College of Agronomy, Shenyang Agricultural University, Shenyang 100866, China.
Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Ying Yong Sheng Tai Xue Bao. 2024 Sep 18;35(9):2581-2591. doi: 10.13287/j.1001-9332.202409.026.
Annual net ecosystem productivity (NEP), the amount of net carbon sequestration during a year, serves as the basis of terrestrial carbon sink. Quantifying the spatial variations of NEP and its trend would enhance our understandings on the response and adaption of ecosystems to environmental change, which also serves for the regional carbon management targeting at carbon neutrality. Based on process-based model and data-driven model simulating NEP, we selected the optimal simulating NEP mostly representing NEP spatial variations with multiple site eddy covariance measurements to develop the spatial downscaling method and generate high resolution NEP data of China, which was used to examine the spatial variations of NEP and its trend and driving factors during 2000-2017. Compared with process-based model results, data-driven model simulating NEP could mostly represent the spatial variation of site measurements. The random forest regression based on climate, soil, and biological data combining with the simple scaling could successfully downscale NEP to a high spatial resolution. From 2000 to 2017, the total amount of NEP in China was (1.30±0.03) Pg C·a, showing a decreasing-increasing pattern with the inflection point in 2009. Chinese NEP decreased from southeast to northwest, showing a descending latitudinal distribution and an ascending longitudinal distribution, with the combined effects of climate and biotic factors. NEP trend decreased from east towards west, which was only accompanied with a slightly ascending longitudinal distribution, while photosynthetically active radiation and soil organic carbon content dominated the spatial variations of NEP trend. Therefore, the spatial patterns of generated NEP obviously differed from those of NEP trend, suggesting the obvious difference between the responses and adaptions of ecosystems to environmental changes.
年净生态系统生产力(NEP)是一年内净碳固存的量,是陆地碳汇的基础。量化 NEP 的空间变化及其趋势将增强我们对生态系统对环境变化的响应和适应的理解,这也有助于实现针对碳中和的区域碳管理。基于过程模型和数据驱动模型模拟 NEP,我们选择了最能代表多个站点涡度协方差测量的 NEP 空间变化的最佳模拟 NEP,以开发空间降尺度方法并生成中国高分辨率 NEP 数据,用于检验 2000-2017 年期间 NEP 的空间变化及其趋势和驱动因素。与过程模型结果相比,数据驱动模型模拟的 NEP 大多可以代表站点测量的空间变化。基于气候、土壤和生物数据的随机森林回归与简单缩放相结合,可以成功地将 NEP 降尺度到高空间分辨率。2000 年至 2017 年期间,中国的 NEP 总量为(1.30±0.03)Pg C·a,呈减少-增加的模式,转折点在 2009 年。中国 NEP 从东南向西北递减,呈现出递减的纬度分布和递增的经度分布,这是气候和生物因素综合作用的结果。NEP 趋势从东向西递减,仅伴有略微递增的经度分布,而光合有效辐射和土壤有机碳含量则主导了 NEP 趋势的空间变化。因此,生成的 NEP 的空间模式明显不同于 NEP 趋势的空间模式,表明生态系统对环境变化的响应和适应存在明显差异。