Naval Research Laboratory Ocean Sciences Division

Experimental batch and miscible-flow cultures were studied in order to determine the mechanistic pathways of microbial Fe(III) respiration in ferruginous smectite clay, NAu-1. The primary purpose was to resolve if alteration of smectite and release of Fe precedes microbial respiration. Alteration of NAu-1, represented by the morphological and mineralogical changes, occurred regardless of the extent of microbial Fe(III) reduction in all of our experimental systems, including those that contained heat-killed bacteria and those in which O2, rather than Fe(III), was the primary terminal electron acceptor. The solid alteration products observed under transmission electron microscopy included poorly crystalline smectite with diffuse electron diffraction signals, discrete grains of Fe-free amorphous aluminosilicate with increased Al/Si ratio, Fe-rich grains, and amorphous Si globules in the immediate vicinity of bacterial cells and extracellular polymeric substances. In reducing systems, Fe was also found as siderite. The small amount of Fe partitioned to the aqueous phase was primarily in the form of dissolved Fe(III) species even in the systems in which Fe(III) was the primary terminal electron acceptor for microbial respiration. From these observations, we conclude that microbial respiration of Fe(III) in our laboratory systems proceeded through the following: (1) alteration of NAu-1 and concurrent release of Fe(III) from the octahedral sheets of NAu-1; and (2) subsequent microbial respiration of Fe(III).

Abstract We examine the temporal means and variability of the semidiurnal internal tide energy fluxes in 1/25° global simulations of the Hybrid Coordinate Ocean Model (HYCOM) and in a global archive of 79 historical moorings. Low‐frequency flows, a major cause of internal tide variability, have comparable kinetic energies at the mooring sites in model and observations. The computed root‐mean‐square (RMS) variability of the energy flux is large in both model and observations and correlates positively with the time‐averaged flux magnitude. Outside of strong generation regions, the normalized RMS variability (the RMS variability divided by the mean) is nearly independent of the flux magnitudes in the model, and of order 23% or more in both the model and observations. The spatially averaged flux magnitudes in observations and the simulation agree to within a factor of about 1.4 and 2.4 for vertical mode‐1 and mode‐2, respectively. The difference in energy flux computed from the full‐depth model output versus model output subsampled at mooring instrument depths is small. The global historical archive is supplemented with six high‐vertical resolution moorings from the Internal Waves Across the Pacific (IWAP) experiment. The model fluxes agree more closely with the high‐resolution IWAP fluxes than with the historical mooring fluxes. The high variability in internal tide energy fluxes implies that internal tide fluxes computed from short observational records should be regarded as realizations of a highly variable field, not as “means” that are indicative of conditions at the measurement sites over all time.

Abstract Wave‐seabed interaction is known to be a fundamental cause of sediment instability and entrainment. Observations of burial for surrogate munitions were made with a high frequency, sector scanning sonar acquiring acoustic images of the seabed every 12 min during a storm event in May 2013 in the northern Gulf of Mexico offshore of Panama City Beach, FL. Surrogate munitions burial depth was verified by divers. Driven by time series of pressure observed near the seabed, an existing poro‐elastic wave‐sediment interaction model in combination with a sediment failure criterion due to liquefaction (loss of vertical effective stress) was used to correctly estimate depth and timing of burial. We introduce the concept of liquefaction degree, which is defined as the portion of vertical effective stress at the sediment surface that is, counteracted by wave‐induced pore pressures. The relationship between wave‐induced pressure at the seabed and liquefaction degree is expressed by a complex transfer function, indicating the importance of the swell band with respect to infra‐gravity and short‐wave components. The liquefaction degree was used to construct and calibrate a dynamical relationship between wave action and acoustic backscatter observations near the seabed, taken as a surrogate for suspended sediment concentration. Predictions of backscatter based on liquefaction degree were accurate at time scales of 15 s–30 min during most of the time span analyzed and superior to predictions of backscatter based on shear stress. The observations and analysis provide evidence for the importance of wave‐induced seepage forces in describing sediment liquefaction and entrainment processes.

Climate change-induced disasters are increasing in intensity and frequency in the United States. Infant feeding in the aftermath of an extreme event is particularly challenging, especially given large variations in community vulnerability and resilience. The aim of this study was to identify the physical, social, and spatial vulnerabilities of communities along the Gulf Coast and highlight locations where high (or low) breastfeeding initiation rates have the potential to offset (or exacerbate) infant feeding challenges in the wake of a disaster. We structured this study as a retrospective, spatial data analysis of breastfeeding initiation, the risk for extreme events, social vulnerability, and community resilience to uncover locations that may need post-disaster intervention. The results suggested that significant gaps in the geographic distribution of community risk, vulnerability, resilience, and breastfeeding initiation existed. While many metropolitan areas benefitted from high breastfeeding initiation rates, they were also the most "at risk" for disasters. Conversely, many rural communities faced less risk for extreme events but exhibited more social vulnerability and less resilience should a disaster strike. Prioritizing emergency response resources to support infant feeding after a disaster is critically important, but urban and rural communities have divergent profiles that will require variable strategies to ensure recovery. Our results highlight this variability and provide prescriptive guidance regarding where to potentially allocate emergency resources.

Abstract The United Nations expressed an interest in reducing subnational (i.e., province and state level) inequality. We propose using a spatial decomposition of the Gini coefficient ( SDGC ) to track changes in subnational inequality. Typically, agencies do not track summary measures of subnational clustering of development indicators. Tracking changes in the SDGC can help measure and reduce regional inequality. To illustrate the use of the SDGC , we first present data for 93 nations to obtain cross‐sectional variation. Next, to illustrate how the SDGC trends over time, changes in the Human Development Index in Mongolia are compared to Russia and China. The SDGC can show improvement, decline and persistent clustering of subnational level inequality. The SDGC is a useful measure for the United Nations' Sustainable Development Goals.

Abstract. The relative influence of both temperature and salinity on the mixed layer depth (MLD) is evaluated using a relationship of binned regressions of MLD on vertical density compensation and isothermal layer depth (ILD) from a global set of in situ profile observations. Our approach is inspired by the observations of the difference between the MLD and the sonic layer depth (SLD) that evolve seasonally around the global ocean. In this article, we hypothesize that vertical density compensation governs SLD-MLD differences and can be used for mapping the relative influence of temperature and salinity on upper ocean structure. The Turner angle, computed between the surface and 200 m (bulk Turner angle, BTA), serves as a measure of vertical density compensation that quantifies times and areas where either temperature or salinity is destabilizing. For temperature destabilization the ocean exhibits cool/fresh overlying hot/salty water. For salinity destabilization the ocean exhibits hot/salty overlying cool/fresh water. These two classes of density compensation have seasonal variability with different geographical characteristics. Profiles with salinity controlled stable density and destabilizing temperature gradient are found most often at high latitudes. Profiles with temperature controlled stable density and destabilizing salinity gradient are found in the tropics and subtropics of all oceans. Results indicate that about half of the ocean has vertical density compensation that is a necessary condition for SLD-MLD differences. While density compensation is necessary, it is not a sufficient condition for predicting the dependence of MLD on BTA. Density compensation is the dominant factor in MLD variability in heavy river input and subduction regions that cover only ~14% of the ocean.

The large variability of the Gulf of Mexico wind field indicates that high-resolution wind data will be required to represent the weather systems affecting ocean circulation. This report presents methods and results of the calculation of a corrected geostrophic wind data set with high temporal and spatial resolution. Corrected geostrophic wind was calculated from surface pressure analyses compiled by the Fleet Numerical Oceanography Center. The correction factors for wind magnitude and direction were calculated using linear regressions of observed Gulf buoy winds and geostrophic winds derived at the buoys. The regressions were performed for each month to determine the seasonal variability of the correction factors. The magnitude correction was found to be nearly constant (0.675) throughout the year, but the direction correction varied seasonally from 8.5 to 26.5 degrees. The corrected geostrophic wind was calculated twice daily store 1967–1982 on a spherical grid over the Gulf, together with the corresponding wind stress and wind stress curl fields. The 12-hourly stress fields show large temporal variations of the wind field for both winter and summer months. Seasonal and monthly climatologies of the stress and corresponding curl show positive curl over the Yucatan and negative curl in the southwest Gulf, which are features not seen in any previous study of Gulf wind stress.

Abstract It is critical to characterize submarine landslide hazards near dense coastal populations, especially in areas with active faults, which can trigger slope failure, subsequent tsunamis, and damage seabed infrastructure during earthquake shaking. Offshore southern California, numerous marine geophysical surveys have been conducted over the past decade, and high‐resolution bathymetric and subsurface data now cover about 60 percent of the total region between Point Conception and the United States‐Mexico border from the California coast out to the base of Patton Escarpment ∼200 km offshore. In a comprehensive compilation and interpretive mapping effort, we find evidence of seafloor failure throughout offshore southern California with nearly 1,500 submarine landslide‐related features, including 63 discrete slide deposits with debris and >1,400 slide‐related scarps. In our analysis, we highlight new mapping of submarine landslides in Catalina Basin, the Del Mar slide, the San Gabriel slide complex, and the 232 km 2 San Nicolas slide, the largest area of any known submarine landslide mass offshore southern California. Analysis of the spatial distribution of submarine landslide features suggests that most mapped slide features are located relatively near coastal sediment sources, particularly during sea‐level lowstand conditions, which underscores the importance of sediment supply and sediment accumulation on low‐gradient slopes as failure preconditioning processes. Tectonically driven uplift at shelf edges and along basin flanks is another key preconditioning factor, and our results also suggest that earthquakes along active faults trigger mass wasting, especially for repeated, small‐scale failures on tectonically steepened slopes.

Big data, the “new oil” of the modern data science era, has attracted much attention in the GIScience community. However, we have ignored the role of code in enabling the big data revolution in this modern gold rush. Instead, what attention code has received has focused on computational efficiency and scalability issues. In contrast, we have missed the opportunities that the more transformative aspects of code afford as ways to organize our science. These “big code” practices hold the potential for addressing some ill effects of big data that have been rightly criticized, such as algorithmic bias, lack of representation, gatekeeping, and issues of power imbalances in our communities. In this article, I consider areas where lessons from the open source community can help us evolve a more inclusive, generative, and expansive GIScience. These concern best practices for codes of conduct, data pipelines and reproducibility, refactoring our attribution and reward systems, and a reinvention of our pedagogy.

Selectivity and cardinality estimation are main driving factors for developing cheap query plans and ultimately faster query processing. Traditionally, database systems use estimation data structures, e.g., histograms, to maintain data summaries. Machine learning models have recently been employed, acting as black boxes, in several database tasks, including cardinality estimation. In the dynamic streaming environments, both estimation data structures and machine learning models struggle with adaptation for dynamic changes in data and query workloads. This paper proposes LATEST; a system module that uses machine learning to enable dynamic adaptation of estimation data structures. For spatial-keyword queries in a streaming environment, it shows on par or better performance than the state-of-the-art estimators. LATEST builds an incremental supervised learning model over a moving time window that helps the underlying system to switch among several estimation structures to keep estimation accuracy high at all times. As an incremental learner, LATEST effectively adapts to dynamic changes of both data and queries in streaming environments. Our extensive experiments on three real datasets and various query workloads verify the effectiveness of LATEST with higher accuracy and lower response times over the state-of-the-art estimators.

This book explores the utility of UAVs for monitoring, measuring, and improving urban sustainability, the urban environment, and community health.

Methodologies inspired by physics-informed neural networks (PINNs) were used to forecast observations recorded by stationary ocean buoys. We combined buoy observations with numerical models to train surrogate deep learning networks that performed better than with either data alone. Numerical model outputs were collected from two sources for training and regularization: the hybrid circulation ocean model and the fifth ECMWF reanalysis experiment. A hyperparameter determines the ratio of observational and modeled data to be used in the training procedure, so we conducted a grid search to find the most performant ratio. Overall, the technique improved the general forecast performance compared with nonregularized models. Under specific circumstances, the regularization mechanism enabled the PINN models to be more accurate than the numerical models. This demonstrates the utility of combining various climate models and sensor observations to improve surrogate modeling.

Geoforensic science investigates the location and time of criminal occurrences by integrating multiple fields, including geography, criminology, ecology, biology, and geology. The ubiquity, durability, and spatial-temporal predictability make pollen a frequently used biomarker in geoforensic investigations to help determine the provenance of hard-to-trace items, including computers, counterfeit products, digging equipment, clothing, and undetonated explosives. The recently developed Geoforensic Interdiction (GOFIND) model links the pollen combination collected from a sample object with the probability of locations traversed by the object. Although the GOFIND model improves over the traditional single-site joint probability approach and can be used to identify multiple locations simultaneously, substantial limitations remain. In particular, GOFIND requires specifying the number of locations traversed by an object in advance—a priori knowledge that is almost impossible to obtain in real-world applications. This article aims to introduce the GOFIND + model that leverages detected and undetected pollen to establish a probabilistic relation between pollen and the corresponding species distribution in the environment. Our simulation tests using the USDA CropScape data for the state of Texas show that the GOFIND + model outperforms the GOFIND model in predictive accuracy. Further, GOFIND + does not require that users specify the number of geographical stops and sites a priori. Key Words: geoforensics, GOFIND+, pollen, spatial optimization.

This work presents the experimental validation of a novel data processing approach to estimate the local depth in a shallow coastal area starting from X-band radar data. The approach is based on the maximization of the Normalized Scalar Product (NSP) between the measured and the theoretical wave dispersion relations, which embed the dependence on the searched for parameters (current vector and depth). The use of NSP approach allows obtaining a high resolution spatial map of the investigated area and a thorough statistical analysis of this approach is carried out by comparison with the ground truth data collected by a multibeam echo-sounder. Finally, the accuracy of the NSP approach is compared with the one achieved by other methods and prove to provide better results.

This paper demonstrates CleanUpOurWorld; a research spatial database that is designed and deployed to collect, process, query, and visualize anthropogenic litter data. Such data has a significant importance in the field of environmental sciences due to its important use cases. We make a major on-going effort to collect and maintain such data worldwide from different sources through a community of environmental scientists and partner organizations. With the increasing volume of data, existing software packages, such as GIS software, do not scale to process, query, and visualize such data. To overcome this, CleanUpOurWorld digests datasets from diferent sources, with different formats, in a scalable backend that cleans, integrates, and unifies them in a structured form in a relational spatial database. Frontend applications are built to visualize litter data at multiple spatial resolutions.

Abstract Pollen is one of the most durable environmental materials that law enforcement agencies recover as trace evidence from people and objects. Although links between objects and geographic locations are essential during legal investigations, the approach of using pollen and other microbial fingerprints to build these links in an analytical framework is still underutilized. This study uses bees as objects that are mobile and collects environmental traces as a test case to determine the efficacy of predictive geolocation efforts with recovered pollen and species distribution models at both subcontinental and global scales. Results demonstrate promising performance in both the predictive capability of species distribution models and identification of possible location history of bees at both study extents. When coupling pollen with other categories of evidentiary items, this geographic attribution framework can aid law enforcement personnel in refining investigation priorities and optimizing search strategies.

As a next-generation DNA sequencing technique, metabarcoding aids in identifying biotic trace materials such as pollen, fungal spores, and other environmental DNA samples. This paper aims to develop a geographic attribution framework using pollen samples associated with objects or persons of interest to reduce search space for law enforcement investigations. We use plant occurrence data from the open-source Global Biodi-versity Information Facility (GBIF) to model individual genus and species distributions which were subsequently combined to inform possible geolocations objects or persons of interest have traveled. Results indicate that the geographic attribution frame-work could potentially aid forensic investigations by eliminating geographic search areas to determine the possible location history of people and objects.

This paper proposes two new measures applicable in a spatial bipartite network model: coverage and coverage robustness. The bipartite network must consist of observer nodes, observable nodes, and edges that connect observer nodes to observable nodes. The coverage and coverage robustness scores evaluate the effectiveness of the observer node placements. This measure is beneficial for stochastic data as it may be coupled with Monte Carlo simulations to identify optimal placements for new observer nodes. In this paper, we construct a Geo-SpatioTemporal Bipartite Network (GSTBN) within the stochastic and dynamical environment of the Gulf of Mexico. This GSTBN consists of GCOOS sensor nodes and HYCOM Region of Interest (RoI) event nodes. The goal is to identify optimal placements to expand GCOOS to improve the forecasting outcomes by the HYCOM ocean prediction model.

Abstract Expendable Bathythermographs (XBTs) are oceanographic instruments that fall through the ocean's water column and measure ocean temperature with depth. In many instances, however, XBTs continue to record temperature after they impact the seabed. Here we show evidence that XBTs produce unique temperature responses when they impact the seabed that depend directly on seabed physical properties. Specifically, standard‐use XBTs (e.g., T‐4s and T‐5s), when deployed above a mud‐rich seabed, require significant time (tens of minutes) to equilibrate to steady‐state seafloor temperatures after seabed impact. In contrast, XBTs deployed above sand‐rich sediments equilibrate to seabed temperatures rapidly (<5 min) after seafloor impact. One explanation for this difference in temperature response is that XBTs deployed above mud‐rich sediment penetrate into low permeability marine muds that jacket the XBT, where diffusive heat flow dominates. Both observations and numerical modeling results support the hypothesis that XBTs impacting muddy seafloors exhibit slow, diffusion‐dominated heat flow, while XBTs impacting harder, sand‐rich seabed sites exhibit rapid seafloor temperature equilibration, consistent with advection‐driven heat flow and little if any XBT seabed penetration. Given that >644k XBT measurements exist publicly (via the National Oceanographic and Atmospheric Administration website), and >74,000 XBTs record temperatures post seabed impact, we suggest that XBT data represents a large, low‐cost, and currently untapped data set for characterizing seabed physical properties globally.

Census blocks are administrative units that serve as statistical areas for the decennial Census in the United States. Visible and nonvisible features bound blocks, including roads, railroads, streams, property lines, and city boundaries. The Census Bureau builds blocks using the Master Address File (MAF), which includes field-verified geographic information about the location of housing unit addresses. Unfortunately, there are substantial errors in the counts of housing units at the block level, even with the purported quality checks by the Census Bureau. This paper aims to detail a method of identifying problematic blocks (i.e., ghost blocks) that report the presence of housing units, but no such units exist. Further, we identify the implications of using ghost blocks in location models using the maximal covering location problem (MCLP) in a case study for sensor locations in Los Angeles, California. We discuss policy implications and strategies to address these errors for developing higher-fidelity location models. • Details a method of identifying problematic U.S. Census blocks that report the presence of housing units, but no such units exist. • Identifies the implications of using ghost blocks in location models. • Discusses policy implications of using ghost blocks. • Provides strategies to address data uncertainty and to develop higher-fidelity location models.