CAMELS-DE-1h: hourly hydro-meteorological time series, weather forecasts, and attributes for 1611 catchments in Germany

Abstract. CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets have been a major driver for advances in large-sample hydrology, facilitating regional studies and the development of deep learning methods and hydrological models by providing homogenized data across large domains, typically at the national scale. However, investigating highly dynamic events, such as flash floods, requires sub-daily resolution, which is often hindered by the daily time steps of most existing CAMELS datasets. Here, we present CAMELS-DE-1h, providing hourly time series of discharge and meteorology for 1,611 catchments in Germany, spanning the period from 2001 to 2024. This dataset homogenizes the extensive but deeply fragmented high-resolution hydrological gauge data managed independently by the German federal states, combining it with high-resolution meteorological forcing from the German Weather Service (DWD). With a median catchment area of 132.4 km², CAMELS-DE-1h includes many small-to-medium-sized basins where hydrological responses occur primarily on sub-daily scales. Alongside the time series, the dataset includes comprehensive static catchment attributes covering soil characteristics, land cover, hydrogeology, and human influences. A novel addition to CAMELS-DE-1h is a readily processed archive of operational short-term weather forecasts (ICON-D2, 48-hours lead time), which are used by German flood forecasting agencies in their operational settings. Including both deterministic runs and precipitation ensembles, these operational forecast data are available at a large scale for the first time for the period from 2021 to 2024. This allows for the evaluation of historical weather forecast quality, the testing of hydrological models in realistic operational settings, and the use of ensemble data to investigate the coupling of meteorological and hydrological forecast uncertainties. Finally, we provide baseline performance benchmarks using a regionally trained Long Short-Term Memory (LSTM) network and a conceptual HBV (Hydrologiska Byråns Vattenbalansavdelning) model. These models achieve median Nash-Sutcliffe Efficiencies (NSE) of 0.82 (LSTM) and 0.69 (HBV) for 1496 catchments selected based on their data availability for training / calibration and testing. By combining high-resolution observations with operational forecasts, CAMELS-DE-1h provides a consistent basis for the systematic comparison and development of hydrological and hydro-meteorological models under realistic conditions. CAMELS-DE-1h is available at: https://doi.org/10.5880/fidgeo.2026.045 (Dolich et al., 2026).