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Trend analysis methodology for water quality time series
Author(s) -
McLeod A. Ian,
Hipel Keith W.,
Bodo Byron A.
Publication year - 1991
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.3770020205
Subject(s) - nonparametric statistics , rank correlation , statistics , series (stratigraphy) , spearman's rank correlation coefficient , trend analysis , time series , rank (graph theory) , statistical hypothesis testing , regression analysis , econometrics , mathematics , computer science , paleontology , combinatorics , biology
A general trend analysis methodology is developed for detecting and modelling trends in water quality time series measured in rivers and streams. The procedure is specifically designed for use with typically ill‐behaved river quality series characterized by problematic features such as non‐normal positively skewed populations, irregularly spaced instantaneous observations, seasonal periodicities, and dependence among water quality variables and riverflows. In order to analyze these “messy” environmental data in a systematic and rigorous fashion, the overall trend analysis approach is divided into the two main categories of graphical studies and trend tests. Within these two main steps, specific graphical, parametric and nonparametric statistical techniques are utilized. Graphical methods used in the procedure include time series plots, robust regression smooths, as well as box and whisker graphs. Nonparametric techniques include the Mann‐Kendall and Kruskal‐Wallis tests. Additionally, a test based on Spearman's partial rank correlation is introduced as a means for eliminating seasonal effects when testing for the presence of a trend. The efficacy of the trend analysis methodology is explained and demonstrated by applying it to water quality time series observed in the Saugeen and Grand Rivers of Southwestern Ontario, Canada.