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Predicting and managing the impacts of fl ash droughts is difficult fi cult owing to their rapid onset and intensifica- fi ca- tion. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that fl ash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens fl ash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence fl uence of vegetation on NLDAS-simulated fl ash 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 fl ash droughts, but OL produces shorter duration events and regional trends in fl ash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, fl ash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, fl ash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of fl ash drought to the representation of vegetation suggests that representing plants with greater fi delity could aid in monitoring fl ash droughts and improve the prediction of fl ash drought transitions to more persistent and damaging long-term droughts.

来源平台：JOURNAL OF HYDROMETEOROLOGY