Repository logo

Browsing by Author "Lee, Dong-Jin, author"

Now showing 1 - 1 of 1
  • Thumbnail Image
    ItemOpen Access
    The effect of parameter uncertainty in stochastic streamflow simulation
    (Colorado State University. Libraries, 2009) Lee, Dong-Jin, author; Salas, Jose D., advisor
    Hydrologic time series simulation based on a stochastic model is intended to obtain a set of equally likely hydrologic sequences that could possibly occur in the future and might be useful for determining the uncertainty of decision variables such as the storage capacity of a reservoir. Since stochastic models generally hinge on parameters that are estimated based on a limited historical sample, the model parameters become uncertain and so are any decision variables that are derived from the generated samples. The main objective of this study is to propose and analyze methods for quantifying the effect of parameter uncertainty of the models that are used in the generation of synthetic streamflow series. As a way of quantifying parameter uncertainty of a stochastic model, asymptotic and Bayesian approaches have been implemented and their performances compared through extensive simulation experiments. Alternative streamflow simulation techniques have been utilized with parameter uncertainty incorporated such as stochastic models of annual streamflows at single and multiple sites as well as temporal and spatial disaggregation models. The impact of parameter uncertainty is shown to increase the variability of generated flow statistics and resultant design related variables, which is visible even with a relatively large sample size, e.g. sample size of 200. The Bayesian approach produces larger variability of generated statistics for small sample sizes than the asymptotic approach, and the difference between the two approaches is more evident for the case of generation of streamflows with high serial correlations. The effect of parameter uncertainty within disaggregation models is not as significant on the first and second moments of disaggregated flows as the effect of parameter uncertainty of the models that generate the input variables; whereas the effect of parameter uncertainty of disaggregation models results in more variability of month-to-month, month-to-annual, and cross correlations than those induced by the uncertainty of the model parameters of input variables.