An interdisciplinary team of researchers, led by University of Minnesota Twin Cities data scientists and supported by the U.S. National Science Foundation and NASA, has published a first-of-its-kind comprehensive global dataset of the lakes and reservoirs on Earth showing how they have changed over the last 30+ years.
The data will provide environmental researchers with new information about land and fresh water use as well as how lakes and reservoirs are being impacted by humans and climate change. The research is also a major advancement in machine learning techniques.
A paper highlighting the Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset was recently published in Scientific Data, a peer-reviewed, open-access journal published by Nature.
Highlights of the study include:
"Around the world, we are seeing lakes and reservoirs changing rapidly with seasonal precipitation patterns, long-term changes in climate, and human management decisions," said Vipin Kumar, the senior author of the study and Regents Professor and William Norris Endowed Chair in the University of Minnesota Twin Cities Department of Computer Science and Engineering. "This new dataset greatly improves the ability of scientists to understand the impact of changing climate and human actions on our fresh water across the globe."
Building a global dataset of lakes and reservoirs and how they are changing required a new type of machine learning algorithms that meld knowledge of the physical dynamics of water bodies with satellite imagery.
"ReaLSAT is a shining example where environmental challenges motivated a new class of knowledge-guided machine learning algorithms that are now being used in numerous scientific applications," Kumar said.
Scientists who study the environment agree that ReaLSAT will improve their work.
"The availability and quality of surface fresh water is central to sustainable use of our planet," said Paul C. Hanson, a Distinguished Research Professor at the University of Wisconsin-Madison Center for Limnology and a co-author of the study. "Because ReaLSAT shows changes in lakes and their boundaries, rather than just water pixels across the landscape, we can now connect ecosystem process about water quality with hundreds of thousands of lakes around the world."
The research was supported by the U.S. National Science Foundation Awards 1029711, 1838159, 1934633, and NASA grant NNX12AP37G. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute.
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