Nonparametric harmonic regression for estuarine water quality data

Abstract

AbstractPeriodicity is omnipresent in environmental time series data. For modeling estuarine water quality variables, harmonic regression analysis has long been the standard for dealing with periodicity. Generalized additive models (GAMs) allow more flexibility in the response function. They permit parametric, semiparametric, and nonparametric regression functions of the predictor variables. We compare harmonic regression, GAMs with cubic regression splines, and GAMs with cyclic regression splines in simulations and using water quality data collected from the National Estuarine Reasearch Reserve System (NERRS). While the classical harmonic regression model works well for clean, near‐sinusoidal data, the GAMs are competitive and are very promising for more complex data. The generalized additive models are also more adaptive and require less‐intervention. Copyright © 2009 John Wiley & Sons, Ltd.