Assessing the vulnerability of urban drinking water intakes to water scarcity under global change: A bottom-up approach
Why this work is in the frame
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Bibliographic record
Abstract
Drinking water intakes (DWIs) face significant pressure due to global changes, including urbanization and climate change. The common approach relies mainly on climate projections generated by global climate models to simulate large scale hydroclimatic conditions. However, it is crucial to discern the impact of global changes on water scarcity at the local level, including in regions where available data are limited. This paper proposes an approach that focuses on studying the vulnerability of surface DWIs to low water levels and water demand in current and future climates within a cold-climate region. Low flows at DWIs were simulated using historical water level data obtained from hydrometric stations situated along the studied river. After defining four scenarios for climate change and anthropogenic activities affecting raw water withdrawals at DWIs, the full potential range of level variations was simulated. This study employed a combined water scarcity index derived from two sub-indices based on water level and water demand. The resulting index ranges from 0 to 1, where a higher value indicates a greater vulnerability to water scarcity. The simulation results demonstrate the vulnerability of water scarcity in both current and future climates. The calculated index, selecting the current vulnerability to water scarcity for the five studied DWIs, ranged from 0.61 to 0.76. The results for the vulnerability of these DWIs under future climate conditions exhibited significant variability across the different scenarios representing possible maximum daily withdrawal. These scenarios were defined to encompass a spectrum of options related to the government's policy for drinking water conservation strategy implementation. While exploring the full range of potential risks, the study's results demonstrated that the DWIs were especially vulnerable to anthropogenic changes affecting water demand. The framework developed in this study can provide a decision-support basis for municipalities and water managers to adapt to global change and achieve greater water supply resilience.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it