Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): Streamflow observations, forcing data and geospatial data for hydrologic studies across North America

Abstract. We present a new data set aimed at hydrologic studies across North America, with a particular focus on facilitating spatially distributed studies. The data set includes basin outlines, stream observations, meteorological data and geospatial data for 1426 basins in the United States and Canada. To facilitate a wide variety of studies, we provide the basin outlines at a lumped and semi-distributed resolution; streamflow observations at daily and hourly time steps; variables suitable for running a wide range of models obtained and derived from different meteorological data sets at daily (1 data set) and hourly (3 data sets) time steps; and geospatial data and derived attributes from 11 different data sets that broadly cover climatic conditions, vegetation properties, land use, and subsurface characteristics. Forcing data are provided at their native gridded resolution, as well as averaged at the basin and sub-basin level. Geospatial data are provided as maps per basin, as well as summarized as catchment attributes at the basin and sub-basin level with various statistics. Attributes are further complemented with statistics derived from the forcing data and streamflow, and have a particular focus on quantifying the variability of natural processes and catchment characteristics in space and time. Our goal with this data set is to build upon existing large-sample data sets and provide the means for more detailed investigation of hydrologic behavior across large geographical scales. In particular, we hope that this data sets provide others with the data needed to implement a wide range of modeling approaches, and to investigate the impact of basin heterogeneity on hydrologic behaviour and similarity. The CAMELS-SPAT (Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis) is available at: https://dx.doi.org/10.20383/103.01216.