Water Supply-Demand Management under Climate Change


  • Tianyi Xu School of Civil & Environmental Engineering, Nanyang Technological University, Singapore 639798
  • Xiaosheng Qin Nanyang Technological University


This study aimed to investigate optimal multiple reservoir operations and water demand management considering climate change impact. In this study, conditional density estimation network creation and evaluation (CaDENCE) method was used for downscaling precipitation, and support vector machine (SVM) was used for downscaling temperature. The Bayesian neural network (BNN) model was applied to simulate the monthly reservoir inflows, which was used as the input to the optimization model. A multi-reservoir system was used for methodology demonstration, where three reservoirs were delivering water to an urban area. Several water-saving measures including long-term and short-term measures were involved in the optimization model to mitigate water shortage problem. The model aimed to maximize the total revenue obtained from water release of three reservoirs subject to constraints of available water supply, demand of water users, and cost of water demand management. The optimal water release schemes and adoption of water-saving measures under current and future climate-change conditions were obtained. The results showed that the water releases would increase at spring and decrease at winter under HadCM3 A2 emission scenario compared to the current condition.

Author Biographies

Tianyi Xu, School of Civil & Environmental Engineering, Nanyang Technological University, Singapore 639798

PhD candidate in School of Civil & Environmental Engineering, Nanyang Technological University

Xiaosheng Qin, Nanyang Technological University

Assistant Professor


BC Hydro (2005). Coquitlam-buntzen project water use plan, Revised for acceptance by the Comptroller of Water Rights. https://www.placespeak.com/uploads/assets/environment30819_1.pdf

BC Ministry of Community & Rural Development (2009). Water conservation calculator. http://waterconservationcalculator.ca/

Cannon, A.J. (2012). Neural networks for probabilistic environmental prediction: Conditional density estimation network creation and evaluation (cadence) in r. Computers & Geosciences, 41, 126-135.

Eum, H.-I. and Simonovic, S. (2010). Integrated reservoir management system for adaptation to climate change: The nakdong river basin in korea. Water Resources Management, 24(13), 3397-3417.

Gordon, C., Cooper, C., Senior, C.A., Banks, H., Gregory, J.M., Johns, T.C., Mitchell, J.F. and Wood, R.A. (2000). The simulation of sst, sea ice extents and ocean heat transports in a version of the hadley centre coupled model without flux adjustments. Climate Dynamics, 16(2-3), 147-168.

Hessami, M., Gachon, P., Ouarda, T.B.M.J. and St-Hilaire, A. (2008). Automated regression-based statistical downscaling tool. Environmental Modelling & Software, 23(6), 813-834.

Huang, G.H., Yin, Y.Y., Luo, B., Nie, X.H., Li, H.L., Cai, Y.P., Liu, Z.F., Qin, X.S., He, L., Lin, Q.G. and Huang, Y.F. (2006). An optimization-simulation approach for watershed management under changing climate in the georgia basin, University of Regina & Environment Canada, Final Report to CCIAD, Earth Sciences Sector, Natural Resources Canada.

Islam, Z. and Gan, T. (2014). Effects of climate change on the surface-water management of the south saskatchewan river basin. Journal of Water Resources Planning and Management, 140(3), 332-342.

Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Jenne, R. and Joseph, D. (1996). The ncep/ncar 40-year reanalysis project. Bulletin of the American Meteorological Society, 77(3), 437-471.

Lu, Y. and Qin, X.S. (2014). A coupled k-nearest neighbour and bayesian neural network model for daily rainfall downscaling. International Journal of Climatology, DOI: 10.1002/joc.3906.

Lu, Y., Qin, X.S., and Xie, Y.J. (2014). An integrated statistical and data-driven framework for supporting flood risk analysis under climate change. Submitted to Journal of Hydrology.

Milly, P.C.D., Betancourt, J., Falkenmark, M., Hirsch, R.M., Kundzewicz, Z.W., Lettenmaier, D.P. and Stouffer, R.J. (2008). Climate change - stationarity is dead: Whither water management? Science, 319(5863), 573-574.

Nowak, K., Prairie, J., Rajagopalan, B. and Lall, U. (2010). A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow. Water Resources Research, 46(8), W08529.

Pope, V., Gallani, M., Rowntree, P. and Stratton, R. (2000). The impact of new physical parametrizations in the hadley centre climate model: Hadam3. Climate Dynamics, 16(2-3), 123-146.






Environmental Modeling, Risk Assessment and Decision Making (EMR)