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Markdown Text - 5.1 KB - MD5: 30831e1ba9bab6fbaa91fd347ce5d855
application/x-yaml - 1.6 KB - MD5: dcf653a6d147f9c6eb577b7f65ce000a
Oct 1, 2025
Shams Eddin, Mohamad Hakam; Zhang, Yikui; Kollet, Stefan; Gall, Juergen, 2025, "RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [data set]", https://doi.org/10.60507/FK2/T8QYWE, bonndata, V1
This is the dataset used in the RiverMamba paper (see https://arxiv.org/abs/2505.22535). The aim of the RiverMamba project is to develop a deep learning model that is pretrained with long-term reanalysis data and fine-tuned on observations to forecast global river discharge and floods up to 7 days lead time on a 0.05° grid. The dataset includes the...
7Z Archive - 12.7 GB - MD5: 16ff28b332feecf7cd477c69698dd64f
Unknown - 50.0 GB - MD5: 4b367c6b66e494a0bdc4e6d07553da4a
Unknown - 50.0 GB - MD5: d2650ec976351aa9d1d3363cef18f073
Unknown - 50.0 GB - MD5: 09d80b1392300f153f8fa13706dab08e
Unknown - 33.6 GB - MD5: 54022e3f0c170010ecbedecf1b612c25
7Z Archive - 12.7 GB - MD5: 15b3b816729e80ccb244100d9be19a6b
Unknown - 50.0 GB - MD5: 4fd1ec6d5184f052423c7409b66ffaf1
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