Continental United States Research Articles

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.

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.

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.

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.

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.

Internet-based surveillance to track trends in seasonal allergies across the United States.

Over a quarter of adults in the United States suffer from seasonal allergies, yet the broader spatiotemporal patterns in seasonal allergy trends remain poorly resolved. This knowledge gap persists due to difficulties in quantifying allergies as symptoms are seldom severe enough to warrant hospital visits. We show that we can use machine learning to extract relevant data from Twitter posts and Google searches to examine population-level trends in seasonal allergies at high spatial and temporal resolution, validating the approach against hospital record data obtained from selected counties in California, United States. After showing that internet-derived data can be used as a proxy for aeroallergen exposures, we demonstrate the utility of our approach by mapping seasonal allergy-related online activity across the 144 most populous US counties at daily time steps over an 8-year period, highlighting the spatial and temporal dynamics in allergy trends across the continental United States.

Provider Guidance for the Prevention of Respiratory Syncytial Virus in Infants: Maternal Vaccination Versus Infant Monoclonal Antibody Treatment.

In 2023, the Food and Drug Administration approved 2 new products to reduce the risk of lower respiratory infections caused by respiratory syncytial virus (RSV) in infants: Beyfortus (nirsevimab; AstraZeneca/Sanofi), a single-dose monoclonal antibody for infant administration, and Abrysvo (bivalent RSVpreF vaccine; Pfizer), a single-dose maternal vaccination. We aimed to synthesize data from the literature and the leading professional organizations to provide guidance on RSV and strategies to reduce the risk of infant infection. This information will assist prenatal care clinicians in counseling their patients regarding the choice between maternal vaccination and the infant monoclonal antibody. A descriptive review of the guidelines from the Centers for Disease Control and Prevention, American College of Obstetrics and Gynecology (ACOG), American Academy of Pediatrics, Society for Maternal-Fetal Medicine, and the American Academy of Family Physicians. All 5 organizations recommend that RSV vaccination should be offered to all pregnant people during the RSV season (September-January in the continental United States). Infants younger than 8 months entering into their first RSV season born to those who did not receive maternal vaccination or received vaccination less than 14 days prior to birth should be offered monoclonal antibody administration. RSV vaccination and monoclonal antibodies have the potential to significantly reduce the burden of lower respiratory tract infections due to RSV in infants. Future studies should further evaluate the durability of protection and other strategies to further protect the infant, including cocooning and the role of breast milk in immunity.

The Assessment and Management of Biliary Atresia in Hawai'i, 2009-2023.

Although biliary atresia (BA) is a rare neonatal disorder, it remains the leading cause of pediatric end-stage liver disease. Early diagnosis of BA and treatment with the Kasai procedure can significantly reduce the need for pediatric liver transplant. Current data suggests that performing the Kasai procedure at 30-45 days of life is associated with longer native liver survival rates and reduction of the need for liver transplant. The incidence rate of BA in the state of Hawai'i is nearly double the incidence rate in the continental US. International studies have demonstrated that screening programs for BA reduce the age at diagnosis and treatment. However, there has been no statewide analysis on the ages at diagnosis or at Kasai, nor does a statewide screening program for BA exist. The purpose of this study is to review the age of diagnosis and treatment of BA to determine if the current practice in Hawai'i is in line with the published data. A retrospective chart review of all patients diagnosed with BA at the state's primary children's hospital was performed (2009-2023) and 19 patients who underwent the Kasai procedure were identified. The mean age at diagnosis is 71.4 days (n=19) and the mean age at Kasai procedure is 72.0 days (n=19). Both the average age at diagnosis and treatment for BA in Hawai'i is significantly higher than published data suggesting best outcomes at 30-45 days of life. This review suggests that the implementation of a statewide screening program for BA in Hawai'i is warranted.

Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting

Abstract Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This study explores an application of machine learning (ML) models as postprocessing tools for subseasonal forecasting. Lagged numerical ensemble forecasts (i.e., an ensemble where the members have different initialization dates) and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods to predict monthly average precipitation and 2-m temperature 2 weeks in advance for the continental United States. For regression, quantile regression, and tercile classification tasks, we consider using linear models, random forests, convolutional neural networks, and stacked models (a multimodel approach based on the prediction of the individual ML models). Unlike previous ML approaches that often use ensemble mean alone, we leverage information embedded in the ensemble forecasts to enhance prediction accuracy. Additionally, we investigate extreme event predictions that are crucial for planning and mitigation efforts. Considering ensemble members as a collection of spatial forecasts, we explore different approaches to using spatial information. Trade-offs between different approaches may be mitigated with model stacking. Our proposed models outperform standard baselines such as climatological forecasts and ensemble means. In addition, we investigate feature importance, trade-offs between using the full ensemble or only the ensemble mean, and different modes of accounting for spatial variability. Significance Statement Accurately forecasting temperature and precipitation on subseasonal time scales—2 weeks–2 months in advance—is extremely challenging. These forecasts would have immense value in agriculture, insurance, and economics. Our paper describes an application of machine learning techniques to improve forecasts of monthly average precipitation and 2-m temperature using lagged physics-based predictions and observational data 2 weeks in advance for the entire continental United States. For lagged ensembles, the proposed models outperform standard benchmarks such as historical averages and averages of physics-based predictions. Our findings suggest that utilizing the full set of physics-based predictions instead of the average enhances the accuracy of the final forecast.

Miscegenation and militarization: undoing racial colonial citizenship for the next century

ABSTRACT This essay considers the Immigration Act of 1924, otherwise known as the Johnson-Reed Act (JRA), within historical and contemporary contexts of anti-miscegenation and Indigenous erasure that are undercurrents of citizenship and immigration processes. By using a framework of settler colonialism carried out by the U.S. nation-state, we explore connections among racialized peoples in U.S. American empire and the push for racial purity through systems of blood quantum and anti-miscegenation laws. With attention to how colonialism from the continental USA flows to locales such as Guåhan/Guam, a place enacting sovereignty despite occupation and colonial possession, we consider how immigration and citizenship are wielded as tools of U.S. American militarization. Ultimately, we argue that critiquing miscegenation and militarism is necessary to overcome racial and colonial violences of empire.

An enhanced Standardized Precipitation–Evapotranspiration Index (SPEI) drought-monitoring method integrating land surface characteristics

Abstract. Atmospheric evaporative demand is a key metric for monitoring agricultural drought. Existing ways of estimating evaporative demand in drought indices do not faithfully represent the constraints imposed by land surface characteristics and become less accurate over nonuniform land surfaces. This study proposes incorporating surface vegetation characteristics, such as vegetation dynamics data, aerodynamic parameters, and physiological parameters, into existing potential-evapotranspiration (PET) methods. This approach is implemented across the continental United States (CONUS) for the period from 1981–2017 and is tested using a recently developed drought index, the Standardized Precipitation–Evapotranspiration Index (SPEI). We show that activating realistic maximum surface conductance and aerodynamic conductance could improve the prediction of soil moisture dynamics and drought impacts by 29 %–41 % on average compared to more simple, widely used methods. We also demonstrate that this is especially effective in forests and humid regions, with improvements of 86 %–89 %. Our approach only requires a minimal amount of ancillary data while allowing for both historical reconstruction and real-time drought forecasting. This offers a physically meaningful yet easy-to-implement way to account for vegetation control in drought indices.