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An exploratory bottom-up resilience assessment framework for integrated water systems
  • Leyang Liu,
  • Ana Mijic
Leyang Liu
Imperial College London

Corresponding Author:[email protected]

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Ana Mijic
Imperial College London
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Abstract

Resilience is the ability of a system to withstand stressors while preserving its structure and functions. Various performance- and attribute-based resilience assessment frameworks have been developed to understand the behaviour and properties of individual water systems. However, in integrated water systems, the increased complexity presents new challenges in the application of these frameworks. This study first reviews the key elements in both frameworks, including system indicators, thresholds, and resilience metrics, across urban water supply, drainage, groundwater, and river systems. Challenges are identified in deriving consistent indicators from siloed subsystem models, robust threshold selection, and resilience metrics synthesis as well as their usefulness for management. Based on the insights, a bottom-up resilience assessment framework for integrated water systems is developed. A water system integration model (WSIMOD) is employed to derive indicators for subsystems. Four performance-based resilience metrics are designed and applied to the indicators to facilitate intercomparison between subsystems. The application of the metrics crosses from event-level assessments for understanding system behaviour to annual-level evaluations of long-term performance, which are ultimately synthesised at the system level for multi-stakeholder decision-making. The efficacy of this framework is demonstrated through a case study in Luton, UK. The findings highlight river water quality as the least resilient subsystem that needs prioritised management. Sensitivity analysis is conducted to examine the impacts of thresholds on resilience results, with subsequent interpretation linking these metrics to specific decision variables for enhanced management. This framework can be extended through stakeholder engagement to improve system performance under deep uncertainties.
31 Oct 2024Submitted to ESS Open Archive
01 Nov 2024Published in ESS Open Archive