CHAPTER 4: SPATIAL DISTRIBUTION OF EXTREME HEAT VULNERABILITY AND
4.4 Discussion
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Figure 4.3: Mapped vulnerability of Harare to high surface temperatures (a) and its link to Surface temperature observed on 30 October 2015 (b).
There was a convincing agreement between the distribution of vulnerability and temperature.
The average temperature was low (32.2oC) where vulnerability was very low compared to very high average temperature (42.2oC) where vulnerability was very high (Table 4.2).
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according to Wilhelmi and Hayden (2010) and Dewan and Corner (2012) ensures effective implementation of localized adaptation and mitigation strategies. Congruently, Johnson, et al.
(2009) pointed out that supplementing socio-demographic data with remotely sensed bio- physical data improves delineation of intra-urban variations in risk from extreme heat events although they mapped vulnerability at census block level.
Heat vulnerability to high surface temperatures was found to be high in the southern areas characterized by a combination of low vegetation fraction, dry surfaces and highly built-up areas occupied by low income residents. Observations along a southwest to northeast direction showed that heat vulnerability was high where high NDBI, low NDVI, low NDWI and high social vulnerability co-existed. This aligns with previous studies which showed that surface temperatures increase with increasing density of buildings, and decrease with increasing surface wetness and vegetation cover (Yuan & Bauer, 2007; Maeda, 2015; Spronken-Smith &
Oke, 1998). In Greater Dhaka, Bangladesh, Dewan and Yamaguchi (2009) also noted that clearing of vegetation resulted in a wide range of environmental impacts including reduction in habitat quality. This study observed that the biophysical properties combine additively to give a measure of vulnerability to high surface temperatures. As such, Maeda (2015) observed that the correlation between surface temperature with a combination of NDVI and elevation was higher than with each of the factors alone. Southern areas of Harare, where heat vulnerability is very high, are mainly occupied by high density residential areas with a low income demographic, thus compromised capacity to cope with heat related pressures during the hot season (Mushore et al., 2016, Kamusoko et al., 2013). According to Brenkert and Malone (2005), the Indian state of Orissa recorded very high vulnerability level due to significant poverty, low level of industrialization and low human development. This is consistent with previous studies which showed that low household income increases heat vulnerability by reducing capacity to adapt (Harlan, et al., 2013; Aubrecht & Özceylan, 2013;
Uejio, et al., 2011; Coates et al., 2014). Harlan et al (2013) observed that deaths from heat exposure in Maricopa County, Arizona were high among people who lack access to cool environments and air conditioning facilities. Coates, et al. (2014) observed that, in Australia, most vulnerable groups live in houses that are poorly adapted to extreme heat.
Differences in levels of heat vulnerability were observed between high density residential areas in the southwest and those in the south. Besides low household income, southwestern suburbs
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have larger populations aged below 15 and above 65 years. This is in tandem which earlier studies that recorded a huge impact of heat stress on the elderly and very young ages (Klein Rosenthal et al., 2014; Scherer et al., 2013). For example, Rosenthal et al. (2014) observed that heat related mortality was high in places where the ratio of people aged above 65 years to the total population was high. The buildings are also more densely packed and older in the southwestern suburbs than elsewhere, hence the very high vulnerability. The old buildings may not be designed to enable effective heat removal by natural ventilation in view of changes in climate since their period of construction and low household income levels. The wide disparity in quality of residential areas between the northern and the southern areas can be linked to the colonial past (Potts, 2011). According to Potts (2011), the southern suburbs have small plots that were meant to host an influx of poor people moving to the city as a labour force. In agreement with Dewan and Corner (2012), packed buildings in the high density residential areas absorb large amounts of heat as indicated by large surface temperatures thus requiring indoor air-conditioning. In Australia, the most vulnerable groups were also found to live in low quality housing units (Coates, et al., 2014). In London, thermo-insulation of homes and high population density were also observed to increase vulnerability (Wolf & McGregor, 2013). In agreement of our finding, lack of wealth was also found to reduce the capacity of a society to access markets, technology and other resources that can be used to adapt to climate change (Brenkert & Malone, 2005). In urban Georgia, low income was also found to combine with physical exposure to increase heat vulnerability in low quality residents (Maier et al., 2014).
In this study, low vegetation cover (NDVI<0.5) and low surface wetness (NDWI<0.5) in the southern suburbs can be linked to resource constraints that prohibit high density residential dwellers from watering and maintaining urban greenery as well as from affording spacious settlements with abundant greenery. Surface wetness and greenery favor evapo-transpiration rather than absorption of heat. Such cooling effect is thus retarded in the southern areas. This agrees with Spronken-Smith and Oke (1998) who observed that during the day, there is a negative correlation between NDWI and surface temperature. Water has high heat capacity such that a lot of energy is required to raise its temperature compared to other surfaces during the day. Open water and high surface wetness favor latent heat transfer thereby lowering surface radiant heat, while surface wetness provides moisture for latent heat transfer thereby reducing amount absorbed by surfaces, thus lowering surface temperatures (Weng & Lu, 2008).
Fanham et al. (2015) observed that daytime temperatures of a city can be reduced using a mist
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fan, which blows moisture on to surfaces, thereby increasing their wetness. Therefore, low level of cooling by latent heat transfer together with limited resources for air conditioning increased heat vulnerability in high density residential areas. Although it was moderate to very high in all high density residential areas, vulnerability was not uniform. Similar to our findings, previous studies also observed that buildings were older while population density, socio- economic pressure and density of buildings were higher in high density areas to the southwest than to the south in Harare (Mlambo, 2008; Zinyama et al., 1993; Wania, et al., 2014). This combination of old buildings and inadequate resources to cope with extreme heat was also labeled as increasing vulnerability in another study (Tomlinson, et al., 2011).
There was a strong spatial correlation between the spatial distribution of vulnerability to extreme temperature and observed surface temperatures (α=0.61). High surface temperatures (40 – 45oC) were observed where vulnerability was in the high to very high categories in southern residential areas. The agreement between observed surface temperature and extent of vulnerability indicate the success of vegetation indices derived from Landsat 8 to accurately measure surface bio-physical properties which in turn strongly correlate with temperature.
Generally, land surface temperature was high where vulnerability was high and vice versa. It has been observed that combining two or more surface properties improves the prediction of surface temperatures by increasing correlation (Maeda, 2015). Maeda (2015) observed that during daytime, the correlation between temperature and elevation alone was 0.68 (R2) but increased to 0.94 when NDVI was included. Therefore, in this study, combining NDVI, NDWI, and NDBI improved vulnerability mapping as evidenced by strong agreement between the mapped vulnerability and observed surface temperatures. The daytime land surface temperature distribution can thus be used to indicate areas where heat vulnerability is high.
Harlan et al (2013) also demonstrated that surface temperature might also be used to indicate heat vulnerability in Maricopa County, Arizona (Xu, et al., 2013). In the western areas of Arizona, high heat vulnerability and high surface temperature were also found to coincide due to physical exposure (Chow, et al., 2012). However, this alone is not sufficient as vulnerability was moderate in some of the southern areas of Harare where temperature was high.
Vulnerability was found to be in the high to very high category in 42% of the total areas of the Metropolitan City of Harare. The large proportion of areas with high vulnerability to extreme surface temperature is due to the extent of built up areas, especially high density dwellings for
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low income groups, that has increased over the years (Kamusoko et al., 2013). This agrees with previous studies that have shown that as a result of urbanization, most of the areas in a city experience high surface temperatures especially when compared with surrounding rural areas where the density of buildings is low (Qiao, et al., 2013). Zhang et al. (2008) also asserted that urbanized surfaces modify the energy and water balance and influence dynamics of air movement, making urbanized areas warmer than the surroundings. It was also observed that population growth and residential developments result in increased temperatures of emerging cities (Zhang, Qi, et al., 2013). Increased density and spatial extent of buildings due to city growth results in elevated temperatures, increased thermal risk and energy consumption through air conditioning (Polydoros & Cartalis, 2014). However, in agreement with Souza, et al. (2009) this makes the low income strata highly vulnerable by raising energy requirements and related costs beyond their reach. Furthermore, Batih and Sorapipatana (2016) observed that the ratio of heat intensity to household income is a strong indicator of vulnerability.
In this study, vulnerability to high surface temperatures in the hot season was observed to be decreasing northwards due to increasing vegetation abundance and reduction in socio- demographic pressures. Except for water bodies which have both low vegetation fraction and low vulnerability, very low to moderate vulnerability were observed north of the CBD where vegetation fraction was greater than 40% and NDVI was between 0.5 and 1. This agrees with Chen et al. (2006) that there is an inverse relation between surface temperature and vegetation abundance represented by high NDVI values. Even in areas where there are buildings, vegetation cover lowers temperature due to latent heat transfer by increasing the surface-air vapour gradient (Chun & Guldmann, 2014). High NDBI values in the CBD implied that the density of buildings was high thus reducing extent of cooling by evaporation as there were few spaces available for vegetation cover and water bodies (Chen, et al., 2006; Yuan & Bauer, 2007). Vegetation lowers surface radiant temperatures as most of the energy received from the sun is used to evaporate water from vegetation surfaces instead of heating the ground and the surrounding (Amiri, et al., 2009; Gottshe & Olesen, 2001; Zhang, et al., 2009). A study by Amiri et al. (2009) showed that human surface alterations can create cool green edges by irrigated plantations due to high thermal capacity and increased latent heat transfer. Zhang et al. (2009) also revealed that urban greenery plays a role in mitigating the heat island effect.
Therefore, a combination of generally high income which increases capacity to adapt and vegetation abundance which enhances coping with extreme temperatures reduces heat
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vulnerability in the northern areas. Abundance of green space was found to reduce heat vulnerability by reducing ambient temperature and providing shelter in Michigan, USA (Gronlund et al., 2015). Vegetation within the urban fabric, such as trees, lowers temperatures and visiting green areas is a good coping strategy during periods of heat stress (Depietri, et al., 2013).
Low to medium density residential areas which occupy the north and eastern parts of the city had moderate vulnerability to extreme surface heating. In these areas, buildings are spaced out, allowing for urban greenery as indicated by vegetation fraction between 40% and 60%, thus higher surface wetness. A study in the same area also revealed that the northern parts of the city are largely occupied by high income strata (Wania, et al., 2014). Therefore, residents of low to medium density residential areas in Harare largely afford to sufficiently maintain urban greenery such as lawns and orchards as indicated by high NDWI and high NDVI compared to the southern suburbs. Increased surface wetness increases heat capacity, increasing latent heat transfer and suppresses temperature of a surface (Cao, et al., 2008; Steeneveld, et al., 2014).
Therefore, surface wetness and greenery reduces vulnerability which supports observations that NDWI and NDVI have an inverse relationship with surface temperature (Chen, et al., 2006). Similarly, Chow, et al. (2012); (Batih & Sorapipatana, 2016) observed that eastern areas of Phoenix had low heat vulnerability due to high income of residents and increased surface greenness due to landscape modification. The significant value of urban greenery in mitigating against extreme surface temperatures was also observed in a recent study (Odindi, et al., 2015).
Odindi et al., (2015) observed that in all seasons, dense vegetation lowers surface temperatures and there was a strong correlation between NDVI and land surface temperature (R2=0.7653).
Surface properties were observed to expose the central business district and industrial areas of the city to high risk of extreme temperatures. In these areas, there was a combination of low NDVI, low NDWI and high NDBI. The NDVI and NDWI ranged between 0.1 and 0.4 while NDBI was generally above 0.5. Thus high vulnerability to extreme temperatures results from high NDBI values, low NDVI and low NDWI, which agrees with several previous works which showed that daytime temperatures are bound to be high where NDBI is high, vegetation fraction is low and the surface is dry (Chen, et al., 2006; Farnham et al., 2015; Spronken-Smith
& Oke, 1998; Chun-ye & Wei-ping, 2011; Steeneveld, et al., 2014). Among densely built areas, high vulnerability was also observed in the CBD and industrial areas. This is due to high
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imperviousness (low NDVI, low NDWI and high NDBI) as well as effects of high rise buildings which characterize the CBD. These buildings increase temperatures by reducing sky view factor, reducing heat removal by wind and by storing large amounts of energy absorbed by the walls of the buildings (Chun & Guldmann, 2014). This agrees with Aubrecht and Ozceylan (2013) who obtained higher levels of heat vulnerability in urbanized areas than in non-urbanized surroundings in the USA. Consistent with our findings, concentration of high rise buildings was also identified as a heterogeneous indicator of potential heat exposure (Rinner et al., 2010). The vulnerability in industrial areas is consistent with Harlan et al (2013) who stressed that people who are physically active in hot environments are highly likely to suffer from heat distress, especially in non- air-conditioned settings.