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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- genRE: A Method to Extend Gridded Precipitation Climatology Data Sets in Near Real‐Time for Hydrological Forecasting Purposes B. van Osnabrugge et al. 10.1002/2017WR021201
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- Development of the consider cubature Kalman filter for state estimation of hydrological models with parameter uncertainty Y. Sun et al. 10.1016/j.jhydrol.2023.130080
- 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
- Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters C. Emery et al. 10.5194/hess-24-2207-2020
- State updating in a distributed hydrological model by ensemble Kalman filtering with error estimation J. Gong et al. 10.1016/j.jhydrol.2023.129450
- 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
- Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods M. Mazzoleni et al. 10.1080/02626667.2018.1430898
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- Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method Y. Fan et al. 10.1016/j.envsoft.2016.09.012
- High-resolution modeling and prediction of urban floods using WRF-Hydro and data assimilation S. Kim et al. 10.1016/j.jhydrol.2021.126236
- Sensitivity analysis of the hyperparameters of an ensemble Kalman filter application on a semi-distributed hydrological model for streamflow forecasting B. Sabzipour et al. 10.1016/j.jhydrol.2023.130251
- Sequential Data Assimilation for Streamflow Forecasting: Assessing the Sensitivity to Uncertainties and Updated Variables of a Conceptual Hydrological Model at Basin Scale G. Piazzi et al. 10.1029/2020WR028390
- The sensitivity of simulated streamflow to individual hydrologic processes across North America J. Mai et al. 10.1038/s41467-022-28010-7
- Improving Forecast Skill of Lowland Hydrological Models Using Ensemble Kalman Filter and Unscented Kalman Filter Y. Sun et al. 10.1029/2020WR027468
- Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. 10.1016/j.advwatres.2023.104560
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- Remote sensing data assimilation A. Nair et al. 10.1080/02626667.2020.1761021
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Saved (final revised paper)
Latest update: 02 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...
