A new publication in Machine Learning: Earth (Wang et al., 2026) develops a method to classify the data quality and then extract snow depth readings automatically from thousands of time-lapse camera images. The demonstrate how vision foundation models allow snow depth monitoring in remote locations with minimal human input. Congratulations to the authors associated with…

A new publication in the journal Atmosphere (Huang et al., 2026) used the Aurora AI weather forecasting model to provide reliable deterministic guidance within 7 days for a series of different types of extreme events. These findings are importance for numerical weather prediction at short-to-medium range. Congratulations to the authors associated with the Center for…

A new publication in Agricultural Water Management (Wei and Xu, 2025) describes a new method to detect irrigation events by applying explainable artificial intelligence (xAI) to groundwater well records, remotely-sensed indices, and precipitation, with applications in the High Plains aquifer. Congratulations to the authors associated with the Center for Hydrologic Innovations!

Congratulations to the team from the Center for Hydrologic Innovations that was recently featured by the Arizona Water Innovation Initiative! Postdoctoral scholar Zhaocheng Wang described the effort as: “Planet imagery is useful for the type of dryland hydrology we have in Arizona. We trained a machine learning model called a convolutional neural network that can…