Abstract
Real-time heatwave risk management with fine-grained spatial resolution is important for analysis of urban heat island (UHI) effects and local heatwaves. This study analyzed the spatio-temporal behavior of ground temperatures and developed methods for modeling them. The developed models consider two higher-order stochastic spatial properties (skewness and kurtosis), which are key to understanding and describing local temperature fluctuations and UHI effects. Application of the developed models to the greater Tokyo metropolitan area demonstrated the feasibility of statistically incorporating a variety of real datasets. Remotely sensed imagery and data from a variety of ground-based monitoring sites were used to build models linking urban covariates to air temperature. Air temperature models were used to capture high-resolution spatial emulator outputs for modeling ground surface temperatures. The main processes studied were the Tukey g-and-h processes for capturing spatial and temporal aspects of heat processes in urban environments. The main finding is that consideration of not only the mean temperature but also the variance, skewness, and kurtosis parameters can reveal hidden heatwave structures.
Highlights
Air temperatures are commonly used for heatwave risk assessment (e.g., [41], [42]), our results suggest that ground temperature is a better indicator of urban heat island (UHI) effects
An l-moment matching approach was used for temporal analysis, and low-rank and sparse Tukey g-and-h (TGH)-RF models were used for spatial analysis
The results demonstrated the importance of considering skewness and kurtosis in heatwave modeling
Summary
Since the first papers on urban heat islands (UHIs) were published (see [3]), the need for quantifying and modeling spatially fine granular temperature processes in urban environments has grown in prominence. Local urban temperature modeling is important because it can inform policy for emission reduction standards The components of such models are increasingly being used as benchmark references to set standards to be achieved by green infrastructure projects for transportation and lowcarbon building initiatives in smart city environments. In the context of developing local temperature models for health-related policies, the outputs of local UHI and heatwave models for both air and ground temperatures can help inform policy for mitigation and severity/exposure reduction for at-risk populations Such models can be used to develop strategies for reducing high mortality rates due to extreme temperature distress, especially in elderly urban populations.
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