The value of stochastic streamflow models in overyear reservoir design applications

The design of storage reservoirs using stochastic streamflow models and synthetic streamflow sequences has received considerable attention in the water resources literature. Fewer studies have addressed the sampling properties of estimates of the design capacity of a storage reservoir obtained using available historical records or using synthetic streamflow sequences generated with models whose parameters were estimated from such data. Our experiments document the bias and root‐mean‐square error of estimates of overyear required storage capacity distribution quantiles corresponding to fixed or to random demand levels. The results show that the use of stochastic streamflow models can lead to improvements in the precision of reservoir design capacity estimates. Estimates of the design capacity of a storage reservoir based upon relatively simple stochastic streamflow models have smaller root‐mean‐square errors than corresponding estimates based solely upon the historic record, even when the correct model form is not known a priori.