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New machine learning model predicts lake ice conditions with 94 per cent accuracy

A University of À¶Ý®ÊÓÆµ press release

To advance climate change monitoring and public safety, researchers at the University of À¶Ý®ÊÓÆµ haveÌýdevelopedÌýnew tools that bring an unprecedented level of accuracy toÌýidentifying lake ice conditions.Ìý

The researchers are the first to use machine learning models for processing satellite radar altimetry data that can identify betweenÌýopen water, thin ice, growing ice, or melting ice with 94 per cent accuracy.Ìý

The Water Institute is pleased to announce that Dr. Dustin GarrickÌýhas been appointed director of the University of À¶Ý®ÊÓÆµâ€™sÌýCollaborative Water ProgramÌýfor a two-year term, effective January 1, 2024. Dustin isÌýUniversity Research Chair in Water and Development Policy, associate professor in the School of Environment, Resources and Sustainability and a Water Institute member. Since 2011, Dr. Garrick has taught interdisciplinary water courses as part of the University of Oxford MSc in water science, policy and management, and he currently serves as the director of research for a European Commission funded doctoral training network, , with 15 PhD students across Europe and Africa.

Water Institute Member David Rudolph, professor in the Department of Earth and Environmental Sciences, is serving as conference co-chair of the . The conference will take place June 17-20, 2024, in San Francisco and is being organized by University of California, Davis and The Water Education Foundation. The Water Institute is a cooperating organization.

Monday, December 11, 2023

Rendering a winter wonderland

A University of À¶Ý®ÊÓÆµ press release.

As snow flurries mark the beginning of winter, a team of University of À¶Ý®ÊÓÆµ researchers have digitized the white stuff into a new model that can be applied to better understand the impact of climate change.

SPLITSnow is a "light transport" model and is part of a larger body of research that simulates how light interacts with complex materials. While previous models exist, SPLITSnow is one of the most comprehensive models to date, which accounts for a variety of snowpack properties, such as density and water content, as well as the size and shape distributions of the individual grains. In addition, SPLITSnow attempts to accountÌýfor the grains' crystalline makeup.

By Kendra Shields, Lujyne Amro, Diana Pena, Favour Ozordi and Cassandra Sherlock.

The recent was a dynamic and insightful gathering, fostering a shared commitment to environmental stewardship. A delegation from the Water Institute along with attendees from diverse backgrounds came together to collaborate on pressing issues regarding our readiness for a clean economy with a focus on clean air, clean water, and a healthy planet.