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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| Format: | Article |
| Language: | English |
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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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| author | F. Börgel S. Karsten K. Rummel U. Gräwe |
| author_facet | F. Börgel S. Karsten K. Rummel U. Gräwe |
| author_sort | F. Börgel |
| collection | DOAJ |
| description | <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> |
| format | Article |
| id | doaj-art-fd9b8e08ec854dbb9eb400c06f87918e |
| institution | OA Journals |
| issn | 1991-959X 1991-9603 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj-art-fd9b8e08ec854dbb9eb400c06f87918e2025-08-20T01:49:47ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032025-03-01182005201910.5194/gmd-18-2005-2025From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecastingF. Börgel0S. Karsten1K. Rummel2U. Gräwe3Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, GermanyLeibniz Institute for Baltic Sea Research Warnemünde, Rostock, GermanyLeibniz Institute for Baltic Sea Research Warnemünde, Rostock, GermanyLeibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany<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>https://gmd.copernicus.org/articles/18/2005/2025/gmd-18-2005-2025.pdf |
| spellingShingle | F. Börgel S. Karsten K. Rummel U. Gräwe From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting Geoscientific Model Development |
| title | From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting |
| title_full | From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting |
| title_fullStr | From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting |
| title_full_unstemmed | From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting |
| title_short | From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting |
| title_sort | from weather data to river runoff using spatiotemporal convolutional networks for discharge forecasting |
| url | https://gmd.copernicus.org/articles/18/2005/2025/gmd-18-2005-2025.pdf |
| work_keys_str_mv | AT fborgel fromweatherdatatoriverrunoffusingspatiotemporalconvolutionalnetworksfordischargeforecasting AT skarsten fromweatherdatatoriverrunoffusingspatiotemporalconvolutionalnetworksfordischargeforecasting AT krummel fromweatherdatatoriverrunoffusingspatiotemporalconvolutionalnetworksfordischargeforecasting AT ugrawe fromweatherdatatoriverrunoffusingspatiotemporalconvolutionalnetworksfordischargeforecasting |