Articles | Volume 21, issue 9
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.
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
CORRESPONDING AUTHOR
Forschungszentrum Jülich, Agrosphere (IBG 3), Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Forschungszentrum Jülich, Jülich, Germany
Harrie-Jan Hendricks Franssen
Forschungszentrum Jülich, Agrosphere (IBG 3), Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Forschungszentrum Jülich, Jülich, Germany
Xujun Han
Forschungszentrum Jülich, Agrosphere (IBG 3), Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Forschungszentrum Jülich, Jülich, Germany
Jasper A. Vrugt
Department of Civil and Environmental Engineering, University of California Irvine, Irvine, USA
Department of Earth Systems Science, University of California Irvine, Irvine, USA
Harry Vereecken
Forschungszentrum Jülich, Agrosphere (IBG 3), Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Forschungszentrum Jülich, Jülich, Germany
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Cited
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41 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
- Bias correction of satellite soil moisture through data assimilation J. Qin et al. 10.1016/j.jhydrol.2022.127947
- 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
- Impact of parameter updates on soil moisture assimilation in a 3D heterogeneous hillslope model N. Brandhorst & I. Neuweiler 10.5194/hess-27-1301-2023
- Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
- A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy A. Roy et al. 10.1029/2022WR033318
- Evaluation of data assimilation strategies on improving the performance of crop modeling based on a novel evapotranspiration assimilation framework C. Yang & H. Lei 10.1016/j.agrformet.2023.109882
- 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
- Development of a disaggregated multi-level factorial hydrologic data assimilation model F. Wang et al. 10.1016/j.jhydrol.2022.127802
- Inflation method for ensemble Kalman filter in soil hydrology H. Bauser et al. 10.5194/hess-22-4921-2018
- Application of particle filter to assess uncertainty for reservoir state and parameter estimation F. Akter et al. 10.1016/j.geoen.2023.211481
- 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
- Assimilation of NDVI data in a land surface – Vegetation model for leaf area index predictions in a tree-grass ecosystem N. Montaldo et al. 10.1080/17538947.2023.2259226
- 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
- Coupling duo-assimilation to hydrological model to enhance flood forecasting D. Dang & T. Anh 10.1080/23249676.2023.2201475
- 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
- Operationalizing crop model data assimilation for improved on-farm situational awareness M. Knowling et al. 10.1016/j.agrformet.2023.109502
- 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
- Recent advances and opportunities in data assimilation for physics-based hydrological modeling M. Camporese & M. Girotto 10.3389/frwa.2022.948832
- 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
- Point-scale multi-objective calibration of the Community Land Model (version 5.0) using in situ observations of water and energy fluxes and variables T. Denager et al. 10.5194/hess-27-2827-2023
- 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
- Combining harmonic pumping with a tracer test for fractured aquifer characterization A. Dodangeh et al. 10.1007/s10040-023-02595-9
- 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
- Assimilation versus optimization for SWAT calibration: accuracy, uncertainty, and computational burden analysis M. Bayat et al. 10.2166/ws.2023.055
- State estimation for one‐dimensional agro‐hydrological processes with model mismatch Z. Liu et al. 10.1002/cjce.25095
- Development of a stochastic hydrological modeling system for improving ensemble streamflow prediction Y. Shen et al. 10.1016/j.jhydrol.2022.127683
- Multiscale Assimilation of Sentinel and Landsat Data for Soil Moisture and Leaf Area Index Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in a Heterogeneous Ecosystem N. Montaldo et al. 10.3390/rs14143458
Saved (preprint)
Latest update: 25 Apr 2024
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...
