CAMELS-DK: Hydrometeorological Time Series and Landscape Attributes for 3330 Catchments in Denmark.

Large samples of hydrometeorological time series and catchment attributes are critical for improving the understanding of complex hydrological processes, hydrological model development and performance benchmarking. CAMELS (Catchment Attributes and Meteorological time series for Large Samples) datasets have been developed in several countries and regions around the world, providing valuable data sources and testbeds for hydrological analysis and new frontiers in data-driven hydrological modelling. Regarding the lack of samples from low-land, groundwater-dominated, small-sized catchments, we develop an extensive repository of a CAMELS-style dataset for Denmark (CAMELS-DK). This CAMELS addition is the first containing both, gauged and ungauged catchments as well as detailed groundwater information. The dataset provides dynamic and static variables for 3330 catchments from various hydrogeological datasets, meteorological observations, and a well-established national-scale hydrological model. The dataset is enhanced with streamflow observations in 304 of those catchments. The spatially dense and full spatial coverage, supplying data for 3330 catchments, instead of only gauged catchments, together with the addition of simulation data from a distributed, process-based model enhance the applicability of such CAMELS data. This is especially relevant for the development of data-driven and hybrid physical informed modelling frameworks. We also provide quantities related to human impact on the hydrological system in Denmark, such as groundwater abstraction and irrigation. The CAMELS-DK dataset is freely available at https://doi.org/10.22008/FK2/AZXSYP (Koch et al., 2024). [ABSTRACT FROM AUTHOR]