Articles | Volume 18, issue 10
https://doi.org/10.5194/hess-18-3923-2014
© Author(s) 2014. 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-18-3923-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
07 Oct 2014
Research article |
| 07 Oct 2014
Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy
X. Xie
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
S. Meng
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Department of Geographical Sciences, University of Maryland, College Park, Maryland, USA
Y. Yao
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
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Cited
28 citations as recorded by crossref.
- Development of a disaggregated multi-level factorial hydrologic data assimilation model F. Wang et al. https://doi.org/10.1016/j.jhydrol.2022.127802
- Exploring uncertainty propagation in coupled hydrologic and hydrodynamic systems via distribution-agnostic state space analysis M. Kazma & A. Taha https://doi.org/10.1016/j.advwatres.2026.105389
- Tracing Temporal Changes of Model Parameters in Rainfall-Runoff Modeling via a Real-Time Data Assimilation S. Meng et al. https://doi.org/10.3390/w8010019
- Runoff Induced Soil Erosion and its Impact on the Quality of Water for Upper-Patiala-Ki-Rao Catchment Lying on Shivalik Hills S. Walia et al. https://doi.org/10.1007/s12594-020-1447-7
- Flood Forecasting via the Ensemble Kalman Filter Method Using Merged Satellite and Measured Soil Moisture Data C. Zhang et al. https://doi.org/10.3390/w14101555
- Combining streamflow observations and hydrologic simulations for the retrospective estimation of daily streamflow for ungauged rivers in southern Quebec (Canada) S. Lachance-Cloutier et al. https://doi.org/10.1016/j.jhydrol.2017.05.011
- Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model S. Tian et al. https://doi.org/10.1029/2021WR031649
- Identification of hydrological model parameter variation using ensemble Kalman filter C. Deng et al. https://doi.org/10.5194/hess-20-4949-2016
- The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review D. Jiang & K. Wang https://doi.org/10.3390/w11081615
- Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation S. Pathiraja et al. https://doi.org/10.1016/j.advwatres.2016.04.021
- Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags S. Meng et al. https://doi.org/10.1016/j.jhydrol.2017.05.024
- Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation S. Pathiraja et al. https://doi.org/10.1002/2018WR022627
- Assimilation versus optimization for SWAT calibration: accuracy, uncertainty, and computational burden analysis M. Bayat et al. https://doi.org/10.2166/ws.2023.055
- Constraining the ensemble Kalman filter for improved streamflow forecasting D. Maxwell et al. https://doi.org/10.1016/j.jhydrol.2018.03.015
- Time-varying parameter models for catchments with land use change: the importance of model structure S. Pathiraja et al. https://doi.org/10.5194/hess-22-2903-2018
- Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review Y. Li et al. https://doi.org/10.3390/rs8060456
- Investigating the impact of surface soil moisture assimilation on state and parameter estimation in SWAT model based on the ensemble Kalman filter in upper Huai River basin Y. Liu et al. https://doi.org/10.1515/johh-2017-0011
- Runoff Predicting and Variation Analysis in Upper Ganjiang Basin under Projected Climate Changes C. Deng & W. Wang https://doi.org/10.3390/su11215885
- Streamflow data assimilation in SWAT model using Extended Kalman Filter L. Sun et al. https://doi.org/10.1016/j.jhydrol.2015.10.060
- Data assimilation using the ensemble Kalman filter in a distributed hydrological model on the Tocantins River, Brasil K. Jiménez et al. https://doi.org/10.1590/2318-0331.241920180031
- Insights on the impact of systematic model errors on data assimilation performance in changing catchments S. Pathiraja et al. https://doi.org/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. https://doi.org/10.1029/2019WR025237
- SWAT_DA: Sequential Multivariate Data Assimilation‐Oriented Modification of SWAT M. Bayat et al. https://doi.org/10.1029/2022WR032397
- Quantifying the contributions of hydrological pre-processor, post-processor, and data assimilator to ensemble streamflow prediction skill J. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.132611
- The Benefits of Continental-Scale High-Resolution Hydrological Modeling in the Detection of Extreme Hydrological Events in China B. Zhu et al. https://doi.org/10.3390/rs15092402
- 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
- Hydrologic modeling in dynamic catchments: A data assimilation approach S. Pathiraja et al. https://doi.org/10.1002/2015WR017192
- ESA CCI Soil Moisture Assimilation in SWAT for Improved Hydrological Simulation in Upper Huai River Basin Y. Liu et al. https://doi.org/10.1155/2018/7301314
28 citations as recorded by crossref.
- Development of a disaggregated multi-level factorial hydrologic data assimilation model F. Wang et al. https://doi.org/10.1016/j.jhydrol.2022.127802
- Exploring uncertainty propagation in coupled hydrologic and hydrodynamic systems via distribution-agnostic state space analysis M. Kazma & A. Taha https://doi.org/10.1016/j.advwatres.2026.105389
- Tracing Temporal Changes of Model Parameters in Rainfall-Runoff Modeling via a Real-Time Data Assimilation S. Meng et al. https://doi.org/10.3390/w8010019
- Runoff Induced Soil Erosion and its Impact on the Quality of Water for Upper-Patiala-Ki-Rao Catchment Lying on Shivalik Hills S. Walia et al. https://doi.org/10.1007/s12594-020-1447-7
- Flood Forecasting via the Ensemble Kalman Filter Method Using Merged Satellite and Measured Soil Moisture Data C. Zhang et al. https://doi.org/10.3390/w14101555
- Combining streamflow observations and hydrologic simulations for the retrospective estimation of daily streamflow for ungauged rivers in southern Quebec (Canada) S. Lachance-Cloutier et al. https://doi.org/10.1016/j.jhydrol.2017.05.011
- Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model S. Tian et al. https://doi.org/10.1029/2021WR031649
- Identification of hydrological model parameter variation using ensemble Kalman filter C. Deng et al. https://doi.org/10.5194/hess-20-4949-2016
- The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review D. Jiang & K. Wang https://doi.org/10.3390/w11081615
- Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation S. Pathiraja et al. https://doi.org/10.1016/j.advwatres.2016.04.021
- Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags S. Meng et al. https://doi.org/10.1016/j.jhydrol.2017.05.024
- Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation S. Pathiraja et al. https://doi.org/10.1002/2018WR022627
- Assimilation versus optimization for SWAT calibration: accuracy, uncertainty, and computational burden analysis M. Bayat et al. https://doi.org/10.2166/ws.2023.055
- Constraining the ensemble Kalman filter for improved streamflow forecasting D. Maxwell et al. https://doi.org/10.1016/j.jhydrol.2018.03.015
- Time-varying parameter models for catchments with land use change: the importance of model structure S. Pathiraja et al. https://doi.org/10.5194/hess-22-2903-2018
- Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review Y. Li et al. https://doi.org/10.3390/rs8060456
- Investigating the impact of surface soil moisture assimilation on state and parameter estimation in SWAT model based on the ensemble Kalman filter in upper Huai River basin Y. Liu et al. https://doi.org/10.1515/johh-2017-0011
- Runoff Predicting and Variation Analysis in Upper Ganjiang Basin under Projected Climate Changes C. Deng & W. Wang https://doi.org/10.3390/su11215885
- Streamflow data assimilation in SWAT model using Extended Kalman Filter L. Sun et al. https://doi.org/10.1016/j.jhydrol.2015.10.060
- Data assimilation using the ensemble Kalman filter in a distributed hydrological model on the Tocantins River, Brasil K. Jiménez et al. https://doi.org/10.1590/2318-0331.241920180031
- Insights on the impact of systematic model errors on data assimilation performance in changing catchments S. Pathiraja et al. https://doi.org/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. https://doi.org/10.1029/2019WR025237
- SWAT_DA: Sequential Multivariate Data Assimilation‐Oriented Modification of SWAT M. Bayat et al. https://doi.org/10.1029/2022WR032397
- Quantifying the contributions of hydrological pre-processor, post-processor, and data assimilator to ensemble streamflow prediction skill J. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.132611
- The Benefits of Continental-Scale High-Resolution Hydrological Modeling in the Detection of Extreme Hydrological Events in China B. Zhu et al. https://doi.org/10.3390/rs15092402
- 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
- Hydrologic modeling in dynamic catchments: A data assimilation approach S. Pathiraja et al. https://doi.org/10.1002/2015WR017192
- ESA CCI Soil Moisture Assimilation in SWAT for Improved Hydrological Simulation in Upper Huai River Basin Y. Liu et al. https://doi.org/10.1155/2018/7301314
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