Aufeis are sheets of ice unique to cold regions that originate from repeated flooding and freezing events during the winter. They have hydrological importance associated with summer flows and winter insulation, but little is known about the seasonal dynamics of the unfrozen sediment layer beneath them. This layer may support perennial groundwater flow in regions with otherwise continuous permafrost. For this study, ground penetrating radar (GPR) were collected in September 2016 (maximum thaw) and April 2017 (maximum frozen) at the Kuparuk aufeis field on the North Slope of Alaska. Supporting surface nuclear magnetic resonance data were collected during the maximum frozen campaign. These point‐in‐time geophysical data sets were augmented by continuous subsurface temperature data and periodic Structure‐from‐Motion digital elevation models collected seasonally. GPR and difference digital elevation model data showed up to 6 m of ice over the sediment surface. Below the ice, GPR and nuclear magnetic resonance identified regions of permafrost and regions of seasonally frozen sediment (i.e., the active layer) underlain by a substantial lateral talik that reached >13‐m thickness. The seasonally frozen cobble layer above the talik was typically 3‐ to 5‐m thick, with freezing apparently enabled by relatively high thermal diffusivity of the overlying ice and rock cobbles. The large talik suggests that year‐round groundwater flow and coupled heat transport occurs beneath much of the feature. Highly permeable alluvial material and discrete zones of apparent groundwater upwelling indicated by geophysical and ground temperature data allows direct connection between the aufeis and the talik below.

Groundwater is critical for maintaining stream baseflow and thermal stability; however, the influence of groundwater on streamflow has been difficult to evaluate at broad spatial scales. Techniques such as baseflow separation necessitate streamflow records and do not directly indicate whether groundwater inflow may be sourced from more dynamic shallow flowpaths. We present a web tool application PASTA (Paired Air and Stream Temperature Analysis; https://cuahsi.shinyapps.io/pasta/) that capitalizes on increased public stream temperature data availability and large‐scale, gridded climate observations to provide new and efficient insights regarding relative groundwater influence on streams. PASTA analyzes paired air and stream water temperature signals to evaluate spatiotemporal patterns in stream thermal sensitivity and relative groundwater influence, including inference regarding the dominant source groundwater depth (shallow or deep (i.e., approximately >6 m depth)). The tool is linked to publicly available stream temperature datasets and accepts user‐uploaded datasets. As local air temperature is not often monitored, PASTA pulls daily air temperature data from the comprehensive Daymet products when directly measured data are unavailable, allowing the repurposing of existing stream temperature data. After data are selected or uploaded, the tool (a) fits sinusoidal curves of daily stream and air temperatures by year (water or calendar) to indicate groundwater influence characteristics and (b) performs linear regressions for stream versus air temperatures to indicate stream thermal sensitivity. Results are exported in ASCII file format, creating an efficient and approachable analysis tool for the adoption of newly developed heat tracing analysis from stream reach to landscape scales.

Geophysical methods can provide three‐dimensional (3D), spatially continuous estimates of soil moisture. However, point‐to‐point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one‐thousand 60/40 training/test cross‐validation folds produced root mean squared errors ranging from 0.025–0.046 cm ³ /cm ³ ). This cross‐validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi‐automated model‐selection process that could be used for other sites and datasets to locally improve accuracy.

The mechanisms that control free phase gas (FPG) dynamics within peatlands, and therefore estimates of past, present, and future gas fluxes to the atmosphere remain unclear. Electrical resistivity imaging (ERI) is capable of autonomously collecting three‐dimensional data on the centimeter to tens of meter scale and thus provides a unique opportunity to observe FPG dynamics in situ. We collected 127 3‐D ERI data sets as well as water level, soil temperature, atmospheric pressure, and limited methane flux data at a site in a northern peatland over the period July–August 2013 to improve the understanding of mechanisms controlling gas releases at a hitherto uncaptured field scale. Our results show the ability of ERI to image the spatial distribution of gas accumulation and infer dynamics of gas migration through the peat column at high (i.e., hourly) temporal resolution. Furthermore, the method provides insights into the role of certain mechanisms previously associated with the triggering of FPG releases such as drops in atmospheric pressure. During these events, buoyancy‐driven gas release primarily occurs in shallow peat as proposed by the “shallow peat model.” Releases from the deeper peat are impeded by confining layers, and we observed a large loss of FPG in deep peat that may likely represent a rupture event, where accumulated FPG escaped the confining layer as suggested by the “deep peat model.” Negative linear correlations between water table elevation and resistivity result from hydrostatic pressure regulating bubble volume, although these variations did not appear to trigger FPG transfer or release.