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February 26, 2026Sensors1 citationsOpen Access

Identification of the Dominant Rainfall Index and Evolution of Multi-Factor Driving Mechanisms for Landslide Activity in Hong Kong (1990–2024)

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JWJiahui WuZMZelang MiaoYXYi Xiong

Key Points

  • The study aims to identify the dominant rainfall index affecting landslide activity and analyze the driving mechanisms over time.
  • Evaluated spatiotemporal associations using Grey Relational Analysis.
  • Developed an integrated framework that combines rainfall index optimization with multi-factor driving analysis.
  • Utilized the Optimal-Parameter Geographical Detector model to assess individual and interacting factors.
  • The maximum 3-day cumulative rainfall index (RX3day) is identified as the dominant rainfall indicator.
  • Geological and topographic factors are shown to control spatial heterogeneity of landslides.
  • The explanatory power of RX3day has significantly increased post-2000.
  • Nonlinear interactions among factors, especially 'geology–topography' and 'rainfall–topography/geology', show strong effects on landslide occurrences.

Abstract

Revealing the spatiotemporal driving mechanisms of landslide activity is fundamental to improving long-term landslide hazard management and risk mitigation in mountainous cities. Focusing on landslide events in Hong Kong from 1990 to 2024, this study develops an integrated framework at the slope-unit scale that combines rainfall index optimization with multi-factor spatiotemporal driving analysis. First, Grey Relational Analysis (GRA) is employed to systematically evaluate the spatiotemporal associations between landslide occurrences and six commonly used rainfall indices, aiming to obtain a consistent and robust representation of rainfall triggering conditions. Subsequently, the Optimal-Parameter Geographical Detector (OPGD) model is introduced to quantitatively assess the explanatory power of individual factors—covering geological, topographic, hydro-meteorological, and human-related variables—as well as their pairwise interactions, thereby revealing the spatiotemporal evolution of landslide driving factors and their multi-factor coupling mechanisms over a 35-year period. The results indicate that the maximum 3-day cumulative rainfall index (RX3day) consistently exhibits the strongest association across different resolution parameter settings and is identified as the dominant rainfall indicator representing dynamic landslide triggering. Geological conditions and topographic factors constitute a stable background controlling the spatial heterogeneity of landslides throughout the entire study period, whereas the explanatory power of RX3day increases markedly after around 2000, gradually emerging as a primary dynamic driving factor of landslide activity. Interaction detection further demonstrates that landslide occurrence is mainly governed by nonlinear enhancement effects among multiple factors, with “geology–topography” and “rainfall–topography/geology” interactions showing the highest explanatory power, and rainfall-related interactions exhibiting continuous strengthening over time. Overall, the spatiotemporal distribution of landslides in Hong Kong is jointly controlled by long-term stable geological–topographic conditions and increasingly intensified extreme rainfall forcing.

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Cite This Study

Wu et al. (2026) studied this question.

synapsesocial.com/papers/699fe40c95ddcd3a253e833chttps://doi.org/10.3390/s26051430
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