11.1 Human Heat Health Index (H3I) for Holistic Assessment of Urban Heat Hazard and Mitigation Strategies

Wednesday, 31 January 2024: 1:45 PM
344 (The Baltimore Convention Center)
Harsh Kamath, Univ. of Texas at Austin, Austin, TX; and A. Martilli, M. Singh, T. Brooks, K. Lanza, P. Bixler, M. Coudert, Z. L. Yang, and D. Niyogi

Urban heat has emerged as a significant contributor to weather-related fatalities in recent years, with its adverse effects disproportionately impacting communities due to the uneven distribution of heat hazards and social vulnerability. Here, we contend that urban heat island intensity is not an appropriate metric to assess daytime urban heat vulnerability. Thus, we propose a novel metric human heat health index (H3I) that can be utilized to: (ⅰ) assess and compare heat hazards in different neighborhoods and (ⅱ) evaluate the effectiveness of environmental interventions for heat mitigation to facilitate the development of if-then analyses for implementing heat mitigation strategies. H3I considers the diurnal profile of heat hazard to estimate the cumulative hours of the day when the hazard exceeds any prescribed threshold along with the social vulnerability and population exposure and is consistent with the IPCC framework for risk assessment.

In this study, the city of Austin, Texas was used as a representative sprawling city to demonstrate H3I. The physiological equivalent temperature (PET) was used as a heat hazard to calculate H3I. The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate mean radiant temperature (TMRT), which is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. Subsequently, PET is calculated using TMRT, humidity air temperature, wind speed, clothing and energy expenditure while performing an activity. SOLWEIG was forced using near-surface ERA-5 meteorological data. Building and vegetation heights along with the digital terrain model used as an input to SOLWEIG was derived from United States Geological Survey 3DEP LiDAR point cloud data.

Our study emphasizes on the importance of considering social vulnerability and involving communities in the decision-making for heat mitigation.

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