Modeling the spatiotemporal variability in subsurface thermal regimes across a low-relief polygonal tundra landscape

Abstract

Abstract. Vast carbon stocks stored in permafrost soils of Arctic tundra are under risk of release to the atmosphere under warming climate scenarios. Ice-wedge polygons in the low-gradient polygonal tundra create a complex mosaic of microtopographic features. This microtopography plays a critical role in regulating the fine-scale variability in thermal and hydrological regimes in the polygonal tundra landscape underlain by continuous permafrost. Modeling of thermal regimes of this sensitive ecosystem is essential for understanding the landscape behavior under the current as well as changing climate. We present here an end-to-end effort for high-resolution numerical modeling of thermal hydrology at real-world field sites, utilizing the best available data to characterize and parameterize the models. We develop approaches to model the thermal hydrology of polygonal tundra and apply them at four study sites near Barrow, Alaska, spanning across low to transitional to high-centered polygons, representing a broad polygonal tundra landscape. A multiphase subsurface thermal hydrology model (PFLOTRAN) was developed and applied to study the thermal regimes at four sites. Using a high-resolution lidar digital elevation model (DEM), microtopographic features of the landscape were characterized and represented in the high-resolution model mesh. The best available soil data from field observations and literature were utilized to represent the complex heterogeneous subsurface in the numerical model. Simulation results demonstrate the ability of the developed modeling approach to capture -without recourse to model calibration -several aspects of the complex thermal regimes across the sites, and provide insights into the critical role of polygonal tundra microtopography in regulating the thermal dynamics of the carbon-rich permafrost soils. Areas of significant disagreement between model results and observations highlight the importance of field-based observations of soil thermal and hydraulic properties for modeling-based studies of permafrost thermal dynamics, and provide motivation and guidance for future observations that will help address model and data gaps affecting our current understanding of the system.

Modeling Climate Change Impacts on an Arctic Polygonal Tundra: 1. Rates of Permafrost Thaw Depend on Changes in Vegetation and Drainage

“…The flat‐centered polygonal (FCP) landform was represented by features with the same dimensions, but the center was level with the rim (Figure b). The landform surfaces were thus 36% centers, 28% rims, and 36% troughs, similar to those derived from a high‐resolution digital elevation model by Kumar et al (). Other landforms such as high‐centered polygons were not represented at this stage of model testing, based on the findings of Wainwright et al () that 47% of the BEO landscape is occupied by FCPs, and most of the remainder by LCPs.…”

Modeling Climate Change Impacts on an Arctic Polygonal Tundra: 2. Changes in CO2 and CH4 Exchange Depend on Rates of Permafrost Thaw as Affected by Changes in Vegetation and Drainage

“…The flat‐centered polygonal (FCP) landform was represented by features with the same dimensions, but the center was level with the rim (Figure 1b in Grant et al, ). The landform surfaces were thus 36% centers, 28% rims, and 36% troughs, similar to those derived from a high‐resolution digital elevation model by Kumar et al (). Other landforms such as high‐centered polygons were not represented at this stage of model testing, based on the findings of Wainwright et al () that 47% of the BEO landscape is occupied by FCPs, and most of the remainder by LCPs.…”

Impacts of microtopographic snow redistribution and lateral subsurface processes on hydrologic and thermal states in an Arctic polygonal ground ecosystem: a case study using ELM-3D v1.0

“…Three recent studies have used other mechanistic models to simulate soil temperature fields at this site and achieved comparably good comparisons with observations (Kumar et al, 2016 applied a 3-D version of PFLOTRAN; Atchley et al, 2015 andHarp et al, 2016 applied a 1-D version of the Arctic Terrestrial Simulator -ATS). However, those models used measured soil temperatures near the surface as the top boundary condition.…”

“…In contrast, the top boundary condition in this work is the climate forcing (air temperature, wind, solar radiation, humidity, precipitation), and the ground heat flux is predicted based on ELM's vegetation and surface energy dynamics. We note that no parameter calibration was done in this work or that of Kumar et al (2016), while the ATS parameterizations were calibrated to match the soil temperature profile.…”