| Summary: | Ensuring proper suspended sediment and turbidity control requires an
understanding of water body response to turbidity-causing events. This thesis
describes an approach which evaluates turbidity control using risk-based
performance indicators, and which is suitable for application in a wide range of water
resource problems. In particular, the performance of a water supply reservoir under
major drawdown conditions is evaluated in terms of meeting water quality turbidity
objectives. The case study presented is based on the Capilano Reservoir located in
North Vancouver, British Columbia. Response to turbidity-causing events is
simulated using artificial neural networks, and turbidity levels are estimated for both
the annual and wet season periods. In addition, artificial neural network models are
developed for short-term forecasting of turbidity levels at the reservoir outlet.
Reservoir reliability and resilience are then estimated using FORM. Reliability and
resilience estimates are derived for various scenarios of interest and, as a result,
can be used to evaluate tradeoffs between meeting proposed drawdown objectives
and the elevated turbidity levels that may occur within the water body. The reservoir
is shown to exhibit poorer performance during the wet season, particularly in the
October to December period, compared to its overall performance on an annual
basis.
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