Comparison of Methods for Normalisation and Trend Testing of Water Quality Data

Claudia Libiseller
Department of Mathematics, Division of Statistics
Linköping University
SE-58183 Linköping, Sweden
E-mail: cllib@mai.liu.se

Abstract
To correctly assess trends in water quality data, influencing variables such as discharge or temperature must be taken into account. This can be done by using (i) one-step procedures like the Partial Mann-Kendall (PMK) test or multiple regression, or (ii) two-step techniques that include a normalisation followed by a trend test on the residuals. Which approach is most appropriate depends strongly on the relationship between the response variable under consideration and the influencing variables. For example, PMK tests can be superior if there are long and varying time lags in the water quality response. Two-step procedures are particularly useful when the shape of the temporal trend is the primary interest, but they can be misleading if one of the influencing variables itself exhibits a trend or long-term tendency. The present study discusses the advantages and disadvantages of some trend testing techniques, using Swedish water quality data to illustrate the properties of the methods.

Keywords: long-term changes in covariate, non-monotonic changes, long memory effects, seasonal variation

In: McRoberts, R. et al. (eds).  Proceedings of the joint meeting of The 6th International Symposium On Spatial Accuracy Assessment In Natural Resources and Environmental Sciences and The 15th Annual Conference of The International Environmetrics Society, June 28 – July 1 2004, Portland, Maine, USA.

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