Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-1-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-1-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
02 Jan 2023
Research article | Highlight paper |
| 02 Jan 2023
| 02 Jan 2023
Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System
Forecast Department, European Centre for Medium-Range Weather
Forecasts (ECMWF), Reading, UK
Ervin Zsoter
Forecast Department, European Centre for Medium-Range Weather
Forecasts (ECMWF), Reading, UK
Department of Geography and Environmental Science, University of
Reading, Reading, UK
Hannah Cloke
Department of Geography and Environmental Science, University of
Reading, Reading, UK
Department of Meteorology, University of Reading, Reading, UK
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Centre of Natural Hazards and Disaster Science, CNDS, Uppsala, Sweden
Peter Salamon
European Commission, Joint Research Centre (JRC), Ispra, Italy
Christel Prudhomme
Forecast Department, European Centre for Medium-Range Weather
Forecasts (ECMWF), Reading, UK
Centre for Ecology and Hydrology (CEH), Wallingford, UK
Department of Geography and Environment, University of Loughborough, Loughborough, UK
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21 citations as recorded by crossref.
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- Improved estimation of extreme floods with data pooling and mixed probability distribution A. Ganapathy et al. 10.1016/j.jhydrol.2024.130633
- Enhancing Flood Risk Management: A Comprehensive Review on Flood Early Warning Systems with Emphasis on Numerical Modeling D. Fernández-Nóvoa et al. 10.3390/w16101408
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
- Assessing robustness in global hydrological predictions by comparing modelling and Earth observations R. Pimentel et al. 10.1080/02626667.2023.2267544
- Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes A. Tursun et al. 10.1016/j.jhydrol.2024.130771
- Fluvial flood inundation and socio-economic impact model based on open data L. Riedel et al. 10.5194/gmd-17-5291-2024
- Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China C. Mo et al. 10.1016/j.atmosres.2023.107116
- EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions T. Hallouin et al. 10.5194/gmd-17-4561-2024
- Towards a coherent flood forecasting framework for Canada: Local to global implications L. Arnal et al. 10.1111/jfr3.12895
- A decision‐led evaluation approach for flood forecasting system developments: An application to the Global Flood Awareness System in Bangladesh S. Hossain et al. 10.1111/jfr3.12959
- Global prediction of extreme floods in ungauged watersheds G. Nearing et al. 10.1038/s41586-024-07145-1
- Insights to key operational questions in forecast-informed dam release operation: case of Hume Dam T. Ng & D. Robertson 10.1080/13241583.2024.2392312
- Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
- Recommendations to improve the interpretation of global flood forecasts to support international humanitarian operations for tropical cyclones L. Speight et al. 10.1111/jfr3.12952
- Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning G. Ascenso et al. 10.1016/j.wace.2024.100724
- The utility of impact data in flood forecast verification for anticipatory actions: Case studies from Uganda and Kenya F. Mitheu et al. 10.1111/jfr3.12911
- Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System G. Matthews et al. 10.5194/hess-26-2939-2022
- Plastic in global rivers: are floods making it worse? C. Roebroek et al. 10.1088/1748-9326/abd5df
- Unfolding the relationship between seasonal forecast skill and value in hydropower production: a global analysis D. Lee et al. 10.5194/hess-26-2431-2022
17 citations as recorded by crossref.
- Technical note: Surface fields for global environmental modelling M. Choulga et al. 10.5194/hess-28-2991-2024
- Developing process-based geomorphic indicators for understanding river dynamics of a highly braided system: Implications for designing resilience based management strategies C. Pradhan et al. 10.1016/j.catena.2023.107411
- Improved estimation of extreme floods with data pooling and mixed probability distribution A. Ganapathy et al. 10.1016/j.jhydrol.2024.130633
- Enhancing Flood Risk Management: A Comprehensive Review on Flood Early Warning Systems with Emphasis on Numerical Modeling D. Fernández-Nóvoa et al. 10.3390/w16101408
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
- Assessing robustness in global hydrological predictions by comparing modelling and Earth observations R. Pimentel et al. 10.1080/02626667.2023.2267544
- Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes A. Tursun et al. 10.1016/j.jhydrol.2024.130771
- Fluvial flood inundation and socio-economic impact model based on open data L. Riedel et al. 10.5194/gmd-17-5291-2024
- Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China C. Mo et al. 10.1016/j.atmosres.2023.107116
- EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions T. Hallouin et al. 10.5194/gmd-17-4561-2024
- Towards a coherent flood forecasting framework for Canada: Local to global implications L. Arnal et al. 10.1111/jfr3.12895
- A decision‐led evaluation approach for flood forecasting system developments: An application to the Global Flood Awareness System in Bangladesh S. Hossain et al. 10.1111/jfr3.12959
- Global prediction of extreme floods in ungauged watersheds G. Nearing et al. 10.1038/s41586-024-07145-1
- Insights to key operational questions in forecast-informed dam release operation: case of Hume Dam T. Ng & D. Robertson 10.1080/13241583.2024.2392312
- Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
- Recommendations to improve the interpretation of global flood forecasts to support international humanitarian operations for tropical cyclones L. Speight et al. 10.1111/jfr3.12952
- Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning G. Ascenso et al. 10.1016/j.wace.2024.100724
4 citations as recorded by crossref.
- The utility of impact data in flood forecast verification for anticipatory actions: Case studies from Uganda and Kenya F. Mitheu et al. 10.1111/jfr3.12911
- Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System G. Matthews et al. 10.5194/hess-26-2939-2022
- Plastic in global rivers: are floods making it worse? C. Roebroek et al. 10.1088/1748-9326/abd5df
- Unfolding the relationship between seasonal forecast skill and value in hydropower production: a global analysis D. Lee et al. 10.5194/hess-26-2431-2022
Latest update: 21 Nov 2024
Executive editor
In agreement with the handling Editor, the study is highlighted in HESS
Short summary
Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System...
