Articles | Volume 27, issue 2
https://doi.org/10.5194/hess-27-501-2023
https://doi.org/10.5194/hess-27-501-2023
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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Latest update: 19 Apr 2024
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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.