Rasifaghihi, Niousha (2019) Impact of climate change on urban water consumption: a case study for Greater Montreal. Masters thesis, Concordia University.
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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.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Rasifaghihi, Niousha |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Civil Engineering |
| Date: | 8 October 2019 |
| Thesis Supervisor(s): | Li, S. Samuel and Haghighat, Fariborz |
| Keywords: | Urban water consumption - Climate change - Bayesian statistics - Regression - Cluster analysis |
| ID Code: | 986167 |
| Deposited By: | Niousha Rasifaghihi |
| Deposited On: | 26 Jun 2020 13:34 |
| Last Modified: | 26 Jun 2020 13:34 |
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