Rising summer river water temperature across Canada
Spatial patterns and hydroclimatic controls
Bibliographic Data
| ID | 15549572 |
|---|---|
| Authors | Rajesh 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) |
| Year | 2024 |
| Volume | 19 |
| Issue | 4 |
| Pages | 044058-044058 |
| Publication date | 2024-03-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ad365f |
| OpenAlex | W4393029595 |
| Language | EN |
| References cited | 52 |
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
Random Forests
Greater vulnerability of snowmelt-fed river thermal regimes to a warming climate
A hybrid model for river water temperature as a function of air temperature and discharge
Monthly river temperature trends across the US confound annual changes
Machine learning and artificial intelligence to aid climate change research and preparedness
| Highly cited | No |
|---|