Global Performance of Remote Sensing-Based and Reanalysis-Driven Models to Estimate Open Water Evaporation

Authors

• Júlia Brusso Rossi, Federal University of Rio Grande do Sul
• Ayan Santos Fleischmann, Mamirauá Institute for Sustainable Development
• Leonardo Laipelt, Federal University of Rio Grande do Sul
• Bruno Comini de Andrade, Federal University of Rio Grande do Sul
• Joshua B. Fisher, Chapman UniversityFollow
• Justin L. Huntington, Desert Research Institute
• Christopher Pearson, Desert Research Institute
• R. Iestyn Woolway, Bangor University
• Roseilson Vale, Federal University of Western Pará
• Júlio Tota, Federal University of Western Pará
• Gabriel B. Senay, U.S. Geological Survey
• Huilin Gao, Texas A&M University
• Anderson Ruhoff, Federal University of Rio Grande do Sul

Document Type

Article

Publication Date

7-22-2026

Abstract

Evaporation plays an essential role in the water cycle, influencing local and regional climates while directly impacting water availability in lakes. However, directly measuring evaporation over water bodies remains challenging due to the high costs of installing and maintaining the required in situ instrumentation. Although several remote sensing algorithms have been providing evaporation estimates, the lack of a global validation hinders our understanding of their relative uncertainties and performances across different regions. Here, we analyze the performance of a suite of models that leverage satellite data and meteorological reanalysis to estimate evaporation over lakes worldwide. We compare 3 remote sensing‐based models, 1 reanalysis‐driven model and 1 ensemble approach, using in situ observations from 27 lakes representing a diverse range of geographic and climatic regions. Our results demonstrate that, overall, the ensemble outperformed any individual model in terms of accuracy, with a RMSE and a bias of 1.3 and 0.3 mm day−1, respectively. These findings highlight the benefits of using an ensemble approach to estimate open water evaporation with satellite-based models at the global scale, leveraging the unique strengths of each model. For the individual models, differences in the representation of heat storage changes and advection effects led to lower values of RMSE and bias, depending on the location and depth of the lakes. This study sets the path for future improvement of open water evaporation algorithms globally, while remote sensing techniques are proven satisfactory to monitoring of water loss in lakes globally, an essential step toward effective large‐scale water resources management.

Comments

This article was originally published in Water Resources Research, volume 62, in 2026. https://doi.org/10.1029/2025WR042363

Recommended Citation

Rossi, J. B., Fleischmann, A. S., Laipelt, L., Comini de Andrade, B., Fisher, J. B., Huntington, J. L., et al. (2026). Global performance of remote sensing-based and reanalysis-driven models to estimate open water evaporation. Water Resources Research, 62, e2025WR042363. https://doi.org/10.1029/2025WR042363

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License