Ученые создали гибридную модель на базе искусственного интеллекта для точного прогнозирования испарения влаги в экосистемах

Water typically moves from the land to the atmosphere through soil evaporation and plant transpiration. This process is called evapotranspiration. It plays a major role in events such as drought and heat waves. However, many existing computer models struggle to distinguish between soil evaporation and plant transpiration. Researchers developed a hybrid model that provides data-informed estimates of parameters that are difficult to estimate. The model combines a physical framework with machine learning to improve predictions of evapotranspiration and its components across diverse conditions, including heat waves and drought. Understanding and predicting how ecosystems use water is essential to helping communities prepare for heat waves and droughts. A new hybrid model combines a form of artificial intelligence and insights from existing land surface models to create a hybrid model that improves scientists' understanding of Earth system processes. This hybrid approach reveals how environmental factors influence evaporation and transpiration across ecosystems. These factors include radiation, soil moisture, and vegetation. These insights can help scientists improve land surface models in Earth system simulations while highlighting where traditional representations of processes break down and where methods that are physically constrained and data-informed may improve robustness. Accurately representing evapotranspiration and its partitioning between soil evaporation and plant transpiration is a long-standing challenge in Earth system science. A multi-institutional team of researchers developed a hybrid evapotranspiration model that integrates machine learning into the Penman–Monteith modeling framework to estimate key surface conductance values while preserving physical constraints. Researchers evaluated the model using observations from 47 National Ecological Observatory Network sites spanning a wide range of environment and vegetation types. The model consistently outperformed traditional physics-based models and purely machine learning approaches, maintained accuracy at new sites, and remained stable under extreme conditions. These results demonstrate a scalable, physically grounded pathway for improving understanding of ecosystem water fluxes. This work was primarily supported by the Environmental System Science program within the U.S. Department of Energy Office of Science's Biological and Environmental Research program. Support was also received from the Zegar Family Foundation and the National Science Foundation Division of Environmental Biology. U.S. Department of Energy Do you have a review, update or anything you would like to add to this news story? Drive costs down on your next hydrogen production project with the hybrid AEM electrolyzer. Measure 100+ gases with research-grade speed and sensitivity using the Antaris™ IGS gas analyzer built for plant and process use. The PDR-1500 delivers superior portable particulate monitoring with real-time data, gravimetric validation and more. Dr. Alex Mellor Naked Energy's approach to heat decarbonization combines proven solar thermal tech with heat pumps, optimizing energy use and lowering costs for businesses. Jim Gale AZoCleantech speaks to Jim Gale, CEO and founder of Food Forest Abundance, about how food forests are a key tool for tackling food insecurity. David Bow David Bow from Power to Hydrogen discusses the features of both AEM and PEM electrolyzers and the different benefits of each system. Please check the box above to proceed. Terms While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided. Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles. Please do not ask questions that use sensitive or confidential information. Read the full Terms & Conditions. Provide Feedback