Open AccessArticle

Scenario Simulation and Scheme Optimization of Water Ecological Security in Hexi Corridor Based on System Dynamics Model

Dongyuan Sun

Shiwei Wang

Zuirong Niu

Yanqiang Cui

Xingfan Wang

Lanzhen Wu

Yali Ma

and

Heping Shu

College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China

Author to whom correspondence should be addressed.

Sustainability 2025, 17(5), 1833; https://doi.org/10.3390/su17051833

Submission received: 14 January 2025 / Revised: 15 February 2025 / Accepted: 18 February 2025 / Published: 21 February 2025

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Abstract

Water ecological security is intricately connected to regional economic development and human survival, exerting a profound influence on regional sustainability. Water ecological security in the study area is a matter of urgency. In this study, the socio-economic, ecological, and water resource data of five cities in the west of the river from 2006 to 2021 are used to construct a dynamic model of the regional water ecological security system and simulate the trend of the regional water ecological security from 2022 to 2035, and the results indicate the following: (1) From 2006 to 2021, Hexi Corridor’s economy exhibited a significant upward trend, while its total population experienced a marked decline. Indicators for the ecological environment system showed notable improvement, whereas those for the water resource system demonstrated a significant downward trend. (2) Spatially, the mean values of system indices in the southeast and northwest regions were higher than those in the central region. (3) Between 2022 and 2035, projections reveal that the total GDP, industrial added value, average sewage discharge, urban green space, and water consumption for ecological purposes will all trend upward. Concurrently, the total population, total water supply, and total water demand are expected to exhibit a continuous decline. (4) The comparative comprehensive scores of the scenario models are as follows: EPS (2.18) > RSS (1.57) > CDS (1.15) > EDS (1.08). This analysis provides valuable insights into the dynamics of water ecological security in the Hexi Corridor and offers critical guidance for sustainable regional development planning.

Keywords:

water resources–social economy–ecological environment; Hexi Corridor; system dynamics model; coupling and coordinated development

1. Introduction

The Hexi Corridor, an oasis nestled within the arid northwest, has fostered vibrant civilizations for millennia. However, escalating climate change and anthropogenic activities have intensified water scarcity and ecological degradation, severely jeopardizing regional water ecological security and impeding sustainable socio-economic development.

According to statistics, the average annual precipitation in the Hexi Corridor is less than 200 mm, while the evaporation is as high as more than 2000 mm, the overexploitation of groundwater has led to a continuous decline in the water level, and some areas have seen a serious groundwater funnel phenomenon, and the utilization rate of water resources has exceeded 70%, far exceeding the internationally recognized 40% warning line. According to 2024 data, only 60% of some areas in the Hexi Corridor have good water quality, far below the national average, and the wetland area in the Heihe River Basin has decreased by about 30% in the past 20 years, and ecological functions have declined significantly.

Water ecological security is defined as the capacity of a water ecosystem to maintain its structural and functional stability within a specific spatiotemporal framework, continuously providing essential ecosystem services to humanity [1,2]. For the Hexi Corridor, water ecological security is crucial not only for the stability of the regional ecological barrier but also for the successful implementation of the national ecological security strategy and the “Belt and Road” initiative. In recent years, researchers have extensively investigated water ecological security issues in the Hexi Corridor, focusing on “the evolution and driving mechanisms of water resources; refs. [3,4] vulnerability assessment and risk early warning of water ecosystems; and the construction and regulation strategies of water ecological security patterns [5]”. Nevertheless, the existing research has limitations in addressing the increasingly complex water ecological environment, such as an insufficient understanding of the multi-scale, multi-process coupling mechanisms of water ecological security, and a lack of in-depth exploration of the coordinated development pathways between water ecological security and socio-economic aspects. In conclusion, studying water ecological security in the Hexi Corridor can deepen the understanding of water ecological security mechanisms in arid regions, enrich the theoretical framework of water ecological security, provide scientific support for regional sustainable development, and contribute Chinese wisdom and solutions to global ecological security research in arid regions.

From the standpoint of theoretical framework construction, several evaluation models have been proposed, such as the Pressure–State–Response (PSR) model [6,7], the Driving–Pressure–State–Influence–Response (DPSIR) model [8,9,10], the SENCE framework [11,12], and the Environment–Economic–Society (EES) model [13]. Common evaluation methods include fuzzy comprehensive evaluation [14,15,16,17,18], principal component analysis (PCA) [19], the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method [20,21,22], and the analytic hierarchy process (AHP) [23,24,25], among others. While the existing studies have significantly enriched and improved the research framework for water ecological security, offering clarity on causal relationships among subsystems within the water ecosystem, they predominantly concentrate on constructing index evaluation systems and formulating safeguard strategies.

System dynamics, initially employed by international researchers, has emerged as a methodological tool for multi-system simulation and water resource management studies within this domain. For instance, Ahmad et al. [26] evaluated the effectiveness of urban water protection policies based on the system dynamics theory, while Xu et al. [27] and Davies et al. [28] explored the sustainability of regional water resources by developing system dynamics models. In China, the system dynamics model has also significantly advanced water resource research. Qin Huanhuan et al. [29] employed this model to forecast future trends in ecological footprints and the ecological carrying capacity of water resources in the Zhangye Basin. Similarly, Zhang Yumian et al. [30] constructed a dynamic model of the water resource supply and demand system in Inner Mongolia, analyzing regional water resource scenarios from the perspectives of resource environment and socio-economic factors. Chen Wenjuan et al. [31] integrated the system dynamics method with the analytic hierarchy process to devise a sustainability-focused water resource model for Tianjin. Kang Yan et al. [32] introduced a coupling model combining logarithmic average Divisia decomposition and system dynamics to identify the key driving factors influencing water consumption changes in irrigation areas within the framework of water demand mechanisms. Finally, Liu Xia et al. [33] utilized system dynamics to predict changes in water resource carrying capacity in the Tarim River Basin. This body of research exemplifies the versatility and effectiveness of system dynamics in examining complex water resource systems, particularly in assessing sustainability, simulating scenarios, and analyzing causal relationships in diverse ecological and socio-economic contexts. To summarize, the system dynamics model offers a straightforward and effective way to illustrate the essential relationships among various factors within complex systems. It efficiently organizes and unveils the interactions and feedback mechanisms both between and within these factors in nonlinear, intricate systems. Utilizing this approach, this paper develops a System Dynamics Model for regional water ecological security in the Hexi Corridor, drawing from relevant data on the social economy, water resources, and ecological environment. By employing the system dynamics method, it investigates the causal feedback relationships among the region’s social economy, water resources, and ecological environment, alongside simulating the future trends of these systems under different scenarios. Additionally, the entropy weight fuzzy evaluation method is applied to calculate the comprehensive scores for each scenario, thereby identifying the optimal strategy to promote and sustain water ecological security in the Hexi Corridor.

2. Materials and Methods

2.1. Overview of the Research Area

The Hexi Corridor lies in the northwest of Gansu Province, stretching from Wushao Mountain in the east to Yumenguan in the west. It is bounded by the southern and northern mountain ranges, with an east–west expanse of approximately 1000 km and a north–south width ranging between 100 and 200 km. Geographically, it is situated between 37°17′ to 42°48′ north latitude and 92°12′ to 103°48′ east longitude, featuring a typical temperate continental climate. The region’s terrain slopes from high elevations in the south to lower elevations in the north, with mountains flanking both sides and a corridor plain running through the center [34]. Covering an area of about 276,000 square kilometers, the Hexi Corridor accounts for over 60% of Gansu Province’s total land area [35]. Administratively, the region encompasses five prefecture-level cities: Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan, an overview map of the study area is shown in Figure 1.

2.2. Research Methods

2.2.1. Linear Tendency Estimation Method

Linear regression uses the established linear regression equation of one variable to reflect its linear change trend and tendency rate, which can directly reflect the change trend of the index in a certain period of time.

A linear trend model is usually expressed as follows:

Y_{t} = a + b \cdot t + ϵ_{t}

It has the advantage of being simple, intuitive, and easy to understand and implement. Disadvantages: it is only applicable to linear trends and does not work well for nonlinear trends.

2.2.2. Five-Year Moving Average

The five-year moving average method is a statistical method used to smooth time-series data by calculating the average of five consecutive years of data to eliminate short-term fluctuations and thus provide a clearer picture of long-term trends. This method is particularly suitable for data with cyclical or seasonal fluctuations. Its advantages are that it effectively smooths out short-term fluctuations and highlights long-term trends, is simple to calculate, and is easy to understand and implement. The disadvantage is that a moving average cannot be calculated for both ends of the data, which may mask important short-term changes.

For the time series data

Y_{t}

the five-year moving average is calculated as follows:

{M A}_{t} = \frac{Y_{t - 2} + Y_{t - 1} + Y_{t} + Y_{t + 1} + Y_{t + 2}}{5}

2.2.3. System Dynamics Model

System dynamics, originally termed Industrial Dynamics, was introduced by Professor Forrester of MIT in 1956. It is a methodology and mathematical modeling technique designed to construct, comprehend, and discuss complex problems. This approach is primarily applied to the comprehensive analysis of large-scale systems—characterized by complexity, multiple levels, multiple sectors, and nonlinear interactions—at both macro and micro levels. At its core, system dynamics involves constructing a causal loop diagram to represent problem–cause–effect relationships and verifying the reliability of the model. This process facilitates the analysis and study of the issue at hand while identifying the system’s development trends.

The variables in the system dynamics model are articulated by state equations, which can be expressed in the following form:

\frac{d X_{i} (t)}{d t} = f (X_{i}, R_{i}, A_{i}, C_{i})

where

X_{i} (t)

is the value of state variable

i

in the system dynamics model at moment

t

;

i

is the state variable ordinal number;

f

is the vector value function;

R_{i}

is the rate variable, which is the rate of change in the state variable;

A_{i}

is the auxiliary variable; and

C_{i}

is the parameter.

Different kinds of foundational metrics make up the subsystems of the system dynamics model, including constants, rate variables, auxiliary variables, and state variables.

The main equations include the following types:

(1): Rate equations: is the equation used to show how the rate variable affects the equation of state.
(2): Equation of state: The continuous cumulative amount of change over time, formulated as follows:

$L t = L_{0} + \int_{0}^{1} (\sum R_{i n} (t) - \sum R_{o u t} (t)) d t$

where $L t$ refers to the value of a state variable at a given time, $L_{0}$ is the initial value of a state variable, and $R_{i n}$ and $R_{o u t}$ denote the input and output rates of the state variable, respectively.
(3): Auxiliary equation: The main role of this equation is to introduce the basic data needed in the system.
(4): Table Functions: For variables with nonlinear relationships in the system, table functions are introduced for nonlinear function representation.

The specific steps of system dynamics modeling are shown in Figure 2.

2.2.4. Scenario Setting

Taking into account the objectives of the 14th Five-Year Plan, this study established four scenarios: Current Development Scenario (CDS), Economic Development Scenario (EDS), ecological protection scenario (EPS), and Resource Saving Scenario (RSS), using 2006 as the reference year.

Six factors, including GDP growth rate, population growth rate, sewage treatment rate, growth rate of urban green space, growth rate of irrigated area, and growth rate of industrial value added were selected, and different values were set for the six parameters under each scenario according to the historical statistics of the Hexi Corridor, as seen in Table 1.

(1): Current Development Scenario (CDS): This hypothetical situation was created using the Hexi Corridor developmental trajectory. Specific parameters, including total GDP, total population, total water supply, total water demand, and sewage discharge, were kept constant to form a natural growth trend.
(2): Economic Development Scenario (EDS): This scenario puts economic efficiency first and does not take into account environmental protection and energy conservation of the resources of the Hexi Corridor. In this scenario, the GDP growth rate increases by 0.8%, the population growth rate increases by 0.3%, and the sewage treatment rate, the growth rate of urban green space, the growth rate of irrigated area, and the growth rate of industrial value added increased by 0.1%, 0.4%, 0.3%, and 1.2%, respectively.
(3): Ecological Protection Scenario (EPS): The focus of the program is on the protection of the ecological environment. Under the program, the growth rate of urban green space and sewage treatment rates were increased by 1%, respectively, and the growth rates of industrial value added decreased by 0.5%, respectively.
(4): Resource Saving Scenario (RSS): Unlike EDS, the main objective of RSS is to conserve resources. In this scenario, population growth and GDP growth are severely constrained, with GDP and population growth rates decreasing by 0.1% and 0.3%, respectively, and sewage treatment rates increasing by 0.2%.

2.2.5. Entropy Weight Comprehensive Evaluation Method

(1): Constructing an assessment matrix.

The evaluation matrix

X

is constructed with

m

evaluation objects and

n

evaluation indicators:

X = [\begin{matrix} x_{11} & x_{12} & \dots & x_{1 n} \\ x_{21} & x_{22} & \dots & x_{2 n} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ x_{m 1} & x_{m 2} & \dots & x_{m n} \end{matrix}]

where

x_{i j}

denotes the value of the

i

th object on the

j

th indicator.

(2): Each index datum standardization.

Forward indicator:

x_{i j}^{'} = \frac{x_{i j} - m i n (x_{j})}{m a x (x_{j}) - m i n (x_{j})}

Negative indicator:

x_{i j}^{'} = \frac{m a x (x_{j}) - x_{i j}}{m a x (x_{j}) - m i n (x_{j})} i = 1, 2, 3, \dots, n; j = 1, 2, 3, \dots, m

(3): Calculate the entropy weight.

The

i

th entropy

H_{i}

can be defined as

H_{i} = - k \sum f_{i j} \ln f_{i j}

In the formula,

f_{i j} = \frac{r_{i j}}{\sum_{j = 1}^{n} r_{i j}}

and

k = \frac{1}{\ln n}

(let us say

f_{i j}

is equal to 0 when

f_{i j} \ln f_{i j}

is equal to 0)

w_{i} = \frac{{1 - H}_{i}}{m - \sum_{i = 1}^{m} H_{i}}

w_{i}

is the weight of each indicator and

m

is the number of evaluation indicators.

(4): Calculate the comprehensive assessment value.

E_{Z} = \sum_{i = 1}^{m} (E_{p i} \times w_{i})

In the formula,

E_{Z}

is the comprehensive evaluation value,

E_{p i}

is the average evaluation value of the

i

index,

m

is the number of evaluation indicators, and

w_{i}

is the weight of the i index.

2.3. Data Sources

This paper mainly selects the socio-economic indicators, water resource indicators, and ecological environment indicators that affect water ecological security in the Hexi Corridor, and the time series is from 2006 to 2021. The indicators of the social and economic system include the following: total population, total GDP, GDP of the primary industry, GDP of the secondary industry, GDP of the tertiary industry, industrial added value, effective irrigation area, and total stock of livestock; ecological environment system indicators include the following: ecological environment water consumption, sewage discharge, sewage treatment rate, and recycling water consumption. Water resource system indicators include the following: total water supply, total water demand, industrial water consumption, agricultural water consumption, domestic water consumption, and water consumption of CNY 10,000 industrial added value; all index data were obtained from the relevant statistical data of Hydrology Bureau of Gansu Province and the Bulletin of Gansu Water Resources.

3. Results

3.1. Interannual Variation Characteristics of Each System Index in Hexi Corridor

3.1.1. Socio-Economic System

In panel a of Figure 3, the total population of the Hexi Corridor region exhibited a decreasing trend from 2006 to 2021, with a reduction of 125,100 individuals (p < 0.05). As illustrated in panels b through h of Figure 3, the total GDP, primary industry GDP, secondary industry GDP, tertiary industry GDP, industrial added value, effective irrigation area, and grain yield in the Hexi Corridor region all demonstrated an increasing trend. Despite the economic upswing in the Hexi Corridor, the total population has been consistently declining. This phenomenon can be attributed to the region’s economic growth being primarily driven by resource exploitation and technological advancements rather than labor-intensive industries. Consequently, economic expansion has not significantly stimulated population growth. Furthermore, the imbalanced economic development within the Hexi Corridor has exacerbated population outflow. These economic and demographic shifts have profound implications for water ecological security. The escalating demand for water resources due to economic development has led to severe groundwater over-extraction. For instance, the groundwater level in the middle reaches of the Heihe River basin has declined by over 5 m, with some areas even forming drawdown cones. The intensive utilization of water resources has intensified the conversion frequency between groundwater and surface water, resulting in increased water mineralization. For example, the groundwater mineralization in the Minqin irrigation area has increased by more than 3.0 g/L, severely impacting agricultural production and ecological equilibrium.

3.1.2. Ecological Environment System

In Panels A to D of Figure 4, the water consumption for the ecological environment, sewage discharge, and renewable water usage increased over 16 years by 481 million m³, 0.54 million tons, and 0.67 million m³, respectively, exhibiting a significant upward trend (p < 0.05). In contrast, urban greening water consumption demonstrated a decreasing trend. The 5-year sliding average curve reveals that ecological environment water consumption and renewable water usage display a fluctuating pattern, initially decreasing before increasing. Sewage discharge follows an opposite pattern, initially increasing and then decreasing. Meanwhile, urban greening water consumption exhibits a more complex trend, decreasing at first, then increasing, and ultimately declining again.

3.1.3. Water Resource System

As illustrated in Figure 5, the water resource indices in the Hexi Corridor exhibited a downward trend from 2006 to 2021. During this period, the total water supply, total water demand, agricultural water consumption, industrial water consumption, domestic water consumption, surface water resources, groundwater resources, and total water resources decreased by 645 million m³, 764 million m³, 941 million m³, 130 million m³, 64 million m³, 1.409 billion m³, 82 million m³, and 1.304 billion m³, respectively. This downward trend was statistically significant (p < 0.05).

The 5-year sliding average curve further reveals the following patterns: the total water supply, total water demand, and industrial water consumption initially increased before declining. In contrast, domestic water consumption and total water resources followed a pattern of first decreasing and then increasing. Agricultural water consumption and surface water resources showed a sequence of reduction, subsequent increase, and then another decline. Conversely, groundwater resources displayed an initial increase, followed by a decrease, and then another period of growth.

3.2. Spatial Variation Characteristics of Each System Index in Hexi Corridor

Based on Figure 6, the average population of cities in the Hexi Corridor region from 2006 to 2021 is ranked as follows: Wuwei City, Zhangye City, Jiuquan City, Jinchang City, and Jiayuguan City. The data indicate a declining trend in population from southeast to northwest. The observed population distribution is a consequence of the interplay between natural conditions, water resource availability, historical development, economic activities, and policy factors. The southeastern region’s favorable natural conditions and advanced economic development have attracted a higher population density, whereas the northwestern region, constrained by water scarcity and a fragile ecological environment, exhibits a more sparse population distribution. Jiuquan City leads in terms of the average total GDP, secondary industry output, tertiary industry output, industrial added value, and effective irrigation area. Meanwhile, Wuwei City records the highest average GDP for the primary industry, and Zhangye City achieves the largest average grain output. Overall, the economic spatial characteristics of the Hexi Corridor reveal that the northwest and southeast regions significantly outperform the central region in key indicators.

As illustrated in Figure 7, the ranking of average water consumption for ecological purposes is as follows: Jiuquan City > Wuwei City > Zhangye City > Jiayuguan City > Jinchang City. Similarly, the ranking of average sewage discharge is as follows: Jiayuguan City > Zhangye City > Jiuquan City > Wuwei City > Jinchang City. Zhangye City exhibits the highest renewable water consumption, whereas Wuwei City leads in urban environmental water usage. Overall, the spatial variation in the ecological environment across the Hexi Corridor demonstrates a distinct pattern, with conditions in the northwest and southeast regions outperforming those in the central area. This is attributed to the ecological fragility of the central region, which is constrained by limited water resources, unsustainable land use practices, and inadequate policy support.

As illustrated in Figure 8, the average performance of water supply, total water demand, and agricultural water consumption across the cities in the Hexi Corridor region follows this order: Jiuquan City > Zhangye City > Wuwei City > Jinchang City > Jiayuguan City. The spatial trend shows an initial decrease followed by an increase from southeast to northwest. Among these cities, Jiuquan City has the highest average industrial water consumption, while Wuwei City ranks highest in both average domestic water consumption and average water consumption per CNY 10,000 of industrial added value.

3.3. Construction and Testing of the Evolution Model of Water Resources, Social Economy, and Ecological Environment in Hexi Corridor

3.3.1. Model Building

Based on the references and considering the specific conditions of the Hexi Corridor, this paper identifies eight state variables: total GDP, total population, industrial added value, actual irrigated area of farmland, surface water supply, groundwater supply, sewage treatment return amount and urban green space area. It also incorporates rate variables such as GDP growth rate, population growth rate, urban green space growth rate, and industrial added value growth rate, along with several auxiliary variables and table functions. These factors are interconnected through their correlations, forming the water ecological security model of the Hexi Corridor, as illustrated in Figure 9.

3.3.2. Model Checking

This study utilized the Vensim7.3.5 software to model the water ecology of the Hexi Corridor. Verifying the model’s accuracy is critical to ensuring its objectivity and scientific reliability.

The dynamic model of the water ecological security system in the Hexi Corridor was analyzed using the historical test method. Key indicators, including total population, total GDP, industrial added value, actual irrigated farmland area, surface water supply, groundwater supply, and sewage reuse, were selected for testing and evaluation.

The results indicate that based on a comparison of historical data from 2006 to 2021, the simulation errors for the seven indicators selected in this study remain below 10%, successfully passing the validation test. Consequently, the system dynamics model is deemed to exhibit strong simulation performance and accuracy. Table 2 provides a detailed overview of the simulation results.

3.4. Simulation and Prediction of Coordinated Development Trend of Water Resources, Social Economy, and Ecological Environment Coupling in Hexi Corridor

3.4.1. Scenario Simulation

Based on the parameters outlined in Section 2.2.4, simulate the variations in the total population, total GDP, urban green space, industrial added value, ecological water consumption, sewage discharge, total water supply, and total water demand in the Hexi Corridor from 2022 to 2035. The results of the simulation are presented in Figure 10.

As illustrated in Figure 10a,d,h, the period from 2022 to 2035 demonstrates a consistent upward trend in the total GDP, industrial added value, and sewage discharge. Notably, the most significant increase is observed under the EDS.

As illustrated in Figure 10b,e,f, the period from 2022 to 2035 demonstrates a consistent downward trend in the total population, total water supply, and total water demand. Among these, the RSS exhibits the most significant decline in both population and water demand, while the EDS shows the smallest decrease. Conversely, for the total water supply, the largest decline is observed in the EDS, with the RSS presenting the most modest reduction.

As illustrated in Figure 10c,g, between 2022 and 2035, both the urban green space area and the water consumption for ecological environments exhibit a consistent upward trend. Among the scenarios analyzed, the EPS demonstrates the most significant increase, while the RSS experiences the smallest growth.

In conclusion, in the CDS and EDS, although the regional economy is growing, the contradiction between the supply and demand of water resources is increasing and the ecological environment is deteriorating. However, in the EPS and RSS, while the regional economy is growing, the ecological environment and water resources are also developing in a better direction.

3.4.2. Scenario Optimization

Based on the simulation data from the water ecological model of the Hexi Corridor spanning 2022 to 2035, comprehensive scenario scores were determined using the entropy-weighted fuzzy comprehensive evaluation method.

The results of the standardization of the scenario indicators are shown in Table 3, Table 4, Table 5 and Table 6, respectively.

The results of the calculation of the weights of the indicators for each scenario are shown in Table 7. The results of the calculation of the composite score for each scenario are shown in Table 8.

4. Discussion

Analyzing the dynamic interplay among the social economy, water resources, and ecological environment in the Hexi Corridor, this paper examines the temporal and spatial changes in each system index. By integrating the feedback relationships between these interconnected systems, a simulation model for water ecological security in the Hexi Corridor is constructed. In comparison to earlier studies [29,30,31], this research addresses the limitations of the conventional PSR model in simulating water ecological security. It offers a fresh perspective and a valuable reference point for enhancing regional water ecological security. The simulation results suggest that the Hexi Corridor’s water resources and ecological environment will face significant pressure from 2022 to 2035. With ongoing economic development, challenges such as diminishing regional water resources and increasing sewage discharge are anticipated, potentially exacerbating the deterioration of the region’s water ecological security. To mitigate the subjectivity associated with previous approaches that relied on the analytic hierarchy process and expert scoring methods, this study adopts the entropy weight fuzzy evaluation method to determine the weights of evaluation indices, ensuring a more objective and reliable analysis.

The entropy-weighted fuzzy evaluation method was employed to calculate the comprehensive scores for each scenario. The findings revealed that the environmental protection scenario and the open-source and throttling scenarios received higher scores, suggesting that water ecological security in the Hexi Corridor would develop more favorably under these two scenarios. Based on the current state of the region’s socio-economic framework, water resources, and ecological environment, this paper simulates and analyzes the potential challenges to water ecological security in the future. Due to the short time series of research data and the few indicators reflecting the ecological status, the research results have certain limitations.

5. Conclusions

(1): From 2006 to 2021, the total population indicator within the socio-economic system of the Hexi Corridor exhibited a notable decline, whereas other socio-economic indicators demonstrated a marked upward trend. Similarly, the ecosystem indicators showed a significant increase over this period. In contrast, the system indicators related to water resources displayed a pronounced downward trend.
(2): From 2006 to 2021, the average population of the Hexi Corridor showed a gradual decrease from southeast to northwest. Economically, the northwest and southeast regions outperformed the central region. Jiuquan City and Wuwei City recorded the highest average levels of ecological water consumption, whereas Jiayuguan City and Zhangye City reported the highest average levels of sewage discharge. On the whole, the ecological environment in the northwest and southeast regions proved to be better than that of the central area. Furthermore, Jiuquan City and Zhangye City exhibited the highest average water supply and demand. Notably, the average water supply and demand initially declined from southeast to northwest but later experienced an upward trend.
(3): The simulation results indicate that between 2022 and 2035, there will be an upward trend in the total GDP, industrial added value, average sewage discharge, urban green space, and water consumption related to the ecological environment. In contrast, the total population, overall water supply, and total water demand are projected to exhibit a consistent downward trend.
(4): The comprehensive scoring results indicated the following ranking: EPS (2.18) > RSS (1.57) > CDS (1.15) > EDS (1.08), with the ecological protection scenario achieving the highest overall score. Based on the principle that a higher score represents a more favorable scenario, it was concluded that the ecological protection scenario is the most suitable choice for the future development of the Hexi Corridor.

Author Contributions

Collecting and organizing data, Y.C. and L.W.; analyzing data and drawing, Y.M. and X.W.; building and testing models, Z.N. and S.W.; manuscript translation and writing, D.S. and H.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research is supported by the Scientific Research Start-up Funds for Openly recruited Doctors of Gansu Agricultural University [Grant No. GAU-KYQD-2019-27], the Key R&D Project for Provincial Ecological Civilization Construction of Gansu Province (Grant No.24YFFF002), the Gansu Province Water Conservancy Science Experimental Research and Technology Promotion Project (23GSLK084, 23GSLK087, and 23GSLK088), and the Discipline Team Construction Project of GAU (GAU-XKTD-2022-08). We also thank the anonymous reviewers for their suggestions and comments, which helped in improving this manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Overview of the study areas.

Figure 2. System dynamics modeling process.

Figure 3. Interannual change trend of socio-economic system indicators.

Figure 4. Interannual trends of ecosystem indicators.

Figure 5. Interannual variation trend of water resource system indicators.

Figure 6. Spatial change in socio-economic system indicators.

Figure 7. Spatial change in ecosystem indicators.

Figure 8. Spatial change in water resource system index.

Figure 9. Dynamic model of water ecological security system in Hexi Corridor.

Figure 10. Simulation results of each scenario index.

Table 1. Set the parameters of each scenario.

Scenario	GDP Growth Rate	Population Growth Rate	Sewage Treatment Rate	Growth Rate of Urban Green Space	Growth Rate of Irrigated Area	Growth Rate of Industrial Value Added
CDS (Current Development Scenario)	Remain unchanged	Remain unchanged	Remain unchanged	Remain unchanged	Remain unchanged	Remain unchanged
EDS (Economic Development Scenario)	Increase by 0.8%	Increase by 0.3%	Increase by 0.1%	Increase by 0.4%	Increase by 3%	Increase by 1.2%
EPS (Ecological Protection Scenario)	Remain unchanged	Remain unchanged	Increase by 1%	Increase by 1%	Remain unchanged	decreases by 0.5%
RSS (Resource Saving Scenario)	decreases by 0.3%	decreases by 0.1%	Increase by 0.2%	decreases by 0.1%	Remain unchanged	decreases by 0.1%

Table 2. Relative errors of model history test (unit: %).

Year	Total GDP	Total Population	Industrial Added Value	Actual Irrigated Area of Farmland	Surface Water Supply	Groundwater Supply	Sewage Treatment Reuse
2006	0%	0%	0%	0%	0%	0%	0%
2007	4.5%	5.0%	0.2%	4.8%	5.6%	0.4%	3.2%
2008	3.1%	6.1%	0.5%	9.0%	6.6%	9.2%	5.1%
2009	4.0%	6.6%	0.1%	7.0%	7.5%	6.0%	5.1%
2010	9.0%	6.0%	2.5%	7.7%	7.6%	2.3%	8.6%
2011	3.7%	8.8%	4.9%	9.3%	7.0%	5.3%	8.4%
2012	7.7%	8.2%	3.0%	6.5%	9.0%	5.0%	8.8%
2013	8.6%	7.3%	2.2%	9.4%	8.3%	7.5%	6.1%
2014	7.5%	8.7%	1.9%	9.0%	7.2%	4.8%	0.1%
2015	8.4%	9.5%	8.0%	8.2%	9.7%	8.4%	5.8%
2016	8.0%	4.9%	8.9%	8.4%	7.1%	8.2%	3.6%
2017	7.0%	6.0%	8.3%	9.3%	9.3%	5.1%	5.3%
2018	5.3%	8.6%	4.6%	6.1%	8.4%	5.4%	9.4%
2019	6.2%	8.8%	5.5%	9.7%	9.0%	3.5%	8.3%
2020	9.2%	8.0%	6.0%	7.1%	4.3%	7.7%	5.8%
2021	7.6%	8.7%	2.2%	7.7%	7.6%	3.1%	8.5%
2022	5.2%	4.4%	2.2%	3.6%	9.0%	3.7%	2.6%

Table 3. Current Development Scenario standardization of indicators.

Year	Total GDP	Total Population	Industrial Added Value	Total Water Supply	Total Water Demand	Ecosystem Water Use	Sewage Treatment Reuse	Urban Green Space
2022	0	1	0	1	1	0	0	0
2023	0.0404	0.9217	0.0409	0.9197	0.9212	0.0609	0.0623	0.0459
2024	0.0848	0.8436	0.0858	0.8401	0.8427	0.1241	0.1267	0.0956
2025	0.1338	0.7658	0.1352	0.7609	0.7646	0.1897	0.1933	0.1494
2026	0.1878	0.6881	0.1895	0.6824	0.6867	0.2579	0.2622	0.2075
2027	0.2472	0.6108	0.2493	0.6044	0.6092	0.3286	0.3335	0.2705
2028	0.3127	0.5336	0.3150	0.5269	0.5320	0.4020	0.4073	0.3385
2029	0.3848	0.4567	0.3873	0.4500	0.4551	0.4781	0.4836	0.4122
2030	0.4642	0.3800	0.4668	0.3737	0.3785	0.5572	0.5625	0.4919
2031	0.5517	0.3035	0.5542	0.2979	0.3022	0.6393	0.6442	0.5782
2032	0.6481	0.2273	0.6503	0.2226	0.2262	0.7245	0.7287	0.6715
2033	0.7543	0.1513	0.7560	0.1479	0.1505	0.8129	0.8161	0.7725
2034	0.8712	0.0755	0.8722	0.0737	0.0751	0.9047	0.9065	0.8818
2035	1	0	1	0	0	1	1	1

Table 4. Economic Development Scenario standardization of indicators.

Year	Total GDP	Total Population	Industrial Added Value	Total Water Supply	Total Water Demand	Ecosystem Water Use	Sewage Treatment Reuse	Urban Green Space
2022	0	1	0	1	1	0	1	0
2023	0.0404	0.9217	0.0409	0.9197	0.9212	0.0609	0.9377	0.0459
2024	0.0848	0.8436	0.0858	0.8401	0.8427	0.1241	0.8733	0.0956
2025	0.1338	0.7658	0.1352	0.7609	0.7646	0.1897	0.8067	0.1494
2026	0.1878	0.6881	0.1895	0.6824	0.6867	0.2579	0.7378	0.2075
2027	0.2472	0.6108	0.2493	0.6044	0.6092	0.3286	0.6665	0.2705
2028	0.3127	0.5336	0.3150	0.5269	0.5320	0.4020	0.5927	0.3385
2029	0.3848	0.4567	0.3873	0.4500	0.4551	0.4781	0.5164	0.4122
2030	0.4642	0.3800	0.4668	0.3737	0.3785	0.5572	0.4375	0.4919
2031	0.5517	0.3035	0.5542	0.2979	0.3022	0.6393	0.3558	0.5782
2032	0.6481	0.2273	0.6503	0.2226	0.2262	0.7245	0.2713	0.6715
2033	0.7543	0.1513	0.7560	0.1479	0.1505	0.8129	0.1839	0.7725
2034	0.8712	0.0755	0.8722	0.0737	0.0751	0.9047	0.0935	0.8818
2035	1	0	1	0	0	1	0	1

Table 5. Ecological protection scenario standardization of indicators.

Year	Total GDP	Total Population	Industrial Added Value	Total Water Supply	Total Water Demand	Ecosystem Water Use	Sewage Treatment Reuse	Urban Green Space
2022	0	1	0	1	1	0	1	0
2023	0.0431	0.9207	0.0457	0.9210	0.9197	0.0543	0.9287	0.0441
2024	0.0902	0.8419	0.0952	0.8423	0.8400	0.1117	0.8565	0.0922
2025	0.1416	0.7634	0.1488	0.7640	0.7608	0.1723	0.7834	0.1444
2026	0.1977	0.6853	0.2068	0.6860	0.6822	0.2363	0.7093	0.2013
2027	0.2589	0.6077	0.2696	0.6084	0.6042	0.3039	0.6344	0.2631
2028	0.3258	0.5304	0.3376	0.5312	0.5268	0.3752	0.5585	0.3304
2029	0.3987	0.4534	0.4112	0.4543	0.4499	0.4506	0.4816	0.4037
2030	0.4783	0.3769	0.4909	0.3777	0.3735	0.5301	0.4038	0.4833
2031	0.5652	0.3008	0.5772	0.3015	0.2977	0.6141	0.3251	0.5700
2032	0.6601	0.2250	0.6707	0.2256	0.2225	0.7029	0.2453	0.6643
2033	0.7636	0.1496	0.7719	0.1501	0.1478	0.7966	0.1646	0.7669
2034	0.8766	0.0746	0.8814	0.0749	0.0736	0.8955	0.0828	0.8785
2035	1	0	1	0	0	1	0	1

Table 6. Resource Saving Scenario standardization of indicators.

Year	Total GDP	Total Population	Industrial Added Value	Total Water Supply	Total Water Demand	Ecosystem Water Use	Sewage Treatment Reuse	Urban Green Space
2022	0	1	0	1	1	0	1	0
2023	0.0440	0.9198	0.0472	0.9216	0.9189	0.0660	0.9317	0.0503
2024	0.0919	0.8401	0.0981	0.8434	0.8385	0.1337	0.8620	0.1040
2025	0.1440	0.7611	0.1530	0.7655	0.7588	0.2031	0.7909	0.1614
2026	0.2008	0.6825	0.2121	0.6879	0.6798	0.2743	0.7185	0.2227
2027	0.2625	0.6045	0.2758	0.6104	0.6016	0.3472	0.6447	0.2881
2028	0.3298	0.5271	0.3444	0.5333	0.5240	0.4219	0.5694	0.3580
2029	0.4030	0.4502	0.4184	0.4564	0.4471	0.4985	0.4927	0.4326
2030	0.4826	0.3738	0.4982	0.3797	0.3709	0.5770	0.4145	0.5123
2031	0.5693	0.2980	0.5841	0.3033	0.2954	0.6575	0.3348	0.5975
2032	0.6637	0.2227	0.6767	0.2271	0.2206	0.7400	0.2535	0.6884
2033	0.7664	0.1480	0.7765	0.1511	0.1464	0.8245	0.1706	0.7855
2034	0.8783	0.0737	0.8841	0.0755	0.0729	0.9112	0.0861	0.8892
2035	1	0	1	0	0	1	0	1

Table 7. Indicator weights for each scenario.

Scenario	CDS	EDS	EPS	RSS
Total GDP	0.15	0.15	0.15	0.15
Total population	0.11	0.11	0.11	0.11
Industrial added value	0.15	0.15	0.11	0.14
Total water supply	0.11	0.11	0.14	0.12
Total water demand	0.11	0.11	0.11	0.14
Ecosystem water use	0.14	0.12	0.13	0.12
Sewage treatment reuse	0.10	0.10	0.11	0.11
Urban green space	0.12	0.14	0.14	0.11

Table 8. Comprehensive score for each scenario.

Scenario	CDS	EDS	EPS	RSS
Mark	1.15	1.08	2.18	1.57
Sort	3	4	1	2

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Sun, D.; Wang, S.; Niu, Z.; Cui, Y.; Wang, X.; Wu, L.; Ma, Y.; Shu, H. Scenario Simulation and Scheme Optimization of Water Ecological Security in Hexi Corridor Based on System Dynamics Model. Sustainability 2025, 17, 1833. https://doi.org/10.3390/su17051833

AMA Style

Sun D, Wang S, Niu Z, Cui Y, Wang X, Wu L, Ma Y, Shu H. Scenario Simulation and Scheme Optimization of Water Ecological Security in Hexi Corridor Based on System Dynamics Model. Sustainability. 2025; 17(5):1833. https://doi.org/10.3390/su17051833

Chicago/Turabian Style

Sun, Dongyuan, Shiwei Wang, Zuirong Niu, Yanqiang Cui, Xingfan Wang, Lanzhen Wu, Yali Ma, and Heping Shu. 2025. "Scenario Simulation and Scheme Optimization of Water Ecological Security in Hexi Corridor Based on System Dynamics Model" Sustainability 17, no. 5: 1833. https://doi.org/10.3390/su17051833

APA Style

Sun, D., Wang, S., Niu, Z., Cui, Y., Wang, X., Wu, L., Ma, Y., & Shu, H. (2025). Scenario Simulation and Scheme Optimization of Water Ecological Security in Hexi Corridor Based on System Dynamics Model. Sustainability, 17(5), 1833. https://doi.org/10.3390/su17051833

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