8 May 2024

Management Strategy for Overwintering Cyanobacteria in Sediments Contributing to Harmful Algal Blooms (HABs)

Purpose: Cyanobacteria that cause harmful algal blooms (HABs) can overwinter in sediments as resting cells (akinetes or vegetative colonies) and contribute to seasonal bloom resurgences. However, to date there has been limited focus on management tactics specifically targeting the control of cyanobacterial sources from sediments. Targeting resting cells in sediments for preventative management may provide a viable approach to delay onset and mitigate blooms (Calomeni et al. 2022). However, there are limited resources for this novel strategy. Given the growing global impact of HABs, there is a need to develop management strategies focused on sediments as a potential source and contributor to HABs. Therefore, the objective of this report is to provide a management strategy in terms of approaches, information, and case study examples for managing overwintering cyanobacteria in sediments with the goal of mitigating seasonal HAB occurrences.

7 May 2024

Spherical Shock Waveform Reconstruction by Heterodyne Interferometry

Abstract: The indirect measurement of shock waveforms by acousto-optic sensing requires a method to reconstruct the field from the projected data. Under the assumption of spherical symmetry, one approach is to reconstruct the field by the Abel inversion integral transform. When the acousto-optic sensing modality measures the change in optical phase difference time derivative, as for a heterodyne Mach–Zehnder interferometer, e.g., a laser Doppler vibrometer, the reconstructed field is the fluctuating refractive index time derivative. A technique is derived that reconstructs the fluctuating index directly by assuming plane wave propagation local to a probe beam. With synthetic data, this approach is compared to the Abel inversion integral transform and then applied to experimental data of laser-induced shockwaves. Time waveforms are reconstructed with greater accuracy except for the tail of the waveform that maps spatially to positions near a virtual origin. Furthermore, direct reconstruction of the fluctuating index field eliminates the required time integration and results in more accurate shock waveform peak values, rise times, and positive phase duration.

6 May 2024

Monitoring Geomorphology to Inform Ecological Outcomes Downstream of Reservoirs Affected by Sediment Release

Abstract: Increasingly, reservoir managers are seeking techniques that improve sediment management while considering long-term sedimentation and reduced operational flexibility. These techniques, often termed sustainable sediment management, involve passing sediment through reservoirs and into downstream rivers. Conceptually, restoring sediment continuity can benefit ecosystem function by increasing floodplain connectivity, contributing to the heterogeneity of channel geomorphology, and supporting the continuity of nutrient cycling. However, when a change is made to operations, geomorphic changes may need to be monitored to document benefits and mitigate any unexpected effects of the change. This investigation develops a geomorphic monitoring plan for downstream reaches affected by sediment-release operations at reservoirs. The monitoring objectives are aligned with potential geomorphic change caused by changes to sediment supply and the associated effects on river function. A tiered approach is presented to explain the quality of information that can be assessed from increasing levels of data collection. A general conceptual model is described in which geomorphic data may be linked to physical habitat conditions and, therefore, ecological processes. The geomorphic monitoring plan for the Tuttle Creek Reservoir water injection dredging (WID) pilot project is presented as a case study. This technical note establishes a general framework for monitoring the design for sustainable sediment management in different ecological and geomorphic contexts.

2 May 2024

Data-Driven Modeling of Groundwater Level Using Machine Learning

Purpose: This US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory engineering technical note (CHETN) documents a preliminary study on the use of specialized machine learning (ML) methods to model the variations in groundwater level (GWL) with time. This approach uses historical groundwater observation data at seven gage locations in Wyoming, USA, available from the USGS database and historical data on several relevant meteorological variables obtained from the ERA5 reanalysis dataset produced by the Copernicus Climate Change Service (usually referred to as C3S) at the European Center for Medium-Range Weather Forecasts to predict future GWL values for a desired period of time. The results presented in this report indicate that the ML method has the potential to predict both short-term (4-hourly) as well as daily variations in GWL several days into the future for the chosen study region, thus alleviating the need for employing sophisticated process-based numerical models with complicated model structure configurations.

30 Apr 2024

Evaluating Soil Conditions to Inform Upper Mississippi River Floodplain Restoration Projects

Abstract: The US Army Corps of Engineers (USACE) has designed and constructed thousands of acres of ecosystem restoration features within the Upper Mississippi River System. Many of these projects incorporate island construction to restore geomorphic diversity and habitat, including floodplain forests. Soils are the foundation of the ecological function and successful establishment of floodplain forests as they are the basis through which plants obtain water and nutrients and provide critical ecosystem services. To improve floodplain forest island restoration outcomes, three natural and four recently (<10 years) constructed restoration sites were studied to compare soil physical, chemical, microbial, and fungal characteristics. Constructed islands had lower soil organic matter and dissolved organic carbon and differed in nutrient concentrations, bacterial assemblages, and fungal communities compared to reference sites. However, soil enzyme activity and some microbial community characteristics were functionally similar between the natural and created sites. Results align with previously established restoration trajectory theories where hydrological and basic microbial ecosystem functions are restored almost immediately, but complex biologically mediated and habitat functions require more time to establish. Data from this and future studies will help increase the long-term success of USACE floodplain forest restoration, improve island design, and help develop region-specific restoration trajectory curves to better anticipate the outcomes of floodplain forest creation projects.

30 Apr 2024

Bioaugmentation for Enhanced Mitigation of Explosives in Surface Soil

Abstract: Residual munition constituents (MCs) generated from live-fire training exercises persist in soil and can migrate to groundwater, surface waters, and off-range locations. Techniques to mitigate this potential migration are needed. Since the MC hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) can be biodegraded, soil inoculation with RDX-degrading bacteria (i.e., bioaugmentation) was investigated as a means to reduce the migration potential of RDX. Metagenomic studies using contaminated soils have suggested that a greater diversity of bacteria are capable of RDX biodegradation. However, these bacteria remain uncultivated and are potentially a source of novel enzymes and pathways for RDX biodegradation. In situ soil cultivation of a novel soil array was used to isolate the uncultivated bacteria that had been inferred to degrade RDX. Approximately 10.5% of the bacteria isolated from the soil arrays degraded RDX by the aerobic denitration pathway. Of these, 26.5% were possibly novel species of RDX-degrading bacteria, based on 16S rRNA sequence similarity. Both cell encapsulation in hydrogels and coating cells onto granules of polymeric carbon sources were investigated as carrier/delivery approaches for soil inoculation. However, neither of these approaches could confirm that the observed RDX degradation was by the inoculated bacteria.

22 Apr 2024

Experimental and Numerical Analyses of Soil Electrical Resistivity under Subfreezing Conditions

Abstract: The engineering behavior of frozen soils is critical to the serviceability of civil infrastructure in cold regions. Among various geophysical techniques, electrical resistivity imaging is a promising technique that is cost effective and provides spatially continuous subsurface information. In this study, under freeze–thaw conditions, we carry out lab–scale 1D electrical resistivity measurements on frost–susceptible soils with varying water content and bulk density properties. We use a portable electrical resistivity meter for temporal electrical resistivity measurements and thermocouples for temperature monitoring. Dynamic temperature-dependent soil properties, most notably unfrozen water content, exert significant influences on the observed electrical resistivity. Below 0 ◦C, soil resistivity increases with the decreasing temperature. We also observe a hysteresis effect on the evolution of electrical resistivity during the freeze–thaw cycle, which effect we characterize with a sigmoidal model. At the same temperature, electrical resistivity during freezing is consistently lower than that during thawing. We have implemented this sigmoidal model into a COMSOL finite element model at both laboratory and field scales which enables the simulation of soil electrical resistivity response under both short–term and long–term sub–freezing conditions. Atmospheric temperature variations induce soil temperature change, and thereby phase transition and electrical resistivity change, with the rate of change being a function of the depth of investigation and soil properties include initial water content and initial temperature. This study advances the fundamental understanding of the electrical behaviors of frozen soils and enhance the application of electrical geophysical investigations in cold regions.

18 Apr 2024

Standardized NEON Organismal Data for Biodiversity Research

Abstract: Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is limited by a lack of standardized, high-quality, empirical data spanning large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the US and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be accessible and easily integrated into investigators’ workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format available through the ecocomDP R package; provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included. Our workflows are open-source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks.

18 Apr 2024

Neural Ordinary Differential Equations for Rotorcraft Aerodynamics

Abstract: High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential Equation (NODE) approach as a machine learning alternative to reduced order models in rotorcraft applications—specifically to predict the pitching moment on a rotor blade section from an initial condition, mach number, chord velocity and normal velocity. The results indicate that NODEs cannot outperform traditional reduced order models, but in some cases they can outperform simple multilayer perceptron networks. Additionally, the mathematical structure provided by NODEs seems to favor time-dependent predictions. We demonstrate how this mathematical structure can be easily modified to tackle more complex problems. The work presented in this report is intended to establish an initial evaluation of the usability of the modified NODE approach for time-dependent modeling of complex dynamics over seen and unseen domains.

17 Apr 2024

Enabling Understanding of Artificial Intelligence (AI) Agent Wargaming Decisions through Visualizations

Abstract: The process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a maritime scenario where the AI agent credibly competes against blue agents in gameplay. However, a limitation of using DRL for agent training relates to the transparency of how the AI agent makes decisions. If leaders were to rely on AI agents for COA development or analysis, they would want to understand those decisions. In or-der to support increased understanding, researchers engaged with stakeholders to determine visualization requirements and developed initial prototypes for stakeholder feedback in order to support increased understanding of AI-generated decisions and recommendations. This report describes the prototype visualizations developed to support the use case of a mission planner and an AI agent trainer. The prototypes include training results charts, heat map visualizations of agent paths, weight matrix visualizations, and ablation testing graphs.