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的生存：树木从土壤中吸取水分，并以水蒸气的形式从树叶中释放出来，水蒸气可以在大气中重新凝结，再次以雨的形式降落。干旱和森林退化有可能破坏这种维持森林的循环系统，将亚马逊雨林从崩溃点推到退化甚至稀树草原状态，这在最近的媒体和科学文献中得到了很多关注。然而，所谓的“临界点”究竟是如何在任何给定的森林地点发生的，目前还不清楚。该项目调查了两种可能导致临界点的原因，预计这两种原因在未来都将变得更加普遍：与El Niño气候条件有关的严重干旱，以及日益频繁的强风暴和大风导致的森林退化。这个奖项利用了一个转瞬即逝的机会来观察持续的干旱，由于之前的森林退化而加剧，如何关闭了树木将水从土壤转移到大气的能力，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亚马逊地区的临界点何时以及如何发生，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，通过对公立大学研究生的培训，以及通过一个专门设计的高中教育项目，将美国学生与亚马逊研究人员联系起来，并从世界上最著名的热带森林的树木中获得真实的科学数据。本研究的重点是整个森林和叶片水平的蒸腾观测-光合作用期间树木从土壤到大气的水分运输-通过干旱和初始恢复。它测试了亚马逊森林临界点范式的三个关键假设。H1)整个森林的干旱敏感性因以往干旱的遗留问题而提高。通过比较涡流通量塔测量的2023/24年干旱响应与之前的干旱响应，特别是2015/16年的极端厄尔尼诺Niño，来检验H1。H2)整个森林的干旱敏感性来自于单个树木对干旱响应的不同生态生理策略。这些策略有助于生态系统尺度的干旱敏感性，并构建临界点的开始。通过观察6种优势物种的反应来测试H2，为基于个体到生态系统特征的尺度化提供了基础。H3)扰动引起的森林退化加剧了森林干旱敏感性。H3被广泛假设，但从未直接测试过，它探讨了森林覆盖损失导致的能量平衡改变与干旱敏感性增加有关的临界点机制。H3是通过比较森林内部和大迎风间隙的树木生态水文和微环境来检验的。这项研究将提供新的、难以观察的数据集，这些数据集将允许对森林干旱响应和生态水文临界点模型进行关键测试（以及随后的改进）。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.