Study on the urban moderate scale of Dongting Lake Ecological Economic Zone based on the water resources carrying capacity

Urbanization is a critical component of Chinese-style modernization and a driving force for China's economic and social development. There is a close relationship between water resources and urbanization, with water serving as a fundamental material foundation for urban growth. The water resources carrying capacity is a crucial metric for assessing the population scale and economic development level that a region's water resources can support (Song et al. 2011). Achieving sustainable urban development depends on judiciously expanding urban scale while ensuring it remains within the limits of the water resources carrying capacity. In 2023, China's urbanization rate has exceeded 65%, but basic public services in cities and towns do not cover all permanent residents, and mega-cities are expanding too rapidly. With the growth of urban population and the economic development, some problems such as water shortages and environmental degradation have become more prominent (Yang et al. 2019). Therefore, both society and academia are paying close attention to the challenge of urban moderate scale development under the constraints of water resources.

Studies on the relationship between water resources and urbanization began early, focusing primarily on the impact of water resources on urban patterns, development levels, and water management, as well as the feedback effects of urbanization on the water environment (Lerner & Barrett 1996; Salman 2002; Al-Kharabsheh & Ta'any 2003). However, in the study area, most research has focused on a specific city or urban agglomeration, which is characterized by more concentrated populations and industries, as well as a more prominent contradiction between the quantity of water supply and demand (Wang et al. 2018a; Zhao et al. 2021). There is a notable lack of research on typical areas that are relatively abundant in water resources but also have higher requirements for maintaining ecological balance. Therefore, this paper focuses on the Dongting Lake Ecological Economic Zone, the second-largest freshwater lake in China and an important retention lake in the Yangtze River basin (Bao & Fang 2012). Despite its relatively rich water resources, the population is rapidly expanding, and living standards are improving as the country accelerates industrialization and urbanization. The resulting tension between the economic and social development and the water resources, as well as the water environment, is becoming increasingly acute. Therefore, this paper is both practically and urgently significant and can provide a reference for other regions with similar conditions globally, guiding the efficient and scientific allocation of water resources and promoting sustainable urban development.

Carrying capacity, originally a physical concept, refers to the maximum load that an object can bear without sustaining damage. Resources carrying capacity refers to the number of people that a region can continuously support by using its local resources, natural assets, intellectual capabilities, technologies, and other conditions within a foreseeable period, maintaining a standard of living in line with its social and cultural norms (Ren et al. 2016). In theoretical research on water resources carrying capacity, most scholars incorporate it into the theory of sustainable development. Keenan et al. (1999) studied the effects of persistent drought on urban economic and social development by taking Southern California as an example. Joardar (1998) explored urban water resources carrying capacity from the perspective of water supply and integrated it into urban development planning. Rijsberman & Van de Ven (2000) studied the evaluation and management system of urban water resources, using carrying capacity as a measure of water security. Varis & Vakkilainen (2001) made preliminary comparisons between the socioeconomic states of the Yangtze River basin and its water resources carrying capacity based on regional development histories. Wang et al. (2019) discussed the constraints of water resources on urban development, noting that excessive urban population concentration leads to severe water shortages.

In China, the concept of water resources carrying capacity was first studied by a soft science research group in Xinjiang in 1989 (Song et al. 2020). However, there is no unified definition of regional water resources carrying capacity. Scholars have proposed various perspectives (Deng et al. 2021; Lv et al. 2021; Wang et al. 2022), generally summarized as the maximum supporting capacity and the maximum development capacity of water resources. This paper defines water resources carrying capacity as the scale of socioeconomic activities and population that a regional water resources system can support under specific historical, technological, and socioeconomic conditions, while maintaining ecological sustainability and living standards in line with social and cultural norms (Qiu et al. 2021).

The urban moderate scale refers to the limits of urban development, where future spatial expansion is not constrained by land or water resources. It is primarily determined by the population, land use, and socioeconomic factors, which are interrelated. Since urban land and socioeconomic scales are often linked to population size, the urban moderate scale is essentially a measure of the moderate population scale. It is typically assessed by evaluating the urban moderate population scale (Yang et al. 2011). Water resources and urban scale interact and restrict each other, with excessive urban population concentration leading to serious water shortages. To comprehensively study urban moderate scale, scholars have developed various analysis methods under the constraints of water resources carrying capacity, including single-factor or multi-factor index methods based on water quantity, water quality, or water rights allocation, and comprehensive evaluation methods (Zhang et al. 2017; Zhu et al. 2019; Qin et al. 2023). While each method has its advantages, most studies have focused on large urban agglomerations where water resource supply and demand conflicts are prominent. Few have explored urban moderate scale development in areas with relatively abundant water resources.

Current prediction methods for urban moderate scale include the Logistic model, ARIMA (Autoregressive Integrated Moving Average) model, BP (Backpropagation) neural network model, and GM (Grey Model) (1,1) model. The Logistic model is suitable for linear fitting but has low classification accuracy and a tendency to overfit (Mulligan 2006). The ARIMA model requires stable time series data (Malarvizhi et al. 2022). The BP neural network model has strong generalization ability but requires significant time and computational resources. The GM (1,1) prediction model is commonly used for urban population scale forecasting, but due to its limitations, some scholars have proposed improvements (Zhang et al. 2023). Guo et al. (2018) used adaptive filtering method to correct the residuals of the traditional GM (1,1) model. Jiang et al. (2011) improved forecasting accuracy by establishing a multi-index PSO-BP (Particle Swarm Optimization-Backpropagation) neural network population scale prediction model. Zhao et al. (2014) improved accuracy by combining the advantages of the GM model and the RBF (Radial Basis Function) neural network model. These studies show that improved models generally achieve higher prediction accuracy than traditional GM models.

To address the gaps identified in previous studies, this paper selects 25 districts and counties in the Dongting Lake Ecological Economic Zone as research subjects. It uses a water resources carrying capacity model to assess the urban moderate scale under water resources constraints and analyzes the spatial evolution trends of water resources carrying state, urban moderate scale, and distance coordination degree. The paper also predicts the future urban moderate scale based on the GM-LSTM (Long Short-Term Memory) model.

Compared to existing research, this paper makes three key contributions:

• (1) Unlike studies that consider a single influencing factor, such as water quantity (Zhu et al. 2019), this paper constructs a two-factor water resources carrying capacity model, incorporating both water quantity and quality.

• (2) Different from the research focused on arid cities (Yang et al. 2011; Qiu et al. 2021), this paper extends the analysis of urban moderate scale within the context of water resources carrying capacity to specific functional areas with relatively abundant water resources. This provides new reference pathways for similar regions globally to promote sustainable urban development.

• (3) Regarding prediction models, while Xiong et al. (2016) used the GM (1,1) model to predict future urban moderate scale, this paper innovatively introduces the LSTM model to enhance the GM (1,1) approach. Specifically, it employs the GM-LSTM model to predict urban moderate scale, overcoming the limitations of single models and improving forecasting accuracy and effectiveness in addressing uncertainties (Wu et al. 2018).

Dongting Lake Ecological Economic Zone is characterized by a dense water network and abundant water resources. However, with the acceleration of urbanization and the increasing demands from agriculture and industry, coupled with the impacts of climate change and pollution, water resources in the area are facing significant challenges. In recent years, the average amount of water entering the lake has decreased from 302.8 billion cubic meters to 205 billion cubic meters, highlighting the increasing pressure on the water resources carrying capacity.

The distance coordination degree is a quantitative index obtained by calculating the distance between the actual value and the ideal value, which is mainly used to measure the level of coordination between two or more systems, indicators, or variables. It enables policymakers to conduct a rigorous quantitative evaluation of the coordination state between systems, indicators, or variables, accurately detect factors contributing to maladjustment, and formulate effective strategies aimed at fostering a more advanced level of coordinated development.

As shown in Table 1, the distance coordination degree between the urban moderate scale and the actual urban population is categorized into two intervals: maladjusted recession and coordinated development (Fu et al. 2016). Furthermore, a corresponding warning level is established for each evaluation type. For instance, if the actual population of a city exceeds its moderate scale, and the distance coordination degree calculated using formula (4) is −0.455, the city would fall into a state of moderate maladjustment according to Table 1. The associated warning level would be a red warning, indicating a critical situation. Policymakers should take immediate action to curb the expansion of the urban population. By assessing and monitoring the distance coordination degree, policymakers can quickly identify and address urban sprawl issues, making this approach highly significant for urban planning and the sustainable development of cities.

Urban economic activity density refers to the intensity of economic activity within a specific urban area relative to its physical space. It reflects the degree of agglomeration and the spatial distribution characteristics of urban economic activities, serving as a key indicator for assessing the level of urban economic development. Typically, urban economic activity density is measured by calculating the number of economic activities per unit land area of a city. Existing research often measures urban economic activity density using population density (Wang et al. 2023). However, while some cities may have high population densities, their economic activity density may remain low due to factors such as low industrialization levels and limited capital stock (Abel & Gabe 2011). To address this discrepancy, this paper further incorporates enterprise density and capital density into the evaluation of urban economic activity density. The comprehensive evaluation index system for urban economic activity density is detailed in Table 2.

• (1) GM (1,1) model

The GM (1,1) model involves generating a cumulative sequence with strong regularity by performing m-times accumulative generation on a set of discrete and random original data sequences, thereby weakening the randomness of the original sequences. After that, modeling is conducted on the generated sequence, and through cumulative subtraction, a predictive sequence is generated (Wang et al. 2018b).

The GM (1,1) model is a widely used prediction method. However, it exhibits limited flexibility, and its prediction accuracy decreases as the degree of data dispersion increases. Therefore, this paper introduces the LSTM model, which has better model generalization capabilities and long-term memory functions, to correct the GM (1,1) model. The GM-LSTM model is employed to predict the urban moderate scale of the Dongting Lake Ecological Economic Zone in the coming years. The specific steps are as follows.

Second, the residual sequence is normalized and used as the input value for the LSTM model, while e⁽⁰⁾(i) serves as the output vector, to train the LSTM model. In this paper, the hidden layer h is set to be 20, and the model is trained for 400 epochs using the PyCharm 2024.1.3, with an RTX3050 GPU.

Data related to water resources, including the total amount of the surface and underground water resources, ecological environmental water demand, agricultural water consumption, industrial water consumption, domestic water consumption, public water consumption, surface runoff, urban wastewater discharged and etc, are mainly collected from the Hunan Water Resources Bulletin from 1980 to 2022, the third evaluation of water resources in Hunan province and the water resources bulletin of districts and counties in Hunan province. The actual urban population data are obtained from the Hunan Statistical Yearbook and the statistical yearbook of districts and counties in Hunan province from 1981 to 2023. To ensure data integrity and consistency, any missing data are completed using interpolation.

In this paper, the formula (1) for calculating the urban moderate scale and the formula (2) for calculating the overloading degree of water resources are applied to evaluate the urban moderate scale in the Dongting Lake Ecological Economic Zone, as summarized in Table 3. The water quantity carrying capacity (c₁) and the water quality carrying capacity (c₂) are obtained from formula (1). Both capacities exhibit a trend of first rising and then falling from 2010 to 2022, with the water quality carrying capacity consistently smaller than the water quantity carrying capacity. In 2017, the water resources began to be overloaded.

In 2010, the water resources carrying state was overloaded in Yueyanglou district and Junshan district of Yueyang city, in Dingcheng district of Changde city, in Yuanjiang county and Ziyang district and Heshan district of Yiyang city, as well as in Wangcheng district of Changsha city. This indicates that the overloading state of water resources is mainly concentrated in the districts of cities within the Dongting Lake Ecological Economic Zone, while the water resources carrying state in the counties is in surplus.

By 2016, the spatial pattern of water resources carrying state still presented a pattern of district overloading and county surplus. However, the number of districts and counties in an overloaded state had increased, with Yunxi district in Yueyang city, Taoyuan county in Changde city and Taojiang county in Yiyang city shifting from surplus to overloading. Meanwhile, Yueyanglou district and Junshan district in Yueyang city remained heavily overloaded.

By 2022, the spatial distribution pattern of the water resources carrying state and urban moderate scale remained similar to those in 2016, indicating that the water resources pressures in overloaded districts and counties had not been effectively mitigated. This highlights the need for enhanced water resources management and urban development planning. Notably, Taojiang county transitioned from a state of water resources overloading to a surplus state. According to the annual statistical bulletins of national economy and social development in Taojiang county, since 2017, a series of comprehensive water environment management measures were implemented. These adhered to core principles such as total quantity control, optimal resources allocation, rational development, and effective conservation, aligned with the philosophy that ‘lucid waters and lush mountains are invaluable assets.’ By 2022, these efforts led to significant improvements in water quality, the establishment of water-saving systems, and effective alleviation of water resources pressures, culminating in a surplus state.

Overall, the water resources carrying state in the districts and counties of the Dongting Lake Ecological Economic Zone indicates that districts are generally overloaded, while counties are in surplus. This suggests that urbanization has led to population migration from counties to districts, resulting in a shortage of water resources in districts. This may also be the primary reason for the overall overloading of water resources in the Dongting Lake Ecological Economic Zone. Therefore, districts and counties should optimize their industrial distribution and lay down appropriate policies for population migration, considering water resources supply, to promote urban moderate scale development.

The distance coordination degree between the urban moderate scale and the actual urban population exhibited a fluctuating downward trend from 2010 to 2022. Specifically, the distance coordination degree from 2010 to 2016 ranged between 0 and 0.045, with an overall decreasing trend. Combined with Table 1, the urban moderate scale and the actual urban population were in a state of primary coordinated development, and the warning level was green warning. However, from 2017 to 2022, the distance coordination degree decreased significantly, reaching −0.021 in 2017, indicating the onset of an impending maladjusted recession. The warning level shifted to yellow warning. And by 2022, the distance coordination degree had further declined to −0.084.

Two main factors can explain this decline. First, the total amount of available water resources decreased while the actual urban population increased, exacerbating the supply-demand imbalance for water resources. Second, with the continued development of urbanization and industrialization, increased sewage discharge has deteriorated water quality, resulting in the reduction of the urban moderate population scale. Therefore, to address these challenges, the Dongting Lake Ecological Economic Zone should reconsider its existing production structure, water supply, and water usage patterns. It is essential to strengthen water resources protection and adopt sustainable urbanization practices.

To further understand the degree of coordinated development between the urban moderate scale and the actual urban population in the Dongting Lake Ecological Economic Zone, this paper examines the distance coordination degree of districts and counties in 2010, 2016, and 2022. Using ArcGIS 10.8 software, the distance coordination degree and its evolution are visually presented.

In 2010, the distance coordination degree in most districts and counties was in a state of primary or intermediate coordinated development, with a green warning level. Only a few districts and counties were in the impending maladjusted recession, marked by a yellow warning level. By 2016, the spatial distribution of the distance coordination degree had slightly deteriorated compared to 2010, with Junshan district and Yunxi district in Yueyang city entering into a state of the mild maladjustment, the warning level is double yellow. In 2022, most districts and counties experiencing maladjustment development were located in districts. Junshan district of Yueyang city showed moderate maladjustment, with a red warning level, while the spatial distribution of other districts and counties remained similar to that in 2016.

In summary, spatial pattern analysis indicates that most districts remain in a state of maladjusted development, while most counties achieve coordinated development. As time progresses, the polarization of these patterns is expected to intensify. This can be attributed to inherent population growth and external inflows in most districts, which lead to continuous expansion of the actual urban population. The resulting population pressure emerges as a primary driver of environmental degradation (Roy et al. 2022a). Without rational and effective interventions, the gap between urban moderate scale and actual urban population is likely to widen, increasing the warning level of the distance coordination degree. Thus, it is imperative for districts in the Dongting Lake Ecological Economic Zone to prioritize effective urban planning, curtail unwarranted urban expansion, and develop integrated water resources management strategies. These measures are essential to ensure efficient water resources allocation and support sustainable development.

Between 2010 and 2022, with the exception of Shimen county and Anhua county, which are located in mountainous and sparsely populated areas, the urban economic activity density in most districts and counties of the Dongting Lake Ecological Economic Zone exhibited an upward trend. Analyzing the overall spatial distribution pattern, districts generally maintained high urban economic activity density, whereas most counties experienced low urban economic activity density. Notably, districts such as Yueyanglou, Heshan, Wuling, and Wangcheng ranked at the forefront in terms of urban economic activity density. These areas are also the primary regions experiencing water resources overload in the Dongting Lake Ecological Economic Zone. Although these districts are situated in the Dongting Lake basin and benefit from relatively abundant water resources, the intensification of urban economic activities has led to a sharp increase in industrial and domestic water demand. This surge has further exacerbated the imbalance between water supply and demand (Qiao et al. 2021). Additionally, the wastewater discharge associated with these activities has significantly increased, escalating the complexity and challenges of urban wastewater treatment.

To address these challenges, districts and counties in the Dongting Lake Ecological Economic Zone, especially areas with high economic activity density and water resources overload, should establish a systematic and comprehensive water resources protection and utilization framework. Such an approach should include strengthening water resources management, improving water-saving measures, enhancing wastewater treatment and reuse systems, and intensifying water environment supervision and protection. These efforts are critical to tackling the water resources challenges posed by increasing urban economic activity density and promoting the green and sustainable development of the regional economy.

Second, the trend analysis method is employed to predict the actual urban population in the coming years. A curve is created to reflect the total population trend by fitting the population data of the Dongting Lake Ecological Economic Zone from 2010 to 2022. Subsequently, the number of urban population from 2023 to 2035 is then predicted based on this trend curve, and the results are shown in Table 4.

Finally, by using formulae (2) and (4), the overloading degree of water resources and the distance coordination degree between the urban moderate scale and the actual population in the Dongting Lake Ecological Economic Zone from 2023 to 2035 are calculated. The specific results are shown in Table 4.

The prediction results indicate that in the short term (2023–2028), the urban moderate scale will exhibit a downward trend. However, from 2028 to 2035, the trend becomes uncertain, with fluctuations leaning toward an upward trajectory. By 2035, the urban moderate scale is predicted to reach 846.52 10,000 people. During the period from 2023 to 2035, the linearly predicted population is expected to show a steady upward trend, with the urban moderate scale remaining smaller than the actual urban population. Consequently, the water resources carrying capacity is anticipated to remain overloaded, and the distance coordination degree is expected to decline year by year. By 2035, the overloading degree of water resources is projected to reach 1.554, while the distance coordination degree will drop to −0.217, indicating a state of mild maladjustment and a double yellow warning level. This suggests that as urbanization advances in the Dongting Lake Ecological Economic Zone, the conflict between water supply and demand will intensify. Without timely governmental interventions to enhance water resources carrying capacity and control urban population growth, the region's sustainable development will face significant constraints.

From an ecological perspective, the continued overload of water resources directly threatens the water safety of Dongting Lake and its surrounding waters (Lv et al. 2021). With the acceleration of urbanization, the discharge of domestic wastewater and industrial wastewater is expected to continue to increase. As urbanization accelerates, domestic and industrial wastewater discharges are expected to rise. Without effective control measures, this may lead to environmental issues such as eutrophication and heavy metal pollution, adversely affecting biodiversity and disrupting the regional ecological balance (Wang et al. 2021). Additionally, excessive groundwater extraction could result in geological problems, including land subsidence and groundwater level decline, causing irreversible damage to the ecological environment.

From an economic standpoint, water resources shortages will act as a bottleneck, restricting industrial upgrading and economic growth in the Dongting Lake Ecological Economic Zone. As water resources are fundamental to industrial production and agricultural irrigation, an overloaded water system will limit high water consumption industries, impacting production efficiency and cost control. Agricultural production may also suffer, threatening food security and agricultural product supply. Over time, these challenges will weaken the region's economic competitiveness and hinder its pursuit of sustainable development (Li et al. 2022).

At the social and livelihood level, water shortages and deteriorating water quality will directly affect the quality of life and health of residents (Roy et al. 2023a). And as the urban population grows, water demand will further increase, placing additional strain on already overloaded water resources.

Urbanization, a key driver of development in the Dongting Lake Ecological Economic Zone, has spurred urban expansion and significantly increased water demand (He et al. 2021). This surge is attributed not only to rising domestic consumption but also to booming industrial and commercial activities, leading to sharp increases in industrial and public water use. Economic growth in the region has heightened the prominence of secondary and tertiary industries. Although agricultural water use has been reduced through water-saving technologies, intensified demand from industrial and service sectors has exacerbated tensions between water supply and demand (Qiao et al. 2021). Additionally, urbanization has transformed residents' lifestyles, with urban dwellers demanding higher water quality and consuming more water for activities like bathing and cleaning, thereby elevating per capita domestic usage. Urbanization policies, including financial support, tax incentives, and land allocation, have further accelerated the concentration of population, enterprises, and capital, indirectly driving escalating water demand.

Against this backdrop, this paper examines the urban moderate scale of the Dongting Lake Ecological Economic Zone based on a two-factor water resources carrying capacity model that considers both water quantity and quality (Yeleliere et al. 2018). The water quantity carrying capacity assesses whether water supply can meet the demands of urban life, production, ecology, and other needs. Meanwhile, the water quality carrying capacity evaluates the availability of water resources, ensuring they meet urban water quality standards.

The impacts of water quantity and quality carrying capacities on urban planning and industrial structure are distinct and significant (Li et al. 2024). Water quantity directly constrains the urban development scale. Under water-scarce conditions, urban planners must limit the expansion of high water-consuming industries, such as textiles and paper manufacturing, while promoting water-efficient sectors like information technology and biomedicine. This structural adjustment ensures that water resources meet urban needs while supporting sustainable economic growth. Water quality carrying capacity, critical for urban water safety and public health, emphasizes the need for water environment protection in urban planning. Policies must prioritize water quality preservation, enforce stringent regulations on high-discharge or difficult-to-treat industries, and restrict or prohibit their development if necessary (Li et al. 2023). Simultaneously, planners should promote eco-friendly and clean industries, such as renewable energy and advanced materials, to minimize environmental impacts and foster green economic growth.

Urban planners must adopt differentiated development strategies based on the specific water resources conditions of each district and county. In areas with intense economic activities and significant water quality issues – such as Yueyanglou district, Wuling district, and Heshan district, priority should be given to improving and protecting water quality. This includes upgrading wastewater treatment facilities to enhance efficiency and effluent standards (Wang et al. 2021), promoting water-saving and emission-reduction technologies such as rainwater collection and reclaimed water reuse, and implementing rigorous water source protection measures to safeguard urban water supply security (Han & Jia 2022). Structural industrial adjustments should restrict the expansion of high-pollution and water-intensive sectors while encouraging the development of low-pollution, water-efficient industries, thereby reducing burdens on water resources and aquatic ecosystems.

Conversely, in water-abundant but economically underdeveloped areas such as Nan county and Hanshou county, urban planners should prioritize expanding water supply (Lv et al. 2021). Strengthening water source exploration and building urban water supply networks can improve the availability, convenience, and stability of water for production and domestic use, thereby establishing a solid foundation for urban development and economic growth. Emphasis should also be placed on preserving water quality, avoiding a ‘pollute first, treat later’ approach, and ensuring that water quality carrying capacity aligns with sustainable urban development.

The study reveals that most districts face water resources overload and maladjusted recession in terms of the distance coordination degree, whereas most counties experience water resources surplus and coordinated development. This disparity highlights a polarization in water resources distribution between districts and counties. The implementation of inter-regional water transfer projects from counties with water surplus to districts experiencing water overload can directly facilitate the reallocation of water resources, thereby enhancing the water resource carrying capacity of these receiving districts. However, the execution of such initiatives is accompanied by substantial investments in human capital, material resources, and financial costs. Furthermore, the intricate process of human intervention is likely to induce a series of cascading reactions and alterations within the existing ecological environment, necessitating careful consideration and assessment. Hence, policymakers should adopt corresponding measures to restrict population influx into districts and encourage the orderly migration of their populations to counties (Li et al. 2017), thereby balancing water demand between districts and counties, alleviating the burden on district water resources, and indirectly achieving the reallocation of water resources.

However, concerns such as infrastructure deficiencies and limited economic development in counties pose significant barriers to the spontaneous migration of population from counties to districts. In light of this, strengthening collaboration between districts and counties appears particularly urgent and crucial. Specifically, investments in county infrastructure development can not only stimulate economic vitality but also effectively expand employment opportunities, thereby enhancing their attractiveness to population and facilitating spontaneous inflow. Furthermore, through rational planning, the appropriate relocation of water-intensive and high-pollution enterprises from districts to counties can significantly reduce water consumption and pollution in districts, while simultaneously bringing new opportunities for economic development to counties. This approach can achieve a win-win situation for water resource utilization and economic development between districts and counties, promoting balanced and coordinated regional development (Zhang & Duan 2024).

Future research should deepen the exploration of water resources carrying capacity by integrating and analyzing the dynamic impacts of multifaceted variables, including climate and topography, on water supply and spatial distribution patterns (He et al. 2021; Roy et al. 2022b). This calls for methodological innovations, including the use of GIS spatial analysis techniques, advanced statistical models, machine learning algorithms, and deep mining of remote sensing data (Roy et al. 2023b). Such approaches can precisely characterize how climatic variations (e.g., precipitation, temperature) and topographic attributes (e.g., altitude, slope, soil type) jointly influence the spatial configuration of water resources. Scenario-based simulations and forecast analyses can further evaluate potential changes in water supply-demand balances under different climate and topographic scenarios, offering scientific insights for the development of sustainable water resources management strategies and urban planning.

Based on the water resources carrying capacity, this paper constructs models to analyze the urban moderate scale of the Dongting Lake Ecological Economic Zone under the constraints of water resources. The main conclusions are as follows:

• (1) From 2010 to 2022, the water quantity carrying capacity and the water quality carrying capacity both showed an inverted U-shaped trend, with water quality carrying capacity being smaller than water quantity carrying capacity. The water resources carrying state became overloaded in 2017.

• (2) The distance coordination degree between the urban moderate scale and the actual urban population shows a fluctuating downward trend, gradually transitioning from a state of primary coordinated development to a state of impending maladjusted recession.

• (3) The spatial distribution patterns of the water resources carrying state and the urban moderate scale in each district and county reveal that districts are overloaded while counties have surplus resources. This indicates a polarization between districts and counties.

• (4) In terms of the spatial distribution of the distance coordination degree, most districts are in a state of maladjustment development, with warning levels being double yellow or red, while most counties are in a state of coordinated development with a green warning level.

• (5) Judging from the overall spatial distribution pattern of urban economic activity density, districts generally maintain high urban economic activity density, while most counties exhibit low urban economic activity density. This disparity significantly influences the formation of water resources overload patterns.

• (6) From 2023 to 2035, the urban moderate scale of the Dongting Lake Ecological Economic Zone is projected to show a downward trend in the short term. In the long term, the direction of change remains uncertain but is predicted to stay lower than the actual urban population. The water resources carrying capacity is expected to remain overloaded, and the distance coordination degree is likely to decline year by year. By 2035, the warning level is anticipated to reach a double yellow warning.

The results of this paper have the following recommendations:

• (1) Optimizing the industrial structure to enhance the water quantity carrying capacity. Policymakers should proactively guide the transformation of primary and secondary industries toward water-conserving and high-efficiency models. This includes implementing restrictive measures such as water quotas and water resources taxes on industries with high water consumption and low efficiency to significantly enhance the utilization efficiency of water resources. Furthermore, considering the spatial distribution patterns of water resources, it is essential to develop scientific and rational industrial layout plans. In regions facing persistent water resources overload, policymakers should prioritize the development of water-saving and high-tech industries while strategically relocating certain water-intensive industries to areas with surplus water resources. Additionally, the government should actively promote and support the growth of water-conserving and high-efficiency industries by establishing specialized support funds, offering financial subsidies, and implementing tax incentives. These measures aim to effectively reduce water consumption in production processes, thereby enhancing the overall water quantity carrying capacity. Such efforts are crucial for fostering sustainable urban development in areas with relatively abundant water resources.

• (2) Strengthening technological innovation in wastewater treatment to improve water quality carrying capacity. The rigorous enforcement of legally binding wastewater discharge standards is of paramount importance in enhancing the water quality carrying capacity. On this basis, policymakers should build a perfect incentive mechanism for scientific research, and actively guide enterprises and scientific research institutions to devote themselves to the research and innovation of wastewater treatment technology by setting up special scientific research projects and providing stable and sufficient financial support. During this process, equal importance should be given to the introduction of international advanced technologies and the independent development of local technology, forming a technology development path driven by the integration of internal and external resources. Through this series of initiatives, the aim is to significantly improve the efficiency and reuse rate of wastewater treatment, fundamentally reducing the pollution and pressure exerted by wastewater discharge on water resources. This will effectively enhance the water quality carrying capacity and provide solid technical support for the sustainable utilization of water resources and environmental protection.

• (3) Adhering to the principle of matching urban scale with available water resources to effectively promote the harmonious symbiosis between humans and water. At first, policymakers should reasonably limit the urban scale in some areas where water resources continue to be overloaded. In addition, it is also necessary to reasonably guide the orderly migration of population between districts and counties. Through the combination of policy regulation and market mechanism, the population is encouraged to migrate from the areas with high economic activity density, to the areas which have great water resources surplus and development potential, so as to alleviate the water shortage and realize the spatial balance between population and water resources distribution.

The authors would like to thank the editors and reviewers for their valuable time giving comments and support.

This research is supported by Key Scientific Research Project of Hunan Provincial Department of Education (Grant No. 22A0468).

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.