Impact of climate data uncertainty on estimated system yield

Time series simulation of reservoir behaviour using synthetically generated streamflow and rainfall data is an integral part of calculating drought risks for many water utilities in Australia. Two key parameters that are required for the generation of the synthetic climate data are the mean and standard deviation of the annual data for each site, these being estimated from historic data. Stedinger and Taylor (1982) explored the impact of uncertainty in these parameters on the simulation of reservoir behaviour and the size of reservoir that would be required in order to maintain a specified target release for a 50 year sequence of inflow. The contemporary focus of drought risk assessments is different and the decision making criteria now require substantially more computational effort. Today, the focus of the assessments is generally on how much water can be supplied from a given set of infrastructure for a given set of reliability targets, rather than on how large the infrastructure needs to be to meet the reliability target. Further, the reliability targets are commonly expressed in terms of low frequency events, which means that many replicates are required per assessment to estimate these low risks. In this paper the impact of uncertainty in the mean and standard deviation of the historic climate data is explored in terms of its impact on a contemporary calculation of system yield. It is found that the uncertainty in these parameters has an appreciable impact on uncertainty associated with the estimated system yield.