Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155408
Title: Nature-based solutions for flood risk reduction : a probabilistic modeling framework
Authors: Lallemant, David
Hamel, Perrine
Balbi, Mariano
Lim, Tian Ning
Schmitt, Rafael
Win, Shelly
Keywords: Science::Geology
Issue Date: 2021
Source: Lallemant, D., Hamel, P., Balbi, M., Lim, T. N., Schmitt, R. & Win, S. (2021). Nature-based solutions for flood risk reduction : a probabilistic modeling framework. One Earth, 4(9), 1310-1321. https://dx.doi.org/10.1016/j.oneear.2021.08.010
Project: NRF-NRFF2018-06 
NRF-NRFF12-2020-0009 
Journal: One Earth 
Abstract: Flooding is the most frequent and damaging natural hazard globally. While nature-based solutions can reduce flood risk, they are not part of mainstream risk management. We develop a probabilistic risk analysis framework to quantify these benefits that (1) accounts for frequent small events and rarer large events, (2) can be applied to large basins and data-scarce contexts, and (3) quantifies economic benefits and reduction in people affected. Measuring benefits in terms of avoided losses enables the integration of nature-based solutions in standard cost-benefit analysis of protective infrastructure. Results for the Chindwin River basin in Myanmar highlight the potential consequences of deforestation on long-term flood risk. We find that loss reduction is driven by small but frequent storms, suggesting that current practice relying on large storms may underestimate the benefits of nature-based solutions. By providing average annual losses, the framework helps mainstream nature-based solutions in infrastructure planning or insurance practice.
URI: https://hdl.handle.net/10356/155408
ISSN: 2590-3330
DOI: 10.1016/j.oneear.2021.08.010
DOI (Related Dataset): 10.21979/N9/BNHSTP
Rights: © 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ASE Journal Articles
EOS Journal Articles

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