Articles | Volume 26, issue 13
https://doi.org/10.5194/hess-26-3377-2022
https://doi.org/10.5194/hess-26-3377-2022
Research article
 | 
05 Jul 2022
Research article |  | 05 Jul 2022

Deep learning rainfall–runoff predictions of extreme events

Jonathan M. Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shalev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, and Grey S. Nearing

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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, 2017a. a, b
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, version 2.0. Boulder, CO, UCAR/NCAR [data set], https://doi.org/10.5065/D6G73C3Q, 2017b. a
Burkey, J.: Log-Pearson Flood Flow Frequency using USGS 17B, MATLAB Central File Exchange [code], https://www.mathworks.com/matlabcentral/fileexchange/22628-log-pearson-flood-flow-frequency-using-usgs-17b (last access: 17 June 2022), 2009. a
Cameron, D., Kneale, P., and See, L.: An evaluation of a traditional and a neural net modelling approach to flood forecasting for an upland catchment, Hydrol. Process., 16, 1033–1046, https://doi.org/10.1002/hyp.317, 2002. a
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Short summary
The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that deep learning models may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis. The deep learning models remained relatively accurate in predicting extreme events compared with traditional models, even when extreme events were not included in the training set.