Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Rising summer river water temperature across Canada

Spatial patterns and hydroclimatic controls

Bibliographic Data

ID15549572
AuthorsRajesh R Shrestha (0000-0001-7781-6495, Environment and Climate Change Canada, corresponding author), Jennifer C Pesklevits (Environment and Climate Change Canada), Jennifer C Guay (Environment and Climate Change Canada), Barrie Bonsal (0000-0002-6641-2556, Environment and Climate Change Canada), Barrie R Bonsal (Environment and Climate Change Canada), Rosa Brannen (0000-0001-7522-6985, Environment and Climate Change Canada), Tammy Guo (Environment and Climate Change Canada), Sydney Hoffman (Environment and Climate Change Canada)
Year2024
Volume19
Issue4
Pages044058-044058
Publication date2024-03-21
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ad365f
OpenAlexW4393029595
LanguageEN
References cited52

Understanding the spatio-temporal variability of climate-induced river water temperature change is critical for identifying hotspots and assessing the impacts on ecological and socioeconomic systems. Here, we employ the air2stream model reconstructed river temperature records for 106 stations in Canada (Nash Sutcliffe coefficient goodness-of-fit: minimum = 0.79; median = 0.93; maximum = 0.97) to analyze summer temperature changes over the years 1980–2018. Results reveal widespread river temperature increases from June to September, with significantly increasing trends for about 40%–60% of stations. Additionally, we find significantly rising 7-day maximum temperature and increasing occurrences over the critical 18 and 20 °C thresholds for about 30%–65% of stations. Furthermore, by employing the Ward’s agglomerative hierarchical clustering machine learning (ML) method, we identify eight regions of spatially coherent variability and change. We find that the south-east, coast and northern prairies are the regions of high vulnerability because of the likely impacts of rising summer water temperatures on cold-water aquatic species. Additionally, by using the random forests ML method, we demonstrate that mean air temperature and its trends are the primary drivers of mean water temperature and trends, respectively. Thus, with the projected enhanced air temperature increase across Canada, an amplified future summer river warming can be expected, which could have severe consequences, particularly in already thermally-stressed river systems

Atmospheric sciences · Climatology · Arctic and Antarctic ice dynamics · Environmental Science · Fish Ecology and Management Studies · Hydrology and Watershed Management Studies · Geology

  • Deep learning and process understanding for data-driven Earth system science

    Open Access•Markus Reichstein, Gustau Camps-Valls et al.•Nature•2019

  • Random Forests

    Open Access•Leo Breiman•Machine Learning•2001

  • Greater vulnerability of snowmelt-fed river thermal regimes to a warming climate

    Open Access•Hongxiang Yan, Ning Sun et al.•Environmental Research Letters•2021

  • A hybrid model for river water temperature as a function of air temperature and discharge

    Open Access•Marco Toffolon, Sebastiano Piccolroaz•Environmental Research Letters•2015

  • Monthly river temperature trends across the US confound annual changes

    Open Access•Christa Kelleher, Heather E Golden et al.•Environmental Research Letters•2021

  • Machine learning and artificial intelligence to aid climate change research and preparedness

    Open Access•Chris Huntingford, Elizabeth S Jeffers et al.•Environmental Research Letters•2019

Highly citedNo

Tools

Open DOIOpen Access
Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae