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Statistical estimation and interpretation of trends in water quality time series
Author(s) -
Zetterqvist Lena
Publication year - 1991
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 - 10.1029/91wr00478
Subject(s) - outlier , autoregressive model , covariate , series (stratigraphy) , statistics , time series , seasonality , econometrics , robustness (evolution) , mathematics , environmental science , geology , paleontology , biochemistry , chemistry , gene
Three approaches to trend analysis of water quality time series are discussed: (1) seasonal model, with a test for trend based on ranks of observations, with observations assumed to be m dependent; (2) transfer function noise model, in which covariate series may be included by means of transfer functions, with the remaining noise modeled as a seasonal autoregressive moving average process; and (3) component model, with the noise decomposed into series which describe trends, and irregular and seasonal variation. Models are studied with regards to their ability to include covariate series, possibility of interpretation of trends, treatment of seasonal variation and serial dependence, and robustness for outliers. We regard the component model being the most realistic and the most informative of the three approaches. Models are applied to series of monthly phosphorus concentration in the Ljungbyån River in Southern Sweden.