Scenario-Based Identification of Critical Thresholds in Interdependent Urban Water–Energy Systems under Climate Extremes

Climate change–driven extremes, including intensified rainfall and heatwaves, increasingly threaten urban systems not through isolated hazards but through cascading failures embedded in infrastructure interdependencies. In urban areas, outdated drainage systems may exacerbate flooding impacts by constraining electricity access and recovery during flooding, whereas concurrent power outages may further impair the pumping capacity, monitoring, and operational control of drainage systems. These coupled dynamics often result in nonlinear, system-wide functional collapse without identifying the respective system’s criticality in their operative conditions. Yet studies have been focused on evaluating water and energy system vulnerability independently and relying on analysis based averaged damage metrics, rendering them structurally incapable of capturing abrupt transitions and amplification processes arising from infrastructural interdependency.

This study develops a scenario-based analytical framework to examine how interdependent urban water–energy systems respond to climate extremes and under what conditions their dynamic behavior undergoes regime shifts. Water and energy infrastructures (i.e., drainage and sewer systems, and power grid systems) are conceptualized as integrated Social–Ecological–Technological Systems (SETs), allowing social capacity, ecological buffering, and technological performance to be analyzed within a unified system structure. Based on this theoretical framework, a Causal Loop Diagram (CLD) is constructed to explicitly represent feedback mechanisms and cascading failure pathways linking drainage capacity, power reliability, and damage recovery dynamics.

Building on the conceptual model, a System Dynamics (SD) approach is employed to explore coupled system behavior across scenarios that vary climate shock intensity, infrastructure functional degradation, interdependency-driven amplification, and the timing of policy intervention. Central to the analysis is the identification of critical transitions through a threshold-state variable that captures shifts from adaptive system functioning to persistent systemic stress. Rather than assuming proportional responses, the model identifies combinations of climatic and infrastructural conditions under which marginal perturbations produce self-reinforcing and potentially irreversible system responses. Results from the scenario analysis indicate that proactive interventions implemented prior to threshold crossings are substantially more effective in suppressing cascading dynamics than reactive measures introduced after system destabilization.

This study aims to advance urban climate adaptation research by reframing infrastructure resilience as a problem of system transition under interdependency, rather than isolated performance failure. By integrating threshold identification analysis, interdependent infrastructure dynamics, and scenario-driven simulation, the proposed framework offers a transferable foundation for designing anticipatory adaptation strategies capable of preventing regime shifts in urban systems under climate extremes.