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.

该项目开发了改进的方法，以改善对不同地区干旱影响的比较理解的方式对干旱严重程度进行分类。该研究的主要目的是识别和量化可能影响干旱分类准确性的不确定性来源。该信息有益于确定不同数据集和指数的使用如何影响干旱评估的准确性。该项目的结果有可能帮助依据当地，州和联邦机构进行的干旱监测和缓解活动。在不同机构的干旱专家指导委员会的帮助下，该研究促进了干旱监测的改进。此外，该项目通过夏季研究实习和课程为本科生提供了培训机会。该项目开发了评估各种地区环境中干旱严重程度的精致方法。研究人员有多种目标。首先，使用档案数据，研究人员开发了客观的干旱阈值，这些阈值可用于跨时间，季节性和空间环境进行准确评估。这些阈值依赖于多种数据来源，包括降水，温度，土壤水分，流量和地下水。其次，使用相关方法和数据，研究人员量化了表征干旱频率和严重性的主要方法的不确定性和偏见。估计这些偏见可以改善未来干旱的预测。第三，研究人员将其阈值与观察到的干旱影响的历史数据进行了比较，这提供了对衍生阈值的有效性和实用性的见解。总体而言，这项研究的结果有助于努力表征和评估过去干旱的影响，同时提供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可能可以转移到全球对干旱严重性和趋势的分析。该奖项反映了NSF的法定任务，并被认为是值得通过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支持的。