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

Abstract. Large-sample hydrology has increasingly relied on harmonized datasets that integrate hydrometeorological observations with catchment attributes across diverse environmental settings. Although the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) initiative has expanded rapidly worldwide, South Korea remains underrepresented despite its distinctive hydroclimatic characteristics, including a monsoon-dominated climate, steep mountainous terrain, rapid runoff generation, and extensive anthropogenic water regulation. Here, we present CAMELS-KR, the first CAMELS-style dataset developed for South Korea. CAMELS-KR provides harmonized hydrometeorological time series, catchment boundaries, and catchment attributes for 282 quality-controlled catchments distributed across the major river basins of the country. The dataset includes daily streamflow and water-level observations, catchment-scale meteorological forcing data from 1981 to 2025, and a comprehensive set of static attributes describing topography, climate, hydrology, land cover, soils, and water infrastructure. CAMELS-KR also includes reconstructed streamflow generated using a regionally trained long short-term memory (LSTM) model and a locally calibrated conceptual HBV model, together with model performance metrics and calibrated parameter sets. By providing open-access, analysis-ready hydrological data from a monsoon-dominated and highly regulated environment, CAMELS-KR fills an important geographic gap within the global CAMELS network. The dataset is expected to support comparative hydrology, prediction in ungauged basins, climate-impact assessments, and the development and benchmarking of next-generation data-driven and process-based hydrological models.