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Integrating social vulnerability into high-resolution global flood risk mapping

2024-04-30  |   Editor : houxue2018  
Category : News


High-resolution global flood risk maps are increasingly used to inform disaster risk planning and response, particularly in lower income countries with limited data or capacity. However, current approaches do not adequately account for spatial variation in social vulnerability, which is a key determinant of variation in outcomes for exposed populations. Here we integrate annual average exceedance probability estimates from a high-resolution fluvial flood model with gridded population and poverty data to create a global vulnerability-adjusted risk index for flooding (VARI Flood) at 90-meter resolution. The index provides estimates of relative risk within or between countries and changes how we understand the geography of risk by identifying ‘hotspots’ characterised by high population density and high levels of social vulnerability. This approach, which emphasises risks to human well-being, could be used as a complement to traditional population or asset-centred approaches.


Flooding is one of the most common natural hazards associated with climate change globally, with nearly a quarter of the world’s population exposed to a 1-in-100-year event. Every year, flooding results in thousands of deaths, the displacement of millions, and hundreds of billions of dollars in damage, and climate change is expected to increase both the frequency and intensity of floods in coming decades. Exposure to flooding is greatest in low- and middle-income countries (LMICs), and this is expected to increase as a consequence of rapid demographic change. This is of particular concern given that the poorest households and communities have the least coping capacity when confronted with a natural hazard event and suffer the greatest well-being losses. Failure to mitigate hazard risks for the most vulnerable contributes to the perpetuation of poverty10 and can exacerbate social inequalities within and between countries.

This study presents global gridded fluvial flood risk estimates that explicitly adjust for variations in social vulnerability at 90 m resolution. Incorporating social vulnerability in this way can alter how they understand the geography of risk within countries by emphasising areas with relatively large populations and low coping capacity. This contrasts with traditional approaches that emphasise either population density or income/asset density.

The results presented here are primarily intended to illustrate an alternative approach to risk estimation with global models rather than reflect definitive estimates of populations at risk within and between countries. These estimates are sensitive to several key assumptions, definitions, uncertainties, and data quality. We assume that poverty is a reasonable proxy for social vulnerability in the absence of more nuanced data (i.e. all else equal, people on lower incomes are generally more vulnerable to stressors than people on higher incomes everywhere). Yet there are many other factors that can affect the vulnerability of individuals, households, and communities. Locally informed vulnerability assessments are preferable where the requisite data are available. Our definition of a ‘flood’ (i.e. events that exceed 10 cm in depth) may not be suitable in all contexts, particularly where such events are frequent and communities have adapted.

Finally, the spatial resolution of our social vulnerability proxies is coarse compared to the available hazard and population data. There is a clear need to substantially improve the resolution of gridded social vulnerability indicators and ensure internal consistency across scales and geographic contexts. Previous research highlights the often very localised nature of social vulnerability dynamics, which can render a useful indicator at one geographic scale (e.g. district or state) misleading at another (e.g. household or neighbourhood). An advantage of the VARI approach using relative deprivation is theoretical and empirical consistency, and the ability to calibrate it to any scale of decision-making. But the accuracy of risk estimates will ultimately depend on the quality of the deprivation data and the scale at which it is considered reliable.


Nature Communications

https://www.nature.com/articles/s41467-024-47394-2 .

Provided by the IKCEST Disaster Risk Reduction Knowledge Service System

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