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.

植物依靠土壤中的储水来继续发展叶子，并在干旱期间经历光合作用。该水源称为动态供水。在干旱期间，一些植物改变了其生长策略以节省动态储水。植物适应干旱条件的这一过程很复杂，但对于记录碳，能量和水周期的文献至关重要。该项目将探讨将植物相互作用与动态水融合在一起是否可以准确估计植物对干旱的弹性。这项研究将开发一个建模框架，该框架结合了动态储水与植被生长和水利用之间的反馈。该框架将在科罗拉多州的森林山地进行测试。该项目将有助于评估植被将来如何适应更频繁和极端的干旱事件的可能性。频率更高的情况似乎正在发生干旱事件。当降雨量很少时，天然植被依赖于地下水库（称为动态储水）来维持新的叶片生长和光合作用。先前的工作记录了由于干旱期间动态储水的减少，植被生产率和水利用效率的变化。先前的研究还观察到植被如何在干旱期间采用不同的水利用策略。但是，知识差距始终存在于动态储藏与植被物候和水利用之间的反馈中。这些反馈动态改变了难以捕获的土地表面和大气之间的水和能量交换。为了解决这一差距，该项目将开发一个建模框架，该框架解释了响应环境条件，植被水利用策略和动态供水的响应候位变化之间的相互作用。该方法将土地表面水文学模型与预后的物候模型融合在一起，以捕获这些相互作用。为了减少预测植物状态的不确定性，使用双重状态参数集合Kalman滤光片吸收了植物绿色的卫星遥感观察（即光合活性辐射的一部分）和植物密度（即叶子面积指数）。这种方法将允许通过估计水，能量和碳的动态储水和土地表面通量来量化环境条件下的不确定性以及这种不确定性的传播。该模型将通过从关键区域网络的动态水群研究从科罗拉多州的森林山区盆地收集的数据进行验证。这项研究将提高西部山区盆地干旱事件的土地大气相互作用的可预测性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。