M. Andy Kass

Evidence for nonuniform permafrost degradation after fire in boreal landscapes

Fire can be a significant driver of permafrost change in boreal landscapes, altering the availability of soil carbon and nutrients that have important implications for future climate and ecological succession. However, not all landscapes are equally susceptible to fire-induced change. As fire frequency is expected to increase in the high latitudes, methods to understand the vulnerability and resilience of different landscapes to permafrost degradation are needed. We present a combination of multiscale remote sensing, geophysical, and field observations that reveal details of both near-surface ( 1 m) impacts of fire on permafrost. Along 11 transects that span burned-unburned boundaries in different landscape settings within interior Alaska, subsurface electrical resistivity and nuclear magnetic resonance data indicate locations where permafrost appears to be resilient to disturbance from fire, areas where warm permafrost conditions exist that may be most vulnerable to future change, and also areas where permafrost has thawed. High-resolution geophysical data corroborate remote sensing interpretations of near-surface permafrost and also add new high-fidelity details of spatial heterogeneity that extend from the shallow subsurface to depths of about 10 m. Results show that postfire impacts on permafrost can be variable and depend on multiple factors such as fire severity, soil texture, soil moisture, and time since fire.

NMR for Near-surface Investigations (Development and Applications)

In porous materials, susceptibility contrasts between the matrix and the pore fluid generate pore-scale inhomogeneities in the magnetic field that are referred to as internal gradients. Internal gradients impact NMR measurements, and can cause large errors in the calculated diffusion coefficient if they are not accounted for. The magnitude of the internal gradients is determined by the susceptibility contrast, the strength of the background magnetic field, and the pore geometry. We use statistical analysis to look for correlation between measured internal gradients and properties of sediment samples. The primary goal of this analysis was to identify parameters that could be used as predictors of internal gradient magnitudes. We measured internal gradients using two different NMR methods: Method 1 estimates an average effective gradient, and Method 2 calculates a distribution of effective gradients. The sediment properties that we consider are magnetic susceptibility, iron content, specific surface area, grain size, and measured NMR parameters including the mean log T2 and the T1/T2 ratio. In our preliminary analysis, conducted with data from 20 sediment samples, we observe linear trends between iron content and measured gradients, and between magnetic susceptibility and measured gradients. We also see that the mineral form of iron appears to impact the relationships between iron content, magnetic susceptibility, and internal gradients. The correlation observed between gradients measured with Method 1 and both the specific surface area and T2 could indicate that this method is biased by relaxation time; this relationship was not observed for the gradients measured with Method 2. We plan to collect data on more sediment samples to better understand these relationships and develop a model for the estimation of internal gradients. Such a model will enable us to include internal gradient values in diffusion coefficient calculations for a range of nearsurface applications.