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Continental and global scale flood forecasting systems
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2016 (English)In: WILEY INTERDISCIPLINARY REVIEWS-WATER, Vol. 3, no 3, 391-418 p.Article in journal (Refereed) Published
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Abstract [en]

Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centers are increasingly using their meteorological output to drive hydrological models, creating hydrometeorological systems capable of forecasting river flow and flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data, and resources in recent years have made it possible to produce global scale flood forecasting systems. In this paper, the current state of operational large-scale flood forecasting is discussed, including probabilistic forecasting of floods using ensemble prediction systems. Six state-of-the-art operational large-scale flood forecasting systems are reviewed, describing similarities and differences in their approaches to forecasting floods at the global and continental scale. Operational systems currently have the capability to produce coarse-scale discharge forecasts in the medium-range and disseminate forecasts and, in some cases, early warning products in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the global scale alongside a move towards multi-model forecasts and grand ensemble techniques, responding to the requirement of developing multi-hazard early warning systems for disaster risk reduction. (C) 2016 The Authors. WIREs Water published by Wiley Periodicals, Inc.

Place, publisher, year, edition, pages
2016. Vol. 3, no 3, 391-418 p.
National Category
Oceanography, Hydrology, Water Resources
Research subject
Hydrology
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URN: urn:nbn:se:smhi:diva-2197DOI: 10.1002/wat2.1137ISI: 000377125900006OAI: oai:DiVA.org:smhi-2197DiVA: diva2:947379
Available from: 2016-07-08 Created: 2016-07-08 Last updated: 2016-07-08Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
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  • vancouver
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More styles
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More languages
Output format
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