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 CAMELS_KR_Data_Description.pdf document. Changelog v1.1 Removed the previous README file and added the CAMELS_KR_Data_Description.pdf document providing detailed information on the dataset.