Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 27, issue 2
Articles | Volume 27, issue 2
https://doi.org/10.5194/hess-27-501-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-501-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 27, issue 2
Research article
 | 
26 Jan 2023
Research article |  | 26 Jan 2023

The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting

Sandra M. Hauswirth, Marc F. P. Bierkens, Vincent Beijk, and Niko Wanders

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Short summary
Forecasts on water availability are important for water managers. We test a hybrid framework based on machine learning models and global input data for generating seasonal forecasts. Our evaluation shows that our discharge and surface water level predictions are able to create reliable forecasts up to 2 months ahead. We show that a hybrid framework, developed for local purposes and combined and rerun with global data, can create valuable information similar to large-scale forecasting models.
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