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

预防事件轨道 2：合作研究：突发干旱：过程、预测以及植被在其演化中的核心作用。

• 批准号: 1854945
• 负责人: Trevor Keenan
• 金额: $ 30.53万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854945&HistoricalAwards=false
• 关键词: PREEVENTS; Track; Collaborative; Research; Flash

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.

干旱通常被认为是一场悄然发生的灾难。随着时间的推移慢慢出现的一种现象。相比之下，“突发干旱”在短短几周内急剧加剧。近年来，许多此类事件袭击了美国，给农业和经济带来了重大和意想不到的损害。目前的预报系统对突发干旱的反映很差，阻碍了干旱准备工作。该项目的动机是需要加深对突发干旱的了解，以提高我们预测干旱的能力。为此，我们将重点关注植物在突发干旱发展中发挥的关键作用。新的卫星技术和现场测量方法使得可以在肉眼看到压力之前数周检测到植物中的水分压力。当植物胁迫迅速增加时，出现突发干旱的风险很高。利用这种理解，我们将制定覆盖整个美国本土的突发干旱定义和检测系统。然后，我们将根据天气和植被相互作用导致干旱的方式对突发干旱进行分类。对于不同的地区或土地用途，这些相互作用可能非常不同，因此识别类别是改进预测的重要一步。利用这些类别，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结合起来，提前两周到三个月预测突发干旱风险。这些时间尺度的预测可以为种植决策和救援工作提供信息。最后，将使用先进的天气模型选择具有高度破坏性的突发干旱进行详细研究，以了解土地管理和气候如何导致特别严重的事件。该项目将通过针对三个已知特征来推进对突发干旱的理解和预测：(1)对植被和土壤湿度的观测可以在重要的时间内提供突发干旱风险的早期迹象； (2) 蒸发需求是突发干旱发生的主要驱动因素，并且适合熟练的次季节到季节 (S2S) 预测； (3) 植被通过土壤湿度和湍流热通量在突发干旱发展中发挥核心作用。为了利用这些特征进行预测，我们基于植被胁迫快速增加是定义突发干旱特征的核心这一认识，提出了一个定义突发干旱的新框架。该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本土的历史性突发干旱事件进行分类，从而使我们能够区分具有不同过程和可预测特征的不同类型的事件。这种分类将支持概率统计和机器学习预测模型，这些模型结合了最近开发的观测数据集和全球 S2S 预测系统的信息。反过来，对干旱类别和可预测性的分析将用于选择案例进行详细的基于动态的模拟研究，以分离植被的作用及其对可预测性的贡献。最后，该项目期间建立的模拟基础设施将用于检查突发干旱的气候和土地覆盖敏感性，有助于预测未来突发干旱风险和评估土地管理方案。总而言之，这些活动将为突发干旱预测带来新工具，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更广泛的气候趋势和陆地碳平衡背景中。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。