CAMELS: Catchment Attributes for Large-Sample Studies

This dataset covers the same 671 catchments as the Large-Sample Hydrometeorological Dataset introduced by Newman et al. (2015). For each catchment, we characterized a wide range of attributes that influence catchment behavior and hydrological processes. Datasets characterizing these attributes have been available separately for some time, but comprehensive multivariate catchment scale assessments have so far been difficult, because these datasets typically have different spatial configurations, are stored in different archives, or use different data formats. By creating catchment scale estimates of these attributes, our aim is to simplify the assessment of their interrelationships.

Topographic characteristics (e.g. elevation and slope) were retrieved from Newman et al. (2015). Climatic indices (e.g., aridity and frequency of dry days) and hydrological signatures (e.g., mean annual discharge and baseflow index) were computed using the time series provided by Newman et al. (2015). Soil characteristics (e.g., porosity and soil depth) were characterized using the STATSGO data set and the Pelletier et al. (2016) data set. Vegetation characteristics (e.g. the leaf area index and the rooting depth) were inferred using MODIS data. Geological characteristics (e.g., geologic class and the subsurface porosity) were computed using the GLiM and GLHYMPS data sets.

An essential feature, that differentiates this data set from similar ones, is that it both provides quantitative estimates of diverse catchment attributes, and involves assessments of the limitations of the data and methods used to compute those attributes (see Addor et al., 2017). The large number of catchments, combined with the diversity of their geophysical characteristics, makes this new data well suited for large-sample studies and comparative hydrology.

The hydrometeorological time series provided by Newman et al. (2015) together with the catchment attributes described here constitute the CAMELS data set: Catchment Attributes and MEteorology for Large-sample Studies.

To Access Resource:

Questions? Email Resource Support Contact:

  • Nans Addor
    University of East Anglia

Temporal Range

  • Begin:  1980-01-01T00:00:00
    End:  2014-12-31T00:00:00


Resource Type dataset
Temporal Range Begin 1980-01-01T00:00:00
Temporal Range End 2014-12-31T00:00:00
Temporal Resolution N/A
Bounding Box North Lat 50
Bounding Box South Lat 25
Bounding Box West Long -125
Bounding Box East Long -66
Spatial Representation vector
Spatial Resolution N/A
Related Links

Datatset Website : The website provides additional information, including related papers, regarding the dataset.

Additional Information N/A
Resource Format ASCII
Standardized Resource Format ASCII
Asset Size 1 MB
Legal Constraints

When using this dataset, users acknowledge and accept the following: - Terms of Use ( - Copyright Issues ( - Privacy Policy ( - Publication and Information Dissemination ( - Public Access (

Access Constraints None
Software Implementation Language N/A

Resource Support Name Nans Addor
Resource Support Email
Resource Support Organization University of East Anglia
Distributor N/A
Metadata Contact Name Nans Addor
Metadata Contact Email
Metadata Contact Organization University of East Anglia

Author Nans Addor
Andrew J. Newman
Naoki Mizukami
Martyn P. Clark
Publisher UCAR/NCAR
Publication Date 2017-06-15
Digital Object Identifier (DOI)
Alternate Identifier Catchment attributes and meteorology for large-sample studies. Version 2.0
Resource Version 2.0
Topic Category N/A
Progress onGoing
Metadata Date 2018-10-12T17:00:12
Metadata Record Identifier 39250224-ccdf-11e8-a6b1-b808cf016134
Metadata Language eng
Suggested Citation Nans Addor, Andrew J. Newman, Naoki Mizukami, Martyn P. Clark. (2017). CAMELS: Catchment Attributes for Large-Sample Studies. 2.0. UCAR/NCAR. Accessed 15 July 2024.

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