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Estimating upper percentiles of surface water monitoring data with sparse samples
Mosquin, P. L., Aldworth, J., Kott, P. S., Chen, W., & Grant, S. (2023). Estimating upper percentiles of surface water monitoring data with sparse samples. Journal of the American Water Resources Association, 59(1), 16-28. https://doi.org/10.1111/1752-1688.13064
The estimation of peak chemical concentrations in surface water systems within sites and regions may be necessary for the assessment of potential risk to ecosystems and human health. Limited sampling size at monitoring sites often limits the use of direct methods to infer upper percentiles of chemical concentrations. In these cases, upper percentiles within regions within a time frame may be estimated by pooling data across sites and years, and then deriving percentile estimates from the resulting dataset. The method uses the observations resulting from either a known probability-sampling design or a pseudo-probability sampling design (i.e. a sampling design treated like a probability sampling design because its observations come close to matching that of a probability-sample). These observations are then weighted to ensure that estimates are representative of a target population across all the sites within the region and the range of years in the time frame. This method of estimating upper percentiles of annual site concentration profiles is demonstrated using atrazine and validated using the monitoring data from two drinking water monitoring programs: 1) the very sparsely sampled non-targeted SDWA (Safe Drinking Water Act) program, and 2) the high-frequency sampling targeted programs of AMP (Atrazine Monitoring Program), SMP (Simazine Monitoring Program), and SVMP (Stewardship Voluntary Monitoring Program). Point and interval estimates of the 90th, 95th, and 99th pooled population percentiles are provided by water body type (static, mixed, or flowing), water treatment (raw or finished), and spatial region.