Estimation of nonlinear trends in water quality: An improved approach using generalized additive models

[1] This paper advocates the use of Generalized Additive Models (GAMs) for the estimation of nonlinear trends in water quality in the presence of serially correlated errors. The GAM methodology is applicable to a range of physical, chemical and biological water quality variates. Comparison with the estimate based on Seasonal Kendall's Slope and robust regression is discussed. An example is given concerning log-transformed stream electrical conductivity, which is adjusted for flow. The monthly data have first order autocorrelation exceeding 0.5 and the trend is markedly nonlinear. Seasonal effects are shown to have been changing over time.