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Data Analysis Considerations in Producing 'Comparable' Information for Water Quality Management Purposes

Water quality monitoring is being used in local, regional, and national scales to measure how water quality variables behave in the natural environment. A common problem, which arises from monitoring, is how to relate information contained in data to the information needed by water resource management for decision-making. This is generally attempted through statistical analysis of the monitoring data. However, how the selection of methods with which to routinely analyze the data affects the quality and comparability of information produced is not as well understood as may first appear. To help understand the connectivity between the selection of methods for routine data analysis and the information produced to support management, the following three tasks were performed. An examination of the methods that are currently being used to analyze water quality monitoring data, including published criticisms of them. An exploration of how the selection of methods to analyze water quality data can impact the comparability of information used for water quality management purposes. Development of options by which data analysis methods employed in water quality management can be made more transparent and auditable. These tasks were accomplished through a literature review of texts, guidance and journals related to water quality. Then, the common analysis methods found were applied to portions of a river water quality dataset from New Zealand. The purpose of this was to establish how information changes as analysis methods change, and to determine if the information produced from different analysis methods is comparable. The results of the literature review and data analysis are then discussed and recommendations are made addressing problems with current data analysis procedures. Options are listed through which to begin solving these problems and produce better information for water quality management. It was found that null hypothesis testing is the most popular method through which to produce information, yet assumptions and hypotheses are loosely explained and alternatives rarely explored to determine the validity and comparability of the results. Other data analysis methods (using graphical, non-null hypotheses or Bayesian methods) that might be more appropriate for producing more comparable information are discussed, along with recommendations for further research and cooperative efforts to establish water quality data analysis protocols for producing information for management.