A joint spatio-temporal characterization of the major meteorological droughts in Europe

• Published in: Environmental research letters Vol. 19; no. 9; pp. 94041 - 94053
• Main Authors: Banfi, Fabiola, Cammalleri, Carmelo, Michele, Carlo De
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01-09-2024
• Subjects: Algorithms; Brittleness; Carbon cycle; Clustering; Cold season; Drought; spatio-temporal characterization; Spatiotemporal data; SPI; Standardized precipitation index; Europe
• ISSN: 1748-9326; 1748-9326
• DOI: 10.1088/1748-9326/ad6ba9

Abstract Drought can be considered one of the most severe and complex weather-related natural hazards. It is a relevant stressor for ecosystems, affecting vegetation, ecosystem productivity, and water and carbon cycles, with a complex web of related impacts. Despite the interlink between the spatial and temporal scales of droughts, these two aspects are often studied separately. In addition, studies generally focus on detecting the events, without trying to investigate similarities among them. In this work, we introduce a set of tools used to summarize the main properties of major droughts in Europe, with the goal of subdividing the events in groups characterized by similar properties. We used a European dataset of meteorological droughts (from 1981 to 2020) that detects events based on the Standardized Precipitation Index using an event-oriented spatio-temporal clustering algorithm. From the analysis, we identified three groups of major meteorological droughts: a first group that is comprised by warm-season events, characterized by a longer duration, a shorter early growing phase, and a longer exhaustion phase; a second group, less numerous, comprised by droughts occurring during the cold season, that tend to have a shorter duration, a longer early growing phase and a shorter exhaustion phase; and a third group comprised of droughts occurring across the two periods. This last class is characterized by a longer duration and a high variability in most of the other characteristics, suggesting that these events may be associated with a large range of driving mechanisms. The proposed procedure allows for a drought classification that can be used for better understanding the mechanisms behind spatio-temporal evolution of these events.