From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
<p>The quality of river runoff determines the quality of regional climate projections for coastal oceans or other estuaries. This study presents a novel approach to river runoff forecasting using convolutional long short-term memory (ConvLSTM) networks. Our method accurately predicts daily run...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-03-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/2005/2025/gmd-18-2005-2025.pdf |
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| Summary: | <p>The quality of river runoff determines the quality of regional climate projections for coastal oceans or other estuaries. This study presents a novel approach to river runoff forecasting using convolutional long short-term memory (ConvLSTM) networks. Our method accurately predicts daily runoff for 97 rivers within the Baltic Sea catchment by modeling runoff as a spatiotemporal sequence defined by atmospheric forcing. The ConvLSTM model predicts river runoff with an accuracy of <span class="inline-formula">±5</span> % when compared to the hydrological model. Compared to more complex process-based hydrological models, ConvLSTM networks offer fast processing times and easy integration into climate models, demonstrating their potential as a powerful tool for climate simulation and water resource management.</p> |
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| ISSN: | 1991-959X 1991-9603 |