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干旱区地理 ›› 2020, Vol. 43 ›› Issue (2): 319-328.doi: 10.12118/j.issn.1000-6060.2020.02.05

• 气候与水文 • 上一篇    下一篇

近30 a甘肃省河东地区极端气温指数时空变化特征及趋势预测

黄浩1，张勃1，黄涛2，王怀军3，马尚谦1， 马彬1，王晓丹1，崔艳强1   

  1. 1 西北师范大学地理与环境科学学院，甘肃 兰州 730070；

    2 西北区域气候中心，甘肃 兰州 730020； 3 淮阴师范学院城市与环境学院，江苏 淮安 223300

  • 收稿日期:2019-06-14 修回日期:2019-10-17 出版日期:2020-03-25 发布日期:2020-03-25
  • 通讯作者: 张勃 (1963-)，男，博士研究生导师，主要从事区域环境与资源开发方面的研究.
  • 作者简介:黄浩（1995-），男，硕士研究生，主要从事气候变化与生态方面研究.E-mail：jannickroad@163.com
  • 基金资助:
    国家自然科学基金项目（41561024）资助

Quantifying and predicting spatial and temporal variations in extreme temperatures since 1990 in Gansu Province,China

HUANG Hao1,ZHANG Bo1,HUANG Tao2,WANG Huai-jun3,MA Shang-qian3,MA Bin1,WANG Xiao-dan1,CUI Yan-qiang1   

  1. 1 College of Geography and Environment Science of Northwest Normal University,Lanzhou 730070,Gansu,China;

    2 Northwest Region Climate Center,Lanzhou 730020,Gansu,China; 3 School of Urban and Environmntal Sciences of Huaiyin Normal University,Huai[JP8]’[JP]an 223300,Jiangsu,China

  • Received:2019-06-14 Revised:2019-10-17 Online:2020-03-25 Published:2020-03-25

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摘要：

基于甘肃省河东地区61个气象站点1988—2017年逐日气温数据，利用Mann-Kendall检验，Sen’s斜率估计方法分析甘肃省河东地区极端气温指数的时空变化趋势，并探讨极端气温指数与其影响因素之间的关系，最后利用NAR神经网络结合Hurst指数对甘肃省河东地区极端气温指数变化进行预测分析。结果表明：(1)从时间上看，冷极值相对指数呈下降趋势，冷极值绝对指数、暖极值以及气温日较差、作物生长期呈上升趋势。(2)从空间上看，对冷极值变化反应最为敏感的是高寒湿润区，对暖极值变化反应最为敏感的是温带半湿润区和北亚热带湿润区，除北亚热带湿润区外各区域作物生长期的变化都达到了显著水平，而气温日较差仅在温带半湿润区达到了显著水平。(3)多数极端气温指数与经纬度、海拔之间有显著相关性，但受区域自然特点影响，经度与海拔对其影响实为一类。(4)亚洲区极涡强度、北半球极涡强度以及青藏高原指数B与极端气温指数变化有密切关系，而太阳黑子等只与个别指数之间存在显著的相关性。(5)预测出的极端气温指数冷极值相对指数仍呈现下降趋势，冷极值的绝对指数、暖极值以及气温日较差、作物生长期仍然呈现增加趋势，但大多数指数与1988—2017年相比变化幅度有所降低。（6）与其他区域相比甘肃省河东地区大多数气温指数变化幅度处于中间水平，表现出其为多种不同气候区、自然区交界地带的特色。

关键词: 河东地区, 极端气温指数, 时空变化, 预测

Abstract:

We assembled a database of daily temperature data from 61 meteorological stations in the Hedong region of Gansu Province,China from 1988 to 2017.We used the MannKendall test and the Sen’s slope estimation method to analyze the patterns of spatiotemporal changes in extreme temperature indices in the Hedong region of Gansu.In addition,we explored the relationship between extreme temperatures and their influencing factors.Finally,we applied the NAR neural network and Hurst index to predict and analyze extreme temperature index changes in Hedong.Our results yielded six major findings:(1) the relative index of cold extreme values has declined over time.The absolute indices of cold and warm extreme values are greater in magnitude,and the temperature index is worse.As a result,the crop growth period is increasing.(2) The areas that are most sensitive to cold extreme changes are the alpine humid regions,while temperate semihumid zones and northern subtropical regions are the most sensitive to warm extreme changes.With the exception of the northern subtropical humid region,the crop growth season changes were significant in all areas.However,diurnal temperature differences reached significance only in temperate semihumid regions.(3) The most extreme temperature indices were significantly correlated with longitude,latitude,and altitude,but were also affected by regional environmental characteristics.(4) The intensity of the polar vortex in Asia,the northern hemisphere polar vortex intensity,and the Tibetan Plateau Index B were all closely related to changes in the extreme temperature index.(5) Our predicted extreme temperature index matched the observed downward trend for the relative index of cold extreme values.It also predicted worsening of extreme heat and cold,and temperatures.Finally,it correctly showed lengthened growing seasons.However,most of the indices deviated slightly from the 1988-2017 observational dataset.(6) Compared with other regions,most of the temperature index changes in Hedong were at intermediate levels,reflecting locally diverse responses in the region’s various climate zones.

Key words: Hedong area, extreme temperature index, temporal and spatial variation, prediction