharge are also different in mountainous and plain areas. In the western Meiling mountain area, the bedrock is exposed, and rock cracks have developed, which become the main channels of groundwater runoff. The groundwater is buried deeply, and the water level changes greatly. This area is dominated by atmospheric precipitation recharge and the vertical and lateral drainage of groundwater. The groundwater level in the Gan and Fu alluvial plain area is shallow. The source of groundwater aquifer recharge is the vertical recharge through atmospheric precipitation, which is mainly discharged by rivers, artificial mining, evaporation, and lateral runoff.

3.4.2. Conceptual Model

The range of the subsurface flow model was determined by taking the range of the surface hydrologic model and the hydrogeological conditions into consideration (Figure 6a). In order to establish a 3D numerical model of groundwater flow to simulate groundwater movement in the study area, it was necessary to accurately describe the aquifer structure first. With the analysis of the hydrogeological map and drilling data in the study area, it can be seen that the pore water aquifer of the quaternary loose rock is the main storage space and the main exploitation layer of groundwater in the area. The thickness of the aquifer is about 5~30 m, and the thickness of the aquifer gradually increases as it moves from upstream to the Poyang Lake waterfront zone. Therefore, the model was generalized into a perched aquifer layer. In accordance with the borehole data (Figure 6a), the hydrogeological profile model (Figure 6b), and the three-dimensional hydrogeological structure model (Figure 6c) of the study area were established. Different boundary conditions have different hydrogeological significance. The upper boundary of the submersible aquifer is the internal boundary of the system at the interface of surface water and groundwater, the lower boundary is the zero-flux boundary, the lateral boundary is the zero-flow boundary at the watershed, and the flow boundary is generalized at the watershed inlet and outlet (Figure 6d). Rivers are the main drainage boundary of groundwater in Nanchang City, and groundwater can also be recharged in the flood season. Most of the riverbeds are located on or exposed to the gravel layer, and surface water and groundwater have close hydraulic relations. Rivers are generalized as river boundaries. Poyang Lake is located downstream of the study area, and there is water exchange with groundwater, so it is generalized as a universal water head boundary (Figure 6d).
The main recharge items of groundwater include precipitation infiltration recharge and lateral runoff recharge. The precipitation infiltration amount can be determined in accordance with the precipitation data and infiltration coefficient of Nanchang Station, and the lateral runoff recharge can be calculated from the hydraulic characteristics at the boundary according to the principle of Darcy’s law. The artificial recharge mainly comes from agricultural irrigation. The main drainage factors of groundwater are evaporation, artificial mining, and lateral runoff discharge. The level of evaporation can be calculated using the evaporation intensity in the study area, the data of artificial mining can be derived from the statistical data of the water resources bulletin, and the lateral runoff excretion amount can be calculated using Darcy’s law. The calculation results of water balance are shown in Table 2.
According to the hydrogeological map of the study area, the permeability coefficient of the main aquifer was determined to be divided (Figure 6e). The permeability coefficient ranged from 0.10 to 120 m/d. The aquifer is a heterogeneous isotropic submersible aquifer with no difference in the spatial direction of water supply, set at 0.2. Figure 6f shows the groundwater flow field in the study area during the 2018 flood period, which was taken as the initial flow field of the forecast period. It can be seen from the figure that the overall flow direction of the groundwater in the study area is from southwest to northeast and discharged downstream to Poyang Lake. The simulation period was from January 2006 to December 2018, with one stress period every month and a total of 156 stress periods. The prediction period was from January 2019 to December 2030, with 144 stress periods.

3.4.3. Numerical Model and Software

In accordance with the hydrogeological structure of Nanchang City, the groundwater flow system can be generalized into a layer of a submersible aquifer, which mainly receives precipitation infiltration, lateral runoff, and artificial recharge and is discharged to rivers and lakes through evaporation and artificial mining. Therefore, a two-dimensional heterogeneous and isotropic numerical simulation model was established using GMS 10.4.5 software to simulate changes in the groundwater flow field and its impact on the surrounding environment.
To ensure the model’s accuracy, GMS software was used to discretize the model grid into 400 m × 400 m cells. This resulted in a total of 303 columns in the east–west direction and 423 rows in the north–south direction, yielding a sum of 41,313 active cells. The RCH package is used to model the locally distributed recharge of groundwater systems, the EVT package models the effect of plant transpiration and direct evaporation on removing water from saturated groundwater states, and the WEL package is designed to model features, such as wells, which take water from or recharge water to aquifers at a constant rate during periods of pressure, and the RIV package models the flow effects between surface water characteristics and groundwater systems. The GHB package is used to model flow in either an inflow or outflow unit.

3.5. Calibration and Validation

To ensure the accuracy of the model, the validation of the coupling process is essential. In this study, the SD model underwent verification utilizing statistical data spanning from 2006 to 2018, encompassing the key socio-economic indicators of Nanchang City. These indicators include the total population, industrial added value, green area, and total water supply. Figure 7 shows that the simulation results for the primary variables within the model closely align with the actual observations. The relative error typically remains within the 10% range, with the majority of variables exhibiting a relative error within the 5% threshold. The simulation outcomes pertaining to the water resource supply and demand ratio are displayed in Figure 8. Over the simulation period, seasonal imbalances in water resource supply and demand occurred in three distinct years, specifically in 2007 (April and September), 2008 (April and September), and 2009 (April). Actual data corroborate the presence of spring or autumn drought events in Nanchang during 2007, 2009, and 2011 to varying degrees. Importantly, the simulated water resource supply and demand situation closely aligns with the observed conditions, demonstrating a strong fitting effect.
Due to the lack of relevant surface runoff data, the hydrological similarity theory and data collection method were used to determine the main parameters of the SWAT model comprehensively. The simulated average value of river–groundwater exchange from 2013 to 2018 simulated with the coupled model is 1.002 billion m3/year, with groundwater mainly discharged to rivers and rivers recharging groundwater in local areas or local periods. According to the statistical data from the Nanchang Water Resources Data Bulletin, from 2013 to 2018, the average exchange volume of surface water and groundwater in Nanchang City was 1.155 billion m3/year, the simulated value was close to the actual value, and the simulation effect was good.
The PEST module in GMS software was used to optimize the groundwater level monitoring data in 2017, and the flow field simulation results in the study area were used for parameter inversion. The permeability coefficient (K) of aquifers in each zone was determined in accordance with the actual hydrogeological conditions. The error in the simulated water head and the actual monitored water head after parameter calibration is shown in Figure 9a. The error between the simulated water level and the actual water level at monitoring wells is generally within 1 m except at some points. Figure 9b shows the fitting between the simulated groundwater flow field and the actual groundwater level monitoring well, which once again proves that the fitting of the model is good.

4. Results and Discussion

4.1. Scenario Development

Residents’ lives, agricultural production, industrial development, and the ecosystem all depend on water resources. Therefore, social and economic development is the main driving force leading to the increase in water demand. Moreover, with the continuous increase in water consumption, the contradiction between the supply and demand of water resources will become more and more prominent. In order to solve the shortage of water resources and realize the sustainable development of water resources, this paper comprehensively considers the simulation results of Nanchang City from 2006 to 2018, analyzes the water consumption quota of various industries and the speed of social and economic development, and sets up three scenarios.
In scenario A (status quo), it is assumed that the variables have a moderate development trend at the 2018 level. In scenario B (developing), based on scenario A, more attention is paid to economic development, and the growth rate of industrial added value is increased from 0.007/month to 0.01/month. In scenario C (water-saving), based on scenario A, the industrial water consumption quota, ecological water consumption quota, and agricultural water consumption quota are reduced by 25%, 20%, and 20%, respectively, by 2030. In this paper, the historical reconstruction method was used to define the climate scenario, and the meteorological data from 2007 to 2018 were used to replace the simulation for the forecast period from 2019 to 2030.

4.2. Analysis of Water Resource Supply and Demand Balance

The basis of the analysis of water resource supply and demand balance is the calculation of social and economic water demand and water resource supply. In the different simulation scenarios, the total water demand shows an increasing trend with economic and social development, and the overall relationship of water demand is as follows water-saving < status quo < developing scenario. By the year 2030, in the current economic and social development scenario, total water demand is projected to reach 4.306 billion m3/year. In the developing scenario, where there is a pronounced focus on industrial growth, the industrial added value is anticipated to surge by 44.03% compared with the status quo scenario, resulting in a total water demand of 5.091 billion m3/year. This represents an 18.22% increase relative to the present state. Conversely, the water-saving scenario, characterized by the implementation of water conservation measures, is projected to entail a total water demand of 3.512 billion m3/year by 2030. This signifies a relatively modest increase of 7.92% compared with the total water demand of 3.254 billion m3/year recorded in 2018. It is worth noting that the water-saving scenario, while necessitating less water demand, effectively mitigates the inherent contradiction between social and economic development and water resource availability. Additionally, this scenario yields a 1.50% increase in industrial added value compared with the status quo scenario.
With the development of the economy and society, the demand for water resources continues to increase. When the demand for water resources is greater than the available water resources, an imbalance between the supply and demand of water resources appears. The results of water resource supply and demand balance in each simulation scenario are shown in Figure 10a. The developing scenario has the largest water shortage degree, followed by the status quo scenario, and the water-saving scenario has the least. It is anticipated that the shortage of water resources will be the most serious in 2030, and the minimum water resources supply and demand ratio of the status quo scenario, the developing scenario, and the water-saving scenario are 0.68, 0.52, and 0.77, respectively. In the status quo scenario, the imbalance of water supply and demand is 11 years, the average number of water shortage months is 3.7 months, and the maximum number of water shortage months is 7 months. In the developing scenario, the imbalance of water supply and demand is 12 years, and the average number of months and the maximum number of months in a year are 4.8 months. In the water-saving scenario, the imbalance of water supply and demand is 9 years, the average number of months in a water shortage year is 3.1 months, and the maximum number of months in a water shortage year is 5 months (Figure 10b). If appropriate water resources management measures are not taken, the water resources shortage problem will continue to worsen.

4.3. Comprehensive Evaluation of Emergency Groundwater Supply

As an important water supply source in Nanchang City, groundwater is closely related to the development of the urban economy and society. Based on the collection of relevant data on Nanchang’s groundwater emergency water source and previous research results, the geological and hydrogeological conditions of Nanchang were comprehensively and systematically analyzed, and three emergency water sources were identified, namely, the Youkou water source north of Nanchang, the Xiebu water source southeast of Nanchang, and the Taohua water source west of Nanchang. The average thicknesses of aquifers in the Youkou, Xiebu, and Taohua water sources are 29.4 m, 25.4 m, and 18.7 m, respectively. In accordance with the water source areas, 78, 78, and 39 emergency mining wells were set up, respectively, and were spaced 400 m apart. According to the analysis results of the above coupling model, it can be seen that there is an imbalance in water resource supply and demand in different economic and social development scenarios. Therefore, in order to meet the water needs of residents living under seasonal drought conditions, an emergency groundwater water supply is required. See Figure 11 for the specific water supply. The maximum daily emergency groundwater water supply demand for the developing, status quo, and water-saving scenarios is 726,300, 487,300, and 273,300 m3/d, respectively.
Emergency groundwater extraction is mainly used to meet the water demand of residents under drought conditions. Therefore, after the need for the emergency groundwater water supply is ended, a lack of water resources will not restrict the development of the population and the population size will expand. According to the simulation results, the total population at the end of 2030 under the condition of adequate residential water supply is 6.12 million, and the total population in the status quo, developing, and water-saving scenarios will increase by 24,000, 49,400, and 11,200, respectively, accounting for 0.39%, 0.81%, and 0.18%, respectively, of the total population at the end of 2030. In summary, emergency groundwater mining will have a positive impact on population growth.
Under the supply conditions of the groundwater emergency water source, the level of exploitation of the emergency water source in the MODFLOW model is obtained from the calculation results of the coupled model. The reduction in groundwater level depth for different water sources in different scenarios is shown in Figure 12.
On the whole, the relationship of the central groundwater level decline ratio of water source is as follows developing > status quo > water-saving scenario. In the status quo scenario, the maximum water level depth of the Yukou, Xiebu, and Taohua water sources accounts for 63%, 90%, and 86% of the aquifer thickness, respectively. The groundwater level depth ratio of the Yukou water source is basically less than 1/3, and only reaches 1/2 but does not exceed 2/3 in 2029 and 2030. The proportion of groundwater level decline in the Xiebu water source is slightly larger than that in the Youkou water source, but it is still less than 1/3 most of the time. The proportion of groundwater depth in the Taohua water source is the largest and exceeds 1/2 almost half the time. Therefore, the groundwater level of the Yukou water source is the least affected by the emergency water supply in the status quo scenario, so it is the most suitable for this purpose. In the developing scenario, the groundwater level depths of the Youkou, Xiebu, and Taohua water sources account for 92%, 91%, and 88% of the aquifer thickness, respectively. Due to the increasing demand for the groundwater emergency water supply in the developing scenario, the proportion of groundwater drawdown will increase rapidly after 2026, and the groundwater drawdown of each aquifer is close to the aquifer thickness, and thus, there is a risk of aquifer drying. In the water-saving scenario, the groundwater level drop depths in the Youkou, Xiebu, and Taohua water sources account for 22%, 30%, and 78% of the aquifer thickness, respectively. The ratio of subsurface water level decline in the Youkou water source and the Xiebu water source is more than 1/3 of aquifer thickness, and the maximum is 0.24 and 0.32, respectively. For the Taohua water source, the rate of groundwater level depth reduction is basically less than 1/2, and the maximum is 0.83. Therefore, in the water-saving scenario, both the Youkou water source and the Xiebu water source can function as emergency water supplies as they are less affected by this condition.

5. Conclusions

In this paper, Nanchang City serves as an illustrative case study. Drawing from the interconnected relationship between socio-economic factors, surface water, and groundwater, we proposed a conceptual model that couples socio-economic aspects with surface water and groundwater dynamics. We developed an SD-SWAT-MODFLOW social hydrology simulation system, building upon prior research. This system enables real-time and spatially explicit dynamic coupling between socio-economic factors and water resources. Using this approach, we assessed water resource supply and demand in various socio-economic development scenarios, presented a groundwater emergency water supply plan, analyzed the capability of existing groundwater emergency water sources, and obtained the following key insights and conclusions.
(1)
The SD and SWAT-MODFLOW coupling simulation programs were used to construct the centralized socio-economic model and the distributed hydrological model. Based on the SWAT-MODFLOW model, the modules for calculating socio-economic water demand, groundwater emergency supply, and groundwater and lake exchange capacity were added, and the SD-SWAT-MODFLOW coupling calculation program was written using the Fortran language. The dynamic coupling simulation of the socio-economic, surface water, and groundwater subsystems in time and space was thus realized. The model identification test results show that the simulation results of water resources supply and demand, surface water and groundwater exchange capacity, and groundwater level are well-fitted to the actual situation. The coupled model can simulate the spatio-temporal evolution of the socio-economic, surface water, and groundwater coupling system.
(2)
The total water demand in 2023 only increased by 7.92% compared with 2018 in the water-saving scenario, and at the same time, part of the contradiction between the social economy and water resources can be alleviated. Different degrees of imbalance in water resources supply and demand exist in different socio-economic development scenarios. The minimum water resource supply and demand ratio is 0.68, 0.52, and 0.77, respectively, in the status quo, developing, and water-saving scenarios. The water resource shortage is most serious in 2030, and the maximum emergency groundwater supply demand is 487,300, 726,300, and 273,300 m3/day, respectively. The emergency groundwater water supply alleviates the restrictive effect of water resources on population growth. In the status quo, developing, and water-saving scenarios, the total population will increase by 24,000, 49,400, and 11,200 in the next 12 years, respectively. Therefore, emergency water supply has a positive impact on the social economy. In order to cope with the problem of seasonal water shortages in Nanchang City and ensure residents’ water safety, it is an effective way to use groundwater emergency water sources.
(3)
According to the simulation results of the SD-SWAT-MODFLOW model proposed in this study, it can be seen that in the water-saving scenario, the Youkou and Xiebu water sources are able to meet the water demand as emergency water sources, and in the status quo scenario, the Youkou water sources are the most suitable as emergency water sources. In the developing scenario, using each water source as an emergency water source alone may have an irreversible impact on the aquifer. The method of using multiple water sources at the same time should be considered, as this could meet the water demand and ensure the sustainable utilization of groundwater resources.

Author Contributions

Z.D., J.Z., and H.C. conceived and designed the research; Q.M. contributed materials and analysis tools; Z.D., Q.M., and J.Z. wrote the paper and analyzed the data; Z.D. and M.C. established the model; Z.N. and G.Z. drew related maps and reviewed and edited the paper; X.J. provided software and conducted project management; Q.F., Q.M., and H.C. funded the research and expenses. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Orderly Cascade Utilization of Hot Dry Rock and Its Technical Support on National Grid (B7280722E072), the Open Fund of Hebei Cangzhou Groundwater and Land Subsidence National Observation (No. CGLOS-2022-02), and the Project for Monitoring and Assessing Geological Safety Risks in National Megacities and Urban Agglomerations (DD20221732).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the memory space for database.

Acknowledgments

We would like to extend special thanks to the editor and reviewers for insightful advice and comments on this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The flowchart of SD-SWAT-MODFLOW coupling model.
Figure 1. The flowchart of SD-SWAT-MODFLOW coupling model.
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Figure 2. A schema of the location of Nanchang and the main rivers and lakes in Nanchang City, China. (a) The map of China and distribution of major rivers. (b) The map of Jiangxi province and distribution of major rivers. (c) The main rivers and lakes in Nanchang City.
Figure 2. A schema of the location of Nanchang and the main rivers and lakes in Nanchang City, China. (a) The map of China and distribution of major rivers. (b) The map of Jiangxi province and distribution of major rivers. (c) The main rivers and lakes in Nanchang City.
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Figure 3. Causal loop diagram.
Figure 3. Causal loop diagram.
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Figure 4. Socio-economic and water resources SD model of Nanchang City.
Figure 4. Socio-economic and water resources SD model of Nanchang City.
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Figure 5. The watershed division and basic database construction of the SWAT model. (a) Digital elevation model of the basin. (b) River distribution and watershed boundaries in the study area. (c) Results of watershed division. (d) The distribution map of land use type. (e) The distribution map of soil type. (f) Topographic slope zoning map.
Figure 5. The watershed division and basic database construction of the SWAT model. (a) Digital elevation model of the basin. (b) River distribution and watershed boundaries in the study area. (c) Results of watershed division. (d) The distribution map of land use type. (e) The distribution map of soil type. (f) Topographic slope zoning map.
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Figure 6. Conceptual model and numerical model of the MODFLOW model. (a) The model boundary of the MODFLOW model. (b) Hydrogeological profile model. (c) Three-dimensional hydrogeological structure model. (d) Boundary condition and production well. (e) Partition map of permeability coefficient. (f) Groundwater flow field.
Figure 6. Conceptual model and numerical model of the MODFLOW model. (a) The model boundary of the MODFLOW model. (b) Hydrogeological profile model. (c) Three-dimensional hydrogeological structure model. (d) Boundary condition and production well. (e) Partition map of permeability coefficient. (f) Groundwater flow field.
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Figure 7. The validation results for major socio-economic variables. (a) Total people simulation results. (b) Industrial added value simulation results. (c) Urban green area simulation results. (d) Total water supply simulation results.
Figure 7. The validation results for major socio-economic variables. (a) Total people simulation results. (b) Industrial added value simulation results. (c) Urban green area simulation results. (d) Total water supply simulation results.
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Figure 8. The supply–demand ratio of water resources simulation results.
Figure 8. The supply–demand ratio of water resources simulation results.
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Figure 9. The supply–demand ratio of water resources simulation results. (a) Groundwater table error between the observed value and the simulated value. (b) Fitting results of water table monitoring well and simulated water table.
Figure 9. The supply–demand ratio of water resources simulation results. (a) Groundwater table error between the observed value and the simulated value. (b) Fitting results of water table monitoring well and simulated water table.
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Figure 10. Analysis of water resource supply and demand balance results. (a) Forecast results of the supply–demand ratio of water resources. (b) The number of months of water shortage in different scenarios.
Figure 10. Analysis of water resource supply and demand balance results. (a) Forecast results of the supply–demand ratio of water resources. (b) The number of months of water shortage in different scenarios.
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Figure 11. Groundwater emergency water supply demand in different scenarios. (a) Status quo scenario, (b) Developing scenario, and (c) Water-saving scenario.
Figure 11. Groundwater emergency water supply demand in different scenarios. (a) Status quo scenario, (b) Developing scenario, and (c) Water-saving scenario.
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Figure 12. Water level drawdown in emergency water sources in different scenarios. (a) The drawdown of Youkou water source in scenario A, (b) The drawdown of Xiebu water source in scenario A, (c) The drawdown of Taohua water source in scenario A, (d) The drawdown of Youkou water source in scenario B, (e) The drawdown of Xiebu water source in scenario B, (f) The drawdown of Taohua water source in scenario B, (g) The drawdown of Youkou water source in scenario C, (h) The drawdown of Xiebu water source in scenario C, and (i) The drawdown of Taohua water source in scenario C.
Figure 12. Water level drawdown in emergency water sources in different scenarios. (a) The drawdown of Youkou water source in scenario A, (b) The drawdown of Xiebu water source in scenario A, (c) The drawdown of Taohua water source in scenario A, (d) The drawdown of Youkou water source in scenario B, (e) The drawdown of Xiebu water source in scenario B, (f) The drawdown of Taohua water source in scenario B, (g) The drawdown of Youkou water source in scenario C, (h) The drawdown of Xiebu water source in scenario C, and (i) The drawdown of Taohua water source in scenario C.
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Table 1. The main data collected and related descriptions.
Table 1. The main data collected and related descriptions.
DataFormat and DescriptionSource
DEMGrid (resolution: 30 × 30 m)Geospatial data cloud
Land cover dataThe year 2015 (resolution: 1000 × 1000 m)Resource and Environmental Science and Data Center, Chinese Academy of Sciences
Soil dataHWSD data (resolution: 1000 × 1000 m)Food and Agriculture Organization of the United Nations (FAO)
Meteorological dataDaily observation data of Nanchang meteorological station (1955–2018)China Meteorological Data Network
RiverDrawing based on satellite imageDraw
Satellite imageGridGoogle Earth
Table 2. The calculation results of water balance.
Table 2. The calculation results of water balance.
Balance IndexWater Volume (104 m3/d)Percentage (%)
RechargePrecipitation infiltration341.8896.47
Lateral inflow12.523.53
Total354.40100
DischargeEvaporation146.2741.27
Artificial mining16.414.63
Lateral outflow18.605.25
River173.1048.85
Total354.38100
Water balance difference (104 m3/d)0.02
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MDPI and ACS Style

Deng, Z.; Ma, Q.; Zhang, J.; Feng, Q.; Niu, Z.; Zhu, G.; Jin, X.; Chen, M.; Chen, H. A New Socio-Hydrology System Based on System Dynamics and a SWAT-MODFLOW Coupling Model for Solving Water Resource Management in Nanchang City, China. Sustainability 2023, 15, 16079. https://0-doi-org.brum.beds.ac.uk/10.3390/su152216079

AMA Style

Deng Z, Ma Q, Zhang J, Feng Q, Niu Z, Zhu G, Jin X, Chen M, Chen H. A New Socio-Hydrology System Based on System Dynamics and a SWAT-MODFLOW Coupling Model for Solving Water Resource Management in Nanchang City, China. Sustainability. 2023; 15(22):16079. https://0-doi-org.brum.beds.ac.uk/10.3390/su152216079

Chicago/Turabian Style

Deng, Zhihui, Qingshan Ma, Jia Zhang, Qingda Feng, Zhaoxuan Niu, Guilin Zhu, Xianpeng Jin, Meijing Chen, and Honghan Chen. 2023. "A New Socio-Hydrology System Based on System Dynamics and a SWAT-MODFLOW Coupling Model for Solving Water Resource Management in Nanchang City, China" Sustainability 15, no. 22: 16079. https://0-doi-org.brum.beds.ac.uk/10.3390/su152216079

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