地理研究  2015 , 34 (12): 2238-2246 https://doi.org/10.11821/dlyj201512003

Orginal Article

中国重点旅游城市气候舒适度及其变化趋势

孔钦钦12, 葛全胜1, 席建超1, 郑景云1

1. 中国科学院地理科学与资源研究所,中国科学院陆地表层格局与模拟重点实验室,北京 100101
2. 中国科学院大学,北京 100049

Thermal comfort and its trend in key tourism cities of China

KONG Qinqin12, GE Quansheng1, XI Jianchao1, ZHENG Jingyun1

1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China

收稿日期: 2015-06-9

修回日期:  2015-11-2

网络出版日期:  2015-12-24

版权声明:  2015 《地理研究》编辑部 《地理研究》编辑部

基金资助:  科技基础性工作专项(2011FY120300)中科院项目XDA05080100中国气象局课题“避暑旅游指数研究”

作者简介:

作者简介:孔钦钦(1989- ),男,河南济源人,博士研究生,主要从事气候变化及其影响研究。E-mail: kongqq.12b@igsnrr.ac.cn

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摘要

基于地面气象观测资料和通用热气候指数,研究中国12个重点旅游城市的气候舒适度及其1960-2013年间的变化趋势。结果显示:① 依据气候舒适度年内分布特征,所有城市可分为5种类型,即春、秋适型,春、秋、冬适型,春、夏、秋适型,冬适型和全年不适型。② 1960-2013年,年均通用热气候指数基本均显著增加;哈尔滨、大连增幅最大,分别达1.73 oC/10a、1.44 oC/10a。月均通用热气候指数也一致增加,且增幅冬季大、夏季小。③ 从年尺度看,各城市“冷不舒适”频率降低,“热不舒适”频率增加。哈尔滨、乌鲁木齐、北京、拉萨、西安、上海、三亚年“舒适”频率增加,尤其拉萨增幅高达8.9 d/10a;呼和浩特、大连、昆明、重庆、广州年“舒适”频率降低。④ 从月尺度看,6-9月的“舒适”频率整体降低;11-2月的“舒适”频率整体增加;3-5月及10月表现为纬度或海拔较高城市的“舒适”频率增加,其余城市降低。

关键词: 气候舒适度 ; 变化趋势 ; 通用热气候指数 ; 重点旅游城市

Abstract

Based on daily observation climate data and the Universal Thermal Climate Index, thermal comfort and its trend during 1960-2013 in 12 key tourism cities of China were investigated. According to the intra-annual distribution of thermally comfortable days, these cities can be classified into 5 categories: (1) Beijing, Dalian, Harbin, Hohhot, Shanghai, Urumqi and Xi'an are thermally comfortable during spring and autumn; Chongqing and Kunming are comfortable during spring, autumn and winter; Lhasa is comfortable during spring, summer and autumn; Guangzhou is comfortable during winter; Sanya is uncomfortable throughout the year. (2) From 1960 to 2013, annual average UTCI shows significant increasing trends for all the cities except Sanya, among which Harbin and Dalian have the largest increases of 1.73 oC/10a and 1.44 oC/10a. Monthly average UTCI also shows consistent positive trends, with larger increases in winter and smaller increases in summer. (3) The annual frequencies of heat stress and cold stress decrease and increase respectively for most cities. The annual number of thermally comfortable days increases in Harbin, Urumqi, Beijing, Lhasa, Xi'an, Shanghai and Sanya, especially in Lhasa where a large increase of 8.9 d/10a was detected. Hohhot, Dalian, Kunming, Chongqing and Guangzhou exhibit decreasing trends in the annual number of thermally comfortable days. (4) The monthly number of thermally comfortable days decreases from June to September, and increases from November to February in most cities. During March, April, May and October, the number of thermally comfortable days tends to increase in cities with relatively high latitudes or altitudes, and decrease in other cities.

Keywords: thermal comfort ; trend ; Universal Thermal Climate Index ; key tourism cities

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孔钦钦, 葛全胜, 席建超, 郑景云. 中国重点旅游城市气候舒适度及其变化趋势[J]. , 2015, 34(12): 2238-2246 https://doi.org/10.11821/dlyj201512003

KONG Qinqin, GE Quansheng, XI Jianchao, ZHENG Jingyun. Thermal comfort and its trend in key tourism cities of China[J]. 地理研究, 2015, 34(12): 2238-2246 https://doi.org/10.11821/dlyj201512003

1 引言

旅游业与气候的关系十分密切。气候不仅是支撑旅游活动的外部环境条件,其本身也是重要的旅游吸引物[1]。气候舒适度是气候影响旅游的一个重要方面。旅游地气候舒适程度及持续时间的长短,是影响游客目的地选择和旅游季节长短的重要因素[2,3]

气候舒适度评价研究已有近百年历史[4]。早期研究多基于统计经验性指标,包括有效温度(effective temperature,ET)[5],湿球黑球温度指数(wet bulb globe temperature, WBGT)[6],以及在中国应用广泛的温湿指数(temperature-humidity index,THI)[7]和风寒指数(wind chill index,WCI)[8]等。经验指标计算简便、易于被公众理解;但过于简单的计算降低了结果的准确性,无法满足指标值和人体热生理状态一一对应的基本要求,其经验性质也导致了指标应用在时间、空间维度的局限性[9]。合理的人体舒适度模型必须以人体热交换机制为基础,综合考虑环境因素、人体代谢呼吸散热和服装热阻等各种因素的影响[10]。20世纪60年代后,生物气象学和计算机技术的进步使得基于人体热交换模型的气候舒适度指标得到了迅速发展[11,12];代表性指标的包括预测平均投票数(predicted mean vote,PMV)[13],体感温度(perceived temperature,PT)[14],标准有效温度(standard effective temperature,SET)[15]和生理等效温度(physiological equivalent temperature,PET)[16]等。然而由于热交换理论的缺陷,上述指标均没有被普遍接受。21世纪后,多学科的高度交叉与融合使得对人体热量传输与能量代谢过程更全面、精确的描述成为可能。在世界气象组织气候学委员会倡导下,欧洲科学与技术合作计划的730号行动召集了来自23个国家的45位科学家,融合生理学、数学、气象学及计算机科学等诸多领域最前沿专业技术知识,共同建立了基于多结点热生理模型[17]的通用热气候指数(Universal Thermal Climate Index,UTCI)[9,18]。相比已有指标,UTCI具有适用多种气候类型、对气候要素改变灵敏、能更好描述热环境变化过程等优势[4]

中国的气候舒适度研究起步较晚,且多基于统计经验性指标[19-23],基于机理模型指标的研究较少[24-26];同时多局限于对月、季平均状态的分析,缺乏时序变化视角的研究。为弥补这些缺陷,本文拟采用UTCI,基于1960-2013年逐日的地面气象观测资料,对1960-2013年间12个重点旅游城市气候舒适度的多年平均状态及其变化趋势进行深入研究;以期进一步丰富旅游气候学的理论研究成果,同时也为旅游资源的开发规划和旅游活动提供科学依据。

2 数据来源与研究方法

2.1 数据来源

本文依据国家旅游局编著的《中国旅游年鉴2013》中关于主要城市接待入境游人次统计的数据,在尽量包含各种气候类型前提下,选取哈尔滨、乌鲁木齐、呼和浩特、北京、大连、西安、上海、拉萨、重庆、昆明、广州、三亚等12个城市作为研究对象。气象数据采用中国气象科学数据共享服务网(http://cdc.cma.gov.cn)提供的1960-2013年覆盖了12城市的18个地面气象站点的逐日观测资料,包括平均气温、平均水汽压、平均风速、日照时数;其中重庆气象要素值由7个站点(奉节、梁平、万县、重庆沙坪坝、涪陵、金佛山、酉阳)取平均得到;缺失数据作抛弃处理。

2.2 研究方法

人体冷热舒适程度除受气温影响外,还与湿度、风速、辐射以及人体代谢、服装热阻等诸多因素有关,是多因素共同作用的结果。UTCI综合了多节点人体热调节模型[17]和目前最先进的自适应穿衣模型[27]图1),是考虑最全面、适用范围最广的人体舒适度指标。UTCI被定义为在标准参照环境下,使人产生与实际环境中相同生理响应的气温;其分级标准如表1所示[18]。关于UTCI的详细介绍,参见相关文献[9,18]

图1   通用热气候指数(UTCI)模型

Fig. 1   The Universal Thermal Climate Index (UTCI) model

表1   UTCI等效温度的热应力及舒适度等级划分

Tab. 1   UTCI equivalent temperatures categorized in terms of thermal stress and thermal perception

UTCI范围(oC)热应力等级舒适度等级UTCI范围(oC)热应力等级舒适度等级
>46极强热应力极热0~9轻微冷应力
38~46很强热应力很热-13~0较强冷应力较冷
32~38强热应力-27~-13强冷应力
26~32较强热应力较热-40~-27很强冷应力很冷
9~26无热应力舒适<-40极强冷应力极冷

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UTCI的计算由两步完成:① 将日照百分比和100%的差值作为近似云覆盖总量[28],Hoyt论证了其合理性[29];② 将气温、水汽压、风速和云覆盖总量输入BioKlima 2.6软件中计算UTCI[30]

3 研究结果

3.1 气候舒适度多年平均特征

根据笔者之前的研究(① 笔者关于中国气候舒适度空间格局的研究,尚未发表。),中国UTCI的空间分布呈现出明显的纬向分布规律和由地形因素导致的经向分布规律。中东部地区UTCI值由北向南递增,西部地区由于海拔因素影响表现出UTCI值北高南低的分布特征;同时在大兴安岭—太行山—巫山—雪峰山和昆仑山脉—祁连山脉—横断山脉一线附近等地形变化较大地区,UTCI值呈现出较大梯度,并形成条状分布特征。

图2给出了12个重点旅游城市逐月UTCI的多年平均值和各旬“舒适”频率。表2总结了各城市气候舒适度的多年平均特征。其中,昆明、拉萨年“舒适”日数最长,分别达241天、234天;广州、哈尔滨较短,分别为104天、109天;三亚最短,仅44天。另外,各城市气候舒适度表现出不同程度的季节差异特征;依照UTCI和“舒适”频率的年内分布,本文揭示了不同城市的“舒适”月和“舒适”旬,并据此将12个城市分为5种类型(表2)。可以看出,除广州、三亚外,其余城市春、秋季节均较为舒适;其中重庆、昆明由于纬度较低,且四川盆地北部山岭屏障阻挡了冬季冷空气的侵袭,以及印度洋西南暖流的影响使得重庆和昆明冬季也较为舒适;拉萨由于海拔较高,夏季也较为舒适。广州、三亚则由于纬度、海拔皆较低而分别为冬适型和全年不适型。

图2   1960-2013年重点旅游城市各旬“舒适”频率及月均UTCI的多年平均值

Fig. 2   Long-term average of monthly UTCI and the frequency of thermally comfortable day in each ten-day interval during 1960-2013 in 12 tourism cities

表2   重点旅游城市气候舒适度多年平均特征

Tab. 2   Long-term average characteristics of thermal comfort in 12 tourism cities

城市年“舒适”日数“舒适”月“舒适”旬类型
北京1164、5、104月上旬-5月上旬、9月下旬-11月上旬春、秋适型
大连1265-6、9-105月上旬-6月上旬、9月下旬-10月中旬
哈尔滨1095、6、95月上旬-6月上旬、9月上旬-9月下旬
呼和浩特1304-5、9-104月中旬-5月下旬、9月上旬-10月中旬
上海1543-5、10-113月中旬-4月下旬、10月中旬-11月下旬
乌鲁木齐1424-5、9-104月中旬-5月下旬、9月上旬-10月中旬
西安1543-4、10-113月上旬-4月下旬、10月上旬-11月中旬
重庆1811-4、10-1210月下旬-4月上旬春、秋、冬适型
昆明2411-5、10-1210月上旬-5月上旬
拉萨2344-104月上旬-10月下旬春、夏、秋适型
广州1041-3、11-1212月上旬-2月上旬冬适型
三亚441全年不适型

注:年“舒适”日数定义为一年中逐日UTCI处于“舒适”范围的日数;“舒适”月指月均UTCI值处于“舒适”范围的月份;“舒适”旬指“舒适”频率大于70%的旬。

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3.2 气候舒适度变化趋势

图3所示,1960-2013年间12个重点旅游城市年均UTCI均呈增加趋势,除三亚外,其余城市增加趋势达到了0.05显著性水平。其中,哈尔滨、大连增幅最大,分别达1.73 oC/10a、1.44 oC/10a;拉萨、西安、北京、上海次之,增幅介于0.59~0.73 oC/10a;呼和浩特、广州、昆明增幅较小,介于0.34~0.45 oC/10a;乌鲁木齐、重庆、三亚增幅最小,在0.23 oC/10a以下,其中三亚增幅仅0.03 oC/10a。图4显示了各城市月均UTCI的变化趋势。可以看出,除乌鲁木齐3月、7月和三亚5-9月外,月均UTCI一致增加,并表现出明显的季节差异特征:冬季增幅大、夏季增幅小;且整体增幅越大的城市,季节差异也越显著,例如大连、哈尔滨冬季增幅较夏季普遍高出0.86~2.17 oC/10a。

图3   1960-2013年重点旅游城市年均UTCI变化趋势(oC/10a)(注:实心标志代表达到0.05显著性水平)

Fig. 3   Trends in annual UTCI in 12 tourism cities during 1960-2013; The filled symbols denote statistically significant trends at the 0.05 level

图4   1960-2013年重点旅游城市月均UTCI的变化趋势(oC/10a)(注:实心标志代表达到0.05显著性水平)

Fig. 4   Trends in monthly average UTCI in 12 tourism cities during 1960-2013; The filled symbols denote statistically significant trends at the 0.05 level

表3为1960-2013年各城市不同等级UTCI年频率的变化趋势。整体而言,“冷不舒适”频率降低,“热不舒适”频率增加。哈尔滨、乌鲁木齐、北京、拉萨、西安、上海和三亚年“舒适”频率增加,其中拉萨、西安、上海的增加趋势达到了0.05显著性水平,尤其是拉萨增幅高达8.9 d/10a;呼和浩特、大连、昆明、重庆和广州年“舒适”频率降低,其中昆明、广州达到了0.05显著性水平。

表3   1960-2013年重点旅游城市不同等级UTCI年频率的变化趋势(d/10a)

Tab. 3   Linear trends estimates of the annual frequencies of different thermal perception categories in the 12 tourism cities during 1960-2013 (d/10a)

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为进一步揭示气候舒适度变化的年内差异特征,本文对1960-2013年各城市月“舒适”频率的变化趋势进行了研究(表4)。可以看出,三亚和其他城市有明显不同。三亚3-11月“舒适”频率呈增加趋势,其中8月、10月达到了0.05显著性水平;12月-次年2月“舒适”频率减小。除三亚外,其余城市6-9月“舒适”频率基本呈降低趋势,6月降幅最大,哈尔滨、大连降幅明显高于其他城市;11月-次年2月“舒适”频率基本呈增加趋势,其中拉萨、西安、上海增幅较大,且均达到了0.05显著性水平;3-5月和10月不同城市“舒适”频率变化趋势存在差异,基本呈现出纬度或海拔较高的城市(哈尔滨、乌鲁木齐、呼和浩特、北京、大连、拉萨)增加,其余城市(昆明、西安、重庆、上海、广州)降低的特征。整体而言,气候变暖背景下UTCI的增加趋势会缓解冬季和纬度或海拔较高城市的“冷不舒适”状况,加剧夏季及纬度和海拔较低城市的“热不舒适”状况。

表4   1960-2013年重点旅游城市各月“舒适”频率的变化趋势(d/10a)

Tab. 4   Linear trends estimates of the number of thermally comfortable days in each month during 1960-2013 (d/10a)

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4 结论与讨论

4.1 结论

本文利用1960-2013年逐日地面气象观测站点资料和通用热气候指数,对中国12个重点旅游城市的气候舒适度及其变化趋势进行了深入研究。研究发现:① 昆明、拉萨的年“舒适”日数最长(分别为241天、234天),广州、哈尔滨较短(分别为104天、109天),三亚最短(44天);各城市气候舒适度具有不同程度的季节差异特征,据此所有城市可分为5种类型,即春、秋适型(北京、大连、哈尔滨、呼和浩特、上海、乌鲁木齐、西安),春、秋、冬适型(重庆、昆明),春、夏、秋适型(拉萨),冬适型(广州)和全年不适型(三亚)。本文还揭示了不同城市的“舒适”月和“舒适”旬,在这些时段内,相关城市气候冷热适宜,适合进行户外旅游活动。② 1960-2013年间,除三亚外所有城市年均UTCI均呈显著增加趋势;哈尔滨和大连增幅最大(分别为1.73 oC/10a、1.44 oC/10a),拉萨、西安、北京、上海次之(0.59~0.73 oC/10a),呼和浩特、广州、昆明较小(0.34~0.45 oC/10a),乌鲁木齐、重庆、三亚最小(<0.23 oC/10a)。除乌鲁木齐3月、7月和三亚5-9月外,月均UTCI也一致增加,且冬季增幅达、夏季增幅小。③ 从年尺度来看,所有城市整体上“冷不舒适”频率降低,“热不舒适”频率增加。哈尔滨、乌鲁木齐、北京、拉萨、西安、上海和三亚年“舒适”频率增加,其中拉萨、西安和上海达到0.05显著性水平,尤其是拉萨增幅高达8.9 d/10a;呼和浩特、大连、昆明、重庆和广州年“舒适”频率降低,其中昆明、广州达到0.05显著性水平。④ 从月尺度来看,除三亚外,其余城市6-9月“舒适”频率整体降低,6月降幅最大,哈尔滨和大连的降幅较大;11月-次年2月“舒适”频率整体增加,其中拉萨、西安和上海增幅较大;3-5月和10月基本呈现出纬度或海拔较高城市增加,其余城市降低的特征。即气候变暖背景下,UTCI的增加趋势会缓解冬季和纬度或海拔较高地区的“冷不舒适”状况,加剧夏季及纬度和海拔较低地区的“热不舒适”状况。

4.2 讨论

旅游业与气候的关系十分密切。完善的气候舒适度信息对游客制订出游计划,旅行社选择旅游线路,旅游地旅游资源开发、旅游产品的设计与营销,以及旅游地基础设施的建设具有指导意义。而目前中国很多地方旅游部门仍没有提供完善的气候舒适度信息服务。有学者在对地中海和北美地区的研究中发现,气候舒适度的变幅普遍超过气温的变幅[31];这表明基于气温的旅游气候信息会低估冷、热不舒适的程度。因此,本文建议相关旅游部门构建更为完善的旅游天气气候信息服务系统。另外,气候变暖背景下夏季及纬度和海拔较低地区“热不舒适”的加剧会给当地旅游业发展造成不利影响。例如,年“舒适”日数的减少会加重部分旅游地的季节性问题;过热的气候条件会提高对降温设备及能耗的要求,增加旅游运营成本,等等。因此需制定相关应对措施以减轻气候舒适程度恶化的不利影响。例如,建造更多的户外遮蔽物,合理规划、布局旅游地基础设施,保持良好自然通风条件,等等。

与国内已有研究相比,本文应用了目前最为先进的气候舒适度指标,综合考虑了多种气象要素影响,提供了更准确可靠的旅游气候舒适度信息;并且提供了时序变化视角的气候舒适度研究成果。然而,本文也存在一些不足:① 在研究方法上,UTCI模型没有考虑人体对气候环境的适应,降低了研究结果的应用价值;模型假设“标准人体”和固定的代谢速率,然而旅游活动的类型会影响人体代谢速率,人种、性别和年龄的不同也会造成人体舒适度的差异;将这些因素纳入模型将能提供更真实、详细和有针对性的旅游气候舒适度信息[18]。② 在研究视角上,本文主要依据以气温、湿度、风速和太阳辐射为基础的人体舒适度指数进行旅游气候舒适度评价,没有考虑其他气象要素,如气压、降水和各种极端天气事件的影响,因此要建立更加完善的气候与旅游的关系还有待进一步的综合与深入研究。

The authors have declared that no competing interests exist.


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Abstract<br/>Over the past century more than 100 indices have been developed and used to assess bioclimatic conditions for human beings. The majority of these indices are used sporadically or for specific purposes. Some are based on generalized results of measurements (wind chill, cooling power, wet bulb temperature) and some on the empirically observed reactions of the human body to thermal stress (physiological strain, effective temperature). Those indices that are based on human heat balance considerations are referred to as &quot;rational indices&quot;. Several simple human heat balance models are known and are used in research and practice. This paper presents a comparative analysis of the newly developed Universal Thermal Climate Index (UTCI), and some of the more prevalent thermal indices. The analysis is based on three groups of data: global data-set, synoptic datasets from Europe, and local scale data from special measurement campaigns of COST Action 730. We found the present indices to express bioclimatic conditions reasonably only under specific meteorological situations, while the UTCI represents specific climates, weather, and locations much better. Furthermore, similar to the human body, the UTCI is very sensitive to changes in ambient stimuli: temperature, solar radiation, wind and humidity. UTCI depicts temporal variability of thermal conditions better than other indices. The UTCI scale is able to express even slight differences in the intensity of meteorological stimuli.<br/>
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Abstract<br/>Existing procedures for the assessment of the thermal environment in the fields of public weather services, public health systems, precautionary planning, urban design, tourism and recreation and climate impact research exhibit significant shortcomings. This is most evident for simple (mostly two-parameter) indices, when comparing them to complete heat budget models developed since the 1960s. ISB Commission 6 took up the idea of developing a Universal Thermal Climate Index (UTCI) based on the most advanced multi-node model of thermoregulation representing progress in science within the last three to four decades, both in thermo-physiological and heat exchange theory. Creating the essential research synergies for the development of UTCI required pooling the resources of multidisciplinary experts in the fields of thermal physiology, mathematical modelling, occupational medicine, meteorological data handling (in particular radiation modelling) and application development in a network. It was possible to extend the expertise of ISB Commission 6 substantially by COST (a European programme promoting Cooperation in Science and Technology) Action 730 so that finally over 45 scientists from 23 countries (Australia, Canada, Israel, several Europe countries, New Zealand, and the United States) worked together. The work was performed under the umbrella of the WMO Commission on Climatology (CCl). After extensive evaluations, Fiala’s multi-node human physiology and thermal comfort model (FPC) was adopted for this study. The model was validated extensively, applying as yet unused data from other research groups, and extended for the purposes of the project. This model was coupled with a state-of-the-art clothing model taking into consideration behavioural adaptation of clothing insulation by the general urban population in response to actual environmental temperature. UTCI was then derived conceptually as an equivalent temperature (ET). Thus, for any combination of air temperature, wind, radiation, and humidity (stress), UTCI is defined as the isothermal air temperature of the reference condition that would elicit the same dynamic response (strain) of the physiological model. As UTCI is based on contemporary science its use will standardise applications in the major fields of human biometeorology, thus making research results comparable and physiologically relevant.<br/>
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An einem hei08en Sommertag, dem 29. Juli 1985, wurden in drei Stadtstrukturen in München und im Stammraum eines nahegelegenen Fichtenhochwaldes zeitgleich biometeorologische Messungen durchgeführt. Mit den Me08ergebnissen wurden folgende thermophysiologisch relevante biometeorologische Indizes berechnet: Predicted mean vote, Hautbenetzungsgrad und physiologisch 01quivalente Temperatur. Diese drei Indizes beruhen auf verschiedenen Modellen zur menschlichen Energiebilanz. Mit den drei Indizes wurden die thermischen Komponenten der Mikroklimate an den ausgew01hlten Me08pl01tzen im Hinblick auf Stadtplanungsaufgaben bewertet. Die Ergebnisse zeigen quantitativ die relativ gro08e Hitzebelastung bei der Stadtstruktur 67Stra08enschlucht, nach Süd exponiert“, w01hrend im 67Stammraum des Fichtenhochwaldes“ selbst an hei08en Sommertagen nahezu optimale Bedingungen herrschen. Zwischen diesen Extremen liegen die Ergebnisse für die anderen Me08pl01tze, wobei für 67Stra08enschlucht, nach Nord exponiert“ die W01rmebelastung etwas h02her als für 67Innenhof mit B01umen“ ist.
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Abstract<br/>The UTCI-Fiala mathematical model of human temperature regulation forms the basis of the new Universal Thermal Climate Index (UTC). Following extensive validation tests, adaptations and extensions, such as the inclusion of an adaptive clothing model, the model was used to predict human temperature and regulatory responses for combinations of the prevailing outdoor climate conditions. This paper provides an overview of the underlying algorithms and methods that constitute the multi-node dynamic UTCI-Fiala model of human thermal physiology and comfort. Treated topics include modelling heat and mass transfer within the body, numerical techniques, modelling environmental heat exchanges, thermoregulatory reactions of the central nervous system, and perceptual responses. Other contributions of this special issue describe the validation of the UTCI-Fiala model against measured data and the development of the adaptive clothing model for outdoor climates.<br/>
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https://doi.org/10.1007/s00484-011-0454-1      Magsci      [本文引用: 4]      摘要

Abstract<br/>The Universal Thermal Climate Index (UTCI) aimed for a one-dimensional quantity adequately reflecting the human physiological reaction to the multi-dimensionally defined actual outdoor thermal environment. The human reaction was simulated by the UTCI-Fiala multi-node model of human thermoregulation, which was integrated with an adaptive clothing model. Following the concept of an equivalent temperature, UTCI for a given combination of wind speed, radiation, humidity and air temperature was defined as the air temperature of the reference environment, which according to the model produces an equivalent dynamic physiological response. Operationalising this concept involved (1) the definition of a reference environment with 50% relative humidity (but vapour pressure capped at 20 hPa), with calm air and radiant temperature equalling air temperature and (2) the development of a one-dimensional representation of the multivariate model output at different exposure times. The latter was achieved by principal component analyses showing that the linear combination of 7 parameters of thermophysiological strain (core, mean and facial skin temperatures, sweat production, skin wettedness, skin blood flow, shivering) after 30 and 120 min exposure time accounted for two-thirds of the total variation in the multi-dimensional dynamic physiological response. The operational procedure was completed by a scale categorising UTCI equivalent temperature values in terms of thermal stress, and by providing simplified routines for fast but sufficiently accurate calculation, which included look-up tables of pre-calculated UTCI values for a grid of all relevant combinations of climate parameters and polynomial regression equations predicting UTCI over the same grid. The analyses of the sensitivity of UTCI to humidity, radiation and wind speed showed plausible reactions in the heat as well as in the cold, and indicate that UTCI may in this regard be universally useable in the major areas of research and application in human biometeorology.<br/>
[19] 曹伟宏, 何元庆, 李宗省, .

丽江旅游气候舒适度与年内客流量变化相关性分析

. 地理科学, 2012, 32(12): 1459-1464.

Magsci      [本文引用: 1]      摘要

<p>利用丽江1954~2009 年的气候资料, 对丽江旅游气候舒适度进行了评价, 划分出其等级和旅游适宜期年内分布;结合2006~2010 年丽江海外旅游、国内旅游的客流量月指数, 在对气候舒适度及特殊影响因素赋值的基础上, 采用OLS方法建立线性回归方程。结果显示:气候舒适度与旅游地年内客流量变化有密切关系。海外客流量年内变化分2 个时段, 在10 月~次年4 月干季, 受气候舒适度影响极其显著, 此外还受新年、圣诞假期的影响, 海外客流量月指数气候弹性系数为1.31%;在5~9 月雨季则呈现较均匀分布状态, 平均月指数为8.33%。国内游客客流量变化全年主要受气候舒适度影响, 同时还受7、8 月暑假及&ldquo;十一&rdquo;黄金周影响, 客流量月指数气候弹性系数为0.56%。</p>

[Cao Weihong, He Yuanqing, Li Zongsheng, et al.

A correlation analysis between climate comfort degree and monthly variation of tourists in Lijiang.

Scientia Geographica Sinica, 2012, 32(12): 1459-1464.]

Magsci      [本文引用: 1]      摘要

<p>利用丽江1954~2009 年的气候资料, 对丽江旅游气候舒适度进行了评价, 划分出其等级和旅游适宜期年内分布;结合2006~2010 年丽江海外旅游、国内旅游的客流量月指数, 在对气候舒适度及特殊影响因素赋值的基础上, 采用OLS方法建立线性回归方程。结果显示:气候舒适度与旅游地年内客流量变化有密切关系。海外客流量年内变化分2 个时段, 在10 月~次年4 月干季, 受气候舒适度影响极其显著, 此外还受新年、圣诞假期的影响, 海外客流量月指数气候弹性系数为1.31%;在5~9 月雨季则呈现较均匀分布状态, 平均月指数为8.33%。国内游客客流量变化全年主要受气候舒适度影响, 同时还受7、8 月暑假及&ldquo;十一&rdquo;黄金周影响, 客流量月指数气候弹性系数为0.56%。</p>
[20] 马丽君, 孙根年, 王洁洁.

中国东部沿海沿边城市旅游气候舒适度评价

. 地理科学进展, 2009, 28(5): 713-722.

https://doi.org/10.11820/dlkxjz.2009.05.009      Magsci      摘要

<p>在温湿指数、风寒指数和着衣指数测定的基础上,构建了一个新的综合气候舒适度评价模型,该模型具有可比较和可加和等特点。运用新的模型,分析了我国东部沿海26个热点城市气候舒适度,划分出适宜于旅游活动的等级和时段分布,并对气候舒适度的南北变化进行了分析。依据最适期的年内分布将其划分为3种旅游气候类型:夏适型气候以哈尔滨为代表,春秋适宜型气候以北京和南京为代表,冬适型气候以海口为代表。从气候舒适度指数的南北差异来看,夏季气候舒适度指数随纬度升高而增大,北方具有更高的旅游气候舒适性;冬季气候舒适度随纬度降低而升高,南方具有更高的旅游气候舒适性。调查了4个代表城市的入境旅游客流量年内变化,揭示了入境客流量年内变化和南北差异,划分出旅游旺季、旅游平季和旅游淡季,其中哈尔滨客流量年内变化呈&ldquo;W&rdquo;形,北京和南京的客流量年内变化呈&ldquo;M&rdquo;形,海口的客流量年内变化上&ldquo;凹&rdquo;弧形。在客流量月指数与气候舒适度指数比较的基础上,通过对特殊因子的数值化,采用虚拟变量的回归分析方法,建立了入境旅游客流量月指数模拟模型。</p>

[Ma Lijun, Sun Gennian, Wang Jiejie.

Evaluation of tourism climate comfortableness of coastal cities in the eastern China.

Progress in Geography, 2009, 28(5): 713-722.]

https://doi.org/10.11820/dlkxjz.2009.05.009      Magsci      摘要

<p>在温湿指数、风寒指数和着衣指数测定的基础上,构建了一个新的综合气候舒适度评价模型,该模型具有可比较和可加和等特点。运用新的模型,分析了我国东部沿海26个热点城市气候舒适度,划分出适宜于旅游活动的等级和时段分布,并对气候舒适度的南北变化进行了分析。依据最适期的年内分布将其划分为3种旅游气候类型:夏适型气候以哈尔滨为代表,春秋适宜型气候以北京和南京为代表,冬适型气候以海口为代表。从气候舒适度指数的南北差异来看,夏季气候舒适度指数随纬度升高而增大,北方具有更高的旅游气候舒适性;冬季气候舒适度随纬度降低而升高,南方具有更高的旅游气候舒适性。调查了4个代表城市的入境旅游客流量年内变化,揭示了入境客流量年内变化和南北差异,划分出旅游旺季、旅游平季和旅游淡季,其中哈尔滨客流量年内变化呈&ldquo;W&rdquo;形,北京和南京的客流量年内变化呈&ldquo;M&rdquo;形,海口的客流量年内变化上&ldquo;凹&rdquo;弧形。在客流量月指数与气候舒适度指数比较的基础上,通过对特殊因子的数值化,采用虚拟变量的回归分析方法,建立了入境旅游客流量月指数模拟模型。</p>
[21] 马丽君, 孙根年, 谢越法, .

50年来东部典型城市旅游气候舒适度变化分析

. 资源科学, 2010, 32(10): 1963-1970.

Magsci      摘要

利用江苏省1996年-2008年土地利用变更调查数据,分别从土地利用数量、土地利用结构、土地利用程度三方面分析了江苏省土地利用变化特征,结果表明,近10年来,江苏省耕地、牧草地明显减少,交通用地与居民点及工矿用地迅速增加;土地利用结构信息熵与均衡度总体呈增长态势,用地结构不断趋于均质化;土地利用程度不断增强,用地效率明显提高。结合研究区实际情况,选取旅游总收入作为LUCC变化的旅游驱动因子,借助线性回归分析方法,建立江苏省主要土地利用类型的旅游驱动力模型,结果表明,江苏省旅游经济发展对其土地利用变化具有显著影响。建议制定有关土地利用政策时,应充分考虑影响土地利用变化的旅游驱动力因子,以更好促进区域土地资源的可持续利用。

[Ma Lijun, Sun Gennian, Xie Yuefa, et al.

A study on variations of the tourism climate comfort degree in five typical cities in eastern China during the last 50 years.

Resources Science, 2010, 32(10): 1963-1970.]

Magsci      摘要

利用江苏省1996年-2008年土地利用变更调查数据,分别从土地利用数量、土地利用结构、土地利用程度三方面分析了江苏省土地利用变化特征,结果表明,近10年来,江苏省耕地、牧草地明显减少,交通用地与居民点及工矿用地迅速增加;土地利用结构信息熵与均衡度总体呈增长态势,用地结构不断趋于均质化;土地利用程度不断增强,用地效率明显提高。结合研究区实际情况,选取旅游总收入作为LUCC变化的旅游驱动因子,借助线性回归分析方法,建立江苏省主要土地利用类型的旅游驱动力模型,结果表明,江苏省旅游经济发展对其土地利用变化具有显著影响。建议制定有关土地利用政策时,应充分考虑影响土地利用变化的旅游驱动力因子,以更好促进区域土地资源的可持续利用。
[22] 闫业超, 岳书平, 刘学华, .

国内外气候舒适度评价研究进展

. 地球科学进展, 2013, 28(10): 1119-1125.

https://doi.org/10.11867/j.issn.1001-8166.2013.10.1119      Magsci      摘要

<p>开展气候舒适度评价对于科学指导旅游出行、客观评价城市人居环境等都具有重要的理论价值和实践意义, 近年来随着旅游业的蓬勃发展和生活质量的普遍提高, 气候舒适度评价愈发成为当前研究的热点问题。在回顾近百年来该领域研究成果的基础上, 划分出气候舒适度评价的3个重要时期, 介绍了每个时期具广泛影响的人体舒适度模型, 论述了各种模型提出的时代背景、基本思想和适用条件。研究表明:气候舒适度评价由简单的经验模型向复杂的机理模型、由时空局限性模型向客观普适性模型发展, 具有精细化、客观化的发展趋势;目前, 国内气候舒适度评价研究中仍以经验模型的应用较为普遍, 但在模型的选择上要注意其季节及地域适用性;发展具有普适性的气候舒适度评价模型、探索气候舒适度精细化评估技术是今后研究中亟待解决的问题;开展特色宜人气候评价有望拓展舒适气候评价研究的深度和范围, 是今后重要的研究方向。</p>

[Yan Yechao, Yue Shuping, Liu Xuehua, et al.

Advances in assessment of bioclimatic comfort conditions at home and abroad.

Advances in Earth Science, 2013, 28(10): 1119-1125.]

https://doi.org/10.11867/j.issn.1001-8166.2013.10.1119      Magsci      摘要

<p>开展气候舒适度评价对于科学指导旅游出行、客观评价城市人居环境等都具有重要的理论价值和实践意义, 近年来随着旅游业的蓬勃发展和生活质量的普遍提高, 气候舒适度评价愈发成为当前研究的热点问题。在回顾近百年来该领域研究成果的基础上, 划分出气候舒适度评价的3个重要时期, 介绍了每个时期具广泛影响的人体舒适度模型, 论述了各种模型提出的时代背景、基本思想和适用条件。研究表明:气候舒适度评价由简单的经验模型向复杂的机理模型、由时空局限性模型向客观普适性模型发展, 具有精细化、客观化的发展趋势;目前, 国内气候舒适度评价研究中仍以经验模型的应用较为普遍, 但在模型的选择上要注意其季节及地域适用性;发展具有普适性的气候舒适度评价模型、探索气候舒适度精细化评估技术是今后研究中亟待解决的问题;开展特色宜人气候评价有望拓展舒适气候评价研究的深度和范围, 是今后重要的研究方向。</p>
[23] 吴普, 葛全胜.

海南旅游客流量年内变化与气候的相关性分析

地理研究, 2009, 28(4): 1078-1084.

Magsci      [本文引用: 1]      摘要

<p>气候是旅游资源不可或缺的组成部分,也是影响旅游地开发的重要因素,直接影响到旅游季节的长短及旅游客流的年内变化。利用海南9个气象站点自建站以来的气候资料及近5年旅游统计资料,通过特吉旺气候舒适指数、相关分析和回归分析等方法,分析海南气候舒适度及其与旅游客流量年内变化的相关性。结果表明:11月~3月是海南旅游的最适宜期;气候对海南旅游客流量有显著影响,以气温为主导的气候舒适度是海南旅游客流年内淡旺季变化及游客旅游决策的主要影响因素;温度与海南旅游客流量呈显著负相关关系;与海口比较而言,三亚旅游业对气候更加敏感。本项研究对更好地将气候整合到旅游产品中进行宣传促销、提高产品吸引力,对海南旅游业发展规划,对提前预判游客规模尽早做出对策安排及旅游投资有很强的现实指导意义。</p>

[Wu Pu, Ge Quan-sheng.

An analysis of annual variation of tourist flows and climate change in Hainan province.

Geographical Research, 2009, 28(4): 1078-1084.]

Magsci      [本文引用: 1]      摘要

<p>气候是旅游资源不可或缺的组成部分,也是影响旅游地开发的重要因素,直接影响到旅游季节的长短及旅游客流的年内变化。利用海南9个气象站点自建站以来的气候资料及近5年旅游统计资料,通过特吉旺气候舒适指数、相关分析和回归分析等方法,分析海南气候舒适度及其与旅游客流量年内变化的相关性。结果表明:11月~3月是海南旅游的最适宜期;气候对海南旅游客流量有显著影响,以气温为主导的气候舒适度是海南旅游客流年内淡旺季变化及游客旅游决策的主要影响因素;温度与海南旅游客流量呈显著负相关关系;与海口比较而言,三亚旅游业对气候更加敏感。本项研究对更好地将气候整合到旅游产品中进行宣传促销、提高产品吸引力,对海南旅游业发展规划,对提前预判游客规模尽早做出对策安排及旅游投资有很强的现实指导意义。</p>
[24] Cheung C S C, Hart M A.

Climate change and thermal comfort in Hong Kong.

International Journal of Biometeorology, 2014, 58(2): 137-148.

https://doi.org/10.1007/s00484-012-0608-9      URL      PMID: 23150088      [本文引用: 1]      摘要

Thermal comfort is a major issue in cities and it is expected to change in the future due to the changing climate. The objective of this paper is to use the universal thermal comfort index (UTCI) to compare the outdoor thermal comfort in Hong Kong in the past (1971-2000) and the future (2046-2065 and 2081-2100). The future climate of Hong Kong was determined by the general circulation model (GCM) simulations of future climate scenarios (A1B and B1) established by the Intergovernmental Panel on Climate Change (IPCC). Three GCMs were chosen, GISS-ER, GFDL-CM2.1 and MRI-CGCM2.3.2, based on their performance in simulating past climate. Through a statistical downscaling procedure, the future climatic variables were transferred to the local scale. The UTCI is calculated by four predicted climate variables: air temperature, wind speed, relative humidity and solar radiation. After a normalisation procedure, future UTCI profiles for the urban area of Hong Kong were created. Comparing the past UTCI (calculated by observation data) and future UTCI, all three GCMs predicted that the future climate scenarios have a higher mode and a higher maximum value. There is a shift from 'No Thermal Stress' toward 'Moderate Heat Stress' and 'Strong Heat Stress' during the period 2046-2065, becoming more severe for the later period (2081-2100). Comparing the two scenarios, B1 exhibited similar projections in the two time periods whereas for A1B there was a significant difference, with both the mode and maximum increasing by 2℃ from 2046-2065 to 2081-2100.
[25] Lai D Y, Guo D H, Hou Y F, et al.

Studies of outdoor thermal comfort in northern China.

Building and Environment, 2014, 77: 110-118.

https://doi.org/10.1016/j.buildenv.2014.03.026      URL      摘要

Outdoor spaces play important roles in daily lives, and the use of these spaces is determined largely by outdoor thermal comfort. Few studies have been conducted on outdoor thermal comfort in northern China. Using microclimatic monitoring and subject interviews at a park in Tianjin, China, this investigation studied outdoor thermal comfort under different climate conditions. Although outdoor thermal environment varied greatly with air temperature from −5.0 to 34.5 °C, 83.3% of respondents consider it “acceptable”. Preferences in solar radiation, wind speed, and relative humidity were related to air temperature. The higher the air temperature was, the higher the wind speed and the lower the solar radiation and relative humidity desired by the occupants, and vice versa. The data were also used to evaluate three indices. The Universal Thermal Climate Index (UTCI) satisfactorily predicted outdoor thermal comfort, while the Predicted Mean Vote (PMV) overestimated it. The neutral physiological equivalent temperature (PET) range found in this study was 11–24 °C, which was lower than the ranges in Europe and Taiwan. Our study indicated that residents of Tianjin were more adapted to cold environment.
[26] Li R, Chi X.

Thermal comfort and tourism climate changes in the Qinghai-Tibet Plateau in the last 50 years.

Theoretical and Applied Climatology, 2014, 117(3-4): 613-624.

https://doi.org/10.1007/s00704-013-1027-5      URL      [本文引用: 1]      摘要

In this paper, the thermal comfort and its changes in the Qinghai–Tibet Plateau over the last 5002years have been evaluated by using the physiological equivalent temperature (PET), and a more complete tourism climate picture is presented by the Climate–Tourism–Information Scheme (CTIS). The results show that PET classes in the Qinghai–Tibet Plateau cover six out of the nine-point thermal sensation scale — very cold, cold, cool, slightly cool, neutral and slightly warm — and cold stress is prevailing throughout the year. A small number of slightly cool/warm and neutral days occur in summer months. There occur no warm, hot and very hot days. The frequency of PET classes varies among regions, depending on their altitude/latitude conditions. Xining, Lhasa and Yushu are the top three cities in terms of thermal favorability. With global warming, annual cumulative number of thermally favorable days has been increasing, and that of cold stress has been reducing. The change is more obvious in lower elevation than that in higher elevation regions. The improving thermal comfort in the Qinghai–Tibet Plateau might be a glad tiding for local communities and tourists. Besides PET, CTIS can provide a number of additional bioclimatic information related to tourism and recreational activities. CTIS for Lhasa and Xining shows that sunshine is plentiful all the year round, and windy days occur frequently from late January to early May. This is a useful bioclimatic information for tourism authorities, travel agencies, resorts and tourists.
[27] Havenith G, Fiala D, Blazejczyk K, et al.

The UTCI-clothing model.

International Journal of Biometeorology, 2012, 56(3): 461-470.

https://doi.org/10.1007/s00484-011-0451-4      Magsci      [本文引用: 1]      摘要

Abstract<br/>The Universal Thermal Climate Index (UTCI) was conceived as a thermal index covering the whole climate range from heat to cold. This would be impossible without considering clothing as the interface between the person (here, the physiological model of thermoregulation) and the environment. It was decided to develop a clothing model for this application in which the following three factors were considered: (1) typical dressing behaviour in different temperatures, as observed in the field, resulting in a model of the distribution of clothing over the different body segments in relation to the ambient temperature, (2) the changes in clothing insulation and vapour resistance caused by wind and body movement, and (3) the change in wind speed in relation to the height above ground. The outcome was a clothing model that defines in detail the effective clothing insulation and vapour resistance for each of the thermo-physiological model’s body segments over a wide range of climatic conditions. This paper details this model’s conception and documents its definitions.<br/>
[28] Essa K S, Etman S M.

On the relation between cloud cover amount and sunshine duration.

Meteorology and Atmospheric Physics, 2004, 87(4): 235-240.

https://doi.org/10.1007/s00703-003-0046-7      Magsci      [本文引用: 1]      摘要

<a name="Abs1"></a>The total cloud cover is deduced from measurements of monthly mean averages of the percent of possible sunshine duration at three locations in Egypt, Cairo, Bahtim and Sedi-Barrani stations during the period 1987&#x2013;1995. This sunshine-derived total cloud cover (C<sub>s</sub>) is compared to conventional ground-based observations of total cloud covers (C<sub>g</sub>) made by meteorological observers. A linear relationship between the two estimates is calculated, and the difference between the two estimates as a function of C<sub>s</sub> and C<sub>g</sub> is fitted with a least-squares linear equation. It is found that on the average the sunshine-derived values of total cloud cover are about 7% lower than the corresponding ground-based estimated of total cloud cover. Both of these parameters are mainly used in solar radiation models and the error sources are mainly depending upon the way to describe sky cover.
[29] Hoyt D V.

Percent of possible sunshine and the total cloud cover.

Monthly Weather Review, 1977, 105(5): 648-652.

https://doi.org/10.1175/1520-0493(1977)105<0648:POPSAT>2.0.CO;2      URL      [本文引用: 1]      摘要

Abstract The total cloud cover is deduced from measurements of the percent of possible sunshine at 72 locations in the United States. This sunshine-derived total cloud cover is then compared to conventional ground-based observations of total cloud cover made by meteorological observers. A linear relationship between the two estimates is calculated, and the difference between the two estimates as a function of latitude is fitted with a least-squares linear equation. It is found that on the average the sunshine-derived values of total cloud cover are about 13% lower than the corresponding ground-based estimates of total cloud cover. The difference between the two estimates may be attributed to projection problems by the ground-based observer where sides of clouds are viewed and added to the estimate of total cloud cover or to the failure of sunshine recorders to detect thin cirrus clouds. Projection problems by the meteorological observers is probably the most likely cause because satellite and aircraft observations confirm the sunshine observations. The difference between the sunshine-derived and ground-based estimates of total cloud cover as a function of latitude also indicates that the ground-based observers are probably having difficulties with the total cloud cover estimates. It is concluded that a more standard definition of the meaning of “cloud” and “total cloud cover”, is needed for radiation budget and climate modelling studies.
[30] Blazejczyk K..

URL      [本文引用: 1]     

[31] Matzarakis A, Amelung B.

Physiologically equivalent temperature as indicator for impacts of climate change on thermal comfort of humans. In: Thomson M C, Garcia-Herrera R, Beniston M. Seasonal Forecasts, Climatic Change and Human Health

. Berlin: Springer Netherlands, 2008: 161-172.

[本文引用: 1]     

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