發(fā)布時間：2018-04-23 06:18

  本文選題：積雪 + 變異分析��； 參考：《內蒙古農業(yè)大學》2017年碩士論文

【摘要】：錫林郭勒地區(qū)屬于我國北部邊疆重要的生態(tài)屏障,同時也是我國三大積雪高值區(qū)之一。研究區(qū)地貌廣闊,積雪分布差異較大。本文利用錫林郭勒地區(qū)15個氣象觀測站1971～2015年的逐日積雪資料,重點分析了研究區(qū)降雪量、積雪深度及積雪日數(shù)三大積雪指標的時空變化規(guī)律、時空變異特征及周期性特征,揭示了積雪日數(shù)與氣象因子的相關性,并對積雪日數(shù)的未來變化趨勢進行了預測分析。主要研究結果如下:1.錫林郭勒地區(qū)各站點降雪量、積雪深度和積雪日數(shù)隨時間波動幅度較為劇烈,且各站點波動規(guī)律基本一致,整體無顯著的增減趨勢。從年內變化上看,降雪量呈現(xiàn)的是"雙峰型"的波動特征,其中11月份的峰值最大,1月份最少,積雪深度和積雪日數(shù)則呈現(xiàn)的是"單峰型"的波動特征,最大值分別在2月份和1月份,最小值都出現(xiàn)在剛入冬的10月份。2.錫林郭勒地區(qū)降雪量、積雪深度及積雪日數(shù)空間分布差異性顯著,基本呈現(xiàn)的是東多西少、南多北少的分布格局,高值區(qū)主要分布在烏拉蓋、西烏旗、正鑲白旗及太卜寺旗周圍,少雪區(qū)主要出現(xiàn)在西部區(qū)。降雪量、積雪深度的極差將近4倍,積雪日數(shù)相差2倍以上。依據區(qū)劃原則,將研究區(qū)劃分為4種類型:一致偏高區(qū)、中值區(qū)、一少兩多區(qū)、一致偏少區(qū)。在此基礎上利用EOF分別對三個積雪指標做了進一步的空間異常分型。3.利用小波分析對錫林郭勒地區(qū)近45a的積雪周期進行分析,可知降雪量存在7a、11a和18a三個震蕩周期,積雪日數(shù)存在7a,11a和22a三個周期變化,積雪深度存在5a和11a兩個震蕩周期。其中降雪量和積雪日數(shù)的主周期均為7a,積雪深度主周期為5a。通過突變檢驗分析可知,降雪量和積雪深度沒有突變產生,而積雪日數(shù)在1996a附近發(fā)生了一次由多到少的突變。4.錫林郭勒積雪日數(shù)受多種氣象因子共同作用,通過相關性分析得出降雪量、積雪深度、氣溫及濕度分別與積雪日數(shù)有較高的相關性。BP神經網絡模型預測,未來十年間積雪日數(shù)呈逐漸減少的趨勢。
[Abstract]:Xilingol area is an important ecological barrier in the northern frontier of China, and is also one of the three high snow cover areas in China. The landform of the study area is wide and the snow cover distribution is quite different. Based on the daily snow cover data of 15 meteorological observatories in Xilinguole region from 1971 to 2015, the temporal and spatial variation, temporal and spatial variation characteristics and periodic characteristics of snow fall, snow depth and snow days in the study area are analyzed in this paper. The correlation between snow days and meteorological factors is revealed, and the trend of snow days in the future is predicted and analyzed. The main results are as follows: 1. The snowfall amount, snow depth and snow days fluctuate sharply with time in Xilingole area, and the fluctuation law of each station is basically the same, and there is no significant increase or decrease trend on the whole. From the point of view of the change of the year, the snowfall is characterized by "double peak" fluctuation, in which the peak value in November is the highest and the minimum in January, and the snow depth and snow days show the fluctuation characteristics of "single peak". The maximum values were in February and January, respectively, and the minimum values appeared in October, just into winter. The spatial distribution of snowfall, snow depth and snow days in Xilinguole area is significant. It basically presents a pattern of less snow in the east and less in the west and less in the south and north. The high value areas are mainly distributed in the Wulagai and Xiwu Banner. Around Zhengxian White Banner and Taibusi Banner, Shaoxue area mainly appeared in the western region. Snowfall, snow depth of nearly 4 times, more than 2 times the number of snow days. According to the principle of regionalization, the study area is divided into four types: uniform high area, median region, one few, two more regions, and uniform less areas. On this basis, the further spatial anomaly classification. 3. 3 was made by using EOF for the three snow cover indexes. Wavelet analysis is used to analyze the snow cover periods in Xilinguole area in the last 45 years. The results show that there are three periods of snow fall: 7a, 11a, 18a, 7a, 11a, 22a, and the depth of snow, 5a and 11a, respectively. The main periods of snow fall and snow days are both 7a and 5a respectively. According to the analysis of sudden change test, there is no sudden change in snowfall and snow depth, and a mutation from more to less occurred in the snow days around 1996. The number of snow days in Xilinguole is affected by various meteorological factors. Through the correlation analysis, it is concluded that the snowfall amount, snow depth, temperature and humidity have a high correlation with snow days. BP neural network model is used to predict the snowfall days. The number of snow days will gradually decrease in the next ten years.
【學位授予單位】：內蒙古農業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P426.635

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