Exploring spatial feedbacks between adaptation policies and internal migration patterns due to sea-level rise

Understanding the global subnational migration patterns driven by hydrological intrusion exposure

Amid the escalating global climatic challenges, hydrological risks significantly influence human settlement patterns, underscoring the imperative for an in-depth comprehension of hydrological change's ramifications on human migration. However, predominant research has been circumscribed to the national level. The study delves into the nonlinear effects of hydrological risks on migration dynamics in 46,776 global subnational units. Meanwhile, leveraging remote sensing, we procured globally consistent metrics of hydrological intrusion exposure, offering a holistic risk assessment encompassing hazard, exposure, and vulnerability dimensions, thus complementing previous work. Here, we show that exposure is the primary migration driver, surpassing socioeconomic factors. Surrounding disparities further intensified exposure's impact. Vulnerable groups, especially the economically disadvantaged and elderly, tend to remain in high-risk areas, with the former predominantly migrating within proximate vicinities. The nonlinear analysis delineates an S-shaped trajectory for hydrological exposure, transitioning from resistance to migration and culminating in entrapment, revealing dependence on settlement resilience and adaptability.

Comparing built-up area datasets to assess urban exposure to coastal hazards in Europe

Information on urban land use, beyond the urban-rural dichotomy, can improve the assessment of potential impacts of coastal hazards by refining estimates of damages and supporting adaptation planning. However, the lack of a consistent definition of "urban" in previous studies has led to exposure estimates that vary considerably. Here, we explore the sensitivity of exposed population and built-up area in four settlement types, defined by four different built-up area datasets. We find large differences in the exposed population of up to 65% (127 million people) in the "Urban" class. The exposure estimates are highly sensitive to the density thresholds used to distinguish the settlement types, with a difference in exposed urban population of up to 53.5 million people when the threshold varies by 10%. We attribute the high sensitivity of the exposure estimates to the varying definitions of built-up area of the underlying datasets. We argue that the definition of urban land is crucial for coastal impact assessments and make recommendations for the use of the analyzed datasets.