城市局地热气候预测及热岛效应缓解策略

宋晓程¹, 刘京^1,2, 林姚宇³, 刘琳¹, 王丹³

(1.哈尔滨工业大学 市政环境工程学院,150090 哈尔滨; 2.城市水资源与水环境国家重点实验室(哈尔滨工业大学), 150090 哈尔滨; 3.哈尔滨工业大学深圳研究生院 深圳市城市规划与决策仿真重点实验室, 518055 广东 深圳)



摘要:

为量化分析下垫面对局地热环境的影响,采用多用途建筑区域热气候评价模型,以深圳国际低碳城核心启动区为对象,讨论下垫面构成对热岛强度及热舒适性的影响．研究表明:最大局地热岛强度体现最热极端情况的气候差异,平均局地热岛强度反映全天气温的平均差异;夜间平均局地热岛强度高于白天;自然下垫面中,增大水体和树木的覆盖率起到的降温效果更好,而适当增加草地的覆盖率能更有效地提高室外环境舒适度．

关键词:  城市热岛  指标评价  区域热气候预测模型  优化策略

DOI：10.11918/j.issn.0367-6234.2015.02.005

分类号:TU14

基金项目:“城市水资源与水环境国家重点实验室”开放课题(2010TS04); 深圳市科技研发资金项目(JCYJ20120615145601342,JCYJ20130325151523015).



Regional thermal climate prediction and mitigation strategy of local urban heat island

SONG Xiaocheng¹, LIU Jing^1,2, LIN Yaoyu³, LIU Lin¹, WANG Dan³

(1.School of Municipal and Environmental Engineering, Harbin Institute of Technology, 150090 Harbin, China; 2. State Key Laboratory of Urban Water Resource and Environment(Harbin Institute of Technology), 150090 Harbin, China; 3. Shenzhen Key Laboratory of Urban Planning and Decision Making, Harbin Institute of Technology Shenzhen Graduate School, 518055 Shenzhen, Guangdong, China)

Abstract:

To analyze the effect of underlying surfaces on local thermal climate, this paper took the international low-carbon city of Shenzhen as an example, and the effects of different underlying surfaces on the urban heat island (UHI) and outdoor thermal comfort were discussed by using multipurpose regional thermal climate prediction model. The results showed that the maximum urban heat island intensity(UHII) reflected the temperature difference between urban and suburban of the hottest cases while the average local UHII reflected the average difference of the whole day. And the average local UHII of night was higher than the average local UHII of daytime. Besides, the cooling effects of water and trees were better compared with other natural underlying surfaces, while appropriate increase of grass coverage could more effectively improve the outdoor thermal comfort.

Key words:  urban heat island  evaluation indicator  regional thermal climate prediction model  mitigation strategy