scholarly journals Flash Drought Characteristics Based on U.S. Drought Monitor

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 498 ◽  
Author(s):  
L. Gwen Chen ◽  
Jon Gottschalck ◽  
Adam Hartman ◽  
David Miskus ◽  
Rich Tinker ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Warm Season ◽  
The United States ◽  
Rate Of Change ◽  
Disaster Mitigation ◽  
Drought Event ◽  
Central United States ◽  
Fast Development ◽  
Moisture Conditions

Understanding the characteristics of flash drought events and further predicting the onset of such events on subseasonal timescales is of critical importance for impact assessment, disaster mitigation, and loss prevention. In this study, we employ a rate-of-change approach and define a flash drought event as a drought event with greater than or equal to two categories degradation in a four-week period based on the U.S. Drought Monitor. Unlike conventional drought, which can occur year-round and everywhere in the United States, flash drought has preferred seasons and locations to occur, mostly in the warm season and over the central United States. Widespread flash drought over the United States is largely correlated with La Niña episodes. In contrast with conventional drought, which is mainly driven by precipitation deficits, anomalously high evapotranspiration rates, caused by anomalously high temperatures, winds, and/or incoming radiation, are usually present before the onset of flash drought. Comparing to precipitation and soil moisture, evapotranspiration typically has the largest decline rate during the fast-development phase. Three-month Standardized Precipitation Indexes are mostly dry right before flash drought onset, but large deficits are not required. As a result, monitoring rapid changes in evapotranspiration, along with precipitation and soil moisture conditions, can provide early warnings of flash drought development.

Investigating the Relationship between the Evaporative Stress Index and Land Surface Conditions in the Contiguous United States

2020 ◽  
Vol 21 (7) ◽  
pp. 1469-1484
Author(s):  
Yafang Zhong ◽  
Jason A. Otkin ◽  
Martha C. Anderson ◽  
Christopher Hain
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Great Plains ◽  
Land Surface ◽  
The United States ◽  
Stress Index ◽  
Northern Great Plains ◽  
Reference Network ◽  
North Central ◽  
Central United States

AbstractDespite the key importance of soil moisture–evapotranspiration (ET) coupling in the climate system, limited availability of soil moisture and ET observations poses a major impediment for investigation of this coupling regarding spatiotemporal characteristics and potential modifications under climate change. To better understand and quantify soil moisture–ET coupling and relevant processes, this study takes advantage of in situ soil moisture observations from the U.S. Climate Reference Network (USCRN) for the time period of 2010–17 and a satellite-derived version of the evapotranspiration stress index (ESI), which represents anomalies in a normalized ratio of actual to reference ET. The analyses reveal strong seasonality and regional characteristics of the ESI–land surface interactions across the United States, with the strongest control of soil moisture on the ESI found in the southern Great Plains during spring, and in the north-central United States, the northern Great Plains, and the Pacific Northwest during summer. In drier climate regions such as the northern Great Plains and north-central United States, soil moisture control on the ESI is confined to surface soil layers, with subsurface soil moisture passively responding to changes in the ESI. The soil moisture–ESI interaction is more uniform between surface and subsurface soils in wetter regions with higher vegetation cover. These results provide a benchmark for simulation of soil moisture–ET coupling and are useful for projection of associated climate processes in the future.

Understanding the Distinct Impacts of MCS and Non-MCS Rainfall on the Surface Water Balance in the Central United States Using a Numerical Water-Tagging Technique

2020 ◽  
Vol 21 (10) ◽  
pp. 2343-2357
Author(s):  
Huancui Hu ◽  
L. Ruby Leung ◽  
Zhe Feng
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Surface Water ◽  
Water Balance ◽  
Surface Runoff ◽  
Land Surface ◽  
Warm Season ◽  
Surface Model ◽  
Central United States ◽  
Infiltration Excess

ABSTRACTWarm-season rainfall associated with mesoscale convective systems (MCSs) in the central United States is characterized by higher intensity and nocturnal timing compared to rainfall from non-MCS systems, suggesting their potentially different footprints on the land surface. To differentiate the impacts of MCS and non-MCS rainfall on the surface water balance, a water tracer tool embedded in the Noah land surface model with multiparameterization options (WT-Noah-MP) is used to numerically “tag” water from MCS and non-MCS rainfall separately during April–August (1997–2018) and track their transit in the terrestrial system. From the water-tagging results, over 50% of warm-season rainfall leaves the surface–subsurface system through evapotranspiration by the end of August, but non-MCS rainfall contributes a larger fraction. However, MCS rainfall plays a more important role in generating surface runoff. These differences are mostly attributed to the rainfall intensity differences. The higher-intensity MCS rainfall tends to produce more surface runoff through infiltration excess flow and drives a deeper penetration of the rainwater into the soil. Over 70% of the top 10th percentile runoff is contributed by MCS rainfall, demonstrating its important contribution to local flooding. In contrast, lower-intensity non-MCS rainfall resides mostly in the top layer and contributes more to evapotranspiration through soil evaporation. Diurnal timing of rainfall has negligible effects on the flux partitioning for both MCS and non-MCS rainfall. Differences in soil moisture profiles for MCS and non-MCS rainfall and the resultant evapotranspiration suggest differences in their roles in soil moisture–precipitation feedbacks and ecohydrology.

The Relationship between Cool and Warm Season Moisture over the Central United States, 1685–2015

2018 ◽  
Vol 31 (19) ◽  
pp. 7909-7924 ◽  
Author(s):  
Max C. A. Torbenson ◽  
David W. Stahle
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Tree Ring ◽  
Land Surface ◽  
Warm Season ◽  
Instrumental Record ◽  
Moisture Balance ◽  
Central United States ◽  
The Relationship

Land surface feedbacks impart a significant degree of persistence between cool and warm season moisture availability in the central United States. However, the degree of correlation between these two variables is subject to major changes that appear to occur on decadal to multidecadal time scales, even in the relatively short 115-yr instrumental record. Tree-ring reconstructions have extended the limited observational record of long-term soil moisture levels, but such reconstructions do not resolve the seasonal differences in moisture conditions. We present two separate 331-yr-long seasonal moisture reconstructions for the central United States, based on sensitive subannual and annual tree-ring chronologies that have strong and separate seasonal moisture signals: an estimate of the long-term May soil moisture balance and a second estimate of the short-term June–August atmospheric moisture balance. The predictors used in each seasonal reconstruction are not significantly correlated with the alternate season target. Both reconstructions capture over 70% of the interannual variance in the instrumental data for the calibration period and also share significant decadal and multidecadal variability with the instrumental record in both the calibration and validation periods. The instrumental and reconstructed moisture levels are both positively correlated between spring and summer strongly enough to have potential value in seasonal prediction. However, the relationship between spring and summer moisture exhibits major decadal changes in strength and even sign that appear to be related to large-scale ocean–atmosphere dynamics associated with the Atlantic multidecadal oscillation.

The Precipitation Response over the Continental United States to Cold Tropical Pacific Sea Surface Temperatures

2014 ◽  
Vol 27 (13) ◽  
pp. 5036-5055 ◽  
Author(s):  
Hailan Wang ◽  
Siegfried Schubert
Keyword(s):  
United States ◽  
Warm Season ◽  
The United States ◽  
Tropical Pacific ◽  
Eastern United States ◽  
North Atlantic Subtropical High ◽  
Sst Variability ◽  
The Pacific ◽  
Central United States ◽  
Atmospheric Circulation Anomalies

The dominant pattern of SST variability in the Pacific during its cold phase produces pronounced precipitation deficits over the continental United States throughout the annual cycle. This study investigates the observed physical and dynamical processes through which the cold Pacific pattern affects U.S. precipitation, particularly the causes for the peak dry impacts in fall, as well as the nature of the differences between the summer and fall responses. Results show that the peak precipitation deficit over the United States during fall is primarily due to reduced atmospheric moisture transport from the Gulf of Mexico into the central and eastern United States and secondarily a reduction in local evaporation from land–atmosphere feedback. The former is associated with a strong and systematic low-level northeasterly flow anomaly over the southeastern United States that counteracts the northwest branch of the climatological North Atlantic subtropical high. The above northeasterly anomaly is maintained by both diabatic heating anomalies in the nearby intra-American seas and diabatic cooling anomalies in the tropical Pacific. In contrast, the modest summertime precipitation deficit over the central United States is mainly an intensification of the local dry anomaly in the preceding spring from local land–atmosphere feedback; the rather weak and disorganized atmospheric circulation anomalies over and to the south of the United States make little contribution. An evaluation of the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1) AGCM simulations shows it to be deficient in simulating the warm season tropical convection responses over the intra-American seas to the cold Pacific pattern and thereby the precipitation responses over the United States, a problem that appears to be common to many AGCMs.

scholarly journals Early warm-season mesoscale convective systems dominate soil moisture–precipitation feedback for summer rainfall in central United States

2021 ◽  
Vol 118 (43) ◽  
pp. e2105260118
Author(s):  
Huancui Hu ◽  
L. Ruby Leung ◽  
Zhe Feng
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Warm Season ◽  
Summer Rainfall ◽  
Mesoscale Convective Systems ◽  
Rainfall Events ◽  
Convective Systems ◽  
Central United States ◽  
Mesoscale Convective

Land–atmosphere interactions play an important role in summer rainfall in the central United States, where mesoscale convective systems (MCSs) contribute to 30 to 70% of warm-season precipitation. Previous studies of soil moisture–precipitation feedbacks focused on the total precipitation, confounding the distinct roles of rainfall from different convective storm types. Here, we investigate the soil moisture–precipitation feedbacks associated with MCS and non-MCS rainfall and their surface hydrological footprints using a unique combination of these rainfall events in observations and land surface simulations with numerical tracers to quantify soil moisture sourced from MCS and non-MCS rainfall. We find that early warm-season (April to June) MCS rainfall, which is characterized by higher intensity and larger area per storm, produces coherent mesoscale spatial heterogeneity in soil moisture that is important for initiating summer (July) afternoon rainfall dominated by non-MCS events. On the other hand, soil moisture sourced from both early warm-season MCS and non-MCS rainfall contributes to lower-level atmospheric moistening favorable for upscale growth of MCSs at night. However, soil moisture sourced from MCS rainfall contributes to July MCS rainfall with a longer lead time because with higher intensity, MCS rainfall percolates into deeper soil that has a longer memory. Therefore, early warm-season MCS rainfall dominates soil moisture–precipitation feedback. This motivates future studies to examine the contribution of early warm-season MCS rainfall and associated soil moisture anomalies to predictability of summer rainfall in the major agricultural region of the central United States and other continental regions frequented by MCSs.

scholarly journals A SMAP-Based Drought Monitoring Index for the United States

2018 ◽  
Author(s):  
Sara Sadri ◽  
Eric F. Wood ◽  
Ming Pan
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Warm Season ◽  
The United States ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface

Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe between the latitude bands of 85.044° N/S in 2–3 days depending on location. SMAP Level 3 passive radiometer product (SPL3SMP) measures the amount of water in the top 5 cm of soil except for regions of heavy vegetation (vegetation water content >4.5 kg/m2) and frozen or snow covered locations. SPL3SMP retrievals are spatially and temporally discontinuous, so the 33 months offers a short SMAP record length and poses a statistical challenge for meaningful assessment of its indices. The SMAP SPL4SMAU data product provides global surface and root zone soil moisture at 9-km resolution based on assimilating the SPL3SMP product into the NASA Catchment land surface model. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. We describe here SMAP-based indices over the continental United States (CONUS) based on both near-surface and root zone soil moisture percentiles. The percentiles are based on fitting a Beta distribution to the retrieved moisture values. To assess the data adequacy, a statistical comparison is made between fitting the distribution to VIC soil moisture values for the days when SPL3SMP are available, versus fitting to a 1979–2017 VIC data record. For the cold season (November–April), 57 % of grids were deemed to be consistent between the periods, and 68 % in the warm season (May–October), based on a Kolmogorov–Smirnov statistical test. It is assumed that if grids passed the consistency test using VIC data, then the grid had sufficient SMAP data. Our near-surface and root zone drought index on maps are shown to be similar to those produced by the U.S. Drought Monitor (from D0-D4) and GRACE. In a similar manner, we extend the index to include pluvial conditions using indices W0-W4. This study is a step forward towards building a national and international soil moisture monitoring system, without which, quantitative measures of drought and pluvial conditions will remain difficult to judge.

scholarly journals DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

2021 ◽  
Vol 4 ◽  
Author(s):  
Colin Brust ◽  
John S. Kimball ◽  
Marco P. Maneta ◽  
Kelsey Jencso ◽  
Rolf H. Reichle
Keyword(s):  
United States ◽  
Machine Learning ◽  
Soil Moisture ◽  
Local Governments ◽  
Warning System ◽  
The United States ◽  
Drought Indices ◽  
Drought Event ◽  
Mitigation And Adaptation ◽  
Drought Conditions

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

Biennial Wormwood (Artemisia biennis) Early-Season Control with Herbicides

2004 ◽  
Vol 18 (3) ◽  
pp. 611-618 ◽  
Author(s):  
Bradley E. Fronning ◽  
George O. Kegode
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Great Plains ◽  
The United States ◽  
Northern Great Plains ◽  
Early Season ◽  
Herbicide Treatments ◽  
Moisture Conditions

Biennial wormwood has become a problem for soybean producers in the northern Great Plains of the United States. Research was conducted to evaluate control of biennial wormwood with preemergence (PRE) herbicides alone or followed by postemergence (POST) herbicides in 2000 and 2001 at Fargo, Leonard, and Wyndmere, ND. Favorable soil moisture conditions at Leonard resulted in continual emergence and greater densities of biennial wormwood, whereas the soil at Fargo and Wyndmere was dry and few biennial wormwood seedlings emerged at these locations. Biennial wormwood control with PRE herbicides was greater than 89% at Fargo and Wyndmere but was 80% or lower at Leonard. PRE biennial wormwood control was higher with flumetsulam than with sulfentrazone. When POST treatments were applied after PRE herbicides, biennial wormwood control 4 wk after treatment was 92% or better at Fargo and Wyndmere but was 76% or less at Leonard. The combination of PRE and POST herbicide treatments did not improve control greatly at Fargo or Wyndmere but at Leonard reduced the number of biennial wormwood plants.

scholarly journals Composites of Heavy Rain Producing Elevated Thunderstorms in the Central United States

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Laurel P. McCoy ◽  
Patrick S. Market ◽  
Chad M. Gravelle ◽  
Charles E. Graves ◽  
Neil I. Fox ◽  
...  
Keyword(s):  
United States ◽  
Heavy Rainfall ◽  
Warm Season ◽  
Heavy Rain ◽  
The United States ◽  
Large Variability ◽  
Null Case ◽  
Central United States ◽  
Mean Fields ◽  
Event Occurrence

Composite analyses of the atmosphere over the central United States during elevated thunderstorms producing heavy rainfall are presented. Composites were created for five National Weather Service County Warning Areas (CWAs) in the region. Events studied occurred during the warm season (April–September) during 1979–2012. These CWAs encompass the region determined previously to experience the greatest frequency of elevated thunderstorms in the United States. Composited events produced rainfall of >50 mm 24 hr−1 within the selected CWA. Composites were generated for the 0–3 hr period prior to the heaviest rainfall, 6–9 hours prior to it, and 12–15 hours prior to it. This paper focuses on the Pleasant Hill, Missouri (EAX) composites, as all CWA results were similar; also these analyses focus on the period 0–3 hours prior to event occurrence. These findings corroborate the findings of previous authors. What is offered here that is unique is (1) a measure of the interquartile range within the composite mean fields, allowing for discrimination between variable fields that provided a strong reliable signal, from those that may appear strong but possess large variability, and (2) composite soundings of two subclasses of elevated thunderstorms. Also, a null case (one that fits the composite but failed to produce significant rainfall) is also examined for comparison.

scholarly journals Predicting the U.S. Drought Monitor Using Precipitation, Soil Moisture, and Evapotranspiration Anomalies. Part II: Intraseasonal Drought Intensification Forecasts

2017 ◽  
Vol 18 (7) ◽  
pp. 1963-1982 ◽  
Author(s):  
David J. Lorenz ◽  
Jason A. Otkin ◽  
Mark Svoboda ◽  
Christopher R. Hain ◽  
Martha C. Anderson ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
High Amplitude ◽  
The United States ◽  
Skill Score ◽  
Good Reliability ◽  
Additional Information ◽  
The North ◽  
Probabilistic Forecasts ◽  
Central United States

Abstract Probabilistic forecasts of U.S. Drought Monitor (USDM) intensification over 2-, 4-, and 8-week time periods are developed based on recent anomalies in precipitation, evapotranspiration, and soil moisture. These statistical forecasts are computed using logistic regression with cross validation. While recent precipitation, evapotranspiration, and soil moisture do provide skillful forecasts, it is found that additional information on the current state of the USDM adds significant skill to the forecasts. The USDM state information takes the form of a metric that quantifies the “distance” from the next-higher drought category using a nondiscrete estimate of the current USDM state. This adds skill because USDM states that are close to the next-higher drought category are more likely to intensify than states that are farther from this threshold. The method shows skill over most of the United States but is most skillful over the north-central United States, where the cross-validated Brier skill score averages 0.20 for both 2- and 4-week forecasts. The 8-week forecasts are less skillful in most locations. The 2- and 4-week probabilities have very good reliability. The 8-week probabilities, on the other hand, are noticeably overconfident. For individual drought events, the method shows the most skill when forecasting high-amplitude flash droughts and when large regions of the United States are experiencing intensifying drought.

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