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- Author or Editor: Renee A. McPherson x
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Abstract
Harmful algae and cyanobacteria blooms are increasing in frequency and intensity in freshwater systems due to anthropogenic impacts such as nutrient loading in watersheds and engineered alterations of natural waterways. There are multiple physical factors that affect the conditions in a freshwater system that contribute to optimal habitats for harmful algae and toxin-producing cyanobacteria. A growing body of research shows that climate change stressors also are impacting water-body conditions that favor harmful algae and cyanobacteria species over other phytoplankton. The overgrowth of these organisms, or a “bloom,” increases the opportunity for exposure to toxins by humans, companion animals, livestock, and wildlife. As waters warm and precipitation patterns change over time, exposure to these blooms is projected to increase. Hence, it is important that states and tribes develop monitoring and reporting strategies as well as align governmental policies to protect their citizens and ecosystems within their jurisdiction. Currently, the policies and approaches taken to monitor and report on harmful algae and cyanobacteria blooms vary widely among states, and it is undetermined if any tribes have specific policies on harmful algae blooms. This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms.
Significance Statement
Inland bodies of freshwater supply drinking water for humans and animals, water for irrigating crops, habitats for aquatic species, places of cultural significance for Indigenous peoples, and other important functions. Many of these bodies of water have been polluted with runoff from industry, including agriculture, and already support harmful algal blooms during warm conditions. Hot extremes associated with climate change are expected to increase the occurrence and duration of harmful algal blooms, and in some places, initiate blooms where none have been recorded previously. These toxic blooms are harmful to people, companion animals, livestock, and wildlife. It is important to review the interconnections among biological, climate, and water systems to monitor blooms and alert the public about their toxin production.
Abstract
Harmful algae and cyanobacteria blooms are increasing in frequency and intensity in freshwater systems due to anthropogenic impacts such as nutrient loading in watersheds and engineered alterations of natural waterways. There are multiple physical factors that affect the conditions in a freshwater system that contribute to optimal habitats for harmful algae and toxin-producing cyanobacteria. A growing body of research shows that climate change stressors also are impacting water-body conditions that favor harmful algae and cyanobacteria species over other phytoplankton. The overgrowth of these organisms, or a “bloom,” increases the opportunity for exposure to toxins by humans, companion animals, livestock, and wildlife. As waters warm and precipitation patterns change over time, exposure to these blooms is projected to increase. Hence, it is important that states and tribes develop monitoring and reporting strategies as well as align governmental policies to protect their citizens and ecosystems within their jurisdiction. Currently, the policies and approaches taken to monitor and report on harmful algae and cyanobacteria blooms vary widely among states, and it is undetermined if any tribes have specific policies on harmful algae blooms. This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms.
Significance Statement
Inland bodies of freshwater supply drinking water for humans and animals, water for irrigating crops, habitats for aquatic species, places of cultural significance for Indigenous peoples, and other important functions. Many of these bodies of water have been polluted with runoff from industry, including agriculture, and already support harmful algal blooms during warm conditions. Hot extremes associated with climate change are expected to increase the occurrence and duration of harmful algal blooms, and in some places, initiate blooms where none have been recorded previously. These toxic blooms are harmful to people, companion animals, livestock, and wildlife. It is important to review the interconnections among biological, climate, and water systems to monitor blooms and alert the public about their toxin production.
Abstract
Agricultural decision-making that adapts to climate variability is essential to global food security. Crop production can be severely impacted by drought, flood, and heat, as seen in recent years in parts of the United States. Seasonal climate forecasts can help producers reduce crop losses, but many nationwide, publicly available seasonal forecasts currently lack relevance for agricultural producers, in part because they do not reflect their decision needs. This study examines the seasonal forecast needs of winter wheat producers in the southern Great Plains to understand what climate information is most useful and what lead times are most relevant for decision-making. An online survey of 119 agricultural advisers, cooperative extension agents in Oklahoma, Kansas, Texas, and Colorado, was conducted and gave insights into producers’ preferences for forecast elements, what weather and climate extremes have the most impact on decision-making, and the decision timing of major farm practices. The survey participants indicated that winter wheat growers were interested not only in directly modeled variables, such as total monthly rainfall, but also in derived elements, such as consecutive number of dry days. Moreover, these agricultural advisers perceived that winter wheat producers needed seasonal climate forecasts to have a lead time of 0–2.5 months—the planning lead time for major farm practices, like planting or harvesting. A forecast calendar and monthly rankings for forecast elements were created that can guide forecasters and advisers as they develop decision tools for winter wheat producers and that can serve as a template for other time-sensitive decision tools developed for stakeholder communities.
Abstract
Agricultural decision-making that adapts to climate variability is essential to global food security. Crop production can be severely impacted by drought, flood, and heat, as seen in recent years in parts of the United States. Seasonal climate forecasts can help producers reduce crop losses, but many nationwide, publicly available seasonal forecasts currently lack relevance for agricultural producers, in part because they do not reflect their decision needs. This study examines the seasonal forecast needs of winter wheat producers in the southern Great Plains to understand what climate information is most useful and what lead times are most relevant for decision-making. An online survey of 119 agricultural advisers, cooperative extension agents in Oklahoma, Kansas, Texas, and Colorado, was conducted and gave insights into producers’ preferences for forecast elements, what weather and climate extremes have the most impact on decision-making, and the decision timing of major farm practices. The survey participants indicated that winter wheat growers were interested not only in directly modeled variables, such as total monthly rainfall, but also in derived elements, such as consecutive number of dry days. Moreover, these agricultural advisers perceived that winter wheat producers needed seasonal climate forecasts to have a lead time of 0–2.5 months—the planning lead time for major farm practices, like planting or harvesting. A forecast calendar and monthly rankings for forecast elements were created that can guide forecasters and advisers as they develop decision tools for winter wheat producers and that can serve as a template for other time-sensitive decision tools developed for stakeholder communities.
Abstract
Hydrologic extremes of drought and flooding stress water resources and damage communities in the Red River basin, located in the south-central United States. For example, the summer of 2011 was the third driest summer in Oklahoma state history and the driest in Texas state history. When the long-term drought conditions ended in the spring of 2015 as El Niño brought record precipitation to the region, there were also catastrophic floods that caused loss of life and property. Hydrologic extremes such as these have occurred throughout the historical record, but decision-makers need to know how the frequency of these events is expected to vary in a changing climate so that they can mitigate these impacts and losses. Therefore, the goals of this study focus on how these hydrologic extremes impact water resources in the Red River basin, how the frequency of such events is expected to change in the future, and how this study can aid local water-resource managers and decision-makers. Heavy-precipitation events were defined at the historical 90th and 99th percentiles, and severe-drought events were identified at a threshold of the standardized precipitation evapotranspiration index’s value of less than or equal to −1. The results show an increase in the frequency of severe-drought events in the western Red River basin and a rise in heavy-rainfall events in the east by the end of the century, especially under RCP 8.5. Therefore, decision-makers and water-resource managers will likely need to prepare for both hydrologic extremes depending on their location within the basin.
Abstract
Hydrologic extremes of drought and flooding stress water resources and damage communities in the Red River basin, located in the south-central United States. For example, the summer of 2011 was the third driest summer in Oklahoma state history and the driest in Texas state history. When the long-term drought conditions ended in the spring of 2015 as El Niño brought record precipitation to the region, there were also catastrophic floods that caused loss of life and property. Hydrologic extremes such as these have occurred throughout the historical record, but decision-makers need to know how the frequency of these events is expected to vary in a changing climate so that they can mitigate these impacts and losses. Therefore, the goals of this study focus on how these hydrologic extremes impact water resources in the Red River basin, how the frequency of such events is expected to change in the future, and how this study can aid local water-resource managers and decision-makers. Heavy-precipitation events were defined at the historical 90th and 99th percentiles, and severe-drought events were identified at a threshold of the standardized precipitation evapotranspiration index’s value of less than or equal to −1. The results show an increase in the frequency of severe-drought events in the western Red River basin and a rise in heavy-rainfall events in the east by the end of the century, especially under RCP 8.5. Therefore, decision-makers and water-resource managers will likely need to prepare for both hydrologic extremes depending on their location within the basin.
Abstract
Evidence exists that a large-scale alteration of land use by humans can cause changes in the climatology of the region. The largest-scale transformation is the substitution of native landscape by agricultural cropland. This modeling study examines the impact of a direct substitution of one type of grassland for another—in this case, the replacement of tallgrass prairie with winter wheat. The primary difference between these grasses is their growing season: native prairie grasses of the U.S. Great Plains are warm-season grasses whereas winter wheat is a cool-season grass.
Case study simulations were conducted for 27 March 2000 and 5 April 2000—days analyzed in previous observational studies. The simulations provided additional insight into the physical processes involved and changes that occurred throughout the depth of the planetary boundary layer. Results indicate the following: 1) with the proper adjustment of vegetation parameters, land-use type, fractional vegetation coverage, and soil moisture, the numerical simulations were able to capture the overall patterns measured near the surface across a growing wheat belt during benign springtime conditions in Oklahoma; 2) the impacts of the mesoscale belt of growing wheat included increased values of latent heat flux and decreased values of sensible heat flux over the wheat, increased values of atmospheric moisture near the surface above and downstream of the wheat, and a shallower planetary boundary layer (PBL) above and downstream of the wheat; 3) in the sheared environments that were examined, a shallower PBL that resulted from growing wheat (rather than natural vegetation) led to reduced entrainment of higher momentum air into the PBL and, thus, weaker winds within the PBL over and downwind from the growing wheat; 4) for the cases studied, gradients in sensible heat were insufficient to establish an unambiguous vegetation breeze or its corresponding mesoscale circulation; 5) the initialization of soil moisture within the root zone aided latent heat fluxes from growing vegetation; and 6) reasonable specification of land surface parameters was required for the correct simulation and prediction of surface heat fluxes and resulting boundary layer development.
Abstract
Evidence exists that a large-scale alteration of land use by humans can cause changes in the climatology of the region. The largest-scale transformation is the substitution of native landscape by agricultural cropland. This modeling study examines the impact of a direct substitution of one type of grassland for another—in this case, the replacement of tallgrass prairie with winter wheat. The primary difference between these grasses is their growing season: native prairie grasses of the U.S. Great Plains are warm-season grasses whereas winter wheat is a cool-season grass.
Case study simulations were conducted for 27 March 2000 and 5 April 2000—days analyzed in previous observational studies. The simulations provided additional insight into the physical processes involved and changes that occurred throughout the depth of the planetary boundary layer. Results indicate the following: 1) with the proper adjustment of vegetation parameters, land-use type, fractional vegetation coverage, and soil moisture, the numerical simulations were able to capture the overall patterns measured near the surface across a growing wheat belt during benign springtime conditions in Oklahoma; 2) the impacts of the mesoscale belt of growing wheat included increased values of latent heat flux and decreased values of sensible heat flux over the wheat, increased values of atmospheric moisture near the surface above and downstream of the wheat, and a shallower planetary boundary layer (PBL) above and downstream of the wheat; 3) in the sheared environments that were examined, a shallower PBL that resulted from growing wheat (rather than natural vegetation) led to reduced entrainment of higher momentum air into the PBL and, thus, weaker winds within the PBL over and downwind from the growing wheat; 4) for the cases studied, gradients in sensible heat were insufficient to establish an unambiguous vegetation breeze or its corresponding mesoscale circulation; 5) the initialization of soil moisture within the root zone aided latent heat fluxes from growing vegetation; and 6) reasonable specification of land surface parameters was required for the correct simulation and prediction of surface heat fluxes and resulting boundary layer development.
Abstract
Although decision-making in response to tornado warnings is well researched, most studies do not examine whether individual responses to these warnings vary across different geographical locations and demographic groups. This gap is addressed by using data from a decision experiment that places participants virtually in a simulated tornado warning and asks them to minimize the costs of their decisions. The authors examine the following: 1) what demographic attributes may contribute to choices to minimize costs to protect assets at a specific location in a tornado warning, 2) whether there is a spatial component to how these attributes influence decision-making, and 3) if there are specific U.S. regions where individuals do not make protective decisions that minimize their overall cost. Multilevel regression analysis and poststratification are applied to data from the simulated decision experiment to estimate which demographic attributes and National Weather Service County Warning Areas are most associated with the costliest protective decisions. The results are then analyzed using spatial autocorrelation to identify spatial patterns. Results indicate that sex, race, and ethnicity are important factors that influence protection decisions. Findings also show that people across the southern portions of the United States tend to make more costly protective decisions, as defined in this work.
Significance Statement
Tornadoes, although rare, threaten both life and property. Studies have shown that certain demographic groups are more negatively impacted by disasters than others and are at higher risk of severe weather hazards. We ask if there are demographic characteristics or geographic locations in common among people who are more prone to making protection decisions during tornado warnings to minimize economic costs. Results can help warning providers, such as the National Weather Service, direct resources and education to specific types of decision-makers or locations to improve sheltering decisions.
Abstract
Although decision-making in response to tornado warnings is well researched, most studies do not examine whether individual responses to these warnings vary across different geographical locations and demographic groups. This gap is addressed by using data from a decision experiment that places participants virtually in a simulated tornado warning and asks them to minimize the costs of their decisions. The authors examine the following: 1) what demographic attributes may contribute to choices to minimize costs to protect assets at a specific location in a tornado warning, 2) whether there is a spatial component to how these attributes influence decision-making, and 3) if there are specific U.S. regions where individuals do not make protective decisions that minimize their overall cost. Multilevel regression analysis and poststratification are applied to data from the simulated decision experiment to estimate which demographic attributes and National Weather Service County Warning Areas are most associated with the costliest protective decisions. The results are then analyzed using spatial autocorrelation to identify spatial patterns. Results indicate that sex, race, and ethnicity are important factors that influence protection decisions. Findings also show that people across the southern portions of the United States tend to make more costly protective decisions, as defined in this work.
Significance Statement
Tornadoes, although rare, threaten both life and property. Studies have shown that certain demographic groups are more negatively impacted by disasters than others and are at higher risk of severe weather hazards. We ask if there are demographic characteristics or geographic locations in common among people who are more prone to making protection decisions during tornado warnings to minimize economic costs. Results can help warning providers, such as the National Weather Service, direct resources and education to specific types of decision-makers or locations to improve sheltering decisions.
Abstract
The term “tornado outbreak” appeared in the meteorological literature in the 1950s and was used to highlight severe weather events with multiple tornadoes. The exact meaning of “tornado outbreak,” however, evolved over the years. Depending on the availability of scientific data, technological advancements, and the intended purpose of these definitions, authors offered a diverse set of approaches to shape the perception and applications of the term “tornado outbreak.” This paper reviews over 200 peer-reviewed publications—by decade—to outline the evolving nature of the “tornado outbreak” definition and to examine the changes in the “tornado outbreak” definition or its perception. A final discussion highlights the importance, limitations, and potential future evolution of what defines a “tornado outbreak.”
Abstract
The term “tornado outbreak” appeared in the meteorological literature in the 1950s and was used to highlight severe weather events with multiple tornadoes. The exact meaning of “tornado outbreak,” however, evolved over the years. Depending on the availability of scientific data, technological advancements, and the intended purpose of these definitions, authors offered a diverse set of approaches to shape the perception and applications of the term “tornado outbreak.” This paper reviews over 200 peer-reviewed publications—by decade—to outline the evolving nature of the “tornado outbreak” definition and to examine the changes in the “tornado outbreak” definition or its perception. A final discussion highlights the importance, limitations, and potential future evolution of what defines a “tornado outbreak.”
Abstract
Oklahoma Mesonet data were used to measure the impact of Oklahoma's winter wheat belt on the mesoscale environment from 1994 to 2001. Statistical analyses of monthly means of near-surface air temperatures demonstrated that 1) a well-defined cool anomaly existed across the wheat belt during November, December, January, February, and April, and 2) a well-defined warm anomaly existed across the wheat belt during June, July, and August. Data from crop year 2000 indicated a slight moist anomaly over the growing wheat from November 1999 through April 2000. In addition, based upon 21 000 daily statistics over eight unique years, statistical computations indicated less than a 0.1% chance that the moist anomaly during March resulted from random chance.
During the period from 1999 to 2001, about 50 days between 15 March and 1 May showed evidence of heightened values of daily maximum dewpoint over Oklahoma's winter wheat belt as compared to adjacent grasslands. On more than half of these days, the dewpoint was enhanced only across five or six counties in north-central Oklahoma, where the winter wheat production was the largest. Another 90 days between 1 June and 31 July revealed a distinct warm anomaly in daily maximum air temperatures over the wheat belt, particularly across north-central Oklahoma.
These analyses demonstrate that Oklahoma's winter wheat belt has a dramatic impact on the near-surface, mesoscale environment during its growth and after its harvest. Consequently, it is imperative that mesoscale forecasts, whether produced objectively or subjectively, account for the vegetation–air interactions that occur across western Oklahoma and, presumably, across other crop regions in the United States and around the globe.
Abstract
Oklahoma Mesonet data were used to measure the impact of Oklahoma's winter wheat belt on the mesoscale environment from 1994 to 2001. Statistical analyses of monthly means of near-surface air temperatures demonstrated that 1) a well-defined cool anomaly existed across the wheat belt during November, December, January, February, and April, and 2) a well-defined warm anomaly existed across the wheat belt during June, July, and August. Data from crop year 2000 indicated a slight moist anomaly over the growing wheat from November 1999 through April 2000. In addition, based upon 21 000 daily statistics over eight unique years, statistical computations indicated less than a 0.1% chance that the moist anomaly during March resulted from random chance.
During the period from 1999 to 2001, about 50 days between 15 March and 1 May showed evidence of heightened values of daily maximum dewpoint over Oklahoma's winter wheat belt as compared to adjacent grasslands. On more than half of these days, the dewpoint was enhanced only across five or six counties in north-central Oklahoma, where the winter wheat production was the largest. Another 90 days between 1 June and 31 July revealed a distinct warm anomaly in daily maximum air temperatures over the wheat belt, particularly across north-central Oklahoma.
These analyses demonstrate that Oklahoma's winter wheat belt has a dramatic impact on the near-surface, mesoscale environment during its growth and after its harvest. Consequently, it is imperative that mesoscale forecasts, whether produced objectively or subjectively, account for the vegetation–air interactions that occur across western Oklahoma and, presumably, across other crop regions in the United States and around the globe.
Abstract
As a result of climate change, extreme precipitation events are likely to become more common in Oklahoma, requiring cities and municipalities to plan for managing this extra water. There are multiple types of practitioners within communities who are responsible for overseeing planning for the future, including stormwater and floodplain management. These practitioners may be able to integrate weather and climate information into their decision-making to help them prepare for heavy precipitation events and their impacts. Floodplain managers from central and eastern Oklahoma were interviewed to learn what information they currently use and how it informs their decision-making. When making decisions in the short term, floodplain managers relied on weather forecasts; for long-term decisions, other factors, such as constrained budgets or the power of county officials, had more influence than specific climate predictions or projections. On all time scales, social networks and prior experience with flooding informed floodplain managers’ decisions and planning. Overall, information about weather and climate is just one component of floodplain managers’ decision-making processes. The atmospheric science community could work more collaboratively with practitioners so that information about weather and climate is more useful and, therefore, more relevant to the types of decisions that floodplain managers make.
Abstract
As a result of climate change, extreme precipitation events are likely to become more common in Oklahoma, requiring cities and municipalities to plan for managing this extra water. There are multiple types of practitioners within communities who are responsible for overseeing planning for the future, including stormwater and floodplain management. These practitioners may be able to integrate weather and climate information into their decision-making to help them prepare for heavy precipitation events and their impacts. Floodplain managers from central and eastern Oklahoma were interviewed to learn what information they currently use and how it informs their decision-making. When making decisions in the short term, floodplain managers relied on weather forecasts; for long-term decisions, other factors, such as constrained budgets or the power of county officials, had more influence than specific climate predictions or projections. On all time scales, social networks and prior experience with flooding informed floodplain managers’ decisions and planning. Overall, information about weather and climate is just one component of floodplain managers’ decision-making processes. The atmospheric science community could work more collaboratively with practitioners so that information about weather and climate is more useful and, therefore, more relevant to the types of decisions that floodplain managers make.
Abstract
Because of the sensitivity of agricultural production to both short-term weather and long-range climatic patterns, the availability of reliable and relevant meteorological data and climate products can potentially affect the entire production process. This study focuses on the use of information from a dense meteorological network—the Oklahoma Mesonet—and its AgWeather program in support of agricultural production decisions. Production decisions that are particularly dependent on information from the Mesonet are identified. Producers in Oklahoma are influenced by Mesonet data at several levels, including agricultural policy, production choices, and risk management. Additionally, producers use the Mesonet to attain their financial goals, through such measures as cost saving and maximization of quality and quantity, in addition to others. Potential savings from Mesonet data for the state’s agricultural sector are also estimated.
Abstract
Because of the sensitivity of agricultural production to both short-term weather and long-range climatic patterns, the availability of reliable and relevant meteorological data and climate products can potentially affect the entire production process. This study focuses on the use of information from a dense meteorological network—the Oklahoma Mesonet—and its AgWeather program in support of agricultural production decisions. Production decisions that are particularly dependent on information from the Mesonet are identified. Producers in Oklahoma are influenced by Mesonet data at several levels, including agricultural policy, production choices, and risk management. Additionally, producers use the Mesonet to attain their financial goals, through such measures as cost saving and maximization of quality and quantity, in addition to others. Potential savings from Mesonet data for the state’s agricultural sector are also estimated.
Abstract
Extreme precipitation events can cause significant impacts to life, property, and the economy. As forecasting capabilities increase, the subseasonal-to-seasonal (S2S) time scale provides an opportunity for advanced notice of impactful precipitation events. Building on a previous workshop, the Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES2iP) project team conducted a second workshop virtually in the fall of 2021. The workshop engaged a variety of practitioners, including emergency managers, water managers, tribal environmental professionals, and National Weather Service meteorologists. While the team’s first workshop examined the “big picture” in how practitioners define “extreme precipitation” and how precipitation events impact their jobs, this workshop focused on details of S2S precipitation products, both current and potential future decision tools. Discussions and activities in this workshop assessed how practitioners use existing forecast products to make decisions about extreme precipitation, how they interpret newly developed educational tools from the PRES2iP team, and how they manage uncertainty in forecasts. By collaborating with practitioners, the PRES2iP team plans to use knowledge gained going forward to create more educational and operational tools related to S2S extreme precipitation event prediction, helping practitioners to make more informed decisions.
Abstract
Extreme precipitation events can cause significant impacts to life, property, and the economy. As forecasting capabilities increase, the subseasonal-to-seasonal (S2S) time scale provides an opportunity for advanced notice of impactful precipitation events. Building on a previous workshop, the Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES2iP) project team conducted a second workshop virtually in the fall of 2021. The workshop engaged a variety of practitioners, including emergency managers, water managers, tribal environmental professionals, and National Weather Service meteorologists. While the team’s first workshop examined the “big picture” in how practitioners define “extreme precipitation” and how precipitation events impact their jobs, this workshop focused on details of S2S precipitation products, both current and potential future decision tools. Discussions and activities in this workshop assessed how practitioners use existing forecast products to make decisions about extreme precipitation, how they interpret newly developed educational tools from the PRES2iP team, and how they manage uncertainty in forecasts. By collaborating with practitioners, the PRES2iP team plans to use knowledge gained going forward to create more educational and operational tools related to S2S extreme precipitation event prediction, helping practitioners to make more informed decisions.
