Preprints
https://doi.org/10.48550/arXiv.2404.14212
https://doi.org/10.48550/arXiv.2404.14212
03 Jun 2024
 | 03 Jun 2024
Status: this preprint is open for discussion.

Toward Routing River Water in Land Surface Models with Recurrent Neural Networks

Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar, and Tapio Schneider

Abstract. Machine learning is playing an increasing role in hydrology, supplementing or replacing physics-based models. One notable example is the use of recurrent neural networks (RNNs) for forecasting streamflow given observed precipitation and geographic characteristics. Training of such a model over the continental United States has demonstrated that a single set of model parameters can be used across independent catchments, and that RNNs can outperform physics-based models. In this work, we take a next step and study the performance of RNNs for river routing in land surface models (LSMs). Instead of observed precipitation, the LSM-RNN uses instantaneous runoff calculated from physics-based models as an input. We train the model with data from river basins spanning the globe and test it in streamflow hindcasts. The model demonstrates skill at generalization across basins (predicting streamflow in unseen catchments) and across time (predicting streamflow during years not used in training). We compare the predictions from the LSM-RNN to an existing physics-based model calibrated with a similar dataset and find that the LSM-RNN outperforms the physics-based model. Our results give further evidence that RNNs are effective for global streamflow prediction from runoff inputs and motivate the development of complete routing models that can capture nested sub-basis connections.

Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar, and Tapio Schneider

Status: open (until 29 Jul 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar, and Tapio Schneider
Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar, and Tapio Schneider

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Short summary
Machine learning is playing an increasingly important role in hydrological modeling. In this paper, we introduce an adaptation of existing machine learning models forecasting streamflow in river basins, redesigning them with the goal of integrating them into climate models. We demonstrate the effectiveness of our adapted model by showing that it outperforms a physics-based river model. These results motivate further studies of the use of machine learning based river models inside climate models.