CAMELS-DK: Hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations

Large-scale datasets of hydrometeorological time series and catchment attributes are essential for advancing the understanding of hydrological processes, advancing hydrological model development, and enabling performance benchmarking. CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets have already been published for various regions worldwide covering a wide range of hydrometeorology and physiography.

We introduce a CAMELS-style dataset for Denmark (CAMELS-DK) containing predominantly lowland, groundwater-influenced, and small-sized catchments. With respect to already published CAMELS datasets, we see this as a valuable extension that enlarges the variability of catchments.

Moreover, this is the first CAMELS dataset to include both gauged and ungauged catchments as well as detailed groundwater information. CAMELS-DK comprises dynamic and static variables for 3,330 catchments across Denmark, derived from diverse hydrogeological datasets, meteorological observations, and simulated variables provided by the National Hydrological Model of Denmark. From the latter, a comprehensive list of simulated groundwater related variables like phreatic depth or groundwater-surface water interactions, are included. Streamflow observations are available for 304 catchments, while simulated streamflow data are provided for a total of 3,330 catchments. The dataset spans 30 years (1989–2019) at a daily temporal resolution. Additionally, the dataset includes variables capturing human impacts on Denmark's water resources, such as groundwater abstraction and irrigation.

By providing streamflow at almost full spatial coverage of Denmark, and not being limited to gauged sites, along with various simulation outputs from a distributed, process-based hydrological model, CAMELS-DK significantly enhances the utility of CAMELS datasets. This includes supporting the development of data-driven and hybrid/physically informed modeling frameworks.