Deep Learning Advances Arctic River Water Temperature Predictions

Journal Article · Thu May 29 00:00:00 EDT 2025 · Water Resources Research

DOI:https://doi.org/10.1029/2024WR039053· OSTI ID:2574173

Chang, Shuyu Y. ^[1]; Schwenk, Jon ^[2]; Solander, Kurt C. ^[2]

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1. Pennsylvania State Univ., University Park, PA (United States). Earth and Environmental Systems Inst. (EESI); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
2. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

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.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Aeronautics and Space Administration (NASA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 2574173
• Report Number(s): LA-UR--25-22927; 10.1029/2024WR039053; 1944-7973
• Journal Information: Water Resources Research, Journal Name: Water Resources Research Journal Issue: 6 Vol. 61; ISSN 1944-7973; ISSN 0043-1397
• Publisher: American Geophysical Union (AGU)
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
LSTM
arctic
machine learning
permafrost
river temperature
soil temperature