Modeling Thermokarst Dynamics in Boreal and Arctic regions of Alaska
and Northwest Canada: A White Paper
The Thermokarst Working Group of the Integrated Ecosystem Model for Alaska and
Northwest Canada
Distributed on 28 February 2013
Introduction
One of the priority issues that we identified for the development of the Integrated
Ecosystem Model (IEM) for Alaska and Canada was the need for the IEM to consider
landscape-level thermokarst dynamics. Landscape-level thermokarst changes are
important to incorporate into the IEM because subsidence associated with the melting of
previously frozen water in ice-rich permafrost can result in substantial changes in
vegetation and habitat (e.g., turning an upland tundra ecosystem into a wetland tundra
ecosystem). The representation of thermokarst disturbance in the IEM is also important
for representing wetland dynamics in the IEM, which are important to model because
much of Alaska is covered by wetland complexes, and changes in wetland structure and
function has the potential to affect numerous animal species that use wetlands (e.g.,
waterfowl).
Currently, the models in the IEM primarily consider the effects of active layer
thickening in response to climate change, and do not consider subsidence associated with
the thawing of ice-rich permafrost. Such subsidence can cause substantial changes in
hydrology that cause more rapid permafrost thaw and further drive landscape changes
and associated changes in ecosystem processes. The degree of thermokarst subsidence
depends on ground ice distribution and content and thaw magnitude that are influenced
by soil characteristics, topography, and geomorphology (Osterkamp et al. 2000, Shur and
Jorgenson 2007).
While no one, to our knowledge, has yet developed a large-scale model of
landscape evolution associated with thermokarst disturbance, we believe that IEM team
has substantial experience with this issue from several field studies and is poised to
integrate this understanding into the IEM. Also, aircraft photos and high resolution
imagery can now be used to detect specific changes in the land/vegetation surface that
can be related to thawing of permafrost. This includes remote sensing of thermokarst
features (Jorgensen et al. 2008a) and of lake and wetland expansion and contraction
(Rover et al. 2011).
In this white paper, we provide a vision for the development of the Alaska
Thermokarst Model (ATM) that is being designed to simulate landscape evolution in
response to thermokarst disturbance as a result of climate change. This white paper is the
result of a synthesis of presentations and discussions in 2012 that were organized by the
thermokarst/wetland group of the IEM.
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Model Overview
The ATM is a state-and-transition model designed to simulate transitions from
non-thermokarst landscape units to thermokarst landscape units in boreal and arctic
ecosystems in Alaska and Northwest Canada. The model is designed to be integrated into
the IEM (see Figure 1), which includes coupled models of fire disturbance
(ALFRESCO), soil thermal dynamics (GIPL) and ecosystem structure/function (TEM).
Figure 1. The thermokarst model (ATM) is to be integrated into the Integrated Ecosystem
Model for Alaska and Northwest Canada by communicating information on thermokarst
type, initiation and expansion rate through the Terrestrial Ecosystem Model (TEM).
The ATM will use a frame-based methodology similar to that of ALFRESCO to
track transitions among non-thermokarst and thermokarst landscape units (cohorts)
within 1 km grid cells. A frame-based methodology is a particular way to implement a
state-and-transition model by defining frames to determine the fate of each type of
landscape unit. A cohort within a 1 km grid cell has a unique state that differs from other
cohorts within the grid cell. This state is described by a set of attributes that include the
type of landscape unit (e.g., bog or wetland tundra), the time since establishment (e.g., 30
year-old bog), and other features relevant to the fate of the landscape unit (e.g., lowcenter polygon) in the logic of the frame. Although the ATM will not track cohorts in a
spatially explicit fashion within 1 km grid cells, it does need to have initial information
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on the proportion of each cohort within the grid cell. See the section below on “Strategies
for Initialization” for a discussion of options for defining the initial distribution of cohorts
within each grid cell.
The operation of the ATM will be to evaluate the fates of cohorts within the
particular 1 km grid cell being considered by the IEM. If there are thermokarst cohorts
within a grid cell, then the ATM will cycle through each of the cohorts to determine their
individual fates. If there are no thermokarst cohorts within a grid cell, then the ATM will
evaluate whether the grid cell is predisposed to experience thermokarst disturbance.
Predisposing factors include topography, ground ice content, soil texture, and other
conditions that allow the ATM to make a binary decision about whether thermokarst
disturbance is a possibility. See the first section of Appendix 1 for an algorithm for
determining the predisposition of boreal forest to experience thermokarst disturbance. For
example, if only non-thermokarst boreal forest cohorts exist within a grid cell, the
predisposing conditions for the possibility of thermokarst might include whether or not
(1) the grid cell in a poor (lowland) drainage area, (2) permafrost is present, and (3) there
is a sufficient amount of ground ice for thermokarst to cause subsidence. Once the ATM
determines that cohorts are potentially subject to thermokarst disturbance, then the ATM
calls up a frame logic for the landscape unit type of the cohort. The frame logic will use
a logical rule set to calculate the probability that the cohort will remain as its current
landscape unit type (parent cohort) and the probability that it will transition to other
landscape unit types (new cohorts). Our current thinking is that the probability will be
used to determine the areas of these transitions so that the ATM is a deterministic model.
The logical rule set will consider factors such as climate, fire, land use, hydrology,
erosion, soil texture and ice content.
Landscape Unit Transition Sets
The definition of the number of frames in the ATM requires the identification of
transitions among landscape unit types in a transition set. In the ATM, there will be two
sets of landscape unit transitions for thermokarst disturbance in Alaska: (1) those
transitions that occur in boreal forest and (2) those transitions that occur in arctic/boreal
tundra. Below we describe each of these sets of transitions.
Boreal Forest Thermokarst Transition Set
The ATM will represent thermokarst-related transitions in the boreal forest that
can occur among lowland forest stands, thermokarst lakes, open fens, open bogs, and
treed bogs (Figure 2). Thermokarst can occur in forest stands that contain permafrost and
soil with moderate to high ice content. As a response to climate warming or fire,
permafrost may thaw to a point where the forest floor will collapse as a result of melting
ice, which will create a depression where water can potentially accumulate. With changes
in local hydrology, lowland forest stands (e.g., black spruce, white spruce, deciduous
forest) may then transition to thermokarst lakes, fens or bogs depending on the pre-thaw
ice content and the hydrological context. It is also possible for thermokarst lakes to
transition to open fens, for open fens to transition to open bogs, for open bogs to
transition to treed bogs, and for treed bogs to transition to lowland forest.
See Appendix 2 for the description of sample frames for the boreal forest
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thermokarst transition set. See Appendix 3 for some initial progress that we have made
in specifying the rules for the probability of thermokarst disturbance in the boreal forest
thermokarst transition set.
Figure 2. As permafrost thaws, lowland boreal forest stands may transition to
thermokarst lakes (TK lake), fens or bogs depending on pre-thaw ice content and the
hydrological context.
Arctic/Boreal Tundra Transition Set
In arctic/boreal tundra, the ATM will consider thermokarst-related transitions
among wetland tundra, graminoid tundra, shrub tundra, shallow thermokarst lakes, and
deep thermokarst lakes (Figure 3). The transitions among these landscape unit types are
tightly coupled with physical changes in soil temperature and moisture. Cold and wet
soils favor wetland tundra, cold and dry soils favor graminoid tundra, and warm and dry
soils favor shrub tundra. Wetland tundra is dominated by wetland sedges, grasses and
mosses, and is typically found on the coastal plain of Alaska. Graminoid tundra, which is
often synonymous with tussock tundra, is generally dominated by sedges and grasses, but
also contains forbs, mosses, lichens, and low-stature shrubs. Shrub tundra is generally
dominated by short- to tall-stature shrubs. Shallow thermokarst lakes and deep
thermokarst lakes occur in areas of poor drainage where water cannot drain off the
landscape.
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Figure 3. Potential transitions among vegetation/landscape states due to changes in
temperature, soil moisture conditions, and thermokarst disturbance. S, M, and R indicate
slow, moderate, and rapid transitions, respectively. Notation of the various components,
L, H and ?, indicate low, high, and unknown resiliency, respectively.
An important feature of the arctic/boreal tundra thermokarst transition set is that
the frames for wetland, graminoid, and shrub tundra will contain rules that represent the
dynamics of ice wedges in response to climate change or fire to drive landscape evolution
as thermal and moisture conditions change. Ice wedges are most obvious in wetland
tundra where, for example, low-centered polygons in wetland tundra are present where
the ice-wedges are actively forming and growing. Ice-wedge dynamics result in the
formation of micro-patterns of different vegetation and soil types, like wet low-center
polygon centers versus drier polygon rims that may support shrub growth. In the intrapolygonal ponds, high soil moisture conditions will result in shallow standing water.
Degradation of ice-rich permafrost and the melting of wedge ice will lead to surface
collapse and transformation of low center polygons to high center polygons (Meyer,
2003). High-centered polygons form as the polygon rims and troughs, which are
underlain by massive ice wedges and tend to degrade faster than the polygon center. This
results in subsidence and water accumulation in polygonal trough ponds that visually give
the effect of an elevated center of the polygon. High center polygons of wetland tundra
can transition to shrub tundra or to graminoid tundra. Alternatively, extensive
thermokarst of ice-rich permafrost soils can result in the formation of large ponds or lakes
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that form from coalescing smaller ponds and lateral expansion by shore thermal erosion.
If a lake drains, permafrost can be restored within the drained lake basin under
sufficiently cold climate conditions to cause transitions to wetland tundra. If climate
conditions are not cold enough, the drained lake basin will not refreeze.
See Appendix 4 for the description of sample frames for the arctic/boreal tundra
thermokarst transition set. See Appendix 5 for some initial progress that we have made in
specifying the rules for the probability of thermokarst disturbance in the arctic/boreal
tundra thermokarst transition set.
Issues in Coupling the ATM into the IEM
The ATM is designed to be a model that is implemented annually. In Figure 1 we
have depicted the ATM as primarily relying on inputs from and providing output to
TEM. The exchange of data between the models hasn’t been finalized, but our current
thoughts are that the ATM will require inputs on the landscape unit types of the cohorts
within a grid cell, the presence/absence of permafrost, soil temperature, soil moisture,
thaw depth, and organic layer thickness. After the implementation of the ATM for one
annual time step, it would provide TEM with information on the landscape unit types of
each cohort, the relative area of each cohort, and the other attributes of each cohort (e.g.,
age since subsidence). Because the ATM has the ability to create new cohorts from
parent cohorts through landscape unit transitions, TEM would then decide on the fate of
carbon and nitrogen pools based on the transitions being implemented in new cohorts.
This is very similar to how TEM currently handles fire disturbance.
Thus, one of the major changes to the IEM will be to track landscape unit types as
cohorts within 1 km grid cells. The ALFRESCO modeling group will need to think about
how this affects the operation of ALFRESCO, as ALFRESCO currently assumes one
landscape unit type per grid cell. Also, there may be issues that emerge in reconciling
thermokarst-related transitions considered by the ATM with fire- and successionalrelated transitions currently considered by ALFRESCO. It may be that TEM will be the
nexus for reconciling these transitions. At this point in time, our intention is to point out
the possible need to resolve these issues. As a group the IEM team should wait until we
have a mature working version of the ATM before attempting to address the details of
resolving such issues. However, if there are thoughts that affect the development of the
ATM, it would be good to be aware of these in the near future.
Strategies for Initialization of Landscape Units and their Attributes
The current initial vegetation of the IEM describes vegetation types as completely
occupying 1 km grid cells (Figure 4). Because the ATM needs to represent landscape
units (cohorts) within 1km grid cells, there will be the need to represent the proportion of
each of the landscape unit types considered by the ATM along with their attributes (e.g.,
age since thermokarst disturbance, polygon type, etc.) for each 1 km grid cell. One way
to proceed may be to gather as much information on possible cohorts within grid cells by
mining that information from finer resolution land covers. For example, the National
Land Cover Database (NLCD) Alaska is a 30-m resolution data set (Figure 5) that may
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be useful in defining the proportion of 1-km grid cells covered by various landscape unit
types considered by the ATM. For example, open water in the NLCD Alaska might be
able to be related to shallow thermokarst lakes, deep thermokarst lakes, and nonthermokarst lakes; herbaceous wetlands to fens and bogs; and woody wetlands to treed
bogs. There are several tundra vegetation types in the NLCD Alaska that could be used to
define the proportion of wetland, graminoid, and shrub tundra within 1 km grid cells.
Datasets such as NLCD Alaska will not provide all of the information necessary to define
cohorts. For example, some of the issues that need to be resolved include (1) how to
divide boreal wetlands among open fens and open bogs, (2) how to assign attributes for
polygon type (low center, transitional, and high center) to different tundra cohorts, and
(3) how to assign the age since thermokarst disturbance attribute to cohorts. Also, the
USGS National Hydrography Dataset (NHD) for Alaska may be useful for distinguishing
between shallow, deep, and non-thermokarst lakes throughout Alaska.
See Appendix 6 for the results of some research we have conducted on a possible
strategy to divide boreal wetlands into open fens and open bogs based on their
distribution in the Tanana Flats.
Figure 4. The initial land cover input data for the Integrated Ecosystem Model for Alaska
and Northwest Canada. Vegetation types are described at 1 km resolution.
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Figure 5. The National Land Cover Database (NLCD) Alaska is based on Landsat land
cover classification and has 30 m resolution.
Other Spatial Data Sets Required for the Operation of the ATM
Besides the development of the data set that defines the initial cohorts for the
application of the ATM, there are several other data sets that will be required for the
operation of the ATM and may require development. Below is a list of data sets that we
have currently identified as being required by the ATM:
(1) Ground ice maps. One option for developing these maps is from Jorgenson et al
(2008b) NICOP Alaska Permafrost map (distribution and generalized ice content
based on current presence of shallow or deep thermokarst forms).
(2) Surface geology and soil texture. Again, one option is to develop these data sets
from Jorgenson et al. (2008b) NICOP Alaska Permafrost map.
(3) Alaska digital elevation model at 60m resolution; and/or 300m Alaska DEM;
elevation derivatives (slope).
Besides these data sets, we also anticipate that our research over the next year
may define other spatial data sets that may be required for the operation of the frames.
For example, in the arctic/boreal tundra transition set, these may include spatial data sets
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of a distributed drainage efficiency parameter, a distributed ice content/form parameter, a
distributed rate of subsidence, and a distributed protective layer parameter (See Appendix
5). We currently don’t know the specific of what would be required for such spatial data
sets. Our purpose here is to let the IEM data group know that we will be defining the
need for additional spatial data set development as we make progress in model
development.
Strategy for Model Development, Testing, and Application
The 2013 objectives of the thermokarst group of the IEM team are to develop the
code for the ATM and to test the code for its operation at landscape scales. The 2014
objectives are to incorporate the ATM into the IEM and to conduct a proof of concept
study. The 2015 objectives are to apply the version of the IEM with the ATM across the
IEM domain. The 2016 objectives are to develop and apply an impact model that would
make substantial use of the ATM-related outputs of the IEM.
In 2013 we will have one postdoctoral scientist working on development of the
code for the boreal forest thermokarst transition set and another postdoctoral scientist
working on the code for the arctic/boreal tundra thermokarst transition set. The
thermokarst/wetland group will work with the postdoctoral scientists during 2013 to
refine the logic of each of the frames in the two thermokarst transition sets. Much of this
interaction will happen at the monthly meetings of the thermokarst/wetland group. The
Tanana Flats will be the test region for evaluating the dynamics of the boreal forest
thermokarst transition set so that we can take advantage of studies on thermokarst
dynamics in that region (e.g., Jorgensen et al. 1999). Coastal tundra near Barrow will be
the test region for evaluating the dynamics of the tundra thermokarst transition set so that
we can take advantage of studies on thermokarst dynamics in that region (e.g., ongoing
studies that are part of the NGEE-Arctic DOE effort).
Much of the evaluation of the dynamics of the ATM will be to compare the
simulated expansion rates of thermokarst landscape units to what has been observed
(Jorgenson et al. 2001, Jorgenson et al. 2005, Jorgenson et al. 2008a, Grosse et al. in
preparation). After evaluating the operation of the ATM in these two regions, near the
end of 2013 we anticipate applying the ATM as a stand-alone model in the two regions
for climate scenarios to go extend to 2100. This will be an activity that not only
demonstrates the capabilities of the ATM, but will be useful for defining the spatial data
sets that will be required to operate the ATM across the IEM domain. This exercise will
provide important information for the model coupling and the data groups of the IEM to
support the incorporation of the ATM into the IEM during 2014, the proof-of-concept
application of the coupled model in 2014, and the application of the coupled model
across the IEM domain in 2015.
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Appendix 1. An Analysis of the Predisposition of Boreal Forest Grid Cells for
Thermokarst Disturbance.
In the boreal forest, predisposing factors may include topography, ground ice
content, soil texture, and other conditions that allow the ATM to make a binary decision
about whether thermokarst disturbance is a possibility.
The key topographical issue is to separate lowlands from uplands. We developed
the criteria to differentiate lowlands and uplands based on a spatial analysis of the
distribution map of fens and bogs for the Tanana Flats in Interior Alaska (Jorgenson et al.
1999). The analysis was conducted at 1km resolution, the resolution of operation of the
ATM. The topographic parameters used in the analysis were computed from the USGS
Digital Elevation Model (DEM) (Earth Resources Observation and Science Center,
http://eros.usgs.gov). Various parameters were tested: slope, log-transformed flow
accumulation (FA), the compound topographic index (CTI), and the standard deviation of
the log-transformed elevation (ESD; estimated from the eight direct neighbors of each
1*1km pixel). FA is derived from a cumulative count of the number of pixels that
naturally drain into outlets. This calculation uses the flow direction, and the largest
outlets have the largest values. The CTI is a function of slope and FA (Moore et al.
1993). A classification tree analysis was conducted to define the topographic conditions
in which lowlands and uplands occur. In the Tanana Flats, the lowlands were distributed
in (1) flat areas where log(ESD) was < 1.189 and (2) wet areas where CTI > 467. This
model was able to predict 96.7% of the lowlands in the Tanana Flats.
The GIPL model will simulate whether or not permafrost is present in the
landscape unit. A ground ice map possibly based on the map of ground ice content
associated with superficial deposits for Alaska (Jorgenson et al. 2008b) will be used to
assess ice content. Thermokarst is most likely to occur in silty soils and least likely to
occur in sandy soils. Thus, a soil texture map will be used to determine if a grid cell is
predisposed to experience thermokarst.
The logic for integrating the various factors might go something like this:
1. Calculate CTI and ESD to determine whether the grid cell is located in lowland or
upland. If CIT>467 and ESD<1.179, then the grid cell is located in lowland and
go to step 2, otherwise, move onto the next grid cell.
2. Check the soil ice content to determine if there is enough ground ice content for
thermokarst disturbance. We will use the criteria of ice content less than 10%
volume (Jorgenson et al. 2008b) as an indication that conditions are not conducive
to thermokarst. In this case, we move onto the next grid cell. Otherwise, we move
onto step 3.
3. If GIPL indicates that permafrost is present and that the mineral soil is not sandy,
then we cycle through the cohorts of the grid cell, calling up the appropriate frame
logic for each cohort.
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Appendix 2. A Sample Frame for the Boreal Forest Thermokarst Transition Set.
The frame diagram for forest stands in the boreal forest is depicted in Figure A21,
in which there are transitions from forest stands (black spruce, white spruce, or deciduous
forest) to thermokarst lakes, bogs, and fens.
Upwelling
Moderately
ice rich
Warming, Fire
Plateau
Forest
Stands
Warming, Fire
Highly ice rich
Fen
Bog
TK lake
Warming, Fire
Figure A21. The frame diagram for thermokarst-related transitions from forest stands in
the boreal forest to thermokarst lakes, bogs, and fens.
The rules for the initiation of fens, bogs, and thermokarst lakes are outlined below
and diagramed in Figure A22 and A23.
1. Are there fens, bogs, or thermokarst cohorts in the grid cell? ” If yes, then go to
the rules for expansion of existing fens, bogs, and thermokarst lakes. Otherwise
go to the rules for initiation of fens, bogs, and thermokarst lakes.
2. For the new initiation logic, if the probability of thermokarst is greater than the
threshold probability A (see Appendix 3), then a new thermokarst is initiated and
go to step 3.
3. If the soil ice content >40% volume, then create a thermokarst lake. Otherwise, go
to step 4.
4. The logic for deciding if the thermokarst initiated in a forest stand will transition
to a fen or a bog depicted in the decision tree of Figure A22.
Figure A22. The decision tree for deciding the transition of thermokarst that initiates in a
forest stand to a fen, bog, or a co-occurring fen/bog (see Appendix 6 for more details).
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Expansion or
transition of fen,
bog or TK lake
Yes
Boreal
forest
Start
Any bog,
fen or TK
lake area?
No
A new initiation
of thermokarst
No
Initiation
probability
P >A?
Yes
Ice
content>40
%?
Yes
TK lake
No
Yes
Fen
Log(ESD)
No
Log(FA)
>=1.667?
>=0.139?
Bog
Yes
No
Yes
Fen/Bog
Log(ESD)
>=0.284?
Log(FA)
>=3.583?
No
No
No
Yes
Log(ESD)
Fen
>=0.139?
Yes
Fen
Figure A23. A depiction of the frame logic for thermokarst-related transitions from forest
stands in the boreal forest to thermokarst lakes, bogs, and fens.
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Appendix 3. A Possible Strategy for Estimating the Probability of Thermokarst in
Boreal Forest Landscape Units.
The probability distribution for estimating the probability of thermokarst in boreal
forest landscape units was estimated from active layer thickness measurements (Figure
A31) between 1994 and 1998 at 240 sites distributed in various vegetation communities
of the Tanana Flats (Jorgenson et al. 1999).
Figure A31. Mean annual active layer thickness for the different vegetation communities
inventoried in the Tanana Flats. Thermokarst fens are the red bars; thermokarst bogs are
the green bars. The horizontal lines represent standard deviation.
Based on the data in Figure A31, Michaelis-Menten and Logistic models can be
developed to estimate the probability function (P) of thermokarst (fen or bog) initiation
(Figure A32).
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Figure A32. Candidate probability of subsidence function (Michaelis Menten left and
Logistic right) developed from the data in Figure A31.
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Appendix 4. Some Frames for the Arctic/Boreal Tundra Thermokarst Transition
Set.
Below, we provide descriptions of the frames for each of the tundra vegetation
types considered by the tundra thermokarst transition set: wetland tundra (Figure A41),
graminoid tundra (Figure A42), and shrub tundra (Figure A43). In each of the frame
figures, the parent tundra type is displayed in the top center.
Wetland Tundra
By the physical nature of wetland tundra environments, we assume that only
moderate- and high-ice contents of the soils can exist (low ice content is not possible). If
the drainage efficiency threshold is not exceeded, thermokarst will result in either
shallow or deep ponds/lakes, depending upon the ice-content. If the drainage efficiency
threshold is exceeded, the landscape is assumed to be dominated by ice-wedge polygons.
Under the right conditions, low-center polygons will degrade to high-center polygons.
The thermal and moisture conditions that result in high-center polygon centers will be
favorable to graminoid tundra and the surrounding polygon trough edges and troughs
may be favorable to shrub tundra. Troughs may also become water filled in flat terrain
and ultimately connect and expand to larger ponds/lakes. As mentioned above, changes
in vegetation composition will only occur under favorable climatic conditions.
Figure A41. The wetland tundra frame for transitions involving thermokarst disturbance.
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Graminoid Tundra
Simulating the thermokarst process and its associated vegetation trajectories that
begin in the graminoid tundra environment will be complicated. Thermokarst in
graminoid tundra can lead to each of the major landscape unit types (graminoid tundra,
shrub tundra, wetland tundra, ponds/lakes), dependent upon the moisture conditions
(drainage efficiency) and ice-content. There are three different potential pathways for
graminoid tundra.
1. The first pathway is the direct and rapid change due to wildfire. In this scenario,
changes in vegetation composition will be handled by the ALFRESCO based on
the research of the Tundra Fire and Treeline Dynamics Working group of the IEM
team.
2. The second pathway occurs if the drainage efficiency threshold is exceeded. In
this scenario, it is again assumed that ice-wedge polygons dominate landscape. If
the active layer depth exceeds the protective layer over time, low-centered
polygons will eventually degrade to high-center polygons. As opposed to the
wetland high-centered polygons, we believe that the thermal and moisture
conditions will be more conducive for shrubs in both the high-center polygon
centers as well as the troughs.
3. The third and final pathway occurs if the drainage efficiency is not exceeded. In
this scenario, the resulting thermokarst form is dependent upon the underlying
ice-content and form. If a low- to moderate-ice content exists, the landscape will
gradually subside, eventually leading back to a wetland tundra environment. In
the case of high- to massive ice-content, shallow thermokarst ponds/lakes will
appear. Shallow ponds/lakes may continue to degrade into deep ponds/lakes or
they may drain. If they drain, they will eventually return to wetland tundra
vegetation type.
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Figure A42. The graminoid tundra frame for transitions involving thermokarst
disturbance.
Shrub Tundra
Thermokarst in areas with shrub tundra can eventually lead to graminoid tundra
or shrub tundra with high center polygons. This is due to the assumption that shrub
tundra is largely present in well-drained (dry) and relatively warm soils. As shown in
Figure A43 below, there are two main pathways in which shrub tundra can shift to
another vegetation type. The first is due the rapid process of wildfire. Based on the work
of the Tundra Fire and Tree Dynamics working group of the IEM team, ALFRESCO will
simulate that process and resulting vegetation composition. The second pathway occurs
in moderate- to high- ice-content soils and in areas that do not exceed the drainage
efficiency threshold. If all the thresholds are met, thermokarst in this region is again
assumed to occur in areas where ice-wedge polygons dominate. Thermokarst in this
region will result in degradation of the ice-wedges that outline the polygon features,
eventually leading to high-centered polygons. If these high-centered polygons continue
to degrade, shallow ponds/lakes would result.
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Figure A43. The shrub tundra frame for transitions involving thermokarst disturbance.
Thermokarst Lake Dynamics
Thermokarst ponds and lakes in the model will be tracked as cohorts. In Alaska,
typical lake area coverage (limnicity) for permafrost regions range from 0 to about 40%.
In addition to existing thermokarst lakes (based on the NHD lake dataset), the above
thermokarst frames will project where new ponds/lakes are forming. Starting with a
shallow thermokarst pond that expands in area, the trajectory may include 1) drainage, 2)
drying, 3) terrestrialization, 4), stabilization, or 5) growth to a deep lake. Growing into a
deep lake may then later result in 1) drainage, or 2) stabilization. Terrestrialization
(slow/gradual process) or lake drainage (instant process) results in a thermokarst basin
with fen vegetation in the boreal forest and arctic tundra region, and then slowly
transitions to bog in boreal region and wetland tundra in the arctic.
Similar to the active layer and the protective layer, several layers need to be
considered for thermokarst lake dynamics. Once initiated, a lake bottom mean annual
temperature of >0degC results in further thaw and subsidence given that the active layer
(or more precisely, the unfrozen zone under a lake) exceeds the protective layer thickness
and that the layer with excess ice content is still not fully exhausted. A lake depth layer
(“lake thickness”) will track water depth, which is relevant for the mean annual bottom
temperature.
1) Drainage: In thick continuous permafrost lakes drain when reaching a lateral
18
2)
3)
4)
5)
drainage gradient by their gradual shore expansion via thermo-erosion; in the
discontinuous permafrost zone, lakes may drain internally when their talik
breaches the bottom of the permafrost layer.
Drying: depending on P-E, shallow lakes may dry in highly continental regions of
Interior Alaska or the semiarid regions of the Northslope.
Terrestrialization: Shallow lakes may be encroached by highly productive shore
fen vegetation; this process seems currently limited to the boreal forest region and
the western Alaska tundra regions (Seward Peninsula, Yukon-Kuskokwim Delta)
Stabilization: Shallow lakes that either fully penetrate the ice-rich layer or
substantially increase the protective layer may become stabilized landscape
features and only slowly expand laterally
Growth to a deep lake: If the ice-rich layer is sufficiently ice-rich and deep and
other environmental conditions do not prevent growth, a thermokarst lake may
deepen to >10 m depth;
19
Appendix 5. A Possible Strategy for Estimating the Probability of Thermokarst in
the Arctic/Boreal Tundra.
Thermokarst can occur in areas where the active layer increases beyond average
maximum depths and taps into ice-rich soils. The thawing of permafrost can result in
thermokarst pits, the transition from low-center to high-center polygons, and shallow or
deep ponds/lakes formation. The trajectory and form of topographic changes, and
resulting vegetation depends on the 1) the “drainage efficiency” (Figure A51); 2) the
distance between the soil surface and ice-rich soils (the “protective layer” thickness) (Fig
A52); and 3) the amount and form of ice in the permafrost soils. The parameterization of
these processes – specifically, the drainage efficiency and the rate of subsidence and
subsequent changes in the protective layer thickness – is the most difficult task in the
model development process for arctic/boreal tundra environments.
The “drainage efficiency” parameter describes the ability of the landscape (or grid
cell) to store water. The drainage efficiency is a function of topography, climate, active
layer depth, soil texture, and the degree to which the landscape is hydrologically
connected (Figure A51). The drainage efficiency accounts for vertical (climate, soil
storage) and lateral (surface and subsurface runoff governed by topography and soil
properties and hydrologic connectivity) processes of water movement over time. The
drainage efficiency (and whether or not the threshold has been crossed) is important as it
sets the direction of both thermokarst form and pathway changes in vegetation
composition due to a changing landscape. The mathematical derivation of this parameter
still needs to be developed.
Figure A51. The dependence of the drainage efficiency parameter on topography,
climate, soil storage, and lateral flow.
20
The “protective layer” is defined to be the distance between the land surface and
ice-rich soils or the top of massive ice bodies (such as an ice wedge or buried glacial ice)
(Figure A52).
Figure A52. The relationship among the active layer, the protective layer (the lines with
the arrows above the ice wedge), and the top of massive ice bodies in the soil.
Physically, the protective layer serves as a buffer between the processes that occur
at the land surface and the underlying soils that are susceptible to thermokarst,
specifically ice-rich soils. If the seasonal thaw of the soil does not penetrate the protective
layer completely, the thermokarst process will not take place. However, if the seasonal
thaw of soil does penetrate the protective layer and melts ice, the soil structure/column
will be altered. As ground-ice melts and the melt water flows away, the land surface will
gradually subside. In case of melting pure ice (e.g. ice wedge) the surface subsidence
may be as large as the thickness of melted ice layer and the protective layer thickness will
not increase immediately. However, if the subsidence is triggered by thawing of a icerich soil, the subsidence will be less than the thickness of the thawed ice-rich layer by the
amount of mineral or organic soil content in this thawed layer resulting in an increased
protective layer thickness. Moreover, in both cases the deposition of mineral and/or
organic material in the developing depressions may farther increase the thickness of the
protective layer.
However, the subsidence may result in hydrological rerouting or collection of
precipitation and lead to ponding which strongly enhances heat transfer into the ground
and deepens thaw penetration. Bottom temperatures of shallow water bodies have been
shown to be several degrees (sometimes exceeding 10 oC) above that of surrounding
tundra. The heat transfer from a water body into underlying permafrost increases with
water volume and substantially accelerates when reaching a water depth exceeding the
threshold of bottom fast ice, when the water body will have liquid water year-round and
21
exercise its thaw impact on underlying permafrost year-round. A lake talik will develop
at speeds depending on water, sediment, and permafrost temperatures (Ling et al., 2004;
Plug & West, 2009; West & Plug 2008).
The degree (or amount) of increased protective layer also depends on sediment
transfer processes (high energy, hydrologically well-connected environments will have a
relatively small amount of protective layer increase compared to low energy,
hydrologically disconnected environment (a pond or lake)). The increasing thickness of
the protective layer serves as a negative feedback, and potentially a stabilizing factor, to
the thermokarst process; however, the increase in water depth often more than offsets the
growth of the protective water. As the protective layer increases, additional 'heat' energy
is required to penetrate the protective layer. The balance between the annual heat energy
supplied and the thermal properties (e.g. thickness and material) is a significant factor in
determining of the expansion [horizontal and vertical] rate of thermokarst.
For each tundra landscape unit, we ask the following questions to determine
whether thermokarst will or will not be initiated:
1) Has the drainage efficiency threshold been crossed? As described above, this
question relates to the moisture conditions and hydrologic connectivity (the ability
of water to move horizontally across the landscape). If the drainage efficiency
threshold has been exceed, the landscape is relatively dry compared to a region
that is below the drainage efficiency threshold.
2) Is the active layer depth greater than the protective layer? If the active layer
depth (or seasonal thaw layer??) is greater than the protective layer, thermokarst
can occur. The degree (amount) of landscape change is based upon a number of
other factors, especially ice-content and form. There are two different ways that
this question can be answered. The first is if the amount of heat input to the
system changes, i.e., if the heat input to the system increases, the resulting active
layer depth (seasonal thaw layer) will most likely increase and vice versa. Heat
input can be changed via a number of mechanisms that include mean annual air
temperature changes, fire events, water ponding, changes in snow cover thickness,
timing, and duration, and changes in vegetation cover and soil organic layer
thickness. The second way is due to changes in the protective layer. As the landsurface subsides, there will be changes to the protective layer thickness (described
above). If the protective layer increases to a non-penetrable depth (by the active
layer depth/seasonal thaw layer), thermokarst will not take place [at that time
step]. However, changes in topography may result in a reduced protective layer.
If the landscape becomes more 'steep', the ability of surface water to transport
protective layer sediment across the landscape may increase due to increasing
energy; In addition, fire may reduce the thickness of the protective layer by
burning of portions of the soil organic layer.
3) What is the ice-content of the soils? The answer to this question determines the
resulting physical landscape change due to thermokarst. If the ice-content of the
soil is high (including massive ice), changes in the landscape due to thermokarst
has a much greater potential depending upon the other factors described above
(e.g. penetration depth of the protective layer). If the landscape is ice-poor, the
resulting thermokarst will be minimal or absent, despite how well the other
22
factors favor thermokarst. Ice content relevant for thermokarst will need to be
defined as excess ice content (ice content that exceeds the pore volume of the
soil/sediment) that can translate into volume loss and subsidence upon permafrost
thaw (e.g., Pullman et al., 2006).
23
Appendix 6. A Possible Strategy for Defining Fens and Bogs in Boreal Wetland
Complexes of Alaska and Northwest Canada.
Fens can be differentiated from bogs in terms of their water supply. Fens are
defined as wetlands where the vegetation gets its water supply mainly from ground water
flow. The source of water is generally located outside of the fen. Fens can range from
moderately acidic to mineral rich, and are generally dominated by grasses and sedges. In
comparison, bogs are defined as wetlands where the vegetation gets its water supply
primarily from precipitation (poor to no connection between the surface vegetation and
the groundwater flow). These ecosystems are acidic and are generally dominated by moss
species. If the environmental conditions allow moss growth and the disturbance history
allows substantial peat accumulation, a mesotrophic fen can transition to a bog
ecosystem.
To spatially distinguish fens and bogs, we conducted an analysis of the
distribution of fens and bogs from the vegetation map for the Tanana Flats (Jorgenson et
al. 1999; see Figure A61).
Figure A61. Distribution of thermokarst bogs and fens in the Tanana Flats, Interior
Alaska.
Two factors that were useful in distinguishing fens and bogs are flow
accumulation (FA) and the standard deviation of elevation of the surrounding grid cells
(ESD). The classification tree differentiates bogs, fens and co-occurring bogs and fens is
presented in Figure A62. This model predicts 61 % of the cells containing bogs only,
24
89% of the cells containing fens only and 32% of cells containing bogs and fens.
Figure 6: Decision tree in which flow accumulation (FA) and standard deviation of
elevation (ESD) are used to spatially distinguish between the location of bogs and fens.
25
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