Continental United States Research Articles

First Report of Twig Dieback Caused by Neopestalotiopsis rosae on Highbush Blueberry in the Continental United States.

Highbush blueberry (Vaccinium corymbosum) is an important fruit crop for pick-your-own agritourism farms in the Midwest. Declining or diseased plants are a major concern for pick-your-own farms, as consumers prioritize healthy plants and organic practices (Norby and Retallick 2012). In August 2023, leaf spot and dieback symptoms were observed sporadically on the current year's growth throughout an organic berry agritourism farm in Eastern Iowa. Small red spots on the twigs and leaves progressed into larger lesions with gray centers, and brown borders with no observable acervuli or pycnidia. Symptomatic samples from 20 individual 'Duke' blueberry plants were collected for disease identification. Tissue was cut from lesion edges, surface sterilized with 10% bleach for 1 min, rinsed thrice in sterilized water, cultured on potato dextrose agar amended with 800 mg/L yeast extract (PDAY) and 100 µg/ml streptomycin, then kept at 20°C. Twenty isolates were identified morphologically, and three were submitted for molecular analysis. Isolated colonies had white aerial mycelia, irregular margins, and were light yellow underneath. Black, globular acervuli produced five-celled fusoid conidia that ranged from straight to slightly curved. The center three cells were light to dark brown. The basal cell was generally conical, with a small basal appendage. The apical cell ranged from conical to cylindrical, with 2-3 appendages. The spores averaged 23.7 µm long (range 19.7-30.1 µm) and 5.9 µm wide (range 5.0-6.5 µm), n=50. BLAST analysis demonstrated that sequences of ITS (ITS1-F/ITS4; PP764834, PQ344945-46), TUB2 (Bt2a/Bt2b; PP786462, PQ356370-71), and TEF1-α (EF1-526-F/; PP789161, PQ356368-69) were ~98 to 100% similar to KM199360, KM199430, and KM199524 published for Neopestalotiopsis rosae. Using MEGA 11 program (Kumar et al. 2018), maximum likelihood analysis based on the concatenated ITS, TUB2, and TEF1-α sequences placed all three isolates in a high-confidence cluster with N. rosae (Baggio et al. 2021; Jayawardena et al. 2016; Lee et al. 2019), confirming their identification. Pathogenicity of one isolate, NPB-1, was confirmed by inoculating three twigs on three 1-year-old V. corymbosum 'Blue Gold' plants (n=9). Twigs were pierced using a sterile 20-gauge needle with a mycelium plug to mimic pruning wounds, or a PDAY plug as a control (Borrero et al. 2018). Inoculated and control plants were kept at 28°C, 50% relative humidity, with an 18-hour photoperiod. Ten days post inoculation (dpi), inoculated plants showed symptoms of reddish-brown spots along wounded stems which were not present on control plants. At 35 dpi, inoculated plants displayed blight symptoms like the field-observed ones. Inoculated twigs had internal discoloration beyond the inoculation point, unlike the control twigs. N. rosae was successfully reisolated 2 cm away from the inoculation site and confirmed by sequencing in all inoculated twigs (n=9), but not in control plants (n=9). Neopestalotiopsis spp. have been previously reported to cause twig blight on blueberries in Korea, Chile, Uruguay, China, and Spain (Lee et al. 2019; Borrero et al. 2018; Chen et al. 2016; González et al. 2012; Espinoza et al. 2008). To our knowledge, this is the first report of N. rosae causing twig dieback on V. corymbosum in the US, and this knowledge will aid in proper diagnosis and treatment development.

Messaging the Rip Current Threat from Distant Tropical Cyclones in the Carolinas

Rip currents are the number one weather-related killer in the Carolinas, and rip currents generated by swells from distant tropical cyclones pose a unique messaging challenge for the National Weather Service (NWS). A database of surf fatalities from Atlantic basin tropical cyclones was compiled, with 87 surf fatalities along the continental United States coastline between 2000 and 2022 attributed to distant storms, where direct impacts from the storm remained far from the local area. Hurricane Lorenzo in 2019 led to eight drownings along the United States East Coast despite remaining more than 3110 km (1680 n mi) offshore in the central Atlantic. The rip current impacts from Lorenzo led to efforts to increase communication and improve several products from the NWS offices in Wilmington and Morehead City, North Carolina. Efforts have also been made to increase the NWS reach into underserved communities and vulnerable populations, including translating rip current briefings into Spanish and developing a partnership with the North Carolina Division of Services for the Deaf and Hard of Hearing. In the last few years, NWS Wilmington has begun messaging rip current outbreaks similar to other weather threats, such as potential severe weather events.

Sleep Value and Sleep Resilience are Important Dimensions of Sleep Health and We Measured Them: Methods for the Sleep Resilience and Variance in Sleep Valuation (SRVIV) Study

Abstract Sleep health is a multidimensional construct that is essential for general health and well-being. Sleep value, the amount of worth an individual places on their sleep, and sleep resilience, the ability to function emotionally, cognitively, and physically in the presence of sleep disturbances are overlooked dimensions of sleep health. To study these sleep health dimensions, we developed the Sleep Valuation Item Bank 2.0, Values Inventory, Monetary Sleep Value Questionnaire, and Sleep Resilience Questionnaire. This paper describes the methods for the Sleep Resilience and Variance in Sleep Valuation (SRVIV) Study. The SRVIV study was conducted to explore the factor structure of sleep value and sleep resilience and determine how they relate to demographic, sleep, and psychological variables. This study resulted in an analysis sample of 455 participants who were recruited by a Qualtrics team and completed a Qualtrics survey consisting of demographic, anxiety, depression, and sleep-related questionnaires, in addition to sleep value and sleep resilience questionnaires. Adult participants were recruited throughout the continental United States and were predominately female (53%), white (82%), married (50%), and had an average age of 45.4 years. The data resulting from this study can be used to address important questions in the field of sleep psychology.

A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO2 Concentration from Satellite and Ground Monitors.

Accurate estimation of atmospheric chemical concentrations from multiple observations is crucial for assessing the health effects of air pollution. However, existing methods are limited by imbalanced samples from observations. Here, we introduce a novel deep-learning model-measurement fusion method (DeepMMF) constrained by physical laws inferred from a chemical transport model (CTM) to estimate NO2 concentrations over the Continental United States (CONUS). By pretraining with spatiotemporally complete CTM simulations, fine-tuning with satellite and ground measurements, and employing a novel optimization strategy for selecting proper prior emission, DeepMMF delivers improved NO2 estimates, showing greater consistency and daily variation alignment with observations (with NMB reduced from -0.3 to -0.1 compared to original CTM simulations). More importantly, DeepMMF effectively addressed the sample imbalance issue that causes overestimation (by over 100%) of downwind or rural concentrations in other methods. It achieves a higher R2 of 0.98 and a lower RMSE of 1.45 ppb compared to surface NO2 observations, overperforming other approaches, which show R2 values of 0.4-0.7 and RMSEs of 3-6 ppb. The method also offers a synergistic advantage by adjusting corresponding emissions, in agreement with changes (-10% to -20%) reported in the NEI between 2019 and 2020. Our results demonstrate the great potential of DeepMMF in data fusion to better support air pollution exposure estimation and forecasting.

Normalized Hurricane Damage in the United States: 1900-2022

Abstract Since 1900, landfalling hurricanes have been the costliest of all weather-related disasters to afflict the continental United States. To provide a present-day (2022) re-evaluation of this risk, this study employs an improved normalization approach to better understand potential economic event losses in the context of contemporary societal conditions. The updated methodology identifies impacted coastal counties using the newly available radius of maximum winds at landfall. Hurricane Katrina is the most expensive hurricane since 1900, with a likely 2022 normalized cost of US$234 billion. Combined losses from the 50 most expensive hurricane events are ~US$2.9 trillion in normalized economic losses. The study also explores some “analogue storms” where comparisons can be made between two historic storms with similar landfall locations. For example, Category 5 Andrew (1992) has lower 2022 normalized losses than Category 4 Great Miami (1926), at US$125 billion vs US$178, most likely due to the significantly different radius of maximum wind size (10 vs 20 nm). As with previous studies, we conclude that increases in inflation, coastal population, regional wealth, and higher replacement costs remain the primary drivers of observed increases in hurricane-related damage. These upsurges are especially impactful for some coastal regions along the US Gulf and Southeast Coasts that have seen exceptionally high rates of population/housing growth in comparison to country-wide growth. Exposure growth trends are likely to continue in the future and, independent of any influence of climate change on tropical cyclone behavior, are expected to result in greater hurricane-related damage costs than have been previously observed.

Temporal Associations Between Polarimetric Updraft Proxies and Signatures of Inflow and Hail in Supercells

Recurring polarimetric radar signatures in supercells include deep and persistent differential reflectivity (ZDR) columns, hail inferred in low-level scans, and the ZDR arc signature. Prior investigations of supercell polarimetric signatures reveal positive correlations between the ZDR column depth and cross-sectional area and quantitative characteristics of the radar reflectivity field. This study expands upon prior work by examining temporal associations between supercell polarimetric radar signatures, incorporating a dataset of relatively discrete, right-moving supercells from the continental United States observed by the Weather Surveillance Radar 1988-Doppler (WSR-88D) network. Cross-correlation coefficients were calculated between the ZDR column area and depth and the base-scan hail area, ZDR arc area, and mean ZDR arc value. These correlation values were computed with a positive and negative lag time of up to 45 min. Results of the lag correlation analysis are consistent with prior observations indicative of storm cycling, including temporal associations between ZDR columns and inferred hail signatures/ZDR arcs in both tornadic and nontornadic supercells, but were most pronounced in tornadic storms.

Continual Learning and Adaptation In Resource-Constrained Environments (CLAIRE)

As climate-related extreme events continue to increase and impact the world, of particular importance are the threats posed to food, agriculture, and water (FAW) systems. Deep learning could benefit FAW systems for classification of threats and for forecasting potential future events given historical patterns. However, many FAW systems are faced with operational environments that are resource-constrained, which could present challenges in deploying deep learning models. Continual learning offers a way to overcome certain deployment challenges by enabling deep learning models that are more robust to data distribution changes, without the need for GPUs or off-line training. We describe a continual learning approach to forecasting extreme air quality events developed for the National Oceanic and Atmospheric Administration to provide operational air quality guidance to the Continental United States. We describe how this deep learning model is resilient to future data distribution changes by performing curriculum learning, and how it can be deployed as a continual learner, offering better predictive performance for resource-constrained environments.

Herbarium specimens reveal links between leaf shape of Capsella bursa-pastoris and climate.

Studies into the evolution and development of leaf shape have connected variation in plant form, function, and fitness. For species with consistent leaf margin features, patterns in leaf architecture are related to both biotic and abiotic factors. However, for species with inconsistent leaf shapes, quantifying variation in leaf shape and the effects of environmental factors on leaf shape has proven challenging. To investigate leaf shape variation in a species with inconsistently shaped leaves, we used geometric morphometric modeling and deterministic techniques to analyze approximately 500 digitized specimens of Capsella bursa-pastoris collected throughout the continental United States over 100 years. We generated a morphospace of the leaf shapes and modeled leaf shape as a function of environment and time. Leaf shape variation of C. bursa-pastoris was strongly associated with temperature over its growing season, with lobing decreasing as temperature increased. While we expected to see changes in variation over time, our results show that the level of leaf shape variation was consistent over the 100 years. Our findings showed that species with inconsistent leaf shape variation can be quantified using geometric morphometric modeling techniques and that temperature is the main environmental factor influencing leaf shape variation.

Toward Modeling Continental‐Scale Inland Water Carbon Dioxide Emissions

AbstractInland waters emit significant amounts of carbon dioxide (CO2) to the atmosphere; however, the global magnitude and source distribution of inland water CO2 emissions remain uncertain. These fluxes have previously been “statistically upscaled” by independently estimating dissolved CO2 concentrations and gas exchange velocities to calculate fluxes. This scaling, while robust and defensible, has known limitations in representing carbon source limitations and spatial variability. Here, we develop and calibrate a CO2 transport model for the continental United States, simulating carbon transport and transformation in >22 million hydraulically connected rivers, lakes, and reservoirs. We estimate 25% lower CO2 fluxes compared to upscaling estimates forced by the same observational calibration data. While precise CO2 source distribution estimates are limited by the resolution of model parameterizations, our model suggests that stream corridor CO2 production dominates over groundwater inputs at the continental scale. Our results further suggest that the lack of observational networks for groundwater CO2 and scalable metabolic models of aquatic CO2 production remain the most salient barriers to further coupling of our model with other Earth system components.

Reemergence of Oropouche Virus in the Americas and Risk for Spread in the United States and Its Territories, 2024.

Oropouche virus has recently caused outbreaks in South America and the Caribbean, expanding into areas to which the virus was previously not endemic. This geographic range expansion, in conjunction with the identification of vertical transmission and reports of deaths, has raised concerns about the broader threat this virus represents to the Americas. We review information on Oropouche virus, factors influencing its spread, transmission risk in the United States, and current status of public health response tools. On the basis of available data, the risk for sustained local transmission in the continental United States is considered low because of differences in vector ecology and in human-vector interactions when compared with Oropouche virus-endemic areas. However, more information is needed about the drivers for the current outbreak to clarify the risk for further expansion of this virus. Timely detection and control of this emerging pathogen should be prioritized to mitigate disease burden and stop its spread.

Forecasting Costs of U.S. Ballistic Missile Defense Against a Major Nuclear Strike

ABSTRACT How many dollars would the United States need to spend on its ballistic missile defense (BMD) for each dollar the attacker spends to launch land-based intercontinental ballistic missiles (ICBMs) armed with nuclear warheads? A hypothetical scenario is analyzed in which the United States has a functioning BMD technology and enough interceptors to distribute them in a two-layer defense with the overall system efficiency of 90%, as targeted by U.S. war planners. It is assumed that the attacker has enough missiles to deliver a range between 500 and 6000 warheads to the continental United States. Results show that in the most optimistic case for the defender, with a very high individual interceptor kill effectiveness of 90% and with perfect decoy discrimination capability, the United States would need to spend on average 8 times more than the attacker, for a total cost between $60 billion and $500 billion. With a more realistic individual interceptor effectiveness of 50% and if the system is unable to discriminate against decoys, the United States would need to spend on average 70 times more, for a total cost between $430 billion and $5.3 trillion.

Investigation of the characteristics of low-level jets over North America in a convection-permitting Weather Research and Forecasting simulation

Abstract. In this study, we utilized a high-resolution (4 km) convection-permitting Weather Research and Forecasting (WRF) simulation spanning a 13-year period (2000–2013) to investigate the climatological features of low-level jets (LLJs) over North America. The 4 km simulation enabled us to represent the effects of orography and the underlying surface on the boundary layer winds better. Focusing on the continental US and the adjacent border regions of Canada and Mexico, this study not only identified several well-known large-scale LLJs, such as the southerly Great Plains LLJ and the summer northerly California coastal LLJ, but also the winter Quebec northerly LLJ which received less focus before. All these LLJs reach their peak in the nighttime in the diurnal cycle. Thus, the different thermal and dynamic mechanisms forming these three significant LLJs are investigated in this paper. Inertial oscillation theory dominates in the Great Plain LLJ, and the California coastal LLJ is formed by the baroclinic theory, whereas the Quebec LLJ is associated with both theories. Moreover, the high-resolution simulation revealed climatic characteristics of weaker and smaller-scale LLJs or low-level wind maxima in regions with complex terrains, such as the northerly LLJs in the foothill regions of the Rocky Mountains and the Appalachians during the winter. This study provides valuable insights into the climatological features of LLJs in North America, and the high-resolution simulation offers a more detailed understanding of LLJ behavior near complex terrains and other smaller-scale features.

Triple Collocation-Based Model Error Estimation of VIC-Simulated Soil Moisture at Spatial and Temporal Scales in the Continental United States in 2010–2020

Triple Collocation-Based Model Error Estimation of VIC-Simulated Soil Moisture at Spatial and Temporal Scales in the Continental United States in 2010–2020

"Turtles of North America: an Illustrated Field Guide to the Turtles of the Continental United States and Canada" by Kyle Horner, 2024 [book review

"Turtles of North America: an Illustrated Field Guide to the Turtles of the Continental United States and Canada" by Kyle Horner, 2024 [book review

Imaging the May 2024 Extreme Aurora With Ionospheric Total Electron Content

AbstractThe continental United States is well instrumented with facilities for mid‐latitude upper atmosphere research that operate on a continuous basis. In addition, citizen scientists provide a wealth of information when unusual events occur. We combine ionospheric total electron content (TEC) data from distributed arrays of GNSS receivers, magnetometer chains, and auroral observations obtained by citizen scientists, to provide a detailed view of the intense auroral breakup and westward surge occurring at the peak of the 10–11 May 2024 extreme geomagnetic storm. Over a 20‐min interval, vertical TEC (vTEC) increased at unusually low latitude (∼45°) and rapidly expanded azimuthally across the continent. Individual receiver/satellite data sets indicate sharp bursts of greatly elevated of vTEC (∼50 TECu). Intense red aurora was co‐located with the leading edge of the equatorward and westward TEC enhancements, indicating that the large TEC enhancement was created by extremely intense low‐energy precipitation during the rapid substorm breakup.

Cooling benefits from urban planting depend on local background canopy cover: A continental cross-city comparison

Numerous studies have shown that the cooling efficiency (CE) of urban trees varies by cities with different climate backgrounds, and recent research further indicated that there may be large within-city variations in CE. However, how such within-city variations differ across cities, and their relations to the local percent of urban tree canopy (Ptree) remain poorly understood. This study aims to fill this gap based on a comparison study across 118 cities with different biomes and climates in the continental USA. We used the tree canopy layer of the National Land Cover Dataset (NLCD) 2011 to measure urban tree canopy (UTC), and calculated land surface temperature (LST) based on Landsat thermal bands. We found: 1) CE had larger within-city and cross-city spatial variations in cities located in arid and semi-arid biomes. 2) CE was related to Ptree in nonlinear ways in >90 % of the study cities. In most cities (approximately 70 %), CE had an L-shaped relationship with Ptree, showing that CE first declined quickly with the increase of Ptree, but then gradually dropped in a slower way or became relatively stable after Ptree reached a certain threshold. 3) While there was no significant difference in the types of CE-Ptree relationship among biomes and climates, the threshold of Ptree in CE-Ptree nonlinear ways was smaller in arid cities. The results of this threshold linking cooling benefits and current UTC can serve as a useful tool to prioritize locations for urban planting to maximize cooling benefits.

Reinterpreting Chronic Wasting Disease Emergence in the USA in Light of Historical Surveillance Limitations.

We estimated the probabilities of detecting one or more chronic wasting disease (CWD) cases (Pdet) in free-ranging cervids in the continental US during 1997 - 2001. Based on sample sizes reported by respective state authorities at the time and a target for detectable apparent prevalence (i.e., a design prevalence) of 0.001 (one positive per 1,000 animals statewide), estimated Pdet were <50% for 39/46 states where CWD had not been detected in the wild prior to 1997 and were <5% in 20/26 states located east of the Mississippi River. The survey designs and sample sizes reported by most states prior to 2002 would have yielded exceedingly small detection probabilities for focal CWD outbreaks. Although most CWD foci in the US were first detected in 2002 or after, the data presented here and elsewhere suggest it is plausible that an unknown number of these-some established perhaps decades earlier-were already present but had simply eluded detection. These data highlight uncertainty regarding timelines for CWD emergence in the US. Accepting-and to the extent possible quantifying-uncertainty in the historical distribution of CWD throughout the US seems a necessary foundation for better understanding its emergence, its drivers and patterns of spread, and its response to various interventions-past, present, and future.

Access to National Cancer Institute-Designated Cancer Centers Among Native American Cancer Patients

Introduction/Purpose Native Americans (NAs) are subject to high cancer mortality rates in the USA. Despite that, they face significant geographic barriers to access to cancer care. This study aims to estimate the travel distance to a National Cancer Institute (NCI)-designated cancer center for NA patients in Utah and the continental USA. Methods This IRB-approved study utilized retrospective data on genitourinary cancer patients from both NAs and white populations from February 2013 to January 2023. The distance of their geographical location to the Huntsman Cancer Institute (HCI) at the University of Utah was calculated using their home zip code and a GeoData ZIP Code Distance Calculations Matrix Template. A shapefile containing NCI-designated cancer centers was used alongside the Area Deprivation Index (ADI), matched to block groups from the 2020 census, to serve as a national control group. All geographic data was visualized in ArcGIS 10.7 by using the coordinates and a 5-digit zip code tabulation area to map locations. Results A total of 468 NA patients were eligible and included. The median travel distance for NA patients vs. white patients to HCI was 190.6 miles (range: 1.1-596.4 miles) vs. 21.6 miles (range: 1.1-269 miles, p&lt;0.0001). In the continental US, the median travel distance from NA reservations vs. ADI-matched block groups to the nearest NCI-designated cancer centers was 186.5 miles (range 77.8-629 miles) vs. 159 miles (range 1.9-671.3 miles, p&lt;0.01). Conclusion The travel distance to NCI-designated cancer center for NA cancer patients in Utah was around nine times longer than that of white cancer patients. This study highlights the significant disparity in cancer care accessibility faced by NA communities.

Modeling continental US stream water quality using long-short term memory and weighted regressions on time, discharge, and season

The temporal dynamics of solute export from catchments are challenging to quantify and model due to confounding hydrological and biogeochemical processes and sparse measurements. Conventionally, the concentration-discharge relationship (C-Q) and statistical approaches to describe it, such as the Weighted Regressions on Time, Discharge and Seasons (WRTDS), have been widely used. Recently, deep learning (DL) approaches, especially Long-Short-Term-Memory (LSTM) models, have shown predictive capability for discharge, temperature, and dissolved oxygen. However, it is not clear if such advances can be expanded to water quality variables driven by complex subsurface biogeochemical processes. This work evaluates the performance of LSTM and WRTDS for 20 water quality variables across ~500 catchments in the continental US. We find that LSTM does not markedly outperform WRTDS in our dataset, potentially limited by the current measurement capabilities of water quality across CONUS. Both models present similar performance patterns across water quality variables, with the LSTM displaying better performance for nutrients compared to weathering-derived solutes. Additionally, the LSTM does not benefit from flexibility in the inputs. For example, incorporation of climate data that constrains streamflow generation, does not significantly improve the LSTM performance. We also find that data availability is not a straightforward predictor of LSTM model performance, although higher availability tends to stabilize performance. To fully assess the potential of the LSTM model, it may be necessary to use a higher frequency dataset across the CONUS, which does not exist today. To evaluate the dynamics of C-Q patterns relative to model performance, we introduce a “simplicity index” considering both the seasonality in the concentration pattern and the linearity in the C-Q relationship, or the C-Q-t pattern. The simplicity index is strongly correlated with model performance and differentiates the underlying controls on water quality dynamics. Further DL experiments and model-intercomparison highlight the strengths and deficiencies of existing frameworks, pointing to the need for further hydrogeochemical theories that are amenable to complex basins and solutes.

Probe-Based Loop-Mediated Isothermal Amplification Assay for Rapid Detection of Two Clarireedia spp., the Causal Agent of Dollar Spot of Turfgrass.

Dollar spot is a major fungal disease affecting turfgrass worldwide and can quickly destroy turfgrass swards. An assimilating probe-based loop-mediated isothermal amplification (LAMP) assay was developed to detect Clarireedia monteithiana and C. jacksonii, the causal agents of dollar spot within the continental United States. Five LAMP primers were designed to target the calmodulin gene with the addition of a 6-carboxyl-fluorescein florescent assimilating probe, and the temperature amplification was optimized for C. jacksonii and C. monteithiana identification. The minimum amount of purified DNA needed for detection was 0.05 ng μl-1. Specificity assays against host DNA and other turfgrass pathogens were negative. Successful LAMP amplification was also observed for dollar spot-infected turfgrass field samples. Further, a DNA extraction technique via rapid heat-chill cycles and visualization of LAMP results via a florescent flashlight was developed and adapted for fast, simple, and reliable detection in 1.25 h. This assimilating probe-based LAMP assay has proved successful as a rapid, sensitive, and specific method for the detection of C. monteithiana and C. jacksonii in pure cultures and from symptomatic turfgrass leaves blades. The assay represents a promising technology to be used in the field for on-site, point-of-care pathogen detection.