An exploratory bottom-up resilience assessment framework for coupled human-water systems

Resilience has been defined as the ability of a system to withstand stressors while preserving its structure and functions. Various resilience assessment frameworks and metrics have been developed for understanding individual water system behaviour. However, in coupled human-water systems, the increased complexity presents new challenges in the application of these frameworks. This exploratory study first conducted a literature review on system performance indicators, failure thresholds, and resilience metrics, across urban water supply, drainage, wastewater, groundwater, and river systems. Challenges are identified in intercomparison between system performance indicators, robustness of thresholds selection, and resilience metrics synthesis as well as their applicability to inform water management. Based on the insights, a bottom-up resilience assessment framework for coupled human-water systems is developed. This framework sets double thresholds to characterise the vulnerable and critical systems state during a disruptive period. Four shape-based resilience metrics are designed and uniformly applied to various performance indicators to facilitate intercomparison between subsystems. The application of the metrics crosses temporal scales, from event-level assessments for understanding system behaviour to annual-level evaluations of system reliability, which are ultimately synthesised at the system level for multi-stakeholder decision-making. The efficacy of this framework is demonstrated through its application with the integrated water system model (WSIMOD) in Luton, UK, serving as a case study. The findings highlight river water quality as the least resilient subsystem that needs prioritised management. Sensitivity analysis is conducted to examine the robustness of results, with subsequent interpretation linking these metrics to specific design variables for enhanced management. This framework can be further applied with stakeholder engagement and multi-criteria analysis for more effective decision-making to achieve better system performance under deep uncertainties.