Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3853-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-3853-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
25 Aug 2023
Data description paper |
| 25 Aug 2023
| 25 Aug 2023
Routine monitoring of western Lake Erie to track water quality changes associated with cyanobacterial harmful algal blooms
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
Ashley M. Burtner
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
Andrew C. Camilleri
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
Glenn Carter
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
Paul DenUyl
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
David Fanslow
NOAA Great Lakes Environmental Research Laboratory, 4840 South State Road, Ann Arbor, MI 48108, USA
Deanna Fyffe Semenyuk
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
Jacobs, 1999 Bryan Street, Suite 1200, Dallas, TX 75201, USA
Casey M. Godwin
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
Duane Gossiaux
NOAA Great Lakes Environmental Research Laboratory, 4840 South State Road, Ann Arbor, MI 48108, USA
Thomas H. Johengen
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
Holly Kelchner
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
Christine Kitchens
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
Lacey A. Mason
NOAA Great Lakes Environmental Research Laboratory, 4840 South State Road, Ann Arbor, MI 48108, USA
Kelly McCabe
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
Danna Palladino
NOAA Great Lakes Environmental Research Laboratory, 4840 South State Road, Ann Arbor, MI 48108, USA
Dack Stuart
Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
Woods Hole Group, Inc., 107 Waterhouse Road, Bourne, MA 02532, USA
Henry Vanderploeg
NOAA Great Lakes Environmental Research Laboratory, 4840 South State Road, Ann Arbor, MI 48108, USA
Reagan Errera
CORRESPONDING AUTHOR
NOAA Great Lakes Environmental Research Laboratory, 4840 South State Road, Ann Arbor, MI 48108, USA
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Cited
20 citations as recorded by crossref.
- Mapping satellite-derived chlorophyll-a concentrations from 2013 to 2023 in Western Lake Ontario using Landsat 8 and 9 imagery A. Shahvaran et al. 10.1016/j.scitotenv.2025.178881
- Predicting Harmful Algal Blooms Using Explainable Deep Learning Models: A Comparative Study B. Demiray et al. 10.3390/w17050676
- Microcystis strains in Lake Erie explain interactions between a selective filter feeder and the phytoplankton community A. Boegehold et al. 10.1007/s10750-025-05839-9
- Genomic Identification and Characterization of Saxitoxin Producing Cyanobacteria in Western Lake Erie Harmful Algal Blooms P. Den Uyl et al. 10.1021/acs.est.4c10888
- Underway measurement of cyanobacterial microcystins using a surface plasmon resonance sensor on an autonomous underwater vehicle W. Ussler et al. 10.1002/lom3.10627
- Comparing the performance of 10 machine learning models in predicting Chlorophyll a in western Lake Erie Y. Song et al. 10.1016/j.jenvman.2025.125007
- A decade-long chlorophyll-a data record in lakes across China from VIIRS observations Z. Cao et al. 10.1016/j.rse.2023.113953
- Short-term probabilistic microcystin prediction using Bayesian model averaging S. Qian et al. 10.1016/j.jenvman.2025.124378
- Investing in Great Lakes Science is critical for safety and prosperity G. Dick et al. 10.1016/j.jglr.2025.102614
- Modeling attenuation of photosynthetically active radiation across the optical gradient in the Laurentian Great Lakes with application to Lake Erie P. Alsip et al. 10.1016/j.jglr.2024.102364
- Long-term spatiotemporal variability and regime classification of Chlorophyll-a concentrations in Lake Erie using satellite products T. Kim et al. 10.1016/j.hal.2025.102896
- Identification of Novel Peptides Originated from Biodegradation of Microcystins Using UHPLC-HRMS/MS S. Thenuwara et al. 10.1021/acsestwater.4c00308
- Are Harmful Algal Blooms Increasing in the Great Lakes? K. Bosse et al. 10.3390/w16141944
- Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie O. Mermer & I. Demir 10.3390/app15094824
- A methodology to assess the phenology patterns of a eutrophic water body, using remote sensing data and principal component analysis I. Biliani & I. Zacharias 10.1016/j.rsase.2024.101387
- Spatial Pattern Assessment and Prediction of Water and Sedimentary Mud Quality Changes in Lake Maurepas T. Gunawardhana et al. 10.3390/environments11120268
- Dynamic energy budget (DEB) parameter estimation for the globally invasive Quagga Mussel (Dreissena rostriformis bugensis) T. Pu et al. 10.1016/j.ecolmodel.2025.111100
- Forecasting short-term chlorophyll a concentration in Lake Erie using the machine learning XGBoost algorithm Y. Song 10.1088/1748-9326/add6b7
- An assessment of a biosensor system for the quantification of microcystins in freshwater cyanobacterial blooms J. Chaffin et al. 10.1016/j.ab.2023.115429
- A Comparative Study of Ensemble Machine Learning and Explainable AI for Predicting Harmful Algal Blooms O. Mermer et al. 10.3390/bdcc9050138
20 citations as recorded by crossref.
- Mapping satellite-derived chlorophyll-a concentrations from 2013 to 2023 in Western Lake Ontario using Landsat 8 and 9 imagery A. Shahvaran et al. 10.1016/j.scitotenv.2025.178881
- Predicting Harmful Algal Blooms Using Explainable Deep Learning Models: A Comparative Study B. Demiray et al. 10.3390/w17050676
- Microcystis strains in Lake Erie explain interactions between a selective filter feeder and the phytoplankton community A. Boegehold et al. 10.1007/s10750-025-05839-9
- Genomic Identification and Characterization of Saxitoxin Producing Cyanobacteria in Western Lake Erie Harmful Algal Blooms P. Den Uyl et al. 10.1021/acs.est.4c10888
- Underway measurement of cyanobacterial microcystins using a surface plasmon resonance sensor on an autonomous underwater vehicle W. Ussler et al. 10.1002/lom3.10627
- Comparing the performance of 10 machine learning models in predicting Chlorophyll a in western Lake Erie Y. Song et al. 10.1016/j.jenvman.2025.125007
- A decade-long chlorophyll-a data record in lakes across China from VIIRS observations Z. Cao et al. 10.1016/j.rse.2023.113953
- Short-term probabilistic microcystin prediction using Bayesian model averaging S. Qian et al. 10.1016/j.jenvman.2025.124378
- Investing in Great Lakes Science is critical for safety and prosperity G. Dick et al. 10.1016/j.jglr.2025.102614
- Modeling attenuation of photosynthetically active radiation across the optical gradient in the Laurentian Great Lakes with application to Lake Erie P. Alsip et al. 10.1016/j.jglr.2024.102364
- Long-term spatiotemporal variability and regime classification of Chlorophyll-a concentrations in Lake Erie using satellite products T. Kim et al. 10.1016/j.hal.2025.102896
- Identification of Novel Peptides Originated from Biodegradation of Microcystins Using UHPLC-HRMS/MS S. Thenuwara et al. 10.1021/acsestwater.4c00308
- Are Harmful Algal Blooms Increasing in the Great Lakes? K. Bosse et al. 10.3390/w16141944
- Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie O. Mermer & I. Demir 10.3390/app15094824
- A methodology to assess the phenology patterns of a eutrophic water body, using remote sensing data and principal component analysis I. Biliani & I. Zacharias 10.1016/j.rsase.2024.101387
- Spatial Pattern Assessment and Prediction of Water and Sedimentary Mud Quality Changes in Lake Maurepas T. Gunawardhana et al. 10.3390/environments11120268
- Dynamic energy budget (DEB) parameter estimation for the globally invasive Quagga Mussel (Dreissena rostriformis bugensis) T. Pu et al. 10.1016/j.ecolmodel.2025.111100
- Forecasting short-term chlorophyll a concentration in Lake Erie using the machine learning XGBoost algorithm Y. Song 10.1088/1748-9326/add6b7
- An assessment of a biosensor system for the quantification of microcystins in freshwater cyanobacterial blooms J. Chaffin et al. 10.1016/j.ab.2023.115429
- A Comparative Study of Ensemble Machine Learning and Explainable AI for Predicting Harmful Algal Blooms O. Mermer et al. 10.3390/bdcc9050138
Latest update: 26 Jul 2025
Short summary
Western Lake Erie suffers from cyanobacterial harmful algal blooms (HABs) despite decades of international management efforts. In response, the US National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs. Here we describe the data collected from this monitoring program from 2012 to 2021.
Western Lake Erie suffers from cyanobacterial harmful algal blooms (HABs) despite decades of...
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