Resilience Assessment and Governance Strategies for a Complex Watershed System: A Case Study of the Erhai Basin, China

Abstract

Ecological resilience serves as a critical foundation for regional ecological management. As a fundamental unit of ecological governance, a watershed integrates natural, economic, and social subsystems into a complex composite system. However, the mechanisms linking human activities, management behaviors, and natural processes to ecological resilience at the watershed scale remain poorly understood. To address this gap, this study takes China’s Erhai watershed as a representative case and develops an integrated evaluation framework for assessing the resilience of a watershed-scale natural–economic–social composite system. The framework combines resilience measurement, coupling coordination analysis, and scenario simulation using the Ordered Weighted Averaging (OWA) method. The results indicate that the overall resilience of the Erhai watershed increased steadily from 2005 to 2020, with the average value rising from 0.23 to 0.42. However, spatial disparities in resilience widened, reflecting challenges of uncoordinated regional development. Fiscal revenue was identified as a key driver of resilience enhancement, as higher fiscal capacity promotes greater investment in ecological protection and environmental governance. Scenario simulations further revealed that the conservation-priority policy scenario achieved the highest resilience, characterized by stronger infrastructure development, improved environmental management, and increased investment in social security and health, supported by sustainable tourism. These findings provide theoretical and practical insights for promoting coordinated and resilient watershed governance in China and similar regions worldwide.

1. Introduction

The ongoing global crises, including environmental degradation, climate change, and related challenges, have placed ecosystems under severe threat, thereby undermining the foundation of sustainable human development [1,2]. Against this complex natural–social context, the concept of ecosystem resilience entered scholarly discourse in the 1970s. The resilience theory offers a conceptual foundation for addressing ecological crises and has gradually become a central research focus. The term “resilience” was first introduced into ecological research by Holling [3], who defined it as “the ability of an ecosystem to absorb change and continue to maintain it”. Early studies primarily assessed resilience within single ecosystems, emphasizing natural rather than socio-economic factors. Various qualitative and quantitative approaches have been applied to measure and evaluate resilience across diverse ecological contexts [4]. The earliest empirical applications focused on coral reef systems, where researchers examined how coral colonies adapted to anthropogenic disturbances [5,6]. Subsequent research extended to grassland ecosystems, exploring their adaptive responses to climate change [7,8,9]. However, resource and environmental issues often originate from complex socio-institutional causes, including governmental barriers, structural economic imbalances, limited public environmental awareness, and cultural disparities [10]. As the concept of resilience expanded, attention gradually shifted toward complex adaptive systems, particularly coupled human–earth and socio-ecological systems [11,12]. Adger [13] further linked social and ecological resilience, defining the former as the capacity of societies to withstand and recover from external shocks such as environmental, social, or economic disruptions. Resilience thinking provides a valuable lens for exploring the dynamic interactions within socio-ecological production systems [14,15]. Scholars have categorized system resilience into three components: natural, ecological, and socio-economic resilience [16]. This capacity emerges from the dynamic processes of natural, ecological, and socio-economic subsystems and is constrained by the essential functions that must be sustained. In examining the response mechanisms of socio-ecological systems, Adger et al. [17] emphasized that resilience reflects a system’s ability to absorb periodic disturbances while maintaining its core structure, processes, and feedback loops. In the context of watershed governance, resilience thinking provides a conceptual foundation for understanding how management interventions, institutional arrangements, and socio-economic conditions shape a system’s capacity to respond to external pressures. Building on this theoretical evolution, recent studies increasingly link resilience assessment to practical governance strategies aimed at strengthening adaptive capacity and long-term sustainability [18,19,20,21]. Numerous models and conceptual frameworks have been proposed for the quantitative assessment of resilience; however, system resilience is typically influenced by multiple interacting variables. Several critical questions remain unresolved, such as how to select and weight variables appropriately, how to define system boundaries and spatial scales, how to enhance and maintain resilience, and how to reveal the mechanisms underlying resilience dynamics. The nature–economy–society interface constitutes a complex adaptive system in which natural processes, human activities, and policies interact dynamically; therefore, assessing resilience from a single dimension is insufficient. It is thus essential to explore resilience dynamics by integrating natural, socio-economic, and policy dimensions within a coupled system framework [22,23]. Previous studies have primarily examined resilience from a socio-ecological perspective, yet few have quantitatively integrated composite system resilience with the analysis of coupling coordination among subsystems [24].

China is committed to building a community with a shared future for mankind and advancing global ecological governance. The Erhai watershed is one of the outcomes of China’s ecological governance, where numerous ecological policies and governance projects are practiced [25]. Therefore, the Erhai watershed was selected as the study area for this research. The Erhai is the most representative highland lake in China and the “mother lake” in the minds of the Dali people in Yunnan province, with 25% of the population and 40–50% of the total economic volume of Dali Prefecture and rich ecosystem types in the watershed. However, the challenges of controlling domestic sewage, solid waste, and agricultural non-point pollution have intensified over time, occasionally triggering social tensions and conflicts of interest among stakeholders. Nevertheless, through the concerted efforts of government, society, and the public, the deterioration of Erhai’s water quality has been largely curbed, and the overall ecological condition of the lake is considered good [26,27]. However, strict ecological protection measures have considerably constrained local agricultural development. Meanwhile, some local governments remain driven by short-term growth incentives, often prioritizing immediate economic benefits over long-term ecological sustainability. Such behavior neglects environmental costs, national interests, and intergenerational equity [28,29]. Against this background, improving the social, economic, and ecological conditions of the watershed has become a key task for local governments, especially under increasingly strict ecological protection requirements. The tension between promoting economic growth and safeguarding the environment remains difficult to resolve. In this study, governance strategies are understood in this governmental and institutional sense, focusing on policy instruments and regulatory actions aimed at shaping local development pathways. Conducting a resilience assessment of the watershed as a complex system offers an integrated perspective for addressing this issue and provides governance insights for local authorities as well as other basins with similar characteristics.

To address these gaps, this study advances watershed resilience research in two ways. First, it conceptualizes resilience within a social–economic–natural composite system rather than focusing solely on ecological resilience, thereby broadening the analytical scope and providing a basis for coordinated governance across multiple levels. Second, it proposes an integrated “assessment-coupling-simulation-strategy” framework that moves beyond traditional approaches centered primarily on resilience measurement. By linking subsystem coordination with scenario-based development pathways, the framework not only offers a more complete, process-oriented understanding of how watershed resilience can be strengthened over time, but also supports the identification of governance strategies as one of the study’s objectives. Specifically: (i) key disturbance factors were identified, and the spatiotemporal evolution of resilience was analyzed using an integrated evaluation approach; (ii) the coupling coordination degree among natural, economic, and social subsystems was examined; and (iii) based on an Ordered Weighted Averaging (OWA) scenario design, alternative policy scenarios were simulated to predict possible outcomes, identify optimal resilience-enhancing strategies, and propose a management system characterized by high adaptive capacity and sustainable development potential.

2. Materials and Methods

2.1. Study Area

The Erhai watershed is located in the center of Dali Bai Autonomous Prefecture, Yunnan Province of China, encompassing two county-level cities that together include sixteen townships (

Table 1. Erhai watershed contains township areas.

County Name	Number of Townships	Township Name	Area (km2)
Dali	10	Xiaguan, Fengyi, Haidong, Dali, Shuanglang, Wase, Xizhou, Yinqiao, Wanqiao, Shangguan	1639.8
Eryuan	6	Cibihu, Fengyu, Yousuo, Sanying, Dengchuan, Niujie	1288.0
Total	16		2927.8

). Geographically, Dali extends from 99°58′ E to 100°27′ E and from 25°25′ N to 25°58′ N, while Eryuan County spans 99°32′ E–100°20′ E and 25°41′ N–26°16′ N (Figure 1). The region experiences a distinct subtropical monsoon climate, characterized by a dry season from November to April and a rainy season from late May to October, during which most annual precipitation occurs. The topography of the Dali region is generally high in the northwest and low in the southeast, forming a basin-like landform that is elevated on all sides and lower in the center. Most mountains and rivers are oriented from south to north, and diverse geomorphic types, including mountains, basins, hills, and lakes, are interspersed throughout the watershed.

In 2022, Dali had a resident population of approximately 653,000, a gross regional product (GDP) of CNY 53.57 billion, and per capita disposable incomes of CNY 44,686 and CNY 21,054 for urban and rural residents, respectively. The city is rich in tourism resources. In 2022, Eryuan County had a resident population of 246,000 and a gross regional product of CNY 8.70 billion. In 2020, its tourism industry generated CNY 3.15 billion in total revenue.

2.2. Evaluation of Resilience of Complex Ecosystem

According to previous studies, a key step in evaluating the resilience of a composite system lies in assessing its capacity to absorb, adapt to, and respond to external changes [30]. Therefore, this study establishes a composite ecosystem resilience evaluation index system in the target, criterion, and indicator layers [31,32,33,34,35]. The proposed indicator framework comprises 17 indicators across three subsystems (natural, economic, and social), of which ten are positive and seven are negative (

Table 2. Evaluation index system weights.

Dimensions	Standards	Indicators	Units	Properties	Weights
Natural Subsystem (0.511)	Ecological status (0.115)	NDVI (A1)	/	+	0.018
		Land use type (A2)	/	+	0.033
		Soil texture (A3)	/	+	0.064
	Ecological pressures (0.098)	Slope (A4)	°	−	0.062
		Share of arable land (A5)	%	−	0.018
		Food production per capita (A6)	kg/person	−	0.018
	Ecological responses (0.298)	Land-average ecological protection input (A7)	10,000 Yuan/km2	+	0.192
		Land-average agricultural, forestry, and water inputs (A8)	10,000 Yuan/km2	+	0.106
Economic Subsystem (0.296)	Economic pressure (0.03)	Gross industrial product per land (B1)	10,000 Yuan/km2	−	0.009
		The proportion of urban residential industrial and mining land (B2)	%	−	0.021
	Economic Base (0.266)	Local average government revenue (B3)	10,000 Yuan/km2	+	0.266
Social Subsystem (0.193)	Social pressure (0.029)	Population density (C1)	Person/km2	−	0.023
		The annual number of visitors (C2)	10,000 people	−	0.006
	People’s livelihood improvement (0.164)	Distributable income per capita (C3)	%	+	0.136
		Ground average infrastructure investment (C4)	10,000 Yuan/km2	+	0.008
		Social security investment per capita (C5)	Yuan/person	+	0.008
		Per capita investment in health (C6)	Yuan/person	+	0.012

). The selection of these 17 indicators follows established frameworks for evaluating the resilience of complex watershed systems and corresponds to the standards defined for each subsystem in Table 2. For the natural subsystem, indicators were selected to reflect ecological status, ecological pressures, and ecological responses, thereby capturing the system’s capacity to maintain stability under environmental disturbances. The economic subsystem includes indicators representing economic pressure and economic base, which describe both development intensity and the foundational resources that support adaptive capacity. The social subsystem incorporates indicators of social pressure and people’s livelihood improvement to reflect demographic stresses and the social conditions that influence resilience. Overall, indicator selection was guided by theoretical relevance, empirical suitability to the Erhai Basin, and the reliability and consistency of available data. Similar indicator structures have been adopted in previous watershed resilience studies [36,37,38], and the indicators used here were further adapted to the environmental and governance conditions of the Erhai Basin to ensure that they accurately reflect local realities.

2.3. Data Requirement and Preparation

This study employed both remotely sensed and statistical datasets. The Normalized Difference Vegetation Index (NDVI) was derived from Landsat-8 imagery to obtain township-level mean values; land-use data were obtained from the Land Use/Cover Change (LUCC) dataset provided by the Chinese Academy of Sciences [39]. Following previous studies, five land-use categories (forest and grassland, water bodies, unused land, arable land, and construction land) were assigned scores of 0.9, 0.7, 0.5, 0.3, and 0.1, respectively, and township-level values were computed accordingly. These values reflect the relative ecological suitability of each land-use type and are widely adopted in existing land-use and ecological assessment research [35]. Soil texture data were obtained from the 30 m resolution national soil dataset of China. Loamy, clayey, and sandy soils were assigned values of 0.7, 0.5, and 0.3, respectively, and township-level averages were then calculated; these values indicate differences in soil structure, water retention capacity, and ecological stability, following common practices in soil suitability classification [35,40]. Slope data were extracted from the digital elevation model (DEM) of Dali Prefecture, and the mean slope value was computed for each township; the proportion of urban residential, industrial, and mining land was derived from the same LUCC dataset. The remaining socioeconomic and fiscal data were collected from the Dali City Statistical Yearbook (2006, 2011, 2016, 2021) and the Eryuan County Statistical Yearbook (2006, 2011, 2016, 2021), as well as from the final accounts of township-level government departments and the Statistical Bulletins on National Economic and Social Development of Dali City and Eryuan County (available at http://www.yndali.gov.cn/dlszf/index.shtml (accessed on 26 November 2025) and http://www.eryuan.gov.cn/ (accessed on 26 November 2025)). To ensure temporal consistency across datasets, all remotely sensed data (NDVI, LUCC, soil texture, and DEM) were extracted for the same benchmark years as the economic and social statistics (2005, 2010, 2015, and 2020). All variables were processed to the township scale using consistent administrative boundaries. This unified spatial scale also helps reduce potential scale effects and ensures comparability across ecological, economic, and social indicators during the resilience assessment.

2.4. Methods

2.4.1. Entropy Weight Method

The entropy weighting method is an objective weighting approach that determines the relative importance of indicators based on their statistical variability. It effectively mitigates the instability of results that may arise from subjective weighting methods [24,41]. This data-driven approach derives indicator importance from statistical variability rather than researcher judgment, thereby avoiding subjective manipulation and improving transparency. In this study, information entropy is employed to calculate the weight of each indicator, serving as the foundation for a comprehensive multi-indicator evaluation. Given the heterogeneity of natural, economic, and social indicators, the entropy method offers an objective and reliable basis for determining weights. The calculation procedure can be summarized as follows.

(a) Standardize the initial data using the min-max standardization method:

For the positive indicator:

$$Y_{ijk}={\displaystyle \frac{X_{ijk}-\min \left(X_j\right)}{\max \left(X_j\right)-\min \left(X_j\right)}}$$

(1)

For the negative indicator:

$$Y_{ijk}={\displaystyle \frac{\max \left(X_j\right)-X_{ijk}}{\max \left(X_j\right)-\min \left(X_j\right)}}$$

(2)

Establish the evaluation matrix: i represents the 16 townships in the study area, j represents the 17 selected indicators reflecting ecosystem resilience, and k represents the four years of this study from 2005 to 2020.

(b) Establish evaluation matrix:

$$X_k=\left[\begin{array}{ccc}{X}_{1,1}& \dots & X_{1,17}\\ ⋮& \ddots & ⋮\\ X_{16,1}& \dots & X_{16,17}\end{array}\right] k=1,2,\dots ,4$$

(3)

(c) Calculate the probability P for each indicator:

$$P_{ijk}={\displaystyle \frac{X_{ijk}}{{{\displaystyle \sum }}_{i=1}^{16}{{\displaystyle \sum }}_{k=1}^4{Y}_{ijk}}}$$

(4)

(d) Derive the entropy value H_j of each indicator according to the probability P_ijk:

$$H_j=-n{\displaystyle \sum }_{i=1}^{16}{\displaystyle \sum }_{k=1}^4{P}_{ijk}\ln \left(P_{ijk}\right)$$

(5)

where n = 1/ln(i × k);

(e) Calculate the importance of the indicator D_j:

$$D_j=1-H_j$$

(6)

(f) Determine the weight of each indicator W_j according to the ratio of the importance of each indicator D_j to the total importance of all indicators:

$$W_j={\displaystyle \frac{D_j}{{{\displaystyle \sum }}_{j=1}^{17}{D}_j}}$$

(7)

(g) Using the indicator weights and standardized results, the resilience in the study area can be calculated by Equation (8):

$$Z_{ik}={{\displaystyle \sum }}_{j=1}^{17}{w}_j\times Y_{ijk}$$

(8)

2.4.2. Coupling Harmonious Degree Model

Coupling refers to the interactive relationship among subsystems within a larger system. It drives the transformation of the system from a disordered to an ordered state, thereby reflecting the degree of interdependence and interaction among the subsystems [42]. In recent years, numerous studies have examined the coordination degree of coupling among regional ecological, social, and economic subsystems [43,44]. Research on the synergistic coordination of composite systems has proliferated. Methodologically, most existing studies construct indicator systems, assign weights to the indicators, and calculate the coupling coordination degree to evaluate the level of coordinated development within composite systems.

Effectively identifying the degree of coordinated development among social, economic, and ecological subsystems within the watershed provides critical guidance for sustainable regional planning and management. Therefore, following the resilience evaluation, this study employs the coupling coordination degree model to quantify the coordination between the socio-economic and ecological subsystems of the Erhai watershed.

(a) Integrated level of development of computing subsystems

Taking ecosystems and social systems as examples, the indicators of social systems are represented by X = (X₁, X₂, …, X_a), the b indicators of ecosystems are represented by Y = (Y₁, Y₂, …, Y_b), and the formula calculates the development level of each system:

$$F\left(t,x\right)={{\displaystyle \sum }}_{i=1}^a{\omega }_i{X}_i, F\left(t,y\right)={{\displaystyle \sum }}_{j=1}^b{\omega }_j{X}_j$$

(9)

F(t, x) and F(t, y) are the composite indices of the social and ecological systems in period t. Simply speaking, the indicators of each subsystem are aggregated.

(b) Calculate the coupling degree of two subsystems

$$C=\left\{{\displaystyle \frac{F\left(t,x\right)F\left(t,y\right)}{{\left[\frac{F\left(t,x\right)+F\left(t,y\right)}{2}\right]}^r}}\right\}$$

(10)

The above equation is the adjustment coefficient because it is the coupling degree between the two subsystems, so r is taken as 2. For the system coupling degree, its range of values, the more orderly development among the systems, the greater the value will be, the less orderly development of the two systems the smaller the value will be.

(c) Calculate the system coordination coupling degree

$$F=\alpha F\left(t,x\right)+\beta F\left(t,y\right)$$

(11)

$$D=\sqrt{C\times F}$$

(12)

where D is the coordination degree, α, β indicates the weight of the two subsystems, both take 0.5, the two subsystems are considered equally important, the final result is divided into ten levels (

Table 3. Coupling coordination level classification criteria.

D	0 ≤ D < 0.1	0.1 ≤ D < 0.2	0.2 ≤ D < 0.3	0.3 ≤ D < 0.4	0.4 ≤ D < 0.5
level	Extreme disorder	Severe disorder	Moderate disorder	Mild disorder	On the verge of disorder
D	0.5 ≤ D < 0.6	0.6 ≤ D < 0.7	0.7 ≤ D < 0.8	0.8 ≤ D < 0.9	0.9 ≤ D < 1
level	Barely coordinated	Primary Coordination	Mid-level coordination	Good Coordination	Quality Coordination

).

2.4.3. OWA-Based Scenario Simulation

Given the inherent uncertainty and complexity of future developments, many studies have attempted to make projections based on existing data. For instance, land-use models such as SD-CA, FLUS, and Markov have been widely applied to simulate potential land-use changes. In contrast, scenario simulation provides a framework for making informed trade-offs and decisions by modeling multiple plausible future conditions under varying social, economic, and environmental contexts [23].

The complex ecosystem of the Erhai watershed faces increasing pressure from population growth and resource depletion. However, with greater governmental attention, the scale and effectiveness of watershed management have improved considerably in recent years [45]. Scenario simulation analysis is therefore essential for consolidating and enhancing the resilience of the Erhai watershed’s complex ecosystem, as well as promoting coordinated development within the coupled human–land system.

The Ordered Weighted Averaging (OWA) method derives order-based weights that capture interactions among indicators and quantitatively assess trade-offs between decision objectives under different scenarios. This approach allows for more objective and accurate scenario-based predictions [46,47]. The positional weights depend solely on the rank order of the indicators, rather than on their absolute values or categorical attributes [48,49]. In this study, the OWA-based scenario design is used not only as a predictive tool but also as a decision-support mechanism for identifying governance strategies. By simulating alternative combinations of ecological investment, economic development intensity, and social inputs, the OWA framework allows us to evaluate the outcomes of different policy orientations.

(a) Sub-order weights

ω refers to the criterion trade-off role, α represents the decision scenario, w denotes the order weight, and n is the number of criteria, calculated as follows.

$$\max \left(\omega \right)=-{{\displaystyle \sum }}_{j=1}^n{w}_j\ln w_j$$

(13)

$$\alpha ={{\displaystyle \sum }}_{j=1}^n\left({\displaystyle \frac{n-j}{n-1}}\right)w_j$$

(14)

$${{\displaystyle \sum }}_{j=1}^n{w}_j=1, 0\le w_j\le 1$$

(15)

In this study, the above nonlinear joint cubic equation system is programmed to obtain the optimal ordering in different cases (

Table 4. Optimal order weights for different decision scenarios (α) and trade-off levels (ω) (n = 17).

n = 17	α
	0.00	0.10	0.20	0.30	0.40	0.50	0.60	0.70	0.80	0.90	1.00
B3	0.0000	0.0002	0.0038	0.0142	0.0323	0.0588	0.0960	0.1487	0.2292	0.3841	1.0000
A7	0.0000	0.0003	0.0049	0.0165	0.0345	0.0588	0.0897	0.1284	0.1773	0.2366	0.0000
C3	0.0000	0.0004	0.0063	0.0191	0.0370	0.0588	0.0838	0.1109	0.1372	0.1458	0.0000
A8	0.0000	0.0007	0.0082	0.0221	0.0396	0.0588	0.0783	0.0958	0.1062	0.0898	0.0000
A3	0.0000	0.0011	0.0106	0.0256	0.0424	0.0588	0.0731	0.0827	0.0821	0.0553	0.0000
A4	0.0000	0.0019	0.0136	0.0296	0.0454	0.0588	0.0683	0.0714	0.0636	0.0341	0.0000
A2	0.0000	0.0030	0.0176	0.0343	0.0486	0.0588	0.0638	0.0617	0.0492	0.0210	0.0000
C1	0.0000	0.0049	0.0228	0.0397	0.0520	0.0588	0.0596	0.0533	0.0381	0.0129	0.0000
B2	0.0000	0.0080	0.0294	0.0460	0.0557	0.0588	0.0557	0.0460	0.0294	0.0080	0.0000
A1	0.0000	0.0129	0.0381	0.0533	0.0596	0.0588	0.0520	0.0397	0.0228	0.0049	0.0000
A5	0.0000	0.0210	0.0492	0.0617	0.0638	0.0588	0.0486	0.0343	0.0176	0.0030	0.0000
A6	0.0000	0.0341	0.0636	0.0714	0.0683	0.0588	0.0454	0.0296	0.0136	0.0019	0.0000
C6	0.0000	0.0553	0.0821	0.0827	0.0731	0.0588	0.0424	0.0256	0.0106	0.0011	0.0000
B1	0.0000	0.0898	0.1062	0.0958	0.0783	0.0588	0.0396	0.0221	0.0082	0.0007	0.0000
C4	0.0000	0.1458	0.1372	0.1109	0.0838	0.0588	0.0370	0.0191	0.0063	0.0004	0.0000
C5	0.0000	0.2366	0.1773	0.1284	0.0897	0.0588	0.0345	0.0165	0.0049	0.0003	0.0000
C2	1.0000	0.3841	0.2292	0.1487	0.0960	0.0588	0.0323	0.0142	0.0038	0.0002	0.0000

).

(b) Guidelines for Clustering

$$WOWA={{\displaystyle \sum }}_{j=1}^n{\displaystyle \frac{u_j{w}_j{z}_{ij}}{{{\displaystyle \sum }}_{j=1}^n{u}_j{w}_j}}$$

(16)

where u_j and z_ij denote the weights of the indicators determined by the entropy weighting method and the corresponding original criterion values, respectively, after descending order. Here, the data of each township in 2020 are selected and renormalized with i represent 16 townships in turn.

3. Results

3.1. Spatial and Temporal Evolution of the Complex System Resilience in the Erhai Watershed

The mean resilience values across the four study periods indicate that overall ecosystem resilience in the study area has continuously strengthened. Meanwhile, the increasing standard deviation suggests that inter-township disparities in resilience have gradually widened since 2005. From 2005 to 2020, ecosystem resilience exhibited a steady upward trend, with Xiaguan consistently showing the highest values and Niujie the lowest (Figure 2). Using ArcGIS 10.8, the resilience index values ranged from 0.16 to 0.65 and were classified into five categories to visualize spatial variation. High-value areas were mainly concentrated on the western side of Erhai Lake, forming a spatial pattern characterized by “high in the west and low in the east” (Figure 3).

3.2. Resilience Zoning of Complex Systems in the Erhai Watershed

The unique natural attributes of watersheds often transcend traditional administrative boundaries, making policy management and governance more complex. In watershed governance, implementing zoning management that integrates ecological characteristics with administrative boundaries can enhance the precision and practicality of management measures. YANG, et al. [50] divided the Erhai watershed into five primary subregions using a correlation analysis approach, taking sub-basins as the basic unit and considering indicators such as elevation, slope, and the normalized vegetation index. In this study, township boundaries were superimposed on the existing ecological zoning results to delineate new hydrological–ecological management zones at the township level. The spatial pattern of resilience levels corresponds closely with the ecological zoning results. From 2005 to 2020, the overall distribution of high- and low-value areas remained largely stable. Areas with lower resilience values were mainly concentrated in the middle and upper reaches of the Three Rivers and in the eastern region, whereas areas with higher resilience values were predominantly located in the western and southern parts of the watershed (Figure 4).

3.3. Coupling Coordination Degree of Individual System

The natural, economic, and social subsystem indices were calculated for each township in the Erhai watershed for the years 2005, 2010, 2015, and 2020. From 2005 to 2020, the weighted average indices of all subsystems exhibited a clear upward trend (Figure 5). Specifically, the natural subsystem index increased from 0.120 to 0.231, representing a growth of 92.44%; the economic subsystem rose from 0.032 to 0.071, an increase of 121.23%; and the social subsystem grew from 0.056 to 0.120, an increase of 114.72%.

3.3.1. Natural–Economic System

The coupling and coordination between natural and economic subsystems across the townships of the Erhai Basin were generally weak. From 2005 to 2020, more than 82.81% of the basin area remained in a state of moderate maladjustment, while only 14.06% achieved an advanced level of coupling coordination. The darker the color and the larger the number in the figure, the higher the degree of coupling coordination (Figure 6). The natural–economic coupling coordination was relatively high in Xiaguan, Fanyi, and Dali townships. Overall, the degree of coupling coordination in the Erhai Basin exhibited a steady upward trend over the study period.

Dali and Dengchuan townships exhibited high growth rates in their ecological subsystem indices, indicating that they have consistently led in ecological construction investments and expenditures related to agriculture, forestry, and water conservation. These areas also maintain a relatively low proportion of arable land and experience less pressure from agricultural non-point source pollution. In 2020, the ecological subsystem index of Dali Township reached the highest level among all townships in the Erhai watershed. By contrast, Wase, Sanying, and Niujie townships maintained relatively low ecological subsystem indices throughout the study period (Figure 7).

3.3.2. Natural–Social System

The coordination level of the natural–social system coupling in the Erhai watershed was generally moderate to high. Approximately half of the townships were in a state of mild maladjustment, while 70.31% achieved a high level of coupling coordination. From 2005 to 2020, the coordination level of most townships improved steadily, with the average increases reaching 3.45%, 13.03%, and 18.18% in 2010, 2015, and 2020, respectively (Figure 6). From 2005 to 2020, the economic development indices of Xiaguan and Haidong increased from 0.047 and 0.035 to 0.269 and 0.163, representing growth rates of 472.34% and 365.71%, respectively. Both townships reached the highest classification level and showed significant improvement over time. This can be attributed to Xiaguan’s position as the political and economic center of the region, as well as its role as a major transportation hub with distinct locational advantages. Haidong, located on the eastern shore of Erhai Lake within Dali City, benefits from excellent transportation infrastructure, including Dali Airport. In addition, the rapid expansion of its specialty fruit industry has substantially increased farmers’ incomes and contributed to local economic resilience (Figure 8).

3.3.3. Socio-Economic System

The socio-economic systems of the townships in the Erhai watershed exhibited a high level of coupling but a low degree of coordination, with 18.75% of the area remaining in a state of serious maladjustment during the study period. The maximum coupling coordination value of the economic–social system was only 0.47, representing a state of high coupling but low coordination, which indicates a risk of disorder. This indicates that economic development and social progress in the watershed have not yet achieved full coordination (Figure 6).

By 2020, most townships in the Erhai watershed remained in a state of moderate imbalance, primarily due to the extremely low economic index relative to the social development index observed in 2005. This imbalance became more pronounced in Xiaguan and Yinqiao in 2010 but gradually moderated by 2015. In 2020, Xiaguan, Haidong, Dali, and Yinqiao showed particularly strong performance in economic indicators such as fiscal revenue. Consequently, the social and economic systems became more harmoniously integrated, as improvements in the social sector helped narrow the gap between economic and social development (Figure 9).

3.4. Complex Ecosystem Resilience in Different Policy Contexts

This study defines the scenario in which the ecological, production, and social system indicators of the Erhai watershed are maintained at their normal levels (α = 0.5) as the status quo scenario; the conservation-priority scenario (α = 0.3) emphasizes investment in infrastructure construction, environmental governance, social security, and healthcare, with a focus on tourism development and a reduction in the proportion of industrial and mining land; the development-priority scenario (α = 0.7) focuses on economic growth by increasing per capita disposable income while simultaneously enhancing investment in energy conservation and environmental protection. Compared with the baseline year 2020, only Niujie showed a slight improvement under the status quo scenario; under the conservation-priority scenario, ecosystem resilience across all townships improved markedly, with overall resilience grades increasing throughout the watershed; in contrast, under the development-priority scenario, ecosystem resilience declined in most townships, except for Niujie and Dali (Figure 10).

Scenario 1 (Status quo scenario): The simulation results were consistent with the spatial distribution of high- and low-resilience areas in 2020, confirming the validity and robustness of the selected scenario indicators. In this scenario, the ecosystem resilience of each township showed a slight decline, with an average decrease of 0.23% (Figure 11).

Scenario 2 (Protection priority scenario): The study area exhibited strong resilience, with an average increase of 30.93% across all townships. This suggests that continuing ecological restoration and protection efforts remains essential for further enhancing resilience at the watershed scale.

Scenario 3 (Development priority scenario): The overall resilience of the study area declined, with an average reduction of 17.6% across townships. However, Dali and Niujie still experienced improvements in resilience, indicating that targeted spatial development strategies and quality-oriented economic policies could further enhance resilience in these areas. For Niujie, which lags behind in economic development, the development-priority pathway appears to be the optimal strategy for enhancing resilience.

4. Discussion

4.1. What Are the Mechanisms of Change in the Resilience of the Complex Ecosystem in the Erhai Watershed?

4.1.1. The Level of Investment in Ecological Construction Directly Affects the Level of Resilience

From 2005 to 2010, differences in the resilience of natural subsystems among townships were relatively small. During this period, slope and soil texture exerted the greatest influence on resilience, followed by land-use type and NDVI. After 2010, the situation began to change, as inter-township differences gradually widened. The dominant influencing factors shifted toward financial inputs in agriculture, forestry, and water management, as well as ecological construction investment. Particularly after 2015, the Chinese government intensified ecological management in the Erhai watershed. Between 2016 and 2020, Dali Prefecture invested a total of CNY 33.9 billion in ecological protection and environmental remediation. As a result of these effective ecological management initiatives, the resilience of the natural subsystem in Dengchuan Township increased by approximately 800% over the 15-year period. This pattern is consistent with resilience thinking, which emphasizes that ecological investments strengthen a system’s ability to withstand and recover from disturbances [37]. As financial support expanded, the natural subsystem gained greater capacity to absorb pressures and rebuild ecological functions, leading to the marked increases observed after 2015.

4.1.2. Green Development Can Achieve a Win-Win Situation in Terms of Fiscal Revenue and Resilience Levels

Fiscal revenue emerged as the primary factor influencing the resilience of the economic subsystem. In 2005, all townships exhibited similar levels of economic resilience; however, since 2010, Xiaguan, Dali, and Haidong have stood out due to their exceptionally rapid fiscal revenue growth. Unlike in many developed Western countries, local governments in China play a dual role, serving both as protectors of the ecological environment and, to some extent, as contributors to environmental degradation. The severe pollution previously observed in the Erhai watershed, mainly caused by the rapid expansion of agriculture and tourism, was largely a result of the extensive, growth-oriented development model pursued by municipal and county governments. Notably, the composite ecosystem resilience of Xiaguan and Dali was significantly higher than that of other townships. This raises an important question: how does superior fiscal capacity translate into higher ecosystem resilience?

Fiscal revenue serves as a critical link between government and market, functioning as a key instrument for implementing public policies and achieving economic objectives. Variations in fiscal incentives and pressures within the intergovernmental fiscal system exert a significant influence on local governments’ environmental governance behavior. In this context, fiscal revenue should be interpreted not merely as an economic variable but as a proxy for local governance capacity, including the ability to invest in ecological protection, maintain infrastructure, and enforce environmental regulations. Thus, its importance reflects institutional capability rather than the notion that “money determines resilience.” Townships with limited fiscal resources can still enhance resilience through regional coordination, targeted policy support, and cross-township resource sharing. When fiscal capacity declines, local governments often resort to preferential policies, such as easing environmental regulations, to attract new industrial enterprises and meet fiscal and growth targets. This trade-off underscores the necessity of transitioning toward a green and resilient development path (Figure 12).

4.1.3. The More Stable the Social System Is in Ecologically Sound Regions

Among the social indicators, per capita disposable income and population density exhibited the greatest variability. For instance, Dengchuan and Niujie townships, which are characterized by low social subsystem resilience, have relatively high population densities and low per capita incomes. This is largely attributable to their small administrative areas and the limited potential for agricultural and tourism development. In contrast, ecologically advantageous regions have achieved higher per capita incomes through the expansion of tourism-oriented industries, which in turn has strengthened their social resilience. This pattern also aligns with insights from social resilience research [24]. Townships with limited income sources and high population pressure have fewer options to adjust or cope under stress, which weakens their adaptive capacity. In contrast, areas with more stable and diversified livelihoods, such as those supported by tourism development, tend to exhibit stronger social resilience.

4.2. What Limits the Resilience of the Complex Ecosystem in the Erhai Watershed?

4.2.1. Uncoordinated Regional Development

Throughout the study period, the highest levels of resilience were observed in the southern part of the Erhai watershed, particularly in Xiaguan and Fengyi. These townships demonstrated strong ecological responses and sustained high resilience due to substantial ecological construction investments, despite the pressure of agricultural and urban pollution. In contrast, the valley areas located in the middle and lower reaches of the Three Rivers, characterized by flat terrain and dense populations, suffer from more severe nitrogen and phosphorus pollution. Their economic development lags behind that of leading townships such as Xiaguan, and insufficient ecological management measures have resulted in lower levels of ecological resilience. Although the spatial and temporal variation in ecological subsystem development across the Erhai watershed is pronounced, the overall pattern of inter-township differences has remained relatively stable. Overall, regional disparities in the resilience of natural subsystems are primarily driven by differences in ecological management investment.

Economic development across the townships exhibits a clear gradient and substantial differences in growth rates, largely attributable to disparities in fiscal revenue. Considering these economic disparities, the fiscal capacity of Xiaguan and Haidong far exceeds that of other townships. However, their investment in social undertakings remains comparable to the regional average, suggesting potential shortcomings in infrastructure development and livelihood protection.

4.2.2. Uncoordinated Development of Composite Systems

(1) Far-reaching effects of the watershed’s economic development mode at the cost of resources and environment.

The low coordination level between natural and economic subsystems in the Erhai watershed reflects the enduring impact of the past resource-intensive development model. Despite continuous ecological governance efforts, the system remains in a state of moderate maladjustment. Two contrasting patterns were observed among townships: (i) those with rapid economic growth but insufficient investment in ecological construction, and (ii) those with lagging economies but disproportionately high ecological expenditures.

(2) Insufficient investment in social undertakings and ecological management due to limited economic development.

Economically developed regions within the Erhai watershed are capable of assuming greater responsibilities for ecological management and public service provision. In contrast, economically underdeveloped townships exhibit insufficient investment in both social and ecological sectors due to fiscal constraints. For instance, Niujie’s economic growth remains sluggish, while Xiaguan’s fiscal revenue in 2020 was nearly ten times higher. Xiaguan allocated over CNY 100 million to ecological corridor construction and ecological resettlement, whereas Niujie’s investment in ecological protection was constrained by its limited fiscal capacity.

4.3. How to Improve the Resilience of Watershed Complex Ecosystems?

4.3.1. Restructuring the Economy

The level and pattern of economic development within the watershed directly influence both ecological construction and investment in social undertakings. Therefore, promoting a circular economy and upgrading the industrial structure are essential to achieving coordinated economic and ecological development. In the past, large areas of garlic cultivation in the Erhai watershed consumed substantial amounts of fertilizers and pesticides, resulting in low economic returns and significant environmental emissions. At present, the more developed townships have established relatively mature agricultural systems. Facing stricter Erhai protection policies and the realities of agricultural production, the transition toward green production methods represents both an opportunity for agricultural transformation and a necessary response to the tightening of ecological governance measures [51]. Consequently, adjusting the agricultural planting structure constitutes an effective strategy for reducing ecological pressure, while government-led technical guidance and financial support are essential to ensure its implementation. For townships with limited fiscal resources, the implementation of green agricultural restructuring relies on support from upper-level governments rather than on local funding alone. Targeted fiscal transfers, agricultural extension programs, and integrated watershed-level planning can provide the necessary institutional and technical capacity for these areas to undertake the transition. Such measures ensure that resilience enhancement remains attainable even in low-income or resource-constrained townships.

4.3.2. Watershed Buffer Zone Construction

The previously uncontrolled expansion of tourism in the Erhai watershed led to the proliferation of numerous unauthorized constructions. Subsequent demolition and recycling efforts have significantly improved the watershed’s ecological environment [52]. Recycling construction and demolition waste provides an effective means of reducing the environmental burden associated with large-scale restoration projects. Most ecological investment in the Erhai watershed has focused on the construction of buffer zones surrounding the lake. The construction of the lakeshore buffer zone plays a crucial role within a system-based governance framework for the Erhai watershed. This approach aims to balance ecological preservation and human well-being by reshaping and restoring the natural lakeshore landscape, improving the lake’s ecological health, and establishing a sustainable self-regulation mechanism. Furthermore, developing low-disturbance service systems within the buffer zone helps reduce pollutant inflow and enhances the long-term resilience of the lake ecosystem.

4.3.3. Partition Governance

Achieving sustainable watershed governance and enhancing overall resilience require targeted strategies that address diverse potential hazards and future threats across the upstream, midstream, and downstream regions [53]. For instance, the zoning framework for the Erhai watershed defines multiple levels of ecological protection and management. The primary protection zone focuses on conserving biodiversity, implementing ecological restoration, constructing ecological corridors, expanding lakeside buffer zones, and enhancing the ecological functions of the lake. The secondary protection zone emphasizes strict control over construction and commercial activities, enforcement of the arable land protection red line, prevention of water pollution in lakes and rivers, and preservation of the region’s pastoral landscape. The tertiary protection zone aims to strengthen the integrated management of mountains, water, forests, farmland, lakes, and grasslands; enhance township and village planning and construction control; and optimize the spatial layout of cultural tourism and ecological industries to promote a green economy. Zoning-based governance not only delineates clear management responsibilities but also enables policymakers to align local development objectives with the overall goals of watershed resilience and sustainable management. The zoning system provides a framework for coordination at the watershed level. Its effectiveness, however, relies on implementation within individual townships. Although many ecological pressures extend across township boundaries and therefore require planning at broader spatial scales, the concrete actions that shape ecological outcomes occur locally. Township governments are responsible for adjusting agricultural practices, enforcing land-use rules, and managing ecological projects. This structure enables watershed-level objectives to be translated into measures that reflect local conditions.

5. Conclusions

This study contributes to the understanding of ecological resilience in complex watershed systems by integrating natural, economic, and social dimensions within a unified analytical framework. Using the Erhai watershed in China as a representative case, the research constructed a comprehensive evaluation model combining the entropy weight method, multi-factor comprehensive assessment, coupling coordination degree analysis, and scenario simulation based on the OWA approach. This integrated framework provides a scientific basis for assessing, comparing, and managing the resilience of watershed-scale composite systems, offering insights into the dynamic interactions among human activities, ecological processes, and policy interventions.

The main findings are as follows: (1) From 2005 to 2020, the overall resilience of the Erhai watershed showed a continuous upward trend, with the mean value rising from 0.23 to 0.42 (an increase of 82.6%), while spatial disparities widened, reflecting uneven regional development. (2) Fiscal revenue, ecological protection investment, and per capita income were the most influential indicators, contributing 26.6%, 19.2%, and 13.6%, respectively, to resilience enhancement; areas with higher resilience were mainly concentrated in aquatic ecological zones, showing strong spatial correlation with hydrological characteristics. (3) The coupling coordination analysis indicated that townships with better coordination among natural, economic, and social subsystems generally exhibited higher resilience, but the watershed as a whole remained in a state of moderate maladjustment. (4) Scenario simulations revealed that the conservation-priority scenario achieved the highest resilience through greater investment in infrastructure, environmental protection, and social welfare, supported by sustainable tourism, while the development-priority scenario enhanced resilience in economically lagging townships such as Niujie.

This study underscores the need for resilience-oriented and region-specific watershed governance. Establishing an integrated management framework that links ecological restoration, industrial transformation, and social welfare improvement is essential for strengthening overall watershed resilience and achieving coordinated regional development. Given that the Erhai Basin is a typical example of a complex watershed system, the integrated “assessment-coupling-simulation-strategy” framework is transferable to other watersheds with comparable cross-system interactions and governance pressures.

Despite the strengths of the proposed framework, this study is subject to certain limitations. The resilience assessment is based on static time steps that may not fully capture short-term fluctuations or rapid ecological responses. In addition, the availability of social indicators at the township scale is limited, which may constrain the comprehensiveness of the social subsystem evaluation. Future research should focus on establishing dynamic monitoring frameworks that integrate high-resolution ecological, socio-economic, and climatic data to capture the temporal evolution of watershed resilience. Incorporating climate change scenarios and socio-economic uncertainties into multi-scale modeling will be crucial for predicting resilience trajectories under different policy and environmental conditions. In addition, exploring the feedback mechanisms between policy interventions and ecosystem responses can provide a stronger theoretical foundation for resilience-oriented watershed management in the context of global change. Future research could also incorporate spatial dependence measures, such as Moran’s I or LISA, to further reveal spatial autocorrelation characteristics that may shape watershed resilience.