CAMELS-H: A large-sample hourly hydrometeorological dataset for CONUS watersheds
First large-sample hourly hydrometeorological dataset for 9,008 US catchments (1980-2024) with 11 meteorological variables, 9 climate indices, and comprehensive watershed attributes.
doi.org
At a glance
- Region
- North America
- Coverage
- North America
- Access
- Free
- Category
- Research
- Last updated
- 2025
What's inside
- Streamflow
- Catchment Data
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
Risk screening
Screen projects and assets for climate risk.
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
Bias correction
Correct and downscale climate model output for a site.
Machine access and documentation
Data paper
CAMELS-H: A large-sample hourly hydrometeorological dataset for CONUS watersheds
Scientific Data 2025
Read the paperRelated water data
- BANDAS - Banco Nacional de Datos de Aguas Superficiales (IMTA)North America | Free
- BC Water Availability Tool (BCWAT)North America | Free
- BC Water Resources Atlas (British Columbia)North America | Free
- CAMELS (US): Catchment Attributes and Meteorology for Large-sample StudiesNorth America | Free
- CAMELS-SPAT: Spatially distributed large-sample dataset for North AmericaNorth America | Free
- CAMELSH: Large-sample hourly hydrometeorological dataset for CONUS (OSTI record)North America | Free
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