基于GIS加权核密度场模型的闪电热点探测与影响因素分析

Detection and Analysis of Lightning Hotspots and Influencing Factors Based on GIS Weighted Kernel Density Field Model

摘要:

本研究利用2017—2023年陕西VLF/LF闪电定位系统地闪数据，基于GIS（Geographic Information System）核密度场模型，建立了基于强度权重的地闪核密度表面;应用热点探测模型对地闪密度热点进行提取和等级分类;分析了地闪核密度及其热点的分布与地形地貌、植被覆盖等环境因素的关系。研究结果显示：①利用GIS核密度场模型和热点探测模型可以准确识别闪电活动密集区域，并深入揭示地闪空间分布特征。②陕西地闪活动存在明显的时空差异，地闪在夏季发生次数多强度大，春秋季次之，冬季最少；空间分布总体表现为南北高—中间低的空间格局。③核密度在海拔高度1 km以下与海拔高度正相关，在海拔高度1 km以上与海拔高度负相关；地形地貌、植被及两者交叉项均对核密度产生显著性差异影响。地形地貌影响的差异超过植被。④中海拔黄土梁峁和中海拔中起伏山地的草地和林区是大型及中大型热点的主要分布区。

Abstract:

This paper presents a detailed analysis of cloud-to-ground (CG) lightning data collected by the Shaanxi VLF/LF lightning location system over a period of seven years, from 2017 to 2023. The research begins by constructing a CG lightning kernel density surface, which is derived from the collected data and implemented using intensity weighting through a Geographic Information System (GIS) kernel density field model. This model facilitates the visualisation and quantification of lightning activity across the Shaanxi region, thus providing a robust foundation for subsequent analyses. Upon establishing the kernel density surface, the study utilises a hotspot detection model to identify and categorise regions with high lightning density. This crucial step pinpoints specific areas where lightning activity is exceptionally intense, thereby offering vital data for risk assessment and mitigation strategies. The analysis explores the relationships between the distribution of CG lightning kernel density, its identified hotspots, and various environmental factors such as topography and vegetation coverage, both of which significantly influence lightning activity. Key insights revealed by the findings include the effectiveness of the GIS kernel density field model and the hotspot detection model in accurately identifying regions with intense lightning activity. This enhanced understanding of the spatial distribution characteristics of CG lightning is essential for predicting and managing lightning-related hazards. Additionally, the research identifies significant spatiotemporal variations in CG lightning activity within the Shaanxi region. The analysis shows that lightning activity is most frequent and intense during the summer months, followed by spring and autumn, with winter experiencing the least activity. Spatially, the lightning density is higher in the northern and southern parts of Shaanxi, while the central region shows relatively lower density. Further, the study examines the correlation between kernel density and altitude. A positive correlation is observed below 1 km in altitude, suggesting that lightning activity increases with altitude. Above 1 km, however, this trend reverses, with a decrease in kernel density as altitude increases. The research also investigates how topography and vegetation impact kernel density values, revealing that both factors significantly affect kernel density, with topography having a more pronounced impact. This indicates that variations in terrain play a crucial role in shaping the patterns of lightning activity. In conclusion, the study identifies the primary distribution areas for large and medium-large hotspots, predominantly located in grasslands and forest areas within mid-altitude loess ridges and mounds, as well as moderately undulating mountains. These findings are instrumental for targeting lightning protection measures and enhancing safety in these vulnerable areas. Overall, this paper significantly advances understanding of lightning activity in the Shaanxi region, providing valuable insights for future research and practical applications, thereby enriching the scientific community’s resources for better managing the risks associated with lightning.