摘要
根据1961-2012年海北地区刚察、门源、海晏、祁连4县气温、相对湿度等资料,利用温湿指数分析了该地区旅游气候资源及其与旅游客流量关系,结果表明:海北地区除10月至翌年4月不宜开展户外旅游活动外,其他月份气温适宜、湿度适中,较适宜旅游活动,其中6—8月为旅游黄金期。各地旅游舒适天数呈逐年上升趋势,全州旅游景点平均舒适期为92天,占全年天数的25%,平均旅游舒适期天数祁连的〉门源的〉海晏的〉刚察的。旅游游客量年内分布呈典型的“单峰型”,7月达一年中的最高峰,占全年游客总数的25%~35%,6—8月是一年中游客最集中的时段,游客量占全年的一半以上,1-2月和12月旅游人数处于低谷。海北地区客流量与旅游气候适宜性之间存在显著相关性,约90%左右的客流量同该地的旅游气候因素有关。温湿指数与客流量呈指数函数,基于气候适宜指数建立客流量预测模型能够为旅游部门提前掌握旅游人数、旅游景区规划提供科学依据。
Based on the temperature and relative humidity data during 1961 to 2012 in Haibei area including Gangcha, Menyuan, Haiyan, Qilian four counties, the paper analyzes the relationship between tourism climate resources and passenger traffic with temperature and humidity index. The results show that: except from October to April, the rest of the months are appropriate for tourism activities in Haibei areas for the comfortable temperatures and humidity, and June to August is tourist golden periods. The travel comfort days show an increasing trend. Tourist attractions comfortable days in whole state are 92 days on average, account for 25 % of the annual days. The average travel comfortable days in Qilian 〉 Menyuan 〉 Haiyan 〉 Gangcha. The passenger traffic is typical "single peak" distribution. The highest value is in July, accounts for 25 % - 35 % of the annual passenger traffic; the passenger traffic in June to August is above 50% ; the lower value are in January to February and December. There is remarkable relevance between tourism climate comfort and passenger traffic in Haibei area, and approximately 90% passenger traffic is related with the place tourism climate resources. The relationship between temperature-humidity index and passenger traffic is an exponential function. The passenger traffic forecasting model based on climate comfort index can provide scientific basis for the tourism sector to master passenger traffic and scenic spots planning.
出处
《气象与环境科学》
2014年第1期83-87,共5页
Meteorological and Environmental Sciences
基金
青海省气象局气象科研项目(2012029)资助
关键词
旅游
温湿指数
客流量
预测模型
tourism
temperature and humidity index
passenger traffic
forecasting model