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  Speaker: Yuchen Lu Doctoral Candidate Thesis Defense Department: Civil and Environmental Engineering Location: Ryon Engineering Building 112 Hydroclimate extremes, including extreme rainfall and tropical cyclones, pose substantial threats to communities, infrastructure systems, and ecosystems. Probabilistic characterization of these extremes is essential for risk assessment, infrastructure design, mitigation planning, and climate adaptation. Many commonly used approaches for estimating hydroclimate hazards assume stationarity, even though observations and climate projections show that extreme events are influenced by climate variability and long-term climate change. However, accounting for this nonstationarity poses methodological challenges: observations of rare events are sparse in time and space, while climate model products contain systematic biases. To address these challenges, this dissertation develops statistical frameworks that incorporate nonstationarity into probabilistic analyses of hydroclimate extremes using limited observational records.   The first chapter develops a hierarchical Bayesian space-time framework for nonstationary analysis of extreme rainfall probabilities. This framework mitigates the challenge of larger sampling variability and uncertainty by incorporating time-varying climate covariates and spatial pooling, allowing information to be shared across stations while estimating spatially coherent changes in daily extreme rainfall. The second chapter extends this analysis to multi-duration rainfall frequency estimates by incorporating duration dependence. This work addresses the challenge of maintaining coherence across both space and durations, which is especially important for short-duration rainfall extremes where observations are particularly sparse. The third chapter develops a nonstationary joint probability framework for characterizing tropical cyclone parameters relevant to hazard analysis. This approach mitigates the challenge of limited local tropical cyclone observations by using regional changes in storm characteristics to inform local-scale analyses and by linking dependent storm parameters through physically motivated relationships. Together, these three studies address a common problem in hydroclimate risk analysis: how to estimate changing extremes from limited and spatially heterogeneous observations. Across rainfall and tropical cyclone applications, the proposed frameworks improve the stability, spatial coherence, and physical interpretability of nonstationary hazard estimates. These methods provide a basis for more robust infrastructure design and climate adaptation planning under changing hydroclimate risk. (Department : Civil and Environmental Engineering)
