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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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...
