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ORE-CZ: Integrating Vegetation Phenology to Understand the Sensitivity of Dynamic Water Storage to Drought Using Remote Sensing Data and Hydrology Modeling

ORE-CZ：利用遥感数据和水文学模型，整合植被物候学来了解动态蓄水对干旱的敏感性

基本信息

批准号：
2228047
负责人：
Lauren Lowman
金额：
$ 20万
依托单位：
Wake Forest University
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：
2023-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228047&HistoricalAwards=false
关键词：
ORE CZ Integrating Vegetation Phenology

项目摘要

Plants rely on water storage in the soils to continue developing leaves and undergoing photosynthesis during drought periods. This water source is called dynamic water storage. During drought periods, some plants alter their growth strategies to conserve dynamic water storage. This process by which plants adapt to drought conditions is complex, but critical to document as it impacts carbon, energy, and water cycles. This project will explore whether incorporating plant interaction with dynamic water can provide an accurate estimate of plant resilience to drought. This study will develop a modeling framework that incorporates feedbacks between dynamic water storage and vegetation growth and water-use. The framework will be tested for forested mountain sites in Colorado. This project will help evaluate how vegetation will adapt to the possibility of more frequent and extreme drought events in the future.Severe drought events seem to be occurring with greater frequency. When rainfall is infrequent, natural vegetation relies on subsurface water stores, referred to as dynamic water storage, to sustain new leaf growth and photosynthesis. Prior work has documented changes in vegetation productivity and water-use efficiency as a result of reduced dynamic water storage during drought. Previous studies have also observed how vegetation can adopt different water-use strategies during drought periods. However, a knowledge gap persists around feedbacks between dynamic water storage and vegetation phenology and water-use. These feedbacks dynamically alter exchanges of water and energy between the land-surface and the atmosphere that are difficult to capture. In order to address this gap, this project will develop a modeling framework that accounts for interactions between phenologic changes in response to environmental conditions, vegetation water-use strategies, and dynamic water storage. The approach couples a land-surface hydrology model with a prognostic phenology model to capture these interactions. To reduce uncertainty in forecasting plant states, satellite remote sensing observations of plant greenness (i.e., fraction of photosynthetically active radiation) and plant density (i.e., leaf area index) are assimilated using a dual state-parameter Ensemble Kalman Filter. This approach will permit quantification of uncertainty in environmental conditions and the propagation of this uncertainty through estimates of dynamic water storage and land surface fluxes of water, energy, and carbon. The model will be validated against data collected from forested mountain basins in Colorado that are being studied by the Dynamic Water Cluster of the Critical Zone Network. This research will improve the predictability of land-atmosphere interactions that characterize drought events in western mountain basins.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.

植物依靠土壤中的水储存来继续发育叶子，并在干旱期间进行光合作用。这种水源被称为动态水储存。在干旱期间，一些植物改变其生长策略以保持动态水储存。植物适应干旱条件的过程是复杂的，但对记录至关重要，因为它会影响碳，能量和水循环。该项目将探讨是否将植物与动态水的相互作用可以提供一个准确的估计植物对干旱的适应能力。这项研究将开发一个模型框架，结合动态水存储和植被生长和用水之间的反馈。该框架将在科罗拉多的森林山区进行测试。该项目将帮助评估植被如何适应未来更频繁和极端干旱事件的可能性，严重干旱事件似乎发生得更频繁。当降雨不频繁时，自然植被依赖于地下水储存，称为动态水储存，以维持新叶生长和光合作用。先前的工作已经记录了干旱期间动态水储存减少导致的植被生产力和水利用效率的变化。以前的研究也观察到植被如何在干旱时期采取不同的用水策略。然而，围绕动态水储存与植被物候和水利用之间的反馈存在知识差距。这些反馈动态地改变了地表和大气之间难以捕获的水和能量交换。为了解决这一差距，该项目将开发一个模型框架，考虑气候变化对环境条件，植被用水策略和动态蓄水之间的相互作用。该方法耦合一个陆地表面水文模型预测物候模型捕捉这些相互作用。为了减少预测植物状态的不确定性，植物绿度的卫星遥感观测（即，光合有效辐射的分数）和植物密度（即，叶面积指数）同化使用双状态参数Enhancement卡尔曼滤波器。这种方法将允许量化环境条件的不确定性和传播这种不确定性，通过估计动态水储存和地表水，能源和碳通量。该模型将根据从科罗拉多森林山地盆地收集的数据进行验证，这些数据正在由关键区网络的动态水集群进行研究。这项研究将提高陆地-大气相互作用的可预测性，这些相互作用是西部山区盆地干旱事件的特征。该奖项反映了NSF的法定使命，并被认为值得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来支持。