Decoupling the multi-drivers of urban extreme heat environment in urban agglomerations using ensemble learning

Abstract

Urban extreme heat (UEH) has been imparting a formidable menace to various facets of sustainable urban and regional development. While increasing studies have examined the urban thermal environment, a gap exists in decoupling the nonlinear multi-drivers of UEH environment in intra-urban regions, especially across the urban agglomerations. In this study, we estimated UEH through the land surface temperature (LST) in the extended summer season exceeding a specific threshold computed through cross-regional statistics. Furthermore, an additive interpretable ensemble learning model enhanced via the Bayesian Optimization algorithm (BO) and Monte Carlo Simulation framework (MCS) was employed to dissect the intricate nonlinear interplay between UEH and intra-urban morphology, green-blue composition, and atmospheric determinants. Our results highlight the indispensably suppressive effect of green and blue factors on UEH, whereas urban morphological variables generally exhibit an opposite trend. Interestingly, we reveal heterogeneity responses of building heights, elevated urban structures mitigate UEH in temperate monsoon and subtropical monsoon zones and exhibit diminished marginal utility in temperate continental zones. Furthermore, the arid climatic characteristics are expected to exert an unexpectedly enhancing effect on water bodies' cooling capacity. These findings provide heterogeneous local guidance for landscape and urban planning towards the severe challenges of climate change.