Articles | Volume 23, issue 12
Research article
17 Dec 2019
Research article |  | 17 Dec 2019

Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets

Frederik Kratzert, Daniel Klotz, Guy Shalev, Günter Klambauer, Sepp Hochreiter, and Grey Nearing

Data sets

Pre-trained models F. Kratzert

Benchmark models F. Kratzert

CAMELS extended Maurer forcings F. Kratzert

Model code and software

Code to reproduce paper experiments/results F. Kratzert

Short summary
A new approach for regional rainfall–runoff modeling using long short-term memory (LSTM)-based models is presented and benchmarked against a range of well-known hydrological models. The approach significantly outperforms regionally calibrated hydrological models but also basin-wise calibrated models. Furthermore, we propose an adaption of the LSTM that allows us to extract the learned catchment understanding of the model and show that it matches our hydrology expert knowledge.