Evaporative Stress Index as a flash drought tool in the southeastern U.S.

: This study focuses on the state of Alabama in the southeastern United States, a humid subtropical region characterized by strong seasonal variability in precipitation, vegetation growth, and atmospheric demand. These conditions complicate drought monitoring, particularly the detection of rapid-onset (“flash”) droughts driven by abrupt soil–moisture depletion during the growing season. : The Atmosphere–Land Exchange Inverse (ALEXI) model provides daily evapotranspiration estimates at 4-km resolution across the United States, from which the Evaporative Stress Index (ESI) is derived as a standardized anomaly of the actual-to-potential evapotranspiration ratio (AET/PET). Because evaporative stress reflects vegetation water use, ESI is often linked to root-zone soil moisture, though this relationship varies with season and land cover. This study evaluates the statistical relationship between ALEXI-based ESI and in-situ soil moisture observations in Alabama and examines the behavior of ESI and ESI change anomalies ( Δ ESI ) during soil–moisture-defined flash drought events. : Results show that correlations between ESI and soil moisture are modest when evaluated year-round but strengthen substantially from late spring through fall, peaking during September–November. Δ ESI frequently becomes negative one to two weeks prior to rapid soil–moisture decline, coinciding with approximately 60% of flash drought events on average and up to 90% during fall. These findings demonstrate that, despite seasonal limitations, ESI and Δ ESI provide valuable early information on soil–moisture-driven flash drought onset in humid subtropical systems and offer complementary insight for drought monitoring and early warning across the southeastern United States. • Evaluates ALEXI-based Evaporative Stress Index (ESI) over Alabama SCAN stations. • Assesses ESI–soil moisture correlations by depth, season, land cover, and soil. • Develops soil–moisture-based flash drought climatology for 2001–2020. • Demonstrates that ESI change anomalies provide skill in identifying flash droughts. • Shows ESI identifies 60%–90% of flash droughts, with strongest signals in fall.