Nature-based solutions for flood risk reduction : a probabilistic modeling framework
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...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10356/155408 |
| _version_ | 1811697697378271232 |
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| author | Lallemant, David Hamel, Perrine Balbi, Mariano Lim, Tian Ning Schmitt, Rafael Win, Shelly |
| author2 | Asian School of the Environment |
| author_facet | Asian School of the Environment Lallemant, David Hamel, Perrine Balbi, Mariano Lim, Tian Ning Schmitt, Rafael Win, Shelly |
| author_sort | Lallemant, David |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:59:23Z |
| format | Journal Article |
| id | ntu-10356/155408 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:59:23Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1554082022-02-26T20:43:51Z Nature-based solutions for flood risk reduction : a probabilistic modeling framework Lallemant, David Hamel, Perrine Balbi, Mariano Lim, Tian Ning Schmitt, Rafael Win, Shelly Asian School of the Environment Earth Observatory of Singapore Science::Geology Flood Maps Nature-Based Solutions Satellite Remote Sensing Flooding 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. Ministry of Education (MOE) National Research Foundation (NRF) Published version We acknowledge funding support from the National Research Foundation, Prime Minister’s Office, Singapore under awards NRF-NRFF2018-06 and NRF-NRFF12-2020-0009, as well as from the Nanyang Technological University, the Stanford Woods Institute for the Environment, the Stanford Urban Resilience Initiative, the Natural Capital Project, the Earth Observatory of Singapore, and the Peruilh Scholarship from the School of Engineering of the University of Buenos Aires. This research was partly supported by the Earth Observatory of Singapore via its funding from the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative. This work comprises EOS contribution number 396. 2022-02-25T07:50:18Z 2022-02-25T07:50:18Z 2021 Journal Article 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 2590-3330 https://hdl.handle.net/10356/155408 10.1016/j.oneear.2021.08.010 2-s2.0-85123243911 9 4 1310 1321 en NRF-NRFF2018-06 NRF-NRFF12-2020-0009 One Earth 10.21979/N9/BNHSTP © 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/). application/pdf |
| spellingShingle | Science::Geology Flood Maps Nature-Based Solutions Satellite Remote Sensing Flooding Lallemant, David Hamel, Perrine Balbi, Mariano Lim, Tian Ning Schmitt, Rafael Win, Shelly Nature-based solutions for flood risk reduction : a probabilistic modeling framework |
| title | Nature-based solutions for flood risk reduction : a probabilistic modeling framework |
| title_full | Nature-based solutions for flood risk reduction : a probabilistic modeling framework |
| title_fullStr | Nature-based solutions for flood risk reduction : a probabilistic modeling framework |
| title_full_unstemmed | Nature-based solutions for flood risk reduction : a probabilistic modeling framework |
| title_short | Nature-based solutions for flood risk reduction : a probabilistic modeling framework |
| title_sort | nature based solutions for flood risk reduction a probabilistic modeling framework |
| topic | Science::Geology Flood Maps Nature-Based Solutions Satellite Remote Sensing Flooding |
| url | https://hdl.handle.net/10356/155408 |
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