Articles | Volume 29, issue 6
https://doi.org/10.5194/hess-29-1587-2025
https://doi.org/10.5194/hess-29-1587-2025
Research article
 | 
25 Mar 2025
Research article |  | 25 Mar 2025

Optimising ensemble streamflow predictions with bias correction and data assimilation techniques

Maliko Tanguy, Michael Eastman, Amulya Chevuturi, Eugene Magee, Elizabeth Cooper, Robert H. B. Johnson, Katie Facer-Childs, and Jamie Hannaford

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Cited articles

Arriagada, P., Karelovic, B., and Link, O.: Automatic gap-filling of daily streamflow time series in data-scarce regions using a machine learning algorithm, J. Hydrol. (Amst), 598, 126454, https://doi.org/10.1016/J.JHYDROL.2021.126454, 2021. 
Bell, V. A., Davies, H. N., Kay, A. L., Marsh, T. J., Brookshaw, A., and Jenkins, A.: Developing a large-scale water-balance approach to seasonal forecasting: application to the 2012 drought in Britain, Hydrol. Process., 27, 3003–3012, https://doi.org/10.1002/hyp.9863, 2013. 
Beven, K.: How to make advances in hydrological modelling, Hydrol. Res., 50, 1481–1494, https://doi.org/10.2166/nh.2019.134, 2019. 
Boucher, M.-A., Quilty, J., and Adamowski, J.: Data Assimilation for Streamflow Forecasting Using Extreme Learning Machines and Multilayer Perceptrons, Water Resour. Res., 56, e2019WR026226, https://doi.org/10.1029/2019WR026226, 2020. 
Broderick, C., Matthews, T., Wilby, R. L., Bastola, S., and Murphy, C.: Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods, Water Resour. Res., 52, 8343–8373, https://doi.org/10.1002/2016WR018850, 2016. 
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Short summary
Our research compares two techniques, bias correction (BC) and data assimilation (DA), for improving river flow forecasts across 316 UK catchments. BC, which corrects errors after simulation, showed broad improvements, while DA, adjusting model states before forecast, excelled under specific conditions like snowmelt and high baseflows. Each method's unique strengths suit different scenarios. These insights can enhance forecasting systems, offering reliable and user-friendly hydrological predictions.
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