Articles | Volume 29, issue 18
https://doi.org/10.5194/hess-29-4515-2025
https://doi.org/10.5194/hess-29-4515-2025
Research article
 | 
22 Sep 2025
Research article |  | 22 Sep 2025

Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments

Mozhgan A. Farahani, Andrew W. Wood, Guoqiang Tang, and Naoki Mizukami

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Cited articles

Adams, B. M., Bohnhoff, W. J., Canfield, R. A., Dalbey, K. R., Ebeida, M. S., Eddy, J. P., Eldred, M. S., Geraci, G., Hooper, R. W., Hough, P. D., Hu, K. T., Jakeman, J. D., Carson, K., Khalil, M., Maupin, K. A., Monschke, J. A., Prudencio, E. E., Ridgway, E. M., Rushdi, A. A., Seidl, D. T., Stephens, J. A., Swiler, L. P., Tran, A., Vigil, D. M., von Winckel, G. J., Wildey, T. M., and Winokur, J. G. (with Menhorn, F., and Zeng, X.): Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis (Version 6.18 Developers Manual), Sandia National Laboratories, Albuquerque, NM, http://snl-dakota.github.io (last access: 30 July 2025), 2023. 
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. 
Arsenault, R., Martel, J.-L., Brunet, F., Brissette, F., and Mai, J.: Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models, Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, 2023. 
Baker, E., Harper, A. B., Williamson, D., and Challenor, P.: Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES, Geosci. Model Dev., 15, 1913–1929, https://doi.org/10.5194/gmd-15-1913-2022, 2022. 
Beck, H. E., Pan, M., Lin, P., Seibert, J., Dijk, A. I. J. M., and Wood, E. F.: Global fully distributed parameter regionalization based on observed streamflow from 4,229 headwater catchments, J. Geophys. Res.-Atmos, 125, https://doi.org/10.1029/2019JD031485, 2020. 
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Short summary
We present a new strategy to calibrate large-domain land/hydrology models over diverse regions. Using the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models, our approach integrates catchment attributes, parameters, and performance metrics to optimize streamflow simulations. Leveraging advances in machine learning for hydrology, we improve calibration and enable regionalization to ungauged basins, which is valuable for national-scale water security studies.
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