RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [data set]https://doi.org/10.60507/FK2/T8QYWEShams Eddin, Mohamad HakamZhang, YikuiKollet, StefanGall, Juergenbonndata2025-10-022025-10-01T07:00:52ZThis 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.Computer and Information ScienceEarth and Environmental SciencesarXiv, 10.48550/arXiv.2505.22535, https://doi.org/10.48550/arXiv.2505.225352025-10-02Shams Eddin, Mohamad Hakam2025-09-12ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS): https://cds.climate.copernicus.eu/datasetsECMWF-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.a4fdd6b9The 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.htmlLISFLOOD static and parameter maps, European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/68050d73-9c06-499c-a441-dc5053cb0c86HydroRIVERS and HydroATLAS, https://www.hydrosheds.org/products/hydroriversCC BY 4.0