New York State's WAVE Method - Evaluation of a Method for Water Quality Monitoring by Citizen Scientists using Benthic Macroinvertebrates

New York State Department of Environmental Conservation (NYSDEC) has developed a robust citizen science macroinvertebrate sampling method. The metric relies on the presence and not the absence of key macroinvertebrates and therefore is resistant to collection and sorting errors. It identifies unimpaired streams with high confidence (0.1% type 1 errors) and at a reasonable efficiency compared to NYSDEC's multimetric index of biological integrity (54%). We rank remaining stream samples for further investigation using a calculated probability of impairment. This method is valuable as a tool for large monitoring programs with limited resources for quality assurance checks. The value of this method goes beyond data collection, however, as data of known quality is an effective communication tool between citizen scientists and state regulatory agencies and/or local decision makers.

Use of power analysis to determine the number of samples needed to assess water quality in lakes and flowing waters

The Clean Water Act requires states to develop methods for assessing water quality. Assessment methods serve as decision-making procedures for including waterbodies on the Section 303(d) List of Impaired Waters. We used 17 years of ambient water quality data to explore statistical analyses for assessment methods that represent New York's waterbodies. Power analyses were performed to determine how many samples are needed to evaluate exceedances of water quality criteria using one-sample t-tests in lakes and flowing waters. Results suggest six samples for lakes and eight samples for flowing waters are needed to obtain at least 80% power, which is fewer samples than most other types of statistical assessment methodologies. This smaller number was possible because the power analysis was applied to the actual variability found in monitoring data to calculate the effect size as opposed to more conservative statistical estimates based on random data. Water quality criteria can have different analysis requirements such as single samples or means above the threshold, so we compared how many impairments would occur in the dataset if the six or eight samples were assessed as two single exceedances or a mean above the water quality criteria. Because the power analysis gives no indication of the time frame of when samples should be collected, the intra- and interannual variability of the data was assessed to determine whether sampling over a growing season in one year or sampling over multiple years is more representative of the water quality status. Results demonstrated that data collected over the growing season captured more variability in water quality data than data collected over multiple years in both waterbody types. With the prevalence of regulatory agencies having large, historical datasets rising, it would be possible for other agencies to apply these types of analyses to their assessment methodologies. Integr Environ Assess Manag 2022;18:1621-1628. © 2021 SETAC.