CAMELS (US): Catchment Attributes and Meteorology for Large-sample Studies
The original CAMELS dataset: daily hydrometeorological forcing (Daymet, NLDAS, Maurer), USGS streamflow, and catchment attributes (topography, climate, soil, geology, land cover, hydrologic signatures) for 671 minimally disturbed catchments across the contiguous United States, 1980-2014. The reference AI/ML-ready large-sample hydrology benchmark used to train LSTM rainfall-runoff models.
data.ucar.edu
At a glance
- Region
- United States (North America)
- Coverage
- 671 catchments (CONUS)
- Access
- Free
- Category
- Research Institute
- Last updated
- 2017
What's inside
- streamflow
- meteorological forcing
- catchment attributes
- hydrologic signatures
- 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
Design storms
Build rainfall intensity-duration-frequency curves for drainage design.
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
Downscaling
Turn coarse rainfall grids into field or ward scale estimates.
Related water data
- CAMELSH: Large-sample hourly hydrometeorological dataset for CONUS (OSTI record)North America | Free
- CAMELS-SPAT: Spatially distributed large-sample dataset for North AmericaNorth America | Free
- CANOPEX: Canadian hydrometeorological watershed databaseNorth America | Free
- CAMELS-GB: 671 catchments in Great BritainEurope | Free
- CAMELS-SE: 50 catchments in Sweden (1961-2020)Europe | Free
- HYSETS: 14,425 watershed hydrometeorological database for North AmericaNorth America | Free
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