Study on Evaluation and Dynamic Early Warning of Urban Water Resources Security

Abstract

Water resources security is crucial to the survival and development of human society. A water resources security assessment and dynamic early warning system was constructed. The weights of water resources evaluation indexes were calculated by the entropy weight method, and the water resources security was evaluated with the comprehensive index method. The obstacle degree model was used to identify and analyze the main obstacle factors. The grey model was adopted to predict the future water resources security situation. The empirical study was carried out in Jinan. The results showed that the grade of water resources security in Jinan from 2008 to 2021 showed a gradually increasing trend. The obstacle factors were mainly concentrated in the pressure subsystem, indicating that the contradiction between supply and demand of water resources was the main problem affecting water resources security, which was accorded with the actual situation. The comprehensive index of water resources security from 2022 to 2026 shows a gradually increasing trend on the whole, and the warning situation develops towards a good trend, indicating that remarkable results in comprehensively building a water-saving society and vigorously promoting water pollution control have been achieved. The measures such as optimizing economic structure, improving water use structure, and improving water use efficiency will promote the further development of water resources security in Jinan.

1. Introduction

Climate change and human activities in the global context have a major impact on water resources, and water resources security issues caused by the increasingly prominent contradiction between supply and demand of water resources, aggravated water pollution, water ecological degradation, and water management imbalance have become the focus of global attention [1]. Depletion of natural resources needs quantification and efficiency analysis of the use of resources to improve sustainability [2]. As a country with a large population, the safe and rational use of water resources has always been an important issue in social development and ecological civilization construction. In recent years, with the rapid advancement of industrialization and urbanization, the demand for water resources is growing day by day, and problems such as uneven distribution, pollution, and overexploitation of water resources are becoming increasingly prominent. Especially in Jinan, these problems are particularly serious. Jinan is known as “Spring City”, but its water shortage and water quality problems have become the bottleneck restricting the healthy development of the city’s economy and society.

Water resources security includes water quantity problems caused by water resources shortage and flood disasters, water quality problems caused by water pollution, etc., covering resources, environment, ecology, society, politics, economy, and other aspects [3]. At present, there are numerous studies in the field of water resources security at home and abroad, and many scholars have adopted different research methods in different fields of water resources security. These studies mainly concern urban water use [4], groundwater [5,6,7], alpine water resources [8], and river basins [9]. In terms of research methods, Bagheri et al. [10] used SEEA water accounting framework to assess water security in Rafsanjan Plain, Iran. He et al. [11] used the GA-BP model to evaluate water resources in Anhui Province. Liang et al. [12] employed Bayesian network methods to construct a model for analyzing the factors affecting water resource security in the Qilian Mountain Nature Reserve. Zhang et al. [4] analyzed the security of urban water resources in the Yellow River Basin by constructing an index system and using the entropy weight-matter element model. Gao et al. [13] used the water resources use efficiency evaluation model of projection pursuit and genetic algorithm to evaluate the water resources use efficiency of 31 provincial administrative regions in China. Zhang et al. [14] employed the evaluation model of entropy weight method–analytic hierarchy process to conduct a comprehensive evaluation of the water resources security in the People’s Victory Canal Irrigation District. Li et al. [15] analyzed the impact of urbanization on water resources security in coastal cities via remote sensing and hydrological models, and Wang and Zhao [16] explored the role of water pricing policies in water conservation and security through econometric analysis.

These research methods and results provide a rich theoretical and practical basis for the study of water resources security. However, the state of regional water resources security changes with the different stages, objectives, and conditions of water resources development and utilization. At present, a relatively perfect evaluation system and early warning method have not been put forward for the research on water resources security.

Based on research from domestic and foreign scholars on water resources security evaluation, this paper aims to construct a water resources security evaluation and dynamic early warning system combining the comprehensive index method, obstacle degree model, and grey prediction model. The comprehensive index method is used to evaluate water resources security, the obstacle degree model is used to identify and analyze the main obstacle factors affecting water resources security, and the future water resources situation is predicted with the grey prediction model. The comprehensive evaluation system proposed in this study can not only monitor the situation of water resources security in real time but also predict the trend of future changes, provide timely and effective early warning information for the government and relevant departments, provide scientific decision-making support for water resources management, and provide the reference value for similar cities.

2. Materials and Methods

2.1. Study Area

Jinan is located in the eastern part of the Chinese mainland and the central part of Shandong Province, as shown in Figure 1. It is an important city in the Yellow River Basin, with an administrative area of 10,244 km² and a permanent population of 9,414,700 in 2024. It is an important part of the ecological protection and high-quality development of the Yellow River Basin. In 2019, the proportion of groundwater extraction in Jinan was 8.5%, a decrease of 11.5% from 9.6% in 2015 [17]. In the same year, the per capita water resources reached 1000 m³, a decrease of 9.1% compared to 1100 m³ in 2015 [18]. As a spring city, Jinan is rich in spring water resources, but it also faces problems such as water shortage, water environment pollution, and water ecological degradation. Therefore, it is of great significance to evaluate the situation of water resources security scientifically and predict the trend of its time series.

2.2. Comprehensive Evaluation Index System

Based on the principles of accessibility, operability, and representativeness of evaluation indexes, the DPSIR model [19,20] was used to establish five subsystems of driving, pressure, state, impact, and response. A total of 27 indexes were selected to construct a comprehensive evaluation index system of water resources security, and the weight of each index was calculated by the Equations (1)–(6), as shown in

Table 1. The comprehensive evaluation index system of water resources security and the weight of each index in Jinan.

Target Layer	Criterion Layer	Index Layer	Index Property	Weight
The comprehensive evaluation index system of water resources security in Jinan	Driving	Per capita GDP (D1)	Positive	0.0362
		Per capita disposable income of urban residents (D2)	Positive	0.0391
		Total social fixed asset investment (D3)	Positive	0.0510
		Value added of the tertiary industry as a proportion of GDP (D4)	Positive	0.0363
		Rural Engel’s coefficient (D5)	Negative	0.0330
		Non-farm output as a percentage of GDP (D6)	Positive	0.0333
		Fiscal expenditure as a percentage of GDP (D7)	Negative	0.0188
		GDP growth rate (D8)	Positive	0.0277
		Labor productivity of the whole society (D9)	Positive	0.0388
	Pressure	Water consumption per CNY 10,000 GDP (P10)	Negative	0.0263
		Comprehensive water consumption per capita (P11)	Negative	0.0497
		Proportion of industrial water consumption to total water consumption (P12)	Negative	0.0532
		Industrial water consumption per CNY 10,000 added value (P13)	Negative	0.0286
		Proportion of irrigation water consumption to total water consumption (P14)	Negative	0.0312
		Agricultural water consumption per CNY 10,000 added value (P15)	Negative	0.0241
		Irrigation water use per unit area (P16)	Negative	0.0493
		Daily domestic water consumption per capita (P17)	Negative	0.0617
		Proportion of domestic water consumption to total water consumption (P18)	Negative	0.0160
	State	Annual precipitation (S19)	Positive	0.0358
		Per capita precipitation resources (S20)	Positive	0.0345
		Per mu precipitation resources (S21)	Positive	0.0417
	Impact	Hardened area ratio (I22)	Negative	0.0321
		CNY 100 million GDP wastewater discharge (I23)	Negative	0.0441
		Proportion of ecological water consumption to total water consumption (I24)	Positive	0.0627
	Response	Greening rate of built-up area (R25)	Positive	0.0434
		Annual planted area (R26)	Positive	0.0342
		Sewage treatment rate (R27)	Positive	0.0170

.

2.3. Data Sources

The data were derived from the Jinan Water Resources Bulletin, the Jinan 14th Five-Year Plan for Ecological and Environmental Protection, and the Jinan Statistical Yearbook from 2008 to 2021. Part of the evaluation index data was obtained from the Shandong Water Resources Bulletin.

2.4. Evaluation and Dynamic Early Warning Methods of Water Resources Security

2.4.1. Entropy Weight Method

At present, domestic and foreign researchers have adopted a variety of methods to determine the weight value of the index system, but it can be roughly divided into two categories, namely the subjective weighting method and the objective weighting method. The former mainly gives the weight value through the subjective importance of the researchers to each evaluation index, such as the analytic hierarchy process (AHP method), the Delphi method, the binomial coefficient method, etc. The latter assigns weight value to each evaluation index according to objective information, such as the entropy weight method, multi-objective programming method, principal component analysis method, etc. [21].

The entropy weight method is an objective weighting method that can evaluate multiple indexes and objects comprehensively. Its evaluation results are mainly based on objective data, almost unaffected by subjective factors, and can largely avoid interference by human factors [22]. In this paper, the entropy weight method is chosen to reduce the influence of subjective factors and make the data analysis more objective and accurate. The principle is to weigh the index according to the amount of information reflected by the changes of each index value in the evaluation process [23]; that is, the lower the entropy, the more information of the index and the higher the weight. On the contrary, the higher the entropy, the less information of the index and the lower the weight [24]. The weight of an index is significant, which means that the index is of high importance in the comprehensive evaluation.

A judgment matrix with n samples and m evaluation indexes is constructed, as shown in Equation (1).

$$X={\left(x_{ij}\right)}_{n\times m}$$

(1)

where $x_{\mathit{ij}}$ is the raw data for the index.

The indexes are divided into positive indexes and negative indexes. The former indicates that the higher the index value, the better the impact. Similarly, the latter indicates that the lower the index value, the better the impact. And they are standardized according to Equations (2) and (3).

$$y_{ij}={\displaystyle \frac{x_{ij}-\min \left\{x_{ij}\right\}}{\max \left\{x_{ij}\right\}-\min \left\{x_{ij}\right\}}}$$

(2)

$$y_{ij}={\displaystyle \frac{\max \left\{x_{ij}\right\}-x_{ij}}{\max \left\{x_{ij}\right\}-\min \left\{x_{ij}\right\}}}$$

(3)

where $y_{ij}$ is the normalized value, and $\max \left\{x_{\mathit{ij}}\right\}$ and $\min \left\{x_{\mathit{ij}}\right\}$ are, respectively, the maximum and minimum values in different samples under the same index.

The entropy value $H_{\mathrm{j}}$ of the jth index is shown in Equation (4).

$$H_j=-{\displaystyle \frac{1}{\ln n}}\left({\displaystyle \sum _{i=1}^n{f}_{ij}\ln f_{ij}}\right)$$

(4)

where $fij$ is calculated by Equation (5).

$$f_{ij}={\displaystyle \frac{1+y_{ij}}{{\displaystyle \sum _{i=1}^n\left(1+y_{ij}\right)}}}$$

(5)

The weight $W_j$ of the jth index is determined by Equation (6).

$$W_j={\displaystyle \frac{1-H_j}{{\displaystyle \sum _{j=1}^m\left(1-H_j\right)}}}$$

(6)

2.4.2. Comprehensive Evaluation Method of Water Resources Security

A single index cannot fully reflect the water resources security status of each system and subsystem, so the water resources security index $ri$ is calculated, as shown in Equation (7).

$$r_i={\displaystyle \sum _{j=1}^m\left(y_{ij}\cdot W_j\right)}$$

(7)

According to the literature [25,26,27] and the actual situation of water resources security in Jinan, water resources safety level and alert status are obtained, as shown in

Table 2. The water resources safety level and alert status.

Water Resources Security Index	Safety Level	Alert Status
0 ≤ r < 0.2	Extremely unsafe	Giant alarm
0.2 ≤ r < 0.4	Less safe	Heavy alarm
0.4 ≤ r < 0.6	Critical safe	Medium alarm
0.6 ≤ r < 0.8	Relatively safe	Light alarm
r ≥ 0.8	Very safe	Non-alarm

.

2.4.3. Obstacle Degree Model

In order to secure rational planning and use of water resources, the formulation and coordination of comprehensive management policies were necessary. Therefore, the obstacle degree model [28,29] was introduced to diagnose and analyze each factor, determine the magnitude of the obstacle degree, and sort it. On this basis, the primary and secondary relations of each factor and the degree of hindrance to the improvement of water resources security in Jinan are compared so as to find the main hindrance factors restricting water resources security [30]. The obstacle degree of each individual index can be calculated by Equation (8).

$$h_j={\displaystyle \frac{D_{ij}\cdot W_j}{{\displaystyle \sum _{i=1}^n\left(D_{ij}\cdot W_j\right)}}}\times 100\%$$

(8)

where $D_{ij}$ can be calculated by Equation (9).

$$D_{ij}=1-y_{ij}$$

(9)

In Equations (8) and (9), $h_j$ is the obstacle degree (a larger value indicates a larger obstacle degree) and $D_{ij}$ is the skewness of the index and represents the difference between the indicator and the optimal value.

2.4.4. Dynamic Grey Prediction Method of Water Resources Security

The GM (1,1) model is used for the dynamic grey prediction of urban water resources security. In the grey system theory, the variables needed for modeling are extracted through correlation analysis and other measures, and a dynamic model of differential equations for discrete data is established on the basis of studying the properties of discrete functions, and then the time response function of variables is obtained. The grey modeling requires less information and has higher precision, so it can better reflect the actual situation of the system [31].

If $x^{(0)}\left(j\right)$ represents the original series of urban water resources security and $x^{(1)}\left(i\right)$ indicates a cumulative generated sequence, then Equation (10) can be obtained.

$$x^{(1)}\left(i\right)=\left\{{\displaystyle \sum _{j=1}^i\left.x^{(0)}\left(j\right)\right|i=1,2,\cdots ,n}\right\}$$

(10)

GM (1,1) is a first-order differential equation model established for Equation (10) with the form as Equation (11).

$${\displaystyle \frac{d{x}^{\left(1\right)}}{dt}}+a{x}^{\left(1\right)}=u$$

(11)

where $a$ is the development coefficient and $u$ is the grey action.

The prediction formula is shown in Equation (12).

$${\widehat{x}}^{\left(1\right)}\left(k+1\right)=\left[x^{\left(1\right)}\left(1\right)-{\displaystyle \frac{\widehat{u}}{\widehat{a}}}\right]e^{-\widehat{a}k}+{\displaystyle \frac{\widehat{u}}{\widehat{a}}}$$

(12)

where ${\widehat{x}}^{\left(1\right)}$ is the predicted value of the $x^{\left(1\right)}$ sequence generated by a single sum, and $\widehat{a}$ and $\widehat{u}$ are estimates of the parameters $a$ and $u$ in Formula (11), respectively.

After the model is constructed, the relative error and stage ratio deviation can be analyzed to verify the model effect.

3. Results

3.1. Evaluation and Analysis of Water Resources Security

The water resources security indexes of annual systems and subsystems were calculated by Equation (7). The results of the water resources security index and safety evaluation level of Jinan from 2008 to 2021 are shown in

Table 3. The water resources security index and safety level of Jinan from 2008 to 2021.

Year	Driving Index	Pressure Index	State Index	Impact Index	Response Index	Comprehensive Index	Safety Level
2008	0.071	0.074	0.029	0.032	0.012	0.218	Less safe
2009	0.084	0.086	0.048	0.038	0.036	0.293	Less safe
2010	0.107	0.131	0.054	0.041	0.046	0.379	Less safe
2011	0.103	0.062	0.029	0.054	0.046	0.295	Less safe
2012	0.110	0.105	0.020	0.045	0.059	0.338	Less safe
2013	0.152	0.128	0.051	0.039	0.066	0.437	Critical safe
2014	0.171	0.127	0.000	0.041	0.075	0.414	Critical safe
2015	0.182	0.184	0.021	0.064	0.078	0.529	Critical safe
2016	0.196	0.188	0.044	0.088	0.080	0.597	Critical safe
2017	0.218	0.245	0.013	0.101	0.084	0.661	Relatively safe
2018	0.238	0.249	0.069	0.110	0.050	0.717	Relatively safe
2019	0.216	0.179	0.028	0.100	0.065	0.588	Critical safe
2020	0.218	0.188	0.051	0.079	0.062	0.598	Critical safe
2021	0.259	0.214	0.112	0.096	0.062	0.743	Relatively safe

. According to Table 3, the change trend charts of the comprehensive index and subsystem index of water resources security in Jinan were made, as shown in Figure 2 and Figure 3. The evaluation results are analyzed vertically and horizontally.

3.1.1. Analysis of Longitudinal Evaluation of Water Resources Security

As can be seen in Table 3 and Figure 2, the water resources safety level from 2008 to 2021 showed an increasing state, gradually changing from less safe to relatively safe. From 2008 to 2013, the comprehensive index of water resources security in Jinan gradually increased, ranging from 0.218 to 0.437, and the safety level in 2013 changed from less safe to critical safe. However, the comprehensive index in 2014 dropped from 0.437 in the previous year to 0.414, mainly because the annual precipitation in that year was the lowest in the years 2008 to 2021, resulting in a state index of zero in 2014. The increase in the daily domestic water consumption per capita and the proportion of industrial water consumption to total water consumption led to a 0.001 decrease in the pressure index in 2014. After that, the comprehensive index increased year by year until 2019, when it plummeted from 0.717 to 0.588. In 2020, it rebounded but only increased to 0.598, which was still within the relatively safe range. The main reason for the decline of the comprehensive index in 2019 was the increase in water consumption in all aspects, which led to a significant decline in the driving index and also a small decrease in the state index and impact index due to the decrease in precipitation. Compared with 2019, the comprehensive index in 2020 had increased, but it was still smaller than that in 2018. In 2020, Jinan, as the central city of the Yellow River Basin, shouldered the important mission of “demonstrating” [18], and finally the comprehensive index rose to 0.743 in 2021. Among them, the driving index increased significantly, which was due to the per capita GDP, per capita disposable income of urban residents, the total social fixed asset investment increased significantly, and the rapid development of society.

3.1.2. Horizontal Evaluation and Analysis of Water Resources Security

As can be seen in Table 3 and Figure 3, the driving index basically increased gradually from 2008 to 2021, slightly decreased in 2011 and 2019, and reached the highest value in 2021. The pressure index showed a fluctuating upward state, but it declined after reaching the highest value in recent years in 2018 and then rebounded in 2021. In addition to the zero state index caused by the lowest annual precipitation in recent years in 2014, the state index and impact index also showed a fluctuating and rising tendency as a whole, and the state index reached the highest value relative to previous years by 2021. The response index increased year by year from 2008 to 2017, but it decreased due to the sharp decline in the total afforestation area in 2018 and rebounded due to the increase in the total afforestation area in 2019. The implementation of the national water resources protection policy has achieved remarkable results in Jinan.

3.2. Identification of Obstacle Factors

According to Equations (8) and (9), the obstacle degree of each evaluation index and subsystem of water resources security in Jinan from 2008 to 2021 was calculated so as to distinguish the obstacle factors.

3.2.1. Obstacle Degree of Index Layer

Table 4. The obstacle degrees of all factors of water resources security in Jinan in 2008 and 2021.

Index Sequence	2008		2021		Index Sequence	2008		2021
	Obstacle Factors	Obstacle Degree/%	Obstacle Factors	Obstacle Degree/%		Obstacle Factors	Obstacle Degree/%	Obstacle Factors	Obstacle Degree/%
1	I24	8.014	P17	21.701	15	P13	3.651	D3	0.000
2	D3	6.515	P12	19.987	16	P10	3.366	D9	0.000
3	P16	6.000	R26	12.629	17	P17	3.358	P10	0.000
4	P12	5.896	I22	12.504	18	S19	3.288	P11	0.000
5	I23	5.642	D6	11.745	19	P15	3.083	P14	0.000
6	R25	5.553	D8	7.720	20	S20	2.974	P15	0.000
7	D2	4.998	P18	6.211	21	R26	2.895	P16	0.000
8	D9	4.966	I23	4.288	22	R27	2.171	S19	0.000
9	P11	4.652	P13	1.173	23	D7	0.619	S20	0.000
10	D4	4.637	D7	1.130	24	D6	0.550	S21	0.000
11	D1	4.632	D5	0.734	25	D8	0.000	I24	0.000
12	S21	4.332	D4	0.178	26	I22	0.000	R25	0.000
13	D5	4.222	D1	0.000	27	P18	0.000	R27	0.000
14	P14	3.986	D2	0.000

shows obstacle degrees of all factors of water resources security in Jinan in 2008 and 2021. As shown in Table 4, in 2008 the top seven obstacle factors were I24, D3, P16, P12, I23, R25, and D2. In 2021, the top 7 obstacle factors were P17, P12, R26, I22, D6, D8, and P18. The full names of all obstacle factors are shown in Table 1.

Figure 4 shows the change trend of the obstacle degree for each factor of water resources security in Jinan from 2008 to 2021, and as shown in Figure 4, barriers varied widely between different factors. The obstacle degrees of D1, D2, and D4 could be summarized as a decreasing tendency with each passing year. Those of D5, D7, D9, P14, P16, P17, P18, I23, and R26 generally fluctuated frequently and irregularly, but the variation of range for P14, P16, P17, P18, I23, and R26 was large. Those of D6, D8, P12, and I22 mainly showed an upward trend, with D8 and I22 rising stably. Those of P10, P13, P15, I24, R25, and R27 decreased significantly, and those of P10 and P15 fluctuated slowly, indicating that the impact of these obstacles on water resources security in Jinan became smaller gradually. Those of D3, P11, S19, S20, and S21 fluctuated significantly, basically rising first and then falling. As Table 1 shows, although the weight of P18 was small, the obstacle degree ranking was relatively high in 2021, indicating that although the government had formulated a series of standards to strengthen water supply management, promote rational water use by residents, and save water, the improvement of water resources security had been affected on account of the unbalance between water supply and demand and other problems. Therefore, Jinan should improve the policies related to residential water consumption, improve the utilization rate of water resources, and further promote the conservation of residential water consumption.

3.2.2. Obstacle Degree of Subsystem

Figure 5 shows the change trend of the obstacle degree for each subsystem of water resources security in Jinan from 2008 to 2021. By analyzing Figure 5, it can be concluded that there were obvious differences in the change trend of the obstacle degree for each subsystem from 2008 to 2021. The obstacle degree of the driving subsystem was basically in a downward trend from 2008 to 2021, but its proportion was still large. That of the pressure subsystem mainly fluctuated between 30% and 40%, with an obvious decrease from 2016 to 2017 and an obvious rise from 2017 to 2021. That of the state subsystem rose first and then fell, and its proportion decreased sharply from 2020 to 2021. The proportion of the impact subsystem and the response subsystem was below 20%, and the trend of the impact subsystem was flatter than that of the response subsystem. On the whole, the pressure subsystem played a dominant role. Therefore, the government should increase policy support and supervision efforts to reduce water consumption in industry, agriculture, and residential life.

3.3. Dynamic Early Warning of Water Resources Security

The GM(1,1) model was utilized to forecast the progression trend of water resources security warning in Jinan from 2022 to 2026. To assess the accuracy of the prediction model, the fitting trend between the original value and the predicted value of the comprehensive index of water resources security in Jinan from 2008 to 2021 was compared, as shown in Figure 6. As can be seen in Figure 6, the predicted value of the model is in good agreement with the original value.

According to Equations (10)–(12), the subsystem indexes and comprehensive indexes of water resources security in Jinan from 2022 to 2026 can be obtained. Combined with the security early warning standards in Table 2, the results of alert status are shown in

Table 5. The prediction of water resources security in Jinan from 2022 to 2026.

Year	Driving Index	Pressure Index	State Index	Impact Index	Response Index	Comprehensive Index	Safety Level	Alert Status
2022	0.276	0.249	0.061	0.111	0.075	0.78	Relatively safe	Light alarm
2023	0.291	0.262	0.063	0.117	0.076	0.823	Very safe	Non-alarm
2024	0.307	0.275	0.066	0.123	0.078	0.868	Very safe	Non-alarm
2025	0.323	0.289	0.069	0.13	0.080	0.915	Very safe	Non-alarm
2026	0.339	0.303	0.072	0.136	0.082	0.962	Very safe	Non-alarm

.

As can be seen in Table 5, the alert status in 2022 is light, and the alert statuses from 2023 to 2026 are non-alarm for four consecutive years, indicating that the alert status is developing in a good trend and the water resources system load is decreasing continuously, which is related to the comprehensive construction of a water-saving society in Jinan and the strong promotion of water pollution control. Although the predicted value indicates that the water resources security is in good condition, with the rapid development of the social economy, it is still facing greater pressure, which should not be taken lightly.

4. Discussion

From 2008 to 2021, the water resources safety level of Jinan showed a gradually increasing trend. From 2008 to 2012, it was in a level. From 2013 to 2016, it rose to the range of [0.4, 0.6], reaching the critical safe level. From 2017 to 2021, it fluctuated greatly, still stable at a relatively safe and critical safe level, showing a good development trend, and is expected to reach a very safe level in the next few years. From 2018 to 2021, the indexes of all subsystems of water resources security showed fluctuating rise. The driving index rose after a slight decline in 2019. The indexes of other subsystems fluctuated over more years, and the whole showed an upward trend. From each subsystem index, remarkable results in the implementation of the national water resources protection policy had been achieved in Jinan.

The obstacle factors of water resources security in Jinan from 2008 to 2021 were mainly concentrated in the pressure subsystem, indicating that the contradiction between supply and demand of water resources was the main problem affecting water resources security. The obstacle degrees of all subsystems of water resources security in Jinan were not consistent, and the pressure subsystem has the highest obstacle degree, indicating that the concept of green development should continue to be observed and water consumption in industry, agriculture, and residents lives should continue to be reduced in Jinan.

From 2022 to 2026, the comprehensive index of water resources security in Jinan is between 0.78 and 0.962 and shows a gradually increasing trend on the whole. The change situation is divided into two stages: “light alarm” in 2022 and “non-alarm” from 2023 to 2026. It shows that remarkable results in building a water-saving society in an all-round way and vigorously promoting water pollution control have been achieved in Jinan. In addition, the measures to optimize the economic structure, promote the industrialization of agriculture, improve industrial economic benefits, improve water use structure, and enhance water use efficiency will promote the further development of water resources security in Jinan.

5. Conclusions

In this study, the evaluation index system of water resources security in Jinan, containing the five subsystems of driving, pressure, state, impact, and response, was constructed. The entropy weight method was adopted to assign weights to the indexes. The comprehensive index method was chosen to conduct a comprehensive evaluation of water resources security. The obstacle degree model was used to analyze the obstacle factors restricting water resources security. Then the grey prediction model was used to predict the future water resources security, and the evaluation results were analyzed.

The proportion of ecological water consumption to total water consumption (I24), per capita daily domestic water consumption (P17), proportion of industrial water consumption to total water consumption (P12), and total social fixed asset investment (D3) were the four indexes with higher weights in Jinan water resources security. The obstacle factors of water resources security in Jinan were mainly concentrated in the pressure subsystem. The water resources safety level of Jinan had experienced a change process of less safe-critical safe-relatively safe, which was basically consistent with the water resources security status of Jinan from 2008 to 2021, indicating that the selected method was reasonable and accurate. As can be seen from the forecast results, the alert status in 2022 is “light alarm”, and those from 2023 to 2026 are “non-alarm” for four consecutive years, illustrating that the alert status is developing towards a good trend and the security situation of water resources is gradually improving. The findings can serve as guidelines for sustainable water development in Jinan.

For future developments, the industrial structure should be improved, and water resources should be optimally allocated on the basis of the proportion of water used by various industries in Jinan. Surface water, groundwater, reclaimed water, and other water sources should be integrated to achieve a diversified supply of water resources. In Jinan, the government should actively coordinate the cooperation of various organizations, ensure the sustainable use of water resources in various fields, adhere to the concept of green development, rationally plan the distribution of water resources, especially in the fields of industry, agriculture, and daily life, and strengthen the publicity of water resources protection. The prevention and control of water pollution and the ecological restoration of water resources should be strengthened. The quality of water sources and the stability of water resources ecosystems should be protected. To achieve real-time monitoring and timely detection and solution of water resources security problems, water resources monitoring and early warning should be strengthened, and a sound water resources monitoring system and early warning system should be established.

The comprehensive evaluation index system of water resources security established in this paper contains 27 indexes mainly related to water quantity, which is not perfect at the moment. In the future, the actual situation of the region should be studied more deeply, relevant indicators such as water quality and water ecology should be further considered, and the evaluation index system should be optimized to make it more scientific, reasonable, comprehensive, and effective. Owing to the availability of data, the years from 2008 to 2021 were chosen as the research period. Assessed over a longer period, the results would be more reliable.

The research area selected in this paper is Jinan, and the research results can serve as a reference for cities with similar subsystems of driving, pressure, state, impact, and response to that of Jinan. In the future, multi-city comparative analysis should be conducted to explore the impact of subsystems or factors on urban water resource security.