7 Jul 2026

FIMOFs: Fiber-Integrated Metal–Organic Frameworks through Electrospinning

Abstract: Green synthesis plays a crucial role in advancing sustainability within materials science. This study explores the integration of metal–organic frameworks (MOFs), obtained through green synthesis, using an electrospinning post-processing technique to develop MOF-based composite materials. The resulting novel multifunctional composites demonstrate enhanced stability and functionality, compared to their control counterparts. The integration of four types of MOFs into an electrospun fiber network was investigated using a specific polymer solution. Characterization and preliminary adsorption studies were conducted to elucidate the chemistry, morphology, and adsorptive capabilities of the resulting MOF composites. Electrospinning MOFs into polymer fibers improved their stability and dye removal capabilities. More specifically, optimization of MOF-to-polymer ratios and processing conditions yielded composites that are thermally stable, with modified surface area and porosity. Post-processing MOFs resulted in a fiber diameter increase of 44 and 109%, enhancing the composites by providing more MOF active sites and improved mechanical strength. Zirconium-based post-processed MOFs demonstrated superior dye removal, different from the copper-based dyes. Electrospinning technology has demonstrated significant potential in the fabrication of high-performance multifunctional MOF composites. This has helped to create advanced sustainable composites with tailored properties, paving the way for more targeted and efficient applications. The applications of these composites show promise for military engineering where durable, light weight, and multifunctional materials are critical in contributing to improved performance, operational efficiency, and safety.

29 Jun 2026

Automated Workflows for Airborne Lidar and Photogrammetry Snow Depth Analyses

Abstract: Lidar and photogrammetry techniques provide highly accurate methods for mapping snow depth distribution. However, postprocessing point clouds for snow depth estimation is more complex compared to other earth science applications. This paper presents ice-road-copters (IRC), an open-source Python toolkit that facilitates processing and georeferencing of lidar and photogrammetry point clouds. Case studies demonstrate the tool’s utility across different sensors and platforms over a complex mountainous study area. Results show that a well-configured digital elevation model (DEM) filter effectively removes most noise and outliers from point cloud data. The Simple Morphological Filter (SMRF) generally perform well for ground segmentation but optimal results across diverse terrains may require site-specific tuning, particularly of the elevation threshold and scalar parameters in more complex landscapes. Different methods, including manual depth measurements or snow-free features, can be used to coregister DEMs, reducing vertical errors, eliminating large bias and achieving comparable accuracy to using exposed control surfaces. Derived snow depth rasters showed strong agreement with in situ probe measurements—root-mean-square error (RMSE) of less than 16 cm. Overall, IRC simplifies the transformation of raw point clouds into high-resolution DEMs and value-added snow products, facilitating efficient multitemporal analysis to support military and hydrology applications.

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.

11 May 2026

Hydrologic Mechanisms for 2022 Yellowstone River Flood and Comparisons to Recent Historic Floods

Abstract: In June 2022, a historic flood event occurred in the headwaters of the Yellowstone River Basin. The flood resulted in millions of dollars in damages and substantial interruptions to Yellowstone National Park. The 2022 flood event was substantially higher in magnitude than other high- peak flow events over the last 30 years. The high discharge was primarily due to the combination of hydrologic mechanisms initiated by rain-on-snow, including a high- elevation snowpack that peaked later than average. However, the contributions of each hydrologic driver, rain and snow, have not been quantified and could be important for understanding future flood events in the region. The contribution of snowmelt to the total terrestrial water input (TWI) varied throughout the area, yet was concentrated in the headwaters of the Yellowstone, Stillwater, and Boulder rivers, along with the headwaters of Rock Creek in Wyoming and Montana. The primary atmospheric contributions to the TWI during the 2022 event were precipitation from moisture transported from the Pacific Ocean that converged over the Greater Yellowstone Area (GYA) and snowmelt from residual snowpack in the northeast part of Yellowstone National Park.

6 May 2026

Two-Dimensional Thermal and Dynamical Strain in Landfast Sea Ice from InSAR: Results From a New Analytical Inverse Method and Field Observations

Abstract: Observing continuous strain in sea ice at geophysical scales of tens of meters to kilometers requires displacement measurements made with millimeter-scale precision. Satellite-based interferometric synthetic aperture radar (InSAR) provides such precise measurements of relative surface displacement over broad spatial areas at regular intervals and, unlike point displacement measurements, it allows confident delineation of continuously deforming regions. However, InSAR only captures the 1-D component of surface displacement parallel to a radar’s lines-of-sight. Additional analysis is required to translate between these 1-D observations and the horizontal or vertical displacements they arose from. Previous studies utilize an iterative inverse model to constrain estimates of horizontal surface displacement from InSAR. Here we build upon that work outlining a new analytical inverse modeling method for quantifying displacement and strain over continuous regions of sea ice and provide comparison between model results and independent displacement observations. We demonstrate the inverse method over both landfast and drifting ice along the Alaskan coastline. These intercomparisons highlight environments in which displacements inverted from interferograms may be used as an independent estimator of surface strain, as well as the potential for the outlined inverse methods to be used in conjunction with other observing methods.

5 May 2026

Permafrost and Rain Influence Summer Hydrologic Flowpaths in Boreal Catchments

Abstract: Flowpaths of water through catchments influence water quality and flow regimes of streams. Depths of dominant flowpaths respond to variation in climate and catchment characteristics, such as topography, vegetation, and soil type. In high‐latitude regions, the depth and spatial extent of permafrost influences catchment hydrology, and thawing permafrost might change sources and pathways of water supplying solutes and flow to streams. We estimated contributions of precipitation, soil water, and groundwater flowpaths to streams during the open‐water period after snowmelt by applying a Bayesian mixing model to 4–6 years of observed solute concentrations in five catchments of boreal Alaska. The relative contribution of groundwater to streams varied from 12% to 82% across catchments and years and declined as spatial extent of permafrost increased from 25% to 58% across catchments, indicating potential for increased infiltration and drainage as permafrost thaws. Temporal patterns in precipitation also influenced flowpaths. The mean annual contribution of precipitation to streamflow increased in years with more rain. Groundwater contribution increased, on average, in years with few large storms, suggesting deepening flows due to seasonal ground thaw or loss of shallow water to evapotranspiration. In contrast, groundwater contributed less in years when large storms delivered most of the year's rain in late summer or autumn. Overall, spatial and temporal variation in relative flowpath contributions to streams suggest that permafrost thaw will deepen flowpaths, but increasing precipitation expected in high‐latitude regions under warming climate might obscure this effect by routing water via shallow flowpaths following large storms.

4 May 2026

Freshwater Wetland Carbon Flux Analysis Pertinent to the Net Emissions Analysis Tool Improvement: Method Development and Testing

Palustrine wetlands are ecosystems of interest due to their capacity to sequester large amounts of greenhouse gases. This field study in Washington and Idaho was conducted as proof of concept of methods for measuring carbon emissions in palustrine wetlands. The regions of Washington and Idaho were chosen as they span three different Environmental Protection Agency (EPA) Level 1 ecoregions in a relatively close geological area. Data were collected across all three ecoregions in an effort to detail the potential differences between palustrine wetlands within them. Carbon dioxide flux measurements were compared across two instruments: LICOR 8,100A and CIRAS-4. Supporting data related to vegetation and site characteristics were incorporated into the overall analyses. Results suggest that carbon dioxide flux varies in relation to several factors. Additional research will be required to inform the application of site-specific data which can improve the application of tools designed to quantify project scale estimates for net greenhouse gas emissions.

28 Apr 2026

Is the Ordinary High Water Mark Ordinarily at Bankfull? Applying A Weight-of-Evidence Approach to Stream Delineation

Abstract: The ordinary high water mark (OHWM) is a regulatory boundary essential to identifying the lateral jurisdictional limits of rivers and streams in the United States (U.S.). Bankfull is a scientific concept that has been defined and identified in a multitude of ways by scientists. Geomorphologist and hydrologist have long recognized that there can be variability in the identification of bankfull depending on how bankfull is defined. Furthermore, this variability is only increased by the inherent variability in stream characteristics that occurs along a reach of channel. Because of the overlap in the regulatory definition of OHWM and the scientific definitions of bankfull, one of the primary purposes of the study is to apply the definition of OHWM and compare it to bankfull in a variety of channel types in different climatic, hydrologic, and geologic settings. Our results show that there is a clear overlap between the identification of the OHWM and bankfull elevations. Regulatory practitioners are generally not specialized in fluvial geomorphology and yet are tasked with consistently and accurately identifying the OHWM in a variety of stream types throughout the U.S. Therefore, we also present how to apply a weight-of-evidence approach through a clear step-by-step process to potentially improve consistency and accuracy in identification of OHWM and bankfull by both scientists and non-scientists.

23 Apr 2026

Applications of Snow-Covered Areas from Unoccupied Aerial Systems (UAS) Visible Imagery: A Demonstration in Southeastern New Hampshire

Abstract: Remote sensing observations of snow-covered areas (SCA) are important for monitoring and modeling energy balances, hydrologic processes, and climate change. For an agricultural field, we produced 12 snow cover maps from UAS imagery during an approximately 3-week-long spring snowmelt period. SCA maps were used to characterize snow cover patterns, validate satellite snow cover products, translate satellite Normalized Difference Snow Index (NDSI) to fractional SCA (fSCA), and downscale satellite SCA observations. Compared to manually delineated SCA, the UAS SCA accuracy was 85%, with misclassifications due to shadows, ice, and patchy snow conditions. During snowmelt, UAS-derived maps of bare earth patches exhibited self-similarity, behaving as fractal objects over scales from 0.01 to 100 m2. As a validation tool, the UAS-derived SCA showed that satellite snow cover observations accurately captured the fSCA evolution during snowmelt (R2 = 0.93−0.98). A random forest satellite downscaling model, trained using 20 m Sentinel-2 NDSI observations and 20 cm vegetation and terrain features, produced realistic (>90%accuracy), high-resolution SCA maps. While similar to traditional Sentinel-2 SCA in most conditions, downscaling snow cover significantly improved performance during periods of patchy snow cover and produced more realistic bare patches. UAS optical sensing demonstrates the potential uses for high-resolution snow cover mapping and recommends future research avenues for using UAS SCA maps.