Advancing Flash Drought Understanding through the Soil Moisture Volatility Index (SMVI)

Flash droughts have emerged as a significant concern, given their severe socioeconomic and ecological impacts. Despite extensive research on flash drought processes, predictability, and trends, a standardized quantitative definition that encompasses all flash drought characteristics and pathways remains elusive. In response to this gap, considerable efforts have been devoted to defining, inventorying, monitoring, and forecasting flash drought events. In our research endeavor, we introduce the Soil Moisture Volatility Index (SMVI) as a powerful tool to assess the onset dates and severity of flash droughts across the Contiguous United States (CONUS) spanning from 1979 to the present. Rigorous evaluations and comparisons with other flash drought definitions and independent vegetation and crop datasets demonstrate the effectiveness of SMVI in capturing flash drought onset, regardless of whether it occurs in humid or semi-arid regions. Leveraging the SMVI inventory, we thoroughly examine and categorize flash drought events based on various land surface and atmospheric conditions that may serve as predictable drivers. Within this comprehensive inventory, we identify three distinct classes of flash droughts. The first class, termed "dry and demanding" droughts, is characterized by high evaporative demand and low soil moisture levels. The second class, comprising "evaporative" events, develops under conditions of elevated demand and accelerated soil drying due to increased evapotranspiration. The third class, referred to as "stealth" events, presents challenges in prediction as they lack clear atmospheric signals despite exhibiting modest anomalies. Our analysis reveals that plant stress and evaporative demand serve as predictive indicators of flash drought, underscoring the critical role of vegetation-soil-atmosphere feedbacks in rapid intensification for specific classes of flash drought. Thus, incorporating vegetation's influence on hydrology and the surface energy balance becomes imperative when forecasting flash droughts within a dynamically-based or process-informed empirical system. Recognizing the composite nature of flash droughts and their underlying drivers is crucial for advancing our understanding and predictive capabilities. This, in turn, paves the way for improved management and mitigation strategies, ultimately aiding in effectively addressing the challenges posed by flash droughts.