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Increased flash drought awareness in recent years has motivated the development of numerous indicators for monitoring, early warning, and assessment. The flash drought indicators can act as a complementary set of tools by which to inform flash drought response and management. However, the limitations of each indicator much be measured and communicated between research and practitioners to ensure effectiveness. The limitations of any flash drought indicator are better understood and overcome through assessment of indicator sensitivity and consistency; however, such assessment cannot assume any single indicator properly represents the flash drought "truth." To better understand the current state of flash drought monitoring, this study presents an intercomparison of nine, widely used flash drought indicators. The indicators represent perspectives and processes that are known to drive flash drought, including evapotranspiration and evaporative demand, precipitation, and soil moisture. We find no single flash drought indicator consistently outperforms all others across the contiguous United States. We do find the evaporative demand- and evapotranspiration-driven indicators tend to lead precipitation- and soil moisture-based indicators in flash drought onset, but also tend to produce more flash drought events collectively. Overall, the regional and definition-specific variability in results supports the argument for a multi-indicator approach for flash drought monitoring, as advocated by recent studies. Furthermore, flash drought research-especially evaluation of historical and potential future changes in flash drought characteristics-should test multiple indicators, datasets, and methods for representing flash drought, and ideally employ a multi-indicator analysis framework over use of a single indicator from which to infer all flash drought information. SIGNIFICANCE STATEMENT: Rapid onset or "flash" drought has been an increasing concern globally, with quickly intensifying impacts to agriculture, ecosystems, and water resources. Many tools and indicators have been developed to monitor and provide early warning for flash drought, ideally resulting in more time for effective mitigation and reduced impacts. However, there remains no widely accepted single method for defining, monitoring, and measuring flash drought, which means most indicators that are developed are compared with other individual indicators or conditions and impacts in one or two flash drought events. In this study, we measure the state of flash drought monitoring through an intercomparison of nine, widely used flash drought indicators that represent different aspects of flash drought. We find that no single flash drought indicator outperformed all others and suggest that a comprehensive flash drought monitor should leverage multiple, complementary indicators, datasets, and methods. Furthermore, we suggest flash drought research-especially that which reflects on historical or projected changes in flash drought characteristics-should seek multiple indicators, datasets, and methods for analyses, thereby reducing the potentially confounding effects of sensitivity to a single indicator. [ABSTRACT FROM AUTHOR]

Characteristics and predictability of drought in the midwestern United States, spanning the from the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916–2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and 3-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for subannual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multiannual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March–November in the NGP and all year in the OV, with a preference for March–May and September–November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is 4 times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons is related to atmospheric wave trains spanning the Pacific–North American sector, longer-lead predictability is limited to the OV in December–February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño–Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwest drought. Significance Statement: The midwestern United States, spanning from the Great Plains to the Ohio Valley, has endured many costly and life-altering droughts. A drought in 2012 led to an estimated $34.5 billion in direct economic losses. This study aims to build a more complete understanding of drought in regions of the Midwest that could be used in drought early warning efforts. Drought is evaluated in four midwestern regions of coherent hydroclimatic variability. The regions were identified by applying a method that groups similar objects to an integrated drought index that includes soil moisture, snow water equivalent, and 3-month runoff from land surface models during 1916–2015. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), droughts in the NGP generally last longer than in the OV. Droughts in the NGP only begin and end during the warm and wet season while droughts in the OV can begin and end during any time of year. El Niño–Southern Oscillation (ENSO), a slow varying phenomenon of the Earth system, may be used as a source of predictability for drought onset and demise in the OV during winter. However, circulation patterns internal to the atmosphere play a key role in shaping drought in all other seasons and regions of the Midwest. [ABSTRACT FROM AUTHOR]

Flash droughts are noted by their unusually rapid rate of onset or intensification, which makes it difficult to anticipate and prepare for them, thus resulting in severe impacts. Although the development of flash drought can be associated with certain atmospheric conditions, vegetation also plays a role in propagating flash drought. This study examines the climatology of warm season (March–September) flash drought occurrence in the United States between 1979 and 2014, and quantifies the possible impacts of vegetation on flash drought based on a set of sensitivity experiments using the Community Earth System Model, version 2 (CESM2). With atmospheric nudging, CESM2 well captures historical flash drought. Compared with NASA's Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and National Climate Assessment–Land Data Assimilation System (NCA-LDAS), CESM2 shows agreement on the high flash drought frequency in the Great Plains and southeastern United States, but overestimates flash drought occurrence in the Midwest. The vegetation sensitivity experiments suggest that vegetation greening can significantly increase the flash drought frequency in the Great Plains and the western United States during the warm seasons through enhanced evapotranspiration. However, flash drought occurrence is not significantly affected by vegetation phenology in the eastern United States and Midwest due to weak land–atmosphere coupling. In response to vegetation greening, the extent of flash drought also increases, but the duration of flash drought is not sensitive to greening. This study highlights the importance of vegetation in flash drought development, and provides insights for improving flash drought monitoring and early warning. [ABSTRACT FROM AUTHOR]

Soil moisture is a key drought indicator; however, current in situ soil moisture infrastructure is inadequate for large‐scale drought monitoring. One initiative of the ongoing National Soil Moisture Network program is the development of a near real‐time drought monitoring product that integrates in situ, model, and satellite remote sensing data. Data integration from diverse sources requires large‐scale validation prior to integration. This study develops a framework for assessing the fidelity of in situ, model, and satellite soil moisture data sets. Here we evaluate data from over 100 in situ monitoring stations that are part of nine monitoring networks; North American Land Data Assimilation System Phase 2 and Climate Prediction Center land surface models; and Soil Moisture Active‐Passive, Soil Moisture and Ocean Salinity, and European Space Agency‐Climate Change Initiative (ESA‐CCI) satellite products. The results indicate the majority of in situ stations exhibit low error variance and are spatially representative; however, some networks and individual stations exhibit anomalously high error variance or are sited in a way that make them not spatially representative of a larger area. Overall, North American Land Data Assimilation System Phase 2 is the modeled product that consistently performed best, and Soil Moisture Active‐Passive L3 is the remotely sensed product that consistently performed the best. They were able to both capture in situ soil moisture variability and provide an accurate depiction of drought conditions. The methods and verification framework applied in this study can be used to evaluate any soil moisture data set in any region of the world. Plain Language Summary: Soil moisture is an important indicator of drought; however, there are very few monitoring stations that directly measure soil moisture across the contiguous United States. High‐quality model and/or satellite remote sensing soil moisture estimates can help fill in the gaps of in‐ground soil moisture measurements. However, integration of soil moisture data from diverse sources requires assessment of data quality. This study applies a series of methods for evaluating the integrity of in‐ground, model, and satellite soil moisture data sets across the United States. The results indicate that the majority of in‐ground sensors tested exhibit high data quality, although some exhibit high measurement error and/or low spatial representativeness. The results also indicate that the land surface models that are part of the North American Land Data Assimilation System Phase 2 and the Soil Moisture Active‐Passive remotely sensed soil moisture products best match in‐ground soil moisture measurement variability. Additionally, North American Land Data Assimilation System Phase 2 and Soil Moisture Active‐Passive data sets accurately depict drought occurrence. The methods applied here can be used to evaluate the quality of soil moisture data sets in any region of the world. Key Points: A framework is applied to validate in situ, model, and satellite soil moisture data for drought monitoringSCAN in situ stations exhibit high relative error variance, compared with other in situ networksNLDAS‐2 models and SMAP L3 remote sensing products outperform other products tested [ABSTRACT FROM AUTHOR]

ABSTRACT Thirty years (1954-1983) of soil moisture data from 60 sites in Iowa are used to identify the dominant modes of spatial and temporal variability. Both soil moisture and precipitation exhibit a northwest to southeast gradient across Iowa, with wetter conditions in the southeast and drier conditions in the northwest. Climate and soil characteristics are responsible for the spatial variations in soil moisture. There is a pronounced annual soil moisture cycle that has three distinct phases: a moist phase (April-June), a drying phase (June-September), and a recharge phase (September-November). Spring soil moisture is strongly controlled by the amount of recharge during the previous fall. There is no evidence of a long-term trend in soil moisture; however there is significant interannual variability and about 20% of the variance is attributable to El Niño-Southern Oscillation (ENSO). The warm (cold) phase of ENSO is associated with above (below) average soil moisture in Iowa. The multi-decadal soil moisture record is used to analyse drought frequency and magnitude in Iowa, and to place the 2012 Midwest drought in historical context. In general, Iowa drought is more common during the latter part of the warm season (August through November), but tends to be more severe during the early part of the warm season (April-July). We find that the 2012 drought was unusual because it persisted and intensified throughout the growing season. [ABSTRACT FROM AUTHOR]

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]