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Impact of climate change on urban water consumption: a case study for Greater Montreal


Impact of climate change on urban water consumption: a case study for Greater Montreal

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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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


Adamowski, J., Adamowski, K., & Prokoph, A. (2013). A spectral analysis based methodology to detect climatological influences on daily urban water demand. Mathematical Geosciences, 45(1), 49–68.
Adamowski, J., Fung Chan, H., Prasher, S. O., Ozga-Zielinski, B., & Sliusarieva, A. (2012). Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in montreal, canada. Water Resources Research, 48(1).
Adamowski, J., & Karapataki, C. (2010). Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ann learning algorithms. Journal of Hydrologic Engineering, 15(10), 729–743.
Ahmadalipour, A., Moradkhani, H., Castelletti, A., & Magliocca, N. (2019). Future drought risk in africa: Integrating vulnerability, climate change, and population growth. Science of the Total Environment, 662, 672–686.
Ahmadalipour, A., Moradkhani, H., & Svoboda, M. (2017). Centennial drought outlook over the conus using nasa-nex downscaled climate ensemble. International Journal of Climatology, 37(5), 2477–2491.
Alexander, L. V., Tapper, N., Zhang, X., Fowler, H. J., Tebaldi, C., & Lynch, A. (2009). Climate extremes: progress and future directions. International Journal of Climatology: A Journal of the Royal Meteorological Society, 29(3), 317–319.
Allison, P. D. (1999). Multiple regression: A primer. Pine Forge Press.
Almutaz, I., Ajbar, A., Khalid, Y., & Ali, E. (2012). A probabilistic forecast of water demand for a tourist and desalination dependent city: Case of mecca, saudi arabia. Desalination, 294, 53–59.
Altunkaynak, A., & Nigussie, T. A. (2017). Monthly water consumption prediction using season algorithm and wavelet transform–based models. Journal of Water Resources Planning and Management, 143(6), 04017011.
Al-Zahrani, M. A., & Abo-Monasar, A. (2015). Urban residential water demand prediction based on artificial neural networks and time series models. Water resources management, 29(10), 3651–3662.
Arturo, O. d. l. C., Alvarez-Chavez, C. R., Ramos-Corella, M. A., & Soto-Hernandez, F. (2017). Determinants of domestic water consumption in hermosillo, sonora, mexico. Journal of cleaner production, 142, 1901–1910.
Ashouri, M., Haghighat, F., Fung, B. C., Lazrak, A., & Yoshino, H. (2018). Development of building energy saving advisory: A data mining approach. Energy and Buildings, 172, 139– 151.
Ba´nbura, M., Giannone, D., & Reichlin, L. (2010). Large bayesian vector auto regressions. Journal of applied Econometrics, 25(1), 71–92.
Bennett, J. C., Grose, M. R., Corney, S. P., White, C. J., Holz, G. K., Katzfey, J. J., . . . Bindoff, N. L. (2014). Performance of an empirical bias-correction of a high-resolution climate dataset. International Journal of Climatology, 34(7), 2189–2204.
Bishop, C. M., & Tipping, M. E. (2003). Bayesian regression and classification. Nato Science Series sub Series III Computer and Systems Sciences, 190, 267–288.
Bo´e, J., Terray, L., Habets, F., & Martin, E. (2007). Statistical and dynamical downscaling of the seine basin climate for hydro-meteorological studies. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(12), 1643–1655.
Brossard. (2019a). Municipal services, by-law. Retrieved from http://www.ville.brossard.qc.ca/services-citoyens/Reglements/Reglements.aspx?lang=en-ca (Accessed: 2019-02-30)
Brossard. (2019b). Municipal services, watering. Retrieved from http://www.ville.brossard.qc.ca/services-citoyens/eau/Eau/Arrosage.aspx?lang=en-ca (Accessed: 2019-02-30)
Buck, S., Soldati, H., & Sunding, D. L. (2015). Forecasting urban water demand in california: Rethinking model evaluation (Tech. Rep.).
Chang, H., Praskievicz, S., & Parandvash, H. (2014). Sensitivity of urban water consumption to weather and climate variability at multiple temporal scales: The case of portland, oregon. International Journal of Geospatial and Environmental Research, 1(1), 7.
Curran, J. M. (2005). An introduction to bayesian credible intervals for sampling error in dna profiles. Law, Probability and Risk, 4(1-2), 115–126.
Davidson-Pilon, C. (2015). Bayesian methods for hackers: probabilistic programming and Bayesian inference. Addison-Wesley Professional.
Eslamian, S. A., Li, S. S., & Haghighat, F. (2016). A new multiple regression model for predictions of urban water use. Sustainable Cities and Society, 27, 419–429.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37–37.
Gato, S., Jayasuriya, N., Hadgraft, R., & Roberts, P. (2005). A simple time series approach to modelling urban water demand. Australasian Journal of Water Resources, 8(2), 153–164.
Gato, S., Jayasuriya, N., & Roberts, P. (2007). Temperature and rainfall thresholds for base use urban water demand modelling. Journal of hydrology, 337(3-4), 364–376.
Ghiassi, M., Zimbra, D. K., & Saidane, H. (2008). Urban water demand forecasting with a dynamic artificial neural network model. Journal of Water Resources Planning and Management, 134(2), 138–146.
Ghosh, B., Basu, B., & O’Mahony, M. (2007). Bayesian time-series model for short-term traffic flow forecasting. Journal of transportation engineering, 133(3), 180–189.
Godsill, S. J. (2001). On the relationship between markov chain monte carlo methods for model uncertainty. Journal of computational and graphical statistics, 10(2), 230–248.
Hamlet, A., Carrasco, P., Deems, J., Elsner, M., Kamstra, T., Lee, C., . . . others (2010). Final report for the columbia basin climate change scenarios project. University of Washington, Climate Impacts Group, Seattle, Washington, DC.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Haque, M. M., Rahman, A., Hagare, D., & Kibria, G. (2014). Probabilistic water demand forecasting using projected climatic data for blue mountains water supply system in australia. Water resources management, 28(7), 1959–1971.
Heckerman, D. (1997). Bayesian networks for data mining. Data mining and knowledge discovery, 1(1), 79–119.
Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning bayesian networks: The combination of knowledge and statistical data. Machine learning, 20(3), 197–243.
Hoffman, M. D., & Gelman, A. (2014). The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo. Journal of Machine Learning Research, 15(1), 1593–1623.
House-Peters, L., Pratt, B., & Chang, H. (2010). Effects of urban spatial structure, sociodemographics, and climate on residential water consumption in hillsboro, oregon. JAWRA Journal of the American Water Resources Association, 46(3), 461–472.
Ismail, M. A., Sadiq, R., Soleymani, H. R., & Tesfamariam, S. (2011). Developing a road performance index using a bayesian belief network model. Journal of the Franklin Institute, 348(9), 2539–2555.
Jain, S., Salunke, P., Mishra, S. K., Sahany, S., & Choudhary, N. (2019). Advantage of nex-gddp over cmip5 and cordex data: Indian summer monsoon. Atmospheric Research.
Jaramillo, P., & Nazemi, A. (2018). Assessing urban water security under changing climate: Challenges and ways forward. Sustainable cities and society, 41, 907–918.
Kenney, D. S., Goemans, C., Klein, R., Lowrey, J., & Reidy, K. (2008). Residential water demand management: lessons from aurora, colorado 1. JAWRA Journal of the American Water Resources Association, 44(1), 192–207.
Khatri, K., & Vairavamoorthy, K. (2009). Water demand forecasting for the city of the future against the uncertainties and the global change pressures: case of birmingham. In World environmental and water resources congress 2009: Great rivers (pp. 1–15).
Kim, H., & Melhem, H. (2004). Damage detection of structures by wavelet analysis. Engineering Structures, 26(3), 347–362.
Koutroulis, A., Papadimitriou, L., Grillakis, M., Tsanis, I., Warren, R., & Betts, R. (2019). Global water availability under high-end climate change: A vulnerability based assessment. Global nd planetary change, 175, 52–63.
Lambert, B. (2018). A student’s guide to bayesian statistics. Sage.
Li, H., Sheffield, J., &Wood, E. F. (2010). Bias correction of monthly precipitation and temperature fields from intergovernmental panel on climate change ar4 models using equidistant quantile matching. Journal of Geophysical Research: Atmospheres, 115(D10).
Li, Q., Gu, L., Augenbroe, G., Wu, C. J., & Brown, J. (2015). Calibration of dynamic building energy models with multiple responses using bayesian inference and linear regression models. Energy Procedia, 78, 979–984.
Liu, G., Yang, J., Hao, Y., & Zhang, Y. (2018). Big data-informed energy efficiency assessment of china industry sectors based on k-means clustering. Journal of cleaner production, 183, 304–314.
Liu, J., Yang, H., Gosling, S. N., Kummu, M., Fl¨orke, M., Pfister, S., . . . others (2017). Water scarcity assessments in the past, present, and future. Earth’s future, 5(6), 545–559.
Mahadevan, S. (1997). Monte carlo simulation. Mechanical Engineering-New York and Basel- Marcel Dekker-, 123–146.
Mandapaka, P. V., & Lo, E. Y. (2018). Assessment of future changes in southeast asian precipitation using the nasa earth exchange global daily downscaled projections data set. International Journal of Climatology, 38(14), 5231–5244.
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M., Lamarque, J.-F., . . . others (2011). The rcp greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic change, 109(1-2), 213.
Miro, M. E., Groves, D. G., Catt, D., Miller, B., & Social, R. (2018). Estimating future water demand for san bernardino valley municipal water district. RAND.
Mooney, C. Z. (1997). Monte carlo simulation (Vol. 116). Sage Publications.
Mouatadid, S., & Adamowski, J. (2017). Using extreme learning machines for short-term urban water demand forecasting. Urban Water Journal, 14(6), 630–638.
Mudgal, A., Hallmark, S., Carriquiry, A., & Gkritza, K. (2014). Driving behavior at a roundabout: A hierarchical bayesian regression analysis. Transportation research part D: transport and environment, 26, 20–26.
Panagopoulos, G. P. (2014). Assessing the impacts of socio-economic and hydrological factors on urban water demand: A multivariate statistical approach. Journal of hydrology, 518, 42–48.
Parandvash, G. H., & Chang, H. (2016). Analysis of long-term climate change on per capita water demand in urban versus suburban areas in the portland metropolitan area, usa. Journal of Hydrology, 538, 574–586.
Polebitski, A. S., & Palmer, R. N. (2009). Seasonal residential water demand forecasting for census tracts. Journal of water resources planning and management, 136(1), 27–36.
Praskievicz, S.,&Chang, H. (2009). Identifying the relationships between urban water consumption and weather variables in seoul, korea. Physical Geography, 30(4), 324–337.
Prusty, R. M., Das, A., & Patra, K. C. (2018). Climate change impact assessment under cordex south-asia rcm scenarios on water resources of the brahmani and baitarini river basin, india.
Raftery, A. E., Madigan, D., & Hoeting, J. A. (1997). Bayesian model averaging for linear regression models. Journal of the American Statistical Association, 92(437), 179–191.
Raghavan, S. V., Hur, J., & Liong, S.-Y. (2018). Evaluations of nasa nex-gddp data over southeast asia: present and future climates. Climatic change, 148(4), 503–518.
Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., . . . Rafaj, P. (2011). Rcp 8.5—a scenario of comparatively high greenhouse gas emissions. Climatic Change, 109(1-2), 33.
Rinaudo, J.-D. (2015). Long-term water demand forecasting. In Understanding and managing urban water in transition (pp. 239–268). Springer.
Romano, G., Salvati, N., & Guerrini, A. (2014). Estimating the determinants of residential water demand in italy. Water, 6(10), 2929–2945.
Rousseeuw, P. J., & Hubert, M. (2011). Robust statistics for outlier detection. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), 73–79.
Ruth, M., Bernier, C., Jollands, N., & Golubiewski, N. (2007). Adaptation of urban water supply infrastructure to impacts from climate and socioeconomic changes: the case of hamilton, new zealand. Water Resources Management, 21(6), 1031–1045.
Schmidli, J., Frei, C., & Vidale, P. L. (2006). Downscaling from gcm precipitation: a benchmark for dynamical and statistical downscaling methods. International journal of climatology, 26(5), 679–689.
Semenov, M. A., & Stratonovitch, P. (2010). Use of multi-model ensembles from global climate models for assessment of climate change impacts. Climate research, 41(1), 1–14.
Sen, P. K. (1968). Estimates of the regression coefficient based on kendall’s tau. Journal of the American statistical association, 63(324), 1379–1389.
Shabani, S., Yousefi, P., & Naser, G. (2017). Support vector machines in urban water demand forecasting using phase space reconstruction. Procedia Engineering, 186, 537–543.
Singh, G., Goel, A., & Choudhary, M. (2015). An inventory of methods and models for domestic water demand forecasting: a review. J. Indian Water Resources Society, 35(3), 34–45.
Singh, S., & Yassine, A. (2018). Big data mining of energy time series for behavioral analytics and energy consumption forecasting. Energies, 11(2), 452.
Sriram, N. (n.d.). A study on machine learning techniques for data mining. Statistics-Canada. (2019). Focus on geography series’. Retrieved from https://www12.statcan.gc.ca/census-recensement/2016/as-sa/fogs-spg/Facts-cma-eng.cfm?LANG=Eng&GK=CMA&GC=462&TOPIC=1 (Accessed: 2019-08-1)
Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., . . . Midgley, P. (2014). Ipcc, 2013: Summary for policymakers in:climate change 2013: The physical science basis.
Stoker, P., & Rothfeder, R. (2014). Drivers of urban water use. Sustainable Cities and Society, 12, 1–8.
Syme, G. J., Shao, Q., Po, M., & Campbell, E. (2004). Predicting and understanding home garden water use. Landscape and Urban Planning, 68(1), 121–128.
Taleb, T., & Kaddour, M. (2017). Hierarchical agglomerative clustering schemes for energyefficiency in wireless sensor networks. Transport and Telecommunication Journal, 18(2), 128–138.
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012). An overview of cmip5 and the experiment design. Bulletin of the American Meteorological Society, 93(4), 485–498.
Thomson, A. M., Calvin, K. V., Smith, S. J., Kyle, G. P., Volke, A., Patel, P., . . . others (2011). Rcp4. 5: a pathway for stabilization of radiative forcing by 2100. Climatic change, 109(1-2), 77.
Tiwari, M. K., & Adamowski, J. (2013). Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resources Research, 49(10), 6486–6507.
Tiwari, M. K., & Adamowski, J. F. (2014). Medium-term urban water demand forecasting with limited data using an ensemble wavelet–bootstrap machine-learning approach. Journal of Water Resources Planning and Management, 141(2), 04014053.
Tiwari, M. K., & Adamowski, J. F. (2017). An ensemble wavelet bootstrap machine learning approach to water demand forecasting: A case study in the city of calgary, canada. Urban Water Journal, 14(2), 185–201.
Wang, Y., Duan, L., Liu, T., Li, J., & Feng, P. (2019). A non-stationary standardized streamflow index for hydrological drought using climate and human-induced indices as covariates. Science of The Total Environment, 134278.
Wong, J. S., Zhang, Q., & Chen, Y. D. (2010). Statistical modeling of daily urban water consumption in hong kong: Trend, changing patterns, and forecast. Water resources research, 46(3).
Yu, Z., Fung, B. C., & Haghighat, F. (2013). Extracting knowledge from building-related data—a data mining framework. In Building simulation (Vol. 6, pp. 207–222).
Yu, Z., Fung, B. C., Haghighat, F., Yoshino, H., & Morofsky, E. (2011). A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and buildings, 43(6), 1409–1417.
Yu, Z., Haghighat, F., Fung, B. C., & Yoshino, H. (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42(10), 1637–1646.
Yu, Z. J., Haghighat, F., Fung, B. C., Morofsky, E., & Yoshino, H. (2011). A methodology for identifying and improving occupant behavior in residential buildings. Energy, 36(11), 6596– 6608.
Yuan, X.-C., Sun, X., Zhao, W., Mi, Z., Wang, B., & Wei, Y.-M. (2017). Forecasting china’s regional energy demand by 2030: A bayesian approach. Resources, Conservation and Recycling, 127, 85–95.
Zhang, X., Flato, G., Kirchmeier-Young, M., Vincent, L.,Wan, H.,Wang, X., . . . Kharin, V. (2019). Changes in temperature and precipitation across canada; chapter 4 in bush e, lemmen ds.(eds.) canada’s changing climate report. Government of Canada, Ottawa, Ontario, 112–193.
Zhou, S. L., McMahon, T. A., Walton, A., & Lewis, J. (2000). Forecasting daily urban water demand: a case study of melbourne. Journal of hydrology, 236(3-4), 153–164.
Zhuang, X., Li, Y., Nie, S., Fan, Y., & Huang, G. (2018). Analyzing climate change impacts on water resources under uncertainty using an integrated simulation-optimization approach. Journal of Hydrology, 556, 523–538.
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