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基于格点数据的1961—2018年中国多种积温时空变化研究
引用本文:白磊,张帆,尚明,师春香,孙帅,刘丽珺,文元桥,苏传程.基于格点数据的1961—2018年中国多种积温时空变化研究[J].地球信息科学,2021,23(8):1446-1460.
作者姓名:白磊  张帆  尚明  师春香  孙帅  刘丽珺  文元桥  苏传程
作者单位:1.武汉理工大学智能交通系统研究中心,武汉 4300702.武汉理工大学航运学院,武汉 4300703.河北工程大学地球科学与工程学院,邯郸 0560384.国家气象信息中心,北京 1000815.广西气象信息中心,南宁 530022
基金项目:国家自然科学基金青年项目(51709218);国家重点研发计划项目(2018YFC1407405);国家重点研发计划项目(2018YFC1506601)
摘    要:积温能够影响植物的物候期和农业作物的产量及空间分布。在研究中,通常将有限数量站点积温数据外推插值到空间进行分析,在站点空间分布稀疏和地形复杂的地区,积温数据在空间上有较大的不确定性。目前,已有高分辨率的格点化气象数据,在此基础上直接计算可解决上述问题。本研究使用1961—2018年长时间序列的高分辨率格点气温数据(CN05.1),分析了中国大陆≥0 ℃、≥ 5 ℃、≥10 ℃和≥ 15 ℃的4种积温时空变化,结论如下:① 在中国大陆范围,4种积温的低值区主要分布在青藏高原、新疆天山和中国东北地区,高值区分布在华南地区;② 在58年间,4种积温均表现出显著增加的趋势,尤其以内蒙古和东北地区积温增温趋势显著;③ 这种积温在空间上趋势变化引起了农业区划中热带和亚热带地区面积显著增加,温带和寒温带面积减少;④ 在58年中,4种积温开始时间显著提前,结束时间显著推后。4种积温开始时间转变间隔时间(0~5 ℃、5~10 ℃和10~15 ℃转变所需时间)在黄土高原和内蒙古地区较为剧烈。对于结束时间转变间隔时间,在华中地区趋势变化剧烈。

关 键 词:区域面积变化  开始时间  结束时间  趋势分析  间隔时间  新疆北疆  青藏高原  东北地区  
收稿时间:2020-09-05

Evolution of the Multiple Accumulated Temperature Across Mainland China in 1961-2018 with the Gridded Meteorological Dataset
BAI Lei,ZHANG Fan,SHANG Ming,SHI Chunxiang,SUN Shuai,LIU Lijun,WEN Yuanqiao,SU Chuancheng.Evolution of the Multiple Accumulated Temperature Across Mainland China in 1961-2018 with the Gridded Meteorological Dataset[J].Geo-information Science,2021,23(8):1446-1460.
Authors:BAI Lei  ZHANG Fan  SHANG Ming  SHI Chunxiang  SUN Shuai  LIU Lijun  WEN Yuanqiao  SU Chuancheng
Institution:1. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430070, China2. School of Navigation, Wuhan University of Technology, Wuhan 430070, China3. School of Earth Science and Engineering, Hebei University of Engineering, Handan 056038, China4. National Meteorological Information Center, Beijing 100081, China5. Guangxi Meteorological Information Center, Nanning 530022, China
Abstract:Accumulated Temperature (AT) could affect plants' phonological period and crops' yield and spatial distribution. AT is usually obtained by extrapolation of surface observations. However, AT would have greater spatial uncertainties in regions where the surface observations are sparsely distributed with complex terrain. In recent years, there have been some gridded meteorological data with well spatial representation. If studies used these high spatial resolution gridded meteorological data to directly calculate AT, the problem mentioned above would be solved. This study used the gridded dataset (CN05.1) with high spatial resolution and long term time series from 1961-2018 to analyze the spatiotemporal changes of the four Accumulated Temperatures (ATs) in mainland China with the thresholds of ≥0 ℃, ≥5 ℃, ≥10 ℃, and ≥15 ℃, respectively. The gridded dataset was made using more than 2400 surface meteorological stations across mainland China and was well extrapolated by the plate spline method. The main conclusions are summarized as follows: ① In mainland China, the four ATs (≥0 ℃, ≥5 ℃, ≥10 ℃ and ≥15 ℃) have low-value areas in the Qinghai-Tibet Plateau, Tianshan Mountains in Xinjiang, and Northeast China, but high-value areas in South China. Their spatial patterns are similar to those of the 2-m air temperature. ② All four ATs show significant increasing trends, especially in Inner Mongolia and Northeast China. ③ Due to changes in the AT spatial trends, the area of tropical and subtropical regions, identified by a threshold of 10 ℃, have a significant increase. In contrast, the area of mid-temperate and cold-temperate regions have a significant decrease. ④ During 1961-2018, starting time of four ATs had significantly advanced while the ending time had significantly delayed in both regional and point scales. The interval period of temperature transition ranges of 0~5 ℃, 5~10 ℃, and 10~15 ℃’s starting time has more severe changes in the Loess Plateau and Inner Mongolia. For interval period of ending time, Central China Plain changes greatly. These significant changes would impact the farming plan, crop physiology, plant diseases, and insect pests. In the future, the gridded dataset with more high spatial resolution and longer time series could be used to study the changes of accumulated temperature under climate change.
Keywords:regional area change  starting time  ending time  trend analysis  interval period for different accumulated temperature  Northern Xinjiang  Qinghai-Tibet Plateau  Northeast China  
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