High-Resolution Permafrost Modeling in Denali National Park and Preserve
Dates
Year
2014
Citation
Panda, Santosh K., Marchenko, Sergey S., and Romanovsky, Vladimir E., 2014, High-Resolution Permafrost Modeling in Denali National Park and Preserve: National Park Service: Fort Collins, CO, v. Natural Resource Technical Report NPS/CAKN/NRTR—2014/858.
Summary
We used the CRU (1950-1959 and 2000-2009) and projected 5-GCM composite (2001-2010, 2051-2060, and 2091-2100) decadal climate forcing, ecotype (Stevens 2001), soil landscape (Clark and Duffy 2006), and snow (unpublished) maps of DENA to model the presence or absence of near-surface permafrost, temperature at the bottom of seasonal freeze-thaw layer and its thickness within DENA. We produced permafrost temperature, and active-layer and seasonally-frozen-layer thickness distribution maps through this modeling effort at a pixel spacing of 28 m. This is an immense improvement over the spatial resolution of existing permafrost maps on any part of Alaska, whether produced through the spatially explicit thermal modeling of ground temperatures [...]
Summary
We used the CRU (1950-1959 and 2000-2009) and projected 5-GCM composite (2001-2010, 2051-2060, and 2091-2100) decadal climate forcing, ecotype (Stevens 2001), soil landscape (Clark and Duffy 2006), and snow (unpublished) maps of DENA to model the presence or absence of near-surface permafrost, temperature at the bottom of seasonal freeze-thaw layer and its thickness within DENA. We produced permafrost temperature, and active-layer and seasonally-frozen-layer thickness distribution maps through this modeling effort at a pixel spacing of 28 m. This is an immense improvement over the spatial resolution of existing permafrost maps on any part of Alaska, whether produced through the spatially explicit thermal modeling of ground temperatures or by visual interpretation of satellite images/ aerial photos using indirect surface evidences of permafrost or by compilation of information from detailed field soil/geology/ecotype surveys. The model predicted ‘stable’ near-surface permafrost under 49% of DENA total area during decade 2000s and its distribution is predicted to decline to 6% by 2050s and 1% by 2090s (Figure i), i.e., near-surface permafrost is predicted to be degrading in the entire DENA and completely degraded in some part of it toward the end of the century. Only tiny areas on the north-facing slopes of high mountains are predicted to have ‘stable’ near-surface permafrost. The accuracy tests of the modeled permafrost, and active-layer and seasonally-frozen-layer thickness maps by comparing them against the field observations of permafrost presence/absence and thaw depth (at 1375 sites within DENA) suggested 86% agreement. We compiled the available ground temperature data from three climate stations within DENA and compared them to the modeled ground temperatures (Table 3). We attributed the air temperature differences between climate stations and the CRU data (input climate forcing) to the difference in scale of these datasets. The difference between recorded near-surface ground temperatures (at 0.02 m) and modeled ground surface temperatures at the three climate stations were smaller (<1°C). We attributed these differences in temperatures to three major factors: difference in scale, ground condition, and snow depth. The GIPL 1.0 model performs competently for DENA and provides reliable permafrost temperature status for different time-periods. As we used past and projected future climate forcing for modeling, the output permafrost maps show the impact of changing climate on near-surface permafrost temperature and its distribution. These permafrost maps will facilitate the park mangers to understand the current status of near-surface permafrost within DENA and how it may evolve in the future with changing climate, also to identify (vulnerable) sites at higher risk of permafrost thawing, with concurrent changes in wildlife habitats and populations. These maps will enable the park managers and decision makers to make informed decision on resource management and design of monitoring programs. Nonetheless, our model is limited in its ability to incorporate temporal changes in vegetation dynamics which could affect near-surface permafrost dynamics. Though we assumed no change in vegetation dynamics for our modeling time periods, the natural disturbances like forest fires and flooding could alter the vegetation structure and composition and consequently the ecotype at the disturbed sites resulting in reduced model prediction accuracy at those sites in the future.
