Articles | Volume 20, issue 10
https://doi.org/10.5194/hess-20-4341-2016
© Author(s) 2016. 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-20-4341-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
26 Oct 2016
Research article |
| 26 Oct 2016
Multivariate hydrological data assimilation of soil moisture and groundwater head
Donghua Zhang
CORRESPONDING AUTHOR
Department of Geosciences and Natural Resource Management, University
of Copenhagen, Copenhagen, Denmark
Henrik Madsen
DHI, Hørsholm, Denmark
Marc E. Ridler
DHI, Hørsholm, Denmark
Jacob Kidmose
Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark
Karsten H. Jensen
Department of Geosciences and Natural Resource Management, University
of Copenhagen, Copenhagen, Denmark
Jens C. Refsgaard
Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark
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Cited
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- Assessing 32-Day Hydrological Ensemble Forecasts in the Lake Champlain–Richelieu River Watershed M. Abaza et al. 10.1061/(ASCE)HE.1943-5584.0001983
- Assessment of an ensemble-based data assimilation system for a shallow estuary M. Khanarmuei et al. 10.1016/j.ecss.2021.107389
- Earth data assimilation in hydrologic models: recent advances S. Jeyalakshmi et al. 10.1080/00207233.2021.1875303
- Hydrological reanalysis across the 20th century: A case study of the Amazon Basin S. Wongchuig et al. 10.1016/j.jhydrol.2019.01.025
- Nordic contributions to stochastic methods in hydrology D. Rosbjerg et al. 10.2166/nh.2022.137
- Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope A. Botto et al. 10.5194/hess-22-4251-2018
- Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation M. Khaki et al. 10.1038/s41598-020-75710-5
- Observational and predictive uncertainties for multiple variables in a spatially distributed hydrological model L. Ehlers et al. 10.1002/hyp.13367
- Data assimilation for flow forecasting in urban drainage systems by updating a hydrodynamic model of Damhusåen Catchment, Copenhagen M. Babel et al. 10.1080/1573062X.2020.1828938
- Water table depth assimilation in integrated terrestrial system models at the larger catchment scale F. Li et al. 10.3389/frwa.2023.1150999
- Long memory of river streams in the canal of Panama watershed R. Coloane Luque & L. Alana 10.15406/ijh.2023.07.00348
- Variable update strategy to improve water quality forecast accuracy in multivariate data assimilation using the ensemble Kalman filter S. Park et al. 10.1016/j.watres.2020.115711
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- Recent advances and opportunities in data assimilation for physics-based hydrological modeling M. Camporese & M. Girotto 10.3389/frwa.2022.948832
- Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
- Assimilation of Groundwater Level and Soil Moisture Data in an Integrated Land Surface‐Subsurface Model for Southwestern Germany C. Hung et al. 10.1029/2021WR031549
30 citations as recorded by crossref.
- Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN Y. Zhang et al. 10.3390/s24010035
- Climate change impacts and uncertainty on spatiotemporal variations of drought indices for an irrigated catchment S. Chan et al. 10.1016/j.jhydrol.2021.126814
- Assessing 32-Day Hydrological Ensemble Forecasts in the Lake Champlain–Richelieu River Watershed M. Abaza et al. 10.1061/(ASCE)HE.1943-5584.0001983
- Assessment of an ensemble-based data assimilation system for a shallow estuary M. Khanarmuei et al. 10.1016/j.ecss.2021.107389
- Earth data assimilation in hydrologic models: recent advances S. Jeyalakshmi et al. 10.1080/00207233.2021.1875303
- Hydrological reanalysis across the 20th century: A case study of the Amazon Basin S. Wongchuig et al. 10.1016/j.jhydrol.2019.01.025
- Nordic contributions to stochastic methods in hydrology D. Rosbjerg et al. 10.2166/nh.2022.137
- Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope A. Botto et al. 10.5194/hess-22-4251-2018
- Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation M. Khaki et al. 10.1038/s41598-020-75710-5
- Observational and predictive uncertainties for multiple variables in a spatially distributed hydrological model L. Ehlers et al. 10.1002/hyp.13367
- Data assimilation for flow forecasting in urban drainage systems by updating a hydrodynamic model of Damhusåen Catchment, Copenhagen M. Babel et al. 10.1080/1573062X.2020.1828938
- Water table depth assimilation in integrated terrestrial system models at the larger catchment scale F. Li et al. 10.3389/frwa.2023.1150999
- Long memory of river streams in the canal of Panama watershed R. Coloane Luque & L. Alana 10.15406/ijh.2023.07.00348
- Variable update strategy to improve water quality forecast accuracy in multivariate data assimilation using the ensemble Kalman filter S. Park et al. 10.1016/j.watres.2020.115711
- Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach G. Mendiguren et al. 10.5194/hess-21-5987-2017
- Temporal prediction of shallow landslides exploiting soil saturation degree derived by ERA5-Land products M. Bordoni et al. 10.1007/s10064-023-03304-2
- Estimation of hydraulic parameters in a heterogeneous low‐lying farmland near Venice E. Zancanaro et al. 10.1002/hyp.14791
- Observations of an Extreme Atmospheric River Storm With a Diverse Sensor Network B. Hatchett et al. 10.1029/2020EA001129
- Real-time simulation of surface water and groundwater with data assimilation X. He et al. 10.1016/j.advwatres.2019.03.004
- Improving modelled streamflow using time-varying multivariate assimilation of remotely sensed soil moisture and in-situ streamflow observations R. Visweshwaran et al. 10.1016/j.advwatres.2024.104676
- Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: the effect of preprocessing and post-processing on skill and statistical consistency D. Lucatero et al. 10.5194/hess-22-3601-2018
- 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
- Monitoring water storage decline over the Middle East M. Khaki & I. Hoteit 10.1016/j.jhydrol.2021.127166
- State updating of root zone soil moisture estimates of an unsaturated zone metamodel for operational water resources management M. Pezij et al. 10.1016/j.hydroa.2019.100040
- Lagrangian Data Assimilation for Improving Model Estimates of Velocity Fields and Residual Currents in a Tidal Estuary N. Mardani et al. 10.3390/app112211006
- 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
- Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model H. Zhang et al. 10.1016/j.advwatres.2017.11.003
- Recent advances and opportunities in data assimilation for physics-based hydrological modeling M. Camporese & M. Girotto 10.3389/frwa.2022.948832
- Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
- Assimilation of Groundwater Level and Soil Moisture Data in an Integrated Land Surface‐Subsurface Model for Southwestern Germany C. Hung et al. 10.1029/2021WR031549
Saved (preprint)
Latest update: 19 Apr 2024
Short summary
We present a method to assimilate observed groundwater head and soil moisture profiles into an integrated hydrological model. The study uses the ensemble transform Kalman filter method and the MIKE SHE hydrological model code. The proposed method is shown to be more robust and provide better results for two cases in Denmark, and is also validated using real data. The hydrological model with assimilation overall improved performance compared to the model without assimilation.
We present a method to assimilate observed groundwater head and soil moisture profiles into an...
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