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 are representative of 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 6 samples for lakes and 8 samples for flowing waters are needed to obtain at least 80% power, which is less samples than most other types of statistical assessment methodologies. This lower 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 6 or 8 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 inter and intra-annual 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 showed 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.

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