CAMELS-SPAT: Spatially distributed large-sample dataset for North America
CAMELS-SPAT: basin outlines (lumped and semi-distributed), daily and hourly streamflow observations, meteorological forcing from four datasets (RDRS, EM-Earth, ERA5, Daymet), and geospatial data/derived attributes from 11 sources for 1,426 streamflow stations across the USA and Canada. Designed for spatially distributed and ML hydrologic modelling.
frdr-dfdr.ca
At a glance
- Region
- United States; Canada (North America)
- Coverage
- 1,426 basins (US and Canada)
- Access
- Free
- Category
- Academic
- Last updated
- 2025
What's inside
- streamflow
- meteorological forcing
- catchment attributes
- geospatial
- time series
Terms explained
What you can do with it
Typical work that data of this type supports.
- 01
Design flows
Estimate 1-in-50 and 1-in-100 year floods for bridges, culverts and spillways.
Because it has: streamflow
- 02
Low flows
Set environmental flows and check what is left in the river in a dry year.
- 03
Model calibration
Calibrate and test catchment models against observed flow.
Where AI adds value
Ideas for machine learning and AI agents built on this kind of data.
- 01
Flow forecasting
Train LSTM-style models on past records to forecast river levels and flow days ahead.
- 02
Ungauged basins
Combine with catchment data to estimate flow where there is no gauge.
- 03
Automatic QC
Flag gauge errors, shifts and gaps before they reach a report.
Related water data
- CAMELS (US): Catchment Attributes and Meteorology for Large-sample StudiesNorth America | Free
- CAMELSH: Large-sample hourly hydrometeorological dataset for CONUS (OSTI record)North America | Free
- CANOPEX: Canadian hydrometeorological watershed databaseNorth America | Free
- HYSETS: 14,425 watershed hydrometeorological database for North AmericaNorth America | Free
- CAMELS-BR: 897 catchments in Brazil (Sextant/Ifremer record)South America | Free
- CAMELS-CH: 331 catchments in hydrologic Switzerland (DORA record)Europe | Free
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