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(2023)ThesisHigh-impact extreme weather and climate events result that threaten society and ecosystems worldwide, from multiple interactions of atmospheric variables linked in dynamic ways. Ongoing global warming necessitates new ways of assessing how extreme events may change in the future. While global climate models (GCMs) have been utilised to assess the simulation of extreme events, the coarse spatial and temporal scales limit their effectiveness at regional or hydrological catchment scales. Regional climate models (RCMs) that use GCM datasets as input boundary conditions are commonly used to improve model predictability for extreme events. Although analyses of extreme events at the regional scale have evolved, systematic bias still exists and is passed onto the RCM simulation through biased boundary conditions simulated using coarser scale GCMs. Despite using various bias correction alternatives to address biases, these approaches often assume that inter-variable bias is not of key importance and that diurnal patterns are properly simulated by the GCM. However, such assumptions can result in substantial anomalies in the simulation of extreme events. Thus, this thesis investigates the impact that several bias correction alternatives can have on RCM boundary conditions with a focus on (1) Precipitation extremes; (2) Spatial, temporal, and multivariate aspects; (3) Multivariate relationships for extreme events; (4) Compound events; (5) Diurnal precipitation cycle; and develops a (6) Software tool for bias correction. The univariate techniques show improvement in precipitation extremes, but the discrepancies in inter-variable relationships are not adequately reduced through RCM boundaries. To address this issue, this study corrects the cross-dependence attributes of these fields, leading to substantial improvements in the statistics used. This study also shows that multivariate bias correction broadly represents the frequency of compound events better. The method is further developed to provide sub-daily corrections that are shown to improve the diurnal cycle of precipitation. Finally, a Python package has been developed as a software tool that simplifies the correction of systematic bias in RCM input boundary conditions. In conclusion, the work in this thesis demonstrates a significant improvement in the regional climate model simulation capacity, thereby enhancing water security and enabling more accurate forecasting of drought and flood events under climate change.
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(2024)ThesisSolar photovoltaic (PV) systems are one of the rapidly growing renewable energy technologies worldwide and will play a crucial role in future decarbonization. The accelerated pace of climate change has become a critical concern with significant implications for PV systems due to its sensitivity to weather-induced variability. Despite large-scale PV deployment worldwide, a comprehensive analysis of the role of meteorology in PV system performance is lacking. Using Australia as a case study, this thesis explores the future changes in PV generation along with addressing concerns related to future intermittency and weather-induced module degradation. Firstly, this thesis explores the effect of climate change on long-term PV potential and the major weather parameters that contribute to future changes in PV potential. The PV potential is expected to decrease ~2.5% by 2079 predominantly due to an increase in temperature. The efficiency of the cell reduces with increased temperature, thus generating less power. Under a future warmer climate, the cell efficiency losses are projected to rise (~1.2%) over Australia. Another major aspect is understanding the changes in solar resource distribution and variability to quantify weather-induced intermittency. PV generation in the eastern regions of Australia is projected to be more reliable in the future due to an increase in resource availability and subsequent reduction in intermittency (~20-minute lull periods). Short-term variability in the power generated (called ramps) can introduce voltage fluctuations that severely impact the grid stability and can also lead to power outages. A concise evaluation of ramps, project a decrease in ramp magnitude (~1%) across Australia by 2100, with ~5% changes in frequency and ramp periods varying with the location. Finally, exposure of the PV modules to outdoor conditions causes modules to degrade with time. However, how climate change will impact the mechanisms related to future degradation remains unclear. The mono-crystalline silicon modules are predicted to degrade ~0.45%/year in the future mainly due to thermal degradation mechanisms. Further, techno-economic implications of future degradation reveal ~15% rise in the cost of future energy. This research helps in identifying regions in Australia where PV systems are susceptible to climate change and provides recommendations to mitigate the risks associated with future PV reliability. The research implications of this thesis can form a benchmark for resource feasibility analysis before large-scale future investments to avoid economic losses. This research can be helpful in appropriate site selection, planning storage systems, material selection for the modules and improving module design to ensure maximum power generation from the PV systems in future.