HR: 1330h
AN: H43A-01    [Abstracts]
TI: Simulation of Monthly Flows Using a Markov Switching Model
AU: * Akintug, B
EM: umakint0@cc.umanitoba.ca
AF: University of Manitoba, Dept. of Civil Engineering University of Manitoba, Winnipeg, MB R3T 5V6 Canada
AU: Rasmussen, P F
EM: rasmusse@cc.umanitoba.ca
AF: University of Manitoba, Dept. of Civil Engineering University of Manitoba, Winnipeg, MB R3T 5V6 Canada
AB: Some annual time series of streamflow and precipitation exhibit extended wet and dry spells that may not be well captured by low-order ARMA models. Markov Switching (MS) models have been suggested as a possible alternative to traditional time series models. MS models employ a Markov chain to simulate a state variable representing the climate regime. Within each climate regime, the hydrologic variable is described by a probability density function whose parameters depend on the state. To simulate realistic multi-year flow regimes, the MS model must be applied to annual data. However, in typical water resource studies, synthetic streamflow data are needed at sub-annual time steps, for example monthly. We present a stochastic model that adapts the well known disaggregation approach to the case of MS models. Parameter estimation will be presented, and analytical and simulated properties of the model will be discussed. We also present some applications of the model and compare it to more conventional time series models used in hydrology.
DE: 1833 Hydroclimatology
DE: 1860 Runoff and streamflow
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2005 Joint Assembly