城市树木风致破坏风险评估技术模型探索

Exploration of technical models for assessing wind-induced damage risk to urban trees

摘要

极端天气下,城市树木的风致损毁对生态环境、社会经济和公共安全构成严峻挑战。本研究融合计算流体力学(CFD)、探地雷达(GPR)、有限元分析(FEA)与修正 GALES 模型,提出一种无损检测 —风场模拟 —力学判稳的树木风险评估框架。通过 GPR 无损检测技术量化树干内部结构和根系分布特征,结合 FEA 分析提出稳定性影响因子,用于优化临界风速预测模型；同时基于实时气象数据与园区地理信息,构建城市局部风场和风向的快速预测模型,实现立木的安全性动态风险评估。以北京林业大学校园典型区域为案例,验证了该方法的可行性。结果表明:参数化的单体树木模型可精准定位树木的薄弱部位,有效提高树木风险评估的准确性；树木形态特征、土壤参数以及树干缺陷与根系分布受不同风速和风向作用,显著影响树干强度和根土锚固力,从而影响树木安全稳固。本研究提供的直观可视化预测结果,为服务城市树木韧性管理提供数据支撑和理论依据。

Abstract

Wind-induced damage to urban trees during extreme weather events poses severe challenges to ecological systems,socioeconomic stability,and public safety. This study integrates Computational Fluid Dynamics (CFD),Ground Penetrating Radar (GPR),Finite Element Analysis (FEA),and a modified GALES model to propose a tree risk assessment framework combining non-destructive testing,wind field simulation,and mechanical stability assessment. By quantifying internal trunk structures and root system distributions using GPR non-destructive testing technology,combined with FEA,we proposed stability influencing factors to optimize the critical wind speed prediction model. Meanwhile,a rapid prediction model for urban micro-wind fields and wind directions was developed based on real-time meteorological data and campus geospatial information,enabling dynamic risk assessment of standing tree safety. The feasibility of this approach was validated using the Beijing Forestry University campus,a typical urban campus setting,as a case study. Results indicate that the parameterized individual tree model can accurately identify vulnerable parts of trees,thereby enhancing the accuracy of risk assessments. Tree morphological characteristics,soil parameters,and trunk defects combined with root distribution are significantly influenced by varying wind speeds and directions,which in turn affect trunk strength and root-soil anchorage capacity,ultimately impacting tree stability and safety. This research provides intuitive visualization of predictive outcomes,offering a data foundation and interdisciplinary theoretical basis for resilient urban tree management.

关键词

城市树木 / 风致破坏风险 / 计算流体力学 / 探地雷达 / 生物力学模型 / 临界风速

Key words

urban trees / wind-induced damage risk / computational fluid dynamics / ground penetrating radar / biomechanical model / critical wind speed

参考文献

[1] 陈峻崎 . 2014. 北京市古树健康评价研究[D]. 北京: 北京林业大学.

[2] Chen J Q . 2014. Health assessment of ancient trees in Beijing[D]. Beijing: Beijing Forestry University.(in Chinese)

[3] 韩付家 . 2013. 树木风险评估研究概况[J]. 山东林业科技, 43(5): 90−94,80.

[4] Han F J . 2013. Overview of tree risk assessment studies[J]. Journal of Shandong Forestry Science and Technology, 43(5): 90−94,80.(in Chinese)

[5] 邵卓平 . 2025. 古树断裂、倒伏方式及其力学机制[J]. 树木医学, 2(3): 61−73.

[6] Shao Z P . 2025. Fracture,lodging mode and mechanical mechanism of ancient trees[J]. Tree Health, 2(3): 61−73.(in Chinese)

[7] 谭旭, 张潇巍, 李爽, 等 . 2024. 风载荷下树木稳定性的无损评估方法[J]. 森林工程, 40(2): 127−141.

[8] Tan X , Zhang X W , Li S ,et al. 2024. A non—destructive method for assessing tree stability under wind loading[J]. Forest Engineering, 40(2): 127−141.(in Chinese)

[9] 文剑, 李伟林, 肖中亮, 等 . 2017. 活立木内部缺陷雷达波检测研究[J]. 农业机械学报, 48(10): 180−188.

[10] Wen J , Li W L , Xiao Z L ,et al. 2017. Radar wave detection of standing trees internal defect[J]. Transactions of the Chinese Society for Agricultural Machinery, 48(10): 180−188.(in Chinese)

[11] 岳小泉, 王立海, 葛晓雯 . 2015. 风荷载作用下树木力学特性研究进展[J]. 森林工程, 31(6): 33−36, 40.

[12] Yue X Q , Wang L H , Ge X W . 2015. Research progress of mechanical properties of trees under wind load action[J]. Forest Engineering, 31(6): 33−36, 40.(in Chinese)

[13] 纭七柒 . 2016. 北京林业大学校园植物导览手册[M]. 北京: 中国林业出版社.

[14] Yun Q Q . 2016. Beijing Forestry University campus plant guide [M]. Beijing: China Forestry Publishing House.(in Chinese)

[15] 中国林业科学研究院木材工业研究所 . 1982. 中国主要树种的木材物理力学性质[M]. 北京:中国林业出版社.

[16] Research Institute of Wood Industry,Chinese Academy of Forestry. 1982. Wood mechanical properties of main tree species in China[M]. Beijing: China Forestry Publishing House Chinese.(in Chinese)

[17] Amorim J H , Rodrigues V , Tavares R ,et al. 2013. CFD modelling of the aerodynamic effect of trees on urban air pollution dispersion[J]. Science of the Total Environment, 461: 541−551.

[18] Ancelin P , Courbaud B , Fourcaud T . 2004. Development of an individual tree‒based mechanical model to predict wind damage within forest stands[J]. Forest Ecology and Management, 203(1/3): 101−121.

[19] Buccolieri R , Santiago J L , Rivas E ,et al. 2018. Review on urban tree modelling in CFD simulations: Aerodynamic,deposition and thermal effects[J]. Urban Forestry & Urban Greening, 31: 212−220.

[20] Carpentieri M , Robins A G . 2015. Influence of urban morphology on air flow over building arrays[J]. Journal of Wind Engineering and Industrial Aerodynamics, 145: 61−74.

[21] Finnigan J . 2000. Turbulence in plant canopies[J]. Annual Review of Fluid Mechanics, 32: 519−571.

[22] Franke J , Hellsten A , Schlunzen K H ,et al. 2011. The COST 732 Best Practice Guideline for CFD simulation of flows in the urban environment: A summary[J]. International Journal of Environment and Pollution, 44(1/2/3/4): 419−427.

[23] Gardiner B , Berry P , Moulia B . 2016. Review: Wind impacts on plant growth,mechanics and damage[J]. Plant Science, 245: 94−118.

[24] Gardiner B , Peltola H , Kellomäki S . 2000. Comparison of two models for predicting the critical wind speeds required to damage coniferous trees[J]. Ecological Modelling, 129(1): 1−23.

[25] Gardiner B A , Stacey G R , Belcher R E ,et al. 1997. Field and wind tunnel assessments of the implications of respacing and thinning for tree stability[J]. Forestry, 70(3): 233−252.

[26] Gromke C , Blocken B . 2015. Influence of avenue‒trees on air quality at the urban neighborhood scale. Part II: Traffic pollutant concentrations at pedestrian level[J]. Environmental Pollution, 196: 176−184.

[27] Gu D L , Zhao P J , Chen W ,et al. 2021. Near real‒time prediction of wind‒induced tree damage at a city scale: Simulation framework and case study for Tsinghua University campus[J]. International Journal of Disaster Risk Reduction, 53: 102003.

[28] Gu Y , Liu Y , Chen Y Q ,et al. 2025. Near real—time warning for urban tree destruction under coupled wind and rain loads: Prediction framework and case study[J]. International Journal of Disaster Risk Reduction, 127: 105663.

[29] Guo X , Gao Z , Buccolieri R ,et al. 2021. Effect of greening on pollutant dispersion and ventilation at urban street intersections[J]. Building and Environment, 203: 108075.

[30] Jalkanen A , Mattila U . 2000. Logistic regression models for wind and snow damage in northern Finland based on the National Forest Inventory data[J]. Forest Ecology and Management, 135(1/3): 315−330.

[31] Kang G , Kim J J , Choi W . 2020. Computational fluid dynamics simulation of tree effects on pedestrian wind comfort in an urban area[J]. Sustainable Cities and Society, 56: 102086.

[32] Keeler B L , Hamel P , McPhearson T ,et al. 2019. Social—ecological and technological factors moderate the value of urban nature[J]. Nature Sustainability, 2(1): 29−38.

[33] Launder B E , Spalding D B . 1983. The numerical computation of turbulent flows[M]// Patankar S V,Pollard A,Singhal A K. Numerical prediction of flow,heat transfer,turbulence and combustion. Amsterdam: Elsevier: 96−116.

[34] Li H B , Zhang X W , Li Z Q ,et al. 2022. A review of research on tree risk assessment methods[J]. Forests, 13(10): 1556.

[35] Mahgoub A O , Ghani S . 2021. Numerical and experimental investigation of utilizing the porous media model for windbreaks CFD simulation[J]. Sustainable Cities and Society, 65: 102648.

[36] Massman W . 1987. A comparative study of some mathematical models of the mean wind structure and aerodynamic drag of plant canopies[J]. Boundary‒Layer Meteorology, 40(1/2): 179−197.

[37] Mochida A , Tabata Y , Iwata T ,et al. 2008. Examining tree canopy models for CFD prediction of wind environment at pedestrian level[J]. Journal of Wind Engineering and Industrial Aerodynamics, 96(10/11): 1667−1677.

[38] Moore J , Gardiner B , Sellier D . 2018. Tree Mechanics and Wind Loading[M]//Geitmann A,Gril J. Plant Biomechanics: From Structure to Function at Multiple Scales. Cham: Springer International Publishing: 79‒106.

[39] Morpurgo J , Remme R P , Van Bodegom P M . 2023. CUGIC: The Consolidated Urban Green Infrastructure Classification for assessing ecosystem services and biodiversity[J]. Landscape and Urban Planning, 234: 104726.

[40] Mortensen N G , Landberg L , Troen I ,et al. 1993. Wind atlas analysis and application program (WAsP)[R]. Roskilde: Risø National Laboratory.

[41] Peltola H , Kellomäki S , Väisänen H ,et al. 1999. A mechanistic model for assessing the risk of wind and snow damage to single trees and stands of Scots pine,Norway spruce,and birch[J]. Canadian Journal of Forest Research, 29(6): 647−661.

[42] Saaroni H , Amorim J H , Hiemstra J A ,et al. 2018. Urban Green Infrastructure as a tool for urban heat mitigation: Survey of research methodologies and findings across different climatic regions[J]. Urban Climate, 24: 94−110.

[43] Säumel I , Weber F , Kowarik I . 2016. Toward livable and healthy urban streets: Roadside vegetation provides ecosystem services where people live and move[J]. Environmental Science & Policy, 62: 24−33.

[44] Tominaga Y , Mochida A , Yoshie R ,et al. 2008. AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings[J]. Journal of Wind Engineering and Industrial Aerodynamics, 96(10/11): 1749−1761.

[45] van Haaften M A , Meuwissen M P M , Gardebroek C ,et al. 2016. Trends in financial damage related to urban tree failure in the Netherlands[J]. Urban Forestry & Urban Greening, 15: 15‒21.

[46] Zeng F X , Lei C W , Liu J L ,et al. 2020. CFD simulation of the drag effect of urban trees: Source term modification method revisited at the tree scale[J]. Sustainable Cities and Society, 56: 102079.

[47] Zhou Z J , Fu J H , Xiao Y Q . 2024. Risk of wind destruction to urban trees: Prediction workflow and relative importance of influencing factors[J]. Sustainable Cities and Society, 112: 105600.

基金资助

国家自然科学基金项目(32071679)

中央高校基本科研业务费专项资金项目(BLX202335)