HR: 1340h
AN: H13A-0396    [Abstracts]
TI: Parsimonious PARMA Models and Their Application to Modeling of Riverflows
AU: * Tesfaye, Y G
EM: yonas@unr.edu
AF: Graduate Program in Hydrologic Sciences, University of Nevada, Reno, NV 89557 United States
AU: Meerschaert, M M
EM: mcubed@unr.edu
AF: Department of Physics, University of Nevada, Reno, NV 89557 United States
AU: Anderson, P L
EM: panderson@albion.edu
AF: Department of Mathematics, Albion College, Albion, MI 49224 United States
AB: For analysis and design of water resources systems, it is sometimes required to synthetically generate riverflow data with high resolution (that is, weekly or daily values). Periodic AutoRegressive Moving Average models provides a powerful tool for modeling such riverflow time series, which are often periodically stationary. The innovations algorithm can be used to obtain parameter estimates for PARMA models with finite fourth moment as well as infinite fourth moment but finite variance. Fitting the PARMA model to historical weekly or daily data, however, requires estimation of too many parameters, which violates the principle of parsimony. In an effort to obtain a parsimonious model representing periodically stationary series, we develop the asymptotic distribution of the discrete Fourier transform of the innovation estimates and then determine those statistically significant Fourier coefficients. We also extend these results to other periodic model parameters. We demonstrate the effectiveness of the technique using simulated data from different PARMA models. An application of the technique is demonstrated through the analysis of a daily riverflow series for the Fraser River in British Columbia.
DE: 1800 HYDROLOGY
DE: 1860 Runoff and streamflow
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting