Integrative Geospatial Analysis of Heat Dynamics Shaping Climate-Resilient Urban Environments

Abstract

Global warming and rapid urbanization are intensifying the frequency, duration, and severity of heat extremes, posing unprecedented challenges to public health, urban infrastructure, and climate resilience. These escalating thermal stresses demand urgent attention from researchers, urban and environmental planners, climate scientists, and policymakers. This study presents a comprehensive, multi-scalar assessment of urban heat dynamics, risks, and mitigation strategies with a particular focus on the context of India and the rapidly urbanizing tropical city of Bhubaneswar. It integrates advanced methodologies including remote sensing, big data analytics, geospatial analysis, climate modeling, and a robust risk assessment framework to address critical gaps in understanding the spatial and temporal patterns of heat extremes and risks, the drivers and mitigation of Urban Heat Island (UHI) effects, and natural cooling mechanisms. The study is structured around four interlinked objectives: (i) utilizing big data analysis to assess spatial and temporal variations in heat extremes across India; (ii) analyzing UHI dynamics and the influence of vegetation using Land Surface Temperature (LST) and landscape indices; (iii) exploring the role of cold-air drainage in mitigating UHI effects and enhancing urban thermal comfort; and (iv) mapping and assessing an urban heat risk index by integrating hazard, exposure, and vulnerability, and proposing adaptation and mitigation measures in Bhubaneswar. At the national scale, the study reveals a marked escalation in both daytime and nighttime heat extremes across India in recent decades, with rising temperatures arriving earlier and lingering longer. The frequency and duration of hot days and warm nights have increased significantly, by an average of 21 days per year in recent years (2020-2024), intensifying public health risks and straining urban infrastructure. To quantify this, a Heat Affected Index (HAI) was developed, capturing four critical dimensions of surface thermal stress: intensity ("how hot"), duration ("how long"), rate of change ("how fast"), and variability ("how uneven"). MODIS-derived Land LST datasets from 2003 to 2024 were used to derive HAI. The multidimensional approach identified persistent and emerging heat hotspots, noting that while western, west-central, and south-central India continue to face intense heat stress, significant shifts are occurring in northern and northeastern regions, indicating a broader transformation in thermal landscape of India. The analysis highlighted a disproportionate rise in the frequency of very warm nights, emphasizing reduced nocturnal cooling as a critical concern for public health.