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.

该项目旨在增进对可持续管理水资源所需的雪地干旱特征的了解，但在了解干旱如何在水循环的不同组成部分中传播和加剧方面存在差距，特别是在季节性融雪的地区。该项目旨在通过描述干旱发生率、其驱动因素以及这些因素如何影响不同类型干旱的传播率来解决悬而未决的问题。 （例如，缺乏雪水当量[雪旱]、径流[水文干旱]和土壤湿度[农业干旱]）将量化一种干旱如何影响的严重程度、持续时间、时间和加剧率。将使用最先进的数据、模型和观察结果来研究这些关系在空间和时间上的变化，旨在帮助建立更可持续的社区，发展气候。有弹性的雪是重要淡水资源的地区的基础设施和水资源管理。该项目将开发一个新的多变量框架，以了解雪地区的干旱特征和传播，研究雪变化的影响、干旱的驱动因素以及干旱发生的速度。将为水管理和干旱监测提供信息。鉴于降雪和径流赤字之间存在滞后性，该分析旨在深入了解干旱预测，评估降雪干旱的发生率、驱动因素以及先期条件的影响程度。中的水量此外，这项研究的新多变量干旱指数将能够量化全球多雪地区的干旱严重程度，这对于气候变暖至关重要。改进水流预测、可持续水资源管理和工程以及次季节和年际时间尺度的干旱评估结果将通过科学会议上的演示、同行评审的期刊出版物和在线数据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，因此研究结果应该影响社会的其他部门并指导农业、经济和城市规划的决策。免费向公众提供，以便水务机构、科学家和工程师可以从其应用中受益。此外，还将开发一个实用的干旱教育工具箱（DET），将新颖的雪和干旱研究整合到工程教育中。该项目负责人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，以研究雪在干旱中的作用，为代表性不足的学生提供研究方面的培训。该奖项反映了 NSF 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。