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Archive: 2022
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  • AIS Data Case Study: Dredge Material Placement Site Evaluation in Frederick Sound near Petersburg, Alaska

    Abstract: The purpose of this Coastal and Hydraulics Laboratory Technical Note (CHETN) is to present an application of historic vessel position information acquired through the Automatic Identification System (AIS), which provides geo-referenced and time-stamped vessel position information. The US Army Corps of Engineers, Alaska District (POA), needed to evaluate potential placement sites for dredged material near Petersburg, AK, and possible impacts to navigation were considered as part of the evaluation process.
  • Calculation of Levee-Breach Widening Rates

    Abstract: Inundation modeling is often conducted for levee systems to understand current flood risks. The extent of inundation caused by a breach in the levee is highly influenced by the widening rate of the levee breach. This study presents an approach for calculating levee-breach widening rates based on average flow velocity through the breach, embankment height, and erosion characteristics of the soil. Estimates of soil erodibility are derived through an analysis of the measurements of soil erodibility presented in the National Cooperative Highway Research Program (NCHRP) Report 915 database. Levee-breach widening rate curves are calculated based on these erosion properties to demonstrate the approach, and default curves are presented for typical levees built from coarse-grained soils and fine-grained soils. While the most accurate approach for a site is to calculate site-specific widening rate curves based on estimates of local soil erodibility, the default curves presented provide a suitable starting point for initial inundation modeling.
  • Sediment Budget Analysis System (SBAS) 2020 User’s Guide: Version 1.0

    Abstract: This special report acts as a user’s guide for the Sediment Budget Analysis System (SBAS) toolbox within ArcGIS Pro. The SBAS toolbox is a free toolset that allows the user to create and visualize a sediment budget using ArcGIS Pro. Included in this report are instructions on how to download the toolbox and create a sediment budget.
  • Summary of Ground-Based Snow Measurements for the Northeastern United States

    ABSTRACT: Snow is an important resource for both communities and ecosystems of the Northeastern United States. Both flood risk management and water supply forecasts for major municipalities, including New York City, depend on the collection of snowpack information. Therefore, the purpose of this study is to summarize all of the snowpack data from ground-based networks currently available in the Northeast. The collection of snow-depth and snow water equivalent information extends back several decades, and there are over 2,200 active sites across the region. Sites are distributed across the entire range of elevations in the region. The number of locations collecting snow information has increased substantially in the last 20 years, primarily from the expansion of the CoCoRaHS (Community Collaborative Rain, Hail, and Snow) network. Our summary of regional snow measurement locations provides a foundation for future studies and analysis, including a template for other regions of the United States.
  • Sustainable Harmful Algal Bloom Mitigation by 3D Printed Photocatalytic Oxidation Devices (3D-PODs)

    Abstract: The impacts of Harmful Algal Blooms (HAB), often caused by cyanobacteria (Figure 1), on water resources are increasing. Innovative solutions for treatment of HABs and their associated toxins are needed to mitigate these impacts and decrease risks without introducing persistent legacy contaminants that cause collateral ecosystem impacts. This technical note (TN) identifies novel opportunities enabled by Additive Manufacturing (AM), or 3D printing, to produce high surface area advanced material composites to rapidly prototype sustainable environmental solutions for aquatic nuisance species control. This innovative research explores deployment of 3D-printable polymer composite structures containing nano-scale photocatalysts for targeted open water treatment of HABs that are customizable to the site-of-concern and also retrievable, reusable, and sustainable. The approach developed to control cyanobacteria HAB events has the potential to augment or replace broadcast, non-specific chemical controls that otherwise put non-target species and ecological resources at long-term risk. It can also augment existing UV-treatment HAB treatment control measures. The expected research outcome is a novel, effective, and sustainable HAB management tool for the US Army Corps of Engineers (USACE) and resource managers to deploy in their HAB rapid response programs. The research will provide a framework for scale-up into other manufacturing methods (e.g., injection molding) to produce the devices in bulk (quickly and efficiently). Research for this project title “Mitigation of Harmful Algal Bloom Toxins using 3D Printed Photocatalytic Materials (FY21-23)” was sponsored by the US Army Engineer Research Development Center’s (ERDC) Aquatic Nuisance Species Research Program (ANSRP).
  • Determination of Nanomaterial Viscosity and Rheology Properties Using a Rotational Rheometer

    Abstract: Rheology studies the flow of matter and is one of the most important methods for materials characterization because flow behavior is responsive to properties such as molecular weight and molecular weight distribution. Rheological properties help practitioners understand fluid flow and how to improve manufacturing processes. Rheometers have been extensively used to determine the viscosity and rheological properties of different materials because the measurements are quick, accurate, and reliable. In this standard operating procedure, a general protocol using a rotational rheometer is developed for characterizing rheological properties of nanomaterials. Procedures and recommendations for sample preparation, instrument preparation, sample measurements, and results analysis are included. The procedure was tested on a variety of carbon-based nanomaterials.
  • South Shore of Long Island, New York Regional Sediment Management Investigation: An Overview of Challenges and Opportunities

    Abstract: The US Army Corps of Engineers (USACE) is conducting the “South Shore of Long Island, New York Regional Sediment Management Investigation” to further understand sediment dynamics and to develop a comprehensive regional sediment management plan for the south shore of Long Island, New York. Regional sediment management is a systems approach using best management practices for more efficient and effective use of sediments in coastal, estuarine, and inland environments. This investigation seeks to characterize sediment movement on the south shore of Long Island as a holistic system across the entire study area. It focuses on the regional system post-Hurricane Sandy (October 2012) as the storm significantly altered the physical landscape with severe shoreline erosion, which resulted in the construction of projects to reduce the risk of future storms and stakeholder priorities with a new emphasis on bay-side sediment dynamics, such as channel shoaling and disappearing wetlands. Despite the fact the storm caused severe erosion, the equilibrium beach profile, depth of closure, and general shoreline orientation seem to be unaffected. Previous studies have characterized sediment movement at specific sections of the south shore, but these data have not been incorporated to create a system-wide perspective. Coordinating sediment management across the six Atlantic Ocean inlets, Great South Bay Channel, Intracoastal Waterway, and coastal storm risk management (CSRM) projects could save the federal government millions of dollars in dredging and sand placement actions. This technical note presents the progress the investigation has made to date and will be followed with a more in-depth technical report titled South Shore of Long Island, New York Regional Sediment Management Investigation: A Post-Hurricane Sandy Shoreline Evaluation, currently in preparation.
  • Tombigbee River: River Miles 81.0–76.0 Sediment Management Study

    Abstract: The US Army Corps of Engineers, St. Louis District, Applied River Engineering Center (AREC), in cooperation with the Operations Branch of the Mobile District, conducted a sediment management study of the Sunflower Bend reach of the Tombigbee River, between River Miles 81.0 and 76.0, near Jackson, AL. The objective of the study was to look at sediment management alternatives to alleviate or eliminate repetitive maintenance dredging. These alternatives involved various river engineering measures including dikes, weirs, channel armoring, disposal armoring, and combinations thereof. A physical Hydraulic Sediment Response model was used to examine the sediment response resulting from these alternatives. During model testing, and after discussions with AREC and Mobile Operations Division staff, a second objective was established to define existing non-erodible bed materials that were located throughout the reach. This was conducted to examine the merits of strategically removing these erosion resistant materials in the river as an additional dredging/excavation alternative. The most favorable alternatives involved removing bedload sand and consolidated clay material from between River Miles 79.1 and 78.0 to improve navigation.
  • Optimization of Reach-Scale Gravel Nourishment on the Green River below Howard Hanson Dam, King County, Washington

    Abstract: The US Army Corps of Engineers, Seattle District, nourishes gravel downstream of Howard Hanson Dam (HHD) on the Green River in Washington State. The study team developed numerical models to support the ongoing salmonid habitat improvement mission downstream of HHD. Recent advancements in computing and numerical modeling software make long-term simulations in steep, gravel, cobble, and boulder river environments cost effective. The team calibrated mobile-bed, sediment-transport models for the pre-dam and post-dam periods. The modeling explored geomorphic responses to flow and sediment regime changes associated with HHD construction and operation. The team found that pre-dam conditions were significantly more dynamic than post-dam conditions and may have had lower spawning habitat quality in the project vicinity. The team applied the Bank Stability and Toe Erosion Model to the site and then calibrated to the post-dam gravel augmentation period. The team implemented a new hiding routine in HEC-RAS that improved the simulated grain size trends but underestimated coarse sediment transport. Models without the hiding function overestimated grain size but matched bed elevations and mass flux very well. Decade-long simulations of four future gravel nourishment conditions showed continued sediment storage in the reach. The storage rate was sensitive to nourishment mass and grain size.
  • A Dynamic Hyperbolic Surface Model for Responsive Data Mining

    Abstract: Data management systems impose structure on data via a static representation schema or data structure. Information from the data is extracted by executing queries based on predefined operators. This paradigm restricts the searchability of the data to concepts and relationships that are known or assumed to exist among the objects. While this is an effective and efficient means of retrieving simple information, we propose that such a structure severely limits the ability to derive breakthrough knowledge that exists in data under the guise of “unknown unknowns.” A dynamic system will alleviate this dependence, allowing theoretically infinite projections of the data to reveal discoverable relationships that are hidden by traditional use case-driven, static query systems. In this paper, we propose a framework for a data-responsive query algebra based on a dynamic hyperbolic surface model. Such a model could provide more intuitive access to analytics and insights from massive, aggregated datasets than existing methods. This model will significantly alter the means of addressing the underlying data by representing it as an arrangement on a dynamic, hyperbolic plane. Consequently, querying the data can be viewed as a process similar to quantum annealing, in terms of characterizing data representation as an energy minimization problem with numerous minima.