Articles | Volume 20, issue 12
https://doi.org/10.5194/hess-20-4949-2016
© 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-20-4949-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
16 Dec 2016
Research article |
| 16 Dec 2016
Identification of hydrological model parameter variation using ensemble Kalman filter
Chao Deng
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
Shenglian Guo
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
Zejun Li
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
Dingbao Wang
Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL, USA
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- Improving structure identifiability of hydrological processes by temporal sensitivity with a flexible modeling framework L. Zhou et al. https://doi.org/10.1016/j.jhydrol.2022.128843
- Simulating Runoff and Actual Evapotranspiration via Time-Variant Parameter Method: The Effects of Hydrological Model Structures Z. Pan et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002220
- Effects of land use/land cover change on hydrological responses of a watershed in the Central Rift Valley of Ethiopia K. Besha et al. https://doi.org/10.2166/nh.2024.042
- Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework Z. Pan et al. https://doi.org/10.5194/hess-23-3405-2019
- The influence of a prolonged meteorological drought on catchment water storage capacity: a hydrological-model perspective Z. Pan et al. https://doi.org/10.5194/hess-24-4369-2020
- Evaluating the Effect of Transpiration in Hydrologic Model Simulation through Parameter Calibration Z. Li et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001895
- Envelope curves for the specific discharge of extreme floods in Malaysia J. Abdullah et al. https://doi.org/10.1016/j.jher.2019.05.002
- Multi-model simulation performance of monthly water balance models for global catchments: Thresholds and structural sensitivity Z. Ning et al. https://doi.org/10.1016/j.jhydrol.2025.134852
- Response of active catchment water storage capacity to a prolonged meteorological drought and asymptotic climate variation J. Tian et al. https://doi.org/10.5194/hess-26-4853-2022
- Reducing the uncertainty of time-varying hydrological model parameters using spatial coherence within a hierarchical Bayesian framework Z. Pan et al. https://doi.org/10.1016/j.jhydrol.2019.123927
- Time-varying characteristics of saturated hydraulic conductivity in grassed swales based on the ensemble Kalman filter algorithm —A case study of two long-running swales in Netherlands F. Yang et al. https://doi.org/10.1016/j.jenvman.2023.119760
- Online state and time‐varying parameter estimation using the implicit equal‐weights particle filter M. Satoh et al. https://doi.org/10.1002/qj.4698
- Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters C. Emery et al. https://doi.org/10.5194/hess-24-2207-2020
- Modelling time-variant parameters of a two-parameter monthly water balance model C. Deng et al. https://doi.org/10.1016/j.jhydrol.2019.04.027
- Real-time flood forecasting using time-varying parameter hydrological model: case study for Ta Trach reservoir C. Tran et al. https://doi.org/10.1007/s13201-025-02503-4
- Variability of spatial patterns of autocorrelation and heterogeneity embedded in precipitation D. Liu et al. https://doi.org/10.2166/nh.2018.054
- Improving Efficiency of Hydrological Prediction Based on Meteorological Classification: A Case Study of GR4J Model X. Wei et al. https://doi.org/10.3390/w13182546
- Temporal variation and scaling of parameters for a monthly hydrologic model C. Deng et al. https://doi.org/10.1016/j.jhydrol.2018.01.049
- Identifying time-varying hydrological model parameters to improve simulation efficiency by the ensemble Kalman filter: A joint assimilation of streamflow and actual evapotranspiration M. Xiong et al. https://doi.org/10.1016/j.jhydrol.2018.11.038
- Dynamics of hydrological-model parameters: mechanisms, problems and solutions T. Lan et al. https://doi.org/10.5194/hess-24-1347-2020
- Estimation of spatiotemporally varying parameters for grid-based distributed hydrologic models X. Zhang et al. https://doi.org/10.1016/j.ejrh.2025.102536
- A Robust Calibration and Evaluation Framework for Dynamic Catchment Characteristics in Hydrological Modeling T. Lan et al. https://doi.org/10.5194/hess-30-2455-2026
Saved (final revised paper)
Latest update: 25 Jul 2026
Short summary
Hydrological model parameters may vary in time under nonstationary conditions, i.e., climate change and anthropogenic activities. The technique of the ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model. Through a synthesis experiment and two case studies, the EnKF is demonstrated to be useful for the identification of parameter variations.
Hydrological model parameters may vary in time under nonstationary conditions, i.e., climate...
