ERI: Investigating different types of drought across snowy regions and their impacts

该项目旨在提高对积雪地区干旱特征的认识，以便可持续地管理水资源。在了解干旱如何在水循环的不同组成部分传播和加剧方面存在差距，特别是在多雪的山区。季节性融雪为美国西部数百万人提供了很大一部分水资源。该项目旨在通过描述干旱发生的速度、驱动因素以及这些因素如何影响干旱影响来解决悬而未决的问题。不同类型干旱（例如，缺乏雪水当量[雪旱]、河流[水文干旱]和土壤湿度[农业干旱]）之间的传播速率将被量化。将研究一种干旱如何影响另一种干旱的特征和滞后性的严重程度、持续时间、时间和加剧率。这些关系如何在空间和时间变化使用最先进的数据，模型和观察将阐明。这项研究的结果旨在帮助建立更可持续的社区，发展适应气候变化的基础设施，并在雪是重要淡水资源的地区管理水资源。该项目将开发一个新的多元框架，以了解干旱特征和在雪域的传播。调查积雪变化的影响、干旱的驱动因素和干旱发生的速度将为水资源管理和干旱监测提供信息。考虑到降雪和水流赤字之间的滞后，该分析旨在深入了解干旱预测。该项目将评估雪旱的发生率、驱动因素和之前的条件将如何以及在多大程度上影响其他状态的水量/流量（例如土壤湿度、径流）。此外，这项研究的新多元干旱指数将使全球雪域干旱严重程度的量化成为可能，这在气候变暖中将是至关重要的。研究结果和方法有望在亚季节和年际时间尺度上改进流量预测、可持续水资源管理和工程以及干旱评估。研究结果将通过在科学会议上的演讲、同行评议的期刊出版物和在线数据库（例如CUAHSI的Hydroshare）与更广泛的社区共享。由于雪和干旱影响到水管理和工程以外的许多领域，研究结果应该影响到社会的其他部门，并指导农业、经济和城市规划的决策。本研究的多元干旱框架将免费提供给公众，以便水务机构、科学家和工程师可以从他们的应用中受益。此外，将开发一个动手干旱教育工具箱（DET），将新的雪和干旱研究整合到工程教育中。DET允许用户研究雪在干旱中的作用。PI与加州州立大学长滩分校现有的促进STEM多样性和少数族裔学生发展的项目合作，指导代表性不足的学生进行研究。该项目将培养水文学、水资源工程、干旱和多元数据分析方面的本科生和研究生。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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to advance knowledge of drought characteristics in snowy areas needed for sustainably managing water resources. Gaps exist in understanding how a drought propagates and intensifies across different components of the water cycle, especially in snowy, mountainous areas. Seasonal snowmelt supplies millions of people across the western U.S. with a significant fraction of their water resources. This project aims to address open questions by characterizing the rate of drought onset, its drivers, and how these factors influence drought impacts. Rates of propagation among different types of droughts (e.g., a lack of snow water equivalent [snow drought], streamflow [hydrological drought], and soil moisture [agricultural drought]) will be quantified. The severity, duration, timing, and intensification rate of how one type of drought influences the characteristics and lag of another will be studied. How these relationships vary in space and time using state-of-the-art data, models, and observations will be elucidated. Findings from this research are targeted to help build more sustainable communities, developing climate resilient infrastructure and managing water resources in areas where snow is a vital freshwater resource.The project will develop a new multivariate framework for understanding drought characteristics and propagation in snowy areas. Investigating the impact of snow variability, drivers of drought, and the rate of drought onset will inform water management and drought monitoring. Given the lag between snow and streamflow deficits, this analysis aims to yield insight into drought prediction. The project will assess how and to what extent the onset rate, drivers of snow drought, and antecedent conditions will influence the amount of water in other states/fluxes (e.g., soil moisture, runoff). Also, new multivariate drought indices from this research will enable quantification of drought severity in snowy areas across the globe, which will be critical in a warming climate. Findings and methods are expected to improve streamflow forecasts, sustainable water resources management and engineering, and drought assessment at sub-seasonal and interannual time scales. Results will be shared with the broader community via presentations at scientific meetings, peer-reviewed journal publications, and online data repositories (e.g., CUAHSI’s Hydroshare). Since snow and drought impact numerous areas beyond water management and engineering, findings should impact other sectors of society and guide decision making in agriculture, economics, and urban planning. The multivariate drought frameworks from this research will be freely provided to the public so water agencies, scientists, and engineers can benefit from their application. Also, a hands-on Drought Education Toolbox (DET) to integrate novel snow and drought research into engineering education will be developed. DET allows users to study the role of snow in drought. The PI works with existing programs that promote diversity in STEM and the development of minority students at California State University, Long Beach, to mentor underrepresented students in research. The project will train undergraduate and graduate students in hydrology, water resources engineering, and drought and multivariate data analysis.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.