摘要
中国西北干旱半干旱区是中国北方重要的生态屏障。在气候变化和人类活动双重影响下,开展该区域植被净生态系统生产力(NEP)时空变化特征及关键驱动因素的研究对区域生态安全与可持续发展意义重大。基于气象、植被、土壤和地形等多源遥感及再分析数据集,利用改进的CASA模型、土壤呼吸模型、地理探测器等方法,分析了2000-2022年中国西北干旱半干旱区植被NEP时空变化特征及其关键影响因子。主要研究结果表明:(1)研究区植被NEP多年均值为135.32g C/m^(2),整体呈碳汇功能,年内碳汇主要集中在5-9月份;碳源区主要集中在山前低植被覆盖区和东部沙地,面积占29%。(2)近23年研究区植被NEP以增加趋势为主,变化速率为3.98g C m^(-2) a^(-1)。空间上,呈增加趋势的面积占比为88.77%,减少趋势主要集中在天山山脉山前平原区。(3)LAI对植被NEP空间分布的解释力最强(0.75),其次是土壤湿度(0.42),不同要素交互对NEP均呈现双因子增强或非线性增强作用;LAI、降水和太阳辐射与研究区植被NEP主要呈显著正相关,显著正相关面积分别占73.79%、16.28%和15.10%,与降水呈显著正相关区域主要分布在西部山区、呼伦湖和东部沙地,与太阳辐射呈显著正相关区域主要分布在东部沙地。(4)西北干旱半干旱区气候变化对NEP的贡献度大于80%的地区占59.42%。人类活动对NEP的贡献率高于60%的区域占比为13.90%,集中分布在阿尔泰山和天山山脉的山前平原区,极少数散落分布在东部沙地。研究结果有助于了解植被NEP对气候变化的响应机制,为实现"双碳"目标提供理论依据。
The arid and semi-arid regions of Northwest China are important ecological barriers in northern China.Under the dual influence of climate change and human activities,it is of great significance to study the spatiotemporal variation characteristics and key driving factors of net ecosystem productivity(NEP)in this region for regional ecological security and sustainable development.Based on multi-source remote sensing and reanalysis datasets such as meteorology,vegetation,soil and topography,and using improved CASA model,soil respiration model,geographic detector and other methods,this study analyzed the spatiotemporal variation characteristics and main influencing factors of vegetation NEP in arid and semi-arid regions of Northwest China from 2000 to 2022.The main findings show that:(1)The multi-year average NEP of vegetation in the study area was 135.32g C/m^(2),which showed the overall carbon sink function,and the carbon sink was mainly concentrated from May to September.The carbon source area is mainly concentrated in the piedmont low vegetation cover area and the eastern sandy land,accounting for 29%.(2)In the past 23years,the NEP of vegetation in the study area has been increasing,with a change rate of 3.98g C m^(-2) a^(-1).Spatially,the area with an increasing trend accounted for 88.77%,and the decreasing trend was mainly concentrated in the piedmont plain area of the Tianshan Mountains.(3)The leaf area index had the strongest explanatory power on the spatial distribution of vegetation NEP(0.75),followed by soil moisture(0.42),and the interaction of different elements showed double factor enhancement or nonlinear enhancement effect on NEP.Leaf area index,precipitation and solar radiation were significantly positively correlated with vegetation NEP.The positive correlation area accounted for 73.79%,16.28%and 15.10%respectively.The areas with significant positive correlation with precipitation are mainly distributed in the western mountainous areas,Hulun Lake and eastern sandy land.There is a significant positive correlation with solar radiation,and the area is mainly distributed in the eastern sandy land.(4)The contribution of climate change to NEP in the arid and semi-arid regions of Northwest China is greater than 80%,accounting for 59.42%.The contribution rate of human activities to NEP is higher than 60%,accounting for 13.90%,which is concentrated in the piedmont plain area of the Altai Mountains and Tianshan Mountains,and a few are scattered in the eastern sandy land.The results of this study are helpful to understand the response mechanism of vegetation NEP to climate change,and provides a theoretical basis for achieving the goal of"carbon peak and carbon neutrality".
作者
赵旭琴
罗敏
孟凡浩
王云倩
萨楚拉
董金义
ZHAO Xuqin;LUO Min;MENG Fanhao;WANG Yunqian;SA Chula;DONG Jinyi(College of Geographical Science,Inner Mongolia Normal University,Hohhot 010022,China;Key Laboratory of Mongolian Plateau Geography Research,Inner Mongolia Autonomous Region,Hohhot 010022,China;School of Geography and Tourism,Qufu Normal University,Rizhao 276826,China)
出处
《生态学报》
北大核心
2025年第15期7415-7427,共13页
Acta Ecologica Sinica
基金
国家自然科学基金(42361024,42261079)
一流学科科研专项项目(YLXKZX-NSD-029)
中央引导地方科技发展资金项目(2024XY0035)。
关键词
西北干旱半干旱区
植被碳源/汇
CASA模型
相对贡献
驱动力分析
arid and semi-arid areas in Northwest China
vegetation carbon source/sink
CASA model
relative contribution
driver analysis
