Articles | Volume 27, issue 14
https://doi.org/10.5194/hess-27-2621-2023
https://doi.org/10.5194/hess-27-2621-2023
Research article
 | 
19 Jul 2023
Research article |  | 19 Jul 2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen

Related authors

Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024,https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion
Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022,https://doi.org/10.5194/hess-26-5163-2022, 2022
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
CONCN: a high-resolution, integrated surface water–groundwater ParFlow modeling platform of continental China
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci., 29, 2201–2218, https://doi.org/10.5194/hess-29-2201-2025,https://doi.org/10.5194/hess-29-2201-2025, 2025
Short summary
Evaluating the effects of topography and land use change on hydrological signatures: a comparative study of two adjacent watersheds
Haifan Liu, Haochen Yan, and Mingfu Guan
Hydrol. Earth Syst. Sci., 29, 2109–2132, https://doi.org/10.5194/hess-29-2109-2025,https://doi.org/10.5194/hess-29-2109-2025, 2025
Short summary
Technical note: What does the Standardized Streamflow Index actually reflect? Insights and implications for hydrological drought analysis
Fabián Lema, Pablo A. Mendoza, Nicolás A. Vásquez, Naoki Mizukami, Mauricio Zambrano-Bigiarini, and Ximena Vargas
Hydrol. Earth Syst. Sci., 29, 1981–2002, https://doi.org/10.5194/hess-29-1981-2025,https://doi.org/10.5194/hess-29-1981-2025, 2025
Short summary
Long short-term memory networks for enhancing real-time flood forecasts: a case study for an underperforming hydrologic model
Sebastian Gegenleithner, Manuel Pirker, Clemens Dorfmann, Roman Kern, and Josef Schneider
Hydrol. Earth Syst. Sci., 29, 1939–1962, https://doi.org/10.5194/hess-29-1939-2025,https://doi.org/10.5194/hess-29-1939-2025, 2025
Short summary
Assessing the value of high-resolution rainfall and streamflow data for hydrological modeling: an analysis based on 63 catchments in southeast China
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 29, 1919–1937, https://doi.org/10.5194/hess-29-1919-2025,https://doi.org/10.5194/hess-29-1919-2025, 2025
Short summary

Cited articles

Anderson, B., Borgonovo, E., Galeotti, M., and Roson, R.: Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?, Risk Anal., 34, 271–293, https://doi.org/10.1111/risa.12117, 2014. a
Atlas, L., Homma, T., and Marks, R.: An Artificial Neural Network for Spatio-Temporal Bipolar Patterns: Application to Phoneme Classification, in: Neural Information Processing Systems, edited by: Anderson, D., vol. 0, American Institute of Physics, https://proceedings.neurips.cc/paper/1987/file/98f13708210194c475687be6106a3b84-Paper.pdf (last access: 20 May 2022), 1987. a
Bennett, A. and Nijssen, B.: Deep Learned Process Parameterizations Provide Better Representations of Turbulent Heat Fluxes in Hydrologic Models, Water Resour. Res., 57, e2020WR029328, https://doi.org/10.1029/2020WR029328, 2021. a
Clark, M. P., Bierkens, M. F. P., Samaniego, L., Woods, R. A., Uijlenhoet, R., Bennett, K. E., Pauwels, V. R. N., Cai, X., Wood, A. W., and Peters-Lidard, C. D.: The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism, Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, 2017. a
Coon, E., Svyatsky, D., Jan, A., Kikinzon, E., Berndt, M., Atchley, A., Harp, D., Manzini, G., Shelef, E., Lipnikov, K., Garimella, R., Xu, C., Moulton, D., Karra, S., Painter, S., Jafarov, E., and Molins, S.: Advanced Terrestrial Simulator, DOECODE [Computer Software], https://doi.org/10.11578/dc.20190911.1, 2019. a, b, c
Download
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.
Share