CHAPTER 7: ASSESSMENT OF IMPACT OF URBAN LAND SURFACE
7.1 Introduction
Urbanization-induced land use and land cover (LULC) distribution and change alter the energy and water balances, causing thermal elevation as natural covers are replaced by impervious surfaces (Nayak & Mandal, 2012). Built up areas absorb and radiate high amounts of heat energy while green-spaces act as heat sinks as they are porous and assimilate local heat (Sithole
& Odindi, 2015). Furthermore, preferential heating of the city, in comparison to the surrounding creates convectional currents which further trap heat (Tursilowati, 2007).
Generally, elevated temperatures increase resident’s thermal discomfort as well as heat related diseases and mortality (Guhathakurta & Gober, 2007; McDonald et al., 2011a; Hallegatte &
Corfee-Morlot, 2010). Urbanization also increases economic strain, particularly in developing countries, as necessary interventions are required to cope with thermal change related impacts (Brown., et al., 2012). Depending on the season, urban thermal characteristics influence energy demand for indoor heating and cooling to ensure human comfort. Thermal elevation arising from urbanization may therefore alter energy requirements due to increased built-up density.
Increased energy requirements to mitigate household thermal elevation like air-conditioning have been associated with rise in greenhouse gas concentration which further raised temperature and household cooling energy demand. Hence there is need to monitor responses of energy demand to localized warming for sustainable urban growth and management of risks associated with indoor thermal discomfort.
Several studies have attempted to estimate the impact of urbanization on energy consumption for heating and cooling, however, each approach has its own limitation. Among others, studies have utilized household electricity bills to determine impact of urban growth on energy consumption through air conditioning (Hirano, et al., 2009; Souza, et al., 2009; Shahmohamadi, et al., 2010; Arifwidodo & Chandrasiri, 2015). Shahmohamadi et al. (2010) for instance established that energy consumption in the United Kingdom, United States of America and Sri Lanka household energy consumption increased with land surface temperature and intensification of urban heat island (UHI). However, the major limitation of this approach is that household electricity usage is not restricted to air conditioning but include other usage like refrigeration, lighting and cooking (Ewing & Rong, 2008). Degree Days derived from temperature have also been as a proxy for energy requirement for indoor cooling or heating (Vardoulakis, et al., 2013; Arifwidodo & Chandrasiri, 2015; Ewing & Rong, 2008). Degree Days are based on a base temperature below or above which human discomfort is triggered, thus a direct measure of need for space heating and cooling (Bolattürk, 2008). Cooling Degree
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Days (CDD) provide a measure for energy for space cooling while Heating Degree Days (HDD) infer energy for household warming (Christenson, et al., 2006). Degree Days strongly relate with energy consumptions. (Balaras, et al., 2005) for instance found a strong positive correlation between CDD and energy in European cities. However, a major limitation in the adoption of Degree Days in previous studies is the use of in-situ measurements of temperature, characterized by limited spatial coverage (Stathopoulou, et al., 2006). (Stathopoulou, et al., 2006) for instance, noted that even in developed countries multiple meteorological stations within 1km2 are rare. Hence in-situ observations are commonly unrepresentative and unable to capture temperature variation, especially in urban landscapes characterized by heterogeneous land-use-land-cover types with high thermal variability (Ogrin & Krevs, 2015). This limitation is even worse in most developing countries, especially in Africa, often characterized by limited meteorological stations coverage, in-adequate to effectively depict urban landscape heterogeneity (Owen, et al., 1998; Shahmohamadi, et al., 2010; Tao, et al., 2013; Zhou &
Wang, 2011a).
The emergence of thermal space-borne remotely sensed data offer great potential in determining intra-urban thermal characteristics, hence spatial characterization of space heating requirements. Furthermore remotely sensed data offer a cost effective means for spatio- temporal analysis and a rich archival data, spanning over 30 years, valuable for climate change analysis (Senanayake, et al., 2013; Tao, et al., 2013; Owen, et al., 1998). However, despite the proliferation of remotely sensed data, its spatial coverage and improvements in data quality such as in radiometric resolution, its adoption to estimate trends in cooling and heating energy has remained limited. To the best of our knowledge, only a single study (Stathopoulou, et al., 2006) has used satellite data to estimate energy consumption in space cooling using Degree Days. In their study, Stathopoulou, et al. (2006) used National Oceanic and Atmospheric Administration’s (NOAA) Advanced Very High Resolution Radiometer (AVHRR) thermal data and estimated Cooling Degree Days (CDD) with an error of 2.2 degree cooling days when compared to retrievals from in-situ temperature data. Furthermore, they obtained a strong correlation (R2=0.78) between estimated and observed CDD for a base temperature of 25 oC.
However, NOAA AVHRR has low spatial resolution of 1.1 km, which may cause errors due to an assumption of uniform temperature over a relatively large and heterogeneous area that often, characterize urban landscapes. Therefore, medium resolution Landsat series data offer great potential to improve estimation of energy requirements for indoor cooling and heating.
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Although not yet used to estimate Degree Days, Landsat data has been instrumental in the estimation of spatial and temporal variations of temperature even in complex urban environments. Landsat has long history of freely-downloadable archival data dating back to 1972, making the series suitable for temperature estimation at single day, seasonal and long term temporal scales (Gusso, et al., 2014; Tao, et al., 2013). In comparison to in-situ observations, surface temperatures estimated from Landsat are on cloud-free days enabling estimation of extreme energy consumption levels. The spatial resolution of the thermal data enables mapping of variations in energy demands between built-up regimes. This is important for identifying the most vulnerable strata and communities, power supply rationalization and in designing of future housing. Furthermore, at the spatial resolution of thermal data from Landsat missions, temperature is estimated over comparatively smaller units than using NOAA AVHRR thus capable of improving accuracy of measurement of Degree Days satellites in urban areas. This is made possible by the capability of Landsat data to produce detailed maps of both LULC and potential thermal stress. At the spatial resolution of multi-spectral data from Landsat, it is possible not only to extract built-up areas but also to further zone them based on characteristics such as density of buildings and vegetation cover fraction. This is important in accurately mapping the complex urban thermal characteristics as well as their impacts which vary within short space. We therefore hypothesize that Landsat data with lower spatial resolution can quantify Degree Days and air-conditioning energy demand in complex urban settings better than in-situ observations.
The objective of this study was thus to quantify the impact of urbanization on energy consumption for indoor heating and cooling energy in Harare, an emerging African city, using remotely sensed data. Specifically, the study adopts LULC changes between 1984 and 2015 to quantify the city’s growth and monitors subsequent response of energy consumption. The study achieves this by quantifying differences in heating and cooling energy requirements based on built-up categories, i.e. Central Business District, high, medium and low density residential areas. The study thus presents a novel approach of estimating Heating and Cooling Degree Days as well as their link with actual energy consumption using medium resolution space- borne satellite remote sensing datasets.
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