[1] Snow distribution patterns are similar from one year to the next because they are largely controlled by the interaction of topography, vegetation, and consistent synoptic weather patterns. On a yearly basis none of these controls changes radically. As a consequence, deep and shallow areas of snow tend to be fixed in space, producing depth differences that may vary in absolute, but not relative, amounts from year to year. While this fact is widely known, the use of patterns in modeling snow cover distribution is limited. Here, on the basis of a training set of nine annual snow depth surveys from a small tundra basin in Alaska, we identify the climatological snow distribution pattern (CSDP). Using this and a few depth measurements, the snow distribution for years that were not included in the training set is predicted and mapped with a near-zero bias and RMSE that ranged from 4.4 to 10.4 cm. The accuracy of this strictly empirical approach to modeling the depth distribution is similar to, or better than, the output from a weather-driven physically based snow model. However, in our view a hybrid approach is best. Ingesting the CSDP into SnowModel, a widely used numerical code that simulates snow processes, the accuracy of the model output is improved by up to 60%. This hybrid approach retains the advantages of running a weather-driven numerical code but adds spatial accuracy currently only obtainable from observed snow patterns. The patterns can be captured in several ways, including aerial photography or satellite remote sensing during snowmelt.

Ambient-noise-based seismic monitoring of the near surface often has limited spatiotemporal resolutions because dense seismic arrays are rarely sufficiently affordable for such applications. In recent years, however, distributed acoustic sensing (DAS) techniques have emerged to transform telecommunication fiber-optic cables into dense seismic arrays that are cost effective. With DAS enabling both high sensor counts (“large N”) and long-term operations (“large T”), time-lapse imaging of shear-wave velocity (V S) structures is now possible by combining ambient noise interferometry and multichannel analysis of surface waves (MASW). Here we report the first end-to-end study of time-lapse V S imaging that uses traffic noise continuously recorded on linear DAS arrays over a three-week period. Our results illustrate that for the top 20 meters the V S models that is well constrained by the data, we obtain time-lapse repeatability of about 2% in the model domain—a threshold that is low enough for observing subtle near-surface changes such as water content variations and permafrost alteration. This study demonstrates the efficacy of near-surface seismic monitoring using DAS-recorded ambient noise.

Field measurements of shallow borehole temperatures in firn across the northern Greenland ice sheet are collected during May 2013. Sites first measured in 1952-1955 are revisited, showing long-term trends in firn temperature. Results indicate a pattern of substantial firn warming (up to +5.7°C) at midlevel elevations (1400-2500 m) and little temperature change at high elevations (>2500 m). We find that latent heat transport into the firn due to meltwater percolation drives the observed warming. Modeling shows that heat is stored at depth for several years, and energy delivered from consecutive melt events accumulates in the firn. The observed warming is likely not yet in equilibrium with recent melt production rates but captures the progression of sites in the percolation facies toward net runoff production.

No abstract

Probability of detection and false alarm rates for current military sensor systems used for detecting buried objects are often unacceptable. One approach to increasing sensor performance and detection reliability is to better understand which physical processes are dominant under certain environmental conditions. Incorporating this understanding into detection algorithms will improve detection performance. Our approach involved studying a small, 3.05 × 3.05 m, test plot at the Engineer Research and Development Center's Cold Regions Research and Engineering Laboratory (ERDC-CRREL) in Hanover, New Hampshire. There we monitored a number of environmental variables (soil temperature moisture, and chemistry as well as air temperature and humidity, cloud cover, and incoming solar radiation) coupled with thermal infrared and electro-optical image collection. Data collection occurred over 4 months with measurements made at 15 minute intervals. Initial findings show that significant spatial and thermal temporal variability is caused by incoming solar radiation; meteorologically driven surface heat exchange; and subsurface-soil temperatures, density, moisture content, and surface roughness. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

Abstract. Permafrost underlies one-quarter of the Northern Hemisphere but is at increasing risk of thaw from climate warming. Recent studies across the Arctic have identified areas of rapid permafrost degradation from both top-down and lateral thaw. Of particular concern is thawing syngenetic “yedoma” permafrost which is ice-rich and has a high carbon content. This type of permafrost is common in the region around Fairbanks, Alaska, and across central Alaska expanding westward to the Seward Peninsula. A major knowledge gap is relating belowground measurements of seasonal thaw, permafrost characteristics, and residual thaw layer development with aboveground ecotype properties and thermokarst expansion that can readily quantify vegetation cover and track surface elevation changes over time. This study was conducted from 2013 to 2020 along four 400 to 500 m long transects near Fairbanks, Alaska. Repeat active layer depths, near-surface permafrost temperature measurements, electrical resistivity tomography (ERT), deep (> 5 m) boreholes, and repeat airborne light detection and ranging (lidar) were used to measure top-down permafrost thaw and map thermokarst development at the sites. Our study confirms previous work using ERT to map surface thawed zones; however, our deep boreholes confirm the boundaries between frozen and thawed zones that are needed to model top-down, lateral, and bottom-up thaw. At disturbed sites seasonal thaw increased up to 25 % between mid-August and early October and suggests measurements to evaluate active layer depth must be made as late in the fall season as possible because the projected increase in the summer season of just a few weeks could lead to significant additional thaw. At our sites, tussock tundra and spruce forest are associated with the lowest mean annual near-surface permafrost temperatures while mixed-forest ecotypes are the warmest and exhibit the highest degree of recent temperature warming and thaw degradation. Thermokarst features, residual thaw layers, and taliks have been identified at all sites. Our measurements, when combined with longer-term records from yedoma across the 500 000 km2 area of central Alaska, show widespread near-surface permafrost thaw since 2010. Projecting our thaw depth increases, by ecotype, across the yedoma domain, we calculate a first-order estimate that 0.44 Pg of organic carbon in permafrost soil has thawed over the past 7 years, which, for perspective, is an amount of carbon nearly equal to the yearly CO2 emissions of Australia. Since the yedoma permafrost and the variety of ecotypes at our sites represent much of the Arctic and subarctic land cover, this study shows remote sensing measurements, top-down and bottom-up thermal modeling, and ground-based surveys can be used predictively to identify areas of the highest risk for permafrost thaw from projected future climate warming.

Amchitka Island, in Alaska, was used for underground nuclear testing from 1965 to 1971. Since the test program concluded, there have been concerns about the possible release of radionuclides into the marine environment of the Aleutian Islands. The hydrogeology of islands such as Amchitka is characterized by a layer of freshwater overlying a saltwater layer, with the salinity increasing across a transition zone ͑TZ͒. Hydrogeologic modeling can provide an estimate of the timing and amount of radionuclide release from the explosions beneath Amchitka Island. This modeling is inconclusive because of a lack of information regarding subsurface structure. To address this problem, magnetotelluric ͑MT͒ data were collected on Amchitka Island in 2004. Broadband MT data were recorded on profiles passing through three explosion sites to give information about subsurface porosity and salinity. A 2D MT inversion produced models of sub-surface electrical resistivity and showed a pattern of increasing, decreasing, and increasing resistivity with depth at each test site. The depth at which resistivity begins to decrease defines the top of the TZ. The deeper increase in resistivity approximates the base of the TZ. The depths of the top and bottom of the TZ were determined as follows: Cannikin 900-2500 m; Long Shot 600-1700 m; Milrow 900-1700 m. Uncertainties were estimated for these depths. Effective porosities were also estimated and ranged from 10%-20% at the surface to 1%-3% at 3-km depth. These porosities are higher than those assumed in several hydrogeologic models, and give longer transit times from the explosion to the marine environment. Subject to the limits of the analysis, it appears that each of the cavities resulting from underground nuclear explosions is located in the TZ from fresh to saltwater. This implies shorter transit times to the marine environment than if the detonations had been located in the saltwater layer.

Global climate change has resulted in a warmer Arctic, with projections indicating accelerated modifications to permafrost in the near future. The thermal, hydrological, and mechanical physics of permafrost thaw have been hypothesized to couple in a complex fashion but data collection efforts to study these feedbacks in the field have been limited. As a result, laboratory and numerical models have largely outpaced field calibration datasets. We present the design, execution, and initial results from the first decameter-scale controlled thawing experiment, targeting coupled thermal/mechanical response, particularly the temporal sequence of surface subsidence relative to permafrost degradation at depth. The warming test was conducted in Fairbanks, AK, and utilized an array of in-ground heaters to induce thaw of a ~11 × 13 × 1.5 m soil volume over 63 days. The 4-D temperature evolution demonstrated that the depth to permafrost lowered 1 m during the experiment. The resulting thaw-induced surface deformation was ~10 cm as observed using a combination of measurement techniques. Surface deformation occurred over a smaller spatial domain than the full thawed volume, suggesting that gradients in cryotexture and ice content were significant. Our experiment provides the first large field calibration dataset for multiphysics thaw models.