Rapid urbanization and climate change are intensifying urban heat stress, flooding, and environmental degradation, increasing risks to human health, infrastructure, ecosystems, and long-term sustainability. Interacting heat islands and extreme precipitation create compound hazards that require integrative approaches across climatology, hydrology, ecology, and urban planning. Green and blue infrastructure (trees, parks, vegetated surfaces, and water bodies) offers nature-based solutions through cooling, stormwater retention, and air quality and carbon benefits, yet their effectiveness is constrained by extreme urban climates, resource limits, and socio-economic factors.
This session brings together research that advances the characterization, modeling, and mitigation of urban heat and flood risks through quantitative, data-driven, and geospatial approaches. We welcome contributions leveraging remote sensing, in-situ observations, numerical and process-based models, spatial statistics, machine learning, as well as the integration of multi-source datasets (satellite, airborne, ground-based, and socio-economic). Particular emphasis is placed on understanding heat–flood interactions, evaluating green adaptation strategies across scales, and assessing their impacts on microclimate, hydrology, energy demand, biodiversity, and human well-being.
Topics of interest include geospatial and AI-based methods to monitor, model, and predict urban heat dynamics, quantitative assessments of urban heat island mitigation and cooling demand, modeling of flood attenuation and runoff reduction through green infrastructure, integrated analyses linking heat with air quality, health, energy use, and social vulnerability, and strategies to optimize the costs and benefits of urban ecosystems under climate stress. By bridging geospatial analyses, modelling frameworks, and urban environmental science, this session aims to deepen understanding of urban climate processes and support the design of resilient, sustainable, and climate-adaptive cities.