Articles | Volume 20, issue 12
https://doi.org/10.5194/hess-20-4999-2016
© Author(s) 2016. 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-20-4999-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
19 Dec 2016
Research article |
| 19 Dec 2016
EnKF with closed-eye period – towards a consistent aggregation of information in soil hydrology
Hannes H. Bauser
CORRESPONDING AUTHOR
Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, Germany
HGS MathComp, Heidelberg University, Heidelberg, Germany
Stefan Jaumann
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
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
21 citations as recorded by crossref.
- Pedotransfer Functions in Earth System Science: Challenges and Perspectives K. Van Looy et al. 10.1002/2017RG000581
- Inflation method for ensemble Kalman filter in soil hydrology H. Bauser et al. 10.5194/hess-22-4921-2018
- Investigation of Data Assimilation Methods for Soil Parameter Estimation with Different Types of Data Y. Zha et al. 10.2136/vzj2019.01.0013
- The value of soil temperature data versus soil moisture data for state, parameter, and flux estimation in unsaturated flow model R. Kandala et al. 10.1002/vzj2.20298
- Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope A. Botto et al. 10.5194/hess-22-4251-2018
- Reducing uncertainties in hydromechanical modeling with a recently developed Rosetta 3 podeotransfer function W. Shao et al. 10.1016/j.enggeo.2023.107250
- An improved Kalman filtering approach for the estimation of unsaturated flow parameters by assimilating photographic imaging data M. Rajabi et al. 10.1016/j.jhydrol.2020.125373
- On the uncertainty of initial condition and initialization approaches in variably saturated flow modeling D. Yu et al. 10.5194/hess-23-2897-2019
- Precipitation and evaporation affecting landfill gas migration into passive methane oxidation biosystems: Models development and verification M. Sun & Y. Yu 10.1016/j.wasman.2024.06.018
- Iterative filter based estimation of fully 3D heterogeneous fields of permeability and Mualem-van Genuchten parameters A. Chaudhuri et al. 10.1016/j.advwatres.2018.10.023
- Covariance resampling for particle filter – state and parameter estimation for soil hydrology D. Berg et al. 10.5194/hess-23-1163-2019
- Reduce uncertainty in soil hydrological modeling: A comparison of soil hydraulic parameters generated by random sampling and pedotransfer function W. Shao et al. 10.1016/j.jhydrol.2023.129740
- Effect of unrepresented model errors on estimated soil hydraulic material properties S. Jaumann & K. Roth 10.5194/hess-21-4301-2017
- <i>STH-net:</i> a soil monitoring network for process-based hydrological modelling from the pedon to the hillslope scale E. Martini et al. 10.5194/essd-13-2529-2021
- Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
- Richards Equation at the Hillslope Scale: Can We Resolve the Heterogeneity of Soil Hydraulic Material Properties? H. Bauser et al. 10.1029/2022WR032294
- Soil hydraulic material properties and layered architecture from time-lapse GPR S. Jaumann & K. Roth 10.5194/hess-22-2551-2018
- Challenges with effective representations of heterogeneity in soil hydrology based on local water content measurements H. Bauser et al. 10.1002/vzj2.20040
- 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
- Comparison of ensemble data assimilation methods for the estimation of time-varying soil hydraulic parameters K. Liu et al. 10.1016/j.jhydrol.2020.125729
- Soil moisture prediction with the ensemble Kalman filter: Handling uncertainty of soil hydraulic parameters N. Brandhorst et al. 10.1016/j.advwatres.2017.10.022
21 citations as recorded by crossref.
- Pedotransfer Functions in Earth System Science: Challenges and Perspectives K. Van Looy et al. 10.1002/2017RG000581
- Inflation method for ensemble Kalman filter in soil hydrology H. Bauser et al. 10.5194/hess-22-4921-2018
- Investigation of Data Assimilation Methods for Soil Parameter Estimation with Different Types of Data Y. Zha et al. 10.2136/vzj2019.01.0013
- The value of soil temperature data versus soil moisture data for state, parameter, and flux estimation in unsaturated flow model R. Kandala et al. 10.1002/vzj2.20298
- Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope A. Botto et al. 10.5194/hess-22-4251-2018
- Reducing uncertainties in hydromechanical modeling with a recently developed Rosetta 3 podeotransfer function W. Shao et al. 10.1016/j.enggeo.2023.107250
- An improved Kalman filtering approach for the estimation of unsaturated flow parameters by assimilating photographic imaging data M. Rajabi et al. 10.1016/j.jhydrol.2020.125373
- On the uncertainty of initial condition and initialization approaches in variably saturated flow modeling D. Yu et al. 10.5194/hess-23-2897-2019
- Precipitation and evaporation affecting landfill gas migration into passive methane oxidation biosystems: Models development and verification M. Sun & Y. Yu 10.1016/j.wasman.2024.06.018
- Iterative filter based estimation of fully 3D heterogeneous fields of permeability and Mualem-van Genuchten parameters A. Chaudhuri et al. 10.1016/j.advwatres.2018.10.023
- Covariance resampling for particle filter – state and parameter estimation for soil hydrology D. Berg et al. 10.5194/hess-23-1163-2019
- Reduce uncertainty in soil hydrological modeling: A comparison of soil hydraulic parameters generated by random sampling and pedotransfer function W. Shao et al. 10.1016/j.jhydrol.2023.129740
- Effect of unrepresented model errors on estimated soil hydraulic material properties S. Jaumann & K. Roth 10.5194/hess-21-4301-2017
- <i>STH-net:</i> a soil monitoring network for process-based hydrological modelling from the pedon to the hillslope scale E. Martini et al. 10.5194/essd-13-2529-2021
- Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
- Richards Equation at the Hillslope Scale: Can We Resolve the Heterogeneity of Soil Hydraulic Material Properties? H. Bauser et al. 10.1029/2022WR032294
- Soil hydraulic material properties and layered architecture from time-lapse GPR S. Jaumann & K. Roth 10.5194/hess-22-2551-2018
- Challenges with effective representations of heterogeneity in soil hydrology based on local water content measurements H. Bauser et al. 10.1002/vzj2.20040
- 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
- Comparison of ensemble data assimilation methods for the estimation of time-varying soil hydraulic parameters K. Liu et al. 10.1016/j.jhydrol.2020.125729
- Soil moisture prediction with the ensemble Kalman filter: Handling uncertainty of soil hydraulic parameters N. Brandhorst et al. 10.1016/j.advwatres.2017.10.022
Latest update: 14 Dec 2024
Short summary
The representation of soil water movement comes with uncertainties in all model components. We assess the key uncertainties for the case of a one-dimensional soil profile with measured water contents. We employ a data assimilation method to represent and reduce the key uncertainties. For intermittent phases where model assumptions are violated, we introduce a "closed-eye period" to bridge the gap. We also demonstrate the need to include heterogeneity.
The representation of soil water movement comes with uncertainties in all model components. We...
