College of Ecology and Environment, Hainan University, Haikou 570228, China.
Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China.
Int J Environ Res Public Health. 2022 Oct 28;19(21):14068. doi: 10.3390/ijerph192114068.
Accurate monitoring of forest carbon flux and its long-term response to meteorological factors is important. To accomplish this task, the model parameters need to be optimized with respect to in situ observations. In the present study, the extended Fourier amplitude sensitivity test (eFAST) method was used to optimize the sensitive ecophysiological parameters of the Biome BioGeochemical Cycles model. The model simulation was integrated from 2010 to 2020. The results showed that using the eFAST method quantitatively improved the model output. For instance, the R increased from 0.53 to 0.72. Moreover, the root-mean-square error was reduced from 1.62 to 1.14 gC·m·d. In addition, it was reported that the carbon flux outputs of the model were highly sensitive to various parameters, such as the canopy average specific leaf area and canopy light extinction coefficient. Moreover, long-term meteorological factor analysis showed that rainfall dominated the trend of gross primary production (GPP) of the study area, while extreme temperatures restricted the GPP. In conclusion, the eFAST method can be used in future studies. Furthermore, eFAST could be applied to other biomes in response to different climatic conditions.
准确监测森林碳通量及其对气象因素的长期响应非常重要。为了完成这项任务，需要根据现场观测优化模型参数。在本研究中，扩展傅里叶幅度灵敏度测试（eFAST）方法用于优化生物地球化学循环模型的敏感生理参数。模型模拟从 2010 年到 2020 年进行。结果表明，使用 eFAST 方法可以定量提高模型输出。例如，R 值从 0.53 增加到 0.72。此外，均方根误差从 1.62 减少到 1.14 gC·m·d。此外，据报道，模型的碳通量输出对各种参数高度敏感，例如冠层平均比叶面积和冠层光衰减系数。此外，长期气象因子分析表明，降雨主导了研究区域总初级生产力（GPP）的趋势，而极端温度限制了 GPP。总之，eFAST 方法可用于未来的研究。此外，eFAST 可应用于其他生物群落，以应对不同的气候条件。
