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Data and code associated with: The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0

Version 3 2022-11-17, 19:25
Version 2 2022-11-16, 20:11
Version 1 2022-10-28, 15:15
posted on 2022-11-17, 19:25 authored by Winslow Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, Werner RammerWerner Rammer



Climate change and increased fire are eroding the resilience of boreal forests. This is problematic because boreal vegetation and the cold soils underneath store approximately 30% of all terrestrial carbon. Society urgently needs projections of where, when, and why boreal forests are most likely to change. Permafrost (i.e., subsurface material that remains frozen for at least two consecutive years) and the thick soil-surface organic layers (SOLs) that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in the process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a new computationally efficient permafrost and SOL module that operates at fine spatial (1 ha) and temporal (daily) resolutions. The module mechanistically simulates daily changes in depth to permafrost, annual SOL accumulation, and their complex effects on boreal forest structure and functions. We coupled the module to an established forest landscape model, iLand, and benchmarked the model in interior Alaska at spatial scales of stands (1 ha) to landscapes (61,000 ha) and over temporal scales of days to centuries. The coupled model could generate intra- and inter-annual patterns of snow accumulation and depth to permafrost consistent with independent observations in 17 instrumented forest stands. The model was also skilled at representing the distribution near-surface permafrost presence in a topographically complex landscape. We simulated 34.6% of forested area in the landscape as underlain by permafrost; nearly identical to the estimate of 33.4% of forested area from the benchmarking product. We further determined that the model could accurately simulate moss biomass, SOL accumulation, fire activity, tree-species composition, and stand structure at the landscape scale. Modular and flexible representations of key biophysical processes that underpin 21st-century ecological change are an essential next step in vegetation simulation to reduce uncertainty in future projections and to support innovative environmental decision making. We show that coupling a new permafrost and SOL module to an existing forest landscape model increases the model’s utility for projecting forest futures at high latitudes. Process-based models that represent relevant dynamics will catalyze opportunities to address previously intractable questions about boreal forest resilience, biogeochemical cycling, and feedbacks to regional and global climate.

File list:
Cary_metadata_Hansen_permafrost_SHARE_2022.docx:  complete metadata for all files, including data tables and definitions for all variables.
analysis R analysis scripts for processing outputs (total of 6 R Markdown files).
executable and source  The QT library and iLand executable used in simulations. Also includes source code for iLand version used in simulations (total of 3 files: ilandc_07-28-2022.exe, iland_07-28-2022.exe, ).
geospatial contains Aspect_repr.tif, an aspect raster for simulated landscape. This product is used in permafrost_SOL-depth_script-1_09-30-2022.Rmd. Project directory for recreating stand level simulations of snow depth and permafrost active layer depth. Zip file includes all inputs, project file, and the sqlite databases of outputs. contains 6 data tables including 342_JFSP_sitedata.txt,  landscape_stand-structure-RSN-validation.txt, SOL_coarse_wood_carbon.txt, 398_2004burns_seedlings.txt, landscape_structure-CAFI-validation.txt, bore.hole.snow.depth.txt, Daily_soil-temp.txt, and AK_CA_Soil_Profile_Synthesis.csv. See Cary_metadata_Hansen_permafrost_SHARE_2022.docx for complete metadata. Project directory for recreating landscape level simulations of permafrost distribution, stand structure, and fire regime including inputs, project file, and sqlite databases of outputs.


Collaborative Research: Will changes in vegetation composition slow climate-driven wildfire growth in the boreal forests of northwestern North America?

Directorate for Geosciences

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Empowering decision makers with state-of-the-art research about forest fires and climate change. Royal Bank of Canada.


Geographic coordinates

65.1366, -147.457

Time period

Begin date: 07/01/2018 End date: 07/01/2023


We used a pattern-oriented modeling framework to evaluate skill of a new module by simulating forests of interior Alaska at stand and landscape scales over days to centuries. Pattern oriented modeling is an approach to benchmarking where patterns of many variables operating at multiple temporal and spatial scales are compared to observational datasets. We chose interior Alaska because it is located in the discontinuous permafrost zone where permafrost presence, moss production, and SOL accumulation vary with dominant forest type, disturbance history, and topography. We first evaluated whether the module could generate realistic daily patterns of snow accumulation/melting and active layer thawing/freezing at the stand level. We then simulated a ~ 61,000 ha forested landscape to test whether the approach could realistically generate complex mosaics of near-surface permafrost presence, moss productivity, and SOL accumulation. To ensure robust simulations, we updated an existing iLand tree-species parameter set for interior Alaska and parameterized the iLand carbon cycle using values derived from the literature. For complete methodology, refer to the metadata document in this archive.

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Data Sharing Statement

The Cary Institute of Ecosystem Studies furnishes data under the following conditions: The data have received quality assurance scrutiny, and, although we are confident of the accuracy of these data, Cary Institute will not be held liable for errors in these data. Data are subject to change resulting from updates in data screening or models used.

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