Examining differential drought responses of forest trees with phylogenies and process-based catchment water age modeling

通过系统发育和基于过程的流域水龄模型检查林木的差异干旱反应

• 批准号: 2243263
• 负责人: James Knighton
• 金额: $ 68.99万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243263&HistoricalAwards=false
• 关键词: Examining; differential; drought; responses; forest

Forested ecosystems provide many benefits to human society and the natural environment, such as alleviating floods, sequestering carbon, and supporting habitats for biodiversity. However, the benefits that forests provide are at risk due to the effects of climate change, including higher frequency and greater intensity of drought in forested landscapes. The resilience of temperate, arid, and tropical forests to drought is declining globally; and the reasons for this decline are not clear. One difficulty is that computer models that are used to study changes in forested ecosystems require researchers to accurately estimate the water uptake strategies of plants. However, the information needed for models, to accomplish this goal, are challenged because of the lack of readily available datasets, particularly in data-poor regions of the world. For this project, researchers will investigate methods to improve the representation of plants in ecosystem models by using the evolutionary histories of trees to estimate trait values. The project will then use these improved models to test the hypothesis that changes in forest resilience are related to changes in the elapsed time between precipitation and water uptake by plant roots. This research will inform management practices designed to promote drought resilience in forests, it will advance ecosystem modeling, and build capacity in the scientific workforce.This research will link recent developments from tracer derived water age tracking in soils and vegetation xylem, ecohydrological simulation of plant hydraulics, and phylogenetic data analytics, which are three key areas that are necessary to answer critical questions that bridge hydrology and ecosystems science. The investigators will develop an approach to parameterize vegetation hydraulic traits in hydrologic and ecosystem models through phylogenetic trait estimation (PTE), supporting forest simulations in data-poor regions. The PTE will be integrated into process-based ecohydrological and land surface models. The researchers will then validate this modeling approach for twelve research catchments with rich hydrologic and water isotopic datasets representing diverse climates, geologies, and tree species compositions. The resulting process-based models will be applied to study the combined and interrelated effects of climate change on catchment water ages, forest primary production and transpiration, tree hydraulic strategies, and their composite effect on drought risk and forest resilience.This award is co-funded by the Hydrologic Sciences and the Division of Environmental Biology – Ecosystem Sciences Cluster programs.This 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.

森林生态系统为人类社会和自然环境提供了许多好处，如缓解洪水、封存碳和支持生物多样性的栖息地。然而，由于气候变化的影响，包括森林景观中更频繁和更强烈的干旱，森林提供的好处面临风险。温带、干旱和热带森林的抗旱能力在全球范围内正在下降；这种下降的原因尚不清楚。其中一个困难是，用于研究森林生态系统变化的计算机模型要求研究人员准确估计植物的吸水策略。然而，由于缺乏现成的数据集，特别是在世界上数据匮乏的地区，模型实现这一目标所需的信息受到了挑战。在这个项目中，研究人员将研究通过使用树木的进化史来估计特征值来改善植物在生态系统模型中的表现的方法。然后，该项目将使用这些改进的模型来检验这一假设，即森林韧性的变化与降水和植物根系吸收水分之间经过的时间的变化有关。这项研究将为旨在促进森林抗旱能力的管理实践提供信息，它将促进生态系统建模，并在科学工作中建设能力。这项研究将把土壤和植被木质部中示踪剂衍生的水年龄跟踪、植物水力学的生态水文学模拟和系统发育数据分析的最新发展联系起来，这三个领域是回答连接水文学和生态系统科学的关键问题所必需的三个关键领域。研究人员将开发一种方法，通过系统发育特征估计(PTE)在水文和生态系统模型中对植被水力特征进行参数化，支持数据匮乏地区的森林模拟。临时环境工程将纳入基于过程的生态水文和陆面模型。然后，研究人员将用代表不同气候、地质和树种组成的丰富的水文和水同位素数据集，对12个研究流域验证这种建模方法。由此产生的基于过程的模型将用于研究气候变化对集水年龄、森林初级生产力和蒸腾作用、树木水力策略的综合和相互关联的影响，以及它们对干旱风险和森林恢复能力的综合影响。该奖项由水文科学和环境生物学-生态系统科学集群计划部门共同资助。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。