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: 1854931
• 负责人: Jason Otkin
• 金额: $ 46.66万
• 依托单位: University of Wisconsin-Madison
• 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=1854931&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灾难。随着时间的流逝，出现了一个缓慢的出现。相比之下，“ Flash Drights”在短短几周内急剧增强。近年来，这些事件中的许多事件袭击了美国，导致对农业和经济的巨大损害。闪存干旱在当前的预测系统中的代表性很差，阻碍了干旱的准备。该项目的激励是有必要提高对闪存干旱的理解，以提高我们预测它们的能力。为此，我们将重点介绍植物在闪光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在植物中发现水应力是可能的，然后几周才能看到这种压力。当植物压力迅速增加时，闪光干旱的风险很高。利用这种理解，我们将生成涵盖整个连续美国的闪光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以引起干旱的方式对闪存干旱进行分类。对于不同地区或土地用途，这些相互作用可能会大不相同，因此识别类别是改善预测的重要步骤。使用这些类别，我们将应用最近开发的统计方法将植物压力观察与天气预报相结合，以预测闪存干旱的风险在两周到三个月的时间内。在这些时间范围内的预测可以为种植决策和救济工作提供信息。最后，使用高级天气模型选择高度破坏性的闪存干旱进行详细研究，以了解土地管理和气候如何有助于特别严重的事件。该项目将通过针对三个已知特征来提高闪光干旱的理解和预测：（1）植被和土壤水分的观察能够在很大的领先时间提供闪光干旱风险的早期指示； （2）蒸发需求是闪存干旱的领先驱动力，它适合熟练的季节至季节（S2S）预测； （3）植被通过土壤水分和湍流的热量在闪光干旱发展中起着核心作用。为了利用这些功能进行预测，我们提出了一个新框架，以确定植被压力的快速增加是定义闪光干旱特征的核心。该框架利用了先进的卫星和地面观测。我们将根据气象，水文和生态因素对整个连续美国的历史性闪存干旱进行分类，从而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。该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察数据集和全球S2S预测系统的信息。对干旱类别和可预测性的分析将依次用于选择详细的基于动态的模拟研究的病例，以隔离植被的作用及其对可预测性的作用。最后，该项目期间建立的模拟基础设施将用于检查闪存干旱的气候和土地覆盖敏感性，这有助于预测未来的闪存干旱风险和对土地管理方案的评估。综上所述，这些活动将带来新的工具来进行闪烁干旱预测，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的更广泛背景下以及陆地碳平衡的更广泛背景下。这奖反映了NSF的法定任务，并通过使用该基金会的知识优点和广泛的criperia来评估，通过评估值得进行评估。

项目成果

Predicting Rapid Changes in Evaporative Stress Index (ESI) and Soil Moisture Anomalies over the Continental United States.

预测美国大陆蒸发应力指数 (ESI) 和土壤湿度异常的快速变化。

• DOI: 10.1175/jhm-d-20-0289.1
• 发表时间: 2021
• 期刊: Journal of Hydrometeorology
• 影响因子: 3.8
• 作者: Lorenz, David J.;Otkin, Jason A.;Zaitchik, Benjamin;Hain, Christopher;Anderson, Martha C.
• 通讯作者: Anderson, Martha C.