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RiverMamba reforecasts |
Copyright notice |
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licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). |
This means you are free to share and adapt the data. ECMWF however must be acknowledged (attributed) as the source. Attribution must be displayed prominently. |
This file was generated on 2025-09-30 by Mohamad Hakam Shams Eddin |
A GENERAL INFORMATION |
1. Title of the dataset: |
RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [data set] |
2. Brief description of the research project and its aims: |
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 necessary data to train and run the model. The dataset includes the following: 1- CPC precipitation data 2- ECMWF-HRES meteorological forecasts 3-ERA5-Land reanalysis data 4- GloFAS reanalysis data 5- GloFAS static data 6- Reforecasts generated by RiverMamba 7- Pretrained RiverMamba models. |
3. Author Information |
A. Project Supervisor (Principal Investigator) Contact Information |
Name:Jürgen Gall |
Institution: Institute of Computer Science III, Department of Information Systems and Artificial Intelligence |
Address: Friedrich-Hirzebruch-Allee 8, 53115 Bonn |
Email: gall@iai.uni-bonn.de |
B. In case of questions related to this dataset, please contact: |
Name: Mohamad Hakam Shams Eddin |
Institution: Institute of Computer Science III, Department of Information Systems and Artificial Intelligence |
Address: Friedrich-Hirzebruch-Allee 8, 53115 Bonn |
Email: shams@iai.uni-bonn.de |
C. Collaborator: |
Name: Yikui Zhang |
Institution: Research Centre Jülich |
Address: Wilhelm-Johnen-Straße, 52428 Jülich |
Email: yik.zhang@fz-juelich.de |
D. Collaborator: |
Name: Stefan Kollet |
Institution: Research Centre Jülich |
Address: Wilhelm-Johnen-Straße, 52428 Jülich |
Email: s.kollet@fz-juelich.de |
4. Date of data collection: |
2024-07-01 |
5. Information about funding sources that supported the collection of the data: |
This work was supported by the Federal Ministry of Research, Technology, and Space under grant no. 01|S24075A-D RAINA and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1502/1–2022 – project no. 450058266 within the Collaborative Research Center (CRC) for the project Regional Climate Change: Disentangling the Role of Land Use and Water Management (DETECT). |
6. Language of the dataset: |
English |
7. Geographic location of data collection: |
Global (90◦N-60◦S, 180◦W-180◦E) |
B DATA | FILE OVERVIEW |
1. File List: |
. |
├── Licenses |
│ └── license_rivermamba_reforecasts_models.txt |
├── Meta |
│ └── GRDC_Meta.txt |
├── RiverMamba_glofas_reanalysis.7z |
├── RiverMamba_glofas_reanalysis_full_map.7z |
└── RiverMamba_grdc_obs.7z |
2. Are there multiple versions of the dataset? |
No |
C SHARING/ACCESS INFORMATION |
2. Licenses/restrictions placed on the data: |
Rivermamba reforecasts and pretrained models: The Creative Commons Attribution 4.0 International (CC BY 4.0) |
See the folder "Licenses" for more details about the licenses of the data |
3. Links to publications that cite or use the data: |
see paper: RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting, NeurIPS 2025. |
link: https://arxiv.org/abs/2505.22535 |
D METHODOLOGICAL INFORMATION |
1. Description of methods used for collection/generation of data: |
- Rivermamba reforecasts: These are the medium-range forecasts of river discahrge as produced by RiverMamba deep learning model. The reforecasts files are generated by the software https://github.com/HakamShams/RiverMamba_code v1.0.0. |
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