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

ERI：调查雪区不同类型的干旱及其影响

• 批准号: 2301815
• 负责人: Laurie Huning
• 金额: $ 20万
• 依托单位: California State University-Long Beach Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2301815&HistoricalAwards=false
• 关键词: ERI; Investigating; different; types; drought

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.

该项目旨在提高对可持续管理水资源所需的雪地干旱特征的了解。理解干旱如何在水周期的不同组成部分（尤其是在雪山地区）中繁殖和加剧的差距存在。季节性融雪可为整个美国西部的数百万人提供其水资源的很大一部分。该项目旨在通过表征干旱的速度，其驱动因素以及这些因素如何影响干旱的影响来解决开放问题。将量化不同类型的干旱（例如，缺乏雪水[雪干]，水流[水文干旱]和土壤水分[农业干旱]）之间的传播率。一种类型的干旱如何影响另一种干旱的特征和滞后的严重性，持续时间，时机和强化率将被研究。这些关系如何使用最先进的数据，模型和观察结果在空间和时间上有所不同。这项研究的发现旨在帮助建立更可持续的社区，开发气候弹性的基础设施，并在雪是重要的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，以了解在雪地地区的干旱特征和传播。调查雪地变异性，干旱驱动因素以及干旱速度的影响将为水管理和干旱监测提供信息。鉴于雪和流量之间的滞后定义，该分析旨在洞悉干旱预测。该项目将评估如何以及在何种程度上，在何种程度上，雪干旱的驱动因素以及先决条件将影响其他状态/通量（例如，土壤水分，径流）的水量。此外，这项研究的新多元干旱指数将能够量化全球白雪皑皑的地区的干旱严重程度，这在温暖的气候中至关重要。期望和方法有望改善流质森林，可持续水资源管理和工程以及在季节和年际时间尺度上的干旱评估。结果将通过科学会议，同行评审的期刊出版物和在线数据存储库（例如Cuahsi的Hydroshare）与更广泛的社区共享。由于雪和干旱影响了水管理和工程以外的许多领域，因此发现应影响社会的其他部门，并指导决策在同意，经济学和城市规划中。这项研究的多元干旱框架将对公众免费，因此水机构，科学家和工程师可以从其应用中受益。此外，还将开发一个手工干旱教育工具箱（DET），以将新颖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与现有的计划合作，这些计划促进了STEM的多样性，并在加州州立大学长滩的加利福尼亚州立大学的少数族裔学生的发展中与精神代表性不足的研究学生进行了研究。该项目将培训水文学，水资源工程以及干旱和多元数据分析的本科生和研究生。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估来诚实地支持支持。