A benchmarking framework to enhance cost-efficiency in water systems resilience planning under deep uncertainty

The increasing variability of climate introduces significant uncertainty into water system planning, posing challenges for enhancing system resilience. Achieving cost-efficient planning outcomes, where planning can address actual pressures with optimal investment, remains a critical goal. While Decision-Making under Deep Uncertainty (DMDU) approaches show promise, their cost-efficiency is rarely evaluated, with post-implementation assessments remaining limited due to their long-term monitoring requirements. This study proposes a novel benchmarking framework for pre-implementation evaluation of DMDU approaches, using historical climate data to simulate the outcomes of planning decisions that could have been taken in the past under conditions of unknown future climates. The framework compares simulated outcomes to theoretical cost-optimal scenarios, providing quantitative insights into refining DMDU approaches for improved cost-efficiency. The framework is applied to a fluvial flood resilience case study in Luton, UK, focusing on the real options DMDU approach. For a proof-of-concept demonstration of the framework's utility, this case study simulates an initial refinement process that decision-makers might undertake, using simplified settings such as a fixed resilience threshold as the planning objective and undiscounted costs estimation. Results reveal that the original real options approach leads to significant underinvestment during early stages, causing substantial resilience deficits. An initial refined increases costs by 16.5% but reduces resilience deficits by 74.7% over the planning period, implying potentially improved cost-efficiency. Further refinement could iteratively benchmark different parameter configurations to identify an optimal setup. The generic benchmarking framework can further facilitate systematic comparison of diverse DMDU approaches across various water management objectives, serving as a valuable tool to improve resilience planning strategies under growing uncertainties.