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1961-2014年中国干湿气候时空变化特征及成因分析
引用本文:胡琦,董蓓,潘学标,姜会飞,潘志华,乔宇,邵长秀,丁梦琳,尹紫薇,胡莉婷.1961-2014年中国干湿气候时空变化特征及成因分析[J].农业工程学报,2017,33(6):124-132.
作者姓名:胡琦  董蓓  潘学标  姜会飞  潘志华  乔宇  邵长秀  丁梦琳  尹紫薇  胡莉婷
作者单位:1. 中国农业大学资源与环境学院,北京 100193;农业部武川农业环境科学观测实验站,呼和浩特 011700;2. 中国农业大学资源与环境学院,北京,100193
基金项目:国家重点研发计划项目 (2016YFD0300105,2016YFD0300106);国家自然科学基金项目(41271053)
摘    要:利用全国701个气象站点1961-2014年逐日地面观测资料,基于降水量和参考作物蒸散量(ET0)计算的湿润指数研究了近54a中国干湿气候时空变化特征,并利用敏感性和贡献率法分析了气候变化背景下主要气象因子对ET0的影响,对干湿气候变化的成因进行了探讨.结果表明:全国气候在3个时间段(时段1:1961-1980;时段2:1981-2000;时段3:2001-2014)中经历了变湿到变干的过程;不同地区干湿状况变化差异很大,干旱趋势主要发生在中国的半干旱半湿润气候区;1961-2014年降水量变化趋势不显著,ET0呈显著下降的趋势,61.6%的站点出现"蒸发悖论"现象.南方大部分地区和新疆的西北部由于降水量增加和ET0减少,气候变湿;西北和西南大部分地区由于年降水量减少和ET0增加,气候呈显著变干的趋势.ET0对相对湿度的变化最敏感,风速的负贡献率是引起ET0变化的主导因子.研究时段内风速和日照时数的减少对ET0的负效应超过温度上升对ET0的增大作用,导致全国ET0总体呈下降趋势.

关 键 词:气候变化  降水量  风速  参考作物蒸散量  湿润指数  敏感系数  贡献率
收稿时间:2016/8/3 0:00:00
修稿时间:2016/12/30 0:00:00

Spatiotemporal variation and causes analysis of dry-wet climate over period of 1961-2014 in China
Hu Qi,Dong Bei,Pan Xuebiao,Jiang Huifei,Pan Zhihu,Qiao Yu,Shao Changxiu,Ding Menglin,Yin Ziwei and Hu Liting.Spatiotemporal variation and causes analysis of dry-wet climate over period of 1961-2014 in China[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(6):124-132.
Authors:Hu Qi  Dong Bei  Pan Xuebiao  Jiang Huifei  Pan Zhihu  Qiao Yu  Shao Changxiu  Ding Menglin  Yin Ziwei and Hu Liting
Institution:1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; 2. Scientific and Observing Experimental Station of Agro-Environment, Ministry of Agriculture, Hohhot 011700, China;,1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; 2. Scientific and Observing Experimental Station of Agro-Environment, Ministry of Agriculture, Hohhot 011700, China;,1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; 2. Scientific and Observing Experimental Station of Agro-Environment, Ministry of Agriculture, Hohhot 011700, China;,1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China;,1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; 2. Scientific and Observing Experimental Station of Agro-Environment, Ministry of Agriculture, Hohhot 011700, China;,1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China;,1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; 2. Scientific and Observing Experimental Station of Agro-Environment, Ministry of Agriculture, Hohhot 011700, China;,1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; 2. Scientific and Observing Experimental Station of Agro-Environment, Ministry of Agriculture, Hohhot 011700, China;,1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; and 1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China;
Abstract:Abstract: Global warming has caused non uniform changes in precipitation intensity and distribution, which will inevitable impact on the wet and dry climate. In order to make clear the distribution of dry-wet climate zone and its changes occurred in recent 54 years, this paper analyzed the temporal and spatial variation characteristics of dry and wet climate in China over the period from 1961 to 2014, studied the influences of main meteorological factors on ET0 by using the sensitivity and contribution rate method, and discussed the probable causes for the dry-wet climate variation. In this study, 701 meteorological stations with 54-year data record were selected in China (Excluding Taiwan, Hongkong and Macao). ET0 was calculated by using Penman-Monteith method. The data were downloaded from the China Meteorological Data Sharing Service Network, including daily mean, maximum and minimum temperature at 2 m height, relative humidity, sunshine hours, wind speed at 10 m height, precipitation and atmospheric pressure. The data processing, including ET0, climate trend, sensitivity coefficient, and contribution rate were achieved by using Matlab2014 software. Spatial analysis was also carried out to study the regional trends at decadal and annual scales. All spatial distribution maps were constructed using the inverse distance weighting interpolation method embedded in the ArcGIS 10.1 software package with a grid cell size of 0.02° (about 2 km). The results showed that the national climate exhibited the process of getting wet to dry in three time periods (period 1:1961-1980; period 2:1981-2000; period 3:2001-2014). The averaged humid index for all stations had increased by 1.3% in recent 54 years with increasing trend rate of 0.002/ decade, and 12.4% (n=87) of stations exhibited the significance changes (P<0.05). However, dry and wet conditions varied greatly in different regions, and the drought trend mainly occurred in the semi-arid and semi-humid climate region in China. These changes for dry-wet conditions were caused by the changes of precipitation and ET0. Precipitation showed non-significant trend over the period of 1961 to 2014 due to great inter-annual variability, while ET0 showed a significant decreasing trend at average rate of -4.2/decade, and 61.6% of the sites appeared "evaporation paradox" phenomenon. In most parts of the south and the northwest of Xinjiang, the climate changed because of the increase of precipitation and ET0. The climate became wet in south China and Xinjiang province of northwest China because of the increasing precipitation and decreasing ET0. On the contrast, most of the northwest and southwest China regions showed dry trend with the decreasing precipitation and increasing ET0. Temperature showed positive sensitive coefficient as well as sunshine hours and wind speed, i.e., ET0 would increase as these three variables increase. On the contrast, relative humidity showed negative sensitive coefficient, which was also the most sensitive variable. Wind speed was the main factor that affected ET0 change with the largest contribution rate, but ET0 is most sensitive to relative humidity. The reason was that the relative importance of the four primary meteorological variables governing ET0 changing trends would vary with both their sensitivity coefficients and relative changing values. Using relative humidity as an example, it showed the least contribution rate due to the smaller relative change rate in recent 54 years compared to temperature, wind speed, and sunshine hours. In summary, China has experienced obviously climate wetting as the climate warming due to the significantly decreasing ET0 over the period of 1961 to 2014. The negative effects caused by the significantly decreasing trend in wind speed and sunshine hours on ET0 exceeded the positive effects caused by the increasing temperature, which resulted in the ET0 decline in China.
Keywords:climate changes  precipitation  wind speed  ET0  humid index  sensitive coefficient  contribution rate
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