Modeling Daily Ice Cover in Northern Hemisphere Lakes With a Long Short‐Term Memory Neural Network

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Abstract
Quantifying lake ice loss is crucial for understanding the impact of climate change on lake ecosystems. In this study, we trained a deep learning model (Long‐Short Term Memory with Landsat observations, 1984–2012) to simulate Northern Hemisphere lake ice changes at a fine spatial scale from 1980 to 2022. The model achieved good performance overall during the test period (2013–2022), and the derived ice‐on and ice‐off matched well with two independent ice phenology data sets. Results reveal a 76.8% increase in intermittently ice‐covered lakes from the 1980s to the 2010s, alongside a 10.7‐day shorter ice duration and a 3.9 percentage‐points reduction in annual mean ice cover fractions. The model can track daily partial ice cover changes, providing a novel contribution to understanding shifts in lake ice cover with climate change. These findings can provide valuable insights for future limnology studies, such as improving estimates of greenhouse gas emissions from lakes.
Funding Information
  • Northeast Climate Adaptation Science Center, University of Massachusetts Amherst (G20AC00354)

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