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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This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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Cited articles

Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013. a
Arnal, L., Cloke, H. L., Stephens, E., Wetterhall, F., Prudhomme, C., Neumann, J., Krzeminski, B., and Pappenberger, F.: Skilful seasonal forecasts of streamflow over Europe?, Hydrol. Earth Syst. Sci., 22, 2057–2072, https://doi.org/10.5194/hess-22-2057-2018, 2018. a, b, c, d
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Candogan Yossef, N., van Beek, R., Weerts, A., Winsemius, H., and Bierkens, M. F. P.: Skill of a global forecasting system in seasonal ensemble streamflow prediction, Hydrol. Earth Syst. Sci., 21, 4103–4114, https://doi.org/10.5194/hess-21-4103-2017, 2017.  a, b
CDS: Seasonal forecast daily and subdaily data on single levels, CDS [data set], https://doi.org/10.24381/cds.181d637e, 2021. a
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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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