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Nature-based solutions for flood risk reduction: A probabilistic modelling framework

Left to right: Assistant Professor David Lallemant and Assistant Professor Perrine Hamel

Nature-based solutions for flood risk reduction: A probabilistic modelling framework

Over the last few months, reports of terrible floods in Peninsular Malaysia have been commonplace in the news. From the devastating ones in December 2021 that affected more than 125,000 people and resulted in about RM6 billion worth of property damage in multiple states, to the latest news in early March 2022 that parts of the capital, Kuala Lumpur, have been inundated by floods. Heavy rainfall, and subsequent flooding, typically comes with the annual Northeast Monsoon when it hits Southeast Asia during the months of December to January. However, these rains and floods have been getting increasingly severe over the years, which experts have attributed to climate change. These floods have been further exacerbated by ever-increasing levels of human activity on the natural environment, such as urbanisation and deforestation. The soil becomes less porous for water to infiltrate into, increasing the volume of surface runoff and causing low-lying areas and rivers to flood more easily.

Current mitigation solutions mostly involve the use of man-made structures to keep flood waters in check. However, these solutions have been criticised as expensive and energy-intensive ventures. This over-reliance on man-made structures stems from the quantifiability of their protective capabilities, an important factor when it comes to economic and financial systems which are typically based on numbers and comparative evaluations. In order to help mainstream the use nature-based solutions, a team of scientists from NTU’s Asian School of the Environment (ASE), led by Assistant Professor David Lallemant and Assistant Professor Perrine Hamel, have designed a probabilistic risk analysis framework capable of addressing the challenges of quantifying nature-based solutions. It considers multiple types of storm events and can be applied to large river basins in data-scarce environments.

Man-made vs nature-based flood mitigation solutions

Flood mitigation solutions can be categorised into two groups: “gray” or man-made infrastructure, and nature-based solutions. Gray infrastructure involves the building of dams, pumps, levees and canals to control water flow. Nature-based solutions, on the other hand, incorporate the use of natural infrastructure, such as the planting of trees, the restoration of wetlands and bogs, and soil conservation measures. These solutions help to trap rainwater and allow it to soak into the ground, rather than letting it flow directly into rivers.

While gray infrastructure flood solutions seemingly present a straightforward choice for organisations due to the ease by which they can be designed with engineering software and its aforementioned quantifiable nature, this type of flood solution also lacks the added co-benefits that nature-based solutions bring. Besides mitigating floods, nature-based solutions provide carbon sequestering benefits from vegetation, biodiversity protection and recreational opportunities. However, despite the many benefits of nature-based solutions, organisations still mostly favour the use of gray infrastructure. Hence there is a need for frameworks like that designed by the ASE team in order to help make the use of nature-based solutions more attractive.

Graphic by Earth Observatory of Singapore

Considering the impact of small and frequent storms

Typical studies tended to focus on rarer and larger storms, usually ones with 100 or even 500-year return periods – the average time between events. The longer the return period, the more intense the disaster will be. However, by focusing only on storms with large return periods, these other studies underestimate the benefits of nature-based flood solutions. Using the Chindwin River basin in Myanmar as a case study, the team found that the cumulative impacts of deforestation on flood volume, flood extent and flood-related losses were greater for smaller frequent storm events compared to rarer larger storms. This result would have otherwise been obscured by studies that only rely on larger storms. Another advantage of the framework was that it could demonstrate how small changes in peak river discharges (from frequent small storms) could amplify into much larger impacts and losses. This was done via the propagation of deforestation effects through their probabilistic risk analysis framework.

Operating in data-scarce river basins

Another obstacle for typical modelling studies is their need for a large amount of data. For some river basins, that data might be scarce, hindering flood risk models and subsequent risk mitigation planning. The case study of the Chindwin River basin lacked much local data, but risk information was urgently needed due to the high rate of deforestation along the river’s banks. Fortunately, the researchers demonstrated that they could perform a robust flood risk analysis using lower resolution and open datasets. The probabilistic framework enables improved uncertainty assessment in such data-scarce situations by the propagation of parameter uncertainty through its modelling chain. This also means that the framework can be adjusted to assess ungauged basins if necessary, helping to inform critical risk mitigation planning without the need for extensive data collection.

Encouraging greater adoption of nature-based solutions

As floods increase around the world, governments and the insurance industry are starting to express more interest in the use of nature-based solutions. A framework such as the one in this study, with its ability to clearly quantify the benefits of natural ecosystems and measure risk reduction in economic terms, will help to further influence flood risk planning ideas towards and strengthen support for nature-based solutions, preserving the natural environment whilst promoting flood-resilient communities.

The entire research team includes David Lallemant, Perrine Hamel, Mariano Balbi, Tian Ning Lim, Rafael Schmitt and Shelly Win. The research is funded from the National Research Foundation, Prime Minister’s Office, Singapore under awards NRF-NRFF2018-06 and NRF-NRFF12-2020-0009 and the Earth Observatory of Singapore.

A graphical abstract of the paper, designed by co-author and PhD Student Mariano Balbi

This post is a re-post from College of Science @ NTU, link below-

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