Articles | Volume 20, issue 1
https://doi.org/10.5194/hess-20-555-2016
© 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-20-555-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
01 Feb 2016
Research article |
| 01 Feb 2016
Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter
D. Erdal
CORRESPONDING AUTHOR
Center for Applied Geoscience, University of Tübingen, 72074 Tübingen, Germany
O. A. Cirpka
Center for Applied Geoscience, University of Tübingen, 72074 Tübingen, Germany
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26 citations as recorded by crossref.
- Comparison of Two Ensemble-Kalman Filter Based Methods for Estimating Aquifer Parameters from Real 3-D Hydraulic and Tracer Tomographic Tests E. Sánchez-León et al. 10.3390/geosciences10110462
- Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data N. Flipo et al. 10.5194/gmd-16-353-2023
- Inflation method for ensemble Kalman filter in soil hydrology H. Bauser et al. 10.5194/hess-22-4921-2018
- ANFIS-MOA models for the assessment of groundwater contamination vulnerability in a nitrate contaminated area H. Elzain et al. 10.1016/j.jenvman.2021.112162
- Data assimilation for real-time subsurface flow modeling with dynamically adaptive meshless node adjustments S. Chen et al. 10.1007/s00366-023-01897-6
- Simultaneous Estimation of a Contaminant Source and Hydraulic Conductivity Field by Combining an Iterative Ensemble Smoother and Sequential Gaussian Simulation S. Jiang et al. 10.3390/w14050757
- Data assimilation in groundwater modelling: ensemble Kalman filter versus ensemble smoothers L. Li et al. 10.1002/hyp.13127
- Combining a land surface model with groundwater model calibration to assess the impacts of groundwater pumping in a mountainous desert basin K. Fang et al. 10.1016/j.advwatres.2019.05.008
- Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods O. Rahmati et al. 10.1016/j.scitotenv.2019.06.320
- A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology B. Ait-El-Fquih et al. 10.5194/hess-20-3289-2016
- Simultaneous identification of contaminant sources and hydraulic conductivity field by combining geostatistics method with self-organizing maps algorithm S. Jiang et al. 10.1016/j.jconhyd.2021.103815
- Comparison of Two Ensemble Kalman-Based Methods for Estimating Aquifer Parameters from Virtual 2-D Hydraulic and Tracer Tomographic Tests E. Sánchez-León et al. 10.3390/geosciences10070276
- Optimal estimation and scheduling in aquifer management using the rapid feedback control method H. Ghorbanidehno et al. 10.1016/j.advwatres.2017.10.011
- Hydrogeological Uncertainty Estimation With the Analytic Element Method M. Ramgraber & M. Schirmer 10.1029/2020WR029509
- Preconditioning an ensemble Kalman filter for groundwater flow using environmental-tracer observations D. Erdal & O. Cirpka 10.1016/j.jhydrol.2016.11.064
- Early Uncertainty Quantification for an Improved Decision Support Modeling Workflow: A Streamflow Reliability and Water Quality Example B. Hemmings et al. 10.3389/feart.2020.565613
- A basin-scale aquifer characterization using an inverse analysis based on groundwater level fluctuation in response to precipitation: Practical application to a watershed in Jeju Island, South Korea E. Park et al. 10.1016/j.ejrh.2021.100933
- Estimability of recharge through groundwater model calibration: Insights from a field-scale steady-state example M. Knowling & A. Werner 10.1016/j.jhydrol.2016.07.003
- Performance improvement of the linear muskingum flood routing model using optimization algorithms and data assimilation approaches A. Salvati et al. 10.1007/s11069-023-06113-8
- Influence of the spatial configuration of available data on hydraulic conductivity estimates for a geostatistical-Kalman filter method H. Júnez-Ferreira et al. 10.2166/ws.2022.396
- The value of simplified models for spin up of complex models with an application to subsurface hydrology D. Erdal et al. 10.1016/j.cageo.2019.01.014
- Adaptive multi-fidelity probabilistic collocation-based Kalman filter for subsurface flow data assimilation: numerical modeling and real-world experiment J. Man et al. 10.1007/s00477-020-01815-y
- Groundwater management under uncertainty using a stochastic multi-cell model A. Joodavi et al. 10.1016/j.jhydrol.2017.06.003
- Transient Recharge Estimability Through Field‐Scale Groundwater Model Calibration M. Knowling & A. Werner 10.1111/gwat.12526
- An application of the ensemble Kalman filter in epidemiological modelling R. Lal et al. 10.1371/journal.pone.0256227
- Towards Improving the Efficiency of Bayesian Model Averaging Analysis for Flow in Porous Media via the Probabilistic Collocation Method L. Xue et al. 10.3390/w10040412
25 citations as recorded by crossref.
- Comparison of Two Ensemble-Kalman Filter Based Methods for Estimating Aquifer Parameters from Real 3-D Hydraulic and Tracer Tomographic Tests E. Sánchez-León et al. 10.3390/geosciences10110462
- Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data N. Flipo et al. 10.5194/gmd-16-353-2023
- Inflation method for ensemble Kalman filter in soil hydrology H. Bauser et al. 10.5194/hess-22-4921-2018
- ANFIS-MOA models for the assessment of groundwater contamination vulnerability in a nitrate contaminated area H. Elzain et al. 10.1016/j.jenvman.2021.112162
- Data assimilation for real-time subsurface flow modeling with dynamically adaptive meshless node adjustments S. Chen et al. 10.1007/s00366-023-01897-6
- Simultaneous Estimation of a Contaminant Source and Hydraulic Conductivity Field by Combining an Iterative Ensemble Smoother and Sequential Gaussian Simulation S. Jiang et al. 10.3390/w14050757
- Data assimilation in groundwater modelling: ensemble Kalman filter versus ensemble smoothers L. Li et al. 10.1002/hyp.13127
- Combining a land surface model with groundwater model calibration to assess the impacts of groundwater pumping in a mountainous desert basin K. Fang et al. 10.1016/j.advwatres.2019.05.008
- Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods O. Rahmati et al. 10.1016/j.scitotenv.2019.06.320
- A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology B. Ait-El-Fquih et al. 10.5194/hess-20-3289-2016
- Simultaneous identification of contaminant sources and hydraulic conductivity field by combining geostatistics method with self-organizing maps algorithm S. Jiang et al. 10.1016/j.jconhyd.2021.103815
- Comparison of Two Ensemble Kalman-Based Methods for Estimating Aquifer Parameters from Virtual 2-D Hydraulic and Tracer Tomographic Tests E. Sánchez-León et al. 10.3390/geosciences10070276
- Optimal estimation and scheduling in aquifer management using the rapid feedback control method H. Ghorbanidehno et al. 10.1016/j.advwatres.2017.10.011
- Hydrogeological Uncertainty Estimation With the Analytic Element Method M. Ramgraber & M. Schirmer 10.1029/2020WR029509
- Preconditioning an ensemble Kalman filter for groundwater flow using environmental-tracer observations D. Erdal & O. Cirpka 10.1016/j.jhydrol.2016.11.064
- Early Uncertainty Quantification for an Improved Decision Support Modeling Workflow: A Streamflow Reliability and Water Quality Example B. Hemmings et al. 10.3389/feart.2020.565613
- A basin-scale aquifer characterization using an inverse analysis based on groundwater level fluctuation in response to precipitation: Practical application to a watershed in Jeju Island, South Korea E. Park et al. 10.1016/j.ejrh.2021.100933
- Estimability of recharge through groundwater model calibration: Insights from a field-scale steady-state example M. Knowling & A. Werner 10.1016/j.jhydrol.2016.07.003
- Performance improvement of the linear muskingum flood routing model using optimization algorithms and data assimilation approaches A. Salvati et al. 10.1007/s11069-023-06113-8
- Influence of the spatial configuration of available data on hydraulic conductivity estimates for a geostatistical-Kalman filter method H. Júnez-Ferreira et al. 10.2166/ws.2022.396
- The value of simplified models for spin up of complex models with an application to subsurface hydrology D. Erdal et al. 10.1016/j.cageo.2019.01.014
- Adaptive multi-fidelity probabilistic collocation-based Kalman filter for subsurface flow data assimilation: numerical modeling and real-world experiment J. Man et al. 10.1007/s00477-020-01815-y
- Groundwater management under uncertainty using a stochastic multi-cell model A. Joodavi et al. 10.1016/j.jhydrol.2017.06.003
- Transient Recharge Estimability Through Field‐Scale Groundwater Model Calibration M. Knowling & A. Werner 10.1111/gwat.12526
- An application of the ensemble Kalman filter in epidemiological modelling R. Lal et al. 10.1371/journal.pone.0256227
Saved (preprint)
Latest update: 30 Jul 2025
Short summary
Groundwater recharge and hydraulic conductivity are both important properties of a groundwater system. However, models using an erroneous conductivity field can be compensated by a false recharge field to construct the same type of hydraulic head observations. In this work we show that prior knowledge is very important when estimating parameter fields from ambiguous data (such as head observations). If the prior information is reasonable, the joint parameter estimation can be possible.
Groundwater recharge and hydraulic conductivity are both important properties of a groundwater...
