Articles | Volume 21, issue 9
Hydrol. Earth Syst. Sci., 21, 4927–4958, 2017
https://doi.org/10.5194/hess-21-4927-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 4927–4958, 2017
https://doi.org/10.5194/hess-21-4927-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
29 Sep 2017
Research article
| 29 Sep 2017
State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter
Hongjuan Zhang et al.
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Cited
27 citations as recorded by crossref.
- Uncertainty quantification using the particle filter for non-stationary hydrological frequency analysis S. Sen et al. 10.1016/j.jhydrol.2020.124666
- 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
- Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
- The TERENO-Rur Hydrological Observatory: A Multiscale Multi-Compartment Research Platform for the Advancement of Hydrological Science H. Bogena et al. 10.2136/vzj2018.03.0055
- Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy F. Ju et al. 10.3390/rs12101556
- Using a Particle Filter to Estimate the Spatial Distribution of the Snowpack Water Equivalent P. Cantet et al. 10.1175/JHM-D-18-0140.1
- Inflation method for ensemble Kalman filter in soil hydrology H. Bauser et al. 10.5194/hess-22-4921-2018
- Estimation of soil gas permeability for assessing radon risk using Rosetta pedotransfer function based on soil texture and water content D. Benavente et al. 10.1016/j.jenvrad.2019.105992
- Adaptive Model Reduction and State Estimation of Agro-hydrological Systems S. Sahoo & J. Liu 10.1016/j.compag.2022.106825
- Probabilistic scheme for joint parameter estimation and state prediction in complex dynamical systems S. Pérez-Vieites et al. 10.1103/PhysRevE.98.063305
- Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
- Covariance resampling for particle filter – state and parameter estimation for soil hydrology D. Berg et al. 10.5194/hess-23-1163-2019
- A Novel Modeling Framework for Computationally Efficient and Accurate Real‐Time Ensemble Flood Forecasting With Uncertainty Quantification V. Tran et al. 10.1029/2019WR025727
- Nested Gaussian filters for recursive Bayesian inference and nonlinear tracking in state space models S. Pérez-Vieites & J. Míguez 10.1016/j.sigpro.2021.108295
- An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters B. Ait-El-Fquih & I. Hoteit 10.1175/MWR-D-16-0485.1
- Improving parameter and state estimation of a hydrological model with the ensemble square root filter N. Li et al. 10.1016/j.advwatres.2020.103813
- Dynamic model reduction and optimal sensor placement for agro-hydrological systems S. Sahoo et al. 10.1016/j.ifacol.2020.12.657
- Consensus‐based approach for parameter and state estimation of agro‐hydrological systems X. Yin et al. 10.1002/aic.17096
- 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
- Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau F. Peng et al. 10.1080/16000870.2019.1704963
- Assimilation of Cosmic‐Ray Neutron Counts for the Estimation of Soil Ice Content on the Eastern Tibetan Plateau S. Mwangi et al. 10.1029/2019JD031529
- 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
- A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time R. Thomas et al. 10.1029/2019WR026138
- Kalman Filter for Linear Systems With Unknown Structural Parameters D. Xin & L. Shi 10.1109/TCSII.2021.3103609
- Optimal sensor placement for agro‐hydrological systems S. Sahoo et al. 10.1002/aic.16795
- Immune Evolution Particle Filter for Soil Moisture Data Assimilation F. Ju et al. 10.3390/w11020211
- Development of a stochastic hydrological modeling system for improving ensemble streamflow prediction Y. Shen et al. 10.1016/j.jhydrol.2022.127683
27 citations as recorded by crossref.
- Uncertainty quantification using the particle filter for non-stationary hydrological frequency analysis S. Sen et al. 10.1016/j.jhydrol.2020.124666
- 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
- Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
- The TERENO-Rur Hydrological Observatory: A Multiscale Multi-Compartment Research Platform for the Advancement of Hydrological Science H. Bogena et al. 10.2136/vzj2018.03.0055
- Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy F. Ju et al. 10.3390/rs12101556
- Using a Particle Filter to Estimate the Spatial Distribution of the Snowpack Water Equivalent P. Cantet et al. 10.1175/JHM-D-18-0140.1
- Inflation method for ensemble Kalman filter in soil hydrology H. Bauser et al. 10.5194/hess-22-4921-2018
- Estimation of soil gas permeability for assessing radon risk using Rosetta pedotransfer function based on soil texture and water content D. Benavente et al. 10.1016/j.jenvrad.2019.105992
- Adaptive Model Reduction and State Estimation of Agro-hydrological Systems S. Sahoo & J. Liu 10.1016/j.compag.2022.106825
- Probabilistic scheme for joint parameter estimation and state prediction in complex dynamical systems S. Pérez-Vieites et al. 10.1103/PhysRevE.98.063305
- Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
- Covariance resampling for particle filter – state and parameter estimation for soil hydrology D. Berg et al. 10.5194/hess-23-1163-2019
- A Novel Modeling Framework for Computationally Efficient and Accurate Real‐Time Ensemble Flood Forecasting With Uncertainty Quantification V. Tran et al. 10.1029/2019WR025727
- Nested Gaussian filters for recursive Bayesian inference and nonlinear tracking in state space models S. Pérez-Vieites & J. Míguez 10.1016/j.sigpro.2021.108295
- An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters B. Ait-El-Fquih & I. Hoteit 10.1175/MWR-D-16-0485.1
- Improving parameter and state estimation of a hydrological model with the ensemble square root filter N. Li et al. 10.1016/j.advwatres.2020.103813
- Dynamic model reduction and optimal sensor placement for agro-hydrological systems S. Sahoo et al. 10.1016/j.ifacol.2020.12.657
- Consensus‐based approach for parameter and state estimation of agro‐hydrological systems X. Yin et al. 10.1002/aic.17096
- 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
- Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau F. Peng et al. 10.1080/16000870.2019.1704963
- Assimilation of Cosmic‐Ray Neutron Counts for the Estimation of Soil Ice Content on the Eastern Tibetan Plateau S. Mwangi et al. 10.1029/2019JD031529
- 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
- A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time R. Thomas et al. 10.1029/2019WR026138
- Kalman Filter for Linear Systems With Unknown Structural Parameters D. Xin & L. Shi 10.1109/TCSII.2021.3103609
- Optimal sensor placement for agro‐hydrological systems S. Sahoo et al. 10.1002/aic.16795
- Immune Evolution Particle Filter for Soil Moisture Data Assimilation F. Ju et al. 10.3390/w11020211
- Development of a stochastic hydrological modeling system for improving ensemble streamflow prediction Y. Shen et al. 10.1016/j.jhydrol.2022.127683
Saved (preprint)
Latest update: 30 Jun 2022
Short summary
Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. We want to investigate the roles of data assimilation methods and land surface models (LSMs) in joint estimation of states and parameters in the assimilation experiments. We find that all DA methods can improve prediction of states, and that differences between DA methods were limited but that the differences between LSMs were much larger.
Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most...
