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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-247
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 14 May 2018

Research article | 14 May 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks

Frederik Kratzert, Daniel Klotz, Claire Brenner, Karsten Schulz, and Mathew Herrnegger Frederik Kratzert et al.
  • Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, 1190, Austria

Abstract. Rainfall-runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data driven models. In this paper, we propose a novel data driven approach, using the Long-Short-Term-Memory (LSTM) network, a special type of recurrent neural networks. The advantage of the LSTM is its ability to learn long-term dependencies between the provided input and output of the network, which are essential for modelling storage effects in e.g. catchments with snow influence. We use 241 catchments of the freely available CAMELS data set to test our approach and also compare the results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine. We also show the potential of the LSTM as a regional hydrological model, in which one model predicts the discharge for a variety of catchments. In our last experiment, we show the possibility to transfer process understanding, learned at regional scale, to individual catchments and thereby increasing model performance when compared to a LSTM trained only on the data of single catchments. Using this approach, we were able to achieve better model performance as the SAC-SMA+Snow-17, which underlines the potential of the LSTM for hydrological modelling applications.

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Frederik Kratzert et al.
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Frederik Kratzert et al.
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In this paper, we propose a novel data driven approach for rainfall-runoff modelling, using the Long-Short-Term-Memory (LSTM) network, a special type of neural networks. We show in three different experiments that this network is able to learn to predict the discharge purely from meteorological input parameters (such as precipitation, temperature etc.) as accurately (or better) as the well established Sacramento-Soil-Moisture-Accounting model, coupled with the Snow-17 snow model.
In this paper, we propose a novel data driven approach for rainfall-runoff modelling, using...
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