ML-based reconstruction of long-term global terrestrial water storage anomalies
Long-term reconstruction of global terrestrial water storage anomalies using machine learning, extending GRACE observations back in time for investigating hydrological extremes under climate change.
doi.org
At a glance
- Region
- Global
- Coverage
- Global
- Access
- Free
- Category
- Research
- Last updated
- 2025
What's inside
- Water Storage
- Climate Projections
- Machine Learning
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: Water Storage
- 02
Change over time
See how lakes and wetlands have grown or shrunk.
- 03
Risk screening
Screen projects and assets for climate risk.
Because it has: Climate Projections
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
Bias correction
Correct and downscale climate model output for a site.
Machine access and documentation
Data paper
ML-based reconstruction of long-term global terrestrial water storage anomalies
Earth System Science Data 2025
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