干旱区研究 ›› 2023, Vol. 40 ›› Issue (12): 1959-1968.doi: 10.13866/j.azr.2023.12.09

• 植物生态 • 上一篇    下一篇

陕西黄河流域植被碳利用率时空特征及对气候的敏感性研究

王娟1,2(),王钊1,2,郭斌3,4,何慧娟1,2(),董金芳1,2   

  1. 1.陕西省农业遥感与经济作物气象服务中心,陕西 西安 710014
    2.陕西省气象局秦岭和黄土高原生态环境气象重点实验室, 陕西 西安 710014
    3.中国气象局成都高原气象研究所,高原与盆地暴雨旱涝灾害四川省重点实验室,四川 成都 610072
    4.四川省阿坝州气象局,四川 马尔康 624000
  • 收稿日期:2023-02-16 修回日期:2023-09-13 出版日期:2023-12-15 发布日期:2023-12-18
  • 通讯作者: 何慧娟. E-mail: 393621703@qq.com
  • 作者简介:王娟(1981-),女,硕士,高级工程师,主要研究生态环境遥感. E-mail: wangj_81@126.com
  • 基金资助:
    陕西省科技厅自然科学基金面上项目(2023-JC-YB-279);陕西省自然科学基金青年项目(2022JQ-232);陕西省气象局秦岭和黄土高原生态环境气象重点实验室开放课题(2022G-12)

Spatiotemporal characteristics of vegetation carbon use efficiency and its sensitivity to climate in the Yellow River Basin in Shaanxi Province

WANG Juan1,2(),WANG Zhao1,2,GUO Bin3,4,HE Huijuan1,2(),DONG Jinfang1,2   

  1. 1. Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crop, Xi’an 710014, Shaanxi, China
    2. Shaanxi Key Laboratory of Eco-environment and Meteorology for the Qinling Mountains and Loess Plateau, Shaanxi Meteorological Bureau, Xi’an 710014, Shaanxi, China
    3. Institute of Plateau Meteorology, China Meteorological Administration, Chengdu/Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, Sichuan, China
    4. Aba Prefecture Meteorological Administration, Maerkang 624000, Sichuan, China
  • Received:2023-02-16 Revised:2023-09-13 Online:2023-12-15 Published:2023-12-18

摘要:

植被碳利用率(Carbon Use Efficiency,CUE)能够较客观地反映植被固定大气碳的效率以及植被对气候变化产生的反馈。利用MOD17、土地利用数据及气象数据,应用Hurst指数、相关分析及敏感性分析等方法,探讨了2001—2021年陕西黄河流域植被CUE时空变异及其对气候因子的敏感性。结果表明:(1) 2001—2021年陕西黄河流域植被总初级生产力(Gross Primary Productivity,GPP)、净初级生产力(Net Primary Productivity,NPP)及植被CUE呈上升趋势,植被CUE均值为0.51。(2) 研究区仅有14.21%的区域植被CUE呈下降趋势,植被CUE高值区主要集中分布在陕北防风固沙区及退耕还林区,未来植被CUE呈现下降趋势的面积占59.96%,且大部分为上升转下降趋势。(3) 气温、降水与植被CUE总体均呈负相关,但与降水的关系较气温更为显著,与气温、降水呈正相关关系的地区分布在陕北防风固沙区;对气温与降水的敏感系数分析表明,植被CUE与气温及降水的阈值分别为10 ℃及500 mm,在气温<10 ℃、降水量<500 mm时植被CUE随气温及降水的增加而增加。植被CUE与气候因子的关系在陕北退耕还林区及防风固沙区等干旱地区较为显著,且敏感性更强。

关键词: 植被CUE, 时空特征, Hurst 指数, 气候因子, 敏感性, 黄河流域

Abstract:

Vegetation carbon use efficiency (CUE) can objectively reflect the efficiency of vegetation in sequestering atmospheric carbon and the response of vegetation to climate change. Using MOD17, land use, and meteorological data, this study applied methods, such as the Hurst exponent, correlation analysis, and sensitivity analysis to explore the spatiotemporal variability of vegetation CUE and its sensitivity to climate factors in the Shaanxi section of the Yellow River Basin from 2001 to 2021. The results showed that (1) From 2001 to 2021, the gross primary productivity, net primary productivity (NPP), and vegetation CUE in the Shaanxi section of the Yellow River Basin exhibited an increasing trend, with an average CUE value of 0.51. (2) The study area was only 14.21% of the region, exhibiting a decreasing trend. The high-value areas of vegetation CUE are primarily concentrated in the windbreak and sand-fixation areas and the Grain for Green Project areas of northern Shaanxi. The areas where vegetation CUE indicated a decreasing trend accounted for 59.96%, most of which transitioned from an increasing trend to a decreasing trend. (3) Overall, temperature and precipitation correlated negatively with vegetation CUE, but the relationship with precipitation is more significant. Regions with positive correlations with temperature and precipitation are distributed in northern Shaanxi’s windbreak and sand-fixation areas. Sensitivity analysis of temperature and precipitation showed that the threshold values were 10 °C and 500 mm, respectively. When the temperature is below 10 °C and the precipitation is below 500 mm, the vegetation CUE increases with increasing temperature and precipitation. The relationship between vegetation CUE and climate factors is more significant and sensitive in arid areas, such as the conversion of farmland to forests and windbreak and sand-fixation areas in northern Shaanxi.

Key words: vegetation CUE, spatiotemporal characteristics, Hurst, climate factors, sensitivity, Yellow River Basin