| Author: | Li, Jierui |
| Title: | Study on responsive relationship between climate dry-wet cycle and regional debris flow susceptibility |
| Advisors: | Ding, Xiao-li (LSGI) |
| Degree: | Ph.D. |
| Year: | 2025 |
| Subject: | Mudflows Landslide hazard analysis Climatic extremes Rain and rainfall Soil mechanics Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Land Surveying and Geo-Informatics |
| Pages: | xviii, 219 pages : color illustrations |
| Language: | English |
| Abstract: | Debris flow is a type of gravity flow involving significant solid material movement, commonly occurring in mountainous areas. However, due to the threat debris flows pose to lives, properties, and infrastructures, preventing unexpected events is crucial. Challenges in quantitatively characterizing the formation and initiation mechanism of debris flows often lead to inaccurate warnings, either missing alarms or causing false ones. The frequency and scale of debris flows have increased due to recent extreme weather events. Though current warning techniques have considered extreme weather influences by extreme rainstorms or tropical cyclones, the impacts of extreme droughts and climate dry-wet cycles have been overlooked. Thus, preventing and mitigating debris flows regionally under extreme dry-wet events is a new scientific challenge. Urgent research is needed to explain the formation and initiation mechanisms of debris flows related to extreme weather. This study focuses on the typical rainfall-induced debris flows in subtropical monsoon climates. Sichuan Province and the Hong Kong Special Administrative Region, both in mountainous areas of China, are selected as study areas. Historical debris flow inventories and geo-environmental databases, including geological, terrain, meteorological, soil, and land use, are compiled. Dry-wet cycle characteristics are derived from long-term historical dry-wet indices based on the geo-environmental databases using autocorrelation function, wavelet analysis, and multifractal spectrum analysis, while debris flow average recurrence intervals are estimated using historical debris flow inventories. Statistically based on dry-wet indices, in the two study areas of this study, debris flows occur when suffering extreme wet, while extreme drought generally exists 6-8 years before debris flow occurrences. The responsive relationship between climatic dry-wet cycles and debris flow susceptibility is explored by analyzing the debris flow average recurrence intervals and the dry-wet cycle characteristics in the study areas. Stronger correlations have been observed between debris flow recurrence interval and dry-wet cycles compared to that between debris flow recurrence interval and maximum rainfall, which is one of the most used factors for debris flow early warning. Soil sampling and testing in typical debris flow gullies help explain the mechanism behind the correlation between debris flow recurrence interval and dry-wet cycles. The dry-wet cycle characteristics are utilized to construct regional debris flow susceptibility assessment models. The responsive relationship between the climatic dry-wet cycles and debris flow susceptibility is further validated by promoting the machine learning model performance by 1-4%. The importance of dry-wet cycle characteristics in debris flow susceptibility assessment is quantitatively explained. Relative influence and partial dependence based on model structure further disclose the main drivers and their impacts on debris flow susceptibility. Factors affecting the performance of dry-wet cycle characteristics in debris flow susceptibility assessment models have been disclosed by comparing the model performance from different study regions. This study reveals the responsive relationship and explains the mechanisms between extreme weather events and regional debris flow susceptibility. A method is proposed to assess the debris flow susceptibility by considering dry-wet cycle characteristics. The contributions of various factors to debris flow susceptibility are quantified, enabling targeted disaster prevention and mitigation plans. |
| Rights: | All rights reserved |
| Access: | open access |
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