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In the two decades, since the advent of the term "flash drought," considerable research has been directed toward the topic. Within the scientific community, we have actively forged a new paradigm that has avoided a chaotic evolution of conventional drought but instead recognizes that flash droughts have distinct dynamics and, particularly, impacts. We have moved beyond the initial debate over the definition of flash drought to a centralized focus on the triad of rapid onset, drought development, and associated impacts. The refinement toward this general set of principles has led to significant progress in determining key variables for monitoring flash drought development, identifying notable case studies, and compiling fundamental physical characteristics of flash drought. However, critical focus areas still remain, including advancing our knowledge on the atmospheric and oceanic drivers of flash drought; developing flash drought‐specific detection indices and monitoring systems tailored to practitioners; improving subseasonal‐to‐seasonal prediction of these events; constraining uncertainty in flash drought and impact projections; and using social science to further our understanding of impacts, particularly with regard to sectors that lie outside of our traditional hydroclimatological focus, such as wildfire management and food‐security monitoring. Researchers and stakeholders working together on these critical topics will assure society is resilient to flash drought in a changing climate. This article is categorized under:Science of Water > Water Extremes [ABSTRACT FROM AUTHOR]

Soil moisture feedbacks that initiate, enhance, or suppress convection initiation and precipitation are important components of regional hydroclimatology. However, soil moisture feedbacks and the processes through which they operate are notoriously challenging to observe and study outside of model environments. In this study, we combine a climatological assessment of event frequency‐based measurements of soil moisture‐precipitation coupling in the central United States with a process‐based analysis of the mechanisms by which wet‐ and dry‐soil feedbacks may operate in the region. We use the Thunderstorm Observation by Radar algorithm to identify the location of convection initiation, circumventing the issue of using precipitation accumulation as a proxy for convection initiation. Results show substantial spatial variability in the climatological sign and strength of soil moisture‐precipitation coupling in the central United States, including regions that exhibit signs of both wet‐ and dry‐soil feedbacks. Within the regions with the strongest feedback signals, we find consistently strong coupling between soil moisture and the partitioning of surface heat flux, and strong coupling between surface heat flux—particularly sensible heat flux—and diurnal change in planetary boundary layer height. In all three regions assessed, the process‐based metrics confirmed the potential of wet‐ and/or dry‐soil feedbacks leading to convection initiation. Plain Language Summary: Soil moisture can affect the occurrence, location, and intensity of precipitation in many global regions. Observations of the drivers of so‐called soil moisture feedbacks are important for constraining models; however, feedbacks are challenging to observe and study outside of model environments. We use a data set of thousands of thunderstorm initiation locations with model‐based soil moisture to measure the sign, strength, and variability in soil moisture feedback to precipitation in the central United States. We denote three regions with strong evidence of both wet‐soil and dry‐soil feedbacks to precipitation. Key Points: The sign and strength of soil moisture‐precipitation coupling was assessed using thousands of convection events points in the central USMultiple regions show signs of both wet and dry soil feedbacks, with strong coupling between soil moisture and surface heat fluxClimatological assessment and process‐based metrics confirmed wet‐ and/or dry‐soil feedbacks leading to convection in the central US [ABSTRACT FROM AUTHOR]

Precipitation extremes present significant risks to Midwest agriculture, water resources, and natural ecosystems. Recently, there is growing attention to the transitions of precipitation extremes, or shifts between heavy precipitation and drought, due to their profound environmental and socio‐economic impacts. Changes in Midwest precipitation extremes and transitions between extremes over the past few decades have been documented; however, their future changes are still unknown. In this study, we estimate the projected changes in transitions of precipitation extremes in the Midwest based on 17 CMIP6 models. Two Standardized Precipitation Index (SPI) based metrics, intra‐annual variability and transitions, are used to quantify the magnitude, duration, and frequency of variability and transitions between wet and dry extremes. Compared with the observation‐based precipitation datasets, the multimodel ensemble median of CMIP6 can reasonably represent the spatial patterns of SPI extremes and transitions. Climate projections show significantly intensified wet extremes across the Midwest by the end of the century, with a greater increase over the northern Midwest and the Great Lakes region. The short‐term SPI also shows intensified dry extremes over the western half of the Midwest. Consequently, there is a significant increase in the magnitude of intra‐annual variability in most areas. Projections also suggest more frequent and rapid transitions between the wet and dry extremes, especially over the Great Lakes region and northern Midwest. Seasonally, more frequent transitions from a wet spring to a dry summer (or from a dry fall to a wet winter/spring) are projected to occur; and generally, the wet and dry conditions between the transitions are projected to be more intense compared to the historical period. Furthermore, the intensified precipitation extremes and accelerated transitions are greatly alleviated under a lower emission scenario, implying that future changes in hydroclimate extremes, and impacts thereof, in the Midwest are sensitive to climate change mitigation. [ABSTRACT FROM AUTHOR]

Severe winter weather is a staple of Midwest United States (U.S.) climate and is an important natural hazard that has significant economic and environmental impacts. Winter season severity varies both spatially and temporally in the Midwest, and past studies have documented significant changes in Midwest winter characteristics, including increases in temperature and decreases in snow depth. In this study, we use the Accumulated Winter Season Severity Index (AWSSI) to characterize winter severity across the Midwest and assess its variability and change on various spatial and temporal scales during the 1951–2020 period. AWSSI is derived from daily records of snowfall (SF), snow depth (SD), maximum surface air temperature (TMAX), and minimum surface air temperature (TMIN) during the winter season. The daily total AWSSI index value reflects the sum of points accumulated from daily values of each of these four variables surpassing pre-determined thresholds, and the total season sum reflects the overall winter season severity. We find AWSSI provides a unique perspective by which to describe climatological differences in winter season character in the Midwest. Namely, over three quarters of winter season days accumulate points in the northern Midwest, compared to just over half in the southern Midwest. Meanwhile, the relative contribution of extreme winter days to total winter severity is much larger in the southern Midwest than farther north. In addition, we find overall winter season severity has significantly decreased at only a quarter of stations and the frequency of extreme winter days has significantly decreased at only a fifth of stations, despite widespread increases in winter daily maximum and minimum temperature. The results suggest Midwest U.S. winter season severity as described by the AWSSI has not significantly changed over the past 70 years. [ABSTRACT FROM AUTHOR]

The Accumulated Winter Season Severity Index (AWSSI) describes the “harshness” of the cold season. This work utilizes AWSSI to explore the interannual teleconnective influences that shaped winter severity between 1951 and 2020 across the Midwest U.S. Annual AWSSI total variability clusters into four distinct geographic areas, each shaped by different teleconnective influences. The regions are closely delineated by the main river basins and include the Ohio (Region 1), upper Mississippi and Missouri (Region 2), central Mississippi and southern Missouri (Region 3), and the Great Lakes (Region 4). Of outmost influence in shaping severe winters across the Midwest have been synoptic flow modifications associated with significant meandering and southern displacement of the Polar jet over the conterminous United States and/or the Midwest that depress near-surface air temperatures and increase the likelihood of snow fall and/or snow depth. These flows are most closely related to the combined influence of the negative (positive) phases of the NAO (PNA and PDO) in Region 1, PNA− in Region 2, NAO− in Region 3, and the negative (positive) phase of the PNA (TNH) across Region 4. Mild winters have been shaped by more zonal flows with the Polar jet positioned north of the Midwest and bringing milder temperatures, less snowfall, and reduced winter severity to the impacted areas. Such modifications have been related to TNH−, PNA+, and NAO+ across Regions 1, 2, and 3, respectively. Mild winters have been shaped by TNH− and to a limited extent NAO+, across Region 4. [ABSTRACT FROM AUTHOR]

Recent studies point to a significant rise in the number of summer extreme weather events that correspond with the presence of amplified, quasistationary midtropospheric planetary waves, weakened atmospheric circulation in the Northern Hemisphere, and coincide with reduced summer Arctic sea ice cover. This study explores potential connections between 1979 and 2016 summer heat wave frequency across the USA and regional Arctic sea ice extent (SIE) in various Arctic basins. Most notable SIE interannual relationships exist across the southern Plains and southeastern US during low Hudson Bay summer SIE. Locally increased frequencies of summer heat waves coincide with unseasonably warm conditions developed and sustained by the presence of an omega blocking pattern situated over the southern US throughout summer. The block appears following anomalous atmospheric warming and reduced mean zonal winds observed throughout spring (March–May) over northeastern Canada, the northwestern Atlantic basin, and Greenland. Spring preconditioning of summer ice melt is favored by the presence of strong negative phase of the North Atlantic Oscillation and positive Greenland Blocking Index. Summer synoptic flow related to Hudson Bay ice melt over North America appears to be influenced by the background state of atmospheric variability, namely, the positive phase of the Atlantic Multidecadal Oscillation. Antecedent local humidity, soil moisture, and precipitation conditions are shown to influence the "flavor" of the heat waves, which are more likely to be oppressive in the southeastern US and extreme across the southern Plains during summers experiencing low Hudson SIE. Key Points: Low summer sea ice in Hudson Bay is statistically linked to an increased frequency of summer U.S. heat wavesExtreme heat waves are more common across southern Plains; oppressive heat waves are more common in the SoutheastHeat wave flavor is shaped by local surface‐atmospheric conditions [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 Arctic sea ice has been shrinking at unprecedented rates over the past three decades. These cryospheric changes have coincided with greater incidence of global extreme weather conditions, including increased severity and frequency of summer heatwaves and extreme rainfall events. Recent studies identify potential physical mechanisms related to Rossby wave and resonance theories that may attribute the observed changes in extreme summer weather patterns to Arctic sea ice decline. This study explores the linkages between summer Arctic sea ice variability and hydroclimate of the north-central United States (US) during the 1979 to 2013 period. Since 1979, summers with low sea ice conditions have coincided with significant increases in mean, minimum, maximum, and dew point air temperatures. Also apparent are increases in seasonal precipitation, the number of wet days, heavy (>95th percentile) precipitation days, and accumulated precipitation over the region. These moisture changes coincide with atmospheric patterns typically observed during anomalously wet summers, known to prompt flooding across the Upper Mississippi River Valley ( UMRV) region. Low sea ice summers have coincided with (1) enhanced southerly air flow and increased activity of the Great Plains Low Level Jet ( GPLLJ) over the study area, (2) increased occurrence of moist tropical air masses over the UMRV region, and (3) amplified 500 hPa flow over the Pacific-North American region with a ridge situated over the central-eastern portions of the North American continent emanating from Greenland and the central Arctic basin. The results suggest summer Arctic sea ice variability has been associated with recent hydroclimate anomalies of the north-central United States and the UMRV region and add to our growing knowledge of the connections between a changing Arctic environment and concurrent mid-latitude climate variability. [ABSTRACT FROM AUTHOR]

ABSTRACT Regions of strong land-atmosphere coupling are often depicted as static in time. However, the mechanisms through which the land surface impacts atmospheric conditions vary on sub-daily to multi-decadal timescales. Therefore, characterizing the long-term variability of land-atmosphere interactions may provide a means of predicting when surface-induced extreme temperature events will be more or less likely to occur. We evaluate the coupling strength between soil moisture, as represented by in situ observations and 1-month Standardized Precipitation Index ( SPI), and subsequent monthly maximum temperature ( TMAX) over the contiguous United States. We find that the utility of SPI as a proxy for soil moisture anomalies is limited to the summer, as the correlations between SPI and TMAX are not significantly related in the other seasons. We examine the variability in summer SPI-TMAX coupling in four regions of the United States. In general, we find the strongest relationships between SPI and TMAX in the Southern Great Plains. However, our results demonstrate that the coupling strength varies considerably over time in most regions of the United States. The long-term variability in SPI-TMAX coupling strength is strongly related to the Pacific Decadal Oscillation in the northwest and midwest United States, and the Atlantic Multidecadal Oscillation in the southeast United States The results of this study suggest that land-atmosphere coupling in the contiguous United States is modulated by multi-decadal oscillations in Pacific and Atlantic sea surface temperatures. [ABSTRACT FROM AUTHOR]

ABSTRACT East China ( EC) has experienced a number of severe droughts in recent decades. Understanding the characteristics of droughts and their persistence will provide operational guidelines for water resource management and agricultural production. This study uses a logistic regression model to measure the probability of drought occurrence in the current season given the previous season's Standardized Precipitation Index ( SPI) and Southern Oscillation Index ( SOI) as well as drought persistence. Results reveal large spatial and seasonal variations in the relationship between the previous season's SPI and the drought occurrence probability in a given season. The drought persistence averaged over the entire study area for all the four seasons is approximately 34% with large variations from season to season and from region to region. The East and Northeast regions have the largest summer drought persistence (∼40%) and lowest fall drought persistence (∼28%). The spatial pattern in winter and spring drought persistence is dissimilar with stronger winter and weaker spring drought persistence in the Southwest and Northeast relative to other regions. Logistic regression analysis indicates a stronger negative relationship in summer-to-fall (or between fall drought occurrence and summer SPI) than other inter-season relationships. Most of the stations with significant relationships in summer-to-fall lie in the East and the southern part of the North region. Significant negative relationships in winter-spring and fall-winter are primarily located on the western portion of the study area. A significant negative spring-summer relationship exists in the North region. Further, spring drought occurrence has a statistically significant positive relationship with winter SOI in the Southeast. Fall drought occurrence and summer SOI are negatively correlated in the northwest portion of EC (i.e. the border of North and Southwest regions). Overall, this study demonstrates that the impact of previous season SPI and SOI on current season drought varies substantially from region to region and from season to season. This study also shows stronger drought persistence in summer than in other seasons. In other words, the probability of fall drought occurrence is closely related to summer moisture conditions in the EC. [ABSTRACT FROM AUTHOR]

ABSTRACT Understanding flash floods in watersheds of the New York City water supply system ( NYCWSS) is important, as turbidity associated with flooding degrades water quality in the unfiltered portions of this water supply system. We examined synoptic-scale atmospheric conditions most frequently associated with flash flooding in this region. Flash floods between 1987 and 2013 were identified in two small watersheds of the NYCWSS using USGS 15-min discharge data at the Esopus Creek near Allaben, NY and Neversink River at Claryville, NY gauges, both located in the Catskill Mountains. A total of 25 flash floods were detected in these watersheds and there were 17 separate flash flood days. The Spatial Synoptic Typer Tools 4.0 were used to characterize the synoptic-scale atmospheric patterns influencing the study area based on NCEP/ NCAR 500-mb geopotential height reanalysis data. Through this procedure, 17 unique synoptic patterns were identified. Three of these types were found to be strongly associated with flash flooding events. Composites of these types show southwesterly flow which suggests advection of moisture from the Gulf of Mexico and Atlantic Ocean. The flash flood days were compared to the National Weather Service flash flood warnings. The flash flood warnings issued for Ulster County compared to the flash floods in the study watersheds highlight the highly localized nature of flash flooding in this region. [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]

Soil moisture monitoring networks can provide real-time and accurate soil moisture measurements; however, missing values and the lack of unified measurement depths across different networks impedes soil moisture applications at regional and national scales. Therefore, methods for vertical extrapolation of soil moisture, i.e., using shallow soil moisture measurements to estimate deeper soil moisture, are needed for standardizing measurements to a set of common depths. This study compared three methods, artificial neural network (ANN), linear regression (LR), and exponential filter (ExpF), for vertical extrapolation of soil moisture using data from the Oklahoma Mesonet. Based on our analysis of intra-annual variations in soil moisture, we divided each year into two seasons, warm and cool. Our results demonstrate that all methods perform better in the warm season than in the cool season, especially at deeper depths. The Kling-Gupta efficiency was used to assess the performance of each method. All methods had similar performance for near-surface extrapolation of soil moisture (top 25 cm). Although the accuracy of all models tended to decrease with depth, the ExpF outperformed the other methods at deeper depths. The soil water index (SWI) is preferred over volumetric water content as input to the ExpF. Incorporating air temperature and an antecedent precipitation index into the ANN and LR methods did not significantly improve their accuracy. We demonstrated that both ExpF and general LR can be used for SWI extrapolation at sites where only surface soil moisture data are available. Soil properties may be useful for further improving the accuracy of the general LR method. [ABSTRACT FROM AUTHOR]