Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
复制标题
DOI：
10.1371/journal.pone.0211510
复制
发表时间：
2019-02-06
期刊：
影响因子：
3.7
通讯作者：
Reichstein, Markus
中科院分区：
文献类型：
--
作者：
Besnard, Simon;Carvalhais, Nuno;Reichstein, Markus
森林在全球碳(C)循环中发挥着至关重要的作用，因为它在陆地生物圈中储存和隔离了大量的碳。由于气候和植被活动的时间动态，森林生物圈和大气之间的二氧化碳(CO2)通量存在显著的区域差异，这正在影响全球碳循环。当前的森林二氧化碳通量动态受瞬时气候、土壤和植被条件的控制，这些条件带有扰动和极端气候事件的遗留影响。由于这些过程的复杂性和长期影响，我们对这些过程遗留下来的二氧化碳净通量的理解水平仍然有限。在这里，我们结合遥感、气候和涡度-协方差通量数据来研究全球185个森林站点的净生态系统二氧化碳交换(NEE)。我们没有使用常用的非动态统计方法，而是采用了一种称为长短期记忆网络(LSTM)的递归神经网络(RNN)，它从植被和气候的时间动态中捕捉信息。由此产生的数据驱动模型通过使用每个站点的陆地卫星和气候数据，整合了气候和植被的年际和季节变化。LSTM算法能够有效地描述NEE的总体季节变化(Nash-Sutcliffe效率，NSE=0.66)和跨站变化(NSE=0.42)，但对特定的季节和年际异常(NSE=0.07)的预测效果较差。这一分析表明，嵌入气候和植被记忆效应的LSTM方法在估计NEE方面优于非动态统计模型(即随机森林)。此外，植被平均季节周期包含了大部分信息量，真实地解释了东北大西洋环流的空间和季节变化。这些发现表明，从气候和植被中捕捉记忆效应在量化森林需求的时空变化方面具有相关性。
Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.