Gleason et al. (2026) SWOT, Empiricism, and River Modeling

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Geophysical Research Letters
• Year: 2026
• Date: 2026-08-24
• Authors: Colin Joseph Gleason, Paul Bates, Michael Durand, Dongmei Feng, Augusto Getirana, Peirong Lin, Hind Oubanas, Tamlin M. Pavelsky, Chaopeng Shen, LAURENCE C. SMITH, Jida Wang, Dai Yamazaki, Yuan Yang, Heejin An, Fiona B Bennitt, Stephen Chuter, Chenqi Fang, Jonathan A. Flores, Elisa Friedmann, Sreelekha Jarugula, Haoyu Ji, Theodore Langhorst, Di Long, Dung Trung Vu, Ziyun Yin, George Henry Allen, Konstantinos M. Andreadis, Sylvain Biancamaria, Casey Brown, Cédric Hervé David, Ming Pan, Jeffrey Charles Neal, Stephen Coss, Merritt E. Harlan, Peyman Saemian, Travis Thomas Simmons, Nikki Tebaldi, Mohammad J. Tourian
• DOI: 10.1029/2026gl124323

Research Groups

• Hydrology Department, University of California, Los Angeles (UCLA)
• NASA's Jet Propulsion Laboratory (JPL)

Short Summary

This study assesses the performance of global river models using Surface Water and Ocean Topography (SWOT) satellite measurements, highlighting areas where these models struggle. The results suggest that leveraging SWOT observations can improve model accuracy if they adapt to represent realistic hydraulics.

Objective

• Evaluate the performance of global river models using inductive empirical framework based on SWOT satellite measurements

Study Configuration

• Spatial Scale: Global river network, covering ∼38% of global discharge
• Temporal Scale: Not specified, but likely focused on contemporary conditions

Methodology and Data

• Models used: Not specified (global river models in general)
• Data sources: Surface Water and Ocean Topography (SWOT) satellite measurements

Main Results

• River models struggle in areas of heavy economic development, multi-channel rivers, arid areas, and many Arctic rivers
• Better skill is observed as rivers get wider
• Parts of Siberia and China are particularly difficult to model
• Large variability and spatial heterogeneity exist in model performance

Contributions

• This study provides a comprehensive assessment of global river models using SWOT observations, highlighting the need for model adaptation to represent realistic hydraulics.

Funding

• NASA's Earth Science Division (Grant No. NNX17AE37G)
• National Science Foundation (Award No. EAR-1929137)

Citation

@article{Gleason2026SWOT,
  author = {Gleason, Colin Joseph and Bates, Paul and Durand, Michael and Feng, Dongmei and Getirana, Augusto and Lin, Peirong and Oubanas, Hind and Pavelsky, Tamlin M. and Shen, Chaopeng and SMITH, LAURENCE C. and Wang, Jida and Yamazaki, Dai and Yang, Yuan and An, Heejin and Bennitt, Fiona B and Chuter, Stephen and Fang, Chenqi and Flores, Jonathan A. and Friedmann, Elisa and Jarugula, Sreelekha and Ji, Haoyu and Langhorst, Theodore and Long, Di and Vu, Dung Trung and Yin, Ziyun and Allen, George Henry and Andreadis, Konstantinos M. and Biancamaria, Sylvain and Brown, Casey and David, Cédric Hervé and Pan, Ming and Neal, Jeffrey Charles and Coss, Stephen and Harlan, Merritt E. and Saemian, Peyman and Simmons, Travis Thomas and Tebaldi, Nikki and Tourian, Mohammad J.},
  title = {SWOT, Empiricism, and River Modeling},
  journal = {Geophysical Research Letters},
  year = {2026},
  doi = {10.1029/2026gl124323},
  url = {https://doi.org/10.1029/2026gl124323}
}