12 Aug 2025

Evaluating and Improving Snow in the National Water Model, Using Observations from the New York State Mesonet

Abstract: This study leverages observations from NYSM to evaluate and improve representation of snow within the NWM and its associated land surface model. Distributed NWM simulations were ran and analyzed, forced by gridded meteorological analyses, and Noah-MP point simulations, forced by NYSM observations. Distributed NWM runs, with a baseline configuration, show substantial SWE biases caused by biases in meteorological forcing used, imperfect representation of snow processes, and mismatches between land cover in the model and NYSM station locations. Noah-MP point simulations, using baseline configuration, reveal a systematic positive bias in SWE accumulation. Noah-MP point simulations, with improved precipitation phase partitioning, reveal a systematic negative bias in SWE ablation rates. Sensitivity experiments highlight uncertain parameters within Noah-MP that strongly affect ablation rates and show particularly large sensitivity to snow albedo decay time-scale parameter, which modulates snow albedo decay rates. Distributed NWM experiments, with precipitation phase partitioning and TAU0 adjusted based on Noah-MP point simulation results, show qualitatively similar sensitivities. However, the distributed experiments do not show clear improvements when compared to SWE and streamflow observations. This is likely due to some combination of sources of bias in the baseline-distributed run and biases in other parameterized processes unrelated to snow in the NWM.

11 Aug 2025

Cracking Performance Characterisation of Aramid Fiber-Reinforced Asphalt Mixtures Using Digital Image Correlation

Abstract: Conventional index-based testing of asphalt mixtures cannot accurately capture local deformation in a sample, limiting the usage of standard test measurements. The non-contact-based measurements proved effective to capture local deformation fields. This study aimed to capture the fatigue and thermal cracking behaviour of fiber-reinforced asphalt mixture by utilising digital image correlation (DIC). One binder (PG76-22), a diabase aggregate and three fibers (polyolefin/ aramid fibers (PFA) at 0.05% dosage and Sasobit-coated aramid fibers at 0.01% and 0.02% dosage) were used to prepare a total of four mixtures (one control and three FRAM). All these mixtures were produced at a local batch plant following manufacturer-recommended mixing methods. DIC analysis was performed for three-point bending beam (3PB) and disk shape compact tension (DCT) tests at intermediate temperature (25°C) and low temperatures of −12°C and −18°C. Based on index values from DCT and 3PB, the thermal and fatigue cracking performance enhancement was not significant. However, DIC analysis showed that, regardless of testing temperature, the crack propagated in a random pattern for FRAM, whereas the crack followed a relatively straight path for the control mix. Finally, based on DIC strain contours, FRAM mixtures exhibit distributed strain over a larger area compared to the control mix.

7 Aug 2025

C-Band Radar Measurements in a Snow-Covered Boreal Forest Environment

Abstract: Sled-based side-looking C-band radar profiles were collected around Fairbanks, Alaska, in March 2023 during the NASA SnowEx campaign to improve the conceptual understanding of C-band radar wave interactions with snow in a boreal forest environment. Seven transects with different vegetation and ground conditions were studied. Significant volume scattering from snow was observed in this shallow snowpack, indicating sensitivity at lower snow depths (SDs) which are common in high-latitude snowpacks. Manual removal of the snowpack decreased the backscatter by more than 2 dB in all polarizations, with a larger decrease in the cross-polarization, supporting the potential use of Sentinel-1 to retrieve SD.

4 Aug 2025

Acoustic and Seismic Wave Transmission Throughout the Multidomain Environment: Experimental Design, Methods, and Construction of a Prototypical Littoral Zone

Abstract: The future operational environment is projected to be a multidomain, transparent battlefield in which the Army must be able to act as both a supported and supporting force. An accurate detection and interpretation of acoustic and seismic signals propagating across land-air-water (LAW) interfaces are required to meet future requirements of a fully “transparent” domain. The LAW domains converge at the significant contested littoral zones. Historically, interpreting signals crossing media boundaries has been studied by stovepiping each distinct medium. These fragmented perspectives led to discrepancies in boundary and adjacent media descriptions and media-specific governing physics. No comprehensive physics framework exists to accurately predict how disorderly waveforms freely traverse LAW media boundaries. To understand these complex phenomena, a highly controlled physical experiment was designed and implemented. Repeatable controls were conducted. Epistemic uncertainty was decreased, and high waveform fidelity was maintained in the experimental setup by not interfering with wave transmission or sensor accuracy between controls. This report summarizes the experimental design, implementation, challenges, and repeatability.

28 Jul 2025

Predicting Frozen Ground and Thaw Risk from Standard Land Model Output: Data, Algorithms, and GeoWATCH Implementation

Abstract: The Geospatial Weather Affected Terrain Conditions and Hazards (GeoWATCH) tool provides real-time mobility predictions at 30 m resolution on demand for any location on the globe. This tool combines dynamic weather data provided by the Air Force 557 Weather Wing (557WW) with static terrain data to downscale soil moisture from global and regional scales to resolutions better suited for terrain analysis applications. Frozen and thawing ground data layers were recently incorporated into the GeoWATCH framework to better support terrain assessment for Warfighter functions in cold regions. This report documents our approach for diagnosing the frozen and thawing ground data layers and provides examples. First, using data from controlled land-surface model simulations, we established simple curve-fitting formulas relating soil temperature to frozen water content. We then added the new formulas to the GeoWATCH code so that end users can generate frozen soil products on demand. Finally, GeoWATCH uses the resultant frozen soil product with a series of soil layers to determine the risk of actively thawing soil and springtime mud conditions. While the new overlays are not integrated into the GeoWATCH mobility diagnostic calculations, they provide insight into soil state conditions critical for operations and weather-based risk assessment in cold regions.

2 Jul 2025

Remote Detection of Soil Shear Strength in Arctic and Subarctic Environments

Abstract: Soil shear strength affects many military activities and is affected significantly by plant roots. Unfortunately, root contribution to soil shear strength is difficult to measure and predict. In the boreal forest ecosystem, soil and hydrologic dynamics make soil shear strength less predictable, while the need for prediction grows due to the rapid changes occurring in this environment. Our current study objectives are to (1) observe possible aboveground vegetation indicators of soil shear strength variation across soils and other environmental heterogeneity, (2) observe possible image-based indicators of soil shear strength variation, and (3) identify the best remote-sensing data source for predicting soil shear strength variation. A total of 65 sites were sampled from a diversity of soil and vegetation types across interior Alaska and Ontario, Canada. Ground-collected data were analyzed to develop a predictive model, while a similar approach was undertaken with Sentinel-2 imagery. Results indicate that both ground-collected data and satellite imagery can reasonably predict boreal forest soil shear strength, with satellite imagery providing the higher predictive ability. A comparison of 10 m Sentinel-2 and submeter Maxar imagery indicated that Sentinel-2 provides a better prediction of soil shear strength.

15 May 2025

Discriminating Buried Munitions Based on Physical Models for Their Thermal Response

Abstract: Munitions and other objects buried near the Earth’s surface can often be recognized in infrared imagery because their thermal and radiative properties differ from the surrounding undisturbed soil. However, the evolution of the thermal signature over time is subject to many complex interacting processes, including incident solar radiation, heat conduction in the ground, longwave radiation from the surface, and sensible and latent heat exchanges with the atmosphere. This complexity makes development of robust classification algorithms particularly challenging. Machine-learning algorithms, although increasingly popular, often require large training datasets including all environments to which they will be applied. Algorithms incorporating an understanding of the physical processes underlying the thermal signature potentially provide improved performance and mitigate the need for large training datasets. To that end, this report formulates a simplified model for the energy exchange near the ground and describes how it can be incorporated into maximum-likelihood ratio and Bayesian classifiers capable of distinguishing buried objects from their surroundings. In particular, a version of the Bayesian classifier is formulated that leverages the differing amplitude and phase response of a buried object over a 24-hour period. These algorithms will be tested on experimental data in a future study.

6 May 2025

Applications of the CRREL–-Geometric Optics Snow Radiative Transfer (GOSRT) Model: Incorporating Diffraction and Simulating Detection of Buried Targets

Abstract: Radiative transfer through a snow surface within the visible and near infrared (NIR) spectra is complicated by the shape, size, and configuration of the snow grains that comprise the snow surface. Ray-tracing and photon-tracking techniques combined with 3D renderings of snow resolved at the microscale have shown promise as a means to directly simulate radiative transfer through snow with no restrictions on the snow grain configuration. This report describes and evaluates the US Army Cold Regions Research and Engineering Laboratory (CRREL) Geometric Optics Snow Radiative Transfer (GOSRT) model. In particular, we describe the incorporation of the diffraction process into the photon-tracking framework and evaluate how accurately the model simulates the spectral albedo of targets buried within the snow. We find that the model simulated spectral albedo is little affected by the incorporation of diffraction for most applications. However, there are nonnegligible impacts on simulated albedo for small grains in the NIR due to a reduction in forward scattering. We conclude by recommending that diffraction is neglected in CRREL–GOSRT for most cases, as including it substantially increases the computational expense with minimal impacts on the result. Finally, we show that buried targets are only distinguishable for very shallow snowpacks.

5 May 2025

Assessing a Mobile Microgrid to Support Electric Vehicle Charging Stations on Army Installations

Abstract: Supplying reliable, off-grid power is critical for transitioning the Army’s fleet to zero carbon emitting vehicles. At the same time, vehicle charging and mission support equipment may require increased electrical loads than currently experienced at Army installations. Other decarbonization initiatives require clean sources of energy. Using microgrids powered with renewable electricity generation systems is a viable, independent solution for powering electric vehicles. Yet, there is a need to fill information gaps in the performance of these systems for realizing sustainable and resilient energy. The goal of this project was to increase the Army’s energy resilience by reducing reliance on the utility grid by using a compact and mobile microgrid that functions as an EV charging station. In this study, a trailered, mobile microgrid that integrates solar panels, a diesel generator, and batteries is evaluated based on performance under varying conditions. The energy generation capabilities are documented and evaluated for capabilities for powering electric vehicles. The outcomes of this research are the advancement of energy resiliency and the addition of performance in temperate and cold regions to the knowledge base. It is also anticipated this research may be leveraged to facilitate power independence and further support decarbonization efforts.

13 Mar 2025

Seasonality of Solute Flux and Water Source Chemistry in a Coastal Glacierized Watershed Undergoing Rapid Change: Wolverine Glacier Watershed, Alaska

Abstract: As glaciers rapidly lose mass, the tight coupling between glaciers and downstream ecosystems results in widespread impacts on global hydrologic and biogeochemical cycling. Knowledge of seasonally changing hydrologic processes and solute sources and signatures is limited. We conducted a broad water sampling campaign to understand the present-day partitioning of water sources and associated solutes in Alaska’s Wolverine Glacier watershed. We established a relationship between electrical conductivity and streamflow at the watershed outlet dividing the melt season into four hydroclimatic periods. Across hydroclimatic periods, we observed a shift in nonglacial source waters from snowmelt-dominated overland and shallow subsurface flow paths to deeper groundwater flow paths. We also observed the shift from a low- to high-efficiency subglacial drainage network and the associated flushing of water stored subglacially with higher solute loads. We used calcium from watershed outlet samples to estimate solute fluxes for each hydroclimatic period across two melt seasons. Between 40% and 55% of Ca2+ export occurred during the late season rainy period. Partitioning of the melt season coupled with a characterization of the chemical makeup and magnitude of solute export provides new insight into a rapidly changing watershed and creates a framework to quantify and predict changes to solute fluxes.