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Nathan Wells, Steve Goddard, and Michael J. Hayes

Abstract

The Palmer Drought Severity Index (PDSI) has been used for more than 30 years to quantify the long-term drought conditions for a given location and time. However, a common critique of the PDSI is that the behavior of the index at various locations is inconsistent, making spatial comparisons of PDSI values difficult, if not meaningless.

A self-calibrating Palmer Drought Severity Index (SC-PDSI) is presented and evaluated. The SC-PDSI automatically calibrates the behavior of the index at any location by replacing empirical constants in the index computation with dynamically calculated values. An evaluation of the SC-PDSI at 761 sites within Nebraska, Kansas, Colorado, Wyoming, Montana, North Dakota, and South Dakota, as well as at all 344 climate divisions shows that it is more spatially comparable than the PDSI, and reports extreme wet and dry conditions with frequencies that would be expected for rare conditions.

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Tsegaye Tadesse, Nicole Wall, Michael Hayes, Mark Svoboda, and Deborah Bathke
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Song Feng, Miroslav Trnka, Michael Hayes, and Yongjun Zhang

Abstract

Vigorous discussions and disagreements about the future changes in drought intensity in the U.S. Great Plains have been taking place recently within the literature. These discussions have involved widely varying estimates based on drought indices and model-based projections of the future. To investigate and understand the causes for such a disparity between these previous estimates, the authors analyzed the soil moisture at the near-surface soil layer and the entire soil column, as well as the Palmer drought severity index, the Palmer Z index, and the standardized precipitation and evaporation index using the output from the Community Climate System Model, version 4 (CCSM4), and 25 state-of-the-art climate models. These drought indices were computed using potential evapotranspiration estimated by the physically based Penman–Monteith method (PE_pm) and the empirically based Thornthwaite method (PE_th). The results showed that the short-term drought indices are similar to modeled surface soil moisture and show a small but consistent drying trend in the future. The long-term drought indices and the total column soil moisture, however, are consistent in projecting more intense future drought. When normalized, the drought indices with PE_th all show unprecedented future drying, while the drought indices with PE_pm show comparable dryness with the modeled soil moisture. Additionally, the drought indices with PE_pm are closely related to soil moisture during both the twentieth and twenty-first centuries. Overall, the drought indices with PE_pm, as well as the modeled total column soil moisture, suggest a widespread and very significant drying in the Great Plains toward the end of the century. The results suggest that the sharp contrasts about future drought risk in the Great Plains discussed in previous studies are caused by 1) comparing the projected changes in short-term droughts with that of the long-term droughts and/or 2) computing the atmospheric evaporative demand using an empirically based method (e.g., PE_th). The analysis here may be applied for drought projections in other regions across the globe.

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Michael Hayes, Mark Svoboda, Nicole Wall, and Melissa Widhalm

No Abstract available.

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Richard W. Reynolds, Ants Leetmaa, Klaus Arpe, Christopher Gordon, Stanley P. Hayes, and Michael J. McPhaden

Abstract

Surface wind analyses from three data assimilation systems are compared with independent wind observations from six buoys located in the Pacific within 8 deg of the equator. The period of comparison is 6 months (February to July 1987), with daily sampling.

The agreement between the assimilation systems and the independent buoy data is disappointing. The longterm mean differences between the buoy and the assimilated zonal and meridional winds are as large as 3.1 m s−1, which is comparable to the size of the means themselves. The zonal and meridional daily wind correlations range between 0.66 and 0.17. The wind field agreement was actually better among the different systems than between any system and the buoys. However, the agreement among the analysis products was usually better for the zonal winds than for the meridional winds. For the time period and locations presented, the comparisons with the independent data show that no assimilation system is clearly superior to any of the others.

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Tsegaye Tadesse, Donald A. Wilhite, Michael J. Hayes, Sherri K. Harms, and Steve Goddard

Abstract

Drought is a complex natural hazard that is best characterized by multiple climatological and hydrological parameters. Improving our understanding of the relationships between these parameters is necessary to reduce the impacts of drought. Data mining is a recently developed technique that can be used to interact with large databases and assist in the discovery of associations between drought and oceanic data by extracting information from massive and multiple data archives.

In this study, a new data-mining algorithm [i.e., Minimal Occurrences With Constraints and Time Lags (MOWCATL)] has been used to identify the relationships between oceanic parameters and drought indices. Rather than using traditional global statistical associations, the algorithm identifies drought episodes separate from normal and wet conditions and then uses drought episodes to find time-lagged relationships with oceanic parameters. As with all association-based data-mining algorithms, MOWCATL is used to find existing relationships in the data, and is not by itself a prediction tool.

Using the MOWCATL algorithm, the analyses of the rules generated for selected stations and state-averaged data for Nebraska from 1950 to 1999 indicate that most occurrences of drought are preceded by positive values of the Southern Oscillation index (SOI), negative values of the multivariate ENSO index (MEI), negative values of the Pacific–North American (PNA) index, negative values of the Pacific decadal oscillation (PDO), and negative values of the North Atlantic Oscillation (NAO). The frequency and confidence of the time-lagged relationships between oceanic indices and droughts at the selected stations in Nebraska indicate that oceanic parameters can be used as indicators of drought in Nebraska.

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Michael J. Hayes, Mark. D. Svoboda, Donald A. Wiihite, and Olga V. Vanyarkho

Droughts are difficult to detect and monitor. Drought indices, most commonly the Palmer Drought Severity Index (PDSI), have been used with limited success as operational drought monitoring tools and triggers for policy responses. Recently, a new index, the Standardized Precipitation Index (SPI), was developed to improve drought detection and monitoring capabilities. The SPI has several characteristics that are an improvement over previous indices, including its simplicity and temporal flexibility, that allow its application for water resources on all timescales. In this article, the 1996 drought in the southern plains and southwestern United States is examined using the SPI. A series of maps are used to illustrate how the SPI would have assisted in being able to detect the onset of the drought and monitor its progression. A case study investigating the drought in greater detail for Texas is also given. The SPI demonstrated that it is a tool that should be used operationally as part of a state, regional, or national drought watch system in the United States. During the 1996 drought, the SPI detected the onset of the drought at least 1 month in advance of the PDSI. This timeliness will be invaluable for improving mitigation and response actions of state and federal government to drought-affected regions in the future.

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Kelly Helm Smith, Andrew J. Tyre, Zhenghong Tang, Michael J. Hayes, and F. Adnan Akyuz

Abstract

State climatologists and other expert drought observers have speculated about the value of monitoring Twitter for #drought and related hashtags. This study statistically examines the relationships between the rate of tweeting using #drought and related hashtags, within states, accounting for drought status and news coverage of drought. We collected and geolocated tweets, 2017–18, and used regression analysis and a diversity statistic to explain expected and identify unexpected volumes of tweets. This provides a quantifiable means to detect state-weeks with a volume of tweets that exceeds the upper limit of the prediction interval. To filter out instances where a high volume of tweets is related to the activities of one person or very few people, a diversity statistic was used to eliminate anomalous state-weeks where the diversity statistic did not exceed the 75th percentile of the range for that state’s diversity statistic. Anomalous state-weeks in a few cases preceded the onset of drought but more often coincided with or lagged increases in drought. Tweets are both a means of sharing original experience and a means of discussing news and other recent events, and anomalous weeks occurred throughout the course of a drought, not just at the beginning. A sum-to-zero contrast coefficient for each state revealed a difference in the propensity of different states to tweet about drought, apparently reflecting recent and long-term experience in those states, and suggesting locales that would be most predisposed to drought policy innovation.

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Tsegaye Tadesse, Brian D. Wardlow, Jesslyn F. Brown, Mark D. Svoboda, Michael J. Hayes, Brian Fuchs, and Denise Gutzmer

Abstract

The vegetation drought response index (VegDRI), which combines traditional climate- and satellite-based approaches for assessing vegetation conditions, offers new insights into assessing the impacts of drought from local to regional scales. In 2011, the U.S. southern Great Plains, which includes Texas, Oklahoma, and New Mexico, was plagued by moderate to extreme drought that was intensified by an extended period of record-breaking heat. The 2011 drought presented an ideal case study to evaluate the performance of VegDRI in characterizing developing drought conditions. Assessment of the spatiotemporal drought patterns represented in the VegDRI maps showed that the severity and patterns of the drought across the region corresponded well to the record warm temperatures and much-below-normal precipitation reported by the National Climatic Data Center and the sectoral drought impacts documented by the Drought Impact Reporter (DIR). VegDRI values and maps also showed the evolution of the drought signal before the Las Conchas Fire (the largest fire in New Mexico’s history). Reports in the DIR indicated that the 2011 drought had major adverse impacts on most rangeland and pastures in Texas and Oklahoma, resulting in total direct losses of more than $12 billion associated with crop, livestock, and timber production. These severe impacts on vegetation were depicted by the VegDRI at subcounty, state, and regional levels. This study indicates that the VegDRI maps can be used with traditional drought indicators and other in situ measures to help producers and government officials with various management decisions, such as justifying disaster assistance, assessing fire risk, and identifying locations to move livestock for grazing.

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Mary Noel, Deborah Bathke, Brian Fuchs, Denise Gutzmer, Tonya Haigh, Michael Hayes, Markéta Poděbradská, Claire Shield, Kelly Smith, and Mark Svoboda

Abstract

The U.S. Drought Monitor (USDM), a weekly map depicting severity and spatial extent of drought, is used to communicate about drought in state and federal decision-making, and as a trigger in response policies, including the distribution of hundreds of millions of dollars for agricultural financial relief in the United States annually. An accompanying classification table helps interpret the map and includes a column of possible impacts associated with each level of drought severity. However, the column describing potential drought impacts is generalized for the entire United States. To provide more geographically specific interpretation of drought, state and regionally specific drought impact classification tables were developed by linking impacts chronicled in the Drought Impact Reporter (DIR) to USDM severity levels across the United States and Puerto Rico and identifying recurrent themes at each level. After creating state-level tables of impacts observed for each level of drought, a nationwide survey was administered to drought experts and decision-makers (n = 89), including the USDM authors, to understand whether the tables provided accurate descriptions of drought impacts in their state. Seventy-six percent of respondents indicated the state table was an acceptable or good characterization of drought impacts for their respective state. This classification scheme was created with a reproducible qualitative methodology that used past observations to identify themes in drought impacts across multiple sectors to concisely describe expected impacts at different levels of drought in each state.

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