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 likely to change. Permafrost (i.e., subsurface material that
remains frozen for at least 2 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 process-based vegetation models used to simulate future
ecosystem trajectories. To address this challenge, we developed a
computationally efficient permafrost and SOL module named the Permafrost and
Organic LayEr module for Forest Models (POLE-FM) 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 generated intra- and inter-annual patterns of snow
accumulation and active layer depth (portion of soil column that thaws
throughout the year) generally consistent with independent observations in
17 instrumented forest stands. The model also represented the distribution
of near-surface permafrost presence in a topographically complex landscape.
We simulated 39.3 % of forested area in the landscape as underlain by
permafrost, compared to the estimated 33.4 % 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.
National Science FoundationOPP 2116863Royal Bank of CanadaNAEuropean Commission101001905Joint Fire Science Program20-2-01-13Introduction
The boreal forest is warming at a rate at least twice the global average
(IPCC, 2021; Chylek et al., 2022), which can reduce
fuel moisture and cause climate-sensitive disturbances, like forest fire, to
increase
(Seidl
et al., 2020; Walker et al., 2020). Together, pronounced warming and larger,
more severe fires are initiating abrupt changes in forest cover, structure,
functions, and tree species composition
(Johnstone
et al., 2010a; Alexander and Mack, 2016; Walker et al., 2019; Mack et al.,
2021; Baltzer et al., 2021), trends that will likely continue for at least
the next several decades
(Mekonnen
et al., 2019; Foster et al., 2019, 2022). This is important because
biophysical properties of the boreal forest underpin feedbacks to regional
climate
(Foley et
al., 1994; Chapin et al., 2008; Rogers et al., 2013; Potter et al., 2020),
and ∼ 30 % of all terrestrial organic carbon stocks
are stored in the biome
(Lorenz and Lal, 2010;
Schurr et al., 2018). Some portion of those stocks could be released to the
atmosphere and further accelerate warming (Anderegg et
al., 2022). Thus, society urgently needs projections of where, when, and why
the boreal forest will change.
Ecological legacies are the organismal adaptations (i.e., information),
physical materials, and energy that persist in ecosystems through multiple
disturbances (Ogle et al., 2015). Legacies will
underpin how the boreal forest responds to climate change and fire
(Turetsky et al., 2016; Johnstone et al., 2016). For example,
adaptive traits, like cone serotiny (cones that stay closed for many years
until heated by fire) and asexual resprouting, are information legacies that
facilitate postfire forest recovery
(Johnstone et al., 2009, 2010a). Thick
moss-dominated soil-surface organic layers (SOLs) form over decades of
postfire forest development, and a portion often escapes burning in the
subsequent fire, leading to accumulation of SOL over multiple fire cycles
(Walker et al., 2018). This serves as a physical
legacy that preserves permafrost (subsurface material that remains frozen
for at least 2 consecutive years)
(Kasischke and Johnstone,
2005; Jorgenson et al., 2010) and shapes tree species composition by
controlling seedling germination and establishment
(Johnstone et al., 2020). In conjunction with
insulative physical legacies, energy legacies of past temperature regimes
also maintain permafrost underneath forests where current air temperature
would otherwise not support it (Schuur and Mack, 2018).
Physical and energy legacies underpin spatio-temporal patterns of permafrost
at multiple scales. In the boreal forest of North America, permafrost is
continuous in the north, becomes discontinuous and sporadic, and is then
eventually absent in the south
(Obu et al., 2019). Within the
discontinuous zone, the permafrost distribution is heterogeneous, varying on
fine spatial scales with topography, dominant forest type, and fire history
(Brown
et al., 2016; Gibson et al., 2018). Permafrost dynamics are particularly
important for shaping boreal forest structure and function as well as
hydrology
(Turetsky
et al., 2010; Baltzer et al., 2014; Dearborn and Baltzer, 2021). Within
permafrost-affected soils, a portion of the soil column termed the “active
layer” undergoes an annual cycle of freezing and thawing. The annual
maximum active layer depth can vary from a few centimeters to several meters
(Smith et al., 2022). This freezing-and-thawing
cycle determines the seasonality, vertical distribution, and amount of
plant-available soil water and influences nutrient availability
(Abbott and Jones, 2015; Young-Robertson et
al., 2017).
In response to continued warming, annual maximum active-layer depth is
predicted to increase, and the distribution of permafrost will likely
contract, with large hydrologic and biogeochemical consequences
(Pastick et al., 2015;
Schuur and Mack, 2018). Increasing wildfire (Veraverbeke et al., 2017;
Phillips et al., 2022) will also impact permafrost by combusting SOLs and
altering tree regeneration pathways (Baltzer et al., 2021; Johnstone et al., 2010a). However, permafrost and the legacies that affect its dynamics are
rarely considered in forest models. In fact, just a handful of models
explicitly simulate permafrost
(Foster
et al., 2019; Gustafson et al., 2020; Kruse et al., 2022), and those that do
often operate at relatively coarse spatial (≥ 25 ha grid cells) and/or
temporal (≥ monthly) resolutions (but
see Kruse et al., 2022, who describe a permafrost module that runs with a
5 min temporal resolution). This makes it difficult to capture the
fine-scale spatial heterogeneity of permafrost distributions and the effects
of daily temperature variability on plant water availability during short
but critical shoulder seasons. Further, most existing permafrost algorithms
rely on computationally intensive numerical methods
(Sitch
et al., 2003; Beer et al., 2007; Karra et al., 2014; Perreault et al., 2021;
Yokohata et al., 2020; Westermann et al., 2016), limiting the
spatio-temporal resolutions at which they can be applied, particularly
across broad domains.
To address this challenge, we present the Permafrost and Organic LayEr
module for Forest Models (POLE-FM) that was designed to mechanistically
simulate daily changes in active layer depth, annual SOL accumulation, and
the associated ecological effects on boreal forests and fire at a fine
spatial resolution (i.e., grain of ∼ 1 ha) in a
computationally efficient manner (Fig. 1). When paired with a
state-of-the-art forest model, such as iLand, the module allows for
simulation of complex feedbacks among forests, fire, and permafrost dynamics
in topographically complex landscapes under historical and future
conditions. In this paper, we describe the module and benchmark its ability
to represent permafrost and SOLs in forest stands to landscapes of interior
Alaska across days to centuries.
Conceptual diagram of the permafrost and soil-surface
organic layer module. State variables are in white, processes are described
in black, and forcing variables are in red.
Model descriptionPermafrost and SOL module
The module represents daily changes in active layer depth and long-term
trends (years to decades) in permafrost presence. Permafrost is represented
based on physical principles of heat transport through vegetation and soil
media with varying thermal resistances affected by soil moisture content. We
incorporate the insulating effects of snow and deep SOLs and capture
transient shifts between permafrost regimes (e.g., a transition from
temporally continuous to sporadic permafrost due to climate change).
Moreover, we aimed for a computationally efficient approach that operates
well within the runtime and memory constraints of forest models. The module
tracks the energy fluxes that thaw and freeze water at the edge of the
active layer (zero isoline, or the depth at which soil temperature is 0 ∘C), requires only a few state variables, and provides daily
values of active layer depth with little computational overhead by avoiding
iterative numerical approximations of differential equations.
To capture daily changes in active layer depth, we first estimate the
thermal resistances R (m2 W-1 K-1) of snow
(when present), SOL, and the mineral soil layer (Eq. 1).
R=Snow.DepthSnow.k+SOL.DepthSOL.k+M.Soil.DepthM.Soil.k
Snow depth is represented as a function of the precipitation that falls
during days with mean air temperature below 0 ∘C and the density
of snowpack (set at 190 kg m-3)
(Bonan, 1991;
Bennett et al., 2019). We set snow thermal conductivity, Snow.k, at 0.3 W m-1 K-1 (Cook et al.,
2008). SOL depth is estimated based on the mass of live and dead mosses and
litter pools in each grid cell. SOL thermal conductivity, SOL.k, is set at
0.09 W m -1 K-1 (Hinzman et
al., 1991; O'Donnell et al., 2009).
Characteristics of the mineral soil layer that determine its conductivity
are explicitly considered. We allow mineral soil thermal conductivity,
M.Soil.k, to vary with soil texture and soil moisture. We derive mineral
soil conductivity following the approach of Farouki (1981)
as described in Bonan (2019) (Eq. 2).
M.Soil.k=M.Soil.k.dry+M.Soil.k.sat-M.Soil.k.dry⋅Ke,
where M.Soil.k is determined by linearly ramping between saturated
conductivity, M.Soil.k.sat, and dry conductivity, M.Soil.k.dry, based on a
factor, Ke, that varies with relative soil moisture and soil texture,
represented separately for unfrozen (Eq. 3) and frozen (Eq. 4) soils.
Ke=1+0.7⋅log10⋅SE,%Sand>501+log10⋅SE,%Sand≤50,
where Ke is the Kersten number, and SE is the volumetric soil water
content (VWC) relative to the volumetric soil water content at saturation
(VWC.sat).
Ke=SE
M.Soil.k.dry is estimated from bulk density (Eq. 5).
M.Soil.k.dry=0.135pb+64.72700-0.947⋅pb,
where pb=2700⋅(1-VWC.sat). M.Soil.k.sat is estimated as a function of
the conductivity of solids, water, and ice in the matrix, modeled separately
for unfrozen (Eq. 6) and frozen (Eq. 7) soils.
6M.Soil.k.sat=Ksolid1-VWC.sat⋅KwaterVWC.sat7M.Soil.k.sat=Ksolid1-VWC.sat⋅KiceVWC.sat
We assume Kwater =0.57 and Kice = 2.29 W m-1 K-1.
Calculation of Ksolid is calculated in Eq. (8).
Ksolid=8.80⋅%sand+2.92⋅(%clay)%sand+%clay
Using the total thermal resistance R from Eq. (1), we can then estimate the
daily sum of energy flow (Einput, MJ d-1) that reaches the zero
isoline from the atmosphere above (Eq. 9).
Einput=1R⋅Air.Temp-Temp.zero.isoline⋅86400sd-11000000JMJ-1,
where Air.Temp is the daily mean air temperature, Temp.zero.isoline = 0 ∘C, and the constant converts from J s-1 to MJ d-1.
Einput is then used to calculate the daily sum of water that thaws or
freezes at the zero isoline based on the enthalpy (or latent heat) of fusion
(Ethaw; 0.33 MJ L-1 water). Equation (9) is also used to estimate the
daily energy flux from soil below the active layer by replacing Air.Temp
with the temperature of the soil below. Deep soil temperatures, set at 5 m,
are assumed to be at equilibrium with mean annual air temperature of the
previous decade (Riseborough, 2004).
We then model the daily amount of water that thaws or freezes, delta.W.mm
(Eq. 10).
delta.W.mm=EinputEthaw,
where delta.W.mm is constrained to values between -10 and +10 mm (only
10 mm of water is allowed to freeze or thaw each day in order to avoid
numerical instabilities close to the soil surface). Finally, the
corresponding depth of soil that freezes or thaws each day in meters, delta.s.m,
is calculated (Eq. 11).
delta.s.m=delta.W.mmVWC.sat⋅11000
Since frozen soil (and the water captured therein) is not accessible for
plants, the actual water holding capacity of the soil is dynamically
modified each day. If soil thaws in a given day, that freshly melted water
is added to the soil water pool and the capacity for soil to hold water
increases. The approach described here also works for estimating seasonal
thawing and freezing of soils in areas not underlain by permafrost.
The SOL component was adapted from Bonan and Korzuhin (1989) and Foster et al. (2019) and represents SOL
depth as a function of annual moss net primary production, biomass
accumulation, respiration, and turnover. It adds live and dead moss to the
fuels for forest fires, and the depth of the SOL influences postfire tree
regeneration. Annual moss productivity is simulated as a function of
environmental scalars that represent effects of light attenuation through
the forest canopy and moss layer and growth inhibition from fresh deciduous
litter. The amount of light that reaches moss for photosynthesis attenuates
with increasing forest canopy cover and with increasing moss biomass.
Effects of light attenuation are represented by first calculating the amount
of light available for photosynthesis in year t as Light.availt (Eq. 12).
Light.availt=e-k⋅(LAI.forestt+LAI.mosst),
where k is the light extinction coefficient, set at 0.92,
LAI.forestt is the leaf area index (square meters of leaf area per square meter of ground) of tree cover in year t. LAI.mosst is the leaf area index of
moss in year t calculated as moss biomass multiplied by the specific leaf area
of moss (1 m2 kg -1)
(Foster et al., 2019, and
https://github.com/UVAFME/UVAFME_model/blob/main/src/Soil.f90, last access: 10 October 2022). The effect of light attenuation on moss
productivity, FLight.availt, is then calculated (Eq. 13).
FLight.availt=(Light.availt-LRmin)(LRmax-LRmin),
where LRmax is the light saturation point, or the amount of light,
relative to the light level above the canopy, above which an increase in
light does not increase moss gross primary production (GPP), set at 0.05. LRmin is the light
compensation point, or the amount of light, relative to light level above
the forest canopy, beyond which moss begins to photosynthesize, set at 0.01.
Field experiments show that fresh leaf litter from deciduous broadleaf tree
species strongly inhibits moss productivity
(Jean et al., 2020). Such inhibitory
effects, FDecidt, are modeled as Eq. (14).
FDecidt=e-0.45⋅Decid.bt-1,
when Decid.bt>0 or 1 when Decid.bt=0, where
Decid.bt-1 is the fresh (previous year's) forest floor deciduous
litter biomass in megagrams per hectare. At, annual assimilation by moss in year
t (kilograms of biomass per square meter of leaf area) is then computed (Eq. 15).
At=AMax⋅FLight.availt⋅FDecidt,
where AMax, the maximum moss productivity per unit leaf area, is 0.3 kg m-2 yr -1
(Foster et al., 2019). We
estimate effective assimilation in year t, A.efft, in kilograms per kilogram
biomass (Eq. 16).
A.efft=SLA⋅At
Moss productivity in year t, Pt, in kilograms per square meter biomass then depends
on turnover, Tt, and respiration, Rt, in year t (Eqs. 17–19).
17Pt=A.efft⋅Moss.bt-1-Tt-Rt,18Tt=Moss.bt-1⋅b,19Pt=Rt=Moss.bt-1⋅q,
where Moss.bt-1 is the previous year's moss biomass in kilograms per square meter, and b and q are empirical parameters set at 0.136 and 0.12, respectively
(Foster et al., 2019). The
moss biomass pool is updated (Eq. 20).
Moss.bt=Moss.bt-1+Pt
Note that the biomass pool can shrink if Pt becomes negative, e.g., due to a
closing canopy.
Thickness of the live moss layer is calculated as biomass divided by a bulk
density of 31 kg m-3 calculated from field observations described in
Walker et al. (2020).
Dead moss and forest floor litter layer thickness is calculated as biomass
divided by bulk density, set at 91 kg m-3
(Walker et al.,
2020).
The permafrost and SOL module is implemented in C++ for computational
efficiency and is relatively compact (< 1000 lines of code). It is
compatible with PC, Linux, or Mac, and full source code and documentation are
available under a GNU General Public License (GNU GPL
http://www.gnu.org/licenses/gpl-3.0.html, last access: 1 October 2022) (see code availability
section). While the design is modular, we note that the complex feedbacks
between vegetation, permafrost dynamics, and SOL accumulation may require
some adaptations and code modifications when integrating our work in
different forest models. Below, we detail the integration into the
individual-based forest landscape and disturbance model iLand
(Seidl et al., 2012a).
Coupling the permafrost and SOL with iLand
The growth and mortality of individual trees in spatially
explicit landscapes are simulated by iLand as a function of canopy light interception, climate,
nutrient availability, and disturbance
(Seidl et al., 2012a, b). The model was originally designed to study effects of natural
disturbances, like forest fire, on forest landscapes in the context of
climate change (Seidl et
al., 2012a). Thus, iLand emphasizes representation of disturbances and the
processes that underpin forest responses to disturbance, including tree seed
production and dispersal, abiotic filters of tree seedling establishment,
and multiple pathways of tree mortality
(Seidl et
al., 2012a, b; Hansen et al., 2018, 2020). For an exhaustive technical
description of iLand, including carbon cycling and simulation of forest
fire, see Appendix A and https://iland-model.org/ (last access: 1 October 2022), which
includes full model source code.
The proportion of moss biomass that turns over (dies) each year in the new
module is fed into the litter layer of iLand's decomposition module. Decomposition is simulated by iLand as a function of climate and pool-specific carbon-to-nitrogen ratios (Seidl et al., 2012b). The C:N ratio of moss
litter is set at 30 (Melvin et al., 2015). Together, live moss,
dead moss, and forest floor litter layers comprise the SOL in iLand. Wildfire
ignition, spread, and severity are partially contingent on downed fuel
availability in iLand (Seidl et al., 2014a), and we now include
live and dead moss as available fuel in the fire module. When a grid cell
burns, the combusted forest floor litter, dead moss, and live moss pools are
subtracted from SOL depth.
The tree species that establish in years following fire shape multi-decadal
successional trajectories (Seidl and Turner, 2022). The depth
of burning in the SOL is an important determinant of seedling establishment
success because the SOL is often dry, and seedlings must expand their roots
into mineral soil to access water (Johnstone and Chapin, 2006;
Brown and Johnstone, 2012). We therefore included the effect of deep SOL as
an additional limiting factor when calculating tree seedling establishment
in iLand. For each 1 ha iLand cell, the probability of establishment is
scaled with a negative exponential function following Trugman et al. (2016) (Eq. 21).
estab.pt=e-c⋅SOL.deptht,
where estab.pt is a multiplicative factor reducing the abiotic
establishment probability in year t; SOL.deptht is the depth of the
SOL (cm) in year t; and c is a species-specific shape parameter, set at 0.50
for trembling aspen (Populus tremuloides Michx.) and Alaskan birch (Betula neoalaskana Sarg.), 0.25 for white
spruce (Picea glauca (Moench) Voss), and 0.15 for black spruce (Picea mariana (P. Mill.) B.S.P.).
Model benchmarking
We used a pattern-oriented modeling framework (Grimm et al.,
2005) to evaluate the 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. For example, areas dominated by mature black spruce in lowland
valley bottoms and north-facing slopes are generally underlain by permafrost
and support a relatively productive forest floor moss layer and thick SOLs.
Upland and south-facing slopes are dominated by deciduous trembling aspen
and Alaskan birch, which are often not underlain by permafrost, and moss is
far less prevalent. White spruce also inhabits upland positions on its own
or mixed with black spruce and contains SOLs of intermediate thickness
(Van Cleve and Viereck, 1981). The multiple interacting
biotic and abiotic drivers of permafrost and moss productivity create
complex landscape mosaics (Johnstone et al., 2010a) that
we wanted to ensure the module could produce.
We first evaluated whether the module could generate reasonably 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 generate
complex mosaics of near-surface permafrost presence, moss productivity, and
SOL accumulation consistent with observations. To ensure robust simulations,
we updated an existing iLand tree species parameter set for interior Alaska
(Hansen et al., 2021) (Table B1) and parameterized the iLand carbon cycle (Table B2) using values derived
from the literature.
Temporal patterns of snow and active layer depth
To evaluate whether the module could generate realistic intra- and
inter-annual patterns of snow accumulation and active layer depth, we
selected 17 forested sites in interior Alaska that span approximately 700 km. The southernmost site sits along the Alaskan highway at the border
between Canada and Alaska. The northernmost site is just south of the Brooks
mountain range along the Dalton highway. Each site was instrumented with
temperature probes to measure daily soil temperature at depths of 0 to
6 m between 2014 and 2018 (https://permafrost.gi.alaska.edu/sites_list, last access: 1 March 2022). Seven of the
sites were recorded as having an annual maximum active layer depth of less
than 2 m (permafrost present). Ten of the sites had an annual maximum active
layer deeper than 2 m (permafrost absent). We used the 2 m depth cutoff
because it is the maximum effective soil depth assumed in iLand. The sites
were initialized from field inventories covering the same domain selected to
match the species composition recorded in the soil temperature database
(Walker and Johnstone, 2014; Johnstone et al., 2020).
Soil information used to initialize iLand was extracted from the global
SoilGrids250m V. 1.0 (for effective soil depth) and 2.0 (for percent sand,
silt, and clay)
(Hengl et al.,
2017). Relative soil fertility, expressed as plant-available nitrogen, was
set to 45 kg ha-1 yr-1
(Hansen et al., 2021). Depth of
the SOL was not recorded in the soil temperature database for the 17 sites.
Thus, we used photos from the instrumented sites and information on the
dominant forest type to assign initial SOL depths to the iLand stands. Sites
where researchers recorded dominance of deciduous trees or where SOLs
appeared absent or shallow in photographs were assigned a depth of 0 or 0.07 m to match independent field estimates of SOL depths in deciduous forests
located in the Tanana Valley near Fairbanks (Melvin et al., 2015).
Sites dominated by black spruce or where photographs suggested a deep SOL
were assigned a depth of 0.25 m based on field surveys of black spruce
stands (Johnstone et al., 2010a). Stands dominated by
white spruce were assigned an intermediate depth of 0.16 m.
Stands were simulated in iLand with 2001–2018 daily climate (minimum and
maximum daily temperature, precipitation, shortwave solar radiation, and
vapor pressure deficit) from the 1 km Daymet product
(Thornton et al., 2021). We benchmarked simulated
maximum annual snow depth and timing of snowmelt for the period 2001–2017
(the period when snow observations were available) using a gridded snow
product (Yi et al., 2020). This product
was developed by integrating downscaled reanalysis data with satellite
imagery to provide a continuous estimate of snow depth at 1 km spatial
grain. When compared with a meteorological station network (SNOTEL), the
gridded observational product had a RMSE of 0.32 m with a bias of -0.09 m in
mid-elevations (400–800 m), where 70 % our forested sites were located, and
a bias of 0.01 m at low elevations (< 400 m), where the rest of our
sites were located (Yi et al., 2020).
We compared simulated and observed maximum annual active layer depth for
2014–2018, the period where soil temperature observations were available, at
the 7 permafrost sites and maximum annual freezing depth for the 10
non-permafrost sites. We converted observed daily soil temperatures at
depths of 0.03, 0.5, 1, 1.5, 2, 4, and 6 m to active layer depth by
identifying the zero isoline with linear interpolation. We also compared the
day of year when maximum active layer depth and freezing depth were reached
in simulations and observations.
Landscape heterogeneity in near-surface permafrost presence, moss
productivity, and SOL accumulation
We evaluated whether the module, coupled with iLand, could simulate
landscape mosaics of near-surface permafrost (≤ 1 m deep), moss
production, and SOL accumulation in a large forested area (∼ 61 000 ha of land area). We initialized the model with a tree species
composition map based on a remotely sensed plant functional type (PFT)
product for Alaska and western Canada that classified vegetation as spruce,
deciduous, mixed forest, or nonforest
(Wang et al., 2020)
and reflected fire history. We further decomposed PFTs into black spruce,
white spruce, trembling aspen, Alaskan birch, mixed forest, potential forest
(i.e., areas currently unforested that could support forest in the future),
and nonforest using rules based on aspect, elevation, and a permafrost map
(Table B3). While this approach allowed us to disaggregate PFTs to the
species level, we lack robust datasets to evaluate the accuracy of the
species composition map. This is a challenge as the dominant tree species
determines SOL accumulation and permafrost distribution. In the future, well-validated, remotely sensed tree species composition maps would markedly
reduce initial condition uncertainty in forest simulations in interior
Alaska (Hermosilla et
al., 2022).
Initial stand densities, tree sizes, and forest floor carbon pools (litter,
coarse wood, live and dead moss; Table B4) for the appropriate tree species
were initialized in the model as early postfire (11 years old) forest based
on field inventories described earlier (Walker and
Johnstone, 2014; Johnstone et al., 2020). Because the forest landscape was
initialized as entirely early postfire, it did not reflect variation in
forest stand age. Thus, we ran a 200-year spin-up as a function of
historical climate (climate years 1950–2005 recycled randomly with
replacement) and simulated fire dynamically to generate spatial
heterogeneity consistent with internal model logic, following protocols
established in previous iLand studies
(Hansen
et al., 2020; Turner et al., 2022). We then simulated forests for another
100 years and used this period in all analyses.
We want to eventually conduct simulations with future 21st-century
climate. Thus, we used daily meteorological data from the historical period
of the CMIP5 generation CCSM4 general circulation model (GCM)
(Gent et al., 2011) to force
landscape-level simulations instead of Daymet (as was used in the
stand-level experiment). This GCM corresponds closely with observed
historical climate in Alaska (Walsh et al., 2018), and we statistically
downscaled it to a 1 km spatial resolution using quantile matching with
Daymet as the observational grid
(Hansen et al., 2021). We
extracted soil data from the same sources as the stand-level experiment
that geographically corresponded to the 1 ha grid cells in our simulated
landscape. Because fire is stochastic in iLand and an important determinant
of permafrost dynamics, SOL depth, tree species composition, and stand
structure, we ran 10 replicates and analyzed output from the run with the
smallest difference between modeled and observed mean annual burned patch
size and annual probability of a fire event.
We compared fire from simulation years 201–300 to observations in the Alaska
Large Fire Database from the period 1980–2021. This database contains
perimeters for larger fires (size threshold for inclusion has varied over
time, ranging from 10–1000 ha) and point locations for smaller fires in
Alaska. We chose years 201–300 for evaluation because a century aligns with
the historical-mean fire return interval in Alaska
(Johnstone et al., 2010b). We combined these
datasets to ensure comprehensive coverage and assumed a circular shape for
the smaller fires when perimeters were unavailable. Fire is a stochastic
process in iLand, so we did not expect perfect correspondence between
modeled and observed individual fire sizes and locations. Instead, we aimed
for the model to generate fire characteristics (i.e., frequency, patch size,
annual area burned, and severity) that were generally consistent with the
observational record. We took two approaches for benchmarking. First, we
compared simulated and observed annual probability of fire occurrence and
mean annual burned patch size, as well as the proportion of stems and basal
area killed by fire. Second, we compared simulated and observed fire
characteristics from the landscape with observed fire characteristics in all
of the forests of interior Alaska broken into 625–61 000 ha
landscapes. This allowed us to determine how the dynamic fire module in
iLand performed for our landscape specifically and how the model performed
relative to the spatial variation in fire regimes across interior Alaska.
We compared the proportion of the landscape underlain by near-surface
permafrost in the last 40 years of simulation (years 261–300) to a remotely
sensed product of near-surface permafrost presence
(Pastick et al., 2015). Forty years
was chosen because we wanted to evaluate permafrost over a multi-decadal
period and because it aligned with the period used to evaluate postfire SOL
combustion and tree seedling density (see below). This product was created
by integrating satellite records and other geospatial datasets to predict
the probability of near-surface permafrost presence at a 30 m spatial
resolution with machine learning. Because iLand operates at 1 ha spatial
resolution for permafrost, we aggregated the remotely sensed data from 30 m
to 1 ha grid cells by calculating the mean probability of near-surface
permafrost presence in each 1 ha grid cell. We then used a ≥ 50 %
probability of permafrost presence, the same cutoff used in the original
analysis (Pastick et al., 2015), to
map the permafrost distribution. In iLand, near-surface permafrost was
considered present in any grid cell where the annual maximum active layer
depth was ≤ 1 m in 15 (38 %) of the last 40 years of simulation. This
cutoff ensured we only included areas that were underlain by consistently
frozen ground. We compared the total proportion of the landscape underlain
by near-surface permafrost and how permafrost presence varied as a function
of aspect in simulations and the benchmarking product. We also evaluated how
permafrost presence varied as a function of simulated dominant tree species
but did not compare to the benchmarking product because we lack tree species
composition maps in interior Alaska.
(a) Observed vs. simulated maximum annual snow depth at 17
sites between 2001–2017. (b) Observed vs. simulated day of spring snowmelt at
17 sites between 2001–2017. (c) Observed vs. simulated maximum annual thaw
depth at seven sites underlain by permafrost between 2014–2018 (only site years
with complete observational records are included). (d) Observed vs. simulated
maximum annual freeze depth at 10 sites not underlain by permafrost between
2014–2018 (only site years with complete observational records are
included). Black lines show one-to-one relationships in all panels.
We compared SOL carbon in simulation year 300 separated by forest type to
field inventories
(Alexander and
Mack, 2016; Walker et al., 2020). While benchmarking data were unavailable,
we also evaluated landscape variability in total SOL and live moss depth. We
assessed SOL combustion by fire in different forest types for model years
261–300 and compared model output to the two extensive sets of postfire
field plots
(Walker
and Johnstone, 2014; Johnstone et al., 2020; Walker et al., 2020) also used
for initialization. The period of analysis was selected to ensure a
sufficient number of fires while balancing the computational intensity of
these calculations.
Because near-surface permafrost presence and moss productivity are affected
by and feed back to influence forest dynamics, we determined whether the
model could realistically represent landscape-level patterns of tree species
composition and stand structure. We explored how landscape patterns of
dominant forest type shifted through 300 years of simulation and compared
simulated stand density and basal area of each forest type from the end of
the simulation with two field inventories. The first was a regional network
of permanent plots in interior Alaska collected by the Bonanza Creek Long-Term Ecological Research Network (Ruess et al.,
2021). The second inventory was the Cooperative Alaska Forest Inventory,
which is a set of permanent plots covering interior Alaska, south-central
Alaska, and the Kenai Peninsula (Malone et al., 2009). We reran
the 300-year simulation with the SOL and permafrost module turned off to
evaluate how the module shaped landscape distributions of tree species
composition.
We also compared simulated aboveground live tree biomass from the end of the
simulation with remotely sensed estimates of aboveground live woody biomass
for interior Alaska and western Canada (Wang
et al., 2021). This dataset is a 30 m product that characterizes annual live
woody biomass for the years 1984–2014. We aggregated 2014 biomass estimates
to the 1 ha spatial resolution of iLand using bilinear interpolation. We
further benchmarked snag and coarse-wood carbon pools in model year 300 with
published field observations (Alexander and Mack, 2015; Melvin et al., 2015).
To quantify the underpinning drivers of landscape variability in
tree species composition and aboveground live and dead biomass, we compared
simulated variation in postfire tree seedling density by species and SOL
depth from years 261–300 with field observations
(Walker and Johnstone, 2014; Johnstone et al., 2020)
using the same fires that were analyzed for postfire SOL combustion.
Finally, we analyzed the computational efficiency of the module by
simulating the landscape with and without the permafrost module turned on to
quantify its memory requirement and runtime.
Dominant forest type was determined using species importance values (IVs), a
measure of stand dominance based on the relative proportions of species
density and basal area. It ranges from zero to two (Hansen et
al., 2020). We considered stands dominated by a particular species if their
IV was greater than one. Stands were considered mixed spruce or
mixed deciduous forest if black spruce and white spruce or aspen and birch
IVs summed to greater than one, respectively. Averages in the text are
presented as medians and inter-quartile ranges (IQRs) (25th–75th
percentiles). When comparing simulated and observed datasets, parametric
statistics were not used because sample sizes can be increased with
simulations to artificially inflate statistical significance. Benchmarking
analyses were conducted in R statistical software V. 4.0.4 (R Core
Team, 2021) using the packages tidyverse (Wickham et al., 2019)
and terra (Hijmans, 2021).
ResultsSnow depth, timing of snowmelt, and active layer depth
When forced with 2001–2017 climate, median simulated maximum annual snow
depth was 0.68 (0.52–0.84) m compared with median observed maximum annual
snow depth of 0.49 (0.39–0.59) m. The model overestimated snow depth for
sites and years where snowfall was above average (Fig. 2a), likely because
snow compaction is not considered in the model. The simulated median number of Julian days
to snowmelt was 122 (116–130) compared to the observed median number of Julian days
of 117 (117–125) (Fig. 2b).
When forced with 2014–2018 climate, simulated median annual maximum active
layer depth was 1.6 (1.3–1.8) m, and observed median annual maximum active
layer depth was 1.4 (1.0–1.5) m in seven forest stands underlain by
permafrost (Fig. 2c). Simulated daily patterns of active layer depth also
corresponded well with observations (Fig. 3). On average, maximum annual
active layer depth occurred 20 d later in iLand than in observations with
an IQR of 10 d earlier to 39 d later. Simulated and observed median
annual maximum freezing depths were 2.0 (1.9–2.0) m and 1.9 (1.9–2.0) m,
respectively (Fig. 2d). On average, the maximum annual freeze depth was
reached 10 d earlier in simulations than in observations with an IQR of
28 d earlier to 7 d later than observations.
(a) Example of daily active layer freezing and thawing.
Data from 2016 at one of seven forest stands underlain by permafrost. (b) Example of daily thawing and freezing. Data from 2016 at 1 of 10 forest
stands not underlain by permafrost. Solid lines represent snow depth. Dots
represent active layer depth or depth of freezing. Gray fill represents
simulated frozen soils. Blue fill represents simulated unfrozen soils.
Landscape-level fire characteristics
Mean annual burned patch size was 3628 ha, and annual probability of a fire
event was 11 % in the best of the 10 replicate landscape simulations. These values differed from observed values by 5 % and 8 %, respectively (Fig. 4a,
b). However, among all 10 replicates, burned patch size and probability of
a fire event differed by as much as 44 % and 42 %, highlighting the
stochastic nature of fire. Both observed and simulated fire metrics for the
landscape were also representative of observed fire characteristics in all
625 sampled 61 000 ha landscapes across the boreal domain of Alaska (Fig. 4c). On average, 73 (70–76) % of stems and 52 (46–60) % of basal
area were killed by fire in the model (Fig. B1).
Simulated and observed (a) annual fire probability and (b) mean burned patch size in a 61 000 ha landscape (Caribou Poker Creek Watershed, CPCRW) in interior Alaska. Model
output is for years 201–300. Observations are from years 1980–2020. The gray
density distribution shows observed values for all sampled 625 61 000 ha
landscapes across the boreal domain of interior Alaska. (c) Map showing all
625 sampled landscapes as dark-gray squares. The red square shows the landscape
simulated in iLand.
Landscape near-surface permafrost, moss, and SOL depth
The model simulated 39.3 % of forested area in the landscape as underlain
by permafrost between years 261–300, compared to the estimated 33.4 %
of forested area from the benchmarking product (Fig. 5a). Aspect was an
important determinant of permafrost presence in the model and in
observations (Fig. 5b). Simulated permafrost was overrepresented on
north-facing slopes, as compared to the benchmarking product, but
corresponded well in all other aspects. Near-surface permafrost presence
also varied with dominant tree species in iLand. A total of 71 % percent of
simulated black spruce forest area was underlain by near-surface permafrost,
followed by 51 % of white spruce forest, 14 % of aspen-dominated stands,
11 % of mixed spruce, 6 % of mixed deciduous forest, and 0.2 % of
birch-dominated forest.
(a) Observed and simulated near-surface (≤ 1 m deep)
permafrost in a 61 000 ha forested landscape in interior Alaska. (b) Observed
and simulated percent of forested area underlain by near-surface permafrost
in the same landscape as a function of aspect. Simulated permafrost presence
is for years 261–300 of the simulation. Benchmarking product is derived from
years 1990–2013.
Soil-surface organic layer C in simulation year 300 averaged 4801 (2965–6575) g m-2. When broken out by dominant forest type, simulated SOL C
closely corresponded to observations for all forest types where comparison
was possible (Fig. 6a). Dead moss and litter depth across the landscape
averaged 11.6 (7.4–15.5) cm in simulation year 300, and live moss depth
averaged 5.4 (2.7–8.7) cm, with pronounced spatial heterogeneity (Fig. 6b). Tree species composition was an important determinant of total SOL
depth (Fig. B2): the SOL was thickest in black-spruce-dominated stands,
averaging 25 (21–27) cm, followed by white spruce: 17 (15–19) cm;
mixed spruce: 14 (7–17) cm; aspen: 9 (4–11) cm; mixed deciduous: 5 (4–10) cm; and birch-dominated forest: 4 (3.7–4.1) cm. Fire occurrence
also strongly influenced SOL depth. In black and white spruce stands, fire
combusted 9 (6–12) cm on average. In contrast, almost no SOL was
combusted in deciduous stands. The memory footprint of the permafrost and
SOL module was approximately 15 MB (∼ 0.1 % of total memory
footprint), and it increased overall runtime by 1 %.
(a) Observed and simulated surface organic layer carbon as
a function of dominant forest type. Bars and whiskers show mean SOL carbon
±1 standard error due to limited availability of raw observational
data. Simulated SOL carbon is from simulation year 300 in a 61 000 ha
forested landscape in interior Alaska. Observations are from field sampling
in other boreal forest stands. (b) Simulated dead moss and tree litter depth
and live moss depth are from simulation year 300 in a 61 000 ha
forested landscape in interior Alaska. Together, these two variables
comprise the total surface organic layer in iLand.
Landscape-level tree species composition and forest structure
Between simulation year 0 and 300, forest cover increased from 48 811 to
60 629 ha, as trees colonized areas initialized as potential forest. The
model was initialized with black spruce forest comprising 41 % of the land
area, followed by white spruce (22 %), aspen (7 %), birch (6 %), and
mixed forest (5 %) (Fig. 7a). By year 300, the land area dominated by
black spruce remained high at 40 % (Fig. 7b). However, white-spruce-dominated forest area declined markedly to 2 % because black spruce
trees colonized white spruce stands, as is commonly found in interior Alaska
(Van Cleve and Viereck, 1981; Burns and
Honkala, 1990). At the end of the simulation, mixed spruce stands comprised
42 % of land area. Aspen and birch also intermixed by year 300, with
mixed deciduous forest covering 11 % of the landscape. The land area
dominated by aspen in year 300 declined to 2 %, and birch-dominated forest
declined to 3 % of the landscape. Rerunning simulations with the
permafrost and SOL module turned off led to markedly different tree species
composition compared to initial conditions and after 300 years of simulation
where permafrost and SOL were dynamically represented (Fig. 8).
(a) Tree species composition in a 61 000 ha forested
landscape of interior Alaska used to initialize iLand. (b) Changes in tree
species dominance over 300 years of simulation. Pima (Picea mariana): black spruce;
Pigl (Picea glauca): white spruce; Potr (Populus tremuloides): trembling aspen; Bene (Betula neoalaskana): Alaskan
birch.
Stand density and basal area in the model corresponded well with multiple
field observation datasets in year 300 (Fig. 9). Aspen and birch stands were
most dense, followed by black-spruce- and white-spruce-dominated stands.
Deciduous-dominated stands also had the greatest basal area, followed by
white spruce and black spruce stands (Fig. 9b).
(a) Initial tree species composition, (b) tree species
composition after 300 years when permafrost and SOL were simulated, and (c) tree species composition after 300 years when permafrost and SOL were not
simulated in a 61 000 ha forested landscape of interior Alaska. Pima (Picea mariana):
black spruce; Pigl (Picea glauca): white spruce; Potr (Populus tremuloides): trembling aspen; Bene
(Betula neoalaskana): Alaskan birch. POLE-FM stands for Permafrost and Organic LayEr module
for Forest Models.
Simulated and observed stand density and basal area
broken out by dominant forest type in a 61 000 ha forested landscape of
interior Alaska. Model output is from simulation year 300. Observations are
from field sampling in other boreal forest stands (see main text for
sources). Pima (Picea mariana): black spruce; Pigl (Picea glauca): white spruce; Potr (Populus tremuloides):
trembling aspen; Bene (Betula neoalaskana): Alaskan birch.
Simulated aboveground live woody biomass across the landscape was within
28 % of the observed average. Aboveground live woody biomass in iLand was
51 931 (20 456–68 200) kg ha-1, on average, and observed biomass was
39 277 (9219–56 246) kg ha-1. Average simulated standing snag
carbon differed from the observed average by 41 % (Fig. B3a). Simulated
downed coarse-wood C varied markedly by dominant forest type and
corresponded closely to field observations (Fig. B3b).
Simulated tree seedling density 2 years after fires closely matched field
observations and varied with depth of postfire SOL (Fig. 10). Birch and
aspen seedlings were most abundant where SOLs were shallow (0–5 cm), with 8.9
(6.1–13.1) and 6.2 (4.9–8.0) seedlings m-2 being established. Black
spruce seedlings were the next most abundant, at 3.6 (0.3–4.8) seedlings m-2, followed by white spruce with 0.3 (0.2–0.4) seedlings m-2.
Where SOLs were thicker (15–20 cm), black spruce density averaged 2.1 (1.3–3.1) seedlings m-2, and aspen, white spruce, and birch were rarely
established.
Simulated and observed tree seedling density 2 years
postfire as a function of surface organic layer depth. Model output is from
recently burned areas in simulation years 261–300 in a 61 000 ha forested
landscape in interior Alaska. Observations are from field sampling in other
boreal forest stands (see main text for sources). Pima (Picea mariana: black spruce;
Pigl (Picea glauca): white spruce; Potr (Populus tremuloides): trembling aspen; Bene (Betula neoalaskana): Alaskan
birch.
Discussion
Ecological legacies will determine how forests are affected by climate
change and increasingly prevalent disturbances, like fire
(Turetsky et al., 2016;
Kannenberg et al., 2020; Hansen et al., 2022a). However, some legacies
uniquely important to the structure and functioning of boreal forests (e.g.,
permafrost and SOLs) are rarely considered in models used to project
21st-century ecological change. Here, we present a new permafrost and
SOL module that operates at fine temporal (daily) and spatial (1 ha) scales
and is computationally efficient. The module simulates daily changes in
active layer depth, moss production, and annual SOL accumulation (Fig. 1).
When coupled to a forest model, it also represents the complex ecological
effects of permafrost and SOLs on boreal forests and fire. With some
exceptions discussed below, benchmarking results demonstrate that the model
recreates temporal and spatial patterns consistent with observations at
stand to landscape scales over days to centuries. Our model will contribute
to improving 21st-century projections of boreal forest change.
Process-based simulation models are powerful tools for assessing how forests
will change
(Seidl, 2017; Albrich et al., 2020; Fisher and Koven, 2020). Forests often respond slowly
to stressors relative to other ecological systems
(Hughes
et al., 2013; Turner et al., 2022). As a result, models must capture dynamic
feedbacks among variables and represent the key legacies that accumulate
over decades to centuries in order to project future trajectories of forests
(Johnstone et al., 2016). Our objective was to mechanistically
represent permafrost and SOLs and capture effects of daily variability in
weather as well as the feedbacks that arise among forest dynamics, fires,
and permafrost in topographically complex landscapes. The model was skilled
at capturing inter-annual variability in maximum thaw depth, but it generally
occurred later in simulations than in observations. There are a number of
potential reasons for this. First, the model does not track the moisture
content of the SOL separately from the mineral soil layer. In reality, the
low bulk density of SOL relative to mineral soils leads to more variable
moisture content and thus a greater range of thermal conductivities, which
could lead to slower thawing in simulations if simulated SOL moisture was
lower during the spring thaw
(Fisher et al., 2016). Another
potential explanation is that forest structure (density and leaf area index)
and tree species composition have been shown to strongly modulate
microclimate and permafrost thaw in complex ways
(Stuenzi et al., 2021). Effects of
forests on microclimate are also not yet included in the model. Finally,
snow depth and melt play a critical role in active layer dynamics. Our
model reasonably recreated snow accumulation patterns for most years but
overestimated depth in years when snowpack was unusually deep. This is
likely because iLand takes a relatively simple approach to simulating snow
derived from Running and Coughlan (1988),
which is not particularly mechanistic. For example, we used a single
snow-density parameter value, which ignores compaction. In reality, snow
density varies tremendously across landscapes and over time. In the future,
our approach would benefit from separately tracking moisture content of the
SOL and from a representation of forest structure and composition effects on
microclimate. Further, a more advanced snow model could be added that includes
key processes affecting snow depth and conductive properties, including the
representation of variation in snow density, freeze–thaw cycles, and
sublimation (Bormann et
al., 2013; Jafarov et al., 2014).
At landscape scales, the model generally captured mosaics of near-surface
permafrost, moss production, and SOL accumulation in a large forested area
(∼ 61 000 ha of land area), but it did overestimate the area
underlain by permafrost on north-facing slopes. This may have occurred for
two reasons. First, the climate data used to force iLand were statistically
downscaled to a 1 km resolution from a global general circulation model and
thus do not perfectly capture variability in climate as a function of
fine-scale variation in aspect and topography. Further downscaling using
lapse rates might help improve simulations. Further, dominant forest type
varies strongly with aspect in interior Alaska and in turn shapes SOL
thickness and permafrost distributions. However, we lack landscape-level
maps of individual tree species distributions to initialize the model. In
the future, well-validated, remotely sensed tree species composition maps
would markedly reduce initial condition uncertainty in forest simulations in
interior Alaska
(Hermosilla et al.,
2022) and could improve landscape level simulations of permafrost
distribution.
The module was designed to represent permafrost and SOL effects on forest
dynamics and fire. In particular, it determines the water available to
plants and accumulation of forest floor biomass, which serves as fuels for
fire and influences postfire tree regeneration. When coupled with iLand, the
model reproduced common secondary successional trajectories found in
interior Alaska, including self-replacement and disturbance-induced abrupt
transitions in forest types (Johnstone et al., 2010a,
2016). For example, when thick SOLs remained after fire in black spruce
stands, self-replacement was common, leading to recovery of forests
functionally and structurally similar to the prefire stands
(Anderson et al., 2003; Johnstone and Kasischke,
2005). In contrast, when fires combusted most of the SOL in black spruce
stands, abrupt transitions from spruce- to deciduous-dominated forest
(mixtures of aspen and birch) occurred, consistent with regional trends
documented in the last few decades (Johnstone
et al., 2010a, 2020).
Conclusions
The boreal forest biome is warming at least 2 times faster than the global
average (IPCC, 2021), causing climate-sensitive disturbances, like
fire, to increase in frequency and severity
(Seidl
et al., 2020; Walker et al., 2020). Our permafrost and SOL module will help
process-based modelers produce more accurate projections of how forests in
the biome are likely to change over the next century. Better projections
will resolve a number of important uncertainties, including (1) where
increased burning due to climate change may reduce boreal fuel loads such
that fire self-limitation emerges
(Héon et al., 2014; Buma et al.,
2022); (2) when shifts in postfire successional trajectories will initiate
biophysical feedbacks that further alter regional climate; and (3) how
climate change, fire, and permafrost thaw will interact to reshape boreal
carbon cycling
(Schurr et al.,
2018; Schuur and Mack, 2018; Mack et al., 2021). Because boreal forests have
disproportionate impacts on the climate system through biogeochemical and
biophysical pathways, such information is essential to inform innovative and
effective global climate mitigation and adaptation strategies.
Carbon cycling in iLand
Carbon in live foliage, branch, stem, and root
compartments and in standing snag, forest floor litter, downed coarse wood,
and mineral soil organic material pools is dynamically modeled by iLand (Seidl et al., 2012b).
Primary production is simulated with a radiation use efficiency approach.
Carbon fixed by trees is then allocated to different tree compartments based
on allometric equations, representing functional balance. Influxes of carbon
from live compartments to dead-organic-matter pools are calculated based on
leaf turnover rates, tree mortality, and snag dynamics. Snag fall occurs
over time based on a species-specific half-life. When snags fall, they are
added to the downed coarse-wood pool. Decomposition of dead-organic-matter
pools is represented with a pool- and species-specific optimal decomposition
rate (10 ∘C, no water limitation) that is then modified by
prevailing temperature and precipitation.
Forest fire in iLand
The model also includes robust representations of several natural
disturbances, including forest fire
(Seidl et al., 2014a, b;
Hansen et al., 2020). Fire occurrence and spread are dynamically simulated
at a 20 m resolution as a function of 20th-century fire probability and
size distributions; landscape topography; model-generated wind speed and
direction; and the proportion of total downed litter and coarse-wood pools
that are burnable, which is determined by fuel moisture (as quantified by
the Keetch–Byram drought index, KBDI). For every 20 m grid cell that burns,
the available fuels are assumed combusted. Percent crown kill of live trees
is estimated as a function of tree size, available fuel loads, and aridity.
For the portion of live tree canopies that are killed, we assumed 90 % of
foliage, 50 % of branch, and 30 % of the burned stem biomass are
combusted. Tree mortality from fire is simulated probabilistically based on
tree size; percent crown kill; and bark thickness, a model parameter that
varies by tree species. If a tree dies, the non-combusted foliage and
branches are added to the downed litter and coarse-wood pools. Portions of
killed tree stems that were not combusted enter the standing snag pool.
Species parameters for interior Alaskan boreal forest.
Pima: Picea mariana (black spruce);
Pigl: Picea glauca (white spruce);
Potr: Populus tremuloides (trembling
aspen); Bene: Betula neoalaskana
(Alaskan birch); dim: dimensionless; exp: expression; sdlings: seedlings. See Hansen et al. (2021) for
sources.
ParameterUnitPimaPiglPotrBeneTree growthSpecific leaf aream2 kg-12.773.971718.5Leaf turnoveryr-10.050.211Root turnoveryr-10.330.330.330.33Height to diameter low adim35.155.0548.5855.37Height to diameter low bdim-0.13-0.2-0.13-0.2Height to diameter high adim330.94357.5402.66577.5Height to diameter high bdim-0.39-0.37-0.36-0.5Wood densitykg m-3380330350480Form factordim[0,1]0.360.40.410.4Biomass allocationStem wood biomass a*0.11790.048440.064010.14796Stem wood biomass b*1.992.512.512.25Stem foliage biomass a*0.05540.025220.0120.012Stem foliage biomass b*1.452.041.451.45Root biomass a*0.027740.027740.0528130.02533Root biomass b*2.2892.2892.2042.417Branch biomass a*0.07380.0011940.000080.01187Branch biomass b*1.38273.047384.132.4MortalityProbability of survival to max age (intrinsic mortality)dim[0,1]0.10.10.10.01Stress-related mortalitydim1111AgingMax ageyears250550250225Max heightm15553530Aging adim0.50.50.50.5Aging bdim2.52.52.52.5Environmental responsesVapor pressure deficit responsedim-0.65-0.65-0.65-0.5Min temperature∘C-8-4-5-6Optimum temperature∘C13201715Nitrogen classdim[1,3]2111Phenologyint[0,2]0011Max canopy conductancem s-10.02120.02120.02070.0207Min soil water potentialMPa-1.5-3-3.5-2Light responsedim[1,5]4.5311Fine-root-to-foliage ratiodim[0,1]0.750.750.750.75
Continued.
ParameterUnitPimaPiglPotrBeneSeed production and dispersal Cone bearing ageyears15305090Seed year intervalyears124.54Non-seed year fractiondim[0,1]00.0030.020.02Seed massmg0.892.20.170.34Germination ratedim[0,1]0.47250.0290.0475*0.038*Fecunditysdlings m-250015185*85*Seed kernel am7110170170Seed kernel bm200600400400Seed kernel cdim[0,1]0.050.50.620.62EstablishmentMin temperature∘C-69-70-80-80Chill requirementdays20424044Min growing degree daysdegree days100130227227Max growing degree daysdegree days3060345944144122Growing degree days base temperature∘C3.02.73.53.7Growing degree days before bud burstdegree days123147189231Frost-free daysdays60608180Frost tolerancedim[0,1]0.90.90.90.9Sapling growthSapling growth adim0.030.0350.120.12Sapling growth bm20352525Max stress yearsyears5225Stress thresholddim[0,1]0.050.220.2*0.25*Height-to-diameter ratiodim8885170119Reineke's Rsaplings ha-140075250650Reference ratiodim[0,1]0.50.6370.80.55SerotinySerotiny formulaexp30,0.99,80,0.99n/an/an/aSerotiny fecunditydim30n/an/an/aCrown parameters for light influence patterns Crown shape coefficientdim0.25930.283570.323260.33303Max crown radius am1.03021.232191.562691.64401Max crown radius bm2.40953.1414.3384.6325Relative crown heightdim[0,1]0.56450.6050.38150.5555
* Adjusted from Hansen et al. (2021) with addition of permafrost module. n/a – not applicable
ParameterPimaPiglBepaPotrLitter C:N ratioa737317.921Fine root C:N ratioa45454545Wood C:N ratioa425.6425.6336.6405.5Standing snag decomposition under optimal climateb0.20.20.20.2Snag half-lifeb25151515Litter decomposition under optimal climateb0.230.330.390.56Coarse-wood decomposition under optimal climateb0.060.020.150.15
a Alexander and Mack (2015). b This study.
Rules for converting plant functional type maps from Wang
et al. (2020) to
species-level maps for initializing iLand.
SpeciesRuleBlack spruce– PFT is spruce, and aspect is north. – PFT is spruce, aspect is flat, and permafrost is present. – PFT is woodland.White spruce– PFT is spruce, and aspect is not north. – PFT is spruce, aspect is flat, and permafrost is not present.Trembling aspen– PFT is deciduous, and aspect is south.Alaskan birch– PFT is deciduous, and aspect is not south.Mixed forest– PFT is mixed forest.Potential forest– PFT is low shrub, tall shrub, open shrub, herbaceous, or tussock tundra.
State variableUnitPimaPiglBepa & PotrMixed forestSourcesForest floor mossKg biomass ha-125 000–45 0005000–15 0001010 000–25 000Johnstone et al. (2020), Walker et al. (2014)Forest floor leaf litter, dead moss, and fine rootsKg C ha-148 682–96 90148 682–96 90117 371–31 76533 026.5–64 336Alexander and Mack (2015)Coarse downed wood coarse root CKg C ha-117 00017 00020 02018 500Alexander and Mack (2015)Organic C in mineralsoilKg C ha-135 00035 00035 00035 000Melvin et al. (2015)
Simulated percent of (a) stems killed and (b) basal area
killed by fire in a 61 000 ha forested landscape in interior Alaska. Model
output is from simulation years 201–300.
Simulated surface organic layer depth as a function of
dominant forest type in a 61 000 ha forested landscape in interior Alaska.
Model output is from simulation year 300.
Observed and simulated (a) standing snag carbon and (b) downed coarse-wood carbon as a function of dominant forest type. Bars and
whiskers show means ±1 standard deviation in plot (a) and means ±1 standard error due to the limited availability of the raw field
observations. Modeled carbon stocks are from simulation year 300 in a 61 000
ha forested landscape in interior Alaska. Observations are from field
sampling in other boreal forest stands.
Code and data availability
The source code is available as a Supplement to this paper. The model
executable and source code, project directories, and analysis R scripts used
in this project are also available at the Cary Institute of Ecosystem
Studies data repository (DOI:
10.25390/caryinstitute.21339090; Hansen et al., 2022). A technical description of
the permafrost and SOL module is available at
https://iland-model.org/permafrost (last access: 1 October 2022).
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-16-2011-2023-supplement.
Author contributions
WR and WDH developed the permafrost and SOL module, and WDH conducted
benchmarking simulations, analyzed outputs, and wrote the paper. All
co-authors contributed to the paper.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We are grateful to Brendan Rogers, Scott Goetz, Michelle Mack, and Xanthe
Walker, who provided feedback on an earlier draft of this paper. Winslow D. Hansen
acknowledges support from the National Science Foundation (grant no. OPP
2116863) and the Royal Bank of Canada. Rupert Seidl and Werner Rammer acknowledge funding from
the European Research Council under the European Union's
Horizon 2020 research and innovation program (grant agreement 101001905).
Benjamin Gaglioti acknowledges support from the Joint Fire Sciences Program (project
20-2-01-13).
Financial support
This research has been supported by the National Science Foundation (grant no. OPP 2116863), the European Research
Council under the European Union’s Horizon 2020 research and innovation
program (grant agreement 101001905), the Joint Fire Sciences Program (project 20-2-01-13), and the Royal Bank of Canada.
Review statement
This paper was edited by Marko Scholze and reviewed by Simone Maria Stuenzi and one anonymous referee.
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