Machine‐learning modeling of hourly potential thermal refuge area: A case study from the Sainte‐Marguerite River (Quebec, Canada)
Abstract To understand the temporal and spatial variability of thermal refuges, this study focused on modeling potential thermal refuge area (PTRA) at a sub‐daily time‐step in two tributary confluences of the Sainte‐Marguerite River (Canada) during the summers of 2020 and 2021. Aquatic ectotherm spe...
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crwiley:10.1002/rra.4191 2024-06-02T08:03:42+00:00 Machine‐learning modeling of hourly potential thermal refuge area: A case study from the Sainte‐Marguerite River (Quebec, Canada) Hani, Ilias St‐Hilaire, André Ouarda, Taha B. M. J. Natural Sciences and Engineering Research Council of Canada 2023 http://dx.doi.org/10.1002/rra.4191 https://onlinelibrary.wiley.com/doi/pdf/10.1002/rra.4191 en eng Wiley http://creativecommons.org/licenses/by-nc-nd/4.0/ River Research and Applications volume 39, issue 9, page 1763-1782 ISSN 1535-1459 1535-1467 journal-article 2023 crwiley https://doi.org/10.1002/rra.4191 2024-05-03T11:26:08Z Abstract To understand the temporal and spatial variability of thermal refuges, this study focused on modeling potential thermal refuge area (PTRA) at a sub‐daily time‐step in two tributary confluences of the Sainte‐Marguerite River (Canada) during the summers of 2020 and 2021. Aquatic ectotherm species, such as Atlantic salmon ( Salmo salar ), seek these refuges to avoid heat stress during high summer river temperatures. To investigate the temporal variability of these PTRA, we employed inverse weighted distance interpolation to delineate the hourly area available at both confluences. We then analyzed the impact of the atypical low flow conditions of summer 2021 on the diel cycle of PTRA extremes using the coefficient of variation and the generalized additive model (GAM). Finally, we used four supervised machine‐learning regression models and three to five hydrometeorological predictors to estimate hourly PTRA availability: multivariate adaptive splines regression (MARS), GAM, support vector machine regression (SVM), and random forest regression (RF). The results showed that tree‐based and kernel‐based regression models, RF and SVM, outperformed GAM and MARS. RF had the highest accuracy at both sites, with a relative root mean square error and Nash–Sutcliffe efficiency coefficient (Nash) of 13% and 93%, respectively. Our study discovered that under warm conditions in August 2021, small perennial tributary inflows in combination with low mainstem discharge could create high and constant PTRA at confluences, potentially providing vital thermal refuges for cold‐water taxa. These refuges may be especially important at the local level, within a specific stretch or section of the river. Given the decreasing availability of thermal refuges for salmonids, it is crucial to monitor stream temperatures at small spatial and temporal scales using data‐driven techniques in order to understand stream temperature heterogeneity at tributary confluences. Article in Journal/Newspaper Atlantic salmon Salmo salar Wiley Online Library Canada Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) Marguerite ENVELOPE(141.378,141.378,-66.787,-66.787) Marguerite River ENVELOPE(-109.929,-109.929,57.560,57.560) Nash ENVELOPE(-62.350,-62.350,-74.233,-74.233) Sutcliffe ENVELOPE(-81.383,-81.383,50.683,50.683) River Research and Applications 39 9 1763 1782 |
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Open Polar |
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Wiley Online Library |
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crwiley |
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English |
description |
Abstract To understand the temporal and spatial variability of thermal refuges, this study focused on modeling potential thermal refuge area (PTRA) at a sub‐daily time‐step in two tributary confluences of the Sainte‐Marguerite River (Canada) during the summers of 2020 and 2021. Aquatic ectotherm species, such as Atlantic salmon ( Salmo salar ), seek these refuges to avoid heat stress during high summer river temperatures. To investigate the temporal variability of these PTRA, we employed inverse weighted distance interpolation to delineate the hourly area available at both confluences. We then analyzed the impact of the atypical low flow conditions of summer 2021 on the diel cycle of PTRA extremes using the coefficient of variation and the generalized additive model (GAM). Finally, we used four supervised machine‐learning regression models and three to five hydrometeorological predictors to estimate hourly PTRA availability: multivariate adaptive splines regression (MARS), GAM, support vector machine regression (SVM), and random forest regression (RF). The results showed that tree‐based and kernel‐based regression models, RF and SVM, outperformed GAM and MARS. RF had the highest accuracy at both sites, with a relative root mean square error and Nash–Sutcliffe efficiency coefficient (Nash) of 13% and 93%, respectively. Our study discovered that under warm conditions in August 2021, small perennial tributary inflows in combination with low mainstem discharge could create high and constant PTRA at confluences, potentially providing vital thermal refuges for cold‐water taxa. These refuges may be especially important at the local level, within a specific stretch or section of the river. Given the decreasing availability of thermal refuges for salmonids, it is crucial to monitor stream temperatures at small spatial and temporal scales using data‐driven techniques in order to understand stream temperature heterogeneity at tributary confluences. |
author2 |
Natural Sciences and Engineering Research Council of Canada |
format |
Article in Journal/Newspaper |
author |
Hani, Ilias St‐Hilaire, André Ouarda, Taha B. M. J. |
spellingShingle |
Hani, Ilias St‐Hilaire, André Ouarda, Taha B. M. J. Machine‐learning modeling of hourly potential thermal refuge area: A case study from the Sainte‐Marguerite River (Quebec, Canada) |
author_facet |
Hani, Ilias St‐Hilaire, André Ouarda, Taha B. M. J. |
author_sort |
Hani, Ilias |
title |
Machine‐learning modeling of hourly potential thermal refuge area: A case study from the Sainte‐Marguerite River (Quebec, Canada) |
title_short |
Machine‐learning modeling of hourly potential thermal refuge area: A case study from the Sainte‐Marguerite River (Quebec, Canada) |
title_full |
Machine‐learning modeling of hourly potential thermal refuge area: A case study from the Sainte‐Marguerite River (Quebec, Canada) |
title_fullStr |
Machine‐learning modeling of hourly potential thermal refuge area: A case study from the Sainte‐Marguerite River (Quebec, Canada) |
title_full_unstemmed |
Machine‐learning modeling of hourly potential thermal refuge area: A case study from the Sainte‐Marguerite River (Quebec, Canada) |
title_sort |
machine‐learning modeling of hourly potential thermal refuge area: a case study from the sainte‐marguerite river (quebec, canada) |
publisher |
Wiley |
publishDate |
2023 |
url |
http://dx.doi.org/10.1002/rra.4191 https://onlinelibrary.wiley.com/doi/pdf/10.1002/rra.4191 |
long_lat |
ENVELOPE(-57.955,-57.955,-61.923,-61.923) ENVELOPE(141.378,141.378,-66.787,-66.787) ENVELOPE(-109.929,-109.929,57.560,57.560) ENVELOPE(-62.350,-62.350,-74.233,-74.233) ENVELOPE(-81.383,-81.383,50.683,50.683) |
geographic |
Canada Gam Marguerite Marguerite River Nash Sutcliffe |
geographic_facet |
Canada Gam Marguerite Marguerite River Nash Sutcliffe |
genre |
Atlantic salmon Salmo salar |
genre_facet |
Atlantic salmon Salmo salar |
op_source |
River Research and Applications volume 39, issue 9, page 1763-1782 ISSN 1535-1459 1535-1467 |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
op_doi |
https://doi.org/10.1002/rra.4191 |
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River Research and Applications |
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39 |
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9 |
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1763 |
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1782 |
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1800748307231801344 |