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半干旱草原型流域植被地上生物量时空分布特征及其影响因子

张俊怡1,刘廷玺1,2*,罗艳云1,2,段利民1,2,李玮1,杨璐1,Buren Scharaw3   

  1. (1内蒙古农业大学水利与土木建筑工程学院, 呼和浩特 010018;2内蒙古自治区水资源保护与利用重点实验室, 呼和浩特 010018;3Application Center for System Technologies, Fraunhofer IOSB, Ilmenau 98693, Germany)
  • 出版日期:2020-02-10 发布日期:2020-02-10

Temporal and spatial distribution of aboveground biomass of vegetation and quantitative analysis of impact factors in semi-arid grassland basin.

ZHANG Jun-yi1, LIU Ting-xi1,2*, LUO Yan-yun1,2, DUAN Li-min1,2, LI Wei1, YANG Lu1, Buren Scharaw3   

  1. 1College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;2Inner Mongolia Water Resource Protection and Utilization Key Laboratory, Hohhot 010018, China; 3Application Center for System Technologies, Fraunhofer IOSB, Ilmenau 98693, Germany)
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  • Online:2020-02-10 Published:2020-02-10

摘要: 锡林河流域作为内蒙古典型草原最具代表性的区域,研究其草地生物量的动态变化特征及其与环境因子的关系,可以为草地生态系统碳循环研究提供基础数据,同时为草地的合理利用和管理提供理论支撑。本研究以半干旱草原型流域——锡林河流域为对象,基于野外实测数据,运用相关分析、分类回归树、非线性回归分析方法,定量分析了流域上游和下游不同时间(2016年6月20日、7月5日、7月20日、8月6日左右)地上生物量与其关键驱动因子的关系。结果表明:(1)随着生长季的推进,地上生物量在7月变化最为显著,并在8月6日达到极值,上、中、下游的地上生物量分别为79.64、74.87、69.34 g·m-2;空间上呈现从东南向西北减小的趋势。(2)影响流域上游不同时间地上生物量的关键因子分别是4月的降水量、7月5日的土壤含水量、7月20日的土壤含水量、8月6日的Simpson指数,除Simpson指数对地上生物量为负效应外,其余因子对地上生物量均有显著的正效应;对应关键因子分别可解释地上生物量变异的77%、72%、79%、65%。(3)经度、6月的降水量、7月的降水量、7月的降水量分别与下游6月20日、7月5日、7月20日、8月6日左右的地上生物量呈显著二次正相关关系;对应关键因子分别可解释地上生物量变异的88%、75%、85%、82%。该研究结果可为今后分析较大尺度上生物量的时空变异特征提供案例参考,同时也可为草地生态系统的深入研究及长期监测提供数据累积。

关键词: 气候变化, 玉米株型, 种植密度, 遮阴, 穗部发育, 生产力

Abstract: Xilin River Basin is the most representative area of typical steppe in Inner Mongolia. Characterizing the dynamics of aboveground biomass and its relationship with environmental factors can provide basic data for the research of ecosystem carbon cycling and a theoretical frame for rational use and management of grassland. Xilin River Basin locates in semiarid region. With filed empirical data, the methods of correlation analysis, classification and regression tree, nonlinear regression analysis were used to quantitatively analyze the relationship between aboveground biomass and its key driving factors in the upstream and downstream of the basin at different times (around June 20, July 5, July 20, and August 6 of 2016). The results showed that: (1) Aboveground biomass changed most significantly in July, with a peak on August 6. The aboveground biomass of the upper, middle and lower basin on August 6 were 79.64, 74.87, 69.34 g·m-2 respectively. Biomass showed a decreasing tendency from the southeast to the northwest. (2) Key factors affecting aboveground biomass at different times in the upstream of the basin were precipitation in April, soil water content on July 5, soil water content on July 20, and Simpson index on August 6. The Simpson index had a negative effect on aboveground biomass, while all other factors had positive effects on aboveground biomass. Corresponding key factors accounted for 77%, 72%, 79%, and 65% of the variation of aboveground biomass, respectively. (3) Longitude, precipitation in June, precipitation in July, and precipitation in July were the key factors affecting aboveground biomass of the downstream on June 20, July 5, July 20, and August 6, respectively. All the factors had a significant positive correlation with aboveground biomass. Corresponding key factors accounted for 88%, 75%, 85%, and 82% of the variation of aboveground biomass, respectively. Our results provide a reference for the analysis of the temporal and spatial variation of plant biomass at a large scale and provide data for in depth research and long-term monitoring of grasslands.

Key words: climate change, shading, productivity., plant type, ear development, planting density