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With the increasing use of the term "flash drought" within the scientific community, Otkin et al. provide a general definition that identifies flash droughts based on their unusually rapid rate of intensification. This study presents an objective percentile-based methodology that builds upon that work by identifying flash droughts using standardized evaporative stress ratio (SESR) values and changes in SESR over some period of time. Four criteria are specified to identify flash droughts: two that emphasize the vegetative impacts of flash drought and two that focus on the rapid rate of intensification. The methodology was applied to the North American Regional Reanalysis (NARR) to develop a 38-yr flash drought climatology (1979–2016) across the United States. It was found that SESR derived from NARR data compared well with the satellite-based evaporative stress index for four previously identified flash drought events. Furthermore, four additional flash drought cases were compared with the U.S. Drought Monitor (USDM), and SESR rapidly declined 1–2 weeks before a response was evident with the USDM. From the climatological analysis, a hot spot of flash drought occurrence was revealed over the Great Plains, the Corn Belt, and the western Great Lakes region. Relatively few flash drought events occurred over mountainous and arid regions. Flash droughts were categorized based on their rate of intensification, and it was found that the most intense flash droughts occurred over the central Great Plains, Corn Belt, and western Great Lakes region. [ABSTRACT FROM AUTHOR]

Reliable indicators of rapid drought onset can help to improve the effectiveness of drought early warning systems. In this study, the evaporative stress index (ESI), which uses remotely sensed thermal infrared imagery to estimate évapotranspiration (ET), is compared to drought classifications in the U.S. Drought Monitor (USDM) and standard precipitation-based drought indicators for several cases of rapid drought development that have occurred across the United States in recent years. Analysis of meteorological time series from the North American Regional Reanalysis indicates that these events are typically characterized by warm air temperature and low cloud cover anomalies, often with high winds and dewpoint depressions that serve to hasten evaporative depletion of soil moisture reserves. Stan-dardized change anomalies depicting the rate at which various multiweek ESI composites changed over different time intervals are computed to more easily identify areas experiencing rapid changes in ET. Overall, the results demonstrate that ESI change anomalies can provide early warning of incipient drought impacts on agricultural systems, as indicated in crop condition reports collected by the National Agricultural Statistics Service. In each case examined, large negative change anomalies indicative of rapidly drying conditions were either coincident with the introduction of drought in the USDM or lead the USDM drought depiction by several weeks, depending on which ESI composite and time-differencing interval was used. Incorporation of the ESI as a data layer used in the construction of the USDM may improve timely depictions of moisture conditions and vegetation stress associated with flash drought events. [ABSTRACT FROM AUTHOR]

Disaggregation of regional-scale (103 m) flux estimates to micrometeorological scales (101–102 m) facilitates direct comparison between land surface models and ground-based observations. Inversely, it also provides a means for upscaling flux-tower information into a regional context. The utility of the Atmosphere–Land Exchange Inverse (ALEXI) model and associated disaggregation technique (DisALEXI) in effecting regional to local downscaling is demonstrated in an application to thermal imagery collected with the Geostationary Operational Environmental Satellite (GOES) (5-km resolution) and Landsat (60-m resolution) over the state of Oklahoma on 4 days during 2000–01. A related algorithm (DisTrad) sharpens thermal imagery to resolutions associated with visible–near-infrared bands (30 m on Landsat), extending the range in scales achievable through disaggregation. The accuracy and utility of this combined multiscale modeling system is evaluated quantitatively in comparison with measurements made with flux towers in the Oklahoma Mesonet and qualitatively in terms of enhanced information content that emerges at high resolution where flux patterns can be identified with recognizable surface phenomena. Disaggregated flux fields at 30-m resolution were reaggregated over an area approximating the tower flux footprint and agreed with observed fluxes to within 10%. In contrast, 5-km flux predictions from ALEXI showed a higher relative error of 17% because of the gross mismatch in scale between model and measurement, highlighting the efficacy of disaggregation as a means for validating regional-scale flux predictions over heterogeneous landscapes. Sharpening the thermal inputs to DisALEXI with DisTrad did not improve agreement with observations in comparison with a simple bilinear interpolation technique because the sharpening interval associated with Landsat (60–30 m) was much smaller than the dominant scale of heterogeneity (200–500 m) in the scenes studied. Greater benefit is expected in application to Moderate Resolution Imaging Spectroradiometer (MODIS) data, where the potential sharpening interval (1 km to 250 m) brackets the typical agricultural field scale. Thermal sharpening did, however, significantly improve output in terms of visual information content and model convergence rate. [ABSTRACT FROM AUTHOR]