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基于CLM5.0的内蒙古呼伦贝尔草地生产力模拟参数优化
引用本文:杨倩,朱小华,欧阳光洲,马灵玲,刘伟,王昆.基于CLM5.0的内蒙古呼伦贝尔草地生产力模拟参数优化[J].农业工程学报,2023,39(6):139-148.
作者姓名:杨倩  朱小华  欧阳光洲  马灵玲  刘伟  王昆
作者单位:1. 中国科学院空天信息创新研究院,北京 100094;2. 中国科学院大学电子电气与通信工程学院,北京 100049;5. 中国科学院定量遥感信息技术重点实验室,北京 100094;1. 中国科学院空天信息创新研究院,北京 100094;5. 中国科学院定量遥感信息技术重点实验室,北京 100094;4. 中国科学院植物研究所,北京 100094;;1. 中国科学院空天信息创新研究院,北京 100094;3. 中国科学院数字地球重点实验室,北京 100049;
基金项目:中国科学院战略性先导科技专项(A类)(XDA26010203-4);内蒙古自治区科技重大专项(2021ZD0044);中科院空天院"未来之星"人才计划项目
摘    要:草地净初级生产力(net primary productivity,NPP)作为评估生态系统结构和功能以及植被质量的重要指标,其精准估算对于草地生态系统保护有着重要作用。陆面过程模型可对大范围NPP进行时序模拟,但受限于对植被生理生化特性的认知,应用于目标区域时,模型参数的默认设定会引起模拟偏差。为了使模型适用于呼伦贝尔草地生产力模拟,该研究基于CESM(community earth system model)框架下的最新陆面过程模型CLM5.0(the community land model 5.0)开展模型参数敏感性分析,并采用DREAM(differential evolution adaptive metropolis)算法对最敏感的10个参数进行优化调整,最后将参数优化后的模型应用于呼伦贝尔草地NPP模拟。结果表明:1)对草地NPP模拟最敏感的是呼吸作用类参数,如冠层叶顶维持呼吸基率的截距参数、凋落池到土壤有机质池转移的呼吸分数,其次是碳循环类参数,如叶片碳氮比、细根碳氮比。2)叶片碳氮比与气孔导度参数优化后的后验概率分布为高斯分布,表明该优化为良性约束,反映了碳氮关联参...

关 键 词:模型  参数优化  草地NPP  敏感性分析  呼伦贝尔草地
收稿时间:2022/11/18 0:00:00
修稿时间:2023/2/15 0:00:00

Optimization of the simulated parameters of grassland productivity using CLM5.0 in Hulunbuir of Inner Mongolia
YANG Qian,ZHU Xiaohu,ONYANG Guangzhou,MA Linglin,LIU Wei,WANG Kun.Optimization of the simulated parameters of grassland productivity using CLM5.0 in Hulunbuir of Inner Mongolia[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(6):139-148.
Authors:YANG Qian  ZHU Xiaohu  ONYANG Guangzhou  MA Linglin  LIU Wei  WANG Kun
Institution:1. Aerospace Information Research Institute,Chinese Academy of Sciences, Beijing 100094, China; 2. University of Chinese Academy of Sciences, School of Electronic, Electrical and Communication Engineering, Beijing 100049, China; 5. Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China;1. Aerospace Information Research Institute,Chinese Academy of Sciences, Beijing 100094, China; 5. Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China;4. Institute of Botany,The Chinese Academy of Sciences Beijing 100049;; 1. Aerospace Information Research Institute,Chinese Academy of Sciences, Beijing 100094, China; 3. Key Laboratory of Digtial Earth Science,Chinese Academy of Sciences,Beijing 100049
Abstract:Abstract: Net primary productivity (NPP) can be one of the most important indicators to evaluate the ecosystem structure and function in grassland protection. Therefore, it is necessary to rapidly and accurately evaluate the NPP under the carbon cycle of the ecosystem. The land surface process model can be used to realize the time series simulation of NPP using the physical mechanism. However, the default parameters of the models cannot be suitable for the target area, leading to the simulation deviation. There is also limited cognition of the physiological and biochemical characteristics of vegetation. Therefore, it is a high demand to optimize the parameters, when applied to the target area. The CESM framework has recently released the land surface model CLM5.0, indicating the most promising land surface process model for the evaluation of grassland productivity. In this study, the spatiotemporal simulation of grassland productivity was carried out in the Hulunbuir grassland, Inner Mongolia, China. Firstly, the local sensitivity analysis was used to choose the ten sensitive parameters of the CLM5.0_BGC module. Taking the global daily NPP data products as the reference, the Differential Evolution Adaptive Metropolis (DREAM) was then utilized to optimize ten sensitive parameters after sensitivity analysis. The accuracy of optimization was verified using global daily NPP data and GLASS annual net primary productivity products. Finally, the parameter setting was established for the CLM5.0 model for the grassland productivity simulation. The results were as follows: 1) Local sensitivity analysis was conducted to determine 74 parameters of the CLM5.0_BGC module. The most sensitive parameters were obtained to simulate the grassland productivity, including respiration, followed by carbon transfer. 2) Similar to the leaf C/N ratio, the Gaussian distribution was found in the posterior probability distribution in the slope parameter of the relationship between the stomatal conductance and photosynthesis. The optimization was observed in the benign constraint. The effectiveness of the carbon/nitrogen correlation and respiration parameters were verified to simulate the optimized model. 3) Parameter optimization effectively improved the simulation performance of CLM5.0 on the productivity of the grassland. The parameter optimization of the model shared a great improvement for the targeted pixel, where the relative error decreased from 33.82% to 10.97%, a relative improvement of 67%, and root mean square error decreased by 0.12 g/m2. The optimization effect of the model in summer was also better than that in autumn. 4) The grassland productivity in the next year was evaluated on the simulation with the optimized parameter, where the relative error decreased from 8.74% to 2.51%, and the absolute difference in annual mean index decreased from 15.64 g/m2 to 13.63 g/m2. Spatially, the accuracy of the optimized model was improved in the different types of grassland. Among them, the relative errors of meadow grassland, lowland meadow, and typical grassland decreased from 43.43% to 37.81%, from 39.23% to 31.17%, and from 42.03% to 33.00%, respectively. The root mean square error was reduced from 0.58 g/m2 to 0.52 g/m2 in the meadow grassland, from 0.62 g/m2 to 0.57 g/m2 in the lowland meadow, and from 0.51 g/m2 to 0.44 g/m2 in the typical grassland. The finding can provide a strong reference to simulate the grassland productivity in the Hulunbuir region using CLM5.0. There can be offered a positive role in the reasonable assessment of the grassland ecosystem under the carbon cycle.
Keywords:CLM5  0  Grassland NPP  Sensitivity analysis  Model parameter optimization  Hulunbuir grassland
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