Sub-seasonal to seasonal ensemble streamflow forecasting using a Handoff forecast LSTM

Despite recent advances in operational streamflow forecasting systems, anticipating and forecasting droughts and associated low-flow conditions remain major challenges in hydrology, with substantial impacts on water-dependent sectors such as agriculture and industry. Enhancing sub-seasonal to seasonal streamflow forecasting is therefore critical for improving water resources management. This study investigates the performance of a Handoff forecast Long Short-Term Memory (LSTM) architecture (Nearing et al., 2024) for probabilistic streamflow forecasting at lead times extending up to six months, with a particular emphasis on low-flow conditions.

The Handoff forecast LSTM is trained regionally on a subset of 292 basins from the Catchment Attributes and MEteorology for Large-sample Studies - FRance dataset (CAMELS-FR) (Delaigue et al., 2025), after excluding basins affected by unreliable low-flow measurements. Model training relies on basin-averaged hydro-meteorological reanalysis data provided by CAMELS-FR. Evaluation of the model is conducted using ensemble streamflow forecasts generated from historical scenarios and meteorological ensemble predictions from the SEAS5 model from the European Center for Medium-Range Weather Forecasts (ECMWF) (Johnson et al., 2019)

The generated ensemble streamflow forecasts are evaluated using a set of probabilistic metrics such as the Continuous Ranked Probability Score (CRPS), the Brier Score, the Area under the ROC curve, and the Talagrand diagram, and using the natural streamflow climatology as a reference. In addition, a sensitivity analysis of static catchment attributes is performed to assess their relative contribution to model performance and to better understand the drivers of predictability across basins.

Delaigue, O., Guimarães, G. M., Brigode, P., Génot, B., Perrin, C., Soubeyroux, J.-M., Janet, B., Addor, N., & Andréassian, V. (2025). CAMELS-FR dataset: a large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking. Earth System Science Data, 17(4), 1461–1479. https://doi.org/10.5194/essd-17-1461-2025

Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., & Monge-Sanz, B. M. (2019). SEAS5: The new ECMWF seasonal forecast system. Geoscientific Model Development, 12(3), 1087–1117. https://doi.org/10.5194/gmd-12-1087-2019

Nearing, G., Cohen, D., Dube, V., Gauch, M., Gilon, O., Harrigan, S., Hassidim, A., Klotz, D., Kratzert, F., Metzger, A., Nevo, S., Pappenberger, F., Prudhomme, C., Shalev, G., Shenzis, S., Tekalign, T. Y., Weitzner, D., & Matias, Y. (2024). Global prediction of extreme floods in ungauged watersheds. Nature, 627(8004), 559–563. https://doi.org/10.1038/s41586-024-07145-1