Accurate assessment and prediction of water shortage risk are essential prerequisites for the rational allocation and risk management of water resources. However, previous water shortage risk assessment models based on copulas have strict requirements for data distribution, making them unsuitable for extreme conditions such as insufficient data volume and indeterminate distribution shapes. These limitations restrict the applicability of the models and result in lower evaluation accuracy. To address these issues, this paper proposes a water shortage risk assessment model based on kernel density estimation (KDE) and copula functions. This approach not only enhances the robustness and stability of the model but also improves its prediction accuracy. The methodology involves initially utilizing kernel density estimation to quantify the random uncertainties in water supply and demand based on historical statistical data, thereby calculating their respective marginal probability distributions. Subsequently, copula functions are employed to quantify the coupled interdependence between water supply and demand based on these marginal probability distributions, thereby computing the joint probability distribution. Ultimately, the water shortage risk is evaluated based on potential loss rates and occurrence probabilities. This proposed model is applied to assess the water shortage risk of the Yuxi water receiving area in the Central Yunnan Water Diversion Project, and compared with existing models through experimental contrasts. The experimental results demonstrate that the model exhibits evident advantages in terms of robustness, stability, and evaluation accuracy, with a rejection rate of 0 for the null hypothesis of edge probability fitting and a smaller deviation in joint probability fitting compared to the most outstanding model in the field. These findings indicate that the model presented in this paper is capable of adapting to non-ideal scenarios and extreme climatic conditions for water shortage risk assessment, providing reliable prediction outcomes even under extreme circumstances. Therefore, it can serve as a valuable reference and source of inspiration for related engineering applications and technical research.

The technical research on determining the drought limit water level can be used as an important basis for starting the emergency response of drought resistance in the basin and guiding the drought resistance scheduling of water conservancy projects. When the concept of drought limit water level was first proposed, the main research object was reservoirs, and the method for determining the lake drought limit water level was not established. Referring to the calculation method of reservoir drought limit water level, the drought limit water level is used as a single warning indicator throughout the year, which lacks graded and staged standards, and also lacks rationality and effectiveness in practical application. Therefore, this article has improved the concept of lake drought limit water level (flow). Under different degrees of drought and water use patterns during the drought period, combined with the characteristics of lake water inflow, considering the factors such as ecology, water supply, and demand, lake inflow, evapotranspiration loss, a graded and staged standard of lake drought limit water level has been developed. For different types of lakes, a general method for determining the lake’s graded and staged drought limit water level has been established. The SCSSA-Elman neural network is used to construct the medium and long-term water inflow prediction model for lakes, and the calculation results of this model are used for the warning and dynamic control analysis of the lake drought limit water level. The application of this method has the characteristics of strong applicability and high reliability. Finally, the determination method and dynamic control method of the lake’s graded and staged drought limit water level have been successfully applied at Dianchi Lake in Yunnan.

Water, soil, and heat are strategic supporting elements for human survival and social development. The degree of matching between human-land-water-heat elements directly influences the sustainable development of a region. However, the current evaluation of the matching of human-land-water-heat elements overlooks the influence of elevation factors on the matching results, especially evident in mountainous areas. Taking the Yunnan Plateau with distinctive mountainous features as the research subject, divided into 11 elevation ranges, the Lorenz Gini coefficient, asymmetry coefficient, matching distance, and imbalance index are used to assess the spatial matching and balance of human-land-water-heat elements. A projection tracing model is employed to analyze its water resource carrying capacity. Analyses revealed that the Gini coefficient of monthly precipitation from the 1950s to 2022 on the Yunnan Plateau increases with increasing latitude, whereas the correlation with elevation is notably lower. The asymmetry coefficient increases gradually from west to east with change in longitude. The mismatch of the human–land–water–heat system in regions at different elevations is in the order 1800–2000 m > 2000–2200 m > 1400–1600 m > 800 m > other areas. The matching of the human–land–water–heat system in different wet–dry years and seasons also fluctuates with elevation, resulting in serious seasonal drought and water shortage problems in mountainous areas with elevations of 1200–1600, 1800–2000 m, and >2600 m. The spatial equilibrium of temperature and precipitation in regions of different elevations is best, followed by that of cultivated land, while that of the population is the worst. The Gini coefficients for different water cycle processes of precipitation, surface runoff, and regulating storage capacity for water supply continue to increase. Specifically, the Gini coefficient of industrial water supply is the highest, reaching 0.576, and that of agricultural irrigation is the lowest (0.424). Through artificial regulation of lake and reservoir water, seasonal changes in the demand for agricultural irrigation water are offset to achieve a demand–supply balance and matching of land and water resources. The water resource capacity of different elevation ranges is evenly underloaded. However, the potential of the water resource capacity varies obviously with elevation in the order 2000–2200 m < 1800–2000 m < 1600–8000 m < 1400–1600 m < other areas. It appears that the greater the human–land–water–heat system mismatch, the smaller the regional potential of the water resource capacity.

This paper adopts the maximum carrying population, industrial added value and agricultural irrigation area to develop a multi-objective model of water resources carrying capacity (WRCC) in Dianchi-Pudu river basin. The model is used to calculate the WRCC for Dianchi-Pudu river basin in 2020 and 2030 respectively in terms of three different water supply schemes. Results show that except Scheme III which can basically support future socio-economic development and population increase, both Scheme I and II cannot support future socio-economic development and population increase of Dianchi-Pudu river basin. Interbasin water transfer would greatly increase the WRCC of Dianchi-Pudu river basin while efficient water saving can merely work in a limited extent. Consequently, interbasin water transfer and efficient water-saving are the necessaries to guarantee the positive balance between the exploitation of water resource and socio-economic development of Dianchi-Pudu river basin.

This study focuses on Qilu Lake to study how to mitigate the impacts of seasonal droughts and provide technical support for drought resistance decision-making in low-latitude plateau lake basins. Using the Standardized Precipitation Index (SPI), the Vegetation Condition Index (VCI), and the Temperature Condition Index (TCI) as bases, in this study, the applicability of the vegetation health index (VHI) within the basin is investigated, and the optimal weight distribution between the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) in the VHI is determined. The VHI is then applied to analyze the correlation between drought frequency and severity within the basin. The results indicate that the method is most effective in assessing agricultural drought in the Qilu Lake Basin when the VCI and TCI are weighted at a 4:6 ratio, optimizing the VHI’s evaluative performance. The drought limit water levels of lakes are further divided into short- and long-term drought limit water levels. The short-term drought limit water level is divided into the drought warning water level and the drought emergency water level. The drought warning water level (corresponding to moderate drought conditions, with a frequency of P = 75%) ranges from 1794.53 m to 1795.11 m, while the drought emergency water level (corresponding to extreme drought conditions, with a frequency of P = 95%) ranges from 1793.94 m to 1794.31 m. These levels are set to meet the emergency water demand during droughts in the basin. The long-term drought limit water levels are calculated by accumulating the water deficits of various sectors within the watershed under different agricultural drought conditions, based on the short-term drought limit water levels. By setting the drought limit water level using this method, as well as considering the original water regulation capacity of the lake resources, when the watershed experiences drought, the scheduling method based on this drought limit water level can better alleviate the water supply pressure on various sectors in the local area.