Resilience framework for urban water supply systems planning
Bibliographic record
Abstract
As the concept of resilience is becoming a criterion in planning, water utilities are seeking support and practical guidance to enhance their conventional risk-based planning processes. This paper presents a resilience framework for urban water supply systems planning during the transition towards integrated water resources management. Based on a synthesis of literature across engineering, ecological and social sciences, resilient system performance is defined using crossings of fail-safe and safe-fail thresholds as key indicators. System performance curves conceptually illustrate the capabilities withstanding, absorptive, restorative, adaptive, transformative, and anticipative (WARATA), during sudden and gradual disruptions. Sustainability goals are explicitly considered in the resilience framework, and the role of transformative and anticipative capabilities to facilitate transitions is discussed. Specifically, the desirability of physical design and predicted community consequences from performance impact and collapse can be used in resilience management to prioritize between fail-safe and safe-fail system capacities. Finally, key considerations for operationalizing the framework are summarized, including how issues related to social justice can be addressed when simulating performance and deriving metrics. While this paper focuses on urban water supply, the framework could be applied to other service-providing infrastructure where resilience-based planning supported by quantitative evidence is required to inform investments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".