Articles | Volume 22, issue 9
https://doi.org/10.5194/hess-22-4921-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-4921-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Sep 2018
Research article |
| 21 Sep 2018
| 21 Sep 2018
Inflation method for ensemble Kalman filter in soil hydrology
Hannes H. Bauser
CORRESPONDING AUTHOR
Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, Germany
HGS MathComp, Heidelberg University, Heidelberg, Germany
Daniel Berg
Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, Germany
HGS MathComp, Heidelberg University, Heidelberg, Germany
Ole Klein
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
Kurt Roth
Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, Germany
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
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Cited
22 citations as recorded by crossref.
- How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation? J. Valdes-Abellan et al. 10.2136/vzj2018.07.0142
- Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau F. Peng et al. 10.1080/16000870.2019.1704963
- A nonparametric sequential data assimilation scheme for soil moisture flow Y. Wang et al. 10.1016/j.jhydrol.2020.125865
- Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter Z. Chen et al. 10.1007/s11004-021-09928-y
- Assimilating shallow soil moisture observations into land models with a water budget constraint B. Dan et al. 10.5194/hess-24-5187-2020
- Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding M. El Gharamti et al. 10.5194/hess-25-5315-2021
- Predicting discharge from a complex karst system using the ensemble smoother with multiple data assimilation A. Pansa et al. 10.1007/s00477-022-02287-y
- Inflation method based on confidence intervals for data assimilation in soil hydrology using the ensemble Kalman filter A. Jamal & R. Linker 10.1002/vzj2.20000
- An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region B. Bonan et al. 10.5194/hess-24-325-2020
- Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN Y. Zhang et al. 10.3390/s24010035
- Covariance resampling for particle filter – state and parameter estimation for soil hydrology D. Berg et al. 10.5194/hess-23-1163-2019
- Improving flood forecasting using conditional bias-aware assimilation of streamflow observations and dynamic assessment of flow-dependent information content H. Shen et al. 10.1016/j.jhydrol.2021.127247
- A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter B. Ait‐El‐Fquih & I. Hoteit 10.1002/qj.3716
- Recursive Bayesian Estimation of Conceptual Rainfall‐Runoff Model Errors in Real‐Time Prediction of Streamflow M. Tajiki et al. 10.1029/2019WR025237
- Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm O. Kozhushko et al. 10.23939/mmc2022.02.203
- Challenges with effective representations of heterogeneity in soil hydrology based on local water content measurements H. Bauser et al. 10.1002/vzj2.20040
- Data assimilation with multiple types of observation boreholes via the ensemble Kalman filter embedded within stochastic moment equations C. Xia et al. 10.5194/hess-25-1689-2021
- Covariance-Based Selection of Parameters for Particle Filter Data Assimilation in Soil Hydrology A. Jamal & R. Linker 10.3390/w14223606
- Joint identification of contaminant source based on the ensemble Kalman filter integrated with relation coefficient L. Jing et al. 10.1016/j.jhydrol.2022.129057
- Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
- Comparison of ensemble Kalman filter application to a prediction model of soil solute transfer into surface runoff by updating different parameters Y. Gu & J. Tong 10.1007/s00477-023-02448-7
- A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation Q. Zhang et al. 10.1016/j.advwatres.2019.103407
21 citations as recorded by crossref.
- How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation? J. Valdes-Abellan et al. 10.2136/vzj2018.07.0142
- Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau F. Peng et al. 10.1080/16000870.2019.1704963
- A nonparametric sequential data assimilation scheme for soil moisture flow Y. Wang et al. 10.1016/j.jhydrol.2020.125865
- Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter Z. Chen et al. 10.1007/s11004-021-09928-y
- Assimilating shallow soil moisture observations into land models with a water budget constraint B. Dan et al. 10.5194/hess-24-5187-2020
- Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding M. El Gharamti et al. 10.5194/hess-25-5315-2021
- Predicting discharge from a complex karst system using the ensemble smoother with multiple data assimilation A. Pansa et al. 10.1007/s00477-022-02287-y
- Inflation method based on confidence intervals for data assimilation in soil hydrology using the ensemble Kalman filter A. Jamal & R. Linker 10.1002/vzj2.20000
- An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region B. Bonan et al. 10.5194/hess-24-325-2020
- Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN Y. Zhang et al. 10.3390/s24010035
- Covariance resampling for particle filter – state and parameter estimation for soil hydrology D. Berg et al. 10.5194/hess-23-1163-2019
- Improving flood forecasting using conditional bias-aware assimilation of streamflow observations and dynamic assessment of flow-dependent information content H. Shen et al. 10.1016/j.jhydrol.2021.127247
- A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter B. Ait‐El‐Fquih & I. Hoteit 10.1002/qj.3716
- Recursive Bayesian Estimation of Conceptual Rainfall‐Runoff Model Errors in Real‐Time Prediction of Streamflow M. Tajiki et al. 10.1029/2019WR025237
- Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm O. Kozhushko et al. 10.23939/mmc2022.02.203
- Challenges with effective representations of heterogeneity in soil hydrology based on local water content measurements H. Bauser et al. 10.1002/vzj2.20040
- Data assimilation with multiple types of observation boreholes via the ensemble Kalman filter embedded within stochastic moment equations C. Xia et al. 10.5194/hess-25-1689-2021
- Covariance-Based Selection of Parameters for Particle Filter Data Assimilation in Soil Hydrology A. Jamal & R. Linker 10.3390/w14223606
- Joint identification of contaminant source based on the ensemble Kalman filter integrated with relation coefficient L. Jing et al. 10.1016/j.jhydrol.2022.129057
- Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
- Comparison of ensemble Kalman filter application to a prediction model of soil solute transfer into surface runoff by updating different parameters Y. Gu & J. Tong 10.1007/s00477-023-02448-7
Latest update: 18 Apr 2024
Short summary
Data assimilation methods like the ensemble Kalman filter (EnKF) can combine models and measurements to estimate states and parameters, but require a proper representation of uncertainties. In soil hydrology, model errors typically vary rapidly in space and time, which is difficult to represent. Inflation methods can account for unrepresented model errors. To improve estimations in soil hydrology, we designed a method that can adjust the inflation of states and parameters to fast varying errors.
Data assimilation methods like the ensemble Kalman filter (EnKF) can combine models and...
