Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River

Abstract

A data-driven approach to characterizing the risk of cyanobacteria-based harmful algal blooms (cyanoHABs) was undertaken for the Ohio River. Twenty-five years of river discharge data were used to develop Bayesian regression models that are currently applicable to 20 sites spread-out along the entire 1579 km of the river's length. Two site-level prediction models were developed based on the antecedent flow conditions of the two blooms that occurred on the river in 2015 and 2019: one predicts if the current year will have a bloom (the occurrence model), and another predicts bloom persistence (the persistence model). Predictors for both models were based on time-lagged average flow exceedances and a site's characteristic residence time under low flow conditions. Model results are presented in terms of probabilities of occurrence or persistence with uncertainty. Although the occurrence of the 2019 bloom was well predicted with the modeling approach, the limited number of events constrained formal model validation. However, as a measure of performance, leave-one-out cross validation returned low misclassification rates, suggesting that future years with flow time series like the previous bloom years will be correctly predicted and characterized for persistence potential. The prediction probabilities are served in real time as a component of a risk characterization tool/web application. In addition to presenting the model's results, the tool was designed with visualization options for studying water quality trends among eight river sites currently collecting data that could be associated with or indicative of bloom conditions. The tool is made accessible to river water quality professionals to support risk communication to stakeholders, as well as serving as a real-time water data monitoring utility.

Keywords: big river; cyanobacteria; harmful algae bloom; predictive modeling; risk characterization.

Figure 1.

Site locations along the Ohio River where historical and real-time flow data were evaluated for modeling.

Figure 2.

Conceptual cause and effects model linking cyanoHAB topreceding river flow conditions.

Figure 3.

Example visualization approach to identify uniqueness of flow conditions during bloom years. Average daily discharge data for the Pike Island site plotted for 1995 through 2021, beginning of May to the end of October each year. Bloom first reported at Pike Island in 2015 (points in red signify bloom period).

Figure 4.

Yearly plots of the ratio of the 1 to 19 day and 21 to 55 day average lagged exceedances at Pike Island.

Figure 5.

Graphical bloom occurrence model results for the Greenup site. Data are yearly 1–19-day:21–55-day maxratios and number of days increasing (inc15s) for the bloom season overlaying gradient in predicted risk probabilities (P) at right. CyanoHABs in 2015 and 2019 years are identified per the legend at the top.

Figure 6.

Daily 1–19day:21–55 day lagged exceedance ratio at Markland Site plotted for each year from 2011 through 2020. Text in each graph denotes the day that the maxratio occurred in each year and what the occurrence model’s prediction probability would have been with the 95% credible interval in parentheses. Data in red are the documented bloom periods.

Figure 7.

Yearly maxratio for the bloom season computed for the Greenup site plotted vs. the number of days since the maxratio occurred for each of the 25 years modeled. Each vertical line of points represents a year. Differences between (A,B) demonstrate how persistence probability increases if the threshold indicator has not been passed (i.e., cone of high probability shifts to the left).

Figure 7.

Yearly maxratio for the bloom season computed for the Greenup site plotted vs. the number of days since the maxratio occurred for each of the 25 years modeled. Each vertical line of points represents a year. Differences between (A,B) demonstrate how persistence probability increases if the threshold indicator has not been passed (i.e., cone of high probability shifts to the left).

Figure 8.

Screen capture of interactive map page of the risk characterization tool. In this image, the Pike Island site has been selected, and discharge data are reported for 2020 (a non-bloom year) compared to 2015 (a bloom year). The results of the lagged exceedances are computed for the day in 2020 that the maxratio occurred and the bloom probability predicted by the occurrence model for this year. In real time, during the bloom season, the flow series would be up-to-date, and the model results would be reported for the most current date that flow had been reported for the site.