CAMELS: Catchment Attributes and MEteorology for Large-sample Studies

The hydrometeorological time series together with the catchment attributes constitute the CAMELS dataset: Catchment Attributes and MEteorology for Large-sample Studies.

TIME SERIES Data citation: A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR. https://dx.doi.org/10.5065/D6MW2F4D

Associated paper: A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan: Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209-223, doi:10.5194/hess-19-209-2015, 2015.

We developed basin scale hydrometeorological forcing data for 671 basins in the United States Geological Survey’s Hydro-Climatic Data Network 2009 (HCDN-2009, Lins 2012) conterminous U.S. basin subset. Retrospective model forcings are derived from Daymet, NLDAS, and Maurer et al. (2002) Daymet and NLDAS forcing data run from 1 Jan 1980 to 31 Dec 2014, and Maurer run from 1 January 1980 to 31 December 2008. Model timeseries output is available for the same time periods as the forcing data. USGS streamflow data are also provided for all basins for all dates available in the 1 Jan to 31 Dec 2014 period. We then implemented the hydrologic model and calibration routine traditionally used by the NWS, the SNOW-17 and Sacramento soil moisture accounting (SAC-SMA) based hydrologic modeling system and the shuffled complex evolution (SCE) optimization approach (Duan et al. 1993).

To retrieve the entire time series dataset, all five .zip files should be downloaded. The basin_timeseries_v1p2_metForcing_obsFlow.zip file contains all the basin forcing data for all three meteorology products, observed streamflow, basin metadata, readme files, and basin shapefiles. The three modelOutput*.zip files contain all the model output for the various forcing datasets denoted in the link names. Finally, the basin_set_full_res.zip file is a full resolution basin shapefile containing the original basin boundaries from the geospatial fabric.

Note there are two versions of the basin shapefiles included in this dataset. The shapefile included with the basin forcing data was used to compute the basin forcing data and is a simplified representation of the basin boundaries which will include small holes in the interior of some basins where sub-basin HRU simplifications do not match. The full resolution shapefile does not have those discontinuities. The user can best determine which shapefile (or both) is appropriate for their needs.

CATCHMENT ATTRIBUTES Data citation: Addor, A. Newman, M. Mizukami, and M. P. Clark, 2017. Catchment attributes for large-sample studies. Boulder, CO: UCAR/NCAR. https://doi.org/10.5065/D6G73C3Q

Association paper: Addor, N., Newman, A. J., Mizukami, N. and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, doi:10.5194/hess-21-5293-2017, 2017.

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 dataset and the Pelletier et al. (2016) dataset. 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 datasets.

An essential feature, that differentiates this dataset 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 these data well suited for large-sample studies and comparative hydrology.