Li Yi Zhe, Zhang Ting Long, Liu Qiu Yu, Li Ying
College of Resources and Environmental Sciences, Northwest A&F University, Yangling 712100, Shaanxi, China.
Ying Yong Sheng Tai Xue Bao. 2018 Jan;29(1):84-92. doi: 10.13287/j.1001-9332.201801.016.
The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation with the spatial heterogeneity under the three vegetation types. According to the temporal and spatial heterogeneity of the optimal values, the parameters of the BIOME-BGC model could be classified in order to adopt different parameter strategies in practical application. The conclusion could help to deeply understand the parameters and the optimal values of the ecological process models, and provide a way or reference for obtaining the reasonable values of parameters in models application.
生态过程模型是目前研究陆地生态系统水碳循环的有力工具。然而，这些模型存在许多参数，且这些参数取值是否合理对模型模拟结果有重要影响。过去，许多研究对模型参数的敏感性和优化进行了分析和讨论。但最优参数的时空异质性较少受到关注。本文以BIOME - BGC模型为例，在常绿阔叶林、落叶阔叶林和C3草地中，每种植被类型选取两个实验站点，通过构建敏感性判断指标筛选模型敏感参数。利用模拟退火算法结合通量数据构建目标函数，获取各站点敏感参数的月最优值。然后构建时间异质性判断指标、空间异质性判断指标以及时空异质性判断指标，定量分析模型敏感参数最优值的时空异质性。结果表明，BIOME - BGC模型参数在不同植被类型下敏感性不同，但所选敏感参数大多一致。BIOME - BGC模型敏感参数的最优值大多呈现出不同程度的时空异质性，且随植被类型而异。与植被生理生态相关的敏感参数时空异质性相对较小，而与环境和物候相关的敏感参数时空异质性一般较大。此外，三种植被类型下模型敏感参数最优值的时间异质性与空间异质性呈显著线性相关。根据最优值的时空异质性，可对BIOME - BGC模型参数进行分类，以便在实际应用中采取不同的参数策略。该结论有助于深入理解生态过程模型的参数及最优值，为模型应用中获取合理参数值提供思路或参考。
