Deep Learning Advances Arctic River Water Temperature Predictions
Abstract The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large,...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR039053 |
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| author | Shuyu Y. Chang Jon Schwenk Kurt C. Solander |
| author_facet | Shuyu Y. Chang Jon Schwenk Kurt C. Solander |
| author_sort | Shuyu Y. Chang |
| collection | DOAJ |
| description | Abstract The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short‐Term Memory (LSTM) model. By incorporating ERA5‐Land reanalysis data and integrating physical understanding into data‐driven processes, our model advanced river water temperature predictions in ungauged, snow‐ and permafrost‐affected basins in Alaska. Our model outperformed existing approaches in high‐latitude regions, achieving a median Nash‐Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0°C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation—factors associated with energy balance—were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts. |
| format | Article |
| id | doaj-art-c69d91146fdf4f2b9c02433e30008df5 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-c69d91146fdf4f2b9c02433e30008df52025-08-20T03:29:48ZengWileyWater Resources Research0043-13971944-79732025-06-01616n/an/a10.1029/2024WR039053Deep Learning Advances Arctic River Water Temperature PredictionsShuyu Y. Chang0Jon Schwenk1Kurt C. Solander2Department of Geography Pennsylvania State University University Park PA USALos Alamos National Laboratory Los Alamos NM USALos Alamos National Laboratory Los Alamos NM USAAbstract The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short‐Term Memory (LSTM) model. By incorporating ERA5‐Land reanalysis data and integrating physical understanding into data‐driven processes, our model advanced river water temperature predictions in ungauged, snow‐ and permafrost‐affected basins in Alaska. Our model outperformed existing approaches in high‐latitude regions, achieving a median Nash‐Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0°C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation—factors associated with energy balance—were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.https://doi.org/10.1029/2024WR039053river temperaturearcticmachine learningLSTMpermafrostsoil temperature |
| spellingShingle | Shuyu Y. Chang Jon Schwenk Kurt C. Solander Deep Learning Advances Arctic River Water Temperature Predictions Water Resources Research river temperature arctic machine learning LSTM permafrost soil temperature |
| title | Deep Learning Advances Arctic River Water Temperature Predictions |
| title_full | Deep Learning Advances Arctic River Water Temperature Predictions |
| title_fullStr | Deep Learning Advances Arctic River Water Temperature Predictions |
| title_full_unstemmed | Deep Learning Advances Arctic River Water Temperature Predictions |
| title_short | Deep Learning Advances Arctic River Water Temperature Predictions |
| title_sort | deep learning advances arctic river water temperature predictions |
| topic | river temperature arctic machine learning LSTM permafrost soil temperature |
| url | https://doi.org/10.1029/2024WR039053 |
| work_keys_str_mv | AT shuyuychang deeplearningadvancesarcticriverwatertemperaturepredictions AT jonschwenk deeplearningadvancesarcticriverwatertemperaturepredictions AT kurtcsolander deeplearningadvancesarcticriverwatertemperaturepredictions |