26 Jun 2026

Examination of Analytical Shear Stress Predictions for Coastal Dune Evolution

Abstract: Existing process-based models for simulating coastal foredune evolution largely use the same analytical approach for estimating wind-induced surface shear stress distributions over spatially variable topography. Originally developed for smooth, low-sloping hills, these analytical models face significant limitations when the topography of interest exhibits large height-to-length ratios and/or steep, localized features. In this work, we utilize computational fluid dynamics (CFD) to examine the error trends of a commonly used analytical shear stress model for a series of idealized two-dimensional dune profiles. It is observed that the prediction error of the analytical model increases compared to the CFD simulations for increasing height-to-length ratio and localized slope values. Furthermore, we explore two data-driven methodologies for generating alternative shear stress prediction models, namely, symbolic regression and linear, projection-based, non-intrusive reduced-order modeling. These alternative modeling strategies demonstrate reduced overall error but still suffer in their generalizability to broader sets of dune profiles outside of the training data. Finally, the impact of these improvements on aeolian sediment transport fluxes is examined to demonstrate that even modest improvements to the shear stress prediction can have significant impacts on dune evolution simulations over engineering-relevant timescales.

26 Jun 2026

Demonstration of a Remotely Operated Vehicle for Inspecting Holt Lock and Dam

Purpose: This report describes the US Army Engineer Research and Development Center–Environmental Laboratory (ERDC-EL), Robotic Characterization of Battlefield and Operational Environments (RCBOE) Team’s application of a small inspection-class remotely operated vehicle (ROV) to inspect underwater structures at the Holt Lock and Dam located near Tuscaloosa, Alabama.

26 Jun 2026

Water Quality and Sediment Dispersal from Placement of Dredged Material over Former Shell Mining Beds in Mobile Bay, Alabama

Abstract: The US Army Corps of Engineers (USACE) continues to advance regional sediment management practices including Beneficial Use of Dredged Material (BUDM) to reduce dredging costs while improving outcomes for coastal communities and ecosystems. This report describes two field studies conducted to better understand sediment retention and water quality implications associated with in-bay strategic placement of dredged material within former oyster-shell mining areas within Mobile Bay, Alabama. Deployed instrumentation and periodic campaigns of bed and water quality sampling provided data prior to dredged-sediment placement through more than a year after placement. Bed sampling and acoustic sub-bottom profiling indicated that the dredged material deposit was spatially variable in thickness and composition. Placed sediment accumulated quickly, within hours of placement, followed by a 2–4 month period with relatively small adjustments. Beyond 6 months, bed elevation changes became stable at near-background levels. Water quality data indicated that impacts to dissolved oxygen and turbidity associated with the dredged material placement are minor and short-lived. Notably, all water quality parameters remained within the normal range of variability observed within the dynamic Mobile Bay ecosystem. Collectively, these sediment bed and water quality studies support future data driven BUDM decision-making within the Mobile Bay region.

22 Jun 2026

Beach-fx Application Guide: A User’s Trail Guide to Beach-fx

Abstract: Beach-fx is a comprehensive analytical framework used for the evaluation of the physical performance and economic benefits of shore-protection projects related to beach nourishment. The model employs an event-driven Monte Carlo simulation of a project’s life cycle and tracks the physical and economic evolution of the beach. The computational architecture of Beach-fx is set up such that the model relies on external databases that are accessed at run time. There are three external databases: the Input Database (IDB), Output Database (ODB), and Shore Response Database (SRD). The IDB and SRD describe the coastal area under study, the environmental forcing that can impact the area, the structures in the area that are susceptible to damages, and estimates of the morphologic response to the environmental forcing. The ODB stores output data and statistics for each simulation. This document summarizes the steps necessary to prepare the external databases, build a Beach-fx study, and understand the results from model runs. The aim is to provide the Beach-fx user with a comprehensive guide that provides insight to the Beach-fx process from beginning to end.

15 Jun 2026

Environmental Fate of Monosodium Methanearsonate (MSMA)—Part 1: Conceptual Model

Abstract: Monosodium methanearsonate (MSMA), the sodium salt of monomethylarsonic acid (MMA), is used as a selective, broad‐spectrum contact herbicide to control weeds in cotton and a variety of turf. In water, MSMA dissociates into ions of sodium (Na+) and of MMA−, which is the herbicide's active component. Certain soil microorganisms can methylate MMA to dimethylarsinic acid (DMA) other microorganisms can demethylate MMA to inorganic arsenic (iAs). To predict the groundwater concentration of iAs that may result from MSMA application, the processes affecting the environmental behavior of MSMA must be quantified and modeled. There is an extensive body of literature regarding the environmental behavior of MSMA. There is a consensus among scientists that the fate of MMA in soil is controlled by microbial activity and sorption to solid surfaces and that iAs sorption is even more extensive than that of MMA. The sorption and transformation of MMA and its metabolites are affected by several factors including aeration condition, temperature, pH, and the availability of nutrients. The precise nature and extent of each of these processes vary depending on site‐specific conditions; however, such variability is constrained in typical MSMA use areas that are highly managed. Monomethylarsonic acid is strongly sorbed on mineral surfaces and becomes sequestered into the soil matrix. Over time, a greater portion of MMA and iAs becomes immobile and unavailable to soil microorganisms and to leaching. This review synthesizes the results of studies that are relevant for the behavior of MSMA used as a herbicide to reliably predict the fate of MSMA in its use conditions.

15 Jun 2026

Natural vs. Genetically Engineered Microbiomes: Understanding Public Attitudes for Indoor Applications and Pathways for Future Engagement

Abstract: This study examines public preferences for natural microbiomes and support for genetically engineered (GE) microbiomes in the built environment, focusing on the demographic, sociographic, and attitudinal factors that influence these preferences. Using data from a nationally representative survey of 1,000 U.S. adults, we employed hierarchical regression analyses to assess the relative contribution of these variables. While demographic and sociographic factors explained limited variance, topic-specific attitudes, including positive perceptions of microbiome engineering’s potential to improve quality of life, were the most significant predictors of support. Conversely, age, distrust in science, and perceived knowledge negatively influenced support for GE microbiomes, reflecting skepticism among some audiences. The findings highlight the potential of the Responsible Research and Innovation (RRI) framework to align the development of microbiome engineering with societal values and to address diverse public perspectives. This research provides actionable insights for policymakers, researchers, and communicators seeking to navigate the complexities of public engagement with emerging biotechnologies.

12 Jun 2026

Automated Snow Cover Detection on Mountain Glaciers Using Spaceborne Imagery and Machine Learning

Abstract: Tracking the extent of seasonal snow on glaciers over time is critical for assessing glacier vulnerability and the response of glacierized watersheds to climate change. We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data. The workflow produces daily to twice monthly time series of several glacier mass balance and snowmelt indicators from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snow lines to manual interpretations at each glacier site. The image classifiers exhibit over-all accuracies of 92 %–98 %, κ scores of 84 %–96 %, and F scores of 93 %–98 % for all image products. The median difference between automatically and manually delineated median snow line altitudes is −31 m across all image products. The Sentinel-2 classifier produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and firn the most reliably. Although they are less accurate, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage of observations. The transient accumulation area ratio produces the least noisy time series, making it the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal the timing of minimum snow cover conditions varies by up to a month between Arctic and midlatitude sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, land ice representations within Earth system models, water resources, and the impacts of climate change on snow cover across broad spatial scales.

12 Jun 2026

Spatiotemporal Patterns of Accumulation and Surface Roughness in Interior Greenland with a GNSS-IR Network

Abstract: The dry-snow zone is the largest region of the Greenland Ice Sheet, yet temporally and spatially dense observations of surface accumulation and surface roughness in this area are lacking. We use the global navigation satellite system interferometric reflectometry (GNSS-IR) technique with a novel, low-cost GNSS network of 12 stations in the vicinity of the ice sheet summit to reveal temporal and spatial patterns of accumulation of the upper snow layer. We show that individual measurements are highly precise, while the aggregate of hundreds of daily measurements across a large spatial footprint can detect millimeter-level surface changes and is biased by −2.7 ± 3.0 cm com-pared to a unique validation data set that covers a similar spatial extent to the instrument sensing footprint. Using the validation data set, we find that the reflectometry technique is most sensitive to the surrounding 4–20 m of the surface, with the GNSS antenna at a height of 1–2 m above ground level. Along with an exceptionally high accumulation rate at the beginning of the study, we also detect an across-slope dependence in accumulation rates at yearly timescales. For the first time, we also validate GNSS-IR sensitivity to meter-scale surface heterogeneities such as sastrugi, and we construct a time series of surface roughness evolution that suggests a seasonal pattern of heightened wintertime roughness features in this region. These surface accumulation and rough-ness measurements provide a novel data set for these critical variables and show a statistically significant relationship with occurrences of both high winds and precipitation events but only moderate correlations, suggesting that other processes may also contribute to accumulation and enhanced surface roughness in the interior region of Greenland.

12 Jun 2026

EcoHydraulic Modeling to Inform Sustainable Sediment Management: A Priori Modeling of Reservoir Sediment Release to Estimate Geomorphic and Ecological Response

Abstract: With decreasing storage capacity and increasing operational costs in reservoir management, sediment release is considered a potential alternative to traditional dredging. However, passing sediment through reservoirs may have unexpected effects on downstream river morphology and ecosystem resources. This study uses numerical modeling and a conceptual ecological model to assess the relative effects of sediment load, stream flow magnitude, and grain size distribution in downstream river morphology and aquatic habitat in a case study system of the Big Blue and Kansas Rivers downstream of Tuttle Creek Reservoir, Manhattan, Kansas. The effects of sediment grain size, clearwater flushing rate, and backwater effects from the Kansas River were all found to be relevant in affecting sediment transport and deposition patterns. High-volume water/sediment releases were found to be most effective at emulating historical conditions. Additionally, sediment release was found to increase desirable physical habitat areas that have been lost in the channel. Clearwater flushing further increased the distribution of sediment to support physical habitat creation. These findings can inform sediment release management decisions regarding the timing, duration, and magnitude of sediment releases, particularly in relation to flows at the downstream confluence and for target ecosystem function goals.

12 Jun 2026

Adsorption of the Microbial Exopolysaccharide from Rhizobium Tropici (ATCC 49,672) on Birnessite Facilitates Mn Reduction and Dissolution: Structural Interactions and Morphological Transformation

Abstract: The study addresses the broader question of how microbial exopolysaccharides (EPS) can modulate the reactivity and stability of manganese (Mn) oxides in environmental systems. The objective of this study is to investigate the adsorption process of EPS from Rhizobium tropici on birnessite (MnO2) and mechanisms of induced Mn reduction and dissolution and its further transformation. EPS adsorption isotherms and kinetics on birnessite and the induced dissolution of birnessite were analyzed. Adsorption kinetics could best be described with the Parabolic Diffusion Model while EPS adsorption isotherm was best fitted with the Freundlich model. Adsorbed EPS facilitated Mn reduction and dissolution, releasing Mn²⁺ into the solution. This was also confirmed by fourier transform infrared spectroscopy (FTIR) results with the Mn-O vibrations, O–H groups and C = O carbonyl stretching. The pre-presence of EPS hindered the formation and growth of crystalline structure of birnessite and further hindered its transformation into more stable β-MnO2. The presence of Mn oxide significantly aggregated EPS, forming large aggregates. This study reveals the importance of EPS in influencing both the adsorption behavior and the redox state of Mn, providing significant insights into the interactions of EPS with birnessite in environmental systems.