# Risk Assessment of Landfalling Tropical Cyclones in China Based on Hazard Risk Theory
## 1 Introduction  
A tropical cyclone is the generic term for a warm-cored, non-frontal synoptic-scale low-pressure system over tropical or subtropical waters. They encompass three major natural hazards: high winds, torrential rains, and storm surges. These cyclones bring copious amounts of rainfall and violent winds, and the secondary hazards they induce pose significant threats to the safety of lives and property in areas near their landfall [1; 2; 3]. Tropical cyclones, characterized by their extensive reach and high frequency, inflict substantial damage to China, a country near the genesis region of these cyclones in the western Pacific [4]. Historical data indicate that approximately 30 tropical cyclones form in the Western Pacific each year, with about 7 of these making landfall in China. On average, these cyclones result in approximately 300 fatalities annually in China and cause direct economic losses amounting to about 44.1 billion yuan per year [5]. Therefore, it is essential to conduct in-depth research on the hazard characteristics of tropical cyclones and to implement effective measures to reduce their occurrence or mitigate their intensity. In the past, research on the risk assessment of tropical cyclones has garnered considerable attention from scholars both domestically and internationally, with many studies focusing on the point of landfall [1; 6; 7]. At the same time, under the influence of factors such as the origin, intensity, and landfall location of tropical cyclones, the path of tropical cyclones can change dynamically [8], and the surrounding areas will be affected, so the risk assessment should not be limited to the range of landfall sites. In 2023, affected by the powerful typhoon \"Doksuri\", regions as far as a thousand miles away from the landfall site, including Beijing and Henan Province, experienced the year's most significant rainfall [9]. In the field of tropical cyclone risk assessment, the selection of the research scale--such as provincial or national level--significantly influences the choice of assessment indicators and the risk assessment methods used [7; 10]. Therefore, a comprehensive evaluation of the impact of tropical cyclones across multiple provinces emphasizes the necessity to establish a robust tropical cyclone hazard assessment system. This system aims to safeguard people's lives and property, as well as to enhance natural hazard resilience, both of which can be expected to continue to receive sustained attention. As research continues to deepen, methods for assessing the risk of tropical cyclones are being continually enriched and refined toward precision and quantification by scholars. [PERSON] has broadly conducted detailed assessments focused on three elements: hazard-causing factors, hazard-prone environments, and carriers [11]. Building on this, [PERSON] proposed a fuzzy risk assessment model for typhoon hazards [12]. [PERSON] has established a hazard loss assessment model by selecting meteorological hazard-causing factors such as maximum wind speed, extreme wind speed, total precipitation, and daily rainfall from local weather stations, and correlating these with indices of human casualties, agricultural damage, and house collapses [13]. However, the above scholars did not consider the impact of the environment on the risk, especially in terms of how the topographic conditions in various regions can change the dynamic path of tropical cyclones. Several scholars, based on different spatial scales (e.g., nationwide, provincial, and regional levels), have conducted in-depth studies on the temporal and spatial distribution of precipitation caused by typhoons, environmental conditions, and cloud types through analyzing both current and historical typhoon data [14; 15; 16]. [PERSON], [PERSON] approached natural hazard risk assessment methodologies from the perspective of their driving forces, categorizing the methods into three main criteria: indicators, data, and scenarios for driving assessments [17].