Adaptive evaluation of urban water resource carrying capacity: Development and empirical study of the URCA model

Abstract

With global climate change and the acceleration of urbanization, urban water resource systems are facing increasingly complex nonlinear risks and dynamic challenges, traditional static assessment methods are gradually becoming in effective. In order to resolve the issues of insufficient dynamic characterization, lack of cross-regional comparability, and inefficient small data prediction, in this study, we combine complex adaptive systems theory (CAS) and resilience cycle theory, propose a six-dimensional dynamic adaptive assessment framework covering “background − pressure − state − response − degradation − upgrade” (BPSRDU), and take Dalian, Qingdao, Ningbo, Xiamen, and Shenzhen as the subjects of our analysis and research. We used the grey relational entropy method for dynamic weighting of indicators, combined with a four-quadrant matrix to examine the coupling relationship between self-organizing states and external environments, and integrated elastic window exponential moving average (EWMA) model to construct an integrated water resource adaptive assessment model (URCA) encompassing “diagnosis − prediction − regulation ”. Empirical results indicate that the water resource carrying capacity of the five cities exhibits significant spatiotemporal differences: Xiamen and Shenzhen are in the “high carrying capacity − low volatility” protection phase, with the best coordination; Qingdao is in the “low carrying capacity − high volatility” rapid transformation phase, while Dalian and Ningbo are in the growth phase, with the weakest self-organization capacity; Dalian and Ningbo are in the growth phase, relying on external drivers to break through bottlenecks. Meanwhile, the level of internal and external coordination in southern cities is significantly higher than that in northern cities. The EWMA model’s prediction accuracy is MAPE = 9.96 %, and dynamic calibration has been completed for cities with highly volatile systems. The innovation of this study lies in combining resilience cycle theory and CAS theory for the first time, proposing a dynamic assessment framework, breaking through the limitations of traditional static calculations, forming a logical closed loop from “static calculation” to “dynamic diagnosis − cycle identification − adaptive prediction” Furthermore, we propose governance strategies for different types of cities, which hold significant scientific value and practical significance for enhancing the water resource resilience of coastal cities.

Topics & Concepts