GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present
Monthly terrestrial water storage anomalies at 0.25° resolution from 1940-2022, reconstructed using machine learning trained on GRACE/GRACE-FO with climatic and hydrological predictors.
doi.org
At a glance
- Region
- Global
- Coverage
- Global
- Access
- Free
- Category
- Research
- Last updated
- 2023
What's inside
- Water Storage
- 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
Design flows
Estimate 1-in-50 and 1-in-100 year floods for bridges, culverts and spillways.
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
Flow forecasting
Train LSTM-style models on past records to forecast river levels and flow days ahead.
Machine access and documentation
Data paper
GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present
Earth System Science Data 2023
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