15 Jun 2022

Simulated Herbicide Spray Retention on Floating Aquatic Plants as Affected by Carrier Volume and Adjuvant Type

Abstract: Foliar delivery of herbicides is a common means for plant management in aquatic environments. Though this technique is decades old, little is known about vegetative spray retention relative to this application method. A more complete understanding of maximizing herbicide retention could lead to improved plant management while simultaneously decreasing pesticide load in aquatic environments. Therefore, outdoor mesocosm experiments were conducted in 2020 to evaluate the effect of adjuvant type on foliar spray retention in waterhyacinth [Eichhornia crassipes (Mart.) Solms]. Additionally, the effect of carrier volume on spray retention in waterhyacinth, waterlettuce (Pistia stratiotes L.), and giant salvinia (Salvinia molesta D.S. Mitchell) was documented. Spray deposition did not differ among the nine adjuvants tested; however, spray retention was reduced 6% to 11% when an adjuvant was excluded from the spray solution. The effect of carrier volume on spray retention in waterhyacinth, waterlettuce, and giant salvinia was also investigated. Decreases in spray retention were most sensitive to increased carrier volume in waterhyacinth, followed by giant salvinia and waterlettuce. Among species, spray retention potential, as determined by intercept estimates, was greatest in water-lettuce and giant salvinia regardless of carrier volume. Asymptotes estimates for waterhyacinth, waterlettuce, and giant salvinia were 33%, 46%, and 79% spray retention, respectively. In other words, spray retention was the lowest and remained relatively constant at these values for the high carrier volumes tested (935 and 1,870 L ha−1), which were likely due to the presence of pubescence on leaves and flatter leaf architecture represented by waterlettuce and giant salvinia compared to the glabrous vertical leaves of waterhyacinth. Future research will evaluate these concepts under field condition.

15 Jun 2022

Foundational Principles in the Development of AdH-SW3, the Three-Dimensional Shallow Water Hydrodynamics and Transport Module within the Adaptive Hydraulics/Hydrology Model

Abstract: This report details the design and development of the three-dimensional shallow water hydrodynamics formulation within the Adaptive Hydraulics/Hydrology model (AdH-SW3) for simulation of flow and transport in rivers, estuaries, reservoirs, and other similar hydrologic environments. The report is intended to communicate principles of the model design for the interested and diligent user. The design relies upon several layers of consistency to produce a stable, accurate, and conservative model. The mesh design can handle rapid changes in bathymetry (e.g., steep-sided navigation channels in estuaries) and maintain accuracy in density-driven transport phenomena (e.g., thermal, or saline stratification and intrusion of salinity).

13 Jun 2022

Evaluation of Multiparameter Water Meter for Environmental Toolkit for Expeditionary Operations

Purpose: A new, commercially available, field-portable water sensor was evaluated for efficacy during operation and compatibility with current Environmental Toolkit for Expeditionary Operations (ETEO) software. The ETEO provides sensors to Soldiers to rapidly identify and quantify environmental contamination in soil, air, and water at potential new base sites during initial reconnaissance to ensure Soldier safety and minimize unnecessary remediation efforts by the Army. The primary objective of this study was to enhance ETEO performance by providing the capability to evaluate multiple water quality properties simultaneously.

13 Jun 2022

A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing

Abstract: Harmful Algal Blooms (HABs) continue to be a global concern, especially since predicting bloom events including the intensity, extent, and geographic location, remain difficult. However, remote sensing platforms are useful tools for monitoring HABs across space and time. The main objective of this review was to explore the scientific literature to develop a near-comprehensive list of spectrally derived empirical algorithms for satellite imagers commonly utilized for the detection and quantification HABs and water quality indicators. This review identified the 29 WorldView-2 MSI algorithms, 25 Sentinel-2 MSI algorithms, 32 Landsat-8 OLI algorithms, 9 MODIS algorithms, and 64 MERIS/Sentinel-3 OLCI algorithms. This review also revealed most empirical-based algorithms fell into one of the following general formulas: two-band difference algorithm (2BDA), three-band difference algorithm (3BDA), normalized-difference chlorophyll index (NDCI), or the cyanobacterial index (CI). New empirical algorithm development appears to be constrained, at least in part, due to the limited number of HAB-associated spectral features detectable in currently operational imagers. However, these algorithms provide a foundation for future algorithm development as new sensors, technologies, and platforms emerge.

10 Jun 2022

Operations & Maintenance (O&M) Facility Data Exchange Pilot Expansion to BUILDER SMS

Abstract: The Army has many enterprise Operation and Maintenance (O&M) systems that require manual input of the same facility data collected through-out the facility life cycle. This manual input of data costs Army installations valuable time and labor. A standardized approach to deliver the O&M information in a consistent, accurate, timely, and digital method for expedited input into the numerous systems is needed. A United States Army Corps of Engineers (USACE)-led team consisting of O&M subject matter experts within USACE and industry developed a standardized process for collecting and exchanging facility data for downstream applications. The process is defined in the Engineering and Construction Bulletin (ECB) 2018-6 and includes utilization of Unified Facilities Guide Specification (UFGS) 01 78 24.00 10. An initial pilot study verified that asset data collected during facility construction could effectively be imported into the Army General Fund Enterprise Business System (GFEBS). This second pilot study focused on facilitating the import of facility asset and equipment data collected during construction into the BUILDER Sustainment Management System (SMS) web-based software application. The project scope included investigation of current Army installations’ processes as they relate to BUILDER SMS as well as initial testing of information transfer approaches.

10 Jun 2022

Inland Waterway Network Mapping of AIS Data for Freight Transportation Planning

Abstract: Travel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84·0% accuracy in detecting stops at ports and 83·5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.

2 Jun 2022

Probabilistic Neural Networks that Predict Compressive Strength of High Strength Concrete in Mass Placements using Thermal History

Abstract: This study explored the use of artificial neural networks to predict UHPC compressive strengths given thermal history and key mix components. The model developed herein employs Bayesian variational inference using Monte Carlo dropout to convey prediction uncertainty using 735 datapoints on seven UHPC mixtures collected using a variety of techniques. Datapoints contained a measured compressive strength along with three curing inputs (specimen maturity, maximum temperature experienced during curing, time of maximum temperature) and five mixture inputs to distinguish each UHPC mixture (ce-ment type, silicon dioxide content, mix type, water to cementitious material ratio, and admixture dosage rate). Input analysis concluded that predictions were more sensitive to curing inputs than mixture inputs. On average, 8.2% of experimental results in the final model fell outside of the predicted range with 67.9%of these cases conservatively underpredicting. The results support that this model methodology is able to make sufficient probabilistic predictions within the scope of the provided dataset but is not for extrapo-lating beyond the training data. In addition, the model was vetted using various datasets obtained from literature to assess its versatility. Overall this model is a promising advancement towards predicting mechanical properties of high strength concrete with known uncertainties.

26 May 2022

Growth Assessments of Starry Stonewort (Nitellopsis obtusa) in Various Substrate Types for Large-scale Cultivation Studies

Purpose: The purpose of this study was to compare multiple substrate types to optimize cultivation conditions for the invasive macroalga Nitellopsis obtusa (Desv. in Loisel.) J. Groves, commonly known as starry stonewort. Large-scale cultivation will allow for tiered approaches to management evaluation research while minimizing the influence of confounding variables.

26 May 2022

Spatial and Temporal Variability of the Alligatorweed Pathogen, Alternaria alternantherae, in Louisiana

Abstract: Alligatorweed leaf spot is a disease of invasive Alternanthera philoxeroides(Alligatorweed) in the southern US, caused by Alternaria alternantherae. However, little is known about when or where this pathogen naturally occurs. To better understand this species’life history, we examined temporal (every 2–3 weeks) and spatial (latitudinal) patterns of A. alternantherae occurrence at sites in Louisiana for 2 y. Pathogen presence reflectedclear within-year temporal and spatial patterns. Overall, the percentage of leaves infectedwith A. alternantherae was low during spring each year (0–20% infected) but increasedthroughout summer (maximum of 50% infected), and plants in northern sites had lowerfrequency of infection relative to southern sites until later in the year (late summer/early fall) but only in 1 of the 2 years of our study. The mean proportion of leaves infected with A. alternantherae declined with latitude both years (P = 0.01) and variability increasedwith latitude (P = 0.04), a pattern suggestive of range limitation in northern areas. We estimate a northern distributional limit of 34°N for A. alternantherae in Louisiana, but Alligatorweed occurs farther north. Although we did not directly examine disease impacts to Alligatorweed during the study, they may be greatest in southern areas, where the pathogenis more common early and throughout the growing season, and thus may be less likely to provide control in northern infestations of the invasive Alligatorweed.

25 May 2022

Improving Container Shipment Analysis

Abstract: US Army Corps of Engineers (USACE) deep-draft navigation economic analyses use assumptions about the sensitivity of vessel operations to channel modification to estimate national economic development benefits. The complexity and proprietary nature of carrier deployment decisions and loading practices adds uncertainty to USACE navigation studies. This report attempts to provide an overview of containership deployment and loading practices as it relates to USACE navigation studies to improve the quality of deep-draft economics. The report relies on trade data, vessel order books, and carrier interviews to study the impact of channel modification on vessel loading and deployment. The report makes recommendations for developing deployment and loading inputs for future economic evaluations.