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Nonlinear Dynamics of the Great Salt Lake: Nonparametric Short‐Term Forecasting
Author(s) -
Lall Upmanu,
Sangoyomi Taiye,
Abarbanel Henry D. I.
Publication year - 1996
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/95wr03402
Subject(s) - autoregressive model , nonparametric statistics , nonlinear system , series (stratigraphy) , chaotic , term (time) , time series , econometrics , scale (ratio) , nonparametric regression , dynamical systems theory , statistical physics , computer science , mathematics , statistics , geology , geography , physics , artificial intelligence , paleontology , cartography , quantum mechanics
Variations in the volume of closed basin lakes, such as the Great Salt Lake, are often driven by large‐scale, persistent climatic fluctuations. 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 for time series analysis and forecasting may be inappropriate for modeling such processes. Here we consider the time series of interest as the outcome of a finite‐dimensional, nonlinear dynamical system and use nonparametric regression to recover the nonlinear, autoregressive “skeleton” of the underlying dynamics. The resulting model can be used for short‐term forecasting, as well as for exploring other properties of the system. The utility of the approach is demonstrated with synthetic periodic data and data from low dimensional, chaotic, dynamical systems. An application to the 1847–1992 Great Salt Lake biweekly volume time series is also reported.