Articles | Volume 16, issue 2
https://doi.org/10.5194/hess-16-375-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-16-375-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter
D. A. Plaza
Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
R. De Keyser
Department of Electrical energy, Systems and Automation, Ghent University, Ghent, Belgium
G. J. M. De Lannoy
Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
L. Giustarini
Centre de Recherche Public – Gabriel Lippmann, Département Environnement et Agrobiotechnologies, Belvaux, Luxembourg
P. Matgen
Centre de Recherche Public – Gabriel Lippmann, Département Environnement et Agrobiotechnologies, Belvaux, Luxembourg
V. R. N. Pauwels
Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
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52 citations as recorded by crossref.
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- Particle filter for high frequency oxygen data assimilation in river systems S. Wang et al. 10.1016/j.envsoft.2022.105382
- Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation M. Leisenring & H. Moradkhani 10.1016/j.jhydrol.2012.08.049
- Assimilation of Surface- and Root-Zone ASCAT Soil Moisture Products Into Rainfall–Runoff Modeling L. Brocca et al. 10.1109/TGRS.2011.2177468
50 citations as recorded by crossref.
- Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method H. Moradkhani et al. 10.1029/2012WR012144
- An Improved Particle Filter Algorithm Based on Ensemble Kalman Filter and Markov Chain Monte Carlo Method H. Bi et al. 10.1109/JSTARS.2014.2322096
- Insights on the impact of systematic model errors on data assimilation performance in changing catchments S. Pathiraja et al. 10.1016/j.advwatres.2017.12.006
- Assimilation of microwave brightness temperatures for soil moisture estimation using particle filter H. Bi et al. 10.1088/1755-1315/17/1/012126
- Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts R. Hostache et al. 10.1029/2017WR022205
- Assimilation of water temperature and discharge data for ensemble water temperature forecasting S. Ouellet-Proulx et al. 10.1016/j.jhydrol.2017.09.027
- A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment G. Piazzi et al. 10.5194/tc-12-2287-2018
- State and parameter update of a hydrodynamic-phytoplankton model using ensemble Kalman filter J. Huang et al. 10.1016/j.ecolmodel.2013.04.022
- Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation S. Pathiraja et al. 10.1002/2018WR022627
- A Mutual Information‐Based Likelihood Function for Particle Filter Flood Extent Assimilation A. Dasgupta et al. 10.1029/2020WR027859
- Application of particle filter to assess uncertainty for reservoir state and parameter estimation F. Akter et al. 10.1016/j.geoen.2023.211481
- Sequential Data Assimilation for Streamflow Forecasting: Assessing the Sensitivity to Uncertainties and Updated Variables of a Conceptual Hydrological Model at Basin Scale G. Piazzi et al. 10.1029/2020WR028390
- A back-fitting algorithm to improve real-time flood forecasting X. Zhang et al. 10.1016/j.jhydrol.2018.04.051
- The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework P. Abbaszadeh et al. 10.1029/2018WR023629
- An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment G. Manoli et al. 10.1016/j.jcp.2014.11.035
- MATLAB algorithm to implement soil water data assimilation with the Ensemble Kalman Filter using HYDRUS J. Valdes-Abellan et al. 10.1016/j.mex.2018.02.008
- 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
- Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review Y. Li et al. 10.3390/rs8060456
- Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method H. Yan et al. 10.1109/TGRS.2015.2432067
- A Modified Particle Filter‐Based Data Assimilation Method for a High‐Precision 2‐D Hydrodynamic Model Considering Spatial‐temporal Variability of Roughness: Simulation of Dam‐Break Flood Inundation Y. Cao et al. 10.1029/2018WR023568
- Closing the Gaps in Our Knowledge of the Hydrological Cycle over Land: Conceptual Problems W. Lahoz & G. De Lannoy 10.1007/s10712-013-9221-7
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- Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes C. Alvarez-Garreton et al. 10.5194/hess-19-1659-2015
- Toward improving drought monitoring using the remotely sensed soil moisture assimilation: A parallel particle filtering framework H. Yan et al. 10.1016/j.rse.2018.07.017
- Evidence of a topographic signal in surface soil moisture derived from ENVISAT ASAR wide swath data D. Mason et al. 10.1016/j.jag.2015.02.004
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- A probabilistic drought forecasting framework: A combined dynamical and statistical approach H. Yan et al. 10.1016/j.jhydrol.2017.03.004
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- Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo P. Abbaszadeh et al. 10.1016/j.advwatres.2017.11.011
- Kalman filters for assimilating near-surface observations into the Richards equation – Part 3: Retrieving states and parameters from laboratory evaporation experiments H. Medina et al. 10.5194/hess-18-2543-2014
- Obtaining soil hydraulic parameters from soil water content data assimilation under different climatic/soil conditions J. Valdes-Abellan et al. 10.1016/j.catena.2017.12.022
- Simultaneous assimilation of in situ soil moisture and streamflow in the SWAT model using the Extended Kalman Filter L. Sun et al. 10.1016/j.jhydrol.2016.10.040
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- Which filter for data assimilation in water quality models? Focus on oxygen reaeration and heterotrophic bacteria activity S. Wang et al. 10.1016/j.jhydrol.2023.129423
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- Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX C. Román-Cascón et al. 10.1016/j.rse.2017.08.022
- Particle filter for high frequency oxygen data assimilation in river systems S. Wang et al. 10.1016/j.envsoft.2022.105382
2 citations as recorded by crossref.
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- Assimilation of Surface- and Root-Zone ASCAT Soil Moisture Products Into Rainfall–Runoff Modeling L. Brocca et al. 10.1109/TGRS.2011.2177468
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