Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
Sci Total Environ. 2021 Feb 20;756:143492. doi: 10.1016/j.scitotenv.2020.143492. Epub 2020 Nov 20.
Dynamic Global Vegetation Models (DGVMs) are commonly used to describe the land biogeochemical processes and regulate carbon and water pools. However, the simulation efficiency and validation of DGVMs are limited to varying temporal and spatial resolutions. Additionally, the uncertainties caused by different interpolation methods used in DGVMs are still not clear. In this study, we employ Socio-Economic and natural Vegetation ExpeRimental (SEVER) DGVM to simulate Net Ecosystem Exchange (NEE) flux with large scale National Centers for Environmental Prediction (NCEP) daily climate data as inputs for the years 1997-2000 at 14 Euroflux sites. It is shown that daily local NEE flux on chosen sites can be reasonably simulated, and daily temperature and shortwave radiation are the most essential inputs for daily NEE simulation compared with precipitation and the ratio of sunshine hours. Different running means (1 to 30 days) methods are analysed for each Euroflux site, and the best results of both averaged regression coefficient and averaged slope of regression are discovered by using 5 days running mean method. SEVER DGVM, driven by linearly interpolated daily climate data is compared at the monthly time step with Lund-Potsdam-Jena (LPJ) DGVM, which combines the linear interpolation of daily temperature with stochastic generation of daily precipitation. The comparison demonstrates that the stochastic generation of daily precipitation provides an acceptable fit to local observed NEE, but with a slight decrease in accuracy. Simulation experiments with SEVER DGVM demonstrate that daily local NEE flux inside a grid cell for a region as large as Europe can be modelled by DGVMs, using only large scale climate data as inputs.
动态全球植被模型（DGVMs）常用于描述陆地生物地球化学过程并调节碳和水储量。然而，DGVM 的模拟效率和验证受到时间和空间分辨率变化的限制。此外，DGVM 中使用的不同插值方法引起的不确定性仍不清楚。本研究采用社会经济和自然植被实验（SEVER）DGVM，以大规模国家环境预报中心（NCEP）日气候数据作为输入，模拟 1997-2000 年 14 个欧洲通量站点的净生态系统交换（NEE）通量。结果表明，所选站点的日本地 NEE 通量可以得到合理的模拟，与降水和日照时数比相比，日温度和短波辐射是日 NEE 模拟的最关键输入。对每个欧洲通量站点分析了不同的运行平均值（1-30 天）方法，发现使用 5 天运行平均值方法可以获得平均回归系数和回归斜率的最佳结果。在每月时间步长上，与线性插值日温度与日降水随机生成相结合的 Lund-Potsdam-Jena（LPJ）DGVM 相比，由线性插值日气候数据驱动的 SEVER DGVM 进行了比较。比较表明，日降水的随机生成可以很好地拟合本地观测到的 NEE，但精度略有下降。SEVER DGVM 的模拟实验表明，使用仅大规模气候数据作为输入，DGVM 可以模拟欧洲等大区域网格单元内的日本地 NEE 通量。
