A framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face of global change

Simple item page
dc.contributor.authorCarey, Cayelan C.en
dc.contributor.authorCalder, Ryan S. D.en
dc.contributor.authorFigueiredo, Renato J.en
dc.contributor.authorGramacy, Robert B.en
dc.contributor.authorLofton, Mary E.en
dc.contributor.authorSchreiber, Madeline E.en
dc.contributor.authorThomas, R. Quinnen
dc.date.accessioned2025-02-19T18:12:13Zen
dc.date.available2025-02-19T18:12:13Zen
dc.date.issued2024-09-20en
dc.description.abstractPhytoplankton blooms create harmful toxins, scums, and taste and odor compounds and thus pose a major risk to drinking water safety. Climate and land use change are increasing the frequency and severity of blooms, motivating the development of new approaches for preemptive, rather than reactive, water management. While several real-time phytoplankton forecasts have been developed to date, none are both automated and quantify uncertainty in their predictions, which is critical for manager use. In response to this need, we outline a framework for developing the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty, thereby enabling managers to adapt operations and mitigate blooms. Implementation of this system calls for new, integrated ecosystem and statistical models; automated cyberinfrastructure; effective decision support tools; and training for forecasters and decision makers. We provide a research agenda for the creation of this system, as well as recommendations for developing real-time phytoplankton forecasts to support management.en
dc.description.versionPublished versionen
dc.format.extentPages 475-487en
dc.format.extent13 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1007/s13280-024-02076-7en
dc.identifier.eissn1654-7209en
dc.identifier.issn0044-7447en
dc.identifier.issue3en
dc.identifier.orcidThomas, Robert [0000-0003-1282-7825]en
dc.identifier.orcidCarey, Cayelan [0000-0001-8835-4476]en
dc.identifier.orcidSchreiber, Madeline [0000-0002-1858-7730]en
dc.identifier.orcidGramacy, Robert [0000-0001-9308-3615]en
dc.identifier.orcidCalder, Ryan [0000-0001-5618-9840]en
dc.identifier.otherPMC11780027en
dc.identifier.other10.1007/s13280-024-02076-7 (PII)en
dc.identifier.pmid39302615en
dc.identifier.urihttps://hdl.handle.net/10919/124655en
dc.identifier.volume54en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/39302615en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectCyanobacteriaen
dc.subjectCyberinfrastructureen
dc.subjectDecision supporten
dc.subjectForecasten
dc.subjectPhytoplankton bloomen
dc.subjectWater managementen
dc.subject.meshPhytoplanktonen
dc.subject.meshEnvironmental Monitoringen
dc.subject.meshForecastingen
dc.subject.meshClimate Changeen
dc.subject.meshLakesen
dc.subject.meshWater Qualityen
dc.titleA framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face of global changeen
dc.title.serialAmbioen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
dcterms.dateAccepted2024-09-05en
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Natural Resources & Environmenten
pubs.organisational-groupVirginia Tech/Natural Resources & Environment/Forest Resources and Environmental Conservationen
pubs.organisational-groupVirginia Tech/Scienceen
pubs.organisational-groupVirginia Tech/Science/Biological Sciencesen
pubs.organisational-groupVirginia Tech/Science/Geosciencesen
pubs.organisational-groupVirginia Tech/Science/Statisticsen
pubs.organisational-groupVirginia Tech/Veterinary Medicineen
pubs.organisational-groupVirginia Tech/Veterinary Medicine/Population Health Sciencesen
pubs.organisational-groupVirginia Tech/Faculty of Health Sciencesen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Natural Resources & Environment/CNRE T&R Facultyen
pubs.organisational-groupVirginia Tech/Science/COS T&R Facultyen
pubs.organisational-groupVirginia Tech/Veterinary Medicine/CVM T&R Facultyen
pubs.organisational-groupVirginia Tech/Post-docsen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face.pdf
Size:
611.86 KB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Plain Text
Description:
Download

Collections

All Faculty Deposits
Scholarly Works, Biological Sciences
Scholarly Works, Forest Resources and Environmental Conservation
Scholarly Works, Geosciences
Scholarly Works, Population Health Sciences
Scholarly Works, Statistics