Event date:
Jun 15 2023 10:00 am

Fast and Accurate Estimation of Evapotranspiration for Smart Agriculture

Dr. Weiyu Li, Stanford University
The ability to quantify evapotranspiration (ET) is crucial for smart agriculture and sustainable groundwater management. Efficient ET estimation strategies often rely on the vertical-flow assumption to assimilate data from soil-moisture sensors. While adequate in some large-scale applications, this assumption fails when the horizontal component of the local flow velocity is not negligible due to, for example, soil heterogeneity or drip irrigation. We present novel implementations of the ensemble Kalman filter (EnKF) and the maximum likelihood estimation (MLE), which enable us to infer spatially varying ET rates and root water uptake profiles from soil-moisture measurements. While the standard versions of EnKF and MLE update the predicted soil moisture prior to computing ET, ours treat the ET sink term in Richards' equation as an updatable observable. We test the prediction accuracy and computational efficiency of our methods in a setting representative of drip irrigation. Our strategies accurately estimate the total ET rates and root-uptake profiles and do so up to two-orders of magnitude faster than the standard EnKF.

The MLSH seminars are targeted towards individuals engaged in research-based activities in the hydrological sciences and will be delivered by scientists who have produced acclaimed research in the field. The concurrent emergence of new technologies, advancements in data-science and new approaches to modelling have expanded the possibility frontier for effective management of our water resources. Not only this, but these developments are uncovering new insights and spawning new discoveries that change our fundamental understanding of the Earth's hydrosphere. "Models, Learning and Sensing in Hydrology" is aimed at propagating the science and techniques of exactly this field of research.

The full schedule for the webinars can be found here, which also includes recordings of past sessions. A link for attending will be sent via email to registered individuals. Colleagues who register once can attend all future talks without the need to register again.

Speaker Introduction: Weiyu Li has received her PhD in Energy Science and Engineering from Stanford University. Her research focuses on data-assimilation and parameter estimation in environmental applications, aiming to provide science-based estimation of evapotranspiration from soil moisture measurements and the quantification of uncertainty inherent in such estimators. Her other research interests include modeling and simulation of electrochemical transport in batteries and biomedical modeling. Prior to her doctoral studies, Weiyu Li obtained her M.Sc. degree in Mechanical and Aerospace Engineering from Princeton University. Weiyu Li is the recipient of the Siebel Scholars Award in Energy Science, class of 2023. Furthermore, she has received prestigious awards, including the Henry J. Ramey Fellowship Award for outstanding research in the Department of Energy Science and Engineering at Stanford University, as well as the Princeton University Fellowship in Natural Sciences and Engineering.