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Abstract Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts. Significance Statement Flash droughts are a subset of droughts with rapid onset and intensification leading to devastating losses to crops. Rapid soil moisture decline is one way to detect flash droughts. Because there is a lack of widespread observational data, we often rely on model outputs of soil moisture. Here, we explore how the representation of vegetation within land surface models influences the U.S. flash drought characteristics covering 1981–2017. We show that the misrepresentation of vegetation status propagates soil moisture biases into flash drought monitoring, impacting our understanding of the onset, magnitude, duration, and trends in flash droughts. Our results suggest that the assimilation of near-real-time vegetation into land surface models could improve the detection, monitoring, and prediction of flash droughts.
Fallah et al. (Fri,) studied this question.