CAMELS-KR: Catchment attributes, meteorology, and reconstructed streamflow for large-sample hydrology in South Korea

CAMELS-KR is the first CAMELS (Catchment Attributes and Meteorological data for Large-sample Studies) dataset developed for South Korea. It provides static catchment attributes, daily hydrological and meteorological time series, and reconstructed streamflow generated using both a regional Long Short-Term Memory (LSTM) model and a locally calibrated HBV conceptual model for 282 catchments across the country. The static attributes are organized into seven categories: location, topography, climate, soil, land cover, hydrology, and human influence. The time series data include observed daily streamflow and water level, catchment-averaged meteorological forcings, and daily simulated streamflow from the LSTM and HBV models. The dataset is designed to support large-sample hydrology, machine learning–based streamflow prediction, regionalization, hydrological model comparison, and benchmarking studies. Detailed descriptions of the dataset structure, variables, and file organization are provided in the accompanying README file and metadata documentation.