Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/4/644 |
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| author | Christopher T. Nietch Leslie Gains-Germain James Lazorchak Scott P. Keely Gregory Youngstrom Emilee M. Urichich Brian Astifan Abram DaSilva Heather Mayfield |
| author_facet | Christopher T. Nietch Leslie Gains-Germain James Lazorchak Scott P. Keely Gregory Youngstrom Emilee M. Urichich Brian Astifan Abram DaSilva Heather Mayfield |
| author_sort | Christopher T. Nietch |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T20:51:08Z |
| format | Article |
| id | doaj.art-f0e7f439e0e74a16a52f104d66ba3b98 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T20:51:08Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-f0e7f439e0e74a16a52f104d66ba3b982023-11-23T22:35:12ZengMDPI AGWater2073-44412022-02-0114464410.3390/w14040644Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio RiverChristopher T. Nietch0Leslie Gains-Germain1James Lazorchak2Scott P. Keely3Gregory Youngstrom4Emilee M. Urichich5Brian Astifan6Abram DaSilva7Heather Mayfield8USEPA Office of Research and Development, Center for Environmental Measurement and Modeling, 26W Martin Luther King Dr, Cincinnati, OH 45268, USANeptune and Company, Inc., 1435 Garrison Street, Suite 201, Lakewood, CO 80215, USAUSEPA Office of Research and Development, Center for Environmental Measurement and Modeling, 26W Martin Luther King Dr, Cincinnati, OH 45268, USAUSEPA Office of Research and Development, Center for Environmental Measurement and Modeling, 26W Martin Luther King Dr, Cincinnati, OH 45268, USAOhio River Valley Water Sanitation Commission, 5735 Kellogg Ave., Cincinnati, OH 45230, USAOhio River Valley Water Sanitation Commission, 5735 Kellogg Ave., Cincinnati, OH 45230, USANational Weather Service, Ohio River Forecast Center, 1901 South State Route 134, Wilmington, OH 45177, USANational Weather Service, Ohio River Forecast Center, 1901 South State Route 134, Wilmington, OH 45177, USAFoundation for Ohio River Education, Ohio River Valley Water Sanitation Commission, 5735 Kellogg Ave., Cincinnati, OH 45230, USAA 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.https://www.mdpi.com/2073-4441/14/4/644cyanobacteriabig riverharmful algae bloomrisk characterizationpredictive modeling |
| spellingShingle | Christopher T. Nietch Leslie Gains-Germain James Lazorchak Scott P. Keely Gregory Youngstrom Emilee M. Urichich Brian Astifan Abram DaSilva Heather Mayfield Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River Water cyanobacteria big river harmful algae bloom risk characterization predictive modeling |
| title | Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River |
| title_full | Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River |
| title_fullStr | Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River |
| title_full_unstemmed | Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River |
| title_short | Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River |
| title_sort | development of a risk characterization tool for harmful cyanobacteria blooms on the ohio river |
| topic | cyanobacteria big river harmful algae bloom risk characterization predictive modeling |
| url | https://www.mdpi.com/2073-4441/14/4/644 |
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