Sustainable, resilient, and reliable urban water systems: making the case for a “one water” approach
Bibliographic record
Abstract
An urban water system (UWS) has three main service components: (i) drinking-water; (ii) waste-water; and (iii) storm-water. Historically, each component in urban water development evolved over time with different objectives for “different” types of water. Even today, the trend continues, as different urban water services are managed in silos. This trend is less sustainable, resilient, and reliable, mainly because of significant pressures on freshwater supplies exerted by the increasing population, demand for high living standards, rapid urbanization, and climate change. To cope with these challenges, the conventional thinking needs to change. This paper identifies a number of significant research gaps related to inter-relationships among various UWS service components. An innovative paradigm, the “one water” approach (OWA), which considers “urban water” as a single entity, is investigated herein. Currently, Australia, the USA, and Singapore are leading the implementation of the OWA, whereas only a few Canadian municipalities have embraced OWA at a very basic level. Among the EU nations, the Netherlands have emphasized the need for integrated water resource management in an urban environment. This review highlights the challenges in adopting the OWA, and also proposes guiding principles in ongoing water management practices. Institutional complexities involving an intricate regulatory structure for different UWS service components, a wider fragmentation in decision making at government levels, and insufficient stakeholder engagement within and between water utilities and other institutions present serious challenges. Various strategies such as, data sharing between water utilities, use of novel technologies (e.g., artificial intelligence, sensor technologies), and visionary leadership at different government levels have been identified as key drivers for the adoption and implementation of the OWA. The authors believe that a paradigm shift from “conventional” approach to OWA is needed to increase resiliency and reliability of water services and assist decision-makers of UWSs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".