A data-driven global flood forecasting system for medium to large rivers
Abstract Losses from catastrophic floods are driving intense efforts to increase preparedness and improve response to disastrous flood events by providing early warnings. Yet accurate flood forecasting remains a challenge due to uncertainty in modeling, calibrating, and validating a useful early war...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-59145-w |
| _version_ | 1797199462225936384 |
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| author | Wahid Palash Ali S. Akanda Shafiqul Islam |
| author_facet | Wahid Palash Ali S. Akanda Shafiqul Islam |
| author_sort | Wahid Palash |
| collection | DOAJ |
| description | Abstract Losses from catastrophic floods are driving intense efforts to increase preparedness and improve response to disastrous flood events by providing early warnings. Yet accurate flood forecasting remains a challenge due to uncertainty in modeling, calibrating, and validating a useful early warning system. This paper presents the Requisitely Simple (ReqSim) flood forecasting system that includes key variables and processes of basin hydrology and atmospheric forcing in a data-driven modeling framework. The simplicity of the modeling structure and data requirements of the system allows for customization and implementation in any medium to large rain-fed river basin globally, provided there are water level or discharge measurements at the forecast locations. The proposed system's efficacy is demonstrated in this paper through providing useful forecasts for various river basins around the world. This include 3–10-day forecasts for the Ganges and Brahmaputra rivers in South Asia, 2–3-day forecast for the Amur and Yangtze rivers in East Asia, 5–10-day forecasts for the Niger, Congo and Zambezi rivers in West and Central Africa, 6–8-day forecasts for the Danube River in Europe, 2–5-day forecasts for the Parana River in South America, and 2–7-day forecasts for the Mississippi, Missouri, Ohio, and Arkansas rivers in the USA. The study also quantifies the effect of basin size, topography, hydrometeorology, and river flow controls on forecast accuracy and lead times. Results indicate that ReqSim's forecasts perform better in river systems with moderate slopes, high flow persistence, and less flow controls. The simple structure, minimal data requirements, ease of operation, and useful operational accuracy make ReqSim an attractive option for effective real-time flood forecasting in medium and large river basins worldwide. |
| first_indexed | 2024-04-24T07:16:08Z |
| format | Article |
| id | doaj.art-573c5552fd964fca8db4010cee594020 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T07:16:08Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-573c5552fd964fca8db4010cee5940202024-04-21T11:17:35ZengNature PortfolioScientific Reports2045-23222024-04-0114111410.1038/s41598-024-59145-wA data-driven global flood forecasting system for medium to large riversWahid Palash0Ali S. Akanda1Shafiqul Islam2Integrated SustainabilityCivil and Environmental Engineering, University of Rhode IslandCivil and Environmental Engineering and The Fletcher School of Law and Diplomacy, Tufts UniversityAbstract Losses from catastrophic floods are driving intense efforts to increase preparedness and improve response to disastrous flood events by providing early warnings. Yet accurate flood forecasting remains a challenge due to uncertainty in modeling, calibrating, and validating a useful early warning system. This paper presents the Requisitely Simple (ReqSim) flood forecasting system that includes key variables and processes of basin hydrology and atmospheric forcing in a data-driven modeling framework. The simplicity of the modeling structure and data requirements of the system allows for customization and implementation in any medium to large rain-fed river basin globally, provided there are water level or discharge measurements at the forecast locations. The proposed system's efficacy is demonstrated in this paper through providing useful forecasts for various river basins around the world. This include 3–10-day forecasts for the Ganges and Brahmaputra rivers in South Asia, 2–3-day forecast for the Amur and Yangtze rivers in East Asia, 5–10-day forecasts for the Niger, Congo and Zambezi rivers in West and Central Africa, 6–8-day forecasts for the Danube River in Europe, 2–5-day forecasts for the Parana River in South America, and 2–7-day forecasts for the Mississippi, Missouri, Ohio, and Arkansas rivers in the USA. The study also quantifies the effect of basin size, topography, hydrometeorology, and river flow controls on forecast accuracy and lead times. Results indicate that ReqSim's forecasts perform better in river systems with moderate slopes, high flow persistence, and less flow controls. The simple structure, minimal data requirements, ease of operation, and useful operational accuracy make ReqSim an attractive option for effective real-time flood forecasting in medium and large river basins worldwide.https://doi.org/10.1038/s41598-024-59145-w |
| spellingShingle | Wahid Palash Ali S. Akanda Shafiqul Islam A data-driven global flood forecasting system for medium to large rivers Scientific Reports |
| title | A data-driven global flood forecasting system for medium to large rivers |
| title_full | A data-driven global flood forecasting system for medium to large rivers |
| title_fullStr | A data-driven global flood forecasting system for medium to large rivers |
| title_full_unstemmed | A data-driven global flood forecasting system for medium to large rivers |
| title_short | A data-driven global flood forecasting system for medium to large rivers |
| title_sort | data driven global flood forecasting system for medium to large rivers |
| url | https://doi.org/10.1038/s41598-024-59145-w |
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