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

在干旱期间，植物依靠土壤中的水分储存来继续发育叶片和进行光合作用。这种水源被称为动态蓄水。在干旱期间，一些植物会改变它们的生长策略，以保存动态的水分储存。植物适应干旱条件的这一过程是复杂的，但由于它影响到碳、能量和水循环，因此对文档来说至关重要。这个项目将探索结合植物与动态水的相互作用是否可以提供对植物抗旱能力的准确估计。这项研究将开发一个包含动态蓄水与植被生长和水分利用之间反馈的建模框架。该框架将在科罗拉多州的森林覆盖的山区进行测试。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。严重干旱事件似乎正在以更频繁的频率发生。当降雨很少时，自然植被依靠地下蓄水，即所谓的动态蓄水，来维持新叶的生长和光合作用。先前的工作已经证明，由于干旱期间动态储水量减少，植被生产力和水利用效率发生了变化。以前的研究也观察到了干旱期间植被如何采取不同的水分利用策略。然而，在动态蓄水与植被物候和水分利用之间的反馈方面，知识差距仍然存在。这些反馈动态地改变了陆地表面和大气之间难以捕捉的水和能量的交换。为了解决这一差距，该项目将开发一个模型框架，该框架考虑了物候变化对环境条件的响应、植被用水策略和动态水分储存之间的相互作用。该方法将陆地-地表水文模型与预测物候模型结合起来，以捕捉这些相互作用。为了减少植物状态预测中的不确定性，利用双状态参数集合卡尔曼滤波对植物绿度(即光合作用有效辐射的比例)和植物密度(即叶面积指数)的卫星遥感观测进行同化。这种方法将可以量化环境条件中的不确定性，并通过估计动态水储量和陆地表面水、能量和碳的通量来传播这种不确定性。该模型将根据从科罗拉多州森林覆盖的山脉盆地收集的数据进行验证，这些数据正在由临界区网络的动态水集群进行研究。这项研究将提高以西部山区盆地干旱事件为特征的陆地-大气相互作用的可预测性。这一奖项反映了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。

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.