Flash Drought Onset and Development Mechanisms Captured With Soil Moisture and Vegetation Data Assimilation. Issue 12 (9th December 2022) - Ahmad, Shahryar K.; Kumar, Sujay V.; Lahmers, Timothy M.; Wang, Shugong; Liu, Pang‐Wei; Wrzesien, Melissa L.; Bindlish, Rajat; Getirana, Augusto; Locke, Kim A.; Holmes, Thomas R.; Otkin, Jason A.
Abstract: Flash droughts evolve and intensify rapidly under the influence of anomalous atmospheric conditions. In this study, we investigate the role of assimilating remotely sensed soil moisture (SM) and vegetation properties in capturing the evolution and impacts of two flash droughts in the Northern Great Plains. We find that during 2016 drought triggered by anomalously high temperatures and excessive evaporative demands, multivariate data assimilation (DA) of MODIS‐derived leaf area index (LAI) and Soil Moisture Active Passive SM within Noah‐Multiparameterization model helps capture elevated transpiration at onset. Assimilation of LAI particularly helped model the resulting rapid decline in SM during onset with as high as 10.0% steeper rate of decline compared to the simulation without any assimilation. Modeled‐SM anomalies exhibit a 7.5% and 11.7% increase in similarity with Evaporative Stress Index (ESI) data and U.S. Drought Monitor (USDM) maps, respectively. In contrast, during 2017 flash drought driven by record‐low precipitation during summers, SM assimilation resulted in largest rates of decline in rootzone SM, as large as 48.4% compared to results from no assimilation. Multivariate DA of SM and LAI results in 6.7% and 14.3% higher spatial similarity with ESI and USDM, respectively, and is necessary to model rapid intensification caused by anomalous precipitation deficits. This study elucidates the need to incorporate multiple observational constraints from remote sensing to effectively capture rapid onset rates, intensification, and severity of flash drought following different propagation mechanisms. This is fundamental for drought early detection to provide a wider window of response and implement efficient mitigation strategies. Plain Language Summary: A class of droughts called flash droughts develop rapidly under unusual weather conditions, often characterized by either warm temperatures or low precipitation or both. In this study, we employ the soil moisture (SM) and leaf area index (LAI) retrievals from the NASA Soil Moisture Active Passive mission and MODIS product, respectively, for characterizing the flash droughts of 2016 and 2017 in the Northern Great Plains. The results demonstrate that LAI observations, when assimilated within a land surface model, are effective in capturing high transpiration at the onset of 2016 drought driven by intense heat waves. The 2017 flash drought, however, was initiated by a precipitation deficit where information on SM is necessary to capture the rapid drying of soils. The modeled outputs not only capture the rapid drying of soil at the onset of droughts but are also spatially and temporally consistent with Evaporative Stress Index data and U.S. Drought Monitor maps. The study highlights the role of multivariate assimilation of remotely sensed vegetation and SM information to capture the rapid rates of onset and contrasting pathways of flash drought development. Key Points: Multivariate assimilation of remotely sensed vegetation and soil moisture helps characterize recent flash droughts in Northern Great Plains Heatwave‐driven warm flash drought requires assimilating vegetation conditions to capture impact on transpiration during rapid development Use of soil moisture data is necessary to represent rapid drying of soils during the dry flash drought intensified by moisture deficit