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Category: Publications: Cold Regions Research and Engineering Laboratory (CRREL)
  • Application of Limited-Field-Data Methods in Reservoir Volume Estimation: A Case Study

    Abstract: The conventional approach to estimating lake or reservoir water volumes hinges on field data collection; however, volume estimation methods are available that use little or no field data. Two such methods—the simplified V-A-h (volume-area-height) and the power function—were applied to a set of six anthropogenic reservoirs on the Fort Jackson, South Carolina, installation and checked against a validation data set. Additionally, seven interpolation methods were compared for differences in total volume estimation based on sonar data collected at each reservoir. The simplified V-A-h method overestimated reservoir volume more than each technique in the power function method, and the categorical technique underestimated the most reservoir volumes of all three techniques. Each method demonstrates high Vₑᵣᵣ variability among reservoirs, and Vₑᵣᵣ for the Power Function techniques applied here is consistent with that found in previous research in that it is near or less than 30%. Compared with Vₑᵣᵣ in other studies evaluating the simplified V-A-h method, Vₑᵣᵣ in this study was found to be 10%–20% higher.
  • Ecological Modeling of Microbial Community Composition under Variable Temperatures

    Abstract: Soil microorganisms interact with one another within soil pores and respond to external conditions such as temperature. Data on microbial community composition and potential function are commonly generated in studies of soils. However, these data do not provide direct insight into the drivers of community composition and can be difficult to interpret outside the context of ecological theory. In this study, we explore the effect of abiotic environmental variation on microbial species diversity. Using a modified version of the Lotka-Volterra Competition Model with temperature-dependent growth rates, we show that environmentally relevant temperature variability may expand the set of temperature-tolerance phenotype pairs that can coexist as two-species communities compared to constant temperatures. These results highlight a potential role of temperature variation in influencing microbial diversity. This in turn suggests a need to incorporate temperature into predictive models of microbial communities in soil and other environments. We recommend future work to parameterize the model applied in this study with empirical data from environments of interest, and to validate the model predictions using field observations and experimental manipulations.
  • Extreme Cold Weather Airfield Damage Repair Testing at Goose Bay Air Base, Canada

    Abstract: Rapid Airfield Damage Recovery (RADR) technologies have proven successful in temperate and subfreezing temperatures but have not been evaluated in extreme cold weather temperatures near 0°F. To address this capability gap, laboratory-scale and full-scale testing was conducted at these temperatures. Methods developed for moderate climates were adapted and demonstrated alongside methods that used snow harvested on-site as compacted backfill. After only a few days of training, seven experimental repairs were conducted by Canadian airmen at Goose Bay Air Base in Labrador, Canada, and load tested with a single-wheel C-17 load cart. Existing RADR technologies performed adequately despite the freezing temperatures, with the main tactic, techniques, and procedures modification being an increased cure time for the rapid-setting concrete surface material. Compacted snow-water slurry methods also performed well, demonstrating their ability to withstand over 500 passes of single-wheel C-17 traffic after sufficient freezing time.
  • The Influence of Mesoscale Atmospheric Convection on Local Infrasound Propagation

    Abstract: Infrasound—that is, acoustic waves with frequencies below the threshold of human hearing—has historically been used to detect and locate distant explosive events over global ranges (≥1,000 km). Simulations over these ranges have traditionally relied on large-scale, synoptic meteorological information. However, infrasound propagation over shorter, local ranges (0–100 km) may be affected by smaller, mesoscale meteorological features. To identify the effects of these mesoscale meteorological features on local infrasound propagation, simulations were conducted using the Weather Research and Forecasting (WRF) meteorological model to approximate the meteorological conditions associated with a series of historical, small-scale explosive test events that occurred at the Big Black Test Site in Bovina, Mississippi. These meteorological conditions were then incorporated into a full-wave acoustic model to generate meteorology-informed predictions of infrasound propagation. A series of WRF simulations was conducted with varying degrees of horizontal resolution—1, 3, and 15 km—to investigate the spatial sensitivity of these infrasound predictions. The results illustrate that convective precipitation events demonstrate potentially observable effects on local infrasound propagation due to strong, heterogeneous gradients in temperature and wind associated with the convective events themselves. Therefore, to accurately predict infrasound propagation on local scales, it may be necessary to use convection-permitting meteorological models with a horizontal resolution ≤4 km at locations and times that support mesoscale convective activity.
  • An MCA Linear Additive Method for Research Project Analysis

    Abstract: This report describes a business intelligence (BI) model developed by the Cold Regions Research and Engineering Laboratory (CRREL) to evaluate multiple projects simultaneously and help researchers learn descriptive phrases found in alignment sources representative of their projects. The BI model combines the linear additive model with the analytical hierarchy process to take advantage of the qualitative and quantitative nature of both methods. The model has five variations, all built along the same objectives but with different criteria due to the specialized emphasis areas of each variation. The BI model operates around three central concepts for evaluating the projects: Alignment Variables, Timing, and Customer Relationship. A use-case scenario with ten projects shows the effectiveness of the model and compares it with another model from the United States Military Academy. This new BI model will assist researchers in developing and proposing research ideas that are more relevant and fundable.
  • CRREL Environmental Wind Tunnel Upgrades and the Snowstorm Library

    Abstract: Environmental wind tunnels are ideal for basic research and applied physical modeling of atmospheric conditions and turbulent wind flow. The Cold Regions Research and Engineering Laboratory's own Environmental Wind Tunnel (EWT)—an open-circuit suction wind tunnel—has been historically used for snowdrift modeling. Recently the EWT has gone through several upgrades, namely the three-axis chassis motors, variable frequency drive, and probe and data acquisition systems. The upgraded wind tunnel was used to simulate various snowstorm conditions to produce a library of images for training machine learning models. Various objects and backgrounds were tested in snowy test conditions and no-snow control conditions, producing a total of 1.4 million training images. This training library can lead to improved machine learning models for image-cleanup and noise-reduction purposes for Army operations in snowy environments.
  • Preliminary Permafrost Predictions within the Chena River Watershed, Alaska, Using Landscape Characteristics

    Purpose: This Technical Note presents a method to create permafrost predictions in the Chena River watershed near Fairbanks, Alaska, using landscape characteristics. We produced probabilities for near-surface permafrost in the Chena River watershed using a published algorithm applied in a nearby region. The methodology presented serves as a proof of concept for developing permafrost maps using similar data in other cold regions.
  • RISC TAMER Framework: Resilient Installation Support Against Compound Threats Analysis and Mitigation for Equipment and Resources Framework

    Every day, decision-makers must allocate resources based on the best available information at the time. Military installations face a variety of threats which challenge sustained functionality of their supporting and supported deployable systems. Considering the compounding and interdependent impacts of the threats, both specified (what is known) and unspecified (what is not known) and the investments needed to address these threats adds value to the decision-making process. Current risk management practices are generally evaluated via scenario analyses that do not consider compound threats, resulting in limited risk management solutions. Current practices also challenge the ability of decision-makers to increase resilience against such threats. The Resilient Installation Support against Compound Threats Analysis and Mitigation for Equipment and Resources (RISC TAMER) Framework establishes a decision support structure to identify and categorize system components, compound threats and risks, and system relationships to provide decision-makers with more complete and comprehensive information from which to base resilience-related decisions, for prevention and response. This paper focuses on the development process for RISC TAMER framework to optimize resilience enhancements for a wide variety of deployable systems in order to implement resilience strategies to protect assets, to increase adaptability, and to support power projection and global operations.
  • Influence of Chemical Coatings on Solar Panel Performance Snow Accumulation

    Abstract: Solar panel performance can be impacted when panel surfaces are coated with substances like dust, dirt, snow, or ice that scatter and/or absorb light and may reduce efficiency. As a consequence, time and resources are required to clean solar panels during and after extreme weather events or whenever surface coating occurs. Treating solar panels with chemical coatings that shed materials may decrease the operating costs associated with solar panel maintenance and cleaning. This study investigates three commercial coatings for use as self-cleaning glass technologies. Optical and thermal properties (reflectivity, absorption, and transmission) are investigated for each coating as well as their surface wettability and particle size. Incoming solar radiation was continuously monitored and snow events were logged to estimate power production capabilities and surface accumulation for each panel. In terms of power output, the commercial coatings made little impact on overall power production compared to the control (uncoated) panels. This was attributable to the overall high transmission, low absorption, and low reflection of each of the commercial coatings, making their presence on the surface of solar panels have minimal impact besides to potentially shed snow While the coatings made no observable difference to increase power production compared to the control panels, the shedding results from video monitoring suggest both the hydrophilic or hydrophobic test coatings decreased snow accumulation to a greater extent than the control panels (uncoated). Controlling the wettability properties of the solar panel surfaces has the potential to limit snow accumulation when compared to uncoated panel surfaces.
  • Using an Object-Based Machine Learning Ensemble Approach to Upscale Evapotranspiration Measured from Eddy Covariance Towers in a Subtropical Wetland

    Abstract: Accurate prediction of evapotranspiration (ET) in wetlands is critical for understanding the coupling effects of water, carbon, and energy cycles in terrestrial ecosystems. Multiple years of eddy covariance (EC) tower ET measurements at five representative wetland ecosystems in the subtropical Big Cypress National Preserve (BCNP), Florida (USA) provide a unique opportunity to assess the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) ET operational product MOD16A2 and upscale tower measured ET to generate local/regional wetland ET maps. We developed an object-based machine learning ensemble approach to evaluate and map wetland ET by linking tower measured ET with key predictors from MODIS products and meteorological variables. The results showed MOD16A2 had poor performance in characterizing ET patterns and was unsatisfactory for estimating ET over four wetland communities where Nash-Sutcliffe model Efficiency (NSE) was less than 0.5. In contrast, the site-specific machine learning ensemble model had a high predictive power with a NSE larger than 0.75 across all EC sites. We mapped the ET rate for two distinctive seasons and quantified the prediction diversity to identify regions easier or more challenging to estimate from model-based analyses. An integration of MODIS products and other datasets through the machine learning upscaling paradigm is a promising tool for local wetland ET mapping to guide regional water resource management.