Robust inference of ecosystem soil water stress from eddy covariance data
Robust inference of ecosystem soil water stress from eddy covariance data
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DOI：
10.1016/j.agrformet.2023.109744
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发表时间：
2023-12
影响因子：
6.2
通讯作者：
Brandon P. Sloan;Xue Feng
中科院分区：
文献类型：
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作者：
Brandon P. Sloan;Xue Feng
涡旋相关数据对于确定土壤水分胁迫下的生态系统水分利用策略具有重要价值。然而，现有的应力推断方法需要大量的主观数据处理和模型规范假设，其对推断的土壤水应力信号的影响很少被量化。这些不确定性可能会使应力推断和生态系统水资源利用策略在多个地点和研究中的推广混淆。在本研究中，我们量化了从涡旋相关数据推断的土壤水应力信号对主流数据和建模假设的敏感性（即其鲁棒性），以编制具有鲁棒性土壤水应力信号的综合站点列表，并评估当前应力推断方法的性能。为了实现这一目标，我们从文献中确定了最普遍的假设，并进行了数字析因实验，以提取151个FLUXNET2015和AmeriFlux-FLUXNET站点的似是而非的土壤水分胁迫信号和模型性能的概率分布。我们开发了一个新的框架，总结了这些概率分布，对每个站点的土壤水分胁迫信号的稳健性进行分类和排名，我们用一个用户友好的热图来显示。我们估计，由于模型性能不足和生态系统用水参数约束不佳，只有5%-36%的地点表现出强大的土壤水分胁迫信号。我们还发现，鲁棒性的缺乏是特定于地点的，这破坏了通过广泛的生态系统类别分组压力信号或在不同假设的研究中比较结果。最后，现有的应力推断方法似乎更适合于草/年植被的涡动相关场地。我们的研究结果呼吁从涡旋相关方差数据中更仔细和一致地推断生态系统的水分胁迫。
Eddy covariance data are invaluable for determining ecosystem water use strategies under soil water stress. However, existing stress inference methods require numerous subjective data processing and model specification assumptions whose effect on the inferred soil water stress signal is rarely quantified. These uncertainties may confound the stress inference and the generalization of ecosystem water use strategies across multiple sites and studies. In this research, we quantify the sensitivity of soil water stress signals inferred from eddy covariance data to the prevailing data and modeling assumptions (i.e., their robustness) to compile a comprehensive list of sites with robust soil water stress signals and assess the performance of current stress inference methods. To accomplish this, we identify the most prevalent assumptions from the literature and perform a digital factorial experiment to extract probability distributions of plausible soil water stress signals and model performance at 151 FLUXNET2015 and AmeriFlux-FLUXNET sites. We develop a new framework that summarizes these probability distributions to classify and rank the robustness of each site’s soil water stress signal, which we display with a user-friendly heat map. We estimate that only 5%–36% of sites exhibit a robust soil water stress signal due to deficient model performance and poorly constrained ecosystem water use parameters. We also find that the lack of robustness is site-specific, which undermines grouping stress signals by broad ecosystem categories or comparing results across studies with differing assumptions. Lastly, existing stress inference methods appear better suited for eddy covariance sites with grass/annual vegetation. Our findings call for more careful and consistent inference of ecosystem water stress from eddy covariance data.