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Global flood forecasting for averting disasters worldwide

Abstract:
Globally, floods are responsible for more than half of the total people affected by all weather-related disasters combined, causing a large number of deaths and significant economic losses. Global-scale flood forecasting systems play a key role in disaster risk reduction: they provide early flood information for several nations who are without local flood early warning systems and function as added value information for national services with their own early warning systems. Global flood forecasting is increasingly becoming attractive due to complete worldwide coverage and improving forecast skills. In this chapter, we present the recent advances in large-scale flood forecasting with a focus on already existing global and continental flood forecasting systems in operation. We also review different scientific methodologies in practice for evaluating and improving the forecast skill such as evaluation methods, precipitation bias corrections, multimodel approaches, and data assimilation. Additionally, we discuss how flood forecast information is used for supporting everyday operations of a humanitarian initiative. We also highlight the remaining challenges of improving the forecast provisions to meet end-users' expectations.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1002/9781119217886.ch12

Authors


More by this author
Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Role:
Author

Contributors

Role:
Editor
Role:
Editor
Role:
Editor
Role:
Editor


Publisher:
Wiley
Host title:
Global Flood Hazard: Applications in Modeling, Mapping and Forecasting
Pages:
205-228
Chapter number:
12
Series:
Geophysical Monograph Series
Series number:
233
Place of publication:
Hoboken
Publication date:
2018-06-11
Edition:
1st
DOI:
EISBN:
9781119217886
ISBN:
9781119217862


Language:
English
Keywords:
Pubs id:
pubs:864368
UUID:
uuid:0e0cf656-0bd7-4784-8304-6965c59ecff5
Local pid:
pubs:864368
Source identifiers:
864368
Deposit date:
2018-07-04

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