Collaborative Research: RAPID: A perfect storm: will the double-impact of 2023/24 El Nino drought and forest degradation induce a local tipping-point onset in the eastern Amazon?

亚马逊雨林通过循环降雨来维持自身：树木从土壤中抽水，并从树叶中释放出水蒸气，这些水蒸气可以在大气中重新凝结，再次降雨。干旱和森林退化有可能破坏这一维持森林的循环系统，使亚马逊雨林从崩溃点变成退化甚至稀树草原状态，这一点最近在媒体和科学文献中引起了广泛关注。然而，在任何特定的森林地点，所谓的“临界点”究竟是如何发生的还不清楚。该项目调查了临界点的两种可能原因，预计这两种原因在未来都将变得更加普遍：与厄尔尼诺气候条件有关的严重干旱，以及日益频繁的强风暴和大风造成的森林退化。这个奖项利用了一个短暂的机会来观察持续的干旱，由于之前的森林退化而加剧，关闭了树木将水从土壤转移到大气中的能力，从而破坏了整个亚马逊地区维持降雨循环的水泵。所产生的知识将帮助科学家预测何时以及如何发生整个亚马逊的临界点，这将对南美洲的天气模式，水资源和经济稳定以及全球气候产生重要影响。这项研究对教育产生了广泛的影响，通过在公立大学培训研究生，并通过定制设计的高中教育计划，将美国学生与亚马逊研究人员和来自世界上最著名的热带森林树木的真实的科学数据联系起来。这项研究的重点是全森林和树叶水平的蒸腾观测-光合作用期间树木将水分从土壤输送到大气-通过干旱和初步恢复。它测试了亚马逊森林引爆点范式核心的三个关键假设。H1）整个森林对干旱的敏感性因以往干旱的影响而提高。H1是通过比较涡流通量塔测量的2023/24年干旱响应与以前的干旱，特别是2015/16年的极端厄尔尼诺现象来测试的。H2）整个森林的干旱敏感性来自于个体树木对干旱反应的不同生理生态策略。这些战略有助于生态系统规模的干旱敏感性和结构的临界点的开始。H2是通过观察6个优势物种的反应进行测试，为基于个体到生态系统特征的缩放提供了基础。H3）干扰引起的森林退化提高了森林干旱敏感性。H3被广泛假设，但从未直接测试过，它探讨了与干旱敏感性增加有关的临界点机制，以改变森林覆盖损失造成的能量平衡。H3是通过比较树木生态水文和森林内部和大的风窗之间的微环境进行测试。这项研究将提供新的，难以观察的数据集，将允许关键的测试（和随后的改进）模型的森林干旱响应和生态水文临界点这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The Amazon rainforest sustains itself by recycling rainfall: trees pump water from the soil and release it from their leaves as vapor, which can be recondensed in the atmosphere and fall as rain again. The potential for drought and forest degradation to break this forest-sustaining recycling system, pushing the Amazon rainforest past a point of collapse into a degraded or even savanna state, has received much recent attention in the media and scientific literature. However, exactly how the so-called ‘tipping point’ occurs in any given forest site is unclear. This project investigates two possible causes of tipping points, both of which are predicted to become more common in the future: severe drought linked to El Niño climate conditions, and forest degradation caused by increasingly frequent strong storms and winds. This award capitalizes on a fleeting opportunity to observe how the ongoing drought, amplified by previous forest degradation, shuts down the capacity of trees to transfer water from the soil to the atmosphere, and thereby breaks the water pump that sustains rainfall recycling throughout the Amazon. The knowledge produced will help scientists predict when and how Amazon-wide tipping points might occur, which would importantly affect weather patterns, water resources, and economic stability in South America, as well as global climate. This study has broad impacts on education, through training of graduate students at public universities and through a custom-designed high school educational program that connects U.S. students with Amazon researchers and real scientific data from trees of the world’s most famous tropical forest.This study focuses on whole-forest and leaf-level observations of transpiration–the transport of water by trees from soil to atmosphere during photosynthesis–through drought and initial recovery. It tests three key hypotheses at the heart of the Amazon forest tipping-point paradigm. H1) Whole-forest drought sensitivity is heightened by the legacy of previous droughts. H1 is tested by comparing eddy-flux-tower measured 2023/24 drought response to those of previous droughts, notably the extreme El Niño of 2015/16. H2) Whole-forest drought sensitivity emerges from individual trees’ differing ecophysiological strategies for drought response. These strategies contribute to ecosystem-scale drought sensitivity and structure the tipping point onset. H2 is tested by observing responses across six dominant species, providing a foundation for individual-to-ecosystem trait-based scaling. H3) Forest drought sensitivity is heightened by disturbance-induced forest degradation. H3, widely postulated but never directly tested, explores the tipping point mechanisms relating increased drought sensitivity to altered energy balance from forest cover loss. H3 is tested by comparing tree ecohydrology and microenvironments between forest interior and large windthrow gaps. This research will provide new, hard-to-observe datasets that will allow critical tests (and subsequent improvement) of models of forest drought response and ecohydrologic tipping pointsThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.