Impact of climate change on urban water consumption: a \ncase study for Greater Montreal
Bibliographic record
Abstract
Smart cities need a sustainable plan and management of urban water consumption (UWC). A reliable long-term forecast of UWC is one of the key tasks to ensure water security and to achieve a balance between future water demand and supply. Long-term forecasts of UWC inevitably contain uncertainties. The uncertainties can associate with: 1) historical data of UWC; 2) existing observations of hydrological/climate variables as potential drivers of UWC; 3) the dependence of UWC on the potential drivers; and 4) projections of future climate change. The purpose of this research is to improve our understanding of the climate-change impact on UWC and of feasible ways to handle the foregoing uncertainties. Specifically, this research seeks answers to two key questions: 1) What quantity of water will be needed in the long-term? 2) To what extent will long-term UWC be affected by climate change? This research took the probabilistic approach to the problem of UWC forecast and made an application to the City of Brossard in the Greater Montreal metropolitan area. The methodologies involve Bayesian statistics as well as cluster analyses, which are a frequently used technique in machine learning. The analyses were performed on multiple year records of daily water consumption (DWC) in the city as well as field measurements of climate variables from the Montreal area. The analyses produced results of decomposed base water consumption (BWC) and seasonal water consumption (SWC). The DWC time-series was shown to have a transition from BWC to SWC at a threshold air temperature. The BWC was independent of climate change but subject to weekend effects, being higher on a weekend than weekdays. The SWC was a function of daily minimum air temperature, daily maximum air-temperature, and daily total precipitation. The SWC forecasts allowed for inherent uncertainties in climate variables. Markov Chain Monte Carlo was used as a sampling method in approximating the posterior distribution of regression parameters. The results from Bayesian linear regression gave a probability distribution of DWC. To quantify the impact of climate change on UWC, future projections of air temperature and precipitation were obtained from 21 General Circulation Models and downscaled for the city. The downscaled daily air temperature and precipitation corresponded to two scenarios of levels of greenhouse gas concentrations. Using quantile mapping methods, bias corrections were made to the downscaled daily minimum temperature, daily maximum temperature and daily total precipitation. These data gave input to the Bayesian linear regression model and produced SWC forecasts for the next three decades. The SWC was shown to display a positive trend over time in response to changing climate. The methodologies discussed in this thesis can be applied to other cities, producing results useful for upgrade and/or construction planning of water supply infrastructures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".