ReaLSAT: Reservoir and Lake Surface Area Timeseries (UMN)
Machine-learning-derived monthly surface-area variations (1984-2015) for ~681,000 water bodies below 50N (0.1-100 km2), with reference shapes and per-waterbody CSV time series. Hosted in the University of Minnesota Digital Conservancy.
conservancy.umn.edu
At a glance
- Region
- Global
- Coverage
- Below 50N latitude
- Access
- Free
- Category
- Academic
- Last updated
- 2022
What's inside
- lake surface area
- reservoir extent
- time series
What you can do with it
Typical work that data of this type supports.
- 01
Storage tracking
Follow storage and releases through the season.
Because it has: lake surface area
- 02
Change over time
See how lakes and wetlands have grown or shrunk.
- 03
Operations
Support release and supply decisions.
Where AI adds value
Ideas for machine learning and AI agents built on this kind of data.
- 01
Storage from space
Estimate volume from satellite water area and altimetry.
- 02
Release decisions
Optimise releases against inflow forecasts.
- 03
Shrinkage alerts
Detect water bodies that are drying or being encroached.
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- Caravan: Global community large-sample hydrology dataset (LEAP/Pangeo zarr)Global | Free
- Caravan dataset collection: 4TU.ResearchDataGlobal | Free
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