Reservoir Sizing at Draft Level of 75% of Mean Annual Flow Using Drought Magnitude Based Method on Canadian Rivers

On a global basis, there is trend that a majority of reservoirs are sized using a draft of 75% of the mean annual flow (0.75 MAF). The reservoir volumes based on the proposed drought magnitude (DM) method and the sequent peak algorithm (SPA) at 0.75 MAF draft were compared at the annual, monthly and weekly scales using the flow sequences of 25 Canadian rivers. In our assessment, the monthly scale is adequate for such analyses. The DM method, although capable of using flow data at any time scale, has been demonstrated using monthly standardized hydrological index (SHI) sequences. The moving average (MA) smoothing of the monthly SHI sequences formed the basis in the DM method for estimating the reservoir volume through the use of the extreme number theorem, and the hypothesis that drought magnitude is equal to the product of the drought intensity and drought length. The truncation level in the SHI sequences was found as SHIo [ = (0.75 ‒ 1) µo/σo], where µo and σo are the overall mean and standard deviation of the monthly flows. The DM-based estimates for the deficit volumes and the SPA-based reservoir volumes were found comparable within an error margin of ±18%.

StEMORS: A Stochastic Eco-Hydrological Model for Optimal Reservoir Sizing

Dams design and their operation cause strong environmental alteration and therefore a long-term debate is ongoing for the scale of these projects. At the same time, the concept of Environmental Flow Assessment (EFA) is a crucial element of modified ecosystems featuring large infrastructure such as dams and reservoirs for mitigating potential environmental degradation while they operate. Nowadays, integrated scientific frameworks are required to quantify the risks caused by large infrastructure. Through the use of stochastic analysis, it is possible to quantify these uncertainties, and present a solution that incorporates long-term persistence and environmental sustainability into a balanced reservoir simulation model. In this work, an attempt is made to determine a benchmark reservoir size incorporating hydrological and ecological criteria though stochastic analysis. The primary goal is to ensure the best possible conditions for the ecosystem, and then secondarily to allow a steady supply of water for other uses. Using a synthetic timeseries based on historical inputs, it is possible to determine and preserve essential statistical characteristics of a river’s streamflow, and use these to detect the optimal reservoir capacity that maximizes environmental and local water demand reliability.