Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie

Qian, Song S.; Stow, Craig A.; Rowland, Freya E.; Liu, Qianqian; Rowe, Mark D.; Anderson, Eric J.; Stumpf, Richard P.; Johengen, Thomas H.

doi:10.1016/j.ecolind.2021.108055

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie

2021
By Qian, Song S. ; Stow, Craig A. ; Rowland, Freya E. ; ...
Source: Ecological Indicators, 130, 108055

[PDF-4.35 MB]

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Journal Title:

Ecological Indicators
Personal Author:

Qian, Song S. ; Stow, Craig A. ; Rowland, Freya E. ; Liu, Qianqian ; Rowe, Mark D. ; ... More +

Qian, Song S. ; Stow, Craig A. ; Rowland, Freya E. ; Liu, Qianqian ; Rowe, Mark D. ; Anderson, Eric J. ; Stumpf, Richard P. ; Johengen, Thomas H. Less -
NOAA Program & Office:

NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science) ; OAR (Oceanic and Atmospheric Research) ; GLERL (Great Lakes Environmental Research Laboratory) ; CIGLR (Cooperative Institute for Great Lakes Research)

NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science) ; OAR (Oceanic and Atmospheric Research) ; GLERL (Great Lakes Environmental Research Laboratory) ; CIGLR (Cooperative Institute for Great Lakes Research) Less -
Description:

We developed a Bayesian hierarchical modeling framework to establish a short-term forecasting model of particulate cyanobacterial toxin concentrations in Western Lake Erie using chlorophyll a concentration as the predictor. The model evolves over time with additional data to reflect the changing dynamics of cyanobacterial toxin production. Specifically, parameters of the empirical relationship between the cyanobacterial toxin microcystin and chlorophyll a concentrations are allowed to vary annually and seasonally under a hierarchical framework. As such, the model updated using the most recent sampling data is suited to provide short-term forecasts. The reduced model predictive uncertainty makes the model a viable tool for risk assessment. Using data from the long-term Western Lake Erie harmful algal bloom monitoring program (2008–2018), we illustrate the model-building and model-updating process and the application of the model for short-term risk assessment. The modeling process demonstrates the use of the Bayesian hierarchical modeling framework for developing informative priors in Bayesian modeling.
Source:

Ecological Indicators, 130, 108055
DOI:

https://doi.org/10.1016/j.ecolind.2021.108055
ISSN:

1470-160X
Publisher:

Elsevier BV
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by-nc-nd/4.0/
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CC BY-NC-ND
Rights Statement:

This manuscript is made available under the Elsevier user license https://www.elsevier.com/open-access/userlicense/1.0/
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urn:sha256:a2136bd23e6297fb931e39b4ef35940cf92fe8e335fef2e417ca30be68139ae2
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https://repository.library.noaa.gov/view/noaa/50666/noaa_50666_DS1.pdf
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Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie

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