Articles | Volume 26, issue 13
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

Data sets

CAMELS return period analysis Jonathan M. Frame

MC-LSTM, model runs Jonathan M. Frame

The CAMELS data set: catchment attributes and meteorology for large-sample studies N. Addor, A. J. Newman, N. Mizukami, and M. P. Clark

NOAA National Water Model CONUS Retrospective Dataset NOAA National Water Model CONUS Retrospective Dataset

Model code and software

Code for calibrating SAC-SMA Grey S. Nearing

NeuralHydrology ( F. Kratzert, M. Gauch, G. Nearing,, and D.Klotz

SPOTting Model Parameters Using a Ready-Made Python Package T. Houska, P. Kraft, A. Chamorro-Chavez, and L.Breuer

jmframe/mclstm_2021_extrapolate: Submit to HESS 5_August_2021 Jonathan Frame

Log-Pearson Flood Flow Frequency using USGS 17B J. Burkey


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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.