Alfieri, L, Cohen, S, Galantowicz, J et al. (17 more authors) (2018) A global network for operational flood risk reduction. Environmental Science and Policy, 84. pp. 149-158. ISSN 1462-9011
Abstract
Every year riverine flooding affects millions of people in developing countries, due to the large population exposure in the floodplains and the lack of adequate flood protection measures. Preparedness and monitoring are effective ways to reduce flood risk. State-of-the-art technologies relying on satellite remote sensing as well as numerical hydrological and weather predictions can detect and monitor severe flood events at a global scale. This paper describes the emerging role of the Global Flood Partnership (GFP), a global network of scientists, users, private and public organizations active in global flood risk management. Currently, a number of GFP member institutes regularly share results from their experimental products, developed to predict and monitor where and when flooding is taking place in near real-time. GFP flood products have already been used on several occasions by national environmental agencies and humanitarian organizations to support emergency operations and to reduce the overall socio-economic impacts of disasters. This paper describes a range of global flood products developed by GFP partners, and how these provide complementary information to support and improve current global flood risk management for large scale catastrophes. We also discuss existing challenges and ways forward to turn current experimental products into an integrated flood risk management platform to improve rapid access to flood information and increase resilience to flood events at global scale.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license http://creativecommons.org/licenses/BY-NC-ND/4.0/). |
Keywords: | Global Flood Partnership (GFP); Disaster risk management; Satellite remote sensing; Flood monitoring; Early warning systems |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Mar 2018 14:22 |
Last Modified: | 25 Jun 2023 21:16 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.envsci.2018.03.014 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128779 |