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.
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.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
27 Mar 2025
Model experiment description paper |
| 27 Mar 2025
| 27 Mar 2025
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
Florian Börgel
CORRESPONDING AUTHOR
Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
Sven Karsten
Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
Karoline Rummel
Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
Ulf Gräwe
Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
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This study investigates the impact of the uncertainty in atmospheric data of a storm event on the transport of microplastics and sediments. The model chain includes the WRF atmospheric model, the WAVEWATCH III® wave model, and the GETM regional ocean model as well as a sediment transport model based on the FABM framework. An ensemble approach based on stochastic perturbations of the WRF model is used. We found a strong impact of atmospheric uncertainty on the amount of transported material.
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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.
Forecasting river runoff, which is crucial for managing water resources and understanding...
