Articles | Volume 26, issue 6
https://doi.org/10.5194/hess-26-1673-2022
https://doi.org/10.5194/hess-26-1673-2022
Research article
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31 Mar 2022
Research article | Highlight paper |  | 31 Mar 2022

Uncertainty estimation with deep learning for rainfall–runoff modeling

Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Johannes Brandstetter, Günter Klambauer, Sepp Hochreiter, and Grey Nearing

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Cited articles

Abramowitz, G.: Towards a benchmark for land surface models, Geophys. Res. Lett., 32, L22702, https://doi.org/10.1029/2005GL024419, 2005. a
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
Althoff, D., Rodrigues, L. N., and Bazame, H. C.: Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble, Stoch. Environ. Res. Risk A., 35, 1051–1067, 2021. a, b
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Berthet, L., Bourgin, F., Perrin, C., Viatgé, J., Marty, R., and Piotte, O.: A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context, Hydrol. Earth Syst. Sci., 24, 2017–2041, https://doi.org/10.5194/hess-24-2017-2020, 2020. a, b
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Short summary
This contribution evaluates distributional runoff predictions from deep-learning-based approaches. We propose a benchmarking setup and establish four strong baselines. The results show that accurate, precise, and reliable uncertainty estimation can be achieved with deep learning.
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