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Flash droughts are characterized by a period of rapid intensification over sub-seasonal time scales that culminates in the rapid emergence of new or worsening drought impacts. This study presents a new flash drought intensity index (FDII) that accounts for both the unusually rapid rate of drought intensification and its resultant severity. The FDII framework advances our ability to characterize flash drought because it provides a more complete measure of flash drought intensity than existing classification methods that only consider the rate of intensification. The FDII is computed using two terms measuring the maximum rate of intensification (FD_INT) and average drought severity (DRO_SEV). A climatological analysis using soil moisture data from the Noah land surface model from 1979–2017 revealed large regional and interannual variability in the spatial extent and intensity of soil moisture flash drought across the US. Overall, DRO_SEV is slightly larger over the western and central US where droughts tend to last longer and FD_INT is ~75% larger across the eastern US where soil moisture variability is greater. Comparison of the FD_INT and DRO_SEV terms showed that they are strongly correlated (r = 0.82 to 0.90) at regional scales, which indicates that the subsequent drought severity is closely related to the magnitude of the rapid intensification preceding it. Analysis of the 2012 US flash drought showed that the FDII depiction of severe drought conditions aligned more closely with regions containing poor crop conditions and large yield losses than that captured by the intensification rate component (FD_INT) alone. [ABSTRACT FROM AUTHOR]

One of the benefits of training a process-based, land surface model is the capacity to use it in ungauged sites as a complement to standard weather stations for predicting energy fluxes, evapotranspiration, and surface and root-zone soil temperature and moisture. In this study, dynamic (i.e., time-evolving) vegetation parameters were derived from remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and coupled with a physics-based land surface model (tin-based Real-time Integrated Basin Simulator (tRIBS)) at four eddy covariance (EC) sites in south-central U.S. to test the predictability of micro-meteorological, soil-related, and energy flux-related variables. One cropland and one grassland EC site in northern Oklahoma, USA, were used to tune the model with respect to energy fluxes, soil temperature, and moisture. Calibrated model parameters, mostly related to the soil, were then transferred to two other EC sites in Oklahoma with similar soil and vegetation types. New dynamic vegetation parameter time series were updated according to MODIS imagery at each site. Overall, the tRIBS model captured both seasonal and diurnal cycles of the energy partitioning and soil temperatures across all four stations, as indicated by the model assessment metrics, although large uncertainties appeared in the prediction of ground heat flux, surface, and root-zone soil moisture at some stations. The transferability of previously calibrated model parameters and the use of MODIS to derive dynamic vegetation parameters enabled rapid yet reasonable predictions. The model was proven to be a convenient complement to standard weather stations particularly for sites where eddy covariance or similar equipment is not available. [ABSTRACT FROM AUTHOR]