Articles | Volume 28, issue 11
https://doi.org/10.5194/hess-28-2505-2024
https://doi.org/10.5194/hess-28-2505-2024
Research article
 | 
13 Jun 2024
Research article |  | 13 Jun 2024

Metamorphic testing of machine learning and conceptual hydrologic models

Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen

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Cited articles

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. a, b, c, d, e, f, g, h
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018. a
Bai, P., Liu, X., and Xie, J.: Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models, J. Hydrol., 592, 125779, https://doi.org/10.1016/j.jhydrol.2020.125779, 2021. a, b
Battjes, J. A. and Labeur, R. J.: Unsteady Flow in Open Channels, Cambridge University Press, Cambridge, UK, ISBN 978-1-107-15029-4, 2017. a, b
Bergström, S.: The HBV Model, Tech. rep., SMHI Reports Hydrology, Sweden, https://www.smhi.se/polopoly_fs/1.83589!/Menu/general/extGroup/attachmentColHold/mainCol1/file/RH_4.pdf (last access: 20 January 2022), 1992. a, b
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Short summary
We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
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