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.