Atmospheric blocking is a long standing structure stalled in the mid-troposphere which is often associated with extreme weather events such as droughts, heatwaves, flood and cold air outbreak. A striking atmospheric blocking is identified to persist over the US during 13–17 August 2007, exacerbating the existing drought over the Southeastern US. This pronounced blocking event not only intensified the concurrent drought conditions, but also led to a record-breaking heatwave over the Southeast of the US. The excessive heat observed during this heatwave is attributable to the subsidence-associated adiabatic warming as well as the dry-and-warm air advection over Alabama and the neighboring states. At the local scale, we choose Birmingham, AL, as the study area for exploring the blocking influence on urban heat island. Based on the remote sensing data, the surface (skin) urban heat island is found to be 8 ${}^{\circ }$ C in this area on the block-onset day. This provides partial evidences that the surface urban heat island intensity is likely amplified by the blocking-induced heat waves. The present work provides a unique case study in which blocking, drought, heatwave and urban heat island all occur concurrently, and interplay across a spectrum of spatial scales. We conclude that atmospheric blocking is capable of reinforcing droughts, initiating heatwaves, and probably amplifying the urban heat island intensity during the concurrent period.

In the southeastern US, Atlanta is always the focus of attention, despite the rapid expansion of small and medium-sized cities (SMSCs) in the region. Clearly, larger cities have more people, resulting in more loss during disasters. However, SMSCs also face natural calamities and must be made robust and sustainable. Keeping this in mind, this study chooses to focus on ten SMSCs in Alabama (Population > 40,000) which have encountered at least a 6% increase in population size between 1990 and 2020, out of which two large cities (Population > 180,000) which experienced loss during the same time. This paper examines the change in urban built-up area between 1990 and 2020 using the random forest algorithm in Google Earth Engine (GEE) and estimates future 2050 urban expansion scenarios using the Cellular Automata (CA) Markov model in TerrSet’s Land Change Modeler (LCM). The results revealed urban built-up areas grew rapidly from 1990 to 2020, with some cities doubling or tripling in size due to population growth. The future growth model predicted growth for most cities and urban expansion along transportation networks. The outcome of this research showcases the importance of proper planning and building sustainably in SMSCs for future natural disaster events.

Urban Heat Island Intensity (UHII) has become a pressing urban climate issue in rapidly developing nations such as Bangladesh. This study presents a nationwide thana-level assessment of UHII trends from 1990 to 2023 using remote sensing, geospatial analysis, and machine learning techniques. Land Surface Temperature (LST) was derived from Landsat imagery to quantify UHII, and a Space–Time Cube framework with Mann–Kendall trend analysis was applied to identify persistent, intensifying, emerging, and diminishing hotspot patterns. Major urban centers, including Dhaka, Narayanganj, and Khulna, exhibited increasing UHII, while Barisal and Mymensingh showed emerging cold spots. A Random Forest model was developed to forecast UHII up to 2040, revealing further intensification in densely populated and industrial zones. The results indicate that major metropolitan areas, particularly Dhaka, Narayanganj, and Khulna, exhibit persistent and intensifying heat hotspots, whereas divisions like Barisal and Mymensingh show emerging cold spots. The findings emphasize the need for climate-responsive urban planning and green infrastructure. This study establishes a baseline for long-term UHII monitoring and serves as a framework for future research aimed at developing predictive models and targeted mitigation strategies to enhance urban climate resilience.