自然资源学报 ›› 2020, Vol. 35 ›› Issue (4): 857-868.doi: 10.31497/zrzyxb.20200409

• 研究论文 • 上一篇    下一篇

黄土塬面保护区潜在蒸发量时空变化及其与气象、环流因子关系分析

孙从建1,2, 郑振婧1, 李新功1, 孙九林1,3   

  1. 1. 山西师范大学地理科学学院,临汾 041000;
    2. 中国科学院荒漠与绿洲生态国家重点实验室,乌鲁木齐 830011;
    3. 中国科学院地理科学与资源研究所,北京 100101
  • 收稿日期:2019-01-18 出版日期:2020-04-28 发布日期:2020-04-28
  • 作者简介:孙从建(1986- ),男,河北沧州人,博士,副教授,主要从事气候变化与水循环研究。E-mail: suncongjian@sina.com
  • 基金资助:
    国家自然科学基金项目(41601317); 荒漠与绿洲生态国家重点实验室开放基金项目(G2018-02-06)

Spatio-temporal distribution of the potential evapotranspiration and its controlling factors in the tableland protected region of the Loess Plateau

SUN Cong-jian1,2, ZHENG Zhen-jing1, LI Xin-gong1, SUN Jiu-lin1,3   

  1. 1. School of Geographical Sciences, Shanxi Normal University, Linfen 041000, Shanxi, China;
    2. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China;
    3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2019-01-18 Online:2020-04-28 Published:2020-04-28

摘要: 黄土塬面是黄土高原地区主要的农业分布区和人口聚居地,地位十分重要。黄土塬面潜在蒸发量(ET0)的研究对于区域水循环研究、水土流失防治及农业的可持续发展具有重要意义。基于黄土塬面保护区1960—2017年的气象数据,利用Penman-Monteith模型、小波分析、Mann-Kendall非参数检验等方法研究了黄土塬面保护区ET0的变化规律及其与气象、环流因子间的关系。结果表明:(1)黄土塬面保护区多年平均ET0为1173.4 mm,总体呈现增长趋势,增长率为21.1 mm/10 a;其生长季平均ET0值及增长率均高于非生长季平均ET0。(2)该区多年平均ET0空间分布特征表现为东部高西部低,西部甘肃塬区多年平均ET0远低于东部山西塬区。(3)过去58年来,区域年均、生长季、非生长季ET0均呈现出增长趋势,但空间差异明显;研究区年均ET0存在着10年、30年和50年的震荡周期,其中以30年周期为主周期。(4)气温是控制区域ET0变化的最重要的气象因子,但气温对ET0的影响具有明显的空间差异,在整个研究区内最低气温影响最显著;而甘肃塬区和陕西塬区的ET0变化主要受平均气温变化的控制,在山西塬区最高气温的变化是区域ET0变化的主要控制因子。(5)遥相关分析结果显示太平洋/北美指数(PNA)与北大西洋年代尺度振荡(AMO)对该区域ET0变化有一定影响,西太平洋海温指数(WPI)的变化影响区域非生长季ET0变化。

关键词: Penman-Monteith模型, 黄土塬面保护区, 影响因子, 潜在蒸发量, 时空分布

Abstract: The tableland protected region, as a major region for agriculture and habitation, plays an important role in the Loess Plateau. The information of the regional potential evapotranspiration (ET0) and its distribution on the Loess Plateau are beneficial to the reorganization in regional water cycle, control on the soil erosion and sustainable development of agriculture. Based on the meteorological data of the tableland protected regions of the Loess Plateau during 1960-2017, the spatial and temporal distribution of the ET0 and its controlling factors were analyzed by using the Penman-Monteith model, wavelet analysis, Mann-Kendall test and ArcGIS. The results are shown as follows: (1) The average ET0 of the study area is 1173.4 mm, which shows a significant increasing trend with a rate of 21.1 mm/10 a. The average ET0 in growing season is much higher than that in the non-growing season. (2) The spatial distribution of the multi-year annual average ET0 in the study area decreased from the east to the west, and the multi-year average ET0 in the western Gansu tableland is far lower than that in the eastern Shanxi tableland. (3) Over the past 58 years, the average ET0 of the whole study area, growth season and non-growth season showed a significant increasing trend, but the spatial variations are remarkable. There were 10-year, 30-year and 50-year oscillation periods in the variation of average ET0 in the study area, of which the 30-year oscillation is the main oscillation period during the past 58 years. (4) Air temperature is the most important meteorological factor controlling ET0 change in the region, but the influence of air temperature on ET0 change shows an obvious spatial difference. The lowest temperature has the most significant effect on the ET0 change of the whole study area. The changes of ET0 in the Gansu tableland and Shaanxi tableland are mainly affected by the change of average temperature. The change of maximum temperature in Shanxi tableland has significant influence on regional ET0. (5) The Pacific/North America index (PNA) and the Atlantic Multi-decadal Oscillation (AMO) are associated with the regional ET0 change, while the ET0 change of the non-growing season is related to the change of the WPI.

Key words: spatio-temporal distribution, the tableland protected region of the Loess Plateau, Penman-Monteith model, potential evapotranspiration, influencing factor