Integrating Urban Form, Function, and Energy Fluxes in a Heat Exposure Indicator in View of Intra-Urban Heat Island Assessment and Climate Change Adaptation

Abstract

Cities worldwide are getting warmer due to the combined effects of urban heat and climate change. To this end, local policy makers need to identify the most thermally vulnerable areas within cities. The Local Climate Zone (LCZ) scheme highlights local-scale variations; however, its classes, although highly valuable, are to a certain extent generalized in order to be universally applicable. High spatial resolution indicators have the potential to better reflect city-specific challenges; in this paper, the Urban Heat Exposure (UHeatEx) indicator is developed, integrating the physical processes that drive the urban heat island (UHI). In particular, the urban form is modeled using remote sensing and geographical information system (GIS) techniques, and used to estimate the canyon aspect ratio and the storage heat flux. The Bowen ratio is calculated using the aerodynamic resistance methodology and downscaled remotely sensed surface temperatures. The anthropogenic heat flux is estimated via a synergy of top-down and bottom-up inventory approaches. UHeatEx is applied to the city of Athens, Greece; it is correlated to air temperature measurements and compared to the LCZs classification. The results reveal that UHeatEx has the capacity to better reflect the strong intra-urban variability of the thermal environment in Athens, and thus can be supportive for adaptation responses. High-resolution climate projections from the EURO-CORDEX ensemble for the region show that the adverse effects of the existing thermal inequity are expected to worsen in the coming decades.These four decisive parameters for the urban climate (H/W, β, ∆Q S , Q F ) are estimated in this work using meteorological, earth observation, and geographical information system (GIS) data; the Climate 2019, 7, 75 3 of 28 different variables are subsequently incorporated in the UHeatEx composite indicator via principal component analysis (PCA). An additional mapping of the study area using the LCZ scheme is applied, and similarities and differences between UHeatEx and LCZs are discussed. Both results are correlated with air temperature measurements from a network of automatic weather stations (AWSs) in the area under examination, for a six-year period (the warm period months). A qualitative and quantitative analysis examines the presence of thermal inequity in Athens and its relationship with socioeconomic characteristics. The growing importance of thermal indicators in sustainable urban planning, due to climate change, is demonstrated by presenting the simulated future conditions of the city. To this end, high-resolution climate projections from an ensemble of regional climate models (RCMs) were used.

Urban Morphological Controls on Surface Thermal Dynamics: A Comparative Assessment of Major European Cities with a Focus on Athens, Greece

“…IMD and BH were aggregated from their initial spatial resolution to 1 km, using a weighted area average. As a next step, the building plan area fraction (λ b ) was calculated, based on the cumulative occupied area by the 10 × 10 m pixels with H > 0, within the corresponding 1 × 1 km windows; for Athens, the available outlines of buildings in vector format from [78] were also used.…”

“…For Athens, 3 additional surface parameters were used: (a) The canyon aspect ratio (H/W), (b) the complete aspect ratio (λ c ), and (c) the impervious ground plan area fraction (λ i ). These were derived in [78] using very high-resolution digital elevation models-a digital surface model (DSM) at 0.8 m and a digital terrain model (DTM) at 5 m-building footprints, and a 10 m land cover classification from Sentinel-2 data. Figure 3 shows the spatial distribution of H/W, λ c , and λ i , as well as the previously mentioned λ imp , λ b , and H. Using the above surface features, the LCZs of Athens were derived at 1000 m following the procedure of [78]; class assignments were based on the distinguishing surface properties of each zone from [11].…”

“…These were derived in [78] using very high-resolution digital elevation models-a digital surface model (DSM) at 0.8 m and a digital terrain model (DTM) at 5 m-building footprints, and a 10 m land cover classification from Sentinel-2 data. Figure 3 shows the spatial distribution of H/W, λ c , and λ i , as well as the previously mentioned λ imp , λ b , and H. Using the above surface features, the LCZs of Athens were derived at 1000 m following the procedure of [78]; class assignments were based on the distinguishing surface properties of each zone from [11]. As discussed in [78], the LCZ scheme did not allow the distinction of the city center of Athens, attributing the vast majority of the central areas as LCZ 2 ("compact midrise").…”

“…Figure 3 shows the spatial distribution of H/W, λ c , and λ i , as well as the previously mentioned λ imp , λ b , and H. Using the above surface features, the LCZs of Athens were derived at 1000 m following the procedure of [78]; class assignments were based on the distinguishing surface properties of each zone from [11]. As discussed in [78], the LCZ scheme did not allow the distinction of the city center of Athens, attributing the vast majority of the central areas as LCZ 2 ("compact midrise"). To address this, following the guidelines in [11], a new subclass was defined for the corresponding pixels of the city center with H/W ≥ 2 or H ≥ 20 m: "Compact midrise with compact highrise" (LCZ 2 1 ) ( Figure 4).…”

“…The average value of Q F per LCZ as well as its daily cycle (one common temporal profile for all classes) were determined from [78] for the same time period (early August) in 2012 under similar weather conditions. Anthropogenic heat releases were derived by a combination of top-down and bottom-up inventory approaches, using detailed electricity consumption and traffic measurements.…”