Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-4761-2025
https://doi.org/10.5194/hess-29-4761-2025
Research article
 | 
30 Sep 2025
Research article |  | 30 Sep 2025

Can causal discovery lead to a more robust prediction model for runoff signatures?

Hossein Abbasizadeh, Petr Maca, Martin Hanel, Mads Troldborg, and Amir AghaKouchak

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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
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark, M. P.: A Ranking of Hydrological Signatures Based on Their Predictability in Space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p
AghaKouchak, A., Pan, B., Mazdiyasni, O., Sadegh, M., Jiwa, S., Zhang, W., Love, C., Madadgar, S., Papalexiou, S., Davis, S., Hsu, K., and Sorooshian, S.: Status and prospects for drought forecasting: Opportunities in artificial intelligence and hybrid physical–statistical forecasting, Philos. T. Roy. Soc. A, 380, 20210288, https://doi.org/10.1098/rsta.2021.0288, 2022. a
Aguilera, P. A., Fernandez, A., Fernandez, R., Rumi, R., and Salmeron, A.: Bayesian networks in environmental modelling, Environ. Modell. Softw., 26, 1376–1388, https://doi.org/10.1016/j.envsoft.2011.06.004, 2011. a
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Short summary
Here, we represented catchments as networks of variables connected by cause-and-effect relationships. By comparing the performance of statistical and machine learning methods with and without incorporating causal information to predict runoff properties, we showed that causal information can enhance models' robustness by reducing the accuracy drop between the training and testing phases, improving the model's interpretability, and mitigating overfitting issues, especially with small training samples.
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