Using machine learning to model and predict water clarity in the Great Lakes

Details

• Journal Title:
  Journal of Great Lakes Research
• Personal Author:
  Lee, Cameron C. ; Barnes, Brian B. ; Sheridan, Scott C. ; Smith, Erik T. ; Hu, Chuanmin ; Pirhalla, Douglas E. ; Ransibrahmanakul, Varis ; Adams, Ryan
• NOAA Program & Office:
  NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science)
• Description:
  Over the last several decades, multiple environmental issues have led to dramatic changes in the water clarity of the Great Lakes. While many of the key factors are well-known and have direct anthropogenic origins, climatic variability and change can also impact water clarity at various temporal scales, but their influence is less often studied. Building upon a recent examination of the univariate relationships between synoptic-scale weather patterns and water clarity, this research utilizes nonlinear autoregressive models with exogenous input (NARX models) to explore the multivariate climate-to-water clarity relationship. Models trained on the observation period (1997–2016) are extrapolated back to 1979 to reconstruct a daily-scale historical water clarity dataset, and used in a reforecast mode to estimate real-time forecast skill. Of the 20 regions examined, models perform best in Lakes Michigan and Huron, especially in spring and summer. The NARX models perform better than a simple persistence model and a seasonal-trend model in nearly all regions, indicating that climate variability is a contributing factor to fluctuations in water clarity. Further, six of the 20 regions also show promise of useful forecasts to at least 1 week of lead-time, with three of those regions showing skill out to two months of lead time.
• Keywords:
  Climatic Changes Climatology Water Quality Climatic Changes Water Quality
• Source:
  Journal of Great Lakes Research, 46(6), 1501-1510
• DOI:
  https://doi.org/10.1016/j.jglr.2020.07.022
• ISSN:
  0380-1330
• Publisher:
  Elsevier BV
• Document Type:
  Journal Article
• Place as Subject:
  Great Lakes (North America)
• Rights Information:
  Accepted Manuscript
• Rights Statement:
  This manuscript is made available under the Elsevier user license https://www.elsevier.com/open-access/userlicense/1.0/
• Compliance:
  Library
• Main Document Checksum:
  urn:sha256:db786d9345fccd03a14e1c1c21d5105c517af6873e10599663e42452aee4d170
• Download URL:
  https://repository.library.noaa.gov/view/noaa/50813/noaa_50813_DS1.pdf
• File Type:
  [PDF - 5.58 MB ]