Reducing the Need for Accurate Stream Flow Forecasting for Water Supply Planning by Augmenting Reservoir Operations with Seawater Desalination and Wastewater Recycling

Accurate stream flow forecasts are critical for reservoir operations for water supply planning. As the world urban population increases, the demand for water in cities is also increasing, making accurate forecasts even more important. However, accurate forecasting of stream flows is difficult owing to short- and long-term weather variations. We propose to reduce this need for accurate stream flow forecasts by augmenting reservoir operations with seawater desalination and wastewater recycling. We develop a robust operating policy for the joint operation of the three sources. With the joint model, we tap into the unlimited reserve of seawater through desalination, and make use of local supplies of wastewater through recycling. However, both seawater desalination and recycling are energy intensive and relatively expensive. Reservoir water on the other hand, is generally cheaper but is limited and variable in its availability, increasing the risk of water shortage during extreme climate events. We operate the joint system by optimizing it using a genetic algorithm to maximize water supply reliability and resilience while minimizing vulnerability subject to a budget constraint and for a given stream flow forecast. To compute the total cost of the system, we take into account the pumping cost of transporting reservoir water to its final destination, and the capital and operating costs of desalinating seawater and recycling wastewater. We produce results for different hydro climatic regions based on artificial stream flows we generate using a simple hydrological model and an autoregressive time series model. The artificial flows are generated from precipitation and temperature data from the Canadian Regional Climate model for present and future scenarios. We observe that the joint operation is able to effectively minimize the negative effects of stream flow forecast uncertainty on system performance at an overall cost that is not significantly greater than the cost of a st