【作者】 王欢；

【导师】 王澄海； 司丽丽；

【作者基本信息】 兰州大学 ， 大气科学·气象学， 2019， 硕士

【摘要】 本文选取保定市11个主要旅游景区(简称景区)为研究对象,采用景区2015-2018年区域自动站气象资料和景区所在县或周边气象站1974年-2018年45年逐日气象资料以及景区物候期调研数据为数据样本,通过相关性分析、回归分析优化研究数据样本库。分析了景区旅游气候、旅游物候、旅游天气的特征,依次建立了3项指数的定量计算和定性评价方法。综合考虑以上3项指标对旅游活动的影响,将3项指标对应的评价方法相联合,最终建立了保定市旅游气象综合指数预报模型,并编制为业务软件投入日常气象服务工作进行了试验和检验,得到如下结论:(1)本文建立了适合于研究区域(保定市)的旅游气候指数、旅游物候指数、旅游天气指数三个指数,在此基础上建立了初步的旅游气象综合指数模型。每个次级指数通过打分评定来建立分级赋值评定标准。根据三个次级指数对旅游活动的影响程度分配权重,建立了一个综合指数计算模型。三个次级指数的定量评定标准乘以权重后得到的值域集合再次进行分级赋值,形成保定市旅游气象综合指数定量定性评定标准。将模型植入专业的旅游气象服务软件,实现了景区气象指数的定时、定点的自动预报。该模型体现了气候、物候、天气三重因素叠加后对旅游活动的综合影响,相对于单一指数更具有参考性,同时该模型由三个独立的次级指数组成,每个次级指数可以体现单一气象因子对旅游活动的影响。初步的应用表明,模型具有应用灵活,可从不同角度为服务对象提供决策意见的优点。(2)得出了各景区旅游气候舒适度分布规律,划分出了各月气候最舒适景区和各景区气候最适宜月。结果表明,保定旅游气候较为舒适的月份为3~11月,对应保定的春、夏、秋三季;而冬季三个月的气候均不适宜旅游。在旅游适宜期内,保定旅游气候最舒适的月份为5月,该月11个景区的气候适宜天数均达到了31天,气候最不舒适的为11月,该月内所有景区气候适宜天数都在15天以下,且有4个景区适宜天数为0。在本文研究的11个景区中,阜平的天生桥景区旅游气候最为宜人,全年气候适宜天数达到了222天,安新的白洋淀景区气候舒适度最差,全年气候适宜天数为151天,这些结论和实际情况相符。(3)给出了各景区旅游物候景观分布规律,得出了各月物候景观最佳的景区和各景区的最佳物候观赏期。其中,各景区对应的花期和红叶盛期为景区的最佳物候观赏期。各景区的红叶期较为集中,除安新的白洋淀景区外,其他景区的红叶期主要分布在9月下旬到10月底。各景区花期分布不均,最早的出现在5月上旬,最晚的出现在8月下旬,且同一月份内存在景区间的花期交叉。各景区从11月初陆续进入凋零初期,到次年4月初或上旬开始进入返青期。凋零初期到返青期期间,是所有景区物候景观观赏性最差的时段。经过初步的业务检验,和实际情况相符。(4)对影响景区的天气现象行了归类,建立了景区不利天气的分类指导建议;得出了各类不利天气在景区间的时间分布和空间分布规律。影响保定旅游景区的不利天气主要有降雨、暴雨、雷暴、冰雹、高温、大风、降雪、雾、霾9种。其中,降雨天气在9种天气中发生最多,是对保定旅游活动影响最大的天气现象。统观全年,各类不利天气在秋季出现较少,在春、夏、冬3季出现较多,夏季最多。其中,降雨、暴雨、雷暴、冰雹、高温多发期在7月,大风多发期在4月,雾多发期在12月,霾多发期在1月,降雪多发期在2月。在11个景区中,涞源白石山景区为不利天气的高发地,该景区不利天气高发期为7月。由于旅游气象服务属于一门新兴的科学。本文仅作为一次探索性的工作,为后期基于量化经济理论基础上建立更为定量化的模型提供参考依据。更多还原

【Abstract】 Eleven major tourist attractions in Baoding City(referred to as scenic spots hereafter)were selected as the research objects;the meteorological data of the regional automatic stations in these scenic spots from 2015 to 2018,the daily meteorological data of the counties where the scenic spots are located or its surrounding weather stations from 1974 to 2018,and the survey data of the scenic spots during phenological periods,were adopted as the data samples,which were optimized through correlation and regression analyses.The characteristics of tourist climate,phenology and weather in the scenic spots were analyzed,and the quantitative calculation and qualitative evaluation methods for the three indexes were established in turn.Considering the impact of the above three indicators on tourism activities,the Comprehensive Index Forecasting Model for Baoding Tourism Meteorology was established at last in combining the evaluation methods of the three indicators.In addition,the model was written into a business software and put into daily weather service for testing and experimenting.The main results of the study are summarized as follows:(1)This study establishes three indexes,that is tourism climate index,tourism phenological index and tourism weather index,which are suitable for the study area(Baoding City).Subsequently,a preliminary comprehensive index model for the tourism meteorology is established.Each sub-index is graded to establish rating assessment criteria.Based on the weight distribution of the three sub-indexes on tourism activities,a comprehensive index calculation model is established.The domain set obtained through multiplying the quantitative evaluation criteria of the three sub-indexes by the weights is to be graded again,producing the quantitative and qualitative evaluation criteria for the tourism meteorological comprehensive indexes in Baoding.The model is implanted into the professional software for tourism weather service to realize the automatic forecasting of the scenic meteorologicalindexes at specific time and places.The preliminary application of the established model has shown that the model reflects the combined impact of climate,phenology and weather on tourism activities,and is of greater referential value compared with a single index.Besides,the model consists of three independent sub-indexes,each of which will indicate the impact of a single meteorological factor on tourism activities,offering opinions for decision-making from different perspectives.Preliminary application shows that the model has the advantages of flexible application and can provide decision-making opinions for service objects from different perspectives.(2)The distribution law of tourism climate comfort degree in each scenic spot is obtained,and the scenic spots with the most comfortable climate every month and the most comfortable months in climate for each scenic spot are summarized.The results show that: the months from March to November are relatively comfortable for tourists in Baoding,just corresponding to the spring,summer and autumn seasons herein but winter.During the appropriate periods,the most comfortable month for tourists is May in Baoding,during which the climate optimum days in the 11 scenic spots reach up to 31;while the most uncomfortable month for tourists is November,when the climate optimum days in all the scenic spots drop below 15,and what’s even worse is that the climate optimum days for four scenic spots are zero.Among the 11 scenic spots studied,the tourist climate of Tianpingqiao Scenic Spot in Fuping is the most pleasant,and its climate optimum days of a year can reach 222.While the climate comfort degree of Baiyang Lake Scenic Spot in Anxin is the worst with the climate optimum days of the whole year being just 151.These conclusions are consistent with the actual situation.After preliminary business tests,it is consistent with the actual situation.(3)The distribution patterns of tourism phenological landscape in each scenic spot are summarized,and the best phenological landscape of each month and the best phenological sightseeing period of each scenic spot are obtained.Among them,the florescene and the acme of red maple leaves are the best phenological sightseeing periods for each scenic spot.The periods of flourished red maple leaves in these scenic spots are relatively concentrated,mainly ranging from late September to the end of October with the exception of the Baiyang Lake scenic spot in Anxin.The florescenes for each scenic spot are unevenly distributed,with the earliest appearing in early May while the latest in late August,and the florescenes may overlap in several scenic spots in a singular month.The scenic spots will enter the primary stageof withering in succession since early November,and return to the regreening stage since the next early or earlier April.The transitional period between the two stages is the worst time for sightseeing the phenological landscape of all scenic spots.(4)The weather phenomena affecting scenic spots are classified,and the classification guidance suggestions of unfavorable weather in scenic spots are established.The time and spatial distribution of various adverse weather conditions are summarized in these scenic spots.The adverse weather affecting Baoding tourist scenic spots mainly includes rainfall,rainstorm,thunderstorm,hail,high temperature,gale,snowfall,fog and haze.Among them,rainfall weather occurs most,becoming a key weather phenomenon influencing the tourism activities in Baoding.Throughout the year,autumn tend to have the least adverse weather conditions,while spring,summer and winter tend to have more with the most occurring in summer.Rainfall,rainstorm,thunderstorm,hail,and high temperature usually occur during July,while strong wind usually occur during April,the fog mostly during December,haze during January,and snowfall during February.Among the 11 scenic spots,the Whitestone Mountain Scenic Spot in Laiyuan is an area mostly hit by unfavorable weather phenomena during July,while the Baiyang Lake Scenic Spot in Anxin is an area least hit by adverse weather phenomena.Tourism meteorological service is a new science.As an exploratory work,this paper can provide a reference for establishing a more quantitative model on the basis of quantitative economic theory.更多还原