地理科学 ›› 2017, Vol. 37 ›› Issue (4): 611-619.doi: 10.13249/j.cnki.sgs.2017.04.015

• 论文 • 上一篇    下一篇

近55年淮河上中游流域气候要素多时间尺度演变特征及关联性分析

王景才1(), 郭佳香1, 徐蛟2, 李帆1   

  1. 1.扬州大学水利与能源动力工程学院,江苏 扬州 225127
    2.江苏省水利工程建设局,江苏 南京 210029
  • 收稿日期:2016-06-18 修回日期:2017-01-25 出版日期:2017-04-25 发布日期:2017-04-25
  • 作者简介:

    作者简介:王景才(1984-),男,河南南阳人,博士,讲师,主要从事水文水资源与自然地理学方面的研究。E-mail:wanguufly@163.com

  • 基金资助:
    江苏省高校自然科学研究面上项目(15KJB170019)、扬州大学科技创新培育基金(2015CXJ032,2016CXJ041)、江苏省水利动力工程重点实验室开放课题(K13019)资助

Multi-time Scales Change Characteristics and Relationship of Meteorological Variables in the Upper and Middle Regions of the Huaihe River Basin in Recent 55 Years

Jingcai Wang1(), Jiaxiang Guo1, Jiao Xu2, Fan Li1   

  1. 1.School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
    2. Water Conservancy Project Construction Bureau of Jiangsu Province, Nanjing 210029, Jiangsu, China
  • Received:2016-06-18 Revised:2017-01-25 Online:2017-04-25 Published:2017-04-25
  • Supported by:
    Natural Science Foundation for Colleges and Universities in Jiangsu Province(15KJB170019), Science and Technology Innovation Fund of Yangzhou University(2015CXJ032, 2016CXJ041), Open Project Program of Jiangsu Province Key Laboratory of Hydrodynamic Engineering(K13019)

摘要:

选取1960~2014年淮河上中游流域19个气象站点的月降水量、气温和日照时数等数据,采用气候倾向率、Mann-Kendall、Morlet小波和相关系数法,对流域年和四季降水、气温和日照时数的变化趋势、多时间尺度演变特征以及相关性进行了研究。结果表明:① 降水在年和四季线性变化趋势不显著;气温除夏季不显著外,年和春、秋、冬季变暖趋势显著;日照时数除春季不显著外,年和夏、秋、冬季节变短趋势显著;② 降水、气温、日照时数在年和四季分别表现出多个时间尺度的相对丰枯、冷暖和长短交替特征;第一主周期尺度及其相应的平均变化周期在年和四季有的较接近有的相差较大,第一主要平均周期介于2~10 a之间;③ 气温的复相关系数均小于降水和日照时数;除冬季气温复相关系数较小外,其他季节各要素均较大。降水-日照的偏相关系数绝对值在年和四季均最大。降水-日照时数、降水-气温大部分情况呈反相关系,冬季气温和日照时数在主周期尺度28 a下呈同相变化。

关键词: 气候变化, 多时间尺度特征, 小波分析, 相关性, 淮河流域

Abstract:

In order to understand the multi-timescales change characteristics and relations of regional meteorological variables (namely the monthly precipitation, temperature and sunshine hours) in the upper and middle regions of the Huaihe River Basin, data of 19 meteorological stations from 1960 to 2014, were analyzed by using the methods of climate tendency rate, Mann-Kendall test, morlet wavelet analysis and correlation coefficient. The results showed that: 1) The linear trends of precipitation in annual and four seasons were not significant. For temperature, its linear trend was not significant in summer, but in annual and other three seasons, a significant trend of getting warm was shown. For sunshine hours, its linear trend was not significant in spring, but in annual and other three seasons, a significant trend of getting short was shown; 2) Precipitation, temperature and sunshine hours in annual and four seasons were characterized by alternatively wet and dry, cold and warm, short and long variations for multiple time scales. Differences between the main period scales and their mean change cycles for the three meteorological variables in annual and four seasons were different, some were close while the others had large gap. The main period scales ranged from 2 to 10 years. 3) Multiple correlation coefficient of temperature was smaller than that of precipitation and sunshine hours in annual and four seasons. Multiple correlation coefficient of winter temperature was the smallest, while for temperature in the annual and other seasons, precipitation and sunshine hours, the multiple correlation coefficient were bigger. The absolute value of partial correlation coefficient between precipitation and sunshine hours were the biggest in annual and all seasons. Precipitation and sunshine hours, temperature and precipitation both had inverse relationship in most cases, while, winter temperature and sunshine hours showed the phase change on the main period scales of 28 a. The regional hydrothermal resources distribution can be better understood by the analysis in this article, which could also provide reference for the formulation of agricultural cropping system, water resources planning and flood and drought management.

Key words: climate change, multi-timescales characteristics, wavelet analysis, correlation coefficient, the Huaihe River Basin

中图分类号: 

  • P339