南岭山地1968到2015年降雨的时空变化特征研究
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摘要
南岭山地位于广东、广西、湖南、江西、福建五省的交界处,是我国南方重要的生态屏障带。认识南岭地区降水的时空分布特征,对于深入了解南岭山地生态屏障作用及气候变化条件下该区域的降水规律意义重大。利用1968—2015年中国南岭区域14个气象站的逐月降水资料,主要采用Mann-Kendall统计检验法、聚类分析法、小波分析方法和Kriging插值法,研究了南岭地区48年内降水的时空分布特征。结果表明:①南岭区域的多年平均雨量分布在1203.19 mm到2019.56 mm之间,总体上来看,南岭地区降雨量主要集中在南部,自南向北呈减少趋势,降水量随着经度增加而增加,随纬度增加而减少,且随海拔的升高而减少。②通过对14个站点的48a降雨情况进行聚类分析可将南岭全区域划分为5个子区域,全区域以及5个子区域春季、秋季的降水量呈下降趋势,夏季和冬季呈上升趋势,汛期大多表现出下降趋势,非汛期大多呈上升趋势,但除了南岭最西部的子区域的汛期降雨量表现出显著的下降趋势以外,其余区域的各时间段降雨量趋势变化均不显著。③全区域和5个子区域的年均降雨量在48 a内没有发生显著的突变,人类活动以及气候变化对于南岭地区的降水尚未造成非常明显的影响。④南岭地区降雨序列存在多个不同时间尺度的周期,仅有2 a和13 a通过了85%的红噪声检验, 13 a周期所对应的小波方差峰值较2 a的峰值更高,因此可将13a作为南岭地区降雨量变化的主周期。研究结果可为南岭地区的农业、林业、旅游业发展决策和生态屏障作用评价提供参考依据。
The Nanling is a mountain range that stretches across five provinces(Hunan, Jiangxi, Guangxi, Guangdong, and Fujian) in China, and it plays an important role as a boundary between tropical and subtropical regions. Understanding the spatial and temporal distribution characteristics of precipitation in Nanling is of great significance for further discovering the precipitation law of Nanling mountains under the conditions of climate change. Based on monthly precipitation data recorded at 14 meteorological stations in the Nanling area from 1968 to 2015, this study used the Mann–Kendall statistical test, wavelet analysis, and Kriging interpolation to analyze the spatial and temporal distribution characteristics of precipitation in the Nanling area. The results showed that rainfall was generally more abundant in the southwest of the area, decreasing from west to east and from south to north, but also decreasing with elevation. Cluster analysis indicated the area could be divided into five subregions, and that one year could not only be divided into spring(Mar.–May), summer(June–Aug.),autumn(Sept.–Nov.), and winter(Dec.–Feb.) but also into the flood season(Apr.–Sept.) and the non-flood season(Oct.–Mar.) as well. The mutative trends of annual seasonal precipitation and precipitation in the flood and non-flood seasons over the entire region, as well as the five subregions, were found not significant. Many different timescales of precipitation sequence were identified for the Nanling area, of which 13 years could be considered the primary period. The research results can provide reference for the development of agriculture,forestry and tourism in Nanling, and lay a foundation for the evaluation of ecological barrier function of Nanling.
The Nanling is a mountain range that stretches across five provinces(Hunan, Jiangxi, Guangxi, Guangdong, and Fujian) in China, and it plays an important role as a boundary between tropical and subtropical regions. Understanding the spatial and temporal distribution characteristics of precipitation in Nanling is of great significance for further discovering the precipitation law of Nanling mountains under the conditions of climate change. Based on monthly precipitation data recorded at 14 meteorological stations in the Nanling area from 1968 to 2015, this study used the Mann–Kendall statistical test, wavelet analysis, and Kriging interpolation to analyze the spatial and temporal distribution characteristics of precipitation in the Nanling area. The results showed that rainfall was generally more abundant in the southwest of the area, decreasing from west to east and from south to north, but also decreasing with elevation. Cluster analysis indicated the area could be divided into five subregions, and that one year could not only be divided into spring(Mar.–May), summer(June–Aug.),autumn(Sept.–Nov.), and winter(Dec.–Feb.) but also into the flood season(Apr.–Sept.) and the non-flood season(Oct.–Mar.) as well. The mutative trends of annual seasonal precipitation and precipitation in the flood and non-flood seasons over the entire region, as well as the five subregions, were found not significant. Many different timescales of precipitation sequence were identified for the Nanling area, of which 13 years could be considered the primary period. The research results can provide reference for the development of agriculture,forestry and tourism in Nanling, and lay a foundation for the evaluation of ecological barrier function of Nanling.
引文
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[22]谢庄,曹鸿兴,李慧,等.近百余年北京气候变化的小波特征[J].气象学报,2000,58(3):362-369.
[23]PRAVEEN K,EFI F.A multicomponent decomposition of spatial rainfall fields:1.Segregation of large-and small-scale features using wavelet transforms[J].Water Resources Research,1993,29(8):2515-2532.
[24]黄奇章.广东降水气候特征及其成因分析[J].热带地理,1990(2):113-124.
[25]沙万英,李克让,尹思明.中国南部沿海地区雨涝灾害时空特征及趋势预测[J].自然灾害学报,1997(1):72-78.
[2]贾文雄.近50年来祁连山及河西走廊降水的时空变化[J].地理学报,2012,67(5):631-644.
[3]王麒翔,范晓辉,王孟本.近50年黄土高原地区降水时空变化特征[J].生态学报,2011,31(19):5512-5523.
[4]张丁玲.青藏高原水资源时空变化特征的研究[D].兰州:兰州大学,2013.
[5]段辉良,曹福祥.中国亚热带南岭山地气候变化特点及趋势[J].中南林业科技大学学报,2012,32(9):110-113.
[6]周平.假如没有南岭[J].中国国家地理.2017(12):16-25.
[7]章文波,付金生.不同类型雨量资料估算降雨侵蚀力[J].资源科学,2003(1):35-41.
[8]闫星光,吴琳娜,周涌,等.喀斯特地区月均降水协克里金插值方法研究--以贵州省为例[J].云南大学学报(自然科学版),2017,39(3):432-439.
[9]杨永利,陈月.基于协同克里金模型的区域降水空间插值方法与应用研究[J].水利技术监督,2016,24(4):104-107.
[10]KENDALL M G,MAURICE G.Rank correlation methods[M].London,Griffin,1948.
[11]MANN H B.Non-parametric Test Against Trend[J].Economet-rica,1945,13:245-259.
[12]简虹,骆云中,谢德体.基于Mann-Kendall法和小波分析的降水变化特征研究--以重庆市沙坪坝区为例[J].西南师范大学学报(自然科学版),2011,36(4):217-222.
[13]陈伟,汤静,卜思阳,等.基于Mann-Kendall法和小波分析的沈阳站1951-2010年降水变化特征研究[J].农业灾害研究,2015,5(11):37-38.
[14]刘娟,陈涛涛,迟道才.基于Daniel及Mann-Kendall检验的辽西北地区降雨量趋势分析[J].沈阳农业大学学报,2014,45(5):599-603.
[15]GRINSTED A,MOORE J C,JEVREJEVA S.Application of the cross wavelet transform and wavelet coherence to geophysical time series[J],Nonlinear Processes in Geophysics.2004,11(5):561-566.
[16]邱海军,曹明明,曾彬.基于小波分析的西安降水时间序列的变化特征[J].中国农业气象,2011,32(1):23-27.
[17]李淼,夏军,陈社明,等.北京地区近300年降水变化的小波分析[J].自然资源学报,2011,26(6):1001-1011.
[18]HUANG Jin,ZHANG Jinchi,ZHANG Zengxin,et al.Spatial and temporal variations in rainfall erosivity during1960-2005 in the Yangtze river basin[J].Stochastic Environmental Research and Risk Assessment.2013,27(2):337-351.
[19]邓自旺,尤卫红,林振山.子波变换在全球气候多时间尺度变化分析中的应用[J].南京气象学院学报,1997(4):89-94.
[20]CHRISTOPHER T,GILBERT P,COMPO.A practical guide to wavelet analysis[J].Bulletin of the American Meteorological Society,1998,79(1):61-78.
[21]王文圣,丁晶,衡彤,等.水文序列周期成分和突变特征识别的小波分析法[J].工程勘察,2003(1):32-35.
[22]谢庄,曹鸿兴,李慧,等.近百余年北京气候变化的小波特征[J].气象学报,2000,58(3):362-369.
[23]PRAVEEN K,EFI F.A multicomponent decomposition of spatial rainfall fields:1.Segregation of large-and small-scale features using wavelet transforms[J].Water Resources Research,1993,29(8):2515-2532.
[24]黄奇章.广东降水气候特征及其成因分析[J].热带地理,1990(2):113-124.
[25]沙万英,李克让,尹思明.中国南部沿海地区雨涝灾害时空特征及趋势预测[J].自然灾害学报,1997(1):72-78.