Date of Award

2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Earth & Environmental Systems

Abstract

Anthropogenic heat flux (Q f ) originates from energy consumption in buildings, industrial plants, vehicle exhaust, and human metabolism. Q f is an important component of the urban Surface Energy Balance (SEB) system and a key to understanding many urban environmental issues. Climate change affects building energy consumption in many ways, and building energy consumption is the largest contributor to Q f in many cities. One of these contributions comprises changes in heating and cooling demands in buildings. The increase in annual energy use in cities results in more carbon emissions and constitutes a great challenge to urban sustainability because traditional fossil fuels remain major resources for the production of electricity for heating and cooling in buildings. The primary objectives of thisdissertation are to 1) develop a high spatial and temporal Q f profile that can be readily incorporated into the urban energy balance models and be used to analyze Q f across multiple spatial and temporal scales; 2) develop a useful database that can allow a city government to foresee the different regional sensitivities to climate change in the city from building energy demand increases at different spatial and temporal scales; and 3) test the potential mitigation effects of green roofs and solar photovoltaic (PV) systems on buildings that are more vulnerable to climate change. Los Angeles County, California, USA, was chosen as the study area, because it was the most populous county in the USA and contained various microclimate conditions. To achieve the objectives of this study, a hybrid Q f modeling approach was developed that combined census inventory data and Geographic Information System (GIS) methods to create a 365-day hourly Q f profile at 120-m spatial resolution for Los Angeles County. In a subsequent step, a GIS-based approach was used to combine climate change modeling, building energy simulation, and fine-scale (individual building) inventory data of building characteristics to quantify the effect of climate change on building energy demand at the sub-city scale. In the final step, the potential mitigation effects of PV-green roofs for building energy demand were assessed based on selected buildings that were predicted to have increased energy needs in the context of climate change. The results showed different magnitudes and diurnal patterns in Q f between workdays, with one peak in the morning and the other in the evening rush hours (dual-peak shape) and weekends/holidays. Additionally, Q f varied seasonally and among different land use types. Building energy consumption was identified as the dominant contributor to Q f in the downtown area of Los Angeles, which was found to have the largest mean Q f among all neighborhoods throughout the entire year. Most building types showed increased energy demands under both scenarios of climate change. Larger changes were observed at finer time scales. The energy demand for buildings increased from April to October, whereas it decreased from November to March. Areas with dense tall commercial buildings would see the largest increase in energy demand. All buildings with green roofs showed positive energy savings with regard to total energy and electricity. In addition, the energy saving ability of green roofs was affected by seasonal effect, building types and technologies, and irrigation saturation, which is the threshold of soil moisture that allows for irrigation. All three objectives of thisdissertation were achieved, and the methodology allows city governments to foresee the sensitivity of building energy demands at different spatiotemporal scales and tailor needed strategies.

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