CAMELS-COL: A Large-Sample Hydrometeorological Dataset for Colombia

Abstract. Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS-COL) is a large-sample hydrological dataset for Colombia that integrates daily meteorological and hydrological time series with comprehensive catchment attributes. The dataset comprises daily precipitation, evapotranspiration, and temperature from CHIRPS and MSWX satellite sources and streamflow records from 347 gauging stations covering the range 1981–2022. Additionally, CAMELS-COL provides a wide range of catchment attributes, including physiographic characteristics, climatic indices, hydrological signatures, land cover, geology, and soil properties, derived primarily from official governmental sources. CAMELS-COL follows the standardized framework of previous CAMELS datasets, such as those developed for Brazil and Chile, to ensure consistency with global hydrological datasets. By incorporating Colombian catchments across diverse hydroclimatic regions, including the Andean, Amazonian, and Caribbean basins, this dataset extends the CAMELS initiative into tropical environments, offering a unique resource for hydrological research. The analysed basins show low flood susceptibility. An analysis of the aridity index reveals that 74.7 % of basins have a subhumid climate, 20.7 % are semiarid, and only 4.6 % are classified as humid. Flow elasticity to precipitation is highest in the Amazon and Orinoco, highlighting their greater streamflow sensitivity to rainfall changes. The Base Flow Index underscores groundwater’s crucial role in stabilizing and regulating surface water, particularly in forested basins. The dataset supports studies on hydrological processes, extreme hydroclimatic events, climate change impacts, and water resource management. Public availability encourages scientific collaboration and facilitates the inclusion of Colombian catchments in continental and global hydrological analyses. The dataset is accessible at https://doi.org/10.5281/zenodo.15554735 (Jimenez et al., 2025).