State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China.
State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China.
Sci Total Environ. 2019 Dec 1;694:133803. doi: 10.1016/j.scitotenv.2019.133803. Epub 2019 Aug 6.
Bamboo forests are an important part of the forest ecosystem, which has strong carbon sequestration potential and plays an important role in the global carbon cycle. As a key parameter for simulating the carbon cycle using forest ecosystem models, the quality of leaf area index (LAI) data has a direct influence on the accuracy of modelling results. Here, we used the particle filter (PF) algorithm and PROSAIL model to assimilate MODIS LAI products, which were then used to drive a boreal ecosystem productivity simulator model to simulate the bamboo forest carbon cycle. The results showed that the relationship between the assimilated and observed LAI values was very significant, with an R of 0.95 and an RMSE of 0.28, greatly improving the precision of MODIS LAI products. The R values for the gross primary productivity (GPP), net ecosystem exchange (NEE), and total ecosystem respiration (TER) simulated by the assimilated LAI values and observed carbon fluxes were 0.65, 0.45 and 0.70, respectively, and the RMSE values were 1.10 g C m day, 1.00 g C m day1 and 0.35 g C m day, respectively. Compared with the results of the carbon cycle simulated by non-assimilated LAI, the R values of the GPP, NEE and TER values that were simulated by assimilated LAI increased by 27.5%, 45.2% and 6.1%, and the RMSE values decreased by 29.9%, 23.7% and 22.2%, respectively. Therefore, coupling the PF and PROSAIL models can greatly improve the simulation precision for the large-scale bamboo forest carbon cycle. This study laid the foundation for simulating the carbon cycle over a large-scale bamboo forest based on low-resolution data in the future.
竹林是森林生态系统的重要组成部分，具有较强的碳固存潜力，在全球碳循环中发挥着重要作用。叶面积指数（LAI）作为森林生态系统模型模拟碳循环的关键参数，其数据质量直接影响模型结果的准确性。本研究利用粒子滤波（PF）算法同化 MODIS LAI 产品，并采用 PROSAIL 模型对同化后的 LAI 产品进行模拟，进而利用同化后的 LAI 产品驱动北方森林生产力模拟器模型（BEPS）模拟竹林生态系统碳循环。结果表明，同化后与观测 LAI 值之间的关系非常显著，R²为 0.95，RMSE 为 0.28，极大地提高了 MODIS LAI 产品的精度。同化后的 LAI 值与观测碳通量模拟的总初级生产力（GPP）、净生态系统交换（NEE）和总生态系统呼吸（TER）的 R²分别为 0.65、0.45 和 0.70，RMSE 分别为 1.10 g C m ⁇ day ⁇ 、1.00 g C m ⁇ day ⁇ 和 0.35 g C m ⁇ day ⁇ 。与非同化 LAI 模拟的碳循环结果相比，同化 LAI 模拟的 GPP、NEE 和 TER 的 R²值分别提高了 27.5%、45.2%和 6.1%，RMSE 值分别降低了 29.9%、23.7%和 22.2%。因此，PF 和 PROSAIL 模型的耦合可以大大提高大规模竹林碳循环的模拟精度。本研究为未来基于低分辨率数据模拟大规模竹林碳循环奠定了基础。
