Universal migration predicts human movements under climate change
Climate change is expected to displace millions of people through impacts like sea level rise, crop failures, and more frequent extreme weather. Yet scientists still cannot predict where these expected climate-induced migrants are likely to go in the coming decades.
A new study, published today in Environmental Research Letters, seeks to address this need by incorporating climate impacts into a universal model of human mobility.
To demonstrate the efficacy of the new approach, the study focused on the case of sea level rise (SLR) and human migration in Bangladesh, where the authors estimate that more than two million Bangladeshis may be displaced from their homes by 2100 because of rising sea levels alone.
The study, led by Columbia University, New York, used a probabilistic model combined with population, geographic, and climatic data to predict the sources, destinations, and flux of potential migrants caused by sea level rise.
Lead author Dr Kyle Davis, from Columbia University, explained: “More than 40 per cent of Bangladesh’s population is especially vulnerable to future sea level rise, as they live in low-lying areas that are often exposed to extreme natural events.
“However, SLR is a very different type of migration driver from short-lived natural hazards, in that it will make certain areas permanently uninhabitable.”
The team’s results using Representative Concentration Pathway (RCP) scenarios showed that mean SLR will cause population displacements in 33 per cent of Bangladesh’s districts, and 53 per cent under more intensive conditions. By mid-century, they estimated nearly 900,000 people are likely to migrate because of direct inundation from mean SLR alone.
Under the most extreme scenario, of up to 2 metre mean SLR, the number of migrants driven by direct inundation could rise to as many as 2.1 million people by the year 2100. For all RCP scenarios, five districts – Barisal, Chandpur, Munshiganj, Narayanganj, and Shariatpur – are the source for 59 per cent of all migrants.
Their analysis considered mean SLR without normal high tides, so the results – both in terms of inundated area and displaced population – are conservative.
The researchers also estimated the extra jobs, housing and food needed to accommodate these migrants at their destinations. They found that to cope with the numbers likely to be displaced by 2050, 600,000 additional jobs, 200,000 residences and 784 billion food calories will be needed.
These results have clear implications for the places that are likely to receive incoming migrants.
Dr Davis said: “SLR migrants are unlikely to search far for an attractive place to move to, and the destination will generally be a trade-off between employment opportunities, its distance from the migrants’ origin, and how vulnerable it is to SLR itself.
“We found that the city of Dhaka was consistently favoured, coming out as the top destination in all scenarios. This means the city will need to prepare for the largest number of migrants, which may compound the area’s already rapid urban growth.”
The study also identified other risks from SLR, most notably on livelihoods and food security.
Dr Davis explained: “Inundation by the sea, and the out-migration it causes, will have significant effects on agriculture and aquaculture. For instance, 1,000km2 of Bangladesh’s cultivated land could be underwater by the end of the century, with an even larger area made unusable by saltwater intrusion. Given that 48 per cent of the labour force works in agriculture, the impact of this would be keenly felt in terms of jobs and food security.
“Similarly, a great deal of the country’s coastal aquaculture is vulnerable to climate change impacts, and this will probably have important nutritional and economic consequences, given that 58 per cent of animal protein in the Bangladeshi diet comes from seafood, and the country is the world’s fifth largest aquaculture producer.
“Ultimately, we hope that the modelling tool we have developed can be used by researchers and planners to accurately predict the relocation of climate-induced migrants, and to enable the development of political and economic strategies to face the challenge.”