How Scale Influences the Resilience of Urban Water Systems: A Literature Review of Trade-Offs and Recommendations
Bibliographic record
Abstract
Climate change severely affects urban water systems (UWSs). Infrastructure historically designed for milder conditions cannot manage growing water demands and extreme events. To obtain a resilient water sector, adaptation and mitigation strategies must address rising water challenges while striving for net-zero emissions. Researchers have noted that extreme decentralization is positively associated with closing cycles while reducing transport costs. However, part of the scientific community defends centralized schemes due to economies of scale. The objective of this systematic review is to understand the trade-offs associated with the adoption of different scales at UWSs design and how this impacts system resilience. This process includes identifying different scale trade-offs and unique environmental aspects that influence the optimal scale suitability. A clear distinction was made in terms of scale concept and classification, considering different design levels. That is, considering the UWS at the city level and water management units (WMUs) at the local level. Similarly, a classification of different scales for each level, covering all water streams—supply, wastewater, and stormwater—was introduced. We defined the key environmental aspects that influence the optimal scale and location suitability: ten at the city-catchment level and eleven at the site-neighbourhood level. Scale impacts three major UWSs functionalities that have repercussions on urban resilience: net energy, net water, and ecosystem services (ES).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".