How do green roofs mitigate urban thermal stress under heat waves?

Abstract

As the climate warms, heat waves (HWs) are projected to be more intense and to last longer, with serious implications for public health. Urban residents face higher health risks because urban heat islands exacerbate HW conditions. One strategy to mitigate negative impacts of urban thermal stress is the installation of green roofs (GRs) given their evaporative cooling effect. However, the effectiveness of GRs and the mechanisms by which they have an effect at the scale of entire cities are still largely unknown. The Greater Beijing Region is modeled for a HW scenario with the Weather Research and Forecasting model coupled with a state‐of‐the‐art urban canopy model (Princeton Urban Canopy Model) to examine the effectiveness of GRs. The results suggest that GR would decrease near‐surface air temperature (ΔT2max = 2.5 K) and wind speed (ΔUV10max = 1.0 m s−1) but increase atmospheric humidity (ΔQ2max = 1.3 g kg−1). GRs are simulated to lessen the overall thermal stress as indicated by apparent temperature (ΔAT2max = 1.7°C). The modifications by GRs scale almost linearly with the fraction of the surface they cover. Investigation of the surface‐atmosphere interactions indicates that GRs with plentiful soil moisture dissipate more of the surface energy as latent heat flux and subsequently inhibit the development of the daytime planetary boundary layer (PBL). This causes the atmospheric heating through entrainment at the PBL top to be decreased. Additionally, urban GRs modify regional circulation regimes leading to decreased advective heating under HW.

A neighbourhood-scale estimate for the cooling potential of green roofs

“…This also brings up the question how efficiently the implementation of green roofs can be scaled up. Li et al [2014] and Sun et al [2016] describe a linear decrease of air temperature with green roof fraction, Mackey et al [2012] compare temperature reduction to the normalised difference vegetation index (NDVI) from satellite measurements and also find a linear relationship. However, this dependency is not well understood.…”

Atmospheric boundary‐layer characteristics from ceilometer measurements. Part 2: Application to London's urban boundary layer

“…geographic location, size, population density, building morphology, orography and surrounding land cover) a wide range of boundary‐layer characteristics need to be quantified. While modelling studies help explore the relative importance of enhanced roughness, anthropogenic heat, the urban heat island effect (Sun et al, ), orography, local circulation patterns, and synoptic conditions, observations to verify results remain scarce.…”

Nature-based approaches to managing climate change impacts in cities

“…An important question is how NBS scale with implementation area, and how their effectiveness alone or in combination with grey infrastructure depends on their physical position in the landscape [84,93,94,116]. For example, a study of green roofs in Beijing, China, found that the reduction in UHI scaled linearly with green roof area: while 100% green roof coverage resulted in significant city-wide cooling (1.5°C), 10% green roof coverage led to only 0.1-0.2°C cooling [124]. On the other hand, a study in Madison (Wisconsin, USA) found that temperature was related to tree canopy cover in nonlinear ways that depended on impervious cover, spatial scale and time of day (daytime versus night-time) [91].…”