A method for assessing water ecological resilience based on DPSIR

Abstract

This invention provides a DPSIR-based method for assessing water ecological resilience, belonging to the field of water ecological assessment technology. The method includes: constructing a five-dimensional DPSIR index system; collecting historical data for each index and performing dimensionless processing to obtain standardized values; determining the weights of each index using the entropy weight method; calculating the evaluation indices for the five dimensions based on the weights and standardized values, and then summing the weighted evaluation indices; performing spatial autocorrelation analysis to reveal the spatial distribution characteristics of water ecological resilience; identifying key factors affecting water ecological resilience by progressively introducing and screening variables and using the least squares method to fit the optimal regression equation; and formulating strategies and recommendations to improve water ecological resilience based on the assessment results. This invention, employing the aforementioned DPSIR-based water ecological resilience assessment method, solves the limitations of traditional assessment methods and the insufficient targeting of water ecological governance.

Description

Technical Field

[0001] This invention relates to the field of water ecological assessment technology, and in particular to a water ecological resilience assessment method based on DPSIR. Background Technology

[0002] As a core element of urban ecosystems, the stable supply and maintenance of water resources are crucial for ensuring regional sustainable development and enhancing urban resilience. With the acceleration of industrialization and urbanization, problems such as water shortage, water pollution, and ecosystem degradation are becoming increasingly prominent. Aquatic ecosystems face the dual pressure of overt and covert disturbances, placing higher demands on their ability to cope with disturbances, maintain structural functions, and restore their initial state.

[0003] Currently, water ecological resilience assessment has become a research hotspot in the field of ecological protection. However, traditional assessment methods have significant limitations. Some studies use a single model, lacking a systematic characterization of the entire chain of "driving force-pressure-state-impact-response," making it difficult to fully reflect the dynamic evolution mechanism of water ecosystems. The selection of indicators is highly subjective, and the determination of weights is easily affected by human factors, resulting in insufficient credibility of the assessment results. At the same time, existing studies focus more on trend analysis in the time dimension, and the analysis of spatial clustering characteristics and regional differences is not in-depth enough, failing to provide accurate support for cross-regional collaborative governance.

[0004] Parts of East China straddle the Yangtze and Huai River basins, and their unique location in the climatic transition zone leads to uneven spatial and temporal precipitation, resulting in frequent floods and droughts. Furthermore, rapid urbanization has exacerbated the pressure on aquatic ecosystems due to human activities such as a surge in industrial water use, increased wastewater discharge, and the expansion of impermeable surfaces. This has created a pattern of varying resilience between the south and north, and between urban and rural areas, with the north even exhibiting a persistent cluster of low-resilience areas, severely hindering regional ecological security and high-quality development. Summary of the Invention

[0005] The purpose of this invention is to provide a water ecological resilience assessment method based on DPSIR, which can accurately analyze the spatiotemporal evolution of water ecological resilience and quantitatively identify influencing factors, providing a scientific tool for regional water ecological protection, differentiated governance and risk early warning, and helping to overcome the limitations of traditional evaluation methods and the lack of targeting in water ecological governance.

[0006] To achieve the above objectives, this invention provides a method for assessing the resilience of aquatic ecosystems based on DPSIR, comprising the following steps: S1. Construct a DPSIR indicator system that includes five dimensions: driving force, pressure, state, impact, and response; S2. Collect historical data for each indicator, perform dimensionless processing on the data to eliminate the influence of dimensions, and obtain standard values; S3. Use the entropy weight method to determine the weight of each indicator; S4. Based on the weights and standardized values, calculate the evaluation indices for the five dimensions of driving force, pressure, state, impact, and response. Sum the evaluation indices of each dimension by weight to obtain the comprehensive water ecological resilience index. S5. Conduct spatial autocorrelation analysis to reveal the spatial distribution characteristics of aquatic ecological resilience; S6. By gradually introducing and screening variables, based on their contribution to the dependent variable, the least squares method is used to fit the optimal regression equation and identify the key factors affecting the resilience of the aquatic ecosystem. S7. Based on the assessment results, formulate strategies and recommendations to enhance the resilience of aquatic ecosystems.

[0007] Preferably, step S2 specifically includes defining the area to be evaluated, collecting relevant data for the target time period in the area, including economic and social development data, water resource utilization data, ecological environment data, and urban construction data, with data sources including regional statistical yearbooks, water resource bulletins, land use datasets, and administrative division boundary datasets.

[0008] Preferably, the DPSIR indicator system is based on the DPSIR model and constructs an evaluation indicator system from five criterion layers: driving force, stress, state, impact, and response. Each criterion layer contains several secondary evaluation indicators, including: The driving force criteria layer includes urbanization rate, regional GDP growth rate, and built-up area growth rate. Urbanization rate and regional GDP growth rate are positive indicators, while built-up area growth rate is a negative indicator. The pressure criterion layer includes precipitation change rate, industrial water consumption, agricultural water consumption, residential water consumption, wastewater discharge, and population density, all of which are negative indicators. The state criteria layer includes annual precipitation, total water resources, impermeable surface ratio, wetland ratio, surface water resources, groundwater resources, and afforestation area. Impermeable surface ratio is a negative indicator, while the others are positive indicators. The influencing criteria include water consumption for ecological environment, green coverage rate of built-up area, and per capita comprehensive water consumption. Per capita comprehensive water consumption is a negative indicator, while the others are positive indicators. The response criteria layer includes the area of ​​parks and green spaces, the total amount of sewage treated, and the centralized treatment rate of urban sewage, all of which are positive indicators.

[0009] Preferably, the process of determining weights using the entropy weight method includes: The initial values ​​are dimensionless to obtain the standard values ​​of the positive and negative indices, expressed as follows: ; ; in, It is a standardized value. It is the original value. and These are the minimum and maximum values ​​of the indicator across all cities; The expressions for the weighting, entropy value, and difference coefficient of the calculated indicators are as follows: ; ; ; in, For specific gravity, The entropy value, The coefficient of variation is... For the sample size, For the number of indicators; The weight of each indicator is determined based on the coefficient of difference, and its expression is as follows: ; in, As weight.

[0010] Preferably, when determining the weight of the weight calculation index, entropy value, difference coefficient and weight using the entropy weight method, batch calculation is performed using computer programming tools, including MATLAB or Python.

[0011] Preferably, the expression for the evaluation index in S4 is: ; The expression for the comprehensive aquatic ecological resilience index is: ; Where F is the urban water ecological resilience index, It is an evaluation index under the driving force dimension. It is an evaluation index under the stress dimension. It is an evaluation index under the state dimension. It is an evaluation index under the influence dimension. It is an evaluation index under the response dimension.

[0012] Preferably, step S6 specifically includes: The comprehensive water ecological resilience index was divided into high-value, relatively high-value, medium-value, relatively low-value, and low-value areas using the natural discontinuity method, and its spatial distribution characteristics were analyzed. Spatial autocorrelation analysis was used to calculate the local Moran's I index, revealing spatial clustering, dispersion, or local anomaly patterns in aquatic ecological resilience. The expression is as follows: ; in, Represents the local Moran's I exponent. and These are the resilience values ​​for city a and city b, respectively. It is the average toughness value of the study area. Represents the spatial weight matrix; Stepwise multiple regression analysis was used to identify key factors affecting the resilience of aquatic ecosystems by fitting the regression equation. The expression is as follows: ; Where i is the sample size and j is the number of variables. It is a coefficient. It is the residual term.

[0013] Preferably, the key factors identified in S6 through stepwise multiple regression analysis include at least three of the following: surface water resources, green space area, total sewage treatment volume, annual precipitation, wastewater discharge, impermeable surface ratio, wetland ratio, and afforestation area.

[0014] Therefore, the present invention employs the above-mentioned DPSIR-based method for assessing the resilience of aquatic ecosystems, and the technical effects are as follows: 1. Breaking through the systematic limitations of traditional evaluation: Based on the DPSIR model, a five-dimensional indicator system of "driving force-pressure-state-impact-response" is constructed, which fully covers the entire chain of disturbance, change, impact and human response of aquatic ecosystems. It solves the problem that traditional single models are difficult to reflect the dynamic evolution mechanism of ecosystems, and makes the assessment more in line with the complex interconnected characteristics of aquatic ecosystems.

[0015] 2. Enhance the objectivity and credibility of evaluation results: The entropy weight method is used to determine the weight of indicators. Through quantitative steps such as standardization, weight calculation, entropy value and difference coefficient analysis, and batch calculation by combining MATLAB or Python, the problem of strong subjectivity in traditional weight determination is avoided, making the weight allocation more in line with the difference characteristics of the data itself and improving the scientific nature of the evaluation results.

[0016] 3. Deepen the assessment depth in the spatiotemporal dimensions: Add a spatial autocorrelation analysis step, and reveal the spatial clustering, dispersion or local anomaly patterns of aquatic ecological resilience by calculating the local Moran's I index. Combined with the natural discontinuity method, resilience level zones are divided, which makes up for the shortcomings of traditional research that focuses on the time dimension and ignores spatial differences, and clearly presents the pattern of resilience differences between regions.

[0017] 4. Enhance the guidance of governance practices: The assessment results not only clarify the spatiotemporal evolution of water ecological resilience, but also identify key driving and limiting factors, which can directly provide scientific tools for regional water ecological protection and differentiated governance. For example, for the low-resilience agglomeration zone in the north, targeted measures such as enhanced wastewater treatment, wetland protection, and impermeable surface control can be formulated based on key factors. Attached Figure Description

[0018] Figure 1 This is a flowchart of a water ecological resilience assessment method based on DPSIR according to the present invention; Figure 2 This is a spatial distribution map of the study area in an embodiment of the present invention; Figure 3 The following are zoning diagrams for the driving force factor toughness index in this embodiment of the invention: (a) is the zoning diagram for 2011; (b) is the zoning diagram for 2017; and (c) is the zoning diagram for 2023. Figure 4 This is a graph showing the average driving force factor resilience index of various cities in this embodiment of the invention. Figure 5 The following are zoning diagrams for the stress factor toughness index in this embodiment of the invention: (a) is the zoning diagram for 2011; (b) is the zoning diagram for 2017; and (c) is the zoning diagram for 2023. Figure 6 This is a graph showing the average pressure factor resilience index of various cities in this embodiment of the invention. Figure 7 The following are zoning diagrams for the resilience index of the state factor in this embodiment of the invention: (a) is the zoning diagram for 2011; (b) is the zoning diagram for 2017; and (c) is the zoning diagram for 2023. Figure 8 This is a graph showing the average state factor resilience index of various cities in this embodiment of the invention. Figure 9 The following are the partitioned maps of the resilience index of the impact factor in this embodiment of the invention: (a) is the partitioned map for 2011; (b) is the partitioned map for 2017; and (c) is the partitioned map for 2023. Figure 10 This is a graph showing the average impact factor resilience index of various cities in this embodiment of the invention. Figure 11 The following are zoning diagrams for the response factor resilience index in this embodiment of the invention: (a) is the zoning diagram for 2011; (b) is the zoning diagram for 2017; and (c) is the zoning diagram for 2023. Figure 12 This is a graph showing the average response factor resilience index of various cities in this embodiment of the invention. Figure 13 The following are zoning maps of the water ecological resilience index in this embodiment of the invention: (a) is the zoning map for 2011; (b) is the zoning map for 2017; and (c) is the zoning map for 2023. Figure 14 This is a graph showing the average water ecological resilience index of various cities in this embodiment of the invention; Figure 15The following are spatial clustering analysis diagrams for Anhui Province in this embodiment of the invention: (a) is the analysis diagram for 2011; (b) is the analysis diagram for 2017; and (c) is the analysis diagram for 2023. Detailed Implementation

[0019] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0021] Example 1 like Figure 1 As shown, this invention provides a water ecological resilience assessment method based on DPSIR. It employs a comprehensive assessment approach encompassing "indicator system construction, data processing, weight determination, index calculation, spatial analysis, factor identification, and strategy formulation." Taking Anhui Province as an example, this invention selects 22 indicators, including urbanization rate and regional GDP growth rate, to construct a water ecological resilience evaluation system. Furthermore, it evaluates the water ecological resilience of Anhui Province, conducting time series analysis, spatial autocorrelation analysis, and stepwise multiple regression analysis on the multi-year water ecological resilience assessment results. Finally, it provides strategies and suggestions for improving urban water ecological resilience.

[0022] Anhui Province, located in East China, spans the Yangtze and Huai River basins and boasts diverse landforms. As an important part of the Yangtze River Delta, it enjoys a strategic location connecting the east and west. The province covers an area of ​​140,100 square kilometers. The north has a warm temperate semi-humid climate, while the south has a subtropical monsoon climate. The average annual rainfall is 800–1800 mm, and the province has abundant water resources.

[0023] However, the province is located in a climate transition zone, with uneven spatial and temporal precipitation, making it prone to floods and droughts. In recent years, with rapid industrialization and urbanization, some areas have experienced water pollution and ecological degradation, posing challenges to the overall resilience of the watershed. Therefore, this paper takes Anhui Province as the research object, assessing its water ecological resilience from a watershed perspective to support regional ecological protection and high-quality development decisions. Its spatial distribution is as follows: Figure 2 As shown.

[0024] The study period in this example is from 2011 to 2023. The data mainly comes from the "Anhui Statistical Yearbook" and the "Anhui Provincial Water Resources Bulletin," with some indicators calculated based on CLCD land use data. The administrative boundary data of prefecture-level cities in Anhui Province are from the Resource and Environmental Science Data Registration and Publication System.

[0025] The concept of urban water ecological resilience is complex and requires the comprehensive application of multiple indicators for measurement. This study, based on the widely used DPSIR model in the field of water resources and adhering to the principles of comprehensiveness, representativeness, and operability of the indicators, constructed an evaluation system comprising 22 secondary indicators, as shown in Table 1. Among these, driving forces reflect economic and social development through indicators such as urbanization rate and GDP growth rate; pressures reflect the negative impacts of human activities on the water system through indicators such as various water consumption and wastewater discharge; state characterizes the current status of the water system through indicators such as total water resources and wetland ratio; impacts show the consequences of system changes through indicators such as ecological water consumption and per capita water consumption; and responses measure the measures taken by society to mitigate ecological risks through indicators such as wastewater treatment rate.

[0026] Table 1 Evaluation system of 22 secondary indicators ;

[0027] To achieve an objective evaluation of the aquatic ecological resilience index, this study employs the entropy weighting method based on data variability to determine the weights. This method effectively reduces subjective interference, and its core lies in utilizing the uniformity of the index data distribution (i.e., information entropy) to calculate the weights.

[0028] The process of determining weights using the entropy weight method includes: The initial values ​​are dimensionless to obtain the standard values ​​of the positive and negative indices, expressed as follows: ; ; in, It is a standardized value. It is the original value. and These are the minimum and maximum values ​​of the indicator across all cities; The expressions for the weighting, entropy value, and difference coefficient of the calculated indicators are as follows: ; ; ; in, For specific gravity, The entropy value, The coefficient of variation is... For the sample size, For the number of indicators; The weight of each indicator is determined based on the coefficient of difference, and its expression is as follows: ; in, As weight.

[0029] The expression for the evaluation index is: ; The expression for the comprehensive aquatic ecological resilience index is: ; Where F is the urban water ecological resilience index, It is an evaluation index under the driving force dimension. It is an evaluation index under the stress dimension. It is an evaluation index under the state dimension. It is an evaluation index under the influence dimension. It is an evaluation index under the response dimension.

[0030] Spatial autocorrelation analysis reveals the clustering, dispersion, or local anomaly patterns of urban water ecological resilience by examining the similarity of spatially adjacent values, thereby highlighting its spatial differences and characteristics.

[0031] It studies whether the value of a given factor shows a strong correlation with the values ​​of adjacent spatial points. The method is applied as follows: The comprehensive water ecological resilience index was divided into high-value, relatively high-value, medium-value, relatively low-value, and low-value areas using the natural discontinuity method, and its spatial distribution characteristics were analyzed. Spatial autocorrelation analysis was used to calculate the local Moran's I index, revealing spatial clustering, dispersion, or local anomaly patterns in aquatic ecological resilience. The expression is as follows: ; in, This is represented by the local Moran's I exponent. and These are the resilience values ​​for city a and city b, respectively. The average toughness value of the study area is represented by the average toughness value. Represented by the spatial weight matrix; Stepwise multiple regression analysis was used to identify key factors affecting the resilience of aquatic ecosystems by fitting the regression equation. The expression is as follows: ; Where i is the sample size and j is the number of variables. It is a coefficient. It is the residual term.

[0032] The weights of each indicator in the Anhui Province Water Ecological Resilience Index were determined through scientific calculation and comprehensive analysis, taking into account the actual characteristics of the regional water ecology. The index fully covers all core dimensions of resilience assessment, and the specific values ​​are shown in Table 2.

[0033] Table 2. Weights of the Water Ecological Resilience Index of Anhui Province ;

[0034] To analyze the resilience of urban water ecosystems, model-based index values ​​were calculated. For clarity of interpretation, the results were categorized into five groups: high-value areas, relatively high-value areas, medium-value areas, relatively low-value areas, and low-value areas. Among various GIS classification methods, the natural breakpoint method was chosen because it can automatically identify the natural distribution of data, objectively capturing inherent patterns, avoiding the subjectivity of manual classification, and providing more accurate and effective grouping for datasets with uneven distribution or extreme values.

[0035] Driving forces are potential factors in economic and social development, which can trigger ecological pressures. The more stable the system, the lower the likelihood of it developing in an unfavorable direction, and the stronger the resilience and defense capacity of the water ecosystem. (2011) Figure 3 (a) High-value areas are concentrated in Hefei and Tongling in the central region, while low-value areas are in Fuyang, Bozhou, and Suzhou in the north. As of 2017 ( Figure 3 (b) The low-value zone has shrunk significantly, with the number of cities decreasing from three to one. Hefei remains at a high value, while Tongling has fallen to the mid-value zone. By 2023 ( Figure 3 (c) The low-value areas have completely disappeared, and the high-value areas have expanded to include Hefei, Wuhu, and Ma'anshan, with a significant increase in the number of higher-value areas. Overall, the resilience of Anhui Province's driving force shows a clear spatial evolution trend, centered on the central region and gradually radiating outwards and improving.

[0036] like Figure 4 As shown, Anhui Province's driving force resilience index has shown a continuous upward trend over the past twelve years, with Hefei City exhibiting the highest resilience in its driving force factors. This growth is primarily attributed to rapid economic development, specifically manifested in the steady increase of key indicators such as urbanization rate and regional GDP growth rate. However, rapid urbanization has also been accompanied by a dramatic expansion of built-up areas, squeezing water ecological space, which has slowed the growth rate of the resilience index to some extent.

[0037] The stress resilience index is a visible factor that directly affects aquatic ecosystems. The more stable the index, the lower the likelihood of the system developing in an unfavorable direction, and the stronger its ecological resilience and defense capabilities. (2011) Figure 5 a) Anhui Province has no low-value areas for the pressure resilience index; high-value areas are distributed in Chizhou and Huangshan in the south. As of 2017 ( Figure 5 (b) The five western cities have developed into the median value zone, but Chizhou and Huangshan still maintain high values. By 2023 ( Figure 5 c) Hefei shifted to a low-value zone, while Fuyang and Bozhou also developed into relatively low-value zones. Overall, the stress resilience index of the six southern cities of Anhui Province (Anqing, Tongling, Wuhu, Chizhou, Huangshan, and Xuancheng) remained relatively stable, with Chizhou and Huangshan consistently in high-value zones; conversely, Hefei deteriorated from a relatively low-value zone to a low-value zone.

[0038] fromFigure 6 As you can see, the average pressure resilience index has been declining year by year, indicating that the overall water ecological pressure in the province continues to increase. This trend is closely related to the level of urban development. For example, Huangshan City consistently has the highest index, indicating relatively low ecological pressure; while Hefei City's index has dropped to the lowest, due to its higher population density and significantly higher pressure in terms of industrial water use, domestic water use, and wastewater discharge compared to Huangshan.

[0039] State refers to the condition of an aquatic ecosystem under stress, generally describing its physical and biological characteristics. A better system state indicates greater ecological resilience and adaptability. (2011) Figure 7 a) Anhui Province has no high-value or high-value areas in its resilience index; from north to south, the values ​​are low, relatively low, and medium. As of 2017 ( Figure 7 (b) Anqing and Chizhou rose to higher value zones, and Lu'an and Fuyang also saw improvements, but none remained at the high value zone. By 2023 ( Figure 7 c) The spatial pattern continued to adjust, with Huangshan replacing Chizhou as the area with higher values, while the values ​​of Fuyang and Chizhou declined. Over the past twelve years, the resilience of Anhui Province has shown an evolutionary pattern of "increasing towards higher values ​​in the south, clustering towards medium values ​​in the central region, and solidifying into a low-value area in the north."

[0040] from Figure 8 As you can see, the average resilience index of Anhui Province shows a fluctuating trend of first rising and then falling, reaching its peak in 2017. Although it declined somewhat in 2023, it was still higher than the 2011 benchmark, indicating that the overall level has improved. Among them, Anqing City's index performance was the most outstanding, while Huaibei City remained in the lowest value range.

[0041] Impact refers to the influence of driving forces, pressures, and conditions on artificial and natural ecological subsystems within an aquatic ecosystem. The smaller the impact, the greater the resilience and adaptability of the aquatic ecosystem. (2011) Figure 9 a) Anhui Province has no high-value areas, Hefei City has a relatively high impact resilience index, and most areas are low-value areas. 2017 ( Figure 9 b) The resilience of various cities has improved, with the northwest region shifting from a low-value area to a lower or medium-value area. Hefei's resilience value remains higher than other cities, placing it in a higher-value area. By 2023 ( Figure 9 c) Hefei City has become a high-value area, and from the perspective of Anhui Province as a whole, the impact resilience index has improved.

[0042] like Figure 10 As shown, the resilience index as a whole shows an upward trend, with Hefei City having the highest resilience index value and the fastest growth rate, while the index values ​​of the other cities are similar. This is related to the increase in ecological water use and green space coverage in Hefei City, as well as the decrease in per capita comprehensive water consumption, thereby improving the score of the water ecological resilience subsystem.

[0043] Response refers to the measures taken by human societies to address adverse changes in aquatic ecosystems. The higher the level of response, the greater the resilience and transformation capacity of the aquatic ecosystem. (2011) Figure 11 a) There are no high-value areas in the province, Hefei City is a relatively high-value area, and most areas are dominated by low values. 2017 ( Figure 11 b) Hefei City is a high-value area, with higher-value areas predominating east of Hefei City and lower-value areas predominating west of Hefei City. 2023 ( Figure 11 (c) Only Chizhou City remains at a low value, most areas are in the medium value range, and Hefei City remains in the high value range, showing the strongest response resilience.

[0044] from Figure 12 It can be seen that the resilience index shows a year-on-year upward trend. Hefei's resilience value is much higher than that of other cities, followed by Wuhu, which also saw rapid growth and ranked second in 2023. The increase in the total amount of wastewater treated reduces pollutant emissions, improves water quality, and thus improves the aquatic ecological environment.

[0045] Taking into account five indicators—driving force, pressure, state, impact, and response—at the criterion level, a comprehensive assessment of the water ecological resilience of Anhui Province was conducted, and the final results are as follows: Figure 13 As shown. 2011 ( Figure 13 a) Anhui Province has no high-value areas for water ecological resilience, with only Hefei City having relatively high values. The resilience value generally increases from north to south. By 2017 ( Figure 13 (b) Hefei has developed into a high-value area, while the medium-value areas are concentrated in central cities, and Anqing, Chizhou, and Huangshan in the south are relatively high-value areas. 2023 ( Figure 13 (c) The high-value area has further expanded to include the cities of Hefei, Anqing, and Huangshan. Overall, from 2011 to 2023, the water ecological resilience value has been increasing year by year, indicating some improvement in resilience.

[0046] Figure 14 The chart shows histograms and annual trends for cities in Anhui Province. The resilience values ​​of all sixteen cities have improved, with Hefei consistently showing the highest and fastest growth, exceeding 0.5 in 2023, a significant increase of 38.12% compared to 2011. Looking at the annual average, the province's resilience level has continued to rise, but the growth rate has slowed: the average annual growth rate was 18.13% between 2011 and 2017, while it slowed to 2.51% between 2017 and 2023. This indicates that, with increased awareness of ecological protection, Anhui Province's water ecological resilience, after a period of rapid improvement, is entering a stage of steady growth.

[0047] Local spatial autocorrelation analysis was conducted on the urban water ecological resilience index of Anhui Province in 2011, 2017, and 2023. This analysis effectively monitored the specific locations of agglomeration areas throughout the region, yielding results such as... Figure 15The clustering diagram shown. (As shown in the image) Figure 15 As shown, the urban resilience level in Anhui Province is mainly characterized by "high-high" and "low-low" clusters, without any "low-high" or "high-low" clusters. This indicates that the city has a spatial spillover effect, meaning that highly resilient cities have a significant impact on surrounding areas.

[0048] From 2011 to 2023, the "low-low" cluster remained consistently distributed in the northern cities of Bozhou, Huaibei, Suzhou, and Bengbu, forming a spatially reinforced low-resilience zone. The "high-high" cluster, concentrated in Chizhou in 2011 and 2017, had a significant driving effect on the surrounding areas; however, by 2023, this cluster had disappeared and become insignificant, presumably due to the general improvement in the resilience of surrounding cities and the narrowing of regional differences.

[0049] Using indicator data from various cities in Anhui Province in 2011, 2017, and 2023, a stepwise multiple regression model was used to calculate and identify the indicators with the greatest impact on spatial clustering. The results are shown in Table 3, where B represents the coefficient of the variable in the fitted equation; a larger value indicates a greater impact on the degree of clustering.

[0050] Table 3. Indicators with the greatest impact on spatial clustering ;

[0051] In 2011, four indicators significantly affected the level of aquatic ecological resilience. The R-squared value of the fitted equation... 2 The R² value is 0.974, indicating a good fit. Among the variables, total wastewater treatment volume and annual precipitation have a significant impact on spatial resilience and its degree. In 2017, there were four input variables. The most influential indicator was surface water resources, while wastewater discharge had a negative impact. In 2023, there were three input variables, and the R² value of the fitted equation was... 2 The value of 0.961 indicates a good fit. Variables with significant influence include the area of ​​green spaces and the amount of surface water resources.

[0052] This invention constructs a water ecological resilience evaluation system comprising 22 indicators based on the DPSIR model. Using entropy weighting, spatial autocorrelation analysis, and stepwise multiple regression, it systematically assesses the spatiotemporal evolution characteristics and driving mechanisms of water ecological resilience in Anhui Province from 2011 to 2023. The main conclusions are as follows: The overall water ecological resilience of Anhui Province shows a steady upward trend, with the average annual value of the comprehensive index continuously increasing, although the growth rate slowed after 2017. Spatially, Huangshan and Chizhou in the south and Hefei in the central region exhibit higher resilience levels, while Bozhou and Huaibei in the north form a persistent low-resilience cluster. Significant spatial clustering characteristics are observed, with high-high clustering areas spreading from Chizhou to surrounding areas, and low-low clustering areas stably distributed in northern Anhui. Stepwise multiple regression indicates that surface water resources, green space area, and total wastewater treatment volume are key factors affecting resilience. Future research could promote cross-city collaborative governance and construct a dynamic monitoring and evaluation system to comprehensively improve regional water ecological resilience and sustainable development capabilities.

[0053] Therefore, this invention adopts the above-mentioned DPSIR-based water ecological resilience assessment method. Based on the DPSIR model, a 22-item index system with five dimensions including driving force, pressure, state, impact, and response is constructed. By collecting historical data of the target area and performing dimensionless processing, the entropy weight method is used to determine the index weights, and the weighted summation is used to obtain the comprehensive water ecological resilience index. Then, spatial autocorrelation analysis is combined to reveal spatial distribution and clustering characteristics, and stepwise multiple regression is used to identify key influencing factors. Finally, it provides a scientific assessment tool for regional water ecological differentiated governance, resilience enhancement, and sustainable development, solving the problems of insufficient systematicity, strong subjectivity, and weak targeting of traditional evaluation methods.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for assessing the resilience of aquatic ecosystems based on DPSIR, characterized in that, Includes the following steps: S1. Construct a DPSIR indicator system that includes five dimensions: driving force, pressure, state, impact, and response; S2. Collect historical data for each indicator, perform dimensionless processing on the data to eliminate the influence of dimensions, and obtain standard values; S3. Use the entropy weight method to determine the weight of each indicator; S4. Based on the weights and standardized values, calculate the evaluation indices for the five dimensions of driving force, pressure, state, impact, and response. Sum the evaluation indices of each dimension by weight to obtain the comprehensive water ecological resilience index. S5. Conduct spatial autocorrelation analysis to reveal the spatial distribution characteristics of aquatic ecological resilience; S6. By gradually introducing and screening variables, based on their contribution to the dependent variable, the least squares method is used to fit the optimal regression equation and identify the key factors affecting the resilience of the aquatic ecosystem. S7. Based on the assessment results, formulate strategies and recommendations to enhance the resilience of aquatic ecosystems.

2. The method for assessing the water ecological resilience based on DPSIR according to claim 1, characterized in that, Step S2 specifically includes defining the area to be assessed, collecting relevant data for the target time period in that area, including economic and social development data, water resource utilization data, ecological environment data, and urban construction data, with data sources including regional statistical yearbooks, water resource bulletins, land use datasets, and administrative division boundary datasets.

3. The method for assessing the water ecological resilience based on DPSIR according to claim 1, characterized in that, The DPSIR indicator system is based on the DPSIR model and constructs an evaluation indicator system from five criterion levels: driving force, stress, state, impact, and response. Each criterion level contains several secondary evaluation indicators, including: The driving force criteria layer includes urbanization rate, regional GDP growth rate, and built-up area growth rate. Urbanization rate and regional GDP growth rate are positive indicators, while built-up area growth rate is a negative indicator. The pressure criterion layer includes precipitation change rate, industrial water consumption, agricultural water consumption, residential water consumption, wastewater discharge, and population density, all of which are negative indicators. The state criteria layer includes annual precipitation, total water resources, impermeable surface ratio, wetland ratio, surface water resources, groundwater resources, and afforestation area. Impermeable surface ratio is a negative indicator, while the others are positive indicators. The influencing criteria include water consumption for ecological environment, green coverage rate of built-up area, and per capita comprehensive water consumption. Per capita comprehensive water consumption is a negative indicator, while the others are positive indicators. The response criteria layer includes the area of ​​parks and green spaces, the total amount of sewage treated, and the centralized treatment rate of urban sewage, all of which are positive indicators.

4. The method for assessing the water ecological resilience based on DPSIR according to claim 1, characterized in that, The process of determining weights using the entropy weight method includes: The initial values ​​are dimensionless to obtain the standard values ​​of the positive and negative indices, expressed as follows: ； ； in, It is a standardized value. It is the original value. and These are the minimum and maximum values ​​of the indicator across all cities; The expressions for the weighting, entropy value, and difference coefficient of the calculated indicators are as follows: ； ； ； in, For specific gravity, The entropy value. The coefficient of variation, For the sample size, For the number of indicators; The weight of each indicator is determined based on the coefficient of difference, and its expression is as follows: ； in, As weight.

5. The method for assessing the water ecological resilience based on DPSIR according to claim 4, characterized in that, When determining the weight of the weight calculation index, entropy value, difference coefficient and weight using the entropy weight method, batch calculation is performed using computer programming tools, including MATLAB or Python.

6. The method for assessing the water ecological resilience based on DPSIR according to claim 5, characterized in that, The expression for the evaluation index in S4 is: ； The expression for the comprehensive aquatic ecological resilience index is: ； Where F is the urban water ecological resilience index, It is an evaluation index under the driving force dimension. It is an evaluation index under the stress dimension. It is an evaluation index under the state dimension. It is an evaluation index under the influence dimension. It is an evaluation index under the response dimension.

7. The method for assessing the water ecological resilience based on DPSIR according to claim 1, characterized in that, The specific steps of S6 include: The comprehensive water ecological resilience index was divided into high-value, relatively high-value, medium-value, relatively low-value, and low-value areas using the natural discontinuity method, and its spatial distribution characteristics were analyzed. Spatial autocorrelation analysis was used to calculate the local Moran's I index, revealing spatial clustering, dispersion, or local anomaly patterns in aquatic ecological resilience. The expression is as follows: ； in, Represents the local Moran's I exponent. and These are the resilience values ​​for city a and city b, respectively. It is the average toughness value of the study area. Represents the spatial weight matrix; Stepwise multiple regression analysis was used to identify key factors affecting the resilience of aquatic ecosystems by fitting the regression equation. The expression is as follows: ； Where i is the sample size and j is the number of variables. It is a coefficient. It is the residual term.

8. The method for assessing the water ecological resilience based on DPSIR according to claim 1, characterized in that, The key factors identified in S6 through stepwise multiple regression analysis include at least three of the following: surface water resources, green space area, total sewage treatment volume, annual precipitation, wastewater discharge, impermeable surface ratio, wetland ratio, and afforestation area.

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