Search Results
You are looking at 11 - 20 of 29 items for
- Author or Editor: Mark Svoboda x
- Refine by Access: All Content x
Abstract
This study examines the evolution of soil moisture, evapotranspiration, vegetation, and atmospheric conditions during an unusual flash drought–flash recovery sequence that occurred across the south-central United States during 2015. This event was characterized by a period of rapid drought intensification (flash drought) during late summer that was terminated by heavy rainfall at the end of October that eliminated the extreme drought conditions over a 2-week period (flash recovery). A detailed analysis was performed using time series of environmental variables derived from meteorological, remote sensing, and land surface modeling datasets. Though the analysis revealed a similar progression of cascading effects in each region, characteristics of the flash drought such as its onset time, rate of intensification, and vegetation impacts differed between regions due to variations in the antecedent conditions and the atmospheric anomalies during its growth. Overall, flash drought signals initially appeared in the near-surface soil moisture, followed closely by reductions in evapotranspiration. Total column soil moisture deficits took longer to develop, especially in the western part of the region where heavy rainfall during the spring and early summer led to large moisture surpluses. Large differences were noted in how land surface models in the North American Land Data Assimilation System depicted soil moisture evolution during the flash drought; however, the models were more similar in their assessment of conditions during the flash recovery period. This study illustrates the need to use multiple datasets to track the evolution and impacts of rapidly evolving flash drought and flash recovery events.
Abstract
This study examines the evolution of soil moisture, evapotranspiration, vegetation, and atmospheric conditions during an unusual flash drought–flash recovery sequence that occurred across the south-central United States during 2015. This event was characterized by a period of rapid drought intensification (flash drought) during late summer that was terminated by heavy rainfall at the end of October that eliminated the extreme drought conditions over a 2-week period (flash recovery). A detailed analysis was performed using time series of environmental variables derived from meteorological, remote sensing, and land surface modeling datasets. Though the analysis revealed a similar progression of cascading effects in each region, characteristics of the flash drought such as its onset time, rate of intensification, and vegetation impacts differed between regions due to variations in the antecedent conditions and the atmospheric anomalies during its growth. Overall, flash drought signals initially appeared in the near-surface soil moisture, followed closely by reductions in evapotranspiration. Total column soil moisture deficits took longer to develop, especially in the western part of the region where heavy rainfall during the spring and early summer led to large moisture surpluses. Large differences were noted in how land surface models in the North American Land Data Assimilation System depicted soil moisture evolution during the flash drought; however, the models were more similar in their assessment of conditions during the flash recovery period. This study illustrates the need to use multiple datasets to track the evolution and impacts of rapidly evolving flash drought and flash recovery events.
Abstract
The U.S. Drought Monitor (USDM) classifies drought into five discrete dryness/drought categories based on expert synthesis of numerous data sources. In this study, an empirical methodology is presented for creating a nondiscrete USDM index that simultaneously 1) represents the dryness/wetness value on a continuum and 2) is most consistent with the time scales and processes of the actual USDM. A continuous USDM representation will facilitate USDM forecasting methods, which will benefit from knowledge of where, within a discrete drought class, the current drought state most probably lies. The continuous USDM is developed such that the actual discrete USDM can be reconstructed by discretizing the continuous USDM based on the 30th, 20th, 10th, 5th, and 2nd percentiles—corresponding with USDM definitions for the D4–D0 drought classes. Anomalies in precipitation, soil moisture, and evapotranspiration over a range of different time scales are used as predictors to estimate the continuous USDM. The methodology is fundamentally probabilistic, meaning that the probability density function (PDF) of the continuous USDM is estimated and therefore the degree of uncertainty in the fit is properly characterized. Goodness-of-fit metrics and direct comparisons between the actual and predicted USDM analyses during different seasons and years indicate that this objective drought classification method is well correlated with the current USDM analyses. In Part II, this continuous USDM index will be used to improve intraseasonal USDM intensification forecasts because it is capable of distinguishing between USDM states that are either far from or near to the next-higher drought category.
Abstract
The U.S. Drought Monitor (USDM) classifies drought into five discrete dryness/drought categories based on expert synthesis of numerous data sources. In this study, an empirical methodology is presented for creating a nondiscrete USDM index that simultaneously 1) represents the dryness/wetness value on a continuum and 2) is most consistent with the time scales and processes of the actual USDM. A continuous USDM representation will facilitate USDM forecasting methods, which will benefit from knowledge of where, within a discrete drought class, the current drought state most probably lies. The continuous USDM is developed such that the actual discrete USDM can be reconstructed by discretizing the continuous USDM based on the 30th, 20th, 10th, 5th, and 2nd percentiles—corresponding with USDM definitions for the D4–D0 drought classes. Anomalies in precipitation, soil moisture, and evapotranspiration over a range of different time scales are used as predictors to estimate the continuous USDM. The methodology is fundamentally probabilistic, meaning that the probability density function (PDF) of the continuous USDM is estimated and therefore the degree of uncertainty in the fit is properly characterized. Goodness-of-fit metrics and direct comparisons between the actual and predicted USDM analyses during different seasons and years indicate that this objective drought classification method is well correlated with the current USDM analyses. In Part II, this continuous USDM index will be used to improve intraseasonal USDM intensification forecasts because it is capable of distinguishing between USDM states that are either far from or near to the next-higher drought category.
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.
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.
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.
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.
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.
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.
Abstract
When drought hits water-scarce regions, there are significant repercussions for food and water security, as well as serious issues for the stability of broader social and environmental systems. To mitigate these effects, environmental monitoring and early warning systems aimed at detecting the onset of drought conditions can facilitate timely and effective responses from government and private sector stakeholders. This study uses multistage, participatory research methods across more than 135 interviews, focus groups, and workshops to assess extant climatic, agricultural, hydrological, and drought monitoring systems; key cross-sector drought impacts; and drought monitoring needs in four countries in the Middle East and North Africa (MENA) region: Morocco, Tunisia, Lebanon, and Jordan. This extensive study of user needs for drought monitoring across the MENA region is informing and shaping the ongoing development of drought early warning systems, a composite drought indicator (CDI), and wider drought management systems in each country. Overarching themes of drought monitoring needs include technical definitions of drought for policy purposes; information-sharing regimes and data-sharing platforms; ground-truthing of remotely sensed and modeled data; improved data quality in observation networks; and two-way engagement with farmers, organizations, and end-users of drought monitoring products. This research establishes a basis for informing enhanced drought monitoring and management in the countries, and the broad stakeholder engagement can help foster the emergence of effective environmental monitoring coalitions.
Abstract
When drought hits water-scarce regions, there are significant repercussions for food and water security, as well as serious issues for the stability of broader social and environmental systems. To mitigate these effects, environmental monitoring and early warning systems aimed at detecting the onset of drought conditions can facilitate timely and effective responses from government and private sector stakeholders. This study uses multistage, participatory research methods across more than 135 interviews, focus groups, and workshops to assess extant climatic, agricultural, hydrological, and drought monitoring systems; key cross-sector drought impacts; and drought monitoring needs in four countries in the Middle East and North Africa (MENA) region: Morocco, Tunisia, Lebanon, and Jordan. This extensive study of user needs for drought monitoring across the MENA region is informing and shaping the ongoing development of drought early warning systems, a composite drought indicator (CDI), and wider drought management systems in each country. Overarching themes of drought monitoring needs include technical definitions of drought for policy purposes; information-sharing regimes and data-sharing platforms; ground-truthing of remotely sensed and modeled data; improved data quality in observation networks; and two-way engagement with farmers, organizations, and end-users of drought monitoring products. This research establishes a basis for informing enhanced drought monitoring and management in the countries, and the broad stakeholder engagement can help foster the emergence of effective environmental monitoring coalitions.
Abstract
Flash droughts, characterized by their unusually rapid intensification, have garnered increasing attention within the weather, climate, agriculture, and ecological communities in recent years due to their large environmental and socioeconomic impacts. Because flash droughts intensify quickly, they require different early warning capabilities and management approaches than are typically used for slower-developing “conventional” droughts. In this essay, we describe an integrated research-and-applications agenda that emphasizes the need to reconceptualize our understanding of flash drought within existing drought early warning systems by focusing on opportunities to improve monitoring and prediction. We illustrate the need for engagement among physical scientists, social scientists, operational monitoring and forecast centers, practitioners, and policy-makers to inform how they view, monitor, predict, plan for, and respond to flash drought. We discuss five related topics that together constitute the pillars of a robust flash drought early warning system, including the development of 1) a physically based identification framework, 2) comprehensive drought monitoring capabilities, and 3) improved prediction over various time scales that together 4) aid impact assessments and 5) guide decision-making and policy. We provide specific recommendations to illustrate how this fivefold approach could be used to enhance decision-making capabilities of practitioners, develop new areas of research, and provide guidance to policy-makers attempting to account for flash drought in drought preparedness and response plans.
Abstract
Flash droughts, characterized by their unusually rapid intensification, have garnered increasing attention within the weather, climate, agriculture, and ecological communities in recent years due to their large environmental and socioeconomic impacts. Because flash droughts intensify quickly, they require different early warning capabilities and management approaches than are typically used for slower-developing “conventional” droughts. In this essay, we describe an integrated research-and-applications agenda that emphasizes the need to reconceptualize our understanding of flash drought within existing drought early warning systems by focusing on opportunities to improve monitoring and prediction. We illustrate the need for engagement among physical scientists, social scientists, operational monitoring and forecast centers, practitioners, and policy-makers to inform how they view, monitor, predict, plan for, and respond to flash drought. We discuss five related topics that together constitute the pillars of a robust flash drought early warning system, including the development of 1) a physically based identification framework, 2) comprehensive drought monitoring capabilities, and 3) improved prediction over various time scales that together 4) aid impact assessments and 5) guide decision-making and policy. We provide specific recommendations to illustrate how this fivefold approach could be used to enhance decision-making capabilities of practitioners, develop new areas of research, and provide guidance to policy-makers attempting to account for flash drought in drought preparedness and response plans.
Abstract
Given the increasing use of the term “flash drought” by the media and scientific community, it is prudent to develop a consistent definition that can be used to identify these events and to understand their salient characteristics. It is generally accepted that flash droughts occur more often during the summer owing to increased evaporative demand; however, two distinct approaches have been used to identify them. The first approach focuses on their rate of intensification, whereas the second approach implicitly focuses on their duration. These conflicting notions for what constitutes a flash drought (i.e., unusually fast intensification vs short duration) introduce ambiguity that affects our ability to detect their onset, monitor their development, and understand the mechanisms that control their evolution. Here, we propose that the definition for “flash drought” should explicitly focus on its rate of intensification rather than its duration, with droughts that develop much more rapidly than normal identified as flash droughts. There are two primary reasons for favoring the intensification approach over the duration approach. First, longevity and impact are fundamental characteristics of drought. Thus, short-term events lasting only a few days and having minimal impacts are inconsistent with the general understanding of drought and therefore should not be considered flash droughts. Second, by focusing on their rapid rate of intensification, the proposed “flash drought” definition highlights the unique challenges faced by vulnerable stakeholders who have less time to prepare for its adverse effects.
Abstract
Given the increasing use of the term “flash drought” by the media and scientific community, it is prudent to develop a consistent definition that can be used to identify these events and to understand their salient characteristics. It is generally accepted that flash droughts occur more often during the summer owing to increased evaporative demand; however, two distinct approaches have been used to identify them. The first approach focuses on their rate of intensification, whereas the second approach implicitly focuses on their duration. These conflicting notions for what constitutes a flash drought (i.e., unusually fast intensification vs short duration) introduce ambiguity that affects our ability to detect their onset, monitor their development, and understand the mechanisms that control their evolution. Here, we propose that the definition for “flash drought” should explicitly focus on its rate of intensification rather than its duration, with droughts that develop much more rapidly than normal identified as flash droughts. There are two primary reasons for favoring the intensification approach over the duration approach. First, longevity and impact are fundamental characteristics of drought. Thus, short-term events lasting only a few days and having minimal impacts are inconsistent with the general understanding of drought and therefore should not be considered flash droughts. Second, by focusing on their rapid rate of intensification, the proposed “flash drought” definition highlights the unique challenges faced by vulnerable stakeholders who have less time to prepare for its adverse effects.
