Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

Abstract

Abstract. This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using groundpenetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided highprecision estimates of snow depth (RMSE = 2.9 cm), with a spatial sampling of 10 cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE = 6.0 cm) and a fine spatial sampling (4 cm × 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro-and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE = 6.0 cm), at 0.5 m resolution and over the lidar domain (750 m × 700 m).

Microtopographic control on the ground thermal regime in ice wedge polygons

“…Although only on the scale of decimeters, this microtopography profoundly influences tundra hydrology (Liljedahl et al, 2012;, and may exert equally strong controls on microbial conversion of soil organic carbon into carbon dioxide and methane (Zona et al, 2011;Wainwright et al, 2017). Polygon microtopography also controls depth variation in the winter snowpack, which accumulates preferentially 5 in low zones, such as the trough space between polygons (Wainwright et al, 2017). It is well known that snow accumulation in periglacial terrain strongly controls winter ground temperatures, by providing insulation from the atmosphere (e.g., Mackay and MacKay, 1974;Goodrich, 1982).…”

Microtopographic control on the ground thermal regime in ice wedge polygons

“…Particularly in coastal regions of the Arctic, the slow growth of ice wedges results in subtle but distinctive surface topography, as pressure between the wedge and the adjacent ground creates rims of raised soil at the perimeters of the polygons. Although only on the scale of decimeters, this microtopography profoundly influences tundra hydrology (Liljedahl et al, 2012(Liljedahl et al, , 2016, and may exert equally strong controls on microbial conversion of soil organic carbon into carbon dioxide and methane (Zona et al, 2011;Wainwright et al, 2017). Polygon microtopography also controls depth variation in the winter snowpack, which accumulates preferentially in low zones, such as the trough space between polygons (Mackay, 1993(Mackay, , 2000Morse and Burn, 2014;Wainwright et al, 2015).…”

Estimating Seasonally Frozen Ground Depth From Historical Climate Data and Site Measurements Using a Bayesian Model

“…Stochastic approaches, such as the Bayesian framework, have become widely accepted methods for parameter estimation and uncertainty analysis (Chen & Dickens, ; H. Dai et al, ; Liu et al, ). Recent studies have applied Bayesian methods to estimate the snow depth (Wainwright et al, ) and soil organic carbon content (Tran et al, ) in permafrost regions using Markov chain Monte Carlo (MCMC) sampling methods. Bayesian methods have been well recognized as effective approaches for inverting complex geophysical data with limited observations in cold regions.…”