Project Team
Students
Sierra Wright
Earth Science
Altoona, University Park
Faculty Mentors
Gabriela Gesualdo
University Park
Department of Geoscience
Antonia Hadjimichael
University Park
Department of Geoscience
Project
Project Video
Project Abstract
Flash droughts are rapid-onset droughts that can develop under any conditions or location. As a relatively new concept with limited research and long-term data, the prediction and preparation for flash droughts are often inadequate. Additionally, the impacts of these events are not well understood. One approach to addressing this gap is through text mining of reports from social media, news articles, and other media content. In this study, we analyzed 31 different sources to identify key terms used to declare a flash drought event, as well as the main affected sectors and their impacts. Our results indicate that the primary affected sectors are agriculture, municipal services, and forestry. The most commonly used terms were “dry spell,” followed by “drought condition” and “sudden and extreme dry spell.” The terms identified in this study can be utilized to predict flash droughts by training machine learning models to recognize their occurrence and impact in literature. Furthermore, the database constructed here can be used to validate flash drought identification methods and improve the accuracy of monitoring and prediction systems, ultimately enhancing the resilience and preparedness of affected sectors.
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