| Author: | You, Jiewen |
| Title: | Detection and attribution of compound hydrometeorological extremes |
| Advisors: | Wang, Shuo (LSGI) |
| Degree: | Ph.D. |
| Year: | 2024 |
| Subject: | Hydrometeorology Climatic extremes Rain and rainfall Heat waves (Meteorology) Flood forecasting Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Land Surveying and Geo-Informatics |
| Pages: | 146 pages : color illustrations |
| Language: | English |
| Abstract: | Compound hydrometeorological extremes refer to the simultaneous or sequential occurrence of multiple climate drivers and hazards (especially floods and heat waves). These compound events often have more severe consequences compared to when these disasters occur individually. Therefore, detecting and understanding compound hydrometeorological extremes are crucial for formulating effective adaptation and mitigation strategies to address the threats posed by climate warming. Although research on the detection and attribution of compound hydrometeorological extremes has received increasing attention in recent years, certain limitations still exist in previous studies. (1) Sparse in-situ tide gauge stations restrict the continuous spatiotemporal assessment of compound flood risk; (2) Despite extensive studies on individual phenomena, the potential lagged dependence between heat waves and heavy rainfall is often overlooked; (3) Knowledge about emerging compound events, particularly humid heat and pluvial flooding, remains poorly understood on a global scale. To address these limitations, innovative methodologies have been developed for a comprehensive assessment of compound hydrometeorological extremes, focusing particularly on compound flood hazards and compound heat-flood hazards. (1) A vine copula ensemble machine learning, combined with a Bayesian hierarchical model is proposed for predicting compound flood risk at ungauged sites. (2) Detection, attribution, and projection methods are employed to address the frequently overlooked lagged dependence between heatwaves and heavy rainfall. (3) A comprehensive global analysis of emerging compound humid heat and pluvial flood events is conducted to improve the understanding of compound hazards using innovative methods for robust detection and attribution. The key findings include: (1) The vine copula ensemble model outperforms traditional methods, showing 29.09% and 19.35% improvements in two datasets. The Bayesian hierarchical model effectively predicts storm surges at ungauged stations with about 11% error. Compound flooding analysis reveals 14.54% of extreme storm surges in Hong Kong coincided with heavy rainfall. (2) In China, 22% of land areas experienced statistically significant consecutive heat wave and heavy rainfall events within 7 days. The shorter and hotter heat waves are more likely to be followed by heavy rainfall. This phenomenon is associated with atmospheric convection and moisture convergence, with projections showing increased frequency and abruptness throughout the 21st century. (3) Global, successive heat-pluvial and pluvial-heat occurred more frequently than expected by chance, with a 20% per decade increase due to warming. These events are associated with vapor pressure deficit anomalies. The findings of this dissertation enhance the understanding of compound hydrometeorological extremes. The probabilistic and continuous estimation of compound flooding advances methodologies in flood risk assessment, emphasizing the importance of addressing multi-hazard flood risks in coastal cities. The investigations into consecutive heat wave and heavy rainfall events provide insights into mitigating or eliminating the impacts of these back-to-back extreme events. The global analysis of compound humid heat and pluvial flooding extremes highlights the need for strategies to cope with overlapping vulnerabilities due to compound hot and wet extremes, particularly in areas prone to such events. |
| Rights: | All rights reserved |
| Access: | open access |
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