The Uses and Limitations of Trend Analyses in Water Quality Studies

Public concerns about water quality often focus on change. In other words, is water quality degradation associated with growth and development occurring? Or, is water quality improving after the implementation of new management actions? Answers to these questions may help guide future decision making, yet clear unambiguous answers can be difficult to obtain.

First, it is important to recognize that even if the management action of interest is affecting water quality, trends may not be apparent in water quality data for several years. This is particularly true for agricultural BMPs, where the small individual impact of a single BMP and the lag time in response to BMP implementation, are important determinants of observable response. Many factors, such as other sources of contaminants, seasonal cycles, precipitation, and natural variability affect measured water quality. As a consequence, it often takes many years of regular water quality data collection to statistically detect a trend. In general, large, abrupt changes in water quality will be detected with fewer samples than will small, gradual changes.

Trend detection involves finding a signal (the trend) in the midst of background variability (noise); the larger the noise, or the smaller the trend, the more data are needed to confidently assess the presence of a trend. More frequent sampling generally helps up to a point; however, samples collected much more frequent than monthly may be serially correlated. As a result of this correlation, sequential samples in time may not be entirely independent of each other, which means that more samples will be needed for statistical trend detection than under conditions of independence. Note that proper data analysis to characterize other patterns (e.g., seasonality) in the data can improve the sensitivity of the test.

Once the water quality data have been collected, we could simply look at a graph of the water quality data versus time, and determine the presence of a trend by visual inspection. However, a more scientifically-defensible approach is to use a statistical technique like the seasonal Kendall test to evaluate data for the presence of a trend, as statistical tests add analytic rigor and a level of objectivity to the conclusion.

The seasonal Kendall (SK) test has become the “industry standard” in water quality trend detection. SK programs are widely available, and the test statistic is relatively easy to compute and interpret. The SK test simply indicates the likely presence (or absence) of a trend at a specified level of significance; other statistics can then be computed to estimate the magnitude of any trend present. Among the shortcomings of the SK test are its restriction to monotonic (unidirectional) trends, and the limited insight it provides in comparison to other methods that might be preferred by an experienced statistician.

Once the application of the SK test indicates the likely presence of a water quality trend, several issues must be addressed to make the analysis useful for management. First, unlike a predictive water quality model, the trend test results provide no information about the likely causes and corrective measures for the trend. Fortunately, a good sampling design may help isolate the cause(s) of a trend. For example, if the impact on river water quality associated with nitrogen removal from a major wastewater treatment plant is of interest, then a reasonable design option is to take samples for nitrogen concentration in the river just below the discharge.

While that particular sampling program may isolate the source, it may be less informative about the meaningful water quality impacts. For example, the treatment plant of concern may be located upstream in the Susquehanna River, while the water quality impact of interest may be downstream in Chesapeake Bay. Processes can occur in the river such that the trend in nitrogen concentration in the Susquehanna due to the treatment plant operations is quite different from that in the Chesapeake. The Bay trend is also likely to be less detectable, since many other factors affect nitrogen concentration in the Chesapeake.

To further complicate matters in this nutrient enrichment example, public response to water quality management actions is probably influenced largely by algal blooms, fishkills, and shellfish harvest in the Chesapeake, not by nitrogen concentration. Unfortunately, measuring trends in algal blooms, fishkills, and shellfish harvest in the Chesapeake Bay and then linking those trends to improvements in nitrogen removal at a specific upstream wastewater treatment plant may be technically and economically infeasible. Thus from a practical perspective, sampling may still focus on the nitrogen trend in the River even though the interesting trend concerns blue crab harvests in the Bay.

In that situation, scientists should describe for policy makers the implications and limitations of assessing trends in a surrogate water quality variable. In the causal chain from a nitrogen source, to nitrogen input, to riverine nitrogen concentration, to estuarine nitrogen concentration, to dissolved oxygen, to blue crabs, trends assessed closer to the source can more easily be related to the underlying cause, but they have less meaning to the public concerning important water quality impacts.

In summary, water quality trend assessment serves primarily as a warning system for change. This can be extremely useful for policy evaluation, but it must be emphasized that definitive conclusions on water quality trends may require years of sampling. Ultimately, if a trend is identified, additional scientific understanding is often essential to understand the implications of the trends and to identify effective corrective actions if the trend reflects water quality degradation.