Articles | Volume 19, issue 6
https://doi.org/10.5194/hess-19-2911-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-2911-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Jun 2015
Research article |
| 23 Jun 2015
Operational aspects of asynchronous filtering for flood forecasting
Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, the Netherlands
UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
A. H. Weerts
Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, the Netherlands
Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
J. Sumihar
Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
R. Uijlenhoet
Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, the Netherlands
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- Optimization of rain gauge sampling density for river discharge prediction using Bayesian calibration A. Wadoux et al. 10.7717/peerj.9558
- Operational hydrological forecasting during the IPHEx-IOP campaign – Meet the challenge J. Tao et al. 10.1016/j.jhydrol.2016.02.019
- A web based tool for operational real-time flood forecasting using data assimilation to update hydraulic states M. Mure-Ravaud et al. 10.1016/j.envsoft.2016.06.002
- State updating in Xin’anjiang model by Asynchronous Ensemble Kalman filtering with enhanced error models J. Gong et al. 10.1016/j.jhydrol.2024.131726
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- Benefits of upstream data for downstream streamflow forecasting: data assimilation in a semi-distributed flood forecasting model P. Royer-Gaspard et al. 10.1080/27678490.2024.2374081
- On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments A. Thiboult & F. Anctil 10.1016/j.jhydrol.2015.09.036
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- The sensitivity of simulated streamflow to individual hydrologic processes across North America J. Mai et al. 10.1038/s41467-022-28010-7
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Saved (final revised paper)
Latest update: 22 Nov 2024
Short summary
This is the first analysis of the asynchronous ensemble Kalman filter in hydrological forecasting. The results of discharge assimilation into a hydrological model for the catchment show that including past predictions and observations in the filter improves model forecasts. Additionally, we show that elimination of the strongly non-linear relation between soil moisture and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting.
This is the first analysis of the asynchronous ensemble Kalman filter in hydrological...
