Drought-induced tree mortality has been increasing worldwide under climate change; therefore, forests will become more vulnerable as warming continues. Meanwhile, carbon starvation and hydraulic failure have been proposed as main drought-induced mortality mechanisms, mostly validated through individual tree-level experiments. However, there lack of a unified way to monitor and assess tree mortality across the different biomes and climate regions. In this sense, process-based biogeochemical (BGC) modeling may be an effective tool for simulating and understanding ecophysiological processes for tree mortality at large spatial scales. In this study, a hydraulic vulnerability curve for percentage loss of conductivity (PLC) was added to the BGC-NSCs model, the modified version of the BIOME-BGC with two additional non-structural carbohydrates (NSCs) pools. And then, we simulate the model at the sites around the world where tree mortality were reported. Using sensitivity analysis and machine learning algorithms for hydraulic stress, PLC and NSCs showed a high sensitivity and significance to tree mortality within the modeling framework. The model simulations also reveal the relationship between PLC and NSCs based on mortality stress intensity, plant functional types, and climate conditions, further validated with the results of previous experiment studies at the plot scale. This study proposes a potential to estimate eocphysiological variables at the regional scale using the BGC model, and to use high sensitivity variable, such as PLC and NSCs, as effective diagnostics for hydraulic stress across different biomes and climate regions.

中文翻译：

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使用基于流程的 BGC 模型和机器学习算法评估全球树木死亡率的关键气候和生态生理参数

在气候变化的影响下，全球范围内由干旱引起的树木死亡率不断增加；因此，随着气候变暖的持续，森林将变得更加脆弱。与此同时，碳饥饿和水力衰竭已被认为是干旱引起的死亡的主要机制，大部分通过个体树级实验进行验证。然而，缺乏统一的方法来监测和评估不同生物群落和气候区域的树木死亡率。从这个意义上说，基于过程的生物地球化学（BGC）建模可能是模拟和理解大空间尺度树木死亡的生态生理过程的有效工具。在这项研究中，BGC-NSCs 模型中添加了电导率损失百分比 (PLC) 的水力脆弱性曲线，该模型是 BIOME-BGC 的修改版本，带有两个额外的非结构碳水化合物 (NSC) 池。然后，我们在世界各地报告树木死亡的地点模拟该模型。通过使用液压应力的敏感性分析和机器学习算法，PLC 和 NSC 在建模框架内显示出对树木死亡率的高度敏感性和重要性。模型模拟还揭示了基于死亡胁迫强度、植物功能类型和气候条件的 PLC 和 NSC 之间的关系，并通过先前小区规模的实验研究结果进一步验证。这项研究提出了使用 BGC 模型估计区域尺度的生态生理变量的潜力，并使用 PLC 和 NSC 等高灵敏度变量作为不同生物群落和气候区域水压力的有效诊断。