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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-221
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-221
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 14 May 2020

Submitted as: research article | 14 May 2020

Review status
This preprint is currently under review for the journal HESS.

A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling

Frederik Kratzert1, Daniel Klotz1, Sepp Hochreiter1, and Grey S. Nearing2,3 Frederik Kratzert et al.
  • 1LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Austria
  • 2Upstream Tech, Natel Energy Inc., Alameda, CA, USA
  • 3Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA

Abstract. A deep learning rainfall-runoff model can take multiple meteorological forcing products as inputs and learn to combine them in spatially and temporally dynamic ways. This is demonstrated using Long Short Term Memory networks (LSTMs) trained over basins in the continental US using the CAMELS data set. Using multiple precipitation products (NLDAS, Maurer, DayMet) in a single LSTM significantly improved simulation accuracy relative to using only individual precipitation products. A sensitivity analysis showed that the LSTM learned to utilize different precipitation products in different ways in different basins and for simulating different parts of the hydrograph in individual basins.

Frederik Kratzert et al.

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Frederik Kratzert et al.

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Short summary
This manuscripts investigates, how deep learning models make use of different meteorological data sets in the task of (regional) rainfall-runoff modeling. We show that performance can be significantly improved, when using different data products as inputs and further show how the model learns to combine those meteorological inputs differently across time and space. The results are carefully benchmarked against classical hydrological models, showing the supremacy of the presented approach.
This manuscripts investigates, how deep learning models make use of different meteorological...
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