干旱区研究

干旱区研究 >

2023 , Vol. 40 >Issue 11: 1824 - 1832

DOI: https://doi.org/10.13866/j.azr.2023.11.12

生态与环境

青海湖流域NPP动态变化及驱动力

  • 吴雪晴 ,
  • 张乐乐 ,
  • 高黎明 ,
  • 李炎坤 ,
  • 刘轩辰
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  • 1.青海师范大学地理科学学院，青海省自然地理与环境过程重点实验室，青海 西宁 810008
    2.青海师范大学，青藏高原地表过程与生态保育教育部重点实验室，青海 西宁 810008
    3.高原科学与可持续发展研究院，青海 西宁 810008
    4.甘肃政法大学网络空间安全学院，甘肃 兰州 730070
吴雪晴（1998-），女，硕士研究生，研究方向为自然地理与生态环境过程. E-mail: 1368418205@qq.ocm

收稿日期: 2023-04-28

  修回日期: 2023-07-17

  网络出版日期: 2023-12-01

基金资助

青海省自然科学基金(2022-ZJ-711);国家自然科学基金(42171467);国家自然科学基金(42001060);国家自然科学基金(41705139)

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Dynamic change and driving force of net primary productivity in Qinghai Lake Basin

  • Xueqing WU ,
  • Lele ZHANG ,
  • Liming GAO ,
  • Yankun LI ,
  • Xuanchen LIU
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  • 1. Qinghai Provincial Key Laboratory of Physical Geography and Environmental Processes, College of Geographical Sciences, Qinghai Normal University, Xining 810008, Qinghai, China
    2. Qinghai Normal University, MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Xining 810008, Qinghai, China
    3. Academy of Plateau Science and Sustainability, Xining 810008, Qinghai, China
    4. School of Cyberspace Security,Gansu University of Political Science and Law, Lanzhou 730070, Gansu, China

Received date: 2023-04-28

  Revised date: 2023-07-17

  Online published: 2023-12-01

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摘要

对青海湖流域植被净初级生产力（Net Primary Productivity，NPP）以及驱动因子进行分析可以为流域生态管理与可持续发展提供一定的参考。本研究基于Carnegie-Ames-Stanford Approach（CASA）模型估算了青海湖流域NPP值，通过趋势分析、Hurst指数、地理探测器等方法，定量评估了2000—2018年青海湖流域NPP的动态变化及驱动因子。结果表明：从空间分布来看，青海湖流域多年平均植被NPP为218.88 g C·m-2，年平均NPP的高值分布在青海湖北部和南部，最高达到375.85 g C·m-2，低值分布在青海湖东岸，最低为0.11 g C·m-2。从时间变化看，2000—2018年流域年平均NPP表现为上升趋势，增幅为1.61 g C· m-2·a-1，2018年达最高值为247.30 g C·m-2。季节变化表明7月NPP最高，1月NPP最低。在NPP未来变化趋势上，Hurst指数小于0.5的区域占比为75.6%，说明青海湖流域植被NPP未来变化趋势可能与现在相反。地理探测器的结果显示单因子探测中土地利用是植被NPP变化的主要驱动力，交互探测中最强主导交互因子是海拔和土地利用。土地利用类型受自然因素影响较大，我们应加强对流域地形因素以及人为活动的关注。

关键词： 植被净初级生产力; CASA模型; 地理探测器; 青海湖流域

本文引用格式

吴雪晴 , 张乐乐 , 高黎明 , 李炎坤 , 刘轩辰 . 青海湖流域NPP动态变化及驱动力[J]. 干旱区研究, 2023 , 40(11) : 1824 -1832 . DOI: 10.13866/j.azr.2023.11.12

Abstract

The analysis of Net Primary Productivity (NPP) and the driving factors in the Qinghai Lake Basin can provide certain references for the ecological management and sustainable development of the basin. This study estimated the NPP value of the Qinghai Lake Basin based on the Carnegie-Ames-Stanford Approach (CASA) model and quantitatively evaluated the dynamic changes and driving factors of NPP in the Qinghai Lake Basin between 2000 and 2018 through trend analysis, Hurst index, and Geographic Detector. From the perspective of spatial distribution, the results show that the annual average NPP value of the Qinghai Lake Basin was 218.88 g C·m-2. The highest value of the annual average NPP was distributed in the north and south of the Qinghai Lake (375.85 g C·m-2) and the lowest value was distributed on the east bank of the Qinghai Lake (0.11 g C·m-2). From the perspective of time change, the annual average NPP of the basin showed an upward trend between 2000 and 2018, with an increase of 1.61 g C·m-2·a-1, reaching the highest value of 247.30 g C·m-2 in 2018. The seasonal variation showed that the NPP value was highest in July and lowest in January. In the future trend of NPP, Hurst index of less than 0.5 accounted for 75.6% of the study area, indicating that the future trend of NPP of vegetation in the Qinghai Lake Basin may be opposite to the present. Land use types are greatly affected by natural factors; therefore, more attention should be paid to watershed topographic factors and human activities.

Key words： Net Primary Productivity of vegetation; CASA mode; Geographic Detector; Qinghai Lake Basin

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