Tracing urban drinking water sources: global state of the art and insights from an IAEA-Coordinated Research Project
Bibliographic record
Abstract
Climate change, inter-annual precipitation variability, recurrent droughts, and flash flooding, coupled with increasing water needs, are shaping the co-evolution of socioeconomic and cultural assemblages, water laws and regulations, and equitable drinking water access and allocation worldwide. Recognizing the need for mitigation strategies for drinking water availability in urban areas, the Isotope Hydrology Section of the International Atomic Energy Agency (IAEA) coordinated a state-of-the-art global assessment to evaluate water sources and distribution of drinking water supply in urban centers, an initiative entitled “Use of Isotope Techniques for the Evaluation of Water Sources for Domestic Supply in Urban Areas (2018-2023)”. Here, we report on a) current research trends for studying urban drinking water systems during the last two decades and b) the development, testing, and integration of new methodologies, aiming for a better assessment, mapping, and management of water resources used for drinking water supply in urban settings. Selected examples of water isotope applications (Canada, USA, Costa Rica, Ecuador, Morocco, Botswana, Romania, Slovenia, India, and Nepal) provide context to the insights and recommendations reported and highlight the versatility of water isotopes to underpin seasonal and temporal variations across various environmental and climate scenarios. The study revealed that urban areas depend on a large spectrum of water recharge across mountain ranges, extensive local groundwater extraction, and water transfer from nearby or distant river basins. The latter is reflected in the spatial isotope snapshot variability. High-resolution monitoring (hourly and sub-hourly) isotope sampling revealed large diurnal variations in the wet tropics (Costa Rica) (up to 1.5‰ in δ 18 O) and more uniform diurnal variations in urban centers fed by groundwater sources (0.08 ‰ in δ 18 O) ([Ljubljana](https://www.google.com/search?client=firefox-b-1-d&sca_esv=f5a20a2e9138d638&sca_upv=1&sxsrf=ADLYWIKR6-DvBtjaWqFYRhn6VgnegOa8kg:1717189104058&q=Ljubljana&stick=H4sIAAAAAAAAAONgVuLQz9U3SMrNNXnEaMwt8PLHPWEprUlrTl5jVOHiCs7IL3fNK8ksqRQS42KDsnikuLjgmngWsXL6ZJUm5WQl5iUCAAFa64FOAAAA&sa=X&ved=2ahUKEwjMrrz047iGAxWyG9AFHSVwCBgQzIcDKAB6BAgTEAE), Slovenia). Similarly, while d -excess was fairly close to the global mean value (+10 ‰) across all urban centers (10-15‰), reservoir-based drinking water systems show significantly lower values (up to ~ -20 ‰) (Arlington, TX, USA and Gaborone, Botswana), as a result of strong evapoconcentration processes. δ 18 O time series and depth-integrated sampling highlighted the influence of the catchment damping ratio in the ultimate intake water composition. By introducing new, traceable spatial and temporal tools that span from the water source to the end-user and are linked to the engineered and socio-economic structure of the water distribution system, governmental, regional, or community-based water operators and practitioners could enhance drinking water treatment strategies (including more accurate surface water blending estimations) and improve urban water management and conservation plans in the light of global warming.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".