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多雪地区干旱特征的认识。在理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，特别是在多雪的山区。季节性的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、其驱动因素以及这些因素如何影响干旱影响来解决未决问题。不同类型干旱之间的传播速度（例如，雪水当量缺乏[雪旱]、河川流量缺乏[水文干旱]和土壤湿度缺乏[农业干旱]）将被量化。将研究一种类型的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上变化，使用最先进的数据，模型和观测将得到阐明。这项研究的结果旨在帮助建立更可持续的社区，发展气候适应性基础设施，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变量框架，用于了解干旱特征和多雪地区的传播。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。由于雪和径流赤字之间的滞后，这项分析的目的是深入了解干旱预测。该项目将评估发病率、雪旱的驱动因素和前期条件如何以及在多大程度上影响其他状态/通量的水量（例如，土壤水分、径流）。 此外，这项研究的新的多变量干旱指数将能够量化地球仪多雪地区的干旱严重程度，这在气候变暖中至关重要。预计研究结果和方法将改善径流预测、可持续水资源管理和工程以及亚季节和年际时间尺度的干旱评估。研究结果将通过科学会议上的演讲、同行评审的期刊出版物和在线数据存储库（例如，CUAHSI的Hydroshare）。由于雪和干旱影响水管理和工程以外的许多领域，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。这项研究的多元干旱框架将免费提供给公众，以便水机构，科学家和工程师可以从其应用中受益。此外，还将开发一个实用的干旱教育系统（DET），将新的雪和干旱研究纳入工程教育。DET允许用户研究雪在干旱中的作用。PI与现有的项目合作，促进STEM的多样性和长滩加州州立大学少数民族学生的发展，以指导研究中代表性不足的学生。该项目将培训水文学、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NSF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。