<p>With the intensification of global climate change, climatic livability has become an important indicator for evaluating regional environmental quality and the suitability of human settlements. As one of China’s core regions with highly concentrated economic activity and population, the Yangtze River Basin (YRB) is facing increasing environmental pressures driven by the combined effects of climate change and human activities. However, a comprehensive understanding of the long-term spatiotemporal patterns of climatic livability at the basin scale remains limited. Based on daily meteorological station data from 1981 to 2023, a climatic livability evaluation indicator system is constructed. An integrated weighting method combining the Analytic Hierarchy Process (AHP), Principal Component Analysis (PCA), Bootstrap resampling, and inverse-variance weighting (IVW) is then applied to quantitatively assess the spatiotemporal patterns and evolutionary characteristics of climatic livability in the YRB. The results show that climatic livability in the YRB exhibits pronounced spatial heterogeneity, with relatively higher levels in regions with favorable hydrothermal conditions and lower levels in high-altitude areas. Over the past 43&#xa0;years, climatic livability has shown an overall increasing trend, with a growth rate of approximately 0.005 per decade, although improvements are not spatially uniform across the basin. These patterns are associated with the combined effects of climatic conditions and regional factors, including topography, urbanization, and climate variability. Variations in climatic livability are closely associated with changes in climatic comfort, climatic livability risk, and ecological environmental conditions. The findings provide new insights into the spatiotemporal variability of climatic livability at the basin scale and contribute to a better understanding of how climatic and environmental factors jointly influence livability patterns.</p>

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Spatiotemporal patterns and trends of climate livability in the Yangtze River Basin, China

  • Qi Meng,
  • Jun Shi,
  • Shitao Song,
  • Dongli Fan

摘要

With the intensification of global climate change, climatic livability has become an important indicator for evaluating regional environmental quality and the suitability of human settlements. As one of China’s core regions with highly concentrated economic activity and population, the Yangtze River Basin (YRB) is facing increasing environmental pressures driven by the combined effects of climate change and human activities. However, a comprehensive understanding of the long-term spatiotemporal patterns of climatic livability at the basin scale remains limited. Based on daily meteorological station data from 1981 to 2023, a climatic livability evaluation indicator system is constructed. An integrated weighting method combining the Analytic Hierarchy Process (AHP), Principal Component Analysis (PCA), Bootstrap resampling, and inverse-variance weighting (IVW) is then applied to quantitatively assess the spatiotemporal patterns and evolutionary characteristics of climatic livability in the YRB. The results show that climatic livability in the YRB exhibits pronounced spatial heterogeneity, with relatively higher levels in regions with favorable hydrothermal conditions and lower levels in high-altitude areas. Over the past 43 years, climatic livability has shown an overall increasing trend, with a growth rate of approximately 0.005 per decade, although improvements are not spatially uniform across the basin. These patterns are associated with the combined effects of climatic conditions and regional factors, including topography, urbanization, and climate variability. Variations in climatic livability are closely associated with changes in climatic comfort, climatic livability risk, and ecological environmental conditions. The findings provide new insights into the spatiotemporal variability of climatic livability at the basin scale and contribute to a better understanding of how climatic and environmental factors jointly influence livability patterns.