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，然后再次以雨的形式落下。干旱和森林退化可能会破坏这个维持森林的循环系统，将亚马逊雨林从崩溃的临界点推向退化甚至稀树草原的状态，这在最近得到了媒体和科学文献的大量关注。然而，所谓的“临界点”究竟是如何在任何给定的森林地点出现的，目前尚不清楚。该项目调查了引爆点的两个可能原因，预计这两个原因在未来都将变得更加常见：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和风暴导致的森林退化。该奖项利用一个转瞬即逝的机会来观察持续的干旱是如何关闭树木将土壤中的水分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。产生的知识将帮助科学家预测亚马逊范围内的临界点可能发生的时间和方式，这将对南美的天气模式、水资源和经济稳定以及全球气候产生重要影响。这项研究通过公立大学对研究生的培训，以及通过一个定制的高中教育项目，将美国学生与亚马逊的研究人员和世界上最著名的热带森林树木的真实科学数据联系起来，对教育产生了广泛的影响。这项研究重点是在整个森林和树叶水平上观察蒸腾作用--树木在光合作用过程中将水分从土壤运输到大气--通过干旱和最初的恢复。它测试了亚马逊森林临界点范式的核心三个关键假设。H1)前几次干旱的后遗症提高了整个森林的干旱敏感性。通过将2023/24年测得的涡流塔干旱响应与以前干旱(特别是2015/16年的极端厄尔尼诺现象)进行比较来测试H1。H2)整个森林对干旱的敏感性来自于个别树木不同的干旱响应生态生理策略。这些战略有助于提高生态系统规模的干旱敏感性，并构成临界点的开始。通过观察六个优势物种的反应来测试H2，为基于个体到生态系统特征的扩展提供了基础。干扰引起的森林退化增强了森林对干旱的敏感性。H3被广泛假设，但从未进行过直接测试，它探索了与森林覆盖损失引起的能量平衡变化有关的干旱敏感性增加的临界点机制。H3是通过比较森林内部和大风空隙之间的树木生态水文和微环境来测试的。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反应和生态水文临界点的模型进行关键测试(以及随后的改进)。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.