Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.153
IF5.153
IF 5-year value: 5.460
IF 5-year
5.460
CiteScore value: 7.8
CiteScore
7.8
SNIP value: 1.623
SNIP1.623
IPP value: 4.91
IPP4.91
SJR value: 2.092
SJR2.092
Scimago H <br class='widget-line-break'>index value: 123
Scimago H
index
123
h5-index value: 65
h5-index65
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.

  14 May 2020

14 May 2020

Review status
A revised version of 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.

Interactive discussion

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Frederik Kratzert et al.

Frederik Kratzert et al.

Viewed

Total article views: 716 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
460 239 17 716 18 19
  • HTML: 460
  • PDF: 239
  • XML: 17
  • Total: 716
  • BibTeX: 18
  • EndNote: 19
Views and downloads (calculated since 14 May 2020)
Cumulative views and downloads (calculated since 14 May 2020)

Viewed (geographical distribution)

Total article views: 574 (including HTML, PDF, and XML) Thereof 574 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 26 Nov 2020
Publications Copernicus
Download
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...
Citation