URBANA, Ill. — In the face of increasing global drought risk, understanding how plants respond to water stress has never been more critical. Researchers at the University of Illinois Agroecosystem Sustainability Center have delivered a pioneering synthesis that reconciles decades of differing approaches to modeling water use and dynamics in plants. The result: a unified theoretical framework and improved tools for predicting plant behavior under drought, and more informed decisions for agriculture and ecosystem management.
Transpiration, the process by which water moves from soil, through plants, and into the atmosphere, is a critical part of Earth’s water and energy cycles. It links plant physiology to weather and climate patterns and returns a significant amount of water that falls on land back to the air. During transpiration, the tiny pores in plants – stomata – release water vapor on the leaf surface and also absorb carbon dioxide to fuel photosynthesis, making transpiration a crucial indicator of plant health and productivity.
Modeling transpiration has a long and evolving history. Scientists have historically used two broad types of models to simulate plant water stress: empirical models based on statistical relationships between water in soil and plants, and mechanistic models based on physical laws that describe biological and biophysical processes. Early mechanistic models introduced empirical approaches, such as the Beta function, which scales transpiration in proportion to the soil water content. The supply-demand balance scheme is a more sophisticated empirical approach used in crop growth models that accounts for how easily water moves through soil and plant roots. These approaches are easy to implement and efficient, but often oversimplify plant physiology and lead to errors. Ideally, a model should be just complex enough to accurately simulate a specific environment.
In contrast, advanced mechanistic approaches such as plant hydraulic models offer detailed simulations of water transport through plants, but require many parameters that are difficult to measure and long computation times. Until now, the relationships among these models (and the best choice for a given application) have remained unclear.
Lead author Dr. Yi Yang and colleagues, led by the project director Dr. Kaiyu Guan, have now developed a novel mathematical framework that formally connects these different approaches. The team was able to see that the simple empirical approaches are specific cases of the complex mechanistic approach. “Scientists have debated which method is the best way to capture how plants respond to drought, but what we really needed was a clear way to connect them all,” said Yang, the former Ph.D. student of Dr. Guan in the Department of Natural Resources and Environmental Sciences. “We show that we don’t have to choose between simplicity and realism. We can understand where each method fits and see that they are connected as special cases within a unified system.”
The researchers used data from three contrasting ecosystems: a humid Midwestern cropland, a semi-arid conifer forest, and an arid grassland, to show how the best choice of model depends on the specific site and conditions. Researchers were then able to see how the models respond to different environments and different plants.
Understanding whether plant stress is caused by dry air, dry soil, or both is key. While droughts in many parts of the world are becoming more common, the Midwest U.S. often experiences ‘atmospheric drought’ (hot, dry air), even when soil moisture is still available. “This framework helps us better simulate how plants respond when the air is dry but the soil may not be,” Yang said. “That’s crucial for predicting stress under future climate conditions.”
In the example of U.S. corn and soy fields, the empirical models strongly overestimated transpiration during periods of atmospheric drought with normal soil moisture but dry air. In this case, detailed models that capture how plants manage internal water flow become more important for accurate predictions.
However, in consistently dry environments or for plants such as grasses with a fairly simple water transport pathway, the simpler Beta model works well. The arid grassland case study demonstrated that the more empirical models performed similarly to the complex mechanistic models, suggesting that the more sophisticated approach isn’t needed for reliable results in these settings.
“Our contribution allows us to determine how the drought stress affects plant transpiration with fewer data and with better efficiency,” Yang said. “If you need to rely on the more complex mechanistic model, you must gather more data and likely must go to the field to measure its hydraulic parameters in order to predict the stress level. With our improved framework and understanding, we can instead estimate that stress level with satellite data in some cases, because we can remove unnecessary parameters from the equation.”
From an agricultural management perspective, more precise estimates of plant water use help farmers optimize irrigation, saving water while protecting yield. By knowing when crops are likely to suffer stress, growers can better time irrigation during critical growth stages like mid-summer, when healthy transpiration supports peak photosynthesis and grain fill.
“How to reconcile these different transpiration modeling theories was a long-standing problem of mine from my own PhD time through my early career as a professor, ” said Guan. “I am so proud that my student Yi made such a breakthrough to close this major scientific gap.”
ASC researchers Bin Peng, Jingwen Zhang, Lisa Ainsworth, and Sheng Wang joined scientists from the University of Minnesota, Cornell University, the University of California-San Diego, University of Texas Austin and the Pacific Northwest Natural Laboratory on this project.
The project received support from an NSF CAREER award managed through the NSF Environmental Sustainability Program and USDA/NSF Cyber-Physical-System Program as well as a an annual small grant award from USGS Illinois Water Resources Center. Funding for AmeriFlux and FLUXNET data resources and core site data was provided by the U.S. Department of Energy’s Office of Science. Funded was also provided by the DOE Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research.
The new study is titled “A Unified Framework to Reconcile Different Approaches of Modeling Transpiration Response to Water Stress: Plant Hydraulics, Supply Demand Balance, and Empirical Soil Water Stress Function” and is published in Journal of Advances for Modeling the Earth System (JAMES) [doi: 10.1029/2023MS003911].