NONLINEAR DYNAMICS OF THE GREAT-SALT-LAKE - NONPARAMETRIC SHORT-TERM FORECASTING

Variations in the volume of closed basin lakes, such as the Great Salt Lake, are often driven by large-scale, persistent climatic fluctuatio ns. There is growing evidence of structure in the recurrence patterns of such fluctuations, their relation to physical mechanisms, and their manifestation in hydrologic time series. Classical, linear methods fo r time series analysis and forecasting may be inappropriate for modeli ng such processes, Here we consider the time series of interest as the outcome of a finite-dimensional, nonlinear dynamical system and use n onparametric regression to recover the nonlinear, autoregressive ''ske leton'' of the underlying dynamics. The resulting model can be used fo r short-term forecasting, as well as for exploring other properties of the system. The utility of the approach is demonstrated with syntheti c periodic data and data from low dimensional, chaotic, dynamical syst ems. An application to the 1847-1992 Great Salt Lake biweekly volume t ime series is also reported.