Articles | Volume 20, issue 5
https://doi.org/10.5194/hess-20-2103-2016
© 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-20-2103-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
30 May 2016
Research article |
| 30 May 2016
Data assimilation in integrated hydrological modelling in the presence of observation bias
Jørn Rasmussen
CORRESPONDING AUTHOR
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Henrik Madsen
DHI, Hørsholm, Denmark
Karsten Høgh Jensen
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Jens Christian Refsgaard
Geological Survey of Denmark and Greenland, Copenhagen, Denmark
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Cited
19 citations as recorded by crossref.
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- Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
- The U. S. Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States R. Regan et al. 10.1016/j.envsoft.2018.09.023
- 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
- Optimization of Bottom Friction Coefficient Using Inverse Modeling in the Persian Gulf Z. Ranji & M. Soltanpour 10.1007/s12601-021-00040-0
- Advances in understanding river‐groundwater interactions P. Brunner et al. 10.1002/2017RG000556
- Recent advances and opportunities in data assimilation for physics-based hydrological modeling M. Camporese & M. Girotto 10.3389/frwa.2022.948832
- Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding M. El Gharamti et al. 10.5194/hess-25-5315-2021
- 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
- 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
- Recursive Bayesian Estimation of Conceptual Rainfall‐Runoff Model Errors in Real‐Time Prediction of Streamflow M. Tajiki et al. 10.1029/2019WR025237
- HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model Q. Tang et al. 10.5194/gmd-17-3559-2024
- 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
- 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
- Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions J. Musuuza et al. 10.3390/rs12050811
- Improving flood forecasting using conditional bias-aware assimilation of streamflow observations and dynamic assessment of flow-dependent information content H. Shen et al. 10.1016/j.jhydrol.2021.127247
- Real-time simulation of surface water and groundwater with data assimilation X. He et al. 10.1016/j.advwatres.2019.03.004
- Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry P. Li et al. 10.1016/j.geoderma.2020.114432
- Toward Discharge Estimation for Water Resources Management with a Semidistributed Model and Local Ensemble Kalman Filter Data Assimilation S. Wongchuig et al. 10.1061/(ASCE)HE.1943-5584.0002027
17 citations as recorded by crossref.
- Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope A. Botto et al. 10.5194/hess-22-4251-2018
- Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
- The U. S. Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States R. Regan et al. 10.1016/j.envsoft.2018.09.023
- 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
- Optimization of Bottom Friction Coefficient Using Inverse Modeling in the Persian Gulf Z. Ranji & M. Soltanpour 10.1007/s12601-021-00040-0
- Advances in understanding river‐groundwater interactions P. Brunner et al. 10.1002/2017RG000556
- Recent advances and opportunities in data assimilation for physics-based hydrological modeling M. Camporese & M. Girotto 10.3389/frwa.2022.948832
- Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding M. El Gharamti et al. 10.5194/hess-25-5315-2021
- 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
- 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
- Recursive Bayesian Estimation of Conceptual Rainfall‐Runoff Model Errors in Real‐Time Prediction of Streamflow M. Tajiki et al. 10.1029/2019WR025237
- HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model Q. Tang et al. 10.5194/gmd-17-3559-2024
- 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
- 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
- Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions J. Musuuza et al. 10.3390/rs12050811
- Improving flood forecasting using conditional bias-aware assimilation of streamflow observations and dynamic assessment of flow-dependent information content H. Shen et al. 10.1016/j.jhydrol.2021.127247
- Real-time simulation of surface water and groundwater with data assimilation X. He et al. 10.1016/j.advwatres.2019.03.004
2 citations as recorded by crossref.
- Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry P. Li et al. 10.1016/j.geoderma.2020.114432
- Toward Discharge Estimation for Water Resources Management with a Semidistributed Model and Local Ensemble Kalman Filter Data Assimilation S. Wongchuig et al. 10.1061/(ASCE)HE.1943-5584.0002027
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Latest update: 13 Dec 2024
Short summary
In the paper, observations are assimilated into a hydrological model in order to improve the model performance. Two methods for detecting and correcting systematic errors (bias) in groundwater head observations are used leading to improved results compared to standard assimilation methods which ignores any bias. This is demonstrated using both synthetic (user generated) observations and real-world observations.
In the paper, observations are assimilated into a hydrological model in order to improve the...
