Predicting open-water thermal regimes of temperate North American lakes. Gillis, D. P., Minns, C. K., & Shuter, B. J. Canadian Journal of Fisheries and Aquatic Sciences, 78(7):820–840, July, 2021. Publisher: NRC Research Press![Predicting open-water thermal regimes of temperate North American lakes [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Temperature profoundly affects the physical, chemical, and biological attributes of lakes and is influenced by several abiotic factors. Lake temperature modelling permits regional estimates of seasonal fish thermal habitat availability; however, this requires models that are accurate across large spatial scales. To address this, we fit a semi-mechanistic seasonal temperature-profile model (STM) to 369 morphometrically diverse North American lakes with data spanning 1971–2016. STM with a fixed-depth thermocline formula accurately modelled lake temperature (median pseudo-R2: 0.95, median lake-year-specific root mean square error (RMSE): 1.13 °C). We used random forests to select candidate predictors, then used linear mixed-effects modelling, based on these predictors, to create empirical equations to predict STM parameters from lake-specific morphometric and climate measures. We tested the accuracy of our equations by predicting thermal profiles in 776 Ontario lakes, finding good agreement between predicted and observed temperatures (median lake-year-specific RMSE: 2.38 °C) and stratification occurrence (91.7%). These findings enhance our understanding of the factors that influence lake temperatures and can be used to identify lake types and regions that may be especially susceptible to climate change.
@article{gillis_predicting_2021,
title = {Predicting open-water thermal regimes of temperate {North} {American} lakes},
volume = {78},
issn = {0706-652X},
url = {https://cdnsciencepub.com/doi/full/10.1139/cjfas-2020-0140},
doi = {10.1139/cjfas-2020-0140},
abstract = {Temperature profoundly affects the physical, chemical, and biological attributes of lakes and is influenced by several abiotic factors. Lake temperature modelling permits regional estimates of seasonal fish thermal habitat availability; however, this requires models that are accurate across large spatial scales. To address this, we fit a semi-mechanistic seasonal temperature-profile model (STM) to 369 morphometrically diverse North American lakes with data spanning 1971–2016. STM with a fixed-depth thermocline formula accurately modelled lake temperature (median pseudo-R2: 0.95, median lake-year-specific root mean square error (RMSE): 1.13 °C). We used random forests to select candidate predictors, then used linear mixed-effects modelling, based on these predictors, to create empirical equations to predict STM parameters from lake-specific morphometric and climate measures. We tested the accuracy of our equations by predicting thermal profiles in 776 Ontario lakes, finding good agreement between predicted and observed temperatures (median lake-year-specific RMSE: 2.38 °C) and stratification occurrence (91.7\%). These findings enhance our understanding of the factors that influence lake temperatures and can be used to identify lake types and regions that may be especially susceptible to climate change.},
number = {7},
urldate = {2023-06-30},
journal = {Canadian Journal of Fisheries and Aquatic Sciences},
author = {Gillis, Daniel P. and Minns, Charles K. and Shuter, Brian J.},
month = jul,
year = {2021},
note = {Publisher: NRC Research Press},
keywords = {Terrestrial Ecoregions (CEC 1997)},
pages = {820--840},
}
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STM with a fixed-depth thermocline formula accurately modelled lake temperature (median pseudo-R2: 0.95, median lake-year-specific root mean square error (RMSE): 1.13 °C). We used random forests to select candidate predictors, then used linear mixed-effects modelling, based on these predictors, to create empirical equations to predict STM parameters from lake-specific morphometric and climate measures. We tested the accuracy of our equations by predicting thermal profiles in 776 Ontario lakes, finding good agreement between predicted and observed temperatures (median lake-year-specific RMSE: 2.38 °C) and stratification occurrence (91.7%). 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