Quantifying Uncertainties in Drought Severity to Improve Drought Monitoring

量化干旱严重程度的不确定性以改进干旱监测

• 批准号: 2117433
• 负责人: Steven Quiring
• 金额: $ 40万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2117433&HistoricalAwards=false
• 关键词: Quantifying; Uncertainties; Drought; Severity; Improve

This project develops improved methods for classifying drought severity in ways that improve comparative understandings of the effects of droughts in different regions. A primary objective of the research is to identify and quantify sources of uncertainty that can affect the accuracy of drought classification. This information is beneficial for determining how the use of different datasets and indices can influence the accuracy of drought assessment. The results of this project have the potential to help to inform drought monitoring and mitigation activities that are undertaken by local, state, and federal agencies. Aided by a steering committee of drought experts from diverse agencies, the study facilitates improved drought monitoring. In addition, the project provides training opportunities for undergraduate students through summer research internships and courses.This project develops refined methods for assessing drought severity in diverse regional settings. The researchers have multiple objectives. First, using archival data, the researchers develop objective drought thresholds that can be used for accurate assessments across temporal, seasonal, and spatial contexts. These thresholds rely on multiple sources of data, including precipitation, temperature, soil moisture, stream flow, and groundwater. Second, using related approaches and data, the researchers quantify the uncertainty and bias in prevailing methods for characterizing drought frequency and severity. Estimating these biases allows for improved projections of future droughts. Third, the researchers compare their thresholds to historical data on observed impacts of droughts, which provides insight into the validity and utility of the derived thresholds. Overall, the results of this study contribute to efforts to characterize and assess the effects of past droughts while providing enhanced forecasting of future drought severity and impacts. The methods developed in this study are potentially transferable to global analyses of drought severity and trends.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.

该项目开发了干旱严重程度分类的改进方法，以提高对不同地区干旱影响的比较理解。这项研究的一个主要目标是确定和量化可能影响干旱分类准确性的不确定性来源。这一信息有助于确定使用不同的数据集和指数如何影响干旱评估的准确性。该项目的结果有可能有助于为地方、州和联邦机构开展的干旱监测和缓解活动提供信息。在由来自不同机构的干旱专家组成的指导委员会的协助下，这项研究有助于改进干旱监测。此外，该项目还通过暑期研究实习和课程为本科生提供培训机会，制定评估不同区域干旱严重程度的完善方法。研究人员有多重目标。首先，利用档案数据，研究人员开发了客观的干旱阈值，可用于跨时间，季节和空间背景的准确评估。这些阈值依赖于多种数据来源，包括降水、温度、土壤湿度、水流和地下水。其次，使用相关的方法和数据，研究人员量化了表征干旱频率和严重程度的现行方法的不确定性和偏差。对这些偏差进行估算，可以改进对未来干旱的预测。第三，研究人员将他们的阈值与观察到的干旱影响的历史数据进行比较，从而深入了解推导出的阈值的有效性和实用性。总的来说，这项研究的结果有助于描述和评估过去干旱的影响，同时加强对未来干旱严重程度和影响的预测。这项研究中开发的方法有可能转移到全球干旱严重程度和趋势的分析。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。