PREEVENTS Track 2: Collaborative Research: Flash droughts: process, prediction, and the central role of vegetation in their evolution

干旱通常被认为是一种缓慢蔓延的灾难；一种随着时间的推移缓慢出现的灾难。相比之下，“闪电干旱”在短短几周内急剧加剧。近年来，许多这样的事件袭击了美国，给农业和经济造成了意想不到的重大损害。在目前的预报系统中，突发性干旱没有得到很好的反映，阻碍了备灾工作。该项目的动机是需要增进对突发性干旱的了解，以便提高我们预测它们的能力。要做到这一点，我们将重点关注植物在突如其来的干旱中所起的关键作用。新的卫星技术和田间测量方法使人们有可能在植物水分胁迫被肉眼看到之前几周检测到这种胁迫。当植物压力迅速增加时，就有很高的风险发生突发性干旱。利用这一理解，我们将制定覆盖整个毗邻美国的快速干旱定义和探测系统。然后，我们将根据天气和植被相互作用导致干旱的方式对闪旱进行分类。这些相互作用对于不同的区域或土地用途可能会有很大的不同，因此识别类别是改进预测的重要步骤。使用这些类别，我们将应用最近开发的统计方法，将植物胁迫观测与天气预报相结合，提前两周至三个月预测闪旱风险。在这些时间尺度上的预测可以为种植决策和救灾工作提供信息。最后，将选择高破坏性的闪旱，利用先进的天气模式进行详细研究，以了解土地管理和气候如何导致特别严重的事件。该项目将通过以下三个已知特征来促进对闪旱的理解和预测：(1)对植被和土壤湿度的观测可以在重要的提前期提供闪旱风险的早期指示；(2)蒸发需求是闪旱发生的主要驱动因素，并且可以进行巧妙的亚季节性(S2S)预报；(3)植被通过土壤水分和湍流热通量在闪旱的发展中发挥核心作用。为了利用这些特征进行预测，我们提出了一个新的定义闪旱的框架，基于这样的理解，即植被胁迫的迅速增加是定义闪旱特征的核心。这一框架利用了先进的卫星和地面观测。我们将根据气象、水文和生态因素对美国毗邻地区的历史闪旱事件进行分类，使我们能够区分具有不同过程和可预测性特征的不同类型的事件。这一分类将支持将来自最近开发的观测数据集和全球S2S预测系统的信息结合在一起的概率统计和机器学习预测模型。对干旱等级和可预测性的分析将反过来用于选择案例，以进行详细的动态模拟研究，以分离植被的作用及其对可预测性的贡献。最后，在项目期间建立的模拟基础设施将用于审查突发干旱对气候和土地覆盖的敏感性，有助于预测未来突发干旱风险和评估土地管理备选方案。综上所述，这些活动将带来新的工具来闪现干旱预测，有助于基于动态的干旱模拟，并将对这些极端事件的理解和预测置于气候趋势和陆地碳平衡的更广泛背景下。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。

Drought is often thought of as a creeping disaster; one that emerges slowly over time. In contrast, "flash droughts" intensify dramatically in just a few weeks. A number of these events have struck the United States in recent years, leading to significant and unexpected damage to agriculture and the economy. Flash droughts are poorly represented in current forecast systems, hindering drought preparedness. This project is motivated by the need to advance understanding of flash droughts in order to improve our ability to predict them. To do this, we will focus on the critical role that plants play in the development of a flash drought. New satellite technologies and field measurement methods make it possible to detect water stress in plants weeks before that stress can be seen by eye. When plant stress increases rapidly there is a high risk of flash drought. Using this understanding, we will produce flash drought definitions and detection systems that cover the entire contiguous United States. We will then categorize flash droughts according to the ways in which weather and vegetation interact to cause the drought. These interactions can be very different for different regions or land uses, so identifying categories is an important step for improving prediction. Using these categories, we will apply recently developed statistical methods to combine plant stress observations with weather forecasts to predict flash drought risk from two weeks to three months in advance. Predictions at these time scales can inform planting decisions and relief efforts. Finally, highly damaging flash droughts will be selected for detailed study using advanced weather models, in order to understand how land management and climate contribute to particularly severe events.This project will advance flash drought understanding and forecasting by targeting three known characteristics: (1) observations of vegetation and soil moisture can provide early indications of flash drought risk at significant lead times; (2) evaporative demand is a leading driver of flash drought onset, and it is amenable to skillful subseasonal-to-seasonal (S2S) forecasts; (3) vegetation plays a central role in flash drought development via soil moisture and turbulent heat fluxes. To leverage these features for prediction, we propose a new framework for defining flash droughts based on the understanding that a rapid increase in vegetation stress is the core defining flash drought characteristic. This framework makes use of advanced satellite and ground observations. We will classify historic flash drought events across the Contiguous United States on the basis of meteorological, hydrological, and ecological factors, allowing us to distinguish different types of event that have distinct processes and predictability characteristics. This classification will support probabilistic statistical and machine learning forecast models that combine information from recently developed observation datasets and global S2S forecasting systems. Analysis of drought classes and predictability will, in turn, be used to select cases for detailed dynamically-based simulation studies that isolate the role of vegetation and its contribution to predictability. Finally, the simulation infrastructure established during the project will be used to examine climate and land cover sensitivities of flash droughts, contributing to projections of future flash drought risk and assessment of land management options. Taken together, these activities will bring new tools to flash drought prediction, contribute to dynamically-based simulation of drought, and place both understanding and prediction of these extreme events into the broader context of climate trends and the terrestrial carbon balance.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.