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In recent years, extreme droughts in the United States have increased in frequency and severity, underlining a need to improve our understanding of vegetation resilience and adaptation. Flash droughts are extreme events marked by the rapid dry down of soils due to lack of precipitation, high temperatures, and dry air. These events are also associated with reduced preparation, response, and management time windows before and during drought, exacerbating their detrimental impacts on people and food systems. Improvements in actionable information for flash drought management are informed by atmospheric and land surface processes, including responses and feedbacks from vegetation. Phenologic state, or growth stage, is an important metric for modeling how vegetation modulates land–atmosphere interactions. Reduced stomatal conductance during drought leads to cascading effects on carbon and water fluxes. We investigate how uncertainty in vegetation phenology and stomatal regulation propagates through vegetation responses during drought and non-drought periods by coupling a land surface hydrology model to a predictive phenology model. We assess the role of vegetation in the partitioning of carbon, water, and energy fluxes during flash drought and carry out a comparison against drought and non-drought periods. We selected study sites in Kansas, USA, that were impacted by the flash drought of 2012 and that have AmeriFlux eddy covariance towers which provide ground observations to compare against model estimates. Results show that the compounding effects of reduced precipitation and high vapor pressure deficit (VPD) on vegetation distinguish flash drought from other drought and non-drought periods. High VPD during flash drought shuts down modeled stomatal conductance, resulting in rates of evapotranspiration (ET), gross primary productivity (GPP), and water use efficiency (WUE) that fall below those of average drought conditions. Model estimates of GPP and ET during flash drought decrease to rates similar to what is observed during the winter, indicating that plant function during drought periods is similar to that of dormant months. These results have implications for improving predictions of drought impacts on vegetation. [ABSTRACT FROM AUTHOR]

Increased flash drought awareness in recent years has motivated the development of numerous indicators for monitoring, early warning, and assessment. The flash drought indicators can act as a complementary set of tools by which to inform flash drought response and management. However, the limitations of each indicator much be measured and communicated between research and practitioners to ensure effectiveness. The limitations of any flash drought indicator are better understood and overcome through assessment of indicator sensitivity and consistency; however, such assessment cannot assume any single indicator properly represents the flash drought "truth." To better understand the current state of flash drought monitoring, this study presents an intercomparison of nine, widely used flash drought indicators. The indicators represent perspectives and processes that are known to drive flash drought, including evapotranspiration and evaporative demand, precipitation, and soil moisture. We find no single flash drought indicator consistently outperforms all others across the contiguous United States. We do find the evaporative demand- and evapotranspiration-driven indicators tend to lead precipitation- and soil moisture-based indicators in flash drought onset, but also tend to produce more flash drought events collectively. Overall, the regional and definition-specific variability in results supports the argument for a multi-indicator approach for flash drought monitoring, as advocated by recent studies. Furthermore, flash drought research-especially evaluation of historical and potential future changes in flash drought characteristics-should test multiple indicators, datasets, and methods for representing flash drought, and ideally employ a multi-indicator analysis framework over use of a single indicator from which to infer all flash drought information. SIGNIFICANCE STATEMENT: Rapid onset or "flash" drought has been an increasing concern globally, with quickly intensifying impacts to agriculture, ecosystems, and water resources. Many tools and indicators have been developed to monitor and provide early warning for flash drought, ideally resulting in more time for effective mitigation and reduced impacts. However, there remains no widely accepted single method for defining, monitoring, and measuring flash drought, which means most indicators that are developed are compared with other individual indicators or conditions and impacts in one or two flash drought events. In this study, we measure the state of flash drought monitoring through an intercomparison of nine, widely used flash drought indicators that represent different aspects of flash drought. We find that no single flash drought indicator outperformed all others and suggest that a comprehensive flash drought monitor should leverage multiple, complementary indicators, datasets, and methods. Furthermore, we suggest flash drought research-especially that which reflects on historical or projected changes in flash drought characteristics-should seek multiple indicators, datasets, and methods for analyses, thereby reducing the potentially confounding effects of sensitivity to a single indicator. [ABSTRACT FROM AUTHOR]

We evaluate two high-resolution Lake Michigan air quality simulations during the 2017 Lake Michigan Ozone Study campaign. These air quality simulations employ identical chemical configurations but use different input meteorology. The AP-XM configuration follows the U.S. Environmental Protection Agency (EPA)-recommended modeling practices, whereas the YNT_SSNG employs different parameterization schemes and satellite-based inputs of sea surface temperatures, green vegetative fraction, and soil moisture and temperature. Overall, we find a similar performance in the model simulations of hourly and maximum daily average 8 h (MDA8) ozone, with the AP-XM and YNT_SSNG simulations showing biases of -11.42 and -13.54 ppbv (parts per billion by volume), respectively, during periods when the observed MDA8 was greater than 70 ppbv. However, for the two monitoring sites that observed high-ozone events, the AP-XM simulation better matched observations at Chiwaukee Prairie, and the YNT_SSNG simulation better matched observations at the Sheboygan Kohler-Andrae (KA) State Park. We find that the differences between the two simulations are largest for column amounts of ozone precursors, particularly NO 2. Across three high-ozone events, the YNT_SSNG simulation has a lower NO 2 column bias (0.17×1015 mol cm -2) compared to the AP-XM simulation (0.31×1015 mol cm -2). The YNT_SSNG simulation also has an advantage in that it better captures the structure of the boundary layer and lake breeze during the 2 June high-ozone event, although the timing of the lake breeze is about 3 h too early at Sheboygan. Our results are useful for informing an air quality modeling framework for the Lake Michigan area. [ABSTRACT FROM AUTHOR]