The air temperature cooling impacts of infrastructure-based adaptation measures in expanding urban areas and under changing climatic conditions are not well understood. We present simulations conducted with the Weather Research and Forecasting (WRF) model, coupled to a multi-layer urban model that explicitly resolves pedestrian-level conditions. Our simulations dynamically downscale global climate projections, account for projected urban growth, and examine cooling impacts of extensive cool roof deployment in Atlanta, Detroit, and Phoenix (USA). The simulations focus on heatwave events that are representative of start-, middle-, and end-of-century climatic conditions. Extensive cool roof implementation is projected to cause a maximum city-averaged daytime air temperature cooling of 0.38 °C in Atlanta; 0.42 °C in Detroit; and 0.66 °C in Phoenix. We propose a means for practitioners to estimate the impact of cool roof treatments on pedestrian-level air temperature, for a chosen roof reflectivity, with a new metric called the Albedo Cooling Effectiveness (ACE). The ACE metric reveals that, on average, cool roofs in Phoenix are 11% more effective at lowering pedestrian-level air temperature than in Atlanta, and 30% more effective than in Detroit. Cool roofs remain similarly effective under future heatwaves relative to contemporary heatwaves for Atlanta and Detroit, with some indication of increased effectiveness under future heatwaves for Phoenix. By highlighting the underlying factors that drive cooling effectiveness in a trio of cities located in different climatic regions, we demonstrate a robust framework for estimating the pedestrian-level cooling impacts associated with reflective roofs without the need for computationally demanding simulations.
Cool materials and rooftop vegetation help achieve urban heating mitigation as they can reduce building cooling demands. This study assesses the cooling potential of different mitigation technologies using Weather Research and Forecasting (WRF)- taking case of a tropical coastal climate in the Kolkata Metropolitan Area. The model was validated using data from six meteorological sites. The cooling potential of eight mitigation scenarios was evaluated for: three cool roofs, four green roofs, and their combination (cool-city). The sensible heat, latent heat, heat storage, 2-m ambient temperature, surface temperature, air temperature, roof temperature, and urban canopy temperature was calculated. The effects on the urban boundary layer were also investigated.
The different scenarios reduced the daytime temperature of various urban components, and the effect varied nearly linearly with increasing albedo and green roof fractions. For example, the maximum ambient temperature decreased by 3.6 °C, 0.9 °C, and 1.4 °C for a cool roof with 85% albedo, 100% rooftop vegetation, and their combination.
The cost of different mitigation scenarios was assumed to depend on the construction options, location, and market prices. The potential for price per square meter and corresponding temperature decreased was related to one another. Recognizing the complex relationship between scenarios and construction options, the reduction in the maximum and minimum temperature across different cool and green roof cases were used for developing the cost estimates. This estimate thus attempted a summary of the price per degree of cooling for the different potential technologies.
Higher green fraction, cool materials, and their combination generally reduced winds and enhanced buoyancy. The surface changes alter the lower atmospheric dynamics such as low-level vertical mixing and a shallower boundary layer and weakened horizontal convective rolls during afternoon hours. Although cool materials offer the highest temperature reductions, the cooling resulting from its combination and a green roof strategy could mitigate or reverse the summertime heat island effect. The results highlight the possibilities for heat mitigation and offer insight into the different strategies and costs for mitigating the urban heating and cooling demands.more » « less
- NSF-PAR ID:
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Computational Urban Science
- Medium: X
- Sponsoring Org:
- National Science Foundation
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