Measuring evaporation from lakes and reservoirs isn’t just an academic exercise—it’s a critical factor in managing water resources, especially for industries like energy that rely on predictable water availability. A new study published in *Water Resources Research* (Águas, Recursos Hídricos in Portuguese) takes a closer look at how well satellite-based models perform in estimating open water evaporation globally, offering insights that could reshape how utilities and energy producers monitor water loss.
Led by Júlia Brusso Rossi from the Federal University of Rio Grande do Sul in Porto Alegre, Brazil, the research analyzed five different models—three satellite-based, one driven by reanalysis meteorological data, and one ensemble approach—that estimate evaporation from lakes. The team compared these models against in situ measurements from 27 lakes across diverse climates and geographies. What they found wasn’t just a technical validation but a potential game-changer for industries dependent on water.
“No single model worked perfectly everywhere,” Rossi explains. “But when we combined them into an ensemble, the accuracy improved significantly.” The ensemble approach reduced errors and bias, delivering a root mean square error (RMSE) of just 1.3 millimeters per day and a bias of 0.3 mm/day—figures that suggest real-world reliability.
Why does this matter for energy? Thermal power plants, hydropower reservoirs, and even renewable energy projects like floating solar farms all depend on accurate water loss data. Overestimating evaporation can lead to inefficient water allocation, while underestimating it risks operational shortfalls during droughts or heatwaves. With climate variability increasing, having a robust, globally consistent method to track evaporation could help utilities optimize cooling water use, manage reservoir levels, and plan for future water scarcity.
The study also highlights why some models perform better in certain regions. “Lake depth and local climate patterns really matter,” Rossi notes. “Shallow lakes in warm climates might lose water faster through evaporation, while deeper ones store heat longer, affecting how models should account for seasonal changes.” This nuance could guide future algorithm development, especially as satellite data becomes more precise and widely accessible.
For commercial players in the energy sector, the implications are clear: integrating ensemble-based evaporation models could enhance water risk assessments, improve regulatory compliance, and support sustainable operations in water-stressed regions. It’s not just about science—it’s about building resilience into global energy infrastructure.
As remote sensing technology advances, Rossi’s work sets the stage for more accurate, scalable water monitoring tools. The next step? Expanding validation efforts and refining models to handle extreme weather events—because when it comes to managing water, every millimeter counts.