地理研究

地理研究 ›› 2008, Vol. 27 ›› Issue (2): 323-331.doi: 10.11821/yj2008020010

• 地表过程研究 • 上一篇    下一篇

长白山森林植被NPP主要影响因子的敏感性分析

王培娟1,2, 谢东辉3, 张佳华1, 朱启疆3   

  1. 1. 中国气象科学研究院,北京 100081;
    2. 成都高原气象研究所,成都 610072;
    3. 北京师范大学 地理学与遥感科学学院,遥感科学国家重点实验室,环境遥感与数字城市北京市重点实验室,北京 100875
  • 收稿日期:2007-02-09 修回日期:2007-11-13 出版日期:2008-03-25 发布日期:2008-03-25
  • 作者简介:王培娟(1978-),女,内蒙古根河人,博士,助理研究员。主要从事生态环境遥感应用研究。
  • 基金资助:

    科技部项目(2006GB24160430);国家自然科学基金(40571109和40601060);十一五科技支撑项目(2006BAD04B04);国家863计划(2006AA10Z213);成都高原气象研究所开放基金(LPM2007013)

Sensitivity analysis for primary factors of the forest net primary productivity in Changbaishan Natural Reserve based on process model

WANG Pei-juan1,2, XIE Dong-hui3, ZHANG Jia-hua1, ZHU Qi-jiang3   

  1. 1. Chinese Academy of Meteorological Science, Beijing 100081, China;
    2. Institute of Plateau Meteorology, CMA, Chengdu 610072, China;
    3. School of Geography,State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China
  • Received:2007-02-09 Revised:2007-11-13 Online:2008-03-25 Published:2008-03-25
  • Supported by:

    科技部项目(2006GB24160430);国家自然科学基金(40571109和40601060);十一五科技支撑项目(2006BAD04B04);国家863计划(2006AA10Z213);成都高原气象研究所开放基金(LPM2007013)

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58 | 12

摘要：

对影响森林植被净第一性生产力的主要因子进行敏感性分析是准确估计NPP的需要。本文以长白山自然保护区为例,利用敏感性矩阵,对北部生态系统生产力模拟模型的三个主要输入因子——叶面积指数、温度和降水量,分别分析了各单一因子对森林植被NPP的敏感性,并建立敏感性分析矩阵。分析结果表明,在长白山自然保护区,森林植被的NPP与叶面积指数呈正相关 ,与温度呈负相关,与降水量无明显相关关系。同时,还统计了不同森林植被类型的NPP对输入参数的敏感性,得出了针叶林对环境的适应性最强、生长最稳定的结论。

关键词: 北部生态系统生产力模拟模型 (BEPS), 净第一性生产力, 敏感性分析, 长白山

Abstract:

The sensitivity analysis of primary factors affecting the forest Net Primary Productivity (NPP) is important to estimate NPP accurately. In this paper, the sensitivity of NPP to primary parameters of BEPS (boreal ecosystem productivity simulator) was explored using uncertainty and sensitivity matrix (USM) at Changbaishan Natural Reserve in southeast Jilin province, China. Three input parameters for BEPS (leaf area index, temperature and precipitation) were selected for single factor analysis. The analysis was based on an uncertainty and sensitivity matrix with two fixed parameters and the third one was given with a change of +/-5% (or 0.5 degree), +/-10% (or 1 degree) and +/-20% (or 2 degree) respectively. The result shows that in Changbaishan Natural Reserve, forest net primary productivity increases with the increase of LAI, drops with the rise of temperature, and has no obvious relationship with precipitation. The sensitivity analysis of different vegetation classes, including coniferous forest, broadleaf forest and mixed forest, was also done. We find the coniferous forest has a strong adaptability to environment and less effect on the environmental changes. This study was just performed at one of the parameters with pre-setting changes, while the other two parameters are fixed at true values. Definitely, any parameter will respond to the change of other parameter. Hence the net forest primary productivity will change with them. Therefore, in the future, we need to strengthen the research of changing more parameters than one simultaneously to study the sensitivity of NPP to input parameters.

Key words: BEPS, NPP, sensitivity analysis, Changbaishan Natural Reserve