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.

Nonlinear forces in urban thermal environment using Bayesian optimization-based ensemble learning.

Urbanization witnessed unprecedented development globally, which causes citizens and urban temperature to become increasingly intertwined. Although researchers were interested in the field, most studies focused on holistic linear links between the characteristics of the urban built-up environment and temperature. The study used Bayesian optimization ensemble learning and Shapley value to decouple the urban thermal environment by Landsat satellite data. This work's novelties reveal the specific driving effect of different value ranges of urban features in the overall process on the urban thermal environment and advancing an optimum observation buffer zone of the urban surface temperature. The study's results were only for daytime and Beijing scope. The following are the main findings: (1) The 2 km observation buffer zone is best to analyze the urban thermal environment for this dataset. (2) The ecological environment factors have a more significant effect on the urban temperature than the urban morphology factors. (3) In summer, when the vegetation coverage exceeds 58.1%, every 10% increase could reduce the temperature by 0.84 °C. In contrast to summer, when vegetation coverage exceeds 64.7% and 73.2%, respectively, in spring and fall, there will be a significant marginal utility. (4) The effect of the building height has seasonal variations. It has the greatest cooling effect in the spring when the height is between 18 m and 75 m, and the daytime surface temperature at the time of Landsat overpass will drop by 1.25 °C. These findings will aid in understanding how building construction influences urban surface temperature and provide statistical support for planners.