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107 records · Page 6

Advanced Concepts Research for Flywheel Technology Applications

The Missile Defense Agency (MDA) (formerly the Ballistic Missile Defense Organization) is embarking on a program to employ the use of High Altitude Airships (HAAs) for surveillance of coastal areas as a part of homeland defense. It is envisioned that these HAAs will fly at 70,000 feet continuously for at least a year, therefore requiring a regenerative electric power system. As part of a program to entice the MDA to utilize the NASA GRC expertise in electric power and propulsion as a means of risk reduction, an internal study program was performed to examine possible configurations that may be employed on a HAA to meet a theoretical surveillance need. This entailed the development of a set of program requirements which were flowed down to system and subsystem level requirements as well as the identification of environmental and infrastructure constraints. Such infrastructure constraints include the ability to construct a reasonably sized HAA within existing airship hangers, as the size of such vehicles could reach in excess of 600 ft. The issues regarding environments at this altitude are similar to those that would be imposed on satellite in Low Earth Orbit. Additionally, operational constraints, due to high winds at certain times of the year were also examined to determine options that could be examined to allow year round coverage of the US coast.

Keith, Theo G., Jr.↗

Evolving Multi-hazard Machine Learning Modeling for Advanced Risk-Informed Infrastructure Resilience Assessment

The socioeconomic impacts of pipeline incidents have escalated over the past three decades, revealing the limitation of traditional risk modeling methods when applied to extensive pipeline networks. This research aims to develop machine learning (ML) models that effectively identify, rank, and predict the diverse hazards and socioeconomic consequences associated with pipeline incidents. Utilizing historical data on pipeline incidents alongside weather and oceanographic data from the 1980s onward, the Houston metropolitan area serves as a testbed for the proposed methodologies. The research segments the combined datasets into three consecutive periods, demonstrating the efficacy of the updated model in predicting future events, particularly concerning precipitation rate data. Despite the challenges posed by a relatively limited dataset, local-level ML modeling offers valuable insights into the spatial and temporal dynamics of multiple hazards that contribute to pipeline incidents. These findings hold significant implications for future research, particularly in understanding and mitigating risks in various locations across the Gulf Coast and other coastal regions.

42 ENGINEERING↗

Influence of Averaging Method on the Evaluation of a Coastal Ocean Color Event on the U.S. Northeast Coast

Application of appropriate spatial averaging techniques is crucial to correct evaluation of ocean color radiometric data, due to the common log-normal or mixed log-normal distribution of these data. Averaging method is particularly crucial for data acquired in coastal regions. The effect of averaging method was markedly demonstrated for a precipitation-driven event on the U.S. Northeast coast in October-November 2005, which resulted in export of high concentrations of riverine colored dissolved organic matter (CDOM) to New York and New Jersey coastal waters over a period of several days. Use of the arithmetic mean averaging method created an inaccurate representation of the magnitude of this event in SeaWiFS global mapped chl a data, causing it to be visualized as a very large chl a anomaly. The apparent chl a anomaly was enhanced by the known incomplete discrimination of CDOM and phytoplankton chlorophyll in SeaWiFS data; other data sources enable an improved characterization. Analysis using the geometric mean averaging method did not indicate this event to be statistically anomalous. Our results predicate the necessity of providing the geometric mean averaging method for ocean color radiometric data in the Goddard Earth Sciences DISC Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni).

Acker, James G.↗

Intelligent Observation Strategies for Geosynchronous Remote Sensing for Natural Hazards

Geosynchronous satellites offer a unique perspective for monitoring environmental factors important to understanding natural hazards and supporting the disasters management life cycle, namely forecast, detection, response, recovery and mitigation. In the NASA decadal survey for Earth science, the GEO-CAPE mission was proposed to address coastal and air pollution events in geosynchronous orbit, complementing similar initiatives in Asia by the South Koreans and by ESA in Europe, thereby covering the northern hemisphere. In addition to analyzing the challenges of identifying instrument capabilities to meet the science requirements, and the implications of hosting the instrument payloads on commercial geosynchronous satellites, the GEO-CAPE mission design team conducted a short study to explore strategies to optimize the science return for the coastal imaging instrument. The study focused on intelligent scheduling strategies that took into account cloud avoidance techniques as well as onboard processing methods to reduce the data storage and transmission loads. This paper expands the findings of that study to address the use of intelligent scheduling techniques and near-real time data product acquisition of both the coastal water and air pollution events. The topics include the use of onboard processing to refine and execute schedules, to detect cloud contamination in observations, and to reduce data handling operations. Analysis of state of the art flight computing capabilities will be presented, along with an assessment of cloud detection algorithms and their performance characteristics. Tools developed to illustrate operational concepts will be described, including their applicability to environmental monitoring domains with an eye to the future. In the geostationary configuration, the payload becomes a networked thing with enough connectivity to exchange data seamlessly with users. This allows the full field of view to be sensed at very high rate under the control of ground infrastructure, resulting in improved efficiencies, accuracy and science benefits. Hence a remote sensing payload and its data may become one of millions of connected objects in the emerging Internet of Things (IoT), and be as easily accessible by a users smart phone as any other smart appliance.

Climate Science for Decision-Making in the New York Metropolitan Region

New York City is one of the world's most vulnerable cities to coastal flooding, due to a high concentration of population and assets near a coastline exposed to warm-season tropical storms and cold -season Nor'easter storms. Among U.S. cities, New York City is second only to New Orleans in population living less than 4 ft above the local high tide. By the 2050s, average annual losses due to coastal flooding alone could exceed $2 billion for the combined New York City-Newark region. Perhaps the most iconic example of a vulnerable New York City asset is the financial district located at the southern tip of Manhattan, however low-lying coastal assets include the full complement of major highways, subways and tunnels, hospitals, schools, wastewater treatment plants, food distribution centers, and people's homes. Given the magnitude of the assets at risk, a compelling case can be made that long-term adaptation makes economic sense for New York City. Given New York's access to economic, human, and technological resources for resilience measures, the City may be able to achieve this resilience. The city's political environment-New York City is a place where climate science is generally not a partisan issue-and the city's experience with uncertainty and overall risk framing (e.g., financing of bond issues for multi-billion dollar infrastructure with multidecade expected lifetimes), encourage climate risk framing.

Horton, Radley↗

Future Nuisance Flooding in Norfolk, VA, From Astronomical Tides and Annual to Decadal Internal Climate Variability

Abstract Increasing sea level rise will lead to more instances of nuisance flooding along the Virginia coastline in the coming decades, causing road closures and deteriorating infrastructure. These minor flood events can be caused by astronomical tides alone, in addition to internal climate variability on annual to decadal timescales. An assessment of nuisance flooding from these two effects is presented up until the year2050 for Norfolk, Virginia. The analysis of water levels indicates that nuisance flooding from tides alone in conjunction with a medium-high sea level scenario will result in flooding beginning in 2030 with frequency increasing thereafter. The addition of climate variability, by use of an empirical mode decomposition, leads to a substantial increase of flooding relative to the tides-alone analysis and shows flood events beginning as soon as 2020. High tides in the future will produce nuisance flooding without the need of other drivers such as coastal storms.

Burgos, A. G.↗

Real Data and Rapid Results: Ocean Color Data Analysis with Giovanni (GES DISC Interactive Online Visualization and ANalysis Infrastructure)

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step addressing the challenge of using archived Earth Observing System (EOS) data for regional or global studies by developing an infrastructure with a World Wide Web interface which allows online, interactive, data analysis: the GES DISC Interactive Online Visualization and ANalysis Infrastructure, or "Giovanni." Giovanni provides a data analysis environment that is largely independent of underlying data file format. The Ocean Color Time-Series Project has created an initial implementation of Giovanni using monthly Standard Mapped Image (SMI) data products from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission. Giovanni users select geophysical parameters, and the geographical region and time period of interest. The system rapidly generates a graphical or ASCII numerical data output. Currently available output options are: Area plot (averaged or accumulated over any available data period for any rectangular area); Time plot (time series averaged over any rectangular area); Hovmeller plots (image view of any longitude-time and latitude-time cross sections); ASCII output for all plot types; and area plot animations. Future plans include correlation plots, output formats compatible with Geographical Information Systems (GIs), and higher temporal resolution data. The Ocean Color Time-Series Project will produce sensor-independent ocean color data beginning with the Coastal Zone Color Scanner (CZCS) mission and extending through SeaWiFS and Moderate Resolution Imaging Spectroradiometer (MODIS) data sets, and will enable incorporation of Visible/lnfrared Imaging Radiometer Suite (VIIRS) data, which will be added to Giovanni. The first phase of Giovanni will also include tutorials demonstrating the use of Giovanni and collaborative assistance in the development of research projects using the SeaWiFS and Ocean Color Time-Series Project data in the online Laboratory for Ocean Color Users (LOCUS). The synergy of Giovanni with high-quality ocean color data provides users with the ability to investigate a variety of important oceanic phenomena, such as coastal primary productivity related to pelagic fisheries, seasonal patterns and interannual variability, interdependence of atmospheric dust aerosols and harmful algal blooms, and the potential effects of climate change on oceanic productivity.

Acker, J. G.↗

Climate Change Adaptation Activities at the NASA John F. Kennedy Space Center, FL., USA

In 2010, the Office of Strategic Infrastructure and Earth Sciences established the Climate Adaptation Science Investigators (CASI) program to integrate climate change forecasts and knowledge into sustainable management of infrastructure and operations needed for the NASA mission. NASA operates 10 field centers valued at $32 billion dollars, occupies 191,000 acres and employs 58,000 people. CASI climate change and sea-level rise forecasts focus on the 2050 and 2080 time periods. At the 140,000 acre Kennedy Space Center (KSC) data are used to simulate impacts on infrastructure, operations, and unique natural resources. KSC launch and processing facilities represent a valued national asset located in an area with high biodiversity including 33 species of special management concern. Numerical and advanced Bayesian and Monte Carlo statistical modeling is being conducted using LiDAR digital elevation models coupled with relevant GIS layers to assess potential future conditions. Results are provided to the Environmental Management Branch, Master Planning, Construction of Facilities, Engineering Construction Innovation Committee and our regional partners to support Spaceport development, management, and adaptation planning and design. Potential impacts to natural resources include conversion of 50% of the Center to open water, elevation of the surficial aquifer, alterations of rainfall and evapotranspiration patterns, conversion of salt marsh to mangrove forest, reductions in distribution and extent of upland habitats, overwash of the barrier island dune system, increases in heat stress days, and releases of chemicals from legacy contamination sites. CASI has proven successful in bringing climate change planning to KSC including recognition of the need to increase resiliency and development of a green managed shoreline retreat approach to maintain coastal ecosystem services while maximizing life expectancy of Center launch and payload processing resources.

plant communities↗

Characterizing Long Island’s Extreme Precipitation and Its Relationship to Tropical Cyclones

Since extreme precipitation impacts society on small scales (i.e., a few kilometers and smaller, it is worthwhile to explore extreme precipitation trends in localized regions, such as Long Island (LI), New York. Its coastal location makes it vulnerable to various extreme events, such as tropical cyclones (TCs). This work aimed to quantify the extreme precipitation events on LI that are caused by TCs, as well as the percentage of TCs passing close to LI that cause extreme precipitation events. Both gauge-based and satellite-based precipitation datasets of varying resolutions (DAYMET, IMERG, and CPC) were used to understand the impact of dataset selection. Results are shown for the common time period of 2001–2020, as well as the full time periods of each dataset. DAYMET shows the highest percentage of extreme precipitation events linked to TCs for 2001–2020 (a maximum of 7.2%) and the highest number of TCs that caused extreme precipitation events (36.5%), with IMERG showing similar results. For the full and common time periods, the highest percentage of extreme precipitation events caused by TCs was found in eastern LI. TC-related extreme precipitation averaged over LI varied year to year, and amounts were dependent on the resolution of the observational dataset, but most datasets showed an increasing trend in the last 19 years that is larger than the trend in mean precipitation. Current infrastructure in the region is likely inadequately prepared for future impacts from TC-related extreme precipitation events in such a population-dense region.

54 ENVIRONMENTAL SCIENCES↗

Tele-Supervised Adaptive Ocean Sensor Fleet

The Tele-supervised Adaptive Ocean Sensor Fleet (TAOSF) is a multi-robot science exploration architecture and system that uses a group of robotic boats (the Ocean-Atmosphere Sensor Integration System, or OASIS) to enable in-situ study of ocean surface and subsurface characteristics and the dynamics of such ocean phenomena as coastal pollutants, oil spills, hurricanes, or harmful algal blooms (HABs). The OASIS boats are extended- deployment, autonomous ocean surface vehicles. The TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy. One feature of TAOSF is the adaptive re-planning of the activities of the OASIS vessels based on sensor input ( smart sensing) and sensorial coordination among multiple assets. The architecture also incorporates Web-based communications that permit control of the assets over long distances and the sharing of data with remote experts. Autonomous hazard and assistance detection allows the automatic identification of hazards that require human intervention to ensure the safety and integrity of the robotic vehicles, or of science data that require human interpretation and response. Also, the architecture is designed for science analysis of acquired data in order to perform an initial onboard assessment of the presence of specific science signatures of immediate interest. TAOSF integrates and extends five subsystems developed by the participating institutions: Emergent Space Tech - nol ogies, Wallops Flight Facility, NASA s Goddard Space Flight Center (GSFC), Carnegie Mellon University, and Jet Propulsion Laboratory (JPL). The OASIS Autonomous Surface Vehicle (ASV) system, which includes the vessels as well as the land-based control and communications infrastructure developed for them, controls the hardware of each platform (sensors, actuators, etc.), and also provides a low-level waypoint navigation capability. The Multi-Platform Simulation Environment from GSFC is a surrogate for the OASIS ASV system and allows for independent development and testing of higher-level software components. The Platform Communicator acts as a proxy for both actual and simulated platforms. It translates platform-independent messages from the higher control systems to the device-dependent communication protocols. This enables the higher-level control systems to interact identically with heterogeneous actual or simulated platforms.

Lefes, Alberto↗

Community-Centric Microgrid Feasibility Analysis Framework to Accelerate Resilience of Island Communities

Marine energy offers a reliable energy solution for island and coastal communities, which often lack traditional local generation, to support their transition to energy independence and reduce reliance on externally imported fuels. Successful deployment of new technologies in these isolated locations requires community acceptance and approval from the outset, as these communities typically lack the financial and technical resources to operate and maintain new systems. This report presents a community-centric microgrid planning framework for remote coastal and island communities. Community engagement is integrated as the first step in the planning process, incorporating community profiles and visions into energy development scenarios. A case study was conducted in St. George, Pribilof Islands, Alaska, which relies entirely on diesel yet has significant wind and wave energy potential. Community engagement revealed a unique history and current economic status, with an interest in adopting advanced energy technologies despite past failures. Various microgrid configurations were optimized, considering different technologies to meet current and future energy needs while balancing cost and energy resilience. Wave energy converters (WECs) were a key component, integrated with other energy sources using the Xendee optimization tool. The Marine Energy Microgrid Toolkit, developed as part of this work, uses commercial power system analysis tools to optimize and analyze microgrid scenarios. The developed framework and toolkit can be applied to island and coastal communities to enhance resilience and support microgrid deployments. Future enhancements will include incorporating new marine resources, developing dynamic models, and automating the integration of Xendee and PowerFactory simulations.

02 PETROLEUM↗

Statistical Considerations of Data Processing in Giovanni Online Tool

The GES DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni) is a web-based interface for the rapid visualization and analysis of gridded data from a number of remote sensing instruments. The GES DISC currently employs several Giovanni instances to analyze various products, such as Ocean-Giovanni for ocean products from SeaWiFS and MODIS-Aqua; TOMS & OM1 Giovanni for atmospheric chemical trace gases from TOMS and OMI, and MOVAS for aerosols from MODIS, etc. (http://giovanni.gsfc.nasa.gov) Foremost among the Giovanni statistical functions is data averaging. Two aspects of this function are addressed here. The first deals with the accuracy of averaging gridded mapped products vs. averaging from the ungridded Level 2 data. Some mapped products contain mean values only; others contain additional statistics, such as number of pixels (NP) for each grid, standard deviation, etc. Since NP varies spatially and temporally, averaging with or without weighting by NP will be different. In this paper, we address differences of various weighting algorithms for some datasets utilized in Giovanni. The second aspect is related to different averaging methods affecting data quality and interpretation for data with non-normal distribution. The present study demonstrates results of different spatial averaging methods using gridded SeaWiFS Level 3 mapped monthly chlorophyll a data. Spatial averages were calculated using three different methods: arithmetic mean (AVG), geometric mean (GEO), and maximum likelihood estimator (MLE). Biogeochemical data, such as chlorophyll a, are usually considered to have a log-normal distribution. The study determined that differences between methods tend to increase with increasing size of a selected coastal area, with no significant differences in most open oceans. The GEO method consistently produces values lower than AVG and MLE. The AVG method produces values larger than MLE in some cases, but smaller in other cases. Further studies indicated that significant differences between AVG and MLE methods occurred in coastal areas where data have large spatial variations and a log-bimodal distribution instead of log-normal distribution.

Suhung, Shen↗

DAISY Benchmark Performance Data

This repository contains the underlying data from benchmark experiments for Drifting Acoustic Instrumentation SYstems (DAISYs) in waves and currents described in "Performance of a Drifting Acoustic Instrumentation SYstem (DAISY) for Characterizing Radiated Noise from Marine Energy Converters" (https://link.springer.com/article/10.1007/s40722-024-00358-6). DAISYs consist of a surface expression connected to a hydrophone recording package by a tether. Both elements are instrumented to provide metadata (e.g., position, orientation, and depth). Information about how to build DAISYs is available at https://www.pmec.us/research-projects/daisy. The repository's primary content is three compressed archives (.zip format), each containing multiple MATLAB binary data files (.mat format). A table relating individual data files to figures in the paper, as well as the structure of each file, is included in the repository as a Word document (Data Description MHK-DR.docx). Most of the files contain time series information for a single DAISY deployment (file naming convention: [site]_DAISY_[Drift #].mat) consisting of processed hydrophone data and associated metadata. For a limited number of DAISY deployments, the hydrophone package was replaced with an acoustic Doppler velocimeter (file naming convention: [site]_DAISY_[Drift #]_ADV.mat). Data were collected over several years at three locations: (1) Sequim Bay at Pacific Northwest National Laboratory's Marine & Coastal Research Laboratory (MCRL) in Sequim, WA, the energetic tidal channel in Admiralty Inlet, WA (Admiralty Inlet), and the U.S. Navy's Wave Energy Test Site (WETS) in Kaneohe, HI. Brief descriptions of data files at each location follow. - MCRL - (1) Drift #4 and #16 contrast the performance of a DAISY and a reference hydrophone (icListen HF Reson), respectively, in the quiescent interior of Sequim Bay (September 2020). (2) Drift #152 and #153 are velocity measurements for a drifting acoustic Doppler velocimeter in in the tidally-energetic entrance channel inside a flow shield and exposed to the flow, respectively (January 2018). (3) Two non-standard files are also included: DAISY_data.mat corresponds to a subset of a DAISY drift over an Adaptable Monitoring Package (AMP) and AMP_data.mat corresponds to approximately co-temporal data for a stationary hydrophone on the AMP (February 2019). - Admiralty Inlet - (1) Drift #1-12 correspond to tests with flow shielded DAISYs, unshielded DAISYs, a reference hydrophone, and drifting acoustic Doppler velocimeter with 5, 10, and 15 m tether lengths between surface expression and hydrophone recording package (July 2022). (2) Drift #13-20 correspond to tests of flow shielded DAISYs with three different tether materials (rubber cord, nylon line, and faired nylon line) in lengths of 5, 10, and 15 m (July 2022). - WETS - (1) Drift #30-32 correspond to tests with a heave plate incorporated into the tether (standard configuration for wave sites), rubber cord only, and rubber cord, but with a flow shielded hydrophone (November 2022). (2) Drift #49-58 and Drift #65-68 correspond to measurements around mooring infrastructure at the 60 m berth where time-delay-of-arrival localization was demonstrated for different DAISY arrangements and hydrophone depths (November 2022).

16 TIDAL AND WAVE POWER↗

Sharks Associated With A Large Sand Shoal Complex: Community Insights From Longline and Acoustic Telemetry Surveys

Offshore sand shoals are a coveted sand source for coastal restoration projects and as sites for wind energy development. Shoals often support unique fish assemblages but their habitat value to sharks is largely unknown due to the high mobility of most species in the open ocean. This study pairs multi-year longline and acoustic telemetry surveys to reveal depth-related and seasonal patterns in a shark community associated with the largest sand shoal complex in east Florida, USA. Monthly longline sampling from 2012–2017 yielded 2,595 sharks from 16 species with Atlantic sharpnose (Rhizoprionodon terraenovae), blacknose (Carcharhinus acronotus), and blacktip (C. limbatus) sharks being the most abundant species. A contemporaneous acoustic telemetry array detected 567 sharks from 16 species (14 in common with longlines) tagged locally and by researchers elsewhere along the US East Coast and Bahamas. PERMANOVA modeling of both datasets indicate that the shark species assemblage differed more across seasons than water depth although both factors were important. Moreover, the shark assemblage detected at an active sand dredge site was similar to that at nearby undisturbed sites. Water temperature, water clarity, and distance from shore were habitat factors that most strongly correlated to community composition. Both sampling approaches documented similar single-species and community trends but longlines underestimated the shark nursery value of the region while telemetry-based community assessments are inherently biased by the number of species under active study. Overall, this study confirms that sharks can be an important component of sand shoal fish communities but suggests that deeper water immediately adjacent to shoals (as opposed to shallow shoal ridges) is more valuable to some species. Potential impacts to these nearby habitats should be considered when planning for sand extraction and offshore wind infrastructure.

Shark Management↗

Ocean Carbon and Biogeochemistry Scoping Workshop on Terrestrial and Coastal Carbon Fluxes in the Gulf of Mexico, St. Petersburg, FL

Despite their relatively small surface area, ocean margins may have a significant impact on global biogeochemical cycles and, potentially, the global air-sea fluxes of carbon dioxide. Margins are characterized by intense geochemical and biological processing of carbon and other elements and exchange large amounts of matter and energy with the open ocean. The area-specific rates of productivity, biogeochemical cycling, and organic/inorganic matter sequestration are high in coastal margins, with as much as half of the global integrated new production occurring over the continental shelves and slopes (Walsh, 1991; Doney and Hood, 2002; Jahnke, in press). However, the current lack of knowledge and understanding of biogeochemical processes occurring at the ocean margins has left them largely ignored in most of the previous global assessments of the oceanic carbon cycle (Doney and Hood, 2002). A major source of North American and global uncertainty is the Gulf of Mexico, a large semi-enclosed subtropical basin bordered by the United States, Mexico, and Cuba. Like many of the marginal oceans worldwide, the Gulf of Mexico remains largely unsampled and poorly characterized in terms of its air-sea exchange of carbon dioxide and other carbon fluxes. The goal of the workshop was to bring together researchers from multiple disciplines studying terrestrial, aquatic, and marine ecosystems to discuss the state of knowledge in carbon fluxes in the Gulf of Mexico, data gaps, and overarching questions in the Gulf of Mexico system. The discussions at the workshop were intended to stimulate integrated studies of marine and terrestrial biogeochemical cycles and associated ecosystems that will help to establish the role of the Gulf of Mexico in the carbon cycle and how it might evolve in the face of environmental change.

Air sea exchanges↗

Data Fusion for the Development of a Multimodal Freight Transload Facilities Dataset in the U.S.

To withstand the growing demand of commodity volume and its strain on the transportation infrastructure, it is necessary to identify the flow of commodities by route and mode. However, a national multimodal freight routing model does not exist for the U.S. The development of such model requires multiple building blocks, such as virtual representations of roadway, railway, and waterway networks, transload facilities (TFs), and access/egress links. Most of these blocks have a robust database in the U.S., except for the TFs. Here, this paper presents the fusion of dispersed and heterogeneous representations of multimodal TFs into a single, comprehensive, geospatial freight TF dataset. The TF dataset is derived from several sources, including the U.S. Army Corps of Engineers Master Docks Plus, the National Transportation Atlas Database, the Intermodal Association of North America, industry publications, and other public information. First, individual datasets were queried and reconciled. A geocoding/reverse geocoding process was applied to get the best street address and latitude/longitude location for each terminal. Then, duplicate terminals were identified by a fuzzy match algorithm based on terminal name and location, and removed. Validation was performed by visual inspection of random facilities. The main contributions of this work are: a publicly available version of the TF dataset, including facility location and multimodal transfer capability of 9,003 facilities, and an enterprise-version with the same facilities but including commodity handling capabilities. The main purpose of developing the TF dataset is to inform multimodal routing algorithms. The proposed TF dataset allows for credibly modeling the multimodal transfer of commodities within shipment routes.

Commodity Routing↗

Sea-Level Rise: Projections for Maryland 2018

In fulfillment of requirements of the Maryland Commission on Climate Change Act of 2015, this report provides updated projections of the amount of sea-level rise relative to Maryland coastal lands that is expected into the next century. These projections represent the consensus of an Expert Group drawn from the Mid-Atlantic region. The framework for these projections is explicitly tied to the projections of global sea-level rise included in the Intergovernmental Panel on Climate Change Fifth Assessment (2014) and incorporates regional factors such as subsidence, distance from melting glaciers and polar ice sheets, and ocean currents. The probability distribution of estimates of relative sea-level rise from the baseline year of 2000 are provided over time and, after 2050, for three different greenhouse gas emissions pathways: Growing Emissions (RCP8.5), Stabilized Emissions (RCP4.5), and meeting the Paris Agreement (RCP2.6). This framework has been recently used in developing relative sea-level rise projections for California, Oregon, Washington, New Jersey, and Delaware as well as several metropolitan areas. The Likely range (66% probability) of the relative rise of mean sea level expected in Maryland between 2000 and 2050 is 0.8 to 1.6 feet, with about a one-in-twenty chance it could exceed 2.0 feet and about a one-in-one hundred chance it could exceed 2.3 feet. Later this century, rates of sea-level rise increasingly depend on the future pathway of global emissions of greenhouse gases during the next sixty years. If emissions continue to grow well into the second half of the 21st century, the Likely range of sea-level rise experienced in Maryland is 2.0 to 4.2 feet over this century, two to four times the sea-level rise experienced during the 20th century. Moreover, there is a one-in-twenty chance that it could exceed 5.2 feet. If, on the other hand, global society were able to bring net greenhouse gas emissions to zero in time to meet the goals of the Paris Climate Agreement and reduce emissions sufficient to limit the increase in global mean temperature to less than 2°Celsius over pre-industrial levels, the Likely range for 2100 is 1.2 to 3.0 feet, with a one-in-twenty chance that it would exceed 3.7 feet. The difference in sea-level rise between these contrasting scenarios would diverge even more during the next century, with the failure to reduce emissions in the near term resulting in much greater sea-level rise 100 years from now. Moreover, recent research suggests that, without imminent and substantial reductions in greenhouse gas emissions, the loss of polar ice sheets-and thus the rate of sea-level rise-may be more rapid than assumed in these projections, particularly under the Growing Emissions scenario. These probabilistic sea-level rise projections can and should be used in planning and regulation, infrastructure siting and design, estimation of changes in tidal range and storm surge, developing inundation mapping tools, and adaptation strategies for high-tide flooding and saltwater intrusion.

Boesch, D. F.↗