Impact of Climate Change on Hydraulic Performance in Water Distribution Networks
Bibliographic record
Abstract
The Intergovernmental Panel on Climate Change (IPCC) has forecast higher mean air temperatures for the mid-latitude region of North America. Studies have shown a strong positive correlation between temperature and municipal water demand. Warmer air temperatures in the future have the potential to increase municipal water demand above levels forecast without climate change considerations. The predicted increase in mean temperature and the onset of hotter and dryer summer weather may create challenges for water providers in the future. Without appropriate network upgrades, higher water demands may degrade the hydraulic performance of existing systems. This creates a need to characterize the impact of higher temperatures on peak water demands and on the hydraulic performance in water distribution networks. The aim of the research is to begin to understand the impact of higher temperatures on nodal demands and pressures in water distribution networks. The sensitivity of municipal water demand to an increase in air temperature is established through previous climate adaptation research completed for the geographical region of central Canada. Results indicate that without adaptation, a 2-4 °C temperature increase causes mean pressure head to fall below the acceptable minimum and produces large uncertainties in pressure head under maximum hour demand (MHD) and maximum day demand (MDD) + fire design conditions in the Anytown network. The combination of low mean pressure head and a high coefficient of variation of pressure head increases the probability of hydraulic failure in the Anytown network. Adaptation strategies are presented as ways to hedge the effects of a warming climate in the Anytown network
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".