长白山森林植被NPP主要影响因子的敏感性分析

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

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。

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.