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In recent decades, climate change has caused a more volatile climate leading to more extreme events such as severe rainstorms, heatwaves and floods which are likely to become more frequent. Aiming to reveal climate change impact on the hydroclimatic extremes in a quantitative sense, this thesis presents a comprehensive analysis from three main strands. The first strand focuses on developing a quantitative modelling framework to quantify the spatiotemporal variation of hydroclimatic extremes for the areas of concern. A spatial random sampling toolbox (SRS-GDA) is designed for randomizing the regions of interest (ROIs) with different geographic locations, sizes, shapes and orientations where the hydroclimatic extremes are parameterised by a nonstationary distribution model whose parameters are assumed to be time-varying. The parameters whose variation with respect to different spatial features of ROIs and climate change are finally quantified by various statistical models such as the generalised linear model. The framework is applied to quantify the spatiotemporal variation of rainfall extremes in Great Britain (GB) and Australia and is further used in a comparison study to quantify the bias between observed and climate projected extremes. Then the framework is extended to a multivariate framework to estimate the time-varying joint probability of more than one hydroclimatic variable in the perspective of non-stationarity. A case study for evaluating compound floods in Ho Chi Minh City, Vietnam is applied for demonstrating the application of the framework. The second strand aims to recognise, classify and track the development of hydroclimatic extremes (e.g., severe rainstorms) by developing a stable computer algorithm (i.e., the SPER toolbox). The SPER toolbox can detect the boundary of the event area, extract the spatial and physical features of the event, which can be used not only for pattern recognition but also to support AI-based training for labelling/cataloguing the pattern from the large-sized, grid-based, multi-scaled environmental datasets. Three illustrative cases are provided; and as the front-end of AI study, an example for training a convolution neural network is given for classifying the rainfall extremes in the last century of GB. The third strand turns to support decision making by building both theory-driven and data-driven decision-making models to simulate the decisions in the context of flood forecasting and early warning, using the data collected via laboratory-style experiments based on various information of probabilistic flood forecasts and consequences. The research work demonstrated in this thesis has been able to bridge the knowledge gaps in the related field and it also provides a precritical insight in managing future risks arising from hydroclimatic extremes, which makes perfect sense given the urgent situation of climate change and the related challenges our societies are facing.

Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes