The goal of this research is to constrain the influence of ice wedge polygon microtopography on near-surface ground temperatures. Ice wedge polygon microtopography is prone to rapid deformation in a changing climate, and cracking in the ice wedge depends on thermal conditions at the top of the permafrost; therefore, feedbacks between microtopography and ground temperature can shed light on the potential for future ice wedge cracking in the Arctic. We first report on a year of sub-daily ground temperature observations at 5 depths and 9 locations throughout a cluster of low-centered polygons near Prudhoe Bay, Alaska, and demonstrate that the rims become the coldest zone of the polygon during winter, due to thinner snowpack. We then calibrate a polygon-scale numerical model of coupled thermal and hydrologic processes against this dataset, achieving an RMSE of less than 1.1 ∘C between observed and simulated ground temperature. Finally, we conduct a sensitivity analysis of the model by systematically manipulating the height of the rims and the depth of the troughs and tracking the effects on ice wedge temperature. The results indicate that winter temperatures in the ice wedge are sensitive to both rim height and trough depth, but more sensitive to rim height. Rims act as preferential outlets of subsurface heat; increasing rim size decreases winter temperatures in the ice wedge. Deeper troughs lead to increased snow entrapment, promoting insulation of the ice wedge. The potential for ice wedge cracking is therefore reduced if rims are destroyed or if troughs subside, due to warmer conditions in the ice wedge. These findings can help explain the origins of secondary ice wedges in modern and ancient polygons. The findings also imply that the potential for re-establishing rims in modern thermokarst-affected terrain will be limited by reduced cracking activity in the ice wedges, even if regional air temperatures stabilize.

In the past three decades, an abrupt, pan‐Arctic acceleration of ice wedge melting has transformed tundra landscapes, spurring the formation of hummock‐like features known as high‐centered polygons (HCPs). This rapid geomorphic transition profoundly alters regional hydrology and influences surface emissions of CO₂ and CH₄. In Arctic Alaska, most recent instances of ice wedge degradation have arrested within 15–20 years of inception, stabilizing HCP microtopography. However, feedbacks between ground surface deformation and permafrost stability are incompletely understood, limiting our capacity to predict trajectories of landscape evolution in a still warmer future. Here, we use field data from a site near Prudhoe Bay, Alaska, to develop a modeling‐based framework for assessing the strength of positive (i.e., exacerbating) feedbacks on ice wedge degradation, focusing on the importance of heterogeneity in surface drainage and microtopographic conditions. Our simulations suggest that, when troughs are narrow, positive feedbacks on ice wedge melting (associated with thermokarst pool formation) are relatively weak. Positive feedbacks are markedly stronger beneath wide troughs, such as those that form above older, larger ice wedges. Seasonal thaw abruptly accelerates once a talik begins to form beneath wide and deep thermokarst pools. Once a talik initiates, winter severity and snowpack thickness increase in importance as predictors of thaw intensity in summer. Our results indicate that meter‐scale heterogeneity in polygonal microtopography potentially exerts strong, nonlinear controls on thermokarst trajectories. These findings are useful for predicting future thermokarst dynamics and for interpreting the results from coarser‐resolution land surface models operating at greater spatial and temporal scales.

We present a workflow for the rapid delineation and microtopographic characterization of ice wedge polygons within high-resolution digital elevation models. At the core of the workflow is a convolutional neural network used to detect pixels representing polygon boundaries. A watershed transformation is subsequently used to segment imagery into discrete polygons. Fast training times (<5 min) permit an iterative approach to improving skill as the routine is applied across broad landscapes. Results from study sites near Utqiaġvik (formerly Barrow) and Prudhoe Bay, Alaska, demonstrate robust performance in diverse tundra settings, with manual validations demonstrating 70–96 % accuracy by area at the kilometer scale. The methodology permits precise, spatially extensive measurements of polygonal microtopography and trough network geometry.

Groundwater discharge into streams helps maintain flows during droughts and provides refugia for thermally sensitive species, as the relatively constant temperature of spring water buffers against instream diurnal and seasonal fluctuations. Unmanned aerial vehicle (UAV)-based thermal imagery represents an attractive option to monitor sites of groundwater-surface water mixing. However, the use of economical thermal sensors presents several challenges to obtaining a stable dataset for mosaicking. Here, we present a method for acquiring and postprocessing thermal infrared imagery at spring discharge sites using an inexpensive, uncooled microbolometer mounted on a small UAV. The procedure involves initial estimation and removal of pixel bias from the sensor output, and then compensation for temporal sensor drift by optimizing the stability of the signal at ground features detected within multiple frames. We illustrate this approach by presenting a case study at the Devils River, a groundwater dependent stream in Texas. Comparison with imagery acquired using a more expensive thermal camera system, designed to compensate for sensor drift at the time of data acquisition using knowledge of internal camera temperature, reveals that our method produces a more consistent final mosaic image. A good linear fit (r² = 0.97) between the signal in the stabilized dataset and ground-based measurements of water temperature underscores the potential for this method to inexpensively produce high quality maps of surface temperature in ecologically important stream reaches.

It is well known that microtopography associated with ice wedge polygons drives pronounced, meter-scale spatial gradients in hydrologic and ecological processes on the tundra. However, high-resolution maps of polygonal geomorphology are rarely available, due to the complexity and subtlety of ice wedge polygon relief at landscape scales. Here we present a sub-meter resolution map of >10⁶ discrete ice wedge polygons across a ~1200 km² landscape, delineated within a lidar-derived digital elevation model. The delineation procedure relies on a convolutional neural network paired with a set of common image processing operations and permits explicit measurement of relative elevation at the center of each ice wedge polygon. The resulting map visualizes meter- to kilometer-scale spatial gradients in polygonal geomorphology across an extensive landscape with unprecedented detail. This high-resolution inventory of polygonal geomorphology provides rich spatial context for extrapolating observations of environmental processes across the landscape. The map also represents an extensive baseline dataset for quantifying contemporary land surface deformation (i.e., thermokarst) at the survey area, through future topographic surveys.

One of the most conspicuous signals of climate change in high‐latitude tundra is the expansion of ice wedge thermokarst pools. These small but abundant water features form rapidly in depressions caused by the melting of ice wedges (i.e., meter‐scale bodies of ice embedded within the top of the permafrost). Pool expansion impacts subsequent thaw rates through a series of complex positive and negative feedbacks which play out over timescales of decades and may accelerate carbon release from the underlying sediments. Although many local observations of ice wedge thermokarst pool expansion have been documented, analyses at continental to pan‐Arctic scales have been rare, hindering efforts to project how strongly this process may impact the global carbon cycle. Here we present one of the most geographically extensive and temporally dense records yet compiled of recent pool expansion, in which changes to pool area from 2008 to 2020 were quantified through satellite‐image analysis at 27 survey areas (measuring 10–35 km² each, or 400 km² in total) dispersed throughout the circumpolar tundra. The results revealed instances of rapid expansion at 44% (15%) of survey areas. Considered alone, the extent of departures from historical mean air temperatures did not account for between site variation in rates of change to pool area. Pool growth was most clearly associated with upland (i.e., hilly) terrain and elevated silt content at soil depths greater than one meter. These findings suggest that, at short time scales, pedologic and geomorphologic conditions may exert greater control on pool dynamics in the warming Arctic than spatial variability in the rate of air temperature increases.