Articles | Volume 22, issue 8
https://doi.org/10.5194/hess-22-4251-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-4251-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Aug 2018
Research article |
| 13 Aug 2018
| 13 Aug 2018
Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope
Anna Botto
CORRESPONDING AUTHOR
Department of Civil, Environmental and Architectural Engineering,
University of Padua, Padua, Italy
Enrica Belluco
Department of Civil, Environmental and Architectural Engineering,
University of Padua, Padua, Italy
Matteo Camporese
Department of Civil, Environmental and Architectural Engineering,
University of Padua, Padua, Italy
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- Iterative filter based estimation of fully 3D heterogeneous fields of permeability and Mualem-van Genuchten parameters A. Chaudhuri et al. https://doi.org/10.1016/j.advwatres.2018.10.023
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28 citations as recorded by crossref.
- A Dual-Cycle Assimilation Approach for Rapid, High-Resolution Fusion of Model-Simulated and Satellite-Observed Land Surface Temperatures Z. Huo et al. https://doi.org/10.1109/JSTARS.2026.3670166
- Combining Models of Root-Zone Hydrology and Geoelectrical Measurements: Recent Advances and Future Prospects B. Mary et al. https://doi.org/10.3389/frwa.2021.767910
- Reducing hydrological uncertainty in large mountainous basins: the role of isotope, snow cover, and glacier dynamics in capturing streamflow seasonality D. Avesani et al. https://doi.org/10.5194/hess-29-5755-2025
- Evaluation of short-term streamflow prediction methods in Urban river basins X. Huang et al. https://doi.org/10.1016/j.pce.2021.103027
- Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data E. Pinnington et al. https://doi.org/10.5194/hess-25-1617-2021
- Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm O. Kozhushko et al. https://doi.org/10.23939/mmc2022.02.203
- Challenges with effective representations of heterogeneity in soil hydrology based on local water content measurements H. Bauser et al. https://doi.org/10.1002/vzj2.20040
- Inflation method for ensemble Kalman filter in soil hydrology H. Bauser et al. https://doi.org/10.5194/hess-22-4921-2018
- Data assimilation with multiple types of observation boreholes via the ensemble Kalman filter embedded within stochastic moment equations C. Xia et al. https://doi.org/10.5194/hess-25-1689-2021
- Comparison of ensemble assimilation methods in a hydrological model dedicated to agricultural best management practices E. Rouzies et al. https://doi.org/10.5194/hess-30-1-2026
- Enhancing inverse modeling in groundwater systems through machine learning: a comprehensive comparative study J. Chen et al. https://doi.org/10.5194/hess-29-4251-2025
- Augmenting observation network design and assimilation frequency in distributed hydrological models: insights from the LISFLOOD-based hydrological data assimilation framework K. Kurugama et al. https://doi.org/10.1016/j.jhydrol.2025.134853
- Assimilation of Groundwater Level and Soil Moisture Data in an Integrated Land Surface‐Subsurface Model for Southwestern Germany C. Hung et al. https://doi.org/10.1029/2021WR031549
- Real-time simulation of surface water and groundwater with data assimilation X. He et al. https://doi.org/10.1016/j.advwatres.2019.03.004
- STH-net: a soil monitoring network for process-based hydrological modelling from the pedon to the hillslope scale E. Martini et al. https://doi.org/10.5194/essd-13-2529-2021
- Establishing a periodic SM model with Fourier analysis for enhancing global soil moisture forecasting J. Zhu et al. https://doi.org/10.1038/s41598-025-97347-y
- Richards Equation at the Hillslope Scale: Can We Resolve the Heterogeneity of Soil Hydraulic Material Properties? H. Bauser et al. https://doi.org/10.1029/2022WR032294
- Estimation of hydraulic parameters in a heterogeneous low‐lying farmland near Venice E. Zancanaro et al. https://doi.org/10.1002/hyp.14791
- Real-time reservoir flood control operation enhanced by data assimilation J. Zhang et al. https://doi.org/10.1016/j.jhydrol.2021.126426
- An efficient and physics-informed regional maize yield estimation scheme by combining data assimilation and machine learning D. Yu et al. https://doi.org/10.1016/j.compag.2025.111142
- Development and Application of a Distributed Hydrological Model Ensemble (DHM-FEWS) for Flash Flood Early Warning X. Liu et al. https://doi.org/10.3390/w18020237
- A new approach for joint assimilation of cosmic-ray neutron soil moisture and groundwater level data into an integrated terrestrial model F. Li et al. https://doi.org/10.5194/hess-29-6419-2025
- State updating of root zone soil moisture estimates of an unsaturated zone metamodel for operational water resources management M. Pezij et al. https://doi.org/10.1016/j.hydroa.2019.100040
- A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation Q. Zhang et al. https://doi.org/10.1016/j.advwatres.2019.103407
- A regional ionospheric assimilation study with GPS and COSMIC measurements using a 3D-var algorithm (IDA4D) S. Jeong et al. https://doi.org/10.1016/j.asr.2021.12.049
- Recent advances and opportunities in data assimilation for physics-based hydrological modeling M. Camporese & M. Girotto https://doi.org/10.3389/frwa.2022.948832
- Iterative filter based estimation of fully 3D heterogeneous fields of permeability and Mualem-van Genuchten parameters A. Chaudhuri et al. https://doi.org/10.1016/j.advwatres.2018.10.023
- Covariance resampling for particle filter – state and parameter estimation for soil hydrology D. Berg et al. https://doi.org/10.5194/hess-23-1163-2019
Saved (final revised paper)
Latest update: 22 Oct 2026
Short summary
We present a multivariate application of the ensemble Kalman filter (EnKF) in hydrological modeling of a real-world hillslope test case with dominant unsaturated dynamics and strong nonlinearities. Overall, the EnKF is able to correctly update system state and soil parameters. However, multivariate data assimilation may lead to significant tradeoffs between model predictions of different variables, if the observation data are not high quality or representative.
We present a multivariate application of the ensemble Kalman filter (EnKF) in hydrological...
