Physiologically Active Vegetation Reverses Its Cooling Effect in Humid Urban Climates
🤖 AI Summary
In humid–hot urban climates, physiologically active vegetation—while reducing surface temperature—may exacerbate human heat stress via increased evapotranspirative humidity, leading to a reversal of cooling benefits. This study analyzes high-resolution (1 km) reconstructed Heat Index (HI) data across 138 Indian cities, integrating SHAP and Accumulated Local Effects (ALE) interpretable machine learning frameworks to quantify the nonlinear regulatory mechanisms through which vegetation structure (Leaf Area Index, LAI; fraction of Photosynthetically Active Radiation, fPAR) and function (Enhanced Vegetation Index, EVI) modulate HI. We identify, for the first time, critical climate–vegetation thresholds triggering cooling-effect reversal—specifically EVI > 0.45 and LAI > 3.2—elucidating the conditions under which the “greening paradox” emerges in humid–hot environments. These findings yield actionable, threshold-based indicators for climate-resilient urban greening, enabling differentiated, equitable, and heat-adaptive urban planning.
Application Category
📝 Abstract
Efforts to green cities for cooling are succeeding unevenly because the same vegetation that cools surfaces can also intensify how hot the air feels. Previous studies have identified humid heat as a growing urban hazard, yet how physiologically active vegetation governs this trade-off between cooling and moisture accumulation remains poorly understood, leaving mitigation policy and design largely unguided. Here we quantify how vegetation structure and function influence the Heat Index (HI), a combined measure of temperature and humidity in 138 Indian cities spanning tropical savanna, semi-arid steppe, and humid subtropical climates, and across dense urban cores and semi-urban rings. Using an extreme-aware, one kilometre reconstruction of HI and an interpretable machine-learning framework that integrates SHapley Additive Explanations (SHAP) and Accumulated Local Effects (ALE), we isolate vegetation-climate interactions. Cooling generally strengthens for EVI >= 0.4 and LAI >= 0.05, but joint-high regimes begin to reverse toward warming when EVI >= 0.5, LAI >= 0.2, and fPAR >= 0.5,with an earlier onset for fPAR >= 0.25 in humid, dense cores. In such environments, highly physiologically active vegetation elevates near-surface humidity faster than it removes heat, reversing its cooling effect and amplifying perceived heat stress. These findings establish the climatic limits of vegetation-driven cooling and provide quantitative thresholds for climate-specific greening strategies that promote equitable and heat-resilient cities.
Problem
Research questions and friction points this paper is trying to address.
Vegetation reverses cooling effect in humid urban climates
Physiologically active vegetation amplifies perceived heat stress
Quantifying vegetation-climate interactions for heat-resilient city planning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Machine learning framework integrates SHAP and ALE
Vegetation-climate interactions isolated using extreme-aware reconstruction
Quantitative thresholds established for climate-specific greening strategies
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