Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 6
Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-2005-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-2005-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 6
Model experiment description paper
 | 
27 Mar 2025
Model experiment description paper |  | 27 Mar 2025

From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting

Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe

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Short summary
Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
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