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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]

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]

The warm season in the United States Great Plains (GP) is characterized by frequent nocturnal low-level jets (LLJs). The GPLLJ serves as a major mechanism of atmospheric moisture transport, contributing to severe weather and precipitation in the region. A combination of synoptic and regional forcing modulates GPLLJ frequency and intensity. The GPLLJ has primarily been studied at the diurnal scale. We hypothesize that, due to the memory of the land surface, longer time scale variability associated with surface moisture also modulates GPLLJ intensity. This work identifies GPLLJ days from ECMWF Reanalysis v5 (ERA5) wind data and isolates extremes using a peaks-over-threshold approach. Extreme GPLLJs are classified by geographic region and synoptic state. Composites of daily soil moisture anomalies show a preference for extreme GPLLJs to occur over anomalously dry soil. Critically, antecedent soil moisture anomalies emerge weeks before the extreme jet occurrence. The dry soil moisture signal coexists with clear skies and drying of the surface at the synoptic time scale. A diurnal PBL heat accumulation, which intensifies the buoyancy oscillation, is also present. The identification of a subseasonal dry anomaly suggests that, although the GPLLJ is generated by diurnally varying oscillations and intensified by synoptic-scale processes, the memory of the land surface can modulate the GPLLJ far beyond the diurnal and synoptic scale. Additionally, the location of the antecedent soil moisture anomalies corresponds with the eventual GPLLJ. The spatiotemporal characteristic of these antecedent anomalies suggests the potential for improved prediction of the GPLLJ activity. [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]

Skillful subseasonal prediction of extreme heat and precipitation greatly benefits multiple sectors, including water management, public health, and agriculture, in mitigating the impact of extreme events. A statistical model is developed to predict the weekly frequency of extreme warm days and 14-day standardized precipitation index (SPI) during boreal summer in the United States. We use a leading principal component of U.S. soil moisture and an index based on the North Pacific sea surface temperature (SST) as predictors. The model outperforms the NCEP Climate Forecast System, version 2 (CFSv2), at weeks 3–4 in the eastern United States. It is found that the North Pacific SST anomalies persist for several weeks and are associated with a persistent wave train pattern, which leads to increased occurrences of blocking and extreme temperature over the eastern United States. Extreme dry soil moisture conditions persist into week 4 and are associated with an increase in sensible heat flux and a decrease in latent heat flux, which may help to maintain the overlying anticyclone. The clear-sky conditions associated with blocking anticyclones further decrease soil moisture and increase the frequency of extreme warm days. This skillful statistical model has the potential to aid in irrigation scheduling, crop planning, and reservoir operation and to provide mitigation of impacts from extreme heat events. [ABSTRACT FROM AUTHOR]

Soil moisture can be obtained from in situ measurements, satellite observations, and model simulations. This study evaluates the importance of in situ observations in soil moisture blending, and compares different weighting and sampling methods for combining model, satellite, and in situ soil moisture data to generate an accurate and spatially continuous soil moisture product at 4-km resolution. Four different datasets are used: the antecedent precipitation index (API); KAPI, which incorporates in situ soil moisture observations with the API using regression kriging; SMOS L3 soil moisture; and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System (NLDAS). Triple collocation, least squares weighting, and equal weighting are used to generate blended soil moisture products. An enumerated weighting scheme is designed to investigate the impact of different weighting schemes. The sensitivity of the blended soil moisture products to sampling schemes, station density, and data formats (absolute, anomalies, and percentiles) are also investigated. The results reveal that KAPI outperforms API. This indicates that incorporating in situ soil moisture improves the accuracy of the blended soil moisture products. There are no statistically significant (p > 0.05) differences between blended soil moisture using triple collocation and equal weighting approaches, and both methods provide suboptimal weighting. Optimal weighting is achieved by assigning larger weights to KAPI and smaller weights to SMOS. Using multiple sources of soil moisture is helpful for reducing uncertainty and improving accuracy, especially when the sampling density is low, or the sampling stations are less representative. These results are consistent regardless of how soil moisture is represented (absolute, anomalies, or percentiles). [ABSTRACT FROM AUTHOR]

Soil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States's current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ-informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting. [ABSTRACT FROM AUTHOR]

Soil moisture can affect precipitation, however there is significant debate about the sign and strength of land–atmosphere coupling. This may be due to the differences regarding the selection of data sources, the presence of atmospheric persistence, and the spatial and temporal scales of the analysis. This study is a sensitivity analysis that quantifies how each of these factors influences land–atmosphere coupling in the central United States from 2005 to 2007. Four different sources of soil moisture are used to represent soil moisture: in situ measurements and three satellite products. The Thunderstorm Observation by Radar (ThOR) algorithm is used to identify convective events. The results show that warm-season afternoon convection initiates preferentially over dry soil, if in situ measurements are used. However, when satellite data are used the sign and strength of the soil moisture-precipitation coupling varies depending on which satellite dataset is used. This demonstrates that evaluations of land–atmosphere coupling strength are sensitive to the source of soil moisture. Our results also show that accounting for atmospheric persistence tends to weaken apparent land–atmosphere coupling. Changing the spatial scale of the analysis can also influence the sign and coupling strength. Analyses that are performed at a coarser spatial resolution tend to indicate a wet preference because they do not capture the local negative feedbacks. This study demonstrates that contradictions in the literature regarding the sign and strength of soil moisture-precipitation feedbacks can be partly attributed to differences in datasets, methods and scale of the analysis. [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]

The complex interactions between soil moisture and precipitation are difficult to observe, and consequently there is a lack of consensus as to the sign, strength, and location of these interactions. Inconsistency between soil moisture-precipitation interaction studies can be attributed to a multitude of factors, including the difficulty of demonstrating causal relationships, dataset differences, and precipitation autocorrelation. The purpose of this study is to explore these potential confounding factors and determine which are most important for consideration when assessing statistical coupling between soil moisture and precipitation. Soil moisture is assessed via three remote sensing datasets: the Advanced Microwave Scanning Radiometer for EarthObserving System, the Tropical Rainfall Measuring Mission Microwave Imager, and the Essential Climate Variable Soil Moisture. Estimates of soil moisture are coupled with afternoon thunderstorm events identified by the Thunderstorm Observation by Radar (ThOR) algorithm, and dry soil or wet soil preferences for convection initiation are determined for over 16 000 thunderstorm events between 2005 and 2007. Differences in soil moisture datasets were found to have the largest impact with regard to determining wet or dry soil preferences. Precipitation autocorrelation is prevalent in the data; however, precipitation autocorrelation did not influence the results with regard to dry or wet soil preferences. Consideration of the convective environment (i.e., weakly or synoptically forced) did result in significant differences in wet/dry soil preference, but only for certain soil moisture datasets. The results suggest that observation-driven soil moisture-precipitation interaction studies should both consider the convective environment and implement multiple soil moisture datasets to assure robust results. [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]

Extreme heat events have been connected with antecedent soil moisture in many global regions, such that dry soils increase sensible heat content of the near-surface atmosphere and impede precipitation through boundary layer growth. However, negative soil moisture-temperature feedbacks (dry soils = higher temperatures) are founded on investigations of maximum temperature that neglect the potentially important latent heating component provided by soil moisture. In this study, the association of spring soil moisture and subsequent summer oppressive heat events is quantified, defined by equivalent temperature. The advantage of equivalent temperature over maximum temperature is that it accounts for both the temperature and moisture components of atmospheric heat content. Quantile regression and composite analysis are used to determine the association between spring soil moisture and summer oppressive heat events using a 25-yr station observation record in Illinois. A consistent response of summer oppressive heat events to antecedent 5-cm soil moisture anomalies is found at all four stations. The frequency of oppressive summer equivalent temperature events is significantly increased following spring seasons with wetter-than-normal soils compared with spring seasons with dry soils. This provides evidence of a possible positive soil moisture-temperature feedback. Further, it is found that oppressive heat events correspond with the combination of wetter-than-normal spring soils and persistent summertime upper-level ridging to the northeast of the region, thereby leading to the conclusion that abundant-to-surplus spring soil moisture is necessary but not sufficient for the occurrence of oppressive heat in Illinois. [ABSTRACT FROM AUTHOR]

The ability to use in situ soil moisture for large-scale soil moisture monitoring, model and satellite validation, and climate investigations is contingent on properly standardizing soil moisture observations. Percentiles are a useful method for homogenizing in situ soil moisture. However, very few stations have been continuously monitoring in situ soil moisture for 20 years or more. Therefore, one challenge in evaluating soil moisture is determining whether the period of record is sufficient to produce a stable distribution from which to generate percentiles. In this study daily in situ soil moisture observations, measured at three separate depths in the soil column at 15 stations in the United States and Canada, are used to determine the record length that is necessary to generate a stable soil moisture distribution. The Anderson-Darling test is implemented, both with and without a Bonferroni adjustment, to quantify the necessary record length. The authors evaluate how the necessary record length varies by location, measurement depth, and month. They find that between 3 and 15 years of data are required to produce stable distributions, with the majority of stations requiring only 3-6 years of data. Not surprisingly, more years of data are required to obtain stable estimates of the 5th and 95th percentiles than of the first, second, and third quartiles of the soil moisture distribution. Overall these results suggest that 6 years of continuous, daily in situ soil moisture data will be sufficient in most conditions to create stable and robust percentiles. [ABSTRACT FROM AUTHOR]

In this study, the robustness of a previously developed classification system that categorizes convective thunderstorm events initiated during various synoptic and dynamic conditions is analyzed. This classification system was used to distinguish between organized and unorganized convection and then used to determine whether unorganized convection occurs preferentially over wet or dry soils. The focus is on 12 events that occurred in synoptically benign (SB) environments where the Great Plains low-level jet was not present (noLLJ), and whether these events were accurately classified as unorganized convection is evaluated. Although there is a small sample size, the results show that the classification system fails to differentiate between local unorganized convection and large-scale organized convection under SB–noLLJ conditions. The authors conclude that past studies that have used this classification to study how soil moisture influences unorganized convection should be revisited. Additional variables and/or alternative precipitation datasets should be employed to enhance the robustness of the classification system. [ABSTRACT FROM AUTHOR]

In this second part of a two-part paper, the impacts of soil texture and vegetation type misclassification and their combined effect on soil moisture, evapotranspiration, and total runoff simulation are investigated using the Noah model. The results show that these impacts are significant for most regions and soil layers, although they vary depending on soil texture classification, vegetation type, and season. The use of site-observed soil texture classification and vegetation type in the model does not necessarily improve anomaly correlations and reduce mean absolute error for soil moisture simulations. Instead, results are mixed when examining all regions and soil layers. This is attributed to the compensation effects (e.g., effect of ill-calibrated model parameters), as Noah has been more or less calibrated with model-specified soil texture classification and vegetation type. The site-based analysis shows that Noah can reasonably simulate the variation of daily evapotranspiration, soil moisture, and total runoff when soil texture classification (vegetation type) is corrected from loam (forest) to clay (grasslands) or vice versa. This suggests that the performance of Noah can be further improved by tuning model parameters when site-observed soil texture and vegetation type are used. [ABSTRACT FROM AUTHOR]

Soil moisture observations from seven observational networks (spanning portions of seven states) with different biome and climate conditions were used in this study to evaluate multimodel simulated soil moisture products. The four land surface models, including Noah, Mosaic, Sacramento soil moisture accounting (SAC), and the Variable Infiltration Capacity model (VIC), were run within phase 2 of the North American Land Data Assimilation System (NLDAS-2), with a ⅛° spatial resolution and hourly temporal resolution. Hundreds of sites in Alabama, Colorado, Michigan, Nebraska, Oklahoma, West Texas, and Utah were used to evaluate simulated soil moisture in the 0-10-, 10-40-, and 40-100-cm soil layers. Soil moisture was spatially averaged in each state to reduce noise. In general, the four models captured broad features (e.g., seasonal variation) of soil moisture variations in all three soil layers in seven states, except for the 10-40-cm soil layer in West Texas and the 40-100-cm soil layer in Alabama, where the anomaly correlations are weak. Overall, Mosaic, SAC, and the ensemble mean have the highest simulation skill and VIC has the lowest simulation skill. The results show that Noah and VIC are wetter than the observations while Mosaic and SAC are drier than the observations, mostly likely because of systematic errors in model evapotranspiration. [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]

The North American Soil Moisture Database (NASMD) was initiated in 2011 to provide support for developing climate forecasting tools, calibrating land surface models, and validating satellite-derived soil moisture algorithms. The NASMD has collected data from over 30 soil moisture observation networks providing millions of in situ soil moisture observations in all 50 states, as well as Canada and Mexico. It is recognized that the quality of measured soil moisture in NASMD is highly variable because of the diversity of climatological conditions, land cover, soil texture, and topographies of the stations, and differences in measurement devices (e.g., sensors) and installation. It is also recognized that error, inaccuracy, and imprecision in the data can have significant impacts on practical operations and scientific studies. Therefore, developing an appropriate quality control procedure is essential to ensure that the data are of the best quality. In this study, an automated quality control approach is developed using the North American Land Data Assimilation System, phase 2 (NLDAS-2), Noah soil porosity, soil temperature, and fraction of liquid and total soil moisture to flag erroneous and/or spurious measurements. Overall results show that this approach is able to flag unreasonable values when the soil is partially frozen. A validation example using NLDAS-2 multiple model soil moisture products at the 20-cm soil layer showed that the quality control procedure had a significant positive impact in Alabama, North Carolina, and west Texas. It had a greater impact in colder regions, particularly during spring and autumn. Over 433 NASMD stations have been quality controlled using the methodology proposed in this study, and the algorithm will be implemented to control data quality from the other ~1200 NASMD stations in the near future. [ABSTRACT FROM AUTHOR]

Soil moisture is an integral part of the climate system and can drive land-atmosphere interactions through the partitioning of latent and sensible heat. Soil moisture feedback to precipitation has been documented in several regions of the world, most notably in the southern Great Plains. However, the impact of soil moisture on precipitation, particularly at short (subdaily) time scales, has not been resolved. Here, in situ soil moisture observations and satellite-based precipitation estimates are used to examine if afternoon precipitation falls preferentially over wet or dry soils in Oklahoma. Afternoon precipitation events during the warm season (May-September) in Oklahoma from 2003 and 2012 are categorized by how favorable atmospheric conditions are for convection, as well as the presence or absence of the Great Plains low-level jet. The results show afternoon precipitation falls preferentially over wet soils when the Great Plains low-level jet is absent. In contrast, precipitation falls preferentially over dry soils when the low-level jet is present. Humidity (temperature) is increased (decreased) as soil moisture increases for all conditions, and convective available potential energy prior to convection is strongest when atmospheric humidity is above normal. The results do not demonstrate a causal link between soil moisture and precipitation, but they do suggest that soil moisture feedback to precipitation could potentially manifest itself over wetter- and drier-than-normal soils, depending on the overall synoptic and dynamic conditions. [ABSTRACT FROM AUTHOR]

Soil moisture-vegetation interactions are an important component of land-atmosphere coupling, especially in semiarid regions such as the North American Great Plains. However, many land surface models parameterize vegetation using an interannually invariant leaf area index (LAI). This study quantifies how utilizing a dynamic vegetation parameter in the variability infiltration capacity (VIC) hydrologic model influences model-simulated soil moisture. Accuracy is assessed using in situ soil moisture observations from 20 stations from the Oklahoma Mesonet. Results show that VIC simulations generated with an interannually variant LAI parameter are not consistently more accurate than those generated with the invariant (static) LAI parameter. However, the static LAI parameter tends to overestimate LAI during anomalously dry periods. This has the greatest influence on the accuracy of the soil moisture simulations in the deeper soil layers. Soil moisture drought, as simulated with the static LAI parameter, tends to be more severe and persist for considerably longer than drought simulated using the interannually variant LAI parameter. Dynamic vegetation parameters can represent interannual variations in vegetation health and growing season length. Therefore, simulations with a dynamic LAI parameter better capture the intensity and duration of drought conditions and are recommended for use in drought monitoring. [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]

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]