Global Sensitivity Analysis of the LPJ Model for?Larix olgensis?Henry Forests NPP in Jilin Province, China

编号 030034702

推送时间 20220613

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Global Sensitivity Analysis of the LPJ Model for?Larix olgensis?Henry Forests NPP in Jilin Province, China

来源期刊 Forests

期 第347期

发表时间 20220602

关键词 global sensitivity analysis;  EFAST;  Morris;  LPJ-DGVM;  net primary productivity; 

摘要 Parameter sensitivity analysis can determine the influence of the input parameters on the model output. Identification and calibration of critical parameters are the crucial points of the process model optimization. Based on the Extended Fourier Amplitude Sensitivity Test (EFAST) and the Morris method, this paper analyzes and compares the parameter sensitivity of the annual mean net primary productivity (NPP) of?Larix olgensis?Henry forests in Jilin Province simulated by the Lund–Potsdam–Jena dynamic global vegetation model (LPJ model) in 2009–2014 and 2000–2019, and deeply examines the sensitivity and influence of the two methods to each parameter and their respective influence on the model’s output. Moreover, it optimizes some selected parameters and re-simulates the NPP of?Larix olgensis?forests in Jilin Province from 2010 to 2019. The conclusions are the following: (1) For the LPJ model, the sensitive and non-influential parameters could be identified, which could guide the optimization order of the model and was valuable for model area applications. (2) The results of the two methods were similar but not identical. The sensitivity parameters were significantly correlated (p?< 0.05); parameter?krp?was the most sensitive parameter, followed by parameters?αm,?αa?and?gm. These sensitive parameters were mainly found in the photosynthesis, water balance, and allometric growth modules. (3) The EFAST method had a higher precision than the Morris method, which could calculate quantitatively the contribution rate of each parameter to the variances of the model results; however, the Morris method involved fewer model running times and higher efficiency. (4) The mean relative error (MRE) and mean absolute error (MAE) of the simulated value of LPJ model after parameter optimization decreases. The optimized annual mean value of NPP from 2010 to 2019 was 580 g C m?2?a?1, with a mean annual growth rate of 2.13%, exhibiting a fluctuating growth trend. The MAE of the simulated value of LPJ model after parameter optimization decreases.?

服务人员 付贺龙

服务院士 唐守正

PDF文件 浏览全文