Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems
Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems
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DOI：
10.3389/fenvs.2022.939238
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发表时间：
2022-07
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通讯作者：
Yihui Wang;F. Yuan;K. Arndt;Jianzhao Liu;Liyuan He;Yunjiang Zuo;D. Zona;D. Lipson;W. Oechel;D. Ricciuto;S. Wullschleger;P. Thornton;Xiaofeng Xu
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作者：
Yihui Wang;F. Yuan;K. Arndt;Jianzhao Liu;Liyuan He;Yunjiang Zuo;D. Zona;D. Lipson;W. Oechel;D. Ricciuto;S. Wullschleger;P. Thornton;Xiaofeng Xu
甲烷（CH 4）通量的空间异质性需要一个可靠的尺度放大方法，以达到准确的区域CH 4预算在北极苔原。在这项研究中，我们结合了CLM-Microbe模型与三种足迹算法，在2013-2015年期间，在阿拉斯加北坡的Utqiag towvik（US-Beo，US-Bes和US-Brw），Atqasuk（US-Atq）和Ivotuk（US-Ivo）的三个站点中，将CH 4通量从地块水平扩展到涡度相关（EC）塔域（200 m × 200 m）。三种足迹算法是均匀足迹（HF），假设所有网格单元的均匀贡献，梯度足迹（GF），假设从中心网格单元到边缘逐渐下降的贡献，和动态足迹（DF），考虑风和陆面异质性的影响。模拟的CH 4年通量与EC测量在US-Beo和US-Bes高度一致。相比之下，通量被高估在US-Brw，US-Atq，和US-Ivo由于较高的模拟CH 4通量在生长季节早期。模拟的月CH 4通量与EC测量结果一致，但足迹算法的精度不同。在2013年9月的US-Bes，使用DF算法的RMSE和NNSE分别为0.002 μmol m−2 s−1和0.782，但使用HF算法的RMSE和NNSE分别为0.007 μmol m−2 s−1和0.758，使用GF算法的RMSE和NNSE分别为0.007 μmol m−2 s−1和0.765。DF算法比HF和GF算法更好地捕捉了每个月CH 4日通量的时间变化，但由于地形平坦，3种算法的模型精度相似。2013-2015年期间CH 4通量的时间变化主要由气温（67-74%）解释，其次是降水（22-36%）。在植被覆盖度和海拔的空间异质性占主导地位的空间变化的CH 4通量的所有五个塔域，尽管相对较弱的差异，模拟CH 4通量之间的足迹算法。CLM-Microbe模式可以模拟地块尺度和景观尺度的CH 4通量，具有较高的时间分辨率，可应用于其他景观。将陆面模式与适当的算法相结合，为陆地生态系统中甲烷通量的放大提供了一个强有力的工具。
Spatial heterogeneity in methane (CH4) flux requires a reliable upscaling approach to reach accurate regional CH4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH4 flux from a plot level to eddy covariance (EC) tower domains (200 m × 200 m) in the Alaska North Slope, for three sites in Utqiaġvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo), for a period of 2013–2015. Three footprint algorithms were the homogenous footprint (HF) that assumes even contribution of all grid cells, the gradient footprint (GF) that assumes gradually declining contribution from center grid cells to edges, and the dynamic footprint (DF) that considers the impacts of wind and heterogeneity of land surface. Simulated annual CH4 flux was highly consistent with the EC measurements at US-Beo and US-Bes. In contrast, flux was overestimated at US-Brw, US-Atq, and US-Ivo due to the higher simulated CH4 flux in early growing seasons. The simulated monthly CH4 flux was consistent with EC measurements but with different accuracies among footprint algorithms. At US-Bes in September 2013, RMSE and NNSE were 0.002 μmol m−2 s−1 and 0.782 using the DF algorithm, but 0.007 μmol m−2 s−1 and 0.758 using HF and 0.007 μmol m−2 s−1 and 0.765 using GF, respectively. DF algorithm performed better than the HF and GF algorithms in capturing the temporal variation in daily CH4 flux each month, while the model accuracy was similar among the three algorithms due to flat landscapes. Temporal variations in CH4 flux during 2013–2015 were predominately explained by air temperature (67–74%), followed by precipitation (22–36%). Spatial heterogeneities in vegetation fraction and elevation dominated the spatial variations in CH4 flux for all five tower domains despite relatively weak differences in simulated CH4 flux among three footprint algorithms. The CLM-Microbe model can simulate CH4 flux at both plot and landscape scales at a high temporal resolution, which should be applied to other landscapes. Integrating land surface models with an appropriate algorithm provides a powerful tool for upscaling CH4 flux in terrestrial ecosystems.