Risk analysis of water resources carrying capacity based on a novel early warning framework

Water Resources Carrying Capacity (WRCC) embodies the fundamental constraint relationship between water resource sustainability and regional economic growth as well as social development, serving as a key indicator for assessing regional sustainable development. Existing studies mainly focus on carrying capacity or status assessment, often deriving deterministic results based solely on a single factor. However, as an indicator of sustainability, WRCC is inevitably influenced by uncertainty risks. Therefore, This study develops a novel Water Resources Carrying Capacity Early Warning (WRCCEW) system based on the Driving force-Pressure-State-Impact-Response-Management (DPSIRM) framework and applies it to evaluate the status of Henan Province from 2011 to 2023.Using the Geographical Detector (GD) model, this study examined the drivers of WRCCEW during different periods along with their interaction effects, while the Bayesian Network (BN) model was applied to simulate the probability of risk occurrence under diverse scenarios. Findings reveal that in Henan Province, the no warning zones are migrating from the west toward the south, the proportion of extreme warning zones has markedly decreased, whereas the proportion of no warning zones has experienced a modest reduction. Meanwhile, the interactive effects of multiple factors exert a significantly stronger influence on WRCCEW than single-factor effects, with the coupling between total water resources and other factors being the most prominent. In the scenario simulations, the S18 scenario can significantly reduce the occurrence of high-risk situations in Henan Province. This study provides a comprehensive framework for the early risk assessment of regional WRCC, highlighting the importance of incorporating uncertainty and probabilistic risks in sustainability evaluation. • Established a novel integrated evaluation system for water resources carrying capacity early warning. • Bayesian methods was introduced for the uncertainty analysis of water resources carrying capacity. • Improved the traditional division of research areas by administrative regions into grid cell units. • Areas classified as light warning or no warning expanded, whereas the share of extreme warning zones dropped by 42%. • The synergistic effects of multiple factors provide better explanatory power for water resources carrying capacity early warning.