Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-2005-2025
https://doi.org/10.5194/gmd-18-2005-2025
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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Cited articles

Ashrafi, M., Chua, L. H. C., Quek, C., and Qin, X.: A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data, J. Hydrol., 545, 424–435, 2017. a, b
Bergström, S. and Carlsson, B.: RIVER RUNOFF TO THE BALTIC SEA – 1950–1990, Ambio, 23, 280–287, 1994. a, b
Börgel, F.: Supporting data ocean model GMD submission: From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting, Zenodo [data set], https://doi.org/10.5281/zenodo.13365099, 2024a. a
Börgel, F.: florianboergel/runoff_prediction: gmd (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.13910136, 2024b. a, b
Cook, B. I., Mankin, J. S., Marvel, K., Williams, A. P., Smerdon, J. E., and Anchukaitis, K. J.: Twenty-First Century Drought Projections in the CMIP6 Forcing Scenarios, Earth's Future, 8, e2019EF001461, https://doi.org/10.1029/2019EF001461, 2020. a
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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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