Yushu Xia, Jonathan Sanderman, Jennifer D. Watts, Megan B. Machmuller, Andrew L. Mullen, Charlotte Rivard, Arthur Endsley, Haydee Hernandez, John Kimball, Stephanie A. Ewing, Marcy Litvak, Tomer Duman, Praveena Krishnan, Tilden Meyers, Nathaniel A. Brunsell, Binayak Mohanty, Heping Liu, Zhongming Gao, Jiquan Chen, Michael Abraha, Russell L. Scott, Gerald N. Flerchinger, Patrick E. Clark, Paul C. Stoy, Anam M. Khan, E. N. Jack Brookshire, Quan Zhang, David R. Cook, Thomas Thienelt, Bhaskar Mitra, Marguerite Mauritz-Tozer, Craig E. Tweedie, Margaret S. Torn, Dave Billesbach
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引用次数: 0
摘要
牧场通过许多生态系统服务提供了显著的环境效益,其中可能包括土壤有机碳(SOC)的固存。然而,由于与牧场碳动态相关的相当大的时空变异性以及有限的数据可用性,量化牧场碳储量和监测碳(C)通量具有挑战性。我们开发了牧场碳跟踪和管理(RCTM)系统,通过利用遥感输入和环境变量数据集以及代表陆地碳循环过程的算法来跟踪SOC和生态系统C通量的长期变化。使用来自美国西部和中西部牧场的61个Ameriflux和NEON通量塔站点的质量控制的C通量数据集进行贝叶斯校准,根据优势植被类别(多年生和/或一年生草、草-灌木混合物和草-树混合物)对模型进行参数化。所得到的RCTM系统在估计年累计总初级生产力(GPP)时产生了更高的模型精度(R2 >;0.6, RMSE <390 g C m−2)相对于净生态系统CO2交换(NEE) (R2 >;0.4, RMSE <180 g cm - 2)。模式在估算牧场碳通量方面的表现因季节和植被类型而异。RCTM捕获了碳储量的空间变异性,与13个NEON站点的碳储量测量结果进行了验证,R2 = 0.6。模式模拟表明,通量塔站点的碳储量在过去10年略有增加,这主要是由降水增加驱动的。未来完善RCTM系统的工作将受益于基于长期网络的植被生物量、碳通量和有机碳储量监测。
Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics
Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.
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