关键词: 微地形, 等级划分, 风速反演, 计算流体力学（CFD）, 多元回归分析

Abstract: Microtopography dominates the spatial distribution of disasters by altering local meteorological conditions. Scientific classification of microtopography is a crucial prerequisite for achieving precise disaster prevention and differentiated management. To accurately quantify the influence of microtopography, this study takes the Liupanshan area as the research object. Based on the classification and identification of typical microtopographic types, multiple regression analysis was employed to reveal a significant coupling relationship between slope, elevation, and local wind speed (R² = 0.987, p < 0.01), verifying that microtopographic heterogeneity serves as the core mechanism driving local micrometeorological differentiation. On this basis, a quantitative classification method for microtopographic grades was proposed, based on inversing wind speed variation characteristics. Through high-resolution wind field simulation of the target area using Computational Fluid Dynamics (CFD), criteria for classifying the intensity of saddle-shaped microtopography based on wind speed response were established. Furthermore, by integrating the national standard "Meteorological Terminology" (GB/T 20481-2006) and relevant wind speed grade specifications, a four-level wind speed classification standard was formulated and mapped onto microtopographic units, achieving a preliminary classification of microtopography. Finally, by combining the wind speed standards with quantitative relationships of topographic factors, a microtopographic intensity classification framework with specific parameter ranges was constructed. This classification framework can not only provide scientific support for disaster early warning and line optimization design of transmission corridors in mountainous areas but also offer a quantitative reference for disaster prevention and control in related fields such as wind load assessment for transportation engineering.

Key words: Micro-topography, Classification, Wind speed inversion, Computational Fluid Dynamics (CFD), Multiple regression analysis