• 目次 •

### 生态过程模型敏感参数最优取值的时空异质性分析——以BIOME-BGC模型为例

1. 西北农林科技大学资源环境学院, 陕西杨凌 712100
• 收稿日期:2017-06-21 出版日期:2018-01-18 发布日期:2018-01-18
• 通讯作者: * E-mail: dargon810614@126.com
• 作者简介:李一哲,男,1993年生,硕士研究生.主要从事生态遥感研究.E-mail: liyizhecn@163.com
• 基金资助:
本文由国家自然科学基金项目(41301451)资助

### Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.

LI Yi-zhe, ZHANG Ting-long*, LIU Qiu-yu, LI Ying

1. College of Resources and Environmental Sciences, Northwest A&F University, Yangling 712100, Shaanxi, China
• Received:2017-06-21 Online:2018-01-18 Published:2018-01-18
• Contact: * E-mail: dargon810614@126.com
• Supported by:
This work was supported by the National Natural Science Foundation of China (41301451).

Abstract: 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.