Creating extreme weather time series through a quantile regression ensemble

Heat waves give rise to order of magnitude higher mortality rates than other weather-related natural disasters. Unfortunately both the severity and amplitude of heat waves are predicted to increase worldwide as a consequence of climate change. Hence, meteorological services have a growing need to identify such periods in order to set alerts, whilst researchers and industry need representative future heat waves to study risk. This paper introduces a new location-specific mortality risk focused definition of heat waves and a new mathematical framework for the creation of time series that represents them. It focuses on identifying periods when temperatures are high during the day and night, as this coincidence is strongly linked to mortality. The approach is tested using observed data from Brazil and the UK. Comparisons with previous methods demonstrate that this new approach represents a major advance that can be adopted worldwide by governments, researchers and industry

Evaluation of human thermal response and building resilience to extreme heat events

Under the current and potential impact of climate change, there is a growing concern about extreme heat events and their challenges to human health and building resilience. The indoor heat-stress situation relates to the interaction of outdoor extreme heat events, building characteristics, and occupants’ vulnerability. The high heat-related mortality rate of older people (aged 65+) and the trend of the population aging worldwide indicate the significant importance of evaluating and predicting heat-stress conditions for older people. Building thermal resilience determines the ability to tolerate extreme heat events and maintain or recover indoor comfort. Models of the relation between outdoor extreme weather data, indoor environment parameters, and human physiological responses are still needed to predict the consequences of global warming. Therefore, this research aims to evaluate building occupants’ thermal response and quantify building thermal resilience against extreme heat events. The Bioheat models applicable to calculating young and older adults’ physiological responses under hot exposure were developed. The validation study shows that the simulation results of the proposed models agree well with the published experimental data. The heat-stress index Standard Effective Temperature (SET) can be calculated based on the proposed Bioheat models and used in the selection of extreme hot years (EHY) and quantification of building thermal resilience. The EHY was selected by quantifying the degree of synchronization between outdoor heatwave events and building indoor overheating conditions based on the concept of POS (Percentage of Synchronization). It has been proved that in building overheating-centric studies, the EHYs should be selected according to the severity and intensity of heatwaves defined by SET. A new quantification framework for building thermal resilience against extreme heat events was developed. The framework includes the conceptual resilience trapezoid curve, Thermal Resilience Index (TRI), and resilience labelling system for zone level and building level resilience. The proposed framework has been implemented in a calibrated building model to quantify the building thermal resilience with different retrofit strategies. With this method, the effect of retrofit strategies and their combinations on the building and zonal thermal resilience can be quantified, labelled, and compared, thereby, a detailed design of resilience enhancement strategies to be achieved. The contributions of the thesis include validated new models and methods to quantify human thermal responses and building resilience to extreme heat events. These new methods and models contribute potentially significant impacts to the research under different climate zones and future climates covering from a single building to large scales to quantify community or city scale resilience to heat