11 Jun 2026

Simulation of Dredged Material Placement in the San Francisco Bay Using a Multi-Dimensional Hydrodynamics and Sediment Transport Model

Abstract: The US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, has developed an Adaptive Hydraulics (AdH) 2D, hydrodynamic and sediment transport model for San Francisco Bay. This model supports the US Army Corps of Engineers, San Francisco District, in informing navigation and sediment management decisions as part of the Regional Dredged Material Management Plan (RDMMP), which evaluates dredging methods and placement alternatives over a 20-year planning horizon. There is a need to assess the long-term fate of dredged material placed at in-bay sites to better understand associated benefits and potential impacts. This report documents the development, calibration, and validation of the AdH 2D model for conditions in 2022. The model was applied to simulate the multimonth dispersion and transport of dredged material from four sites. Model results demonstrate that sediment transport patterns are influenced by seasonal hydrodynamic forcing and grain-size composition, with coarser material forming stable deposits that persist over time. The findings of this study inform sediment management strategies under the San Francisco Bay RDMMP and support efforts to reduce navigation risks and enhance beneficial use opportunities. The study recommends field data collection to improve sediment characterization at placement sites and strengthen predictive modeling and planning efforts.

11 Jun 2026

Proceedings from the Great Lakes Engineering With Nature® Natural and Nature-Based Features Playbook Workshop

Abstract: Communities in the Great Lakes are experiencing increased frequency in coastal flooding and erosion, causing property damage, putting lives at risk, and disrupting local economies. To address these challenges, two workshops were conducted (18 February 2025 [virtual] and 26–27 February 2025 [in person]) to collect knowledge, insights, and feedback from community members, policymakers, and Tribal Nations representatives to inform the development of the Engineering With Nature® Great Lakes Playbook. This report documents the workshop outcomes. The playbook is being designed to advance coastal resilience efforts in the region by identifying natural and nature-based features and multiple lines of resilience strategies that address unique natural hazard-related challenges of the Great Lakes. During the workshops, sustainable, resilient, adaptable, and cost-effective solutions were explored and construction and implementation feasibility were discussed along with regulatory and community challenges that are applicable to coastal risks and opportunities around the Great Lakes. By providing location-appropriate examples and clear guidance on how these nature-based and engineered solutions can be implemented, the playbook will enhance understanding of their potential performance in the region and build confidence among federal, state, and local agencies and Tribal Nations in planning, designing, and implementing these sustainable, adaptable, and cost-effective solutions.

11 Jun 2026

Geofencing for Standardized Navigation Lock Cycle Time Analysis

Abstract: The purpose of this US Army Engineer Research and Development Center (ERDC) technical note (TN) is to describe the motivation for, and development of, a set of geospatial boundaries (geofences) at standard intervals around navigation lock structures owned or operated by the US Army Corps of Engineers (USACE). These geofences will be used for automated time-stamp generation in conjunction with Automatic Identification System (AIS) broadcasts from vessels.

10 Jun 2026

Multisource Knowledge Graph Architecture for Air-Gapped AI Systems: Design Patterns and a Geospatial Reference Implementation

Purpose: This technical note presents a reference architecture for constructing multisource knowledge graphs in air-gapped, domain-specific AI systems. The architecture addresses recurring problems in military, intelligence, and secure-enterprise environments: integrating heterogeneous authoritative data sources while preserving source schema fidelity, enabling deterministic semantic resolution, and operating without external network dependencies. While this is a geospatial implementation, the architectural principles in the core design are transferable. The implementation is organized around four separable layers—schema registry, domain ontology, reasoning patterns, and relationship vocabulary—and adopts a canonical-with-aliasing integration strategy that supports cross-schema reasoning without forcing premature schema con-vergence. These patterns are validated through a geospatial intelligence implementation supporting US Army operations and demonstrate how abstract design principles translate into an operationally relevant system. This knowledge graph is designed to serve as the semantic substrate for router-based AI systems. A companion technical note (Drouillard and Lewis 2026) describes the geospatial AI (GeoAI) agent stack, which is a router-based orchestration architecture that coordinates multiple retrieval backends and reasoning tools. Within that architecture, the knowledge graph functions as a specialized retrieval backend that runs alongside document retrieval and vector search, serving queries that require structured entity-relationship reasoning, provenance tracking, or deterministic semantic resolution. The router directs spatial relationship queries (e.g., “which roads cross this river”), multihop dependency queries (e.g., “what infrastructure depends on this power station”), and schema-resolution queries (e.g., “find all transportation features in Multinational Geospatial Co-production Program [MGCP] format”) to the knowledge graph while routing conceptual or analytical questions to document retrieval. This technical note focuses exclusively on the knowledge graph architecture; the broader orchestration patterns and routing logic are detailed in the companion paper. While the examples presented are geospatial, the architectural principles, validation strategies, and design tradeoffs documented here have broader applicability where deterministic semantic integration is required under air-gapped constraints. The geospatial instantiation serves as a concrete demonstration of abstract patterns that may inform future knowledge graph efforts in other US Army Engineer Research and Development Center (ERDC) research domains.

9 Jun 2026

Considerations for Potential Use of an Agent-Based Model in a Petri Network Framework to Model Roost Tree Dynamics of Bats

Purpose: The US Army Corps of Engineers (USACE) is responsible for numerous projects that involve altering or removing wildlife habitat, including habitat of species listed as threatened, endangered, or sensitive (TES). Before initiating a project that may affect TES species, USACE must determine the project’s impact to these species. Understanding the degree of impact, both positive and negative, allows USACE to compare alternatives to reduce negative effects. Because of this, USACE planners need tools to provide accurate assessments of project impacts. Conservation efforts for bats have focused on protecting habitat, especially diurnal roosting trees. Roost trees serve not only as daytime shelter for bats but also for rearing pups until they are able to fly (Barbour and Davis 1969). Determining the impact of habitat change by empirically testing the response of bats to habitat modification has proved difficult because of the effects it may have on TES bats as well as its cost. Simulating the effects of habitat change using computer models provides an excellent data alternative for USACE planners. This technical note explains how agent-based models within a petri network framework can provide USACE planners with information on how habitat modification will affect bat presence or absence.

9 Jun 2026

Considerations for Potential Use of an Agent-Based Model in a Petri Network Framework to Model Roost Tree Dynamics of Bats

Purpose: The US Army Corps of Engineers (USACE) is responsible for numerous projects that involve altering or removing wildlife habitat, including habitat of species listed as threatened, endangered, or sensitive (TES). Before initiating a project that may affect TES species, USACE must determine the project’s impact to these species. Understanding the degree of impact, both positive and negative, allows USACE to compare alternatives to reduce negative effects. Because of this, USACE planners need tools to provide accurate assessments of project impacts. Conservation efforts for bats have focused on protecting habitat, especially diurnal roosting trees. Roost trees serve not only as daytime shelter for bats but also for rearing pups until they are able to fly (Barbour and Davis 1969). Determining the impact of habitat change by empirically testing the response of bats to habitat modification has proved difficult because of the effects it may have on TES bats as well as its cost. Simulating the effects of habitat change using computer models provides an excellent data alternative for USACE planners. This technical note explains how agent-based models within a petri network framework can provide USACE planners with information on how habitat modification will affect bat presence or absence.

8 Jun 2026

Metabarcoding Stem Residue as a Novel Environmental DNA (eDNA) Tool to Identify Spread of Phragmites australis Biological Control Agents

Abstract: Detection and monitoring of biological control agents are critical for evaluating their establishment and spread yet remain challenging when species are cryptic, and densities are low. We assessed whether environmental DNA (eDNA) metabarcoding of stem residues could be used to detect and distinguish the stem-boring noctuid moths Archanara neurica and Lenisa geminipuncta, biological control agents of introduced Phragmites australis released in Ontario, Canada. Using 16S rDNA metabarcoding supplemented with newly generated reference sequences, we analyzed stem residue samples spanning a gradient of quality, including laboratory culture stems (in which agent larvae were present at the time of sampling), confirmed Canadian release sites, and degraded stems from un-managed Phragmites stands in western New York, USA. Target species were consistently detected in laboratory samples, where they comprised 76.7–100% (L. geminipuncta) and 79.2–100% (A. neurica) of Lepidoptera reads. At Canadian release sites, A. neurica was detected in 5 of 11 samples and L. geminipuncta in all 10 samples, with relative read abundances ranging from trace levels to > 90%. Among 27 damaged stems collected in the United States, A. neurica and L. geminipuncta DNA was detected in three samples at very low abundances (0.003–0.12% of total reads), representing the first molecular evidence consistent with trans-border dispersal of these agents. Although detections were rare and do not confirm population establishment, results demonstrate that stem-residue eDNA metabarcoding provides a sensitive, non-invasive tool for early detection and post-release monitoring of biological control agents for invasive plants.

8 Jun 2026

Evaluating Migratory Fish Passage at Partial Migration Barriers in a Social-Ecological Riverscape

Abstract: Anthropogenic partial barriers, such as low-head locks and dams, fragment social-ecological riverscapes and limit migratory fish access to historical spawning habitats, creating trade-offs between ecological conservation and human needs. Fish passage mitigation strategies at three low-head locks and dams on the Cape Fear River, North Carolina, across two contrasting mitigation regimes included (i) a nature-like fishway at LD1, (ii) conservation locking at LD2 and LD3, and (iii) environmental flow prescriptions when locks were inoperable. We evaluated passage of American shad and striped bass using acoustic telemetry and multistate models within a Bayesian framework to estimate upstream passage probabilities under varying flow conditions and management regimes. Passage probabilities for both species were higher in 2013–2015 when conservation locking was conducted. In contrast, passage declined when locks were inoperable and only e-flows allowed passage during dam submergence events in 2022–2023. Flow positively influenced passage, with strongest effects for striped bass; however, the nature-like fishway exhibited consistently low passage probability, and modifications did not improve passage probabilities. Given low passage probabilities during the recent mitigation period, improving longitudinal connectivity for diadromous fish in this river necessitates flexible, integrated operational, structural, and flow-based strategies. Possible future mitigation actions to improve fish passage could include resuming conservation locking, structural interventions such as bypass channel construction and dam height lowering that extends dam submergence, and continued use of e-flows.

8 Jun 2026

Emulation of Peak Storm Surge Across Extended Spatial Domains Using Separable Gaussian Process Techniques

Abstract: Data-driven emulation of peak storm surge has emerged as a popular strategy for overcoming limitations arising from the computational burden of high-fidelity hydrodynamic numerical models used within coastal risk assessment applications. The surrogate models used for this emulation are developed using suites of synthetic storm simulations, and once calibrated, can replace the original high-fidelity model to establish predictions for new storms. These predictions pertain to the geographic domain, and therefore nodal locations, covered by the original high-fidelity simulation suite. This creates a two-dimensional space for the peak surge predictions, with one corresponding to the storm features and the other to the spatial domain. Gaussian Process techniques have emerged as a widely popular surrogate modeling technique for peak surge emulation. In all GP implementations so far, the spatial variability has been incorporated in the analysis through the metamodel output, considering a multi-output GP implementation. This approach fails to explicitly model spatial dependencies for the peak surge. To address this shortcoming, this study examines an alternative implementation that considers spatial and storm feature variability as part of the metamodel input, establishing a surrogate model that simultaneously predicts the peak storm surge across both the spatial domain and the storm features. For computational tractability, a separable covariance function is considered for the GP, establishing separate kernels for the spatial and storm feature spaces. Particularly for the spatial domain, an adaptive covariance tapering formulation, which infuses sparsity in the corresponding covariance matrix, is adopted to support applications with a large number of nodal locations. A simultaneous calibration approach for the hyperparameters of the separate kernels is further proposed to improve emulation accuracy. Comparisons of computational efficiency and accuracy of the alternative GP implementations are established utilizing the Coastal Hazards System–North Atlantic database, with those employing the adaptive covariance tapering formulation evaluated under varying sparsity levels. The case study demonstrates that the simultaneous hyperparameter calibration is beneficial for the separable GP's predictive accuracy, particularly as it relates to the worst-performing nodes in the domain, and that the imposed sparsity level impacts the separable GP's ability to model non-stationary spatial trends in the domain.

8 Jun 2026

MoistViT: A Vision Transformer Model for Moisture Content Prediction of Wood Chips

Abstract: Moisture content in wood chips is a critical parameter for industries such as pelleting mills, bio-refineries, paper mills, and renewable energy production. The moisture level significantly influences both the quality of the final product and the efficiency of the production process. Consequently, accurate knowledge of moisture content is of substantial importance to wood chip-reliant industries. However, current methods for determining moisture content are either time-consuming or require costly equipment and specialized setups. Therefore, developing a quick and reliable method for assessing wood chip moisture content is imperative. To address this need, we evaluate fourteen Vision Transformer (ViT) architectures and introduce an optimized model, MoistViT, developed using Bayesian Optimization Hyperband (BOHB) for efficient hyperparameter tuning. Experiments on two wood chip image datasets (1600 total images) show that MoistViT achieves 91% accuracy and 92% F1-score on Source 1 and 93% accuracy and 93% F1-score on Source 2, outperforming all baseline models. Subsequently, a thorough analysis of failure cases has been carried out, including the identification of the most challenging groups of moisture levels. These analyses provide valuable insights into the complex task of determining moisture content from inherently heterogeneous wood chips. The proposed MoistViT demonstrates significant potential for real-time applications in relevant industries, which could ultimately lead to a streamlined production process.