Continental United States Research Articles

Access to National Cancer Institute (NCI)–designated cancer centers among Native American patients with cancer.

e18646 Background: Native Americans (NA) have some of the highest mortality rates from cancer in the US. Despite that, they face significant geographic barriers to access to cancer care. However, little study addresses disparities in accessibility in NA population. This study aims to estimate the travel distance to National Cancer Institute (NCI)-designated cancer center for NA patients in Utah and the continental US. Methods: We first retrospectively collected geographic information of patients with genitourinary cancer (NA vs. white) from Feb 1, 2013, to Jan 31, 2023. The travel distance from patients’ home zip code to Huntsman Cancer Institute (HCI) at the University of Utah was calculated using GeoData ZIP Code Distance Calculations Matrix Template. Then, the shapefile was downloaded from the NCI website. Area Deprivation Index (ADI) state decile was used to match between NA reservations and the control block groups within the same state. The travel time was calculated using Google map. Locations were mapped in ArcGIS 10.7 using coordinates and a 5-digit zip code tabulation area (ZCTA). Results: During the ten-year period of time, a total of 468 NA patients were seen in our cancer clinic. The median travel distance for NA patients vs. white patients to HCI is 190.6 mi (range: 1.1-596.4 mi) vs. 21.6 mi (range: 1.1-269 mi, p< 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 is 186.5 mi (range 77.8-629 mi) vs. 159 miles (range 1.9-671.3 mi, p< 0.01) (Table). Map data and travel time will be presented at the meeting. Conclusions: The travel distance to HCI for NA cancer patients in Utah was almost nine times longer than that of white cancer patients. It is also significantly longer from most NA reservations to the nearest NCI-designated cancer centers compared to ADI-matched block groups. This study highlights the significant disparity in cancer care accessibility faced by Native American communities and the urgent need for action to address this issue. [Table: see text]

“The Queer Nuyorican”: Reading Downtown Poetry and Performance Through a Queer Lens

“The Queer Nuyorican”: Reading Downtown Poetry and Performance Through a Queer Lens

Assimilation of FY-3D and FY-3E Hyperspectral Infrared Atmospheric Sounding Observation and Its Impact on Numerical Weather Prediction during Spring Season over the Continental United States

As a part of the World Meteorological Organization (WMO) Global Observing System, HIRAS-1 and HIRAS-2’s observations’ impact on improving the accuracy of numerical weather prediction (NWP) can be summarized into two questions: (1) Will HIRAS observation help the NWP system to improve its accuracy? (2) Which instrument has the greater impact on NWP? To answer the questions, four experiments are designed here: (I) the HIRAS-1 experiment, which assimilates the principal component (PC) scores derived from HIRAS-1 radiance observation from the FY-3D satellite; (II) the HIRAS-2 experiment, which assimilates HIRAS-2 (onboard the FY-3E satellite) radiance-observation-derived PC scores; (III) the J-01 experiment, which assimilates JPSS1 CrIS radiance-observation-derived PC scores; (IV) the control experiment. Each experiment generated a series of forecasts with 24 h lead-time from 16 March 2022 to 12 April 2022 using the Unified Forecast System Short-Range Weather application. Forecast evaluation using radiosonde and aircraft observation reveals: (a) for upper-level variables (i.e., temperature and specific humidity), assimilating HIRAS observation can improve the NWP’s performance by decreasing the standard deviation (Stdev) and increasing the anomaly correlation coefficient (ACC); (b) according to the multi-category Heidke skill score, HIRAS assimilation experiments, especially the HIRAS-2 experiment, have a higher agreement with hourly precipitation observations; (c) based on two tornado-outbreak case studies, which occurred on 30 March 2022 and 5 April 2022, HIRAS observation can increase the predicted intensity of 0–1 km storm relative helicity and decrease the height of the lifted condensation level at tornado outbreak locations; and (d) compared to CrIS, HIRAS-2 still has room for improvement.

Damages Associated with Excessive Rainfall Outlooks (ERO) and Missed Flash Floods

Abstract Flash flooding is the most damaging and deadly type of flooding event in the continental United States (CONUS), and one of the deadliest hazards worldwide. The Weather Prediction Center’s (WPC) Excessive Rainfall Outlook (ERO) is used to highlight regions at risk of receiving excessive rainfall that can lead to flash flooding. While EROs have been validated by the WPC across several metrics, an analysis of flash floods that were not forecast by EROs, which we define as missed flash floods, has not been performed, nor have damages associated with missed flash floods been examined. Using EROs, flash flood data from the Unified Flooding Verification System (UFVS), and flash flood damage data from the National Centers for Environmental Information (NCEI) Storm Events Database, this research investigates the characteristics of missed flash floods. We find that missed damages occur most frequently in summer and least frequently in winter, but that only 9.2% of flash flood damages and 23.2% of damaging events are missed by the ERO. Missed damaging flash flood events occur frequently in the U.S. Southwest. In this region, missed damages and missed events are primarily attributed to the North American monsoon (NAM). We also investigate forecast flash floods by ERO risk category. High risks incur the most damages despite having the fewest number of damaging events, indicating that the ERO is able to distinguish higher-impact events. The ERO predicts damaging flash floods well, although the Southwest and urban areas broadly could be investigated further to ensure that the potential of damaging flash floods are accurately forecast.

Beyond In/Visible Tokens: Complicating Narratives of Blackness and Belonging at an Oceanic University

This photo essay incorporates a creative visual ethnic studies approach to express Black students' experiences, understanding, and concerns on oceanic Blackness and belonging at the University of Hawaiʻi at Mānoa. The students, from Hawaiʻi and the continental United States, share their thoughts on how the university and island provide possibilities for oceanic ways of thinking about Blackness that render them invisible, visible, safe, threatened, complicated, and whole on campus and in Hawaiʻi. These visual vignettes reflect how students negotiate with their presence on a campus with few Black students, staff, and faculty, where they feel a lack of connection in nearly all locations on campus yet strive to develop a sense of belonging through interpersonal relations and possibilities.

(454) Variations in Practice Patterns of Xiaflex Providers Throughout the United States

Abstract Introduction Intralesional Collagenase Clostridium Histolyticum (CCH; Xiaflex; Endo Pharmaceuticals, Dublin, Ireland) is the only FDA-approved intralesional therapy to treat bothersome penile curvature in men with Peyronie’s Disease (PD). Clinicians who administer CCH must be certified through a risk evaluation and management strategy (REMS) program. However, provider availability is highly dependent on geography and many patients must travel great distances to undergo evaluation and treatment with CCH by an experienced injector. Objective The goal of the current study is to describe the landscape of certified CCH injectors with respect to location and clinical background/training. Methods Patients are currently able to use an online “locate a specialist” tool through Endo Pharmaceuticals to identify clinicians in their local geographic area who are certified to administer CCH. Using this tool, a list of all providers who offer CCH injections was compiled throughout the continental United States. Pertinent information that was gathered included geographic location, type of provider or level of training [Medical Doctor (MD), Doctor of Osteopathic Medicine (DO), Physician Assistant (PA), Nurse Practitioner (NP), and Registered Nurse (RN)], and gender of providers. This information was compared to state population data (United State Census Bureau) to determine per capita coverage. Results A total of 1947 Xiaflex providers were identified: 1749 Doctors of Medicine (MD; 90%), 115 Doctor of Osteopathic Medicine (DO; 6%), 60 Physician Assistants (PA; 3%), 21 Nurse Practitioners (NP; 1%), and 1 Registered Nurse (RN; 0.05%). Out of the 1947 providers, only 95 (4.9%) were women. Most women administering Xiaflex were MDs (73%), followed by PAs (12%), NP and DOs (7%), and RN (1%). Xiaflex providers were available in all states except for Hawaii and Alaska. Providers were distributed throughout the United States with the average provider per capita ratio of 230,240. Wyoming, Washington D.C., Rhode Island, and Georgia had the best provider per capita at <10,000 while New Mexico had the worst provider per capita ratio at 2,117,522. Conclusions Our results show significant variation in certified CCH injector availability for patients across the United States. < 5% of all certified clinicians are female and 4% were advanced practice providers. Further work is needed to characterize and remedy gender disparities amongst providers, and ensure greater access for patients who desire an FDA-approved non-surgical treatment for their PD-associated curvature. Disclosure No

Harvesting the Landsat archive for land cover land use classification using deep neural networks: Comparison with traditional classifiers and multi-sensor benefits

The Landsat archive, with a multi-decadal global coverage is a prime candidate for deep learning classification methods due to the large data volume. Local studies have evaluated deep learning methods on Landsat observations. However, these models often saturate at high accuracies due to limited reference dataset size thus do not fully explore the potential of deep classifiers. Furthermore, no provisions are taken to investigate algorithmic performance of challenging classification areas. To address these shortcomings in this research, Landsat 5, 7 and 8 observations were combined within the continental United States to create one of the largest to date reference dataset containing about 21 million labeled annual temporal sequences. Difficult to classify reference samples were isolated by examining labelsin the immediate vicinity. Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) deep learners were integrated to capture temporal and spatial relationships, respectively. Classification mapping accuracy was contrasted with a commonly implemented large-scale mapping method, the Random Forest (RF).Results indicate substantial classification improvements of deep learning methods (DLMs) over the RF. These improvements are more pronounced on challenging to classify pixels in heterogenous areas. RF classification accuracy reaches about 70% on average, while DLMs are at 86%-95% range, depending on model architecture. Grass and bare land classes show the highest accuracy improvements, from 65.5% and 63.5%, respectively for the RF to the 79.4%-96.3% range for the DLMs. Our work also examined the practical value of having two, instead of one, Landsat sensors. Results indicate substantial classification increases (7%-10% in average F1 accuracy) suggesting that having two concurrent Landsat sensors is important not only for redundancy but also for improved mapping capabilities.

County-level assessment of behind-the-meter solar and storage to mitigate long duration power interruptions for residential customers

Customer concerns over electric system resilience could drive early adoption of behind-the-meter solar-plus-storage (BTM PVESS), especially as wildfire, hurricane, and other climate-driven risks to electric grids become more pronounced. However, the resilience benefits of BTM PVESS are poorly understood, especially for residential customers, owing to lack of data and methodological challenges, making it difficult to forecast adoption trends. In this paper, we develop a methodology to model the performance of BTM PVESS in providing backup power across a wide range of customer types, geography / climate conditions, and long duration power interruption scenarios, considering both whole-building backup and backup of specific critical loads. We combine novel, disaggregated end-use load profiles across the continental United States with temporally and geospatially aligned solar generation estimates. We then implement a PVESS dispatch algorithm to calculate the amount of load served during interruptions. We find that PVESS with 10 kWh of storage can meet a limited set of critical loads in most United States counties during any month of the year, though this capability drops to meeting only 86% of critical load, averaged across all counties and months, when heating and cooling are considered critical. Backup performance is lowest in winter months where electric heat is common (southeast and northwest U.S.) and in summer months in places with large cooling loads (southwest and southeast U.S.). Winter backup performance varies by roughly 20% depending on infiltration rates, while summer performance varies by close to 15% depending on the efficiency of the central air-conditioning system. Differences in temperature set-points in Harris County correspond to a 40% range in winter backup performance and a 20% range in summer performance. Economic calculations show that a customer’s resilience value of PVESS must be high to motivate adoption of these systems.

A practical evaluation of micro-hydrokinetic power potential for the Continental United States

Study regionContinental United States (CONUS) Study focusMicro-hydrokinetic turbines are hydropower turbines that can utilize the current of the river to generate power without constructing a dam. Recent advances in micro-hydrokinetic turbine technology have made their use an attractive alternative to traditional hydropower systems that require the construction of a dam. In this paper, we introduce a novel and practical analysis of continental to global scale hydrology and hydraulics that we use to assess continental United States (CONUS) stream locations with optimum hydraulic characteristics for an example micro-hydrokinetic turbine (Volturnus). We then utilize the boundaries of CONUS military installations to determine installations possessing the greatest potential to utilize the Volturnus. New hydrological insights for the regionFor the Volturnus, our results indicate that although less than 1% of CONUS stream locations possess the hydraulic characteristics necessary for the Volturnus to function optimally, this translates into roughly 224–544 million kWh per week, considerably less than previous estimates of CONUS theoretical and recoverable power but substantial, nonetheless. Around 26–40 CONUS military installations possess streams that are sometimes capable of producing hydropower with the Volturnus and 12–20 of those military installations possess optimum hydraulic conditions for the Volturnus year round. In our analysis, reducing either the minimum velocity or minimum depth threshold of the Volturnus nearly double the proportion of CONUS locations with optimum Volturnus hydraulic characteristics.

Assessment of light-duty versus heavy-duty diesel on-road mobile source emissions using general additive models applied to traffic volume and air quality data and COVID-19 responses

ABSTRACT Following the outbreak of the COVID-19 pandemic, several papers have examined the effect of the pandemic response on urban air pollution worldwide. This study uses observed traffic volume and near-road air pollution data for black carbon (BC), oxides of nitrogen (NOx), and carbon monoxide (CO) to estimate the emissions contributions of light-duty and heavy-duty diesel vehicles in five cities in the continental United States. Analysis of mobile source impacts in the near-road environment has several health and environmental justice implications. Data from the initial COVID-19 response period, defined as March to May in 2020, were used with data from the same period over the previous two years to develop general additive models (GAMs) to quantify the emissions impact of each vehicle class. The model estimated that light-duty traffic contributes 4–69%, 14–65%, and 21–97% of BC, NOx, and CO near-road levels, respectively. Heavy-duty diesel traffic contributes an estimated 26–46%, 17–63%, and −7–18% of near-road levels of the three pollutants. The estimated mobile source impacts were used to calculate NOx to CO and BC to NOx emission ratios, which were between 0.21–0.32 μg m−3 NOx (μg m−3 CO)−1 and 0.013–0.018 μg m−3 BC (μg m−3 NOx)−1. These ratios can be used to assess existing emission inventories for use in determining air pollution standards. These results agree moderately well with recent National Emissions Inventory estimates and other empirically-derived estimates, showing similar trends among the pollutants. However, a limitation of this study was the recurring presence of an implausible air pollution impact estimate in 41% of the site-pollutant combinations, where a vehicle class was estimated to account for either a negative impact or an impact higher than the total estimated pollutant concentration. The variations seen in the GAM estimates are likely a result of location-specific factors, including fleet composition, external pollution sources, and traffic volumes. Implications: Drastic reductions in traffic and air pollution during the lockdowns of the COVID-19 pandemic present a unique opportunity to assess vehicle emissions. A General Additive Modeling approach is developed to relate traffic levels, observed air pollution, and meteorology to identify the amount vehicle types contribute to near-road levels of traffic-related air pollutants (TRAPs), which is important for future emission regulation and policy, given the significant health and environmental justice implications of vehicle-related pollution along major roadways. The model is used to evaluate emission inventories in the near-road environment, which can be used to refine existing estimates. By developing a locally data-driven method to readily characterize impacts and distinguish between heavy and light duty vehicle effects, local regulations can be used to target policies in major cities around the country, thus addressing local health disbenefits and disparities occurring as a result of exposure to near-road air pollution.

Strategies to Operationalize Health Equity in Island Area Health Departments.

Strategies to Operationalize Health Equity in Island Area Health Departments.

100 A survey of general road transportation of horses in the United States

An online survey was conducted to understand the most common reasons for travel and the types of journeys horses take when being transported by road in the United States. Qualtrics was used to create a survey, with a subset of 20-questions that asked about the demographics of the survey taker and general questions about road transportation. To participate in the survey, respondents had to be at least 18 years old and own, lease, or be in the full care of one or more horses or ponies that resided in the U.S and were transported by road in 2021. The survey was advertised on Facebook and through various equine media publications where responses were collected from 1294 participants. While there was at lease one response from every state in the continental US, the majority of responses were from the Southeast (41.6%). The most common survey taker was a female (93.9%), adult amateur (81.2%), horse owner (64.4%), who owns 1 to 3 horses (55.6%), and transports their own horse (79.4%). Overall, the most common reasons for travel were trail or leisure riding (34.2%) followed by showing and competition (25.3%); however, this varied by discipline with dressage (42.6%) and eventing (46.2%) riders most common reasons for travel being for lessons and schooling. The most common trip length was less than one hour (46.8%) with only 12.4% of triplengths being 4 h or more in length. The roads traveled most often were highways (60.7%) that were mostly straight with some curves (41.7%) and flat with some hills (46.9%). As the length of trip increased, the frequency in which a horse was transported decreased and the amount of time between trips increased. The length of the most common trip also varied by reason for transportation with the most common length to travel for showing or competition being one to 3 h (51%); traveling for lessons was typically less than an hour (71.2%) and transporting for trail or leisure riding usually took less than 3 h (92.1%) when comparing to other trip lengths. These results provide insight into the main reasons why horses are being transported by road in the US and the types of journeys they are transported most frequently. It also highlights differences in reasons for transportation and journey lengths between horse owners. Although previous equine road transportation research has focused mainly on long-distance trips, these findings support the need for further research on how short-distance road transportation might impact horse health in the US

Robust projections and consequences of an expanding bimodal growing season in the western United States

AbstractPlant growth is restricted to times of the year when actual evapotranspiration (AET) is greater than zero because AET requires both the presence of water in the soil and temperatures warm enough to allow transpiration. Locations where water rather than temperature limits plant growth, such as semi‐arid areas of the southwestern United States, often have a bimodal growing season, such that distinct AET (growth) peaks occur in the spring and late summer, with a period of very limited plant growth occurring during the intervening summer months. We hypothesized that future warming will increase the zone containing bimodal growth seasons, likely resulting in significant changes in the competitive relationships between plant species that differ in their tolerance of a bimodal seasonality. This will likely alter plant distributions. Using climate projections to drive a water balance model, we mapped geographic regions within the continental United States projected to experience bimodal growing seasons in the future. The area containing bimodal seasonality increased under all 13 general circulation models (GCMs) and two representative concentration pathways (RCPs) examined. This robust result (seen in all alternative futures examined) nevertheless showed considerable variability depending on the GCM examined. The bimodal zone was projected to increase 13%–212% (49,000–792,000 km2) by the late 21st century relative to 1981–2010 estimates. Climate futures that contained the greatest temperature increases and greatest precipitation decreases projected the greatest expansion in the bimodal zone. For plant species that depend on relatively long, consistent time periods that are favorable to growth each year, the projected shift in seasonality may be an acute disturbance that could cause widespread mortality. These changes will likely have cascading ecological and management implications, including changes in the dominant plant life history strategies that occur in affected areas.

Direct and Indirect Drivers of Energy and Nutrient Availability in Freshwater Ecosystems Across Spatial Scales

AbstractFreshwater ecosystems reflect the landscapes in which they are embedded. The biogeochemistry of these systems is fundamentally linked to climate and watershed processes that control fluxes of water and the mobilization of energy and nutrients imprinting as variation in stream water chemistry. Disentangling these processes is difficult as they operate at multiple scales varying across space. We examined the relative importance of climate, soil, and watershed characteristics in mediating direct and indirect pathways that influence carbon and nitrogen availability in streams and rivers across spatial scales. Our data set comprised landscape and climatic variables and 37,995 chemistry measurements of carbon and nitrogen across 459 streams and rivers spanning the continental United States. Models explained a small fraction of carbon and nitrogen concentrations at the continental scale (25% and 6%, respectively) but 61% and 40%, respectively, at smaller spatial scales. Hydrometeorological processes were always important in mediating the availability of solutes but the mechanistic implications were variable across spatial scales. The influence of hydrometeorology on concentrations was often not direct, rather it was mediated by soil characteristics for carbon and watershed characteristics for nitrogen. For example, the seasonality of precipitation was often important in determining carbon concentrations through its influence on soil moisture at biogeoclimatic spatial scales, whereas it had a direct influence on concentrations at the continental scale. Our results suggest that hydrometeorological forcing remains the consistent driver of energy and nutrient concentrations but the mechanism influencing patterns varies across broad spatial scales.

Media Mortality Surveillance during Winter Storm Uri, United States – 2021

Introduction:On February 13, 2021, Winter Storm Uri hit the United States beginning in the Pacific Northwest, heading across the central US, and eventually exiting on the East coast. By February 16, roughly 73% of the continental United States had snow coverage leading to ten million households without power. To understand the disaster-related causes and circumstances of death for Winter Storm Uri, we activated media mortality surveillance to help inform preparedness and response efforts.Method:We searched the internet for key terms related to the winter storm, including storm name and type (e.g., winter storm), location-specific terms (e.g., state, county, city), mortality-related terms (e.g., death, mortality), cause of death (e.g., exposure, motor vehicle collision, carbon monoxide), along with other information learned from previous days (e.g., name of individual). We compiled and coded data into a standardized media mortality surveillance database and conducted descriptive statistics.Results:Between February 13 and March 2, 2021, the media reported 136 storm-related deaths from nine states. The winter storm had the largest impact in Texas (n=91). Of decedents with sex data available (n=91), the majority (58%) were male. For decedents with age data available (n=93), the majority (91%) were adults. Exposure to extreme temperatures (47%) was the most common cause of death, followed by blunt force trauma (15%), CO poisoning (7%), and fire (7%). Roughly one-third of deaths (34%) were indirectly related to the winter storm with motor vehicle collision (13%) representing the top indirect circumstance. Twenty-six deaths (19%) have an unknown circumstance and cause of death.Conclusion:This was the first time we activated media mortality surveillance for a winter storm providing timely data for public health action. Media mortality surveillance continues to be a useful tool in assessing the impact of a disaster and guiding response efforts.

Correlation between Rising Temperature and Increasing Wildfires in the Western Continental United States

Correlation between Rising Temperature and Increasing Wildfires in the Western Continental United States

Exploring the Factors Controlling the Long‐Term Trend (1988–2019) of Surface Organic Aerosols in the Continental United States by Simulations

AbstractObserved surface organic aerosols (OA) concentrations slightly increased in the western US (WUS) but significantly decreased in the eastern US (EUS) in summer, and continuously decreased in winter over the US region. To understand the driving factors for the long‐term surface OA trend, we apply a revised version of the Community Atmosphere Model version 6 with comprehensive tropospheric and stratospheric chemistry representation, considering the heterogeneous formation of isoprene‐epoxydiol‐derived secondary organic aerosols (SOAIE) and fast photolysis rate of monoterpene‐derived secondary organic aerosols (MTSOA) to diagnose the OA evolution in 1988–2019. Compared to older versions, the revised model better reproduces the climatology, seasonal cycle, and long‐term trend of surface OA as evaluated against the Interagency Monitoring of Protected Visual Environments measurements. We find the decrease in EUS summertime OA is likely attributed to the interplay between SOAIE and MTSOA. With anthropogenic emissions reduction, primary organic aerosols (POA) declined, SOAIE decreased along with sulfate, while MTSOA increased along with biogenic emissions driven by a warming climate. POA from wildfires with a significant trend of 2.9% yr−1 and considerable interannual variation of 62.8% drive the statistically insignificant but increasing WUS summertime OA, while anthropogenic POA dominates the decreasing wintertime OA in the US. Through sensitivity experiments, we find MTSOA show linear responses to the increasing monoterpenes emissions and negligible responses to NOx emissions reduction due to the mutual offsets between MTSOA components from different oxidation pathways. This study reveals the increasingly important role of MTSOA in summertime OA under a warming climate.

Temporal and spatial variability of energy intensity for atmospheric water generators

Atmospheric water generators (AWGs) produce potable water from the moisture in the air, providing a potentially viable water source in austere locations or emergency response scenarios. In this study, the operating constraints of three existing commercially available AWG devices are investigated, compared to historical weather data from across the continental United States. Utilizing linear regression modeling and weather station data for the years of 1985–2019, the monthly and spatial trends of energy demand to produce water from these devices are estimated. Energy and water production efficiencies for the devices are highly dependent on environmental conditions with relative humidity (RH) and temperature as the two driving factors. Publicly available manufacturer specifications for each AWG system were modeled to predict yield and specific energy consumption (SEC). A spatial analysis depicts the change in SEC in kilowatt-hours per liter (kWh l−1) across the country at a monthly scale. SEC for refrigeration AWG ranged between 0.02 and 3.64 kWh l−1 and solar driven sorption was between 3.19 and 5.29 kWh l−1, significantly larger than conventional water treatment energy demands. Additionally, the results are synthesized based on the Köppen–Geiger climate classification system, to approximate projected water production and energy demand for each environment, with arid climates demanding larger energy consumption per unit volume of water produced. Excluding arid and cold climate classes, solar powered refrigeration devices have the potential to operate more efficiently than solar driven sorption due to advances in photovoltaic solar panel technology, but still require more energy than alternatives.

Accuracy of shoreline forecasting using sparse data

Sandy beaches are important resources providing recreation, tourism, habitat, and coastal protection. They evolve over various time scales due to local winds, waves, storms, and changes in sea level. A common method used to monitor change in sandy beaches is to measure the movement of the shoreline over time. Typically, the rate of change is estimated by fitting a linear regression through a time series of shoreline positions. To best manage the valuable resources within a coastal environment, accurate forecasts of shoreline position are needed. A simple way to estimate future shoreline position is to extrapolate a linear regression into the future, this method is often used to establish management guidelines like construction setback lines. A more recently developed shoreline forecasting technique utilizes the Kalman filter to assimilate shoreline data and modify the linear regression. This paper calculates the uncertainty and accuracy of both the extrapolated linear regression and Kalman filter forecasting methods for 10- and 20-year hindcasts using data collected at five diverse study areas. These data are inherently sparse (8–10 measurements per location, collected over 150 years) and are representative of the observed historical data available for the continental United States for this timeframe. Both methods produced similar results and had regionally averaged forecast accuracies of 5–16 m. We determined that the inaccuracy of the forecasts is largely due to the effects of shorter time scale variability. This variability is roughly proportional to the standard error of the linear regression, which is a useful measure of forecast uncertainty.

How well does the IMERG satellite precipitation product capture the timing of precipitation events?

Precipitation occurs in the form of discrete “events” and the event characteristics (event duration, depth, peak intensity, start/end time) significantly influence the hydrologic response of a basin. Despite this importance, event-based performance of satellite precipitation products has still not been fully investigated to assess limitations in the retrieval algorithms, guide future improvements, and inform hydrologic applications. In this study, we evaluate the precipitation event performance of the GPM IMERG product using as reference the high-resolution ground gauge–radar dataset (GV-MRMS) in the Continental United States (CONUS) at the native IMERG resolution (0.1°×0.1°, 0.5 h), with a primary focus on the detectability and timing of events. Our results show that IMERG generally overestimates the event duration but underestimates the mean event precipitation intensity in the summer, while the opposite is true for winter. This discrepancy is mostly attributed to the under-representation of short-duration intense events in the summer and long-duration moderate events in the winter in IMERG. In terms of the detection of individual events, about 50% of the reference events are properly detected by IMERG, and conversely, 50% of IMERG events do not match a reference event. However, nearly 40% of the missed or false events result from temporal mismatching of less than 3 h between the retrieved and the reference event. The remaining 60% comes from IMERG not detecting an existing event or inventing a nonexistent event. When IMERG successfully detects an event, the average temporal overlap with the reference event is about 70% of its total duration, which mostly stems from the mistiming of IMERG-derived events. IMERG events tend to start, peak, and end earlier than GV-MRMS events, with national mean shifts of −26 min, −17 min, and −7min, respectively. For about only 20% of all situations the starting time of the event is correctly reproduced by IMERG, and the same applies to the peak time and end time. Our results provide guidance for applications of IMERG at sub-daily scales, as well as new insights for the improvement of satellite retrieval algorithms.