Articles | Volume 27, issue 14
https://doi.org/10.5194/hess-27-2621-2023
© Author(s) 2023. 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-27-2621-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jul 2023
Research article |
| 19 Jul 2023
| 19 Jul 2023
Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Pin Shuai
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Department of Civil and Environmental Engineering, Utah Water Research Laboratory, Utah State University, Logan, Utah, USA
Alexander Sun
Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas, USA
Maruti K. Mudunuru
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Xingyuan Chen
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
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Cited
16 citations as recorded by crossref.
- Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
- Sensor Networks, Data Processing, and Inference: The Hydrology Challenge A. Zanella et al. 10.1109/ACCESS.2023.3318739
- 1D-2D hydrodynamic and sediment transport modelling using MIKE models K. Pareta 10.1007/s43832-024-00130-9
- CatBoost-Based Automatic Classification Study of River Network D. Wang & H. Qian 10.3390/ijgi12100416
- Advancing Infiltration Rate Prediction in Algeria’s Mitidja Plain: A Machine Learning and Empirical Model Comparison A. Mazighi et al. 10.1007/s41748-025-00640-z
- Machine Learning-Based Reconstruction and Prediction of Groundwater Time Series in the Allertal, Germany T. Tran et al. 10.3390/w17030433
- Characterization of Reservoir Structures With Knowledge-Informed Neural Network Z. Cui et al. 10.2118/228279-PA
- Estimation of natural vegetation phenology metrics using time series EVI over Jharkhand state, India N. Priyadarshi et al. 10.1080/14498596.2023.2281926
- Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado P. Jiang et al. 10.5194/hess-27-2621-2023
- Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. 10.3390/su16041376
- Improving Hydrological Modeling with Hybrid Models: A Comparative Study of Different Mechanisms for Coupling Deep Learning Models with Process-based Models Y. Wei et al. 10.1007/s11269-024-03780-5
- Parameter optimization method of hydrological model based on neural ordinary differential equations Q. Xiangzhao et al. 10.18307/2025.0342
- Optimizing parameter learning and calibration in an integrated hydrological model: Impact of observation length and information P. Jiang et al. 10.1016/j.jhydrol.2024.131889
- The importance of explicitly representing the streambed in watershed models P. Shuai et al. 10.1002/hyp.15043
- Scalable deep learning for watershed model calibration M. Mudunuru et al. 10.3389/feart.2022.1026479
- Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling P. Jiang et al. 10.1029/2022WR032932
13 citations as recorded by crossref.
- Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
- Sensor Networks, Data Processing, and Inference: The Hydrology Challenge A. Zanella et al. 10.1109/ACCESS.2023.3318739
- 1D-2D hydrodynamic and sediment transport modelling using MIKE models K. Pareta 10.1007/s43832-024-00130-9
- CatBoost-Based Automatic Classification Study of River Network D. Wang & H. Qian 10.3390/ijgi12100416
- Advancing Infiltration Rate Prediction in Algeria’s Mitidja Plain: A Machine Learning and Empirical Model Comparison A. Mazighi et al. 10.1007/s41748-025-00640-z
- Machine Learning-Based Reconstruction and Prediction of Groundwater Time Series in the Allertal, Germany T. Tran et al. 10.3390/w17030433
- Characterization of Reservoir Structures With Knowledge-Informed Neural Network Z. Cui et al. 10.2118/228279-PA
- Estimation of natural vegetation phenology metrics using time series EVI over Jharkhand state, India N. Priyadarshi et al. 10.1080/14498596.2023.2281926
- Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado P. Jiang et al. 10.5194/hess-27-2621-2023
- Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. 10.3390/su16041376
- Improving Hydrological Modeling with Hybrid Models: A Comparative Study of Different Mechanisms for Coupling Deep Learning Models with Process-based Models Y. Wei et al. 10.1007/s11269-024-03780-5
- Parameter optimization method of hydrological model based on neural ordinary differential equations Q. Xiangzhao et al. 10.18307/2025.0342
- Optimizing parameter learning and calibration in an integrated hydrological model: Impact of observation length and information P. Jiang et al. 10.1016/j.jhydrol.2024.131889
3 citations as recorded by crossref.
- The importance of explicitly representing the streambed in watershed models P. Shuai et al. 10.1002/hyp.15043
- Scalable deep learning for watershed model calibration M. Mudunuru et al. 10.3389/feart.2022.1026479
- Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling P. Jiang et al. 10.1029/2022WR032932
Latest update: 06 Jun 2025
Short summary
We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
We developed a novel deep learning approach to estimate the parameters of a computationally...
