TITLE:
Estimation of Reservoir Volumes at Drafts of 40% - 90%: Drought Magnitude Method Applied on Monthly River Flows from Canadian Prairies
AUTHORS:
Tribeni C. Sharma, Umed S. Panu
KEYWORDS:
Draft Ratio, Extreme Number Theorem, Markov Chain, Moving Average Smoothing, Reliability, Standardized Hydrological Index, Sequent Peak Algorithm
JOURNAL NAME:
Journal of Water Resource and Protection,
Vol.14 No.8,
August
5,
2022
ABSTRACT: The draft ratios for sizing the reservoirs can vary within a wide range (40% - 90% of the mean annual flow, MAF), depending upon the demands for water by various users, and environmental and ecological considerations. The reservoir volumes based on the drought magnitude (DM) method were assessed at aforesaid draft ratios using monthly-standardized hydrological index (SHI) sequences of 10 Canadian rivers located in the Canadian prairies and northwestern Ontario. These rivers are typified by a high level of persistence lag-1 autocorrelation, ρ1m ≥ 0.50 and up to 0.94) and coefficient of variation (cvo) in the range of 0.42 to 1.48. The moving average (MA) smoothing of monthly SHI sequences formed the basis of the DM method for estimating reservoir volumes. The truncation or cutoff level in the SHI sequences was found as SHIx [=(α - 1)μo/σo], [(α - 1)μo/σmax], or [(α - 1)μo/σav], where α (=0.40 to 0.90) is the draft ratio i.e. proportion of the MAF, μo and σo are the overall mean and standard deviation of the monthly flows, σmax is the maximum value of standard deviations and σav the average of 12 monthly values. The failure probability levels (PF) were fixed at 5%, 2.5% and 0% (corresponding reliability of 95%, 97.5% and 100%). The study revealed that the coefficient of variation is the most important parameter that influences the reservoir size while the role of lag-1 autocorrelation (ρ1m) appears more pronounced at high draft ratios, α such as 0.90, 0.80 and 0.70 in increasing the reservoir size. The DM based method can be regarded as an alternative to Behavior analysis for sizing reservoirs at the desired probability of failure or reliability level.