Impact of Vegetation Assimilation on Flash Drought Characteristics Across the Continental United States

Predicting and managing the costly impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring tends to rely on assessing changes in root-zone soil moisture. But given the lack of widespread soil moisture measurements, flash drought assessments have to rely on process-based model data like that from the National Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes, which shape land-atmosphere energy and moisture exchange. Here we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments: one, called OL, that uses the operational NLDAS protocol based on prognostic vegetation, and the second, called DA, that instead assimilates near-real-time satellite-derived leaf area index (LAI). Both experiments share the same geographic pattern of flash droughts, but OL produces both shorter events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. For instance, across much of the Midwest and Southern U.S., flash drought events are four weeks (or about 70%) longer on average in the DA than OL experiment. Moreover, across many regions, flash drought occurrence has trended upward according to the DA experiment, which is opposite of that represented in the OL. That conclusions about short- and long-term monitoring of impactful flash droughts are sensitive to how vegetation is represented in our modeling systems is essential—it suggests that representing plants with greater fidelity could aid not just the monitoring of flash droughts, but also improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.