RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [data set]

Name: RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [data set]
Published: 2025-10-02
License: http://creativecommons.org/licenses/by/4.0

Open this dataset in the live repository

Important usage condition: Use and redistribution are governed by CC BY 4.0. Review and comply with the license before using the data.

Persistent identifier: doi:10.60507/FK2/T8QYWE
Published version: 1.0
Publication date: 2025-10-02
License: CC BY 4.0

Description

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.

Creators

Shams Eddin, Mohamad Hakam
Zhang, Yikui
Kollet, Stefan
Gall, Juergen

Keywords

Extreme events, Deep learning, Remote sensing, Climate science, Earth science

Files

Before downloading: Use and redistribution are governed by CC BY 4.0. Review and comply with the license before using the data.

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Citation

Shams Eddin, Mohamad Hakam; Zhang, Yikui; Kollet, Stefan; Gall, Juergen, 2025-10-02, RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [data set], doi:10.60507/FK2/T8QYWE, V1.0

Additional Dataverse fields

Id	478
Dataset Type	dataset
Internal Version Number	348
Latest Version Publishing State	RELEASED
Release Time	2025-10-01T07:00:52Z
Create Time	2025-09-12T10:56:17Z
Citation Date	2025-10-02
File Access Request	True

Export metadata

Static metadata exports available for this published dataset version:

Complete Dataverse metadata

Citation Metadata

Title title

RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [data set]

Alternative URL alternativeURL

https://hakamshams.github.io/RiverMamba/

Author author

Author Name
Shams Eddin, Mohamad Hakam
Author Affiliation
University of Bonn
Author Identifier Scheme
ORCID
Author Identifier
Type Name
authorIdentifier
Multiple
False
Type Class
primitive
Value
https://orcid.org/0000-0003-3136-4619
Expandedvalue
Person Name
Shams Eddin, Mohamad Hakam
@id
https://orcid.org/0000-0003-3136-4619
Scheme
ORCID
@type
https://schema.org/Person
Author Name
Zhang, Yikui
Author Affiliation
Forschungszentrum Jülich GmbH
Author Name
Kollet, Stefan
Author Affiliation
Forschungszentrum Jülich GmbH
Author Identifier Scheme
ORCID
Author Identifier
Type Name
authorIdentifier
Multiple
False
Type Class
primitive
Value
https://orcid.org/0000-0003-0095-1554
Expandedvalue
Person Name
Kollet, Stefan
@id
https://orcid.org/0000-0003-0095-1554
Scheme
ORCID
@type
https://schema.org/Person
Author Name
Gall, Juergen
Author Affiliation
University of Bonn
Author Identifier Scheme
ORCID
Author Identifier
Type Name
authorIdentifier
Multiple
False
Type Class
primitive
Value
https://orcid.org/0000-0002-9447-3399
Expandedvalue
Person Name
Gall, Juergen
@id
https://orcid.org/0000-0002-9447-3399
Scheme
ORCID
@type
https://schema.org/Person

Dataset Contact datasetContact

Dataset Contact Name
Shams Eddin, Mohamad Hakam
Dataset Contact Affiliation
University of Bonn
Dataset Contact Email
mshamsed@uni-bonn.de

Ds Description dsDescription

Ds Description Value
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.
Ds Description Date
2025-09-30

Subject subject

Computer and Information Science
Earth and Environmental Sciences

Free Keyword freeKeyword

Free Keyword Value
Extreme events
Free Keyword Value
Deep learning
Free Keyword Value
Remote sensing
Free Keyword Value
Climate science
Free Keyword Value
Earth science

Publication publication

Publication Relation Type
IsSupplementTo
Publication IDType
arXiv
Publication IDNumber
10.48550/arXiv.2505.22535
Publication URL
https://doi.org/10.48550/arXiv.2505.22535

Grant Number grantNumber

Grant Number Agency
The Federal Ministry of Research, Technology, and Space
Grant Number Value
Grant no. 01IS24075A-D RAINA
Grant Number Agency
The German Research Foundation (DFG)
Grant Number Value
SFB 1502/1–2022 – project no. 450058266

Depositor depositor

Shams Eddin, Mohamad Hakam

Date Of Deposit dateOfDeposit

2025-09-12

Software software

Software Name
https://github.com/HakamShams/RiverMamba_code
Software Version
v1.0.0

Data Sources dataSources

ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS): https://cds.climate.copernicus.eu/datasets
ECMWF-IFS HRES from ECMWF archive catalogue (MARS)
Joint Research Center, Copernicus Emergency Management Service (2019): River discharge and related historical data from the Global Flood Awareness System. Early Warning Data Store (EWDS): https://doi.org/10.24381/cds.a4fdd6b9
The Global Unified Gauge-Based Analysis of Daily Precipitation. National Oceanic and Atmospheric Administration (NOAA), Climate Prediction Center (CPC): https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html
LISFLOOD static and parameter maps, European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/68050d73-9c06-499c-a441-dc5053cb0c86
HydroRIVERS and HydroATLAS, https://www.hydrosheds.org/products/hydrorivers

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