CAMELS-DE-1h: Advancing Large-Sample Hydrological Modeling by Shifting to the Hourly Scale

CAMELS datasets have been primary accelerators for Large-Sample Hydrology (LSH), providing extensive, harmonized hydro-meteorological data and establishing benchmarks that have fundamentally changed how data-driven models in Hydrology are developed and evaluated. However, to date, these efforts have predominantly focused on daily resolution. While the overall performance of deep learning models for daily rainfall-runoff modelling has reached a high standard - often plateauing with "vanilla" LSTMs - significant challenges remain. These include the accurate representation of flood peaks, drought dynamics, performance under non-stationary conditions, and the capturing of rapid events in small catchments. Although initial LSH studies have explored hourly data, fully exploiting sub-daily information remains an open and pressing challenge. The shift to high-resolution datasets offers the potential to improve modeling extreme floods and their dynamics and to capture runoff generation processes also in smaller catchments. However, this transition requires a reassessment of the current state-of-the-art: do the limitations of daily modelling persist at the hourly scale, are they resolved by higher resolution data, and which entirely new challenges arise?
To address these questions and facilitate the transition to sub-daily LSH, we introduce CAMELS-DE-1h, a comprehensive hourly dataset for Germany. It covers 1626 catchments with streamflow and meteorological forcing data spanning 2001 - 2024. Uniquely, CAMELS-DE-1h includes historical short-term meteorological forecasts (ICON-D2, 48 hours lead time) from 2021 - 2024, both as deterministic and ensemble forecasts. This novelty enables rigorous research regarding the propagation of meteorological uncertainty into hydrological predictions and the development of deep learning models for operational settings. With CAMELS-DE-1h, we provide open-source LSTM benchmarks for both discharge simulation and forecasting, and use these benchmarks to evaluate the transition from daily to hourly simulations. Specifically, we analyze how the transition to hourly resolution alters model behavior regarding peak flow timing and hydrograph shape, and discuss the challenges such as computational costs and the need for evaluation metrics adapted to sub-daily Large-Sample Hydrology.