Soil saturation effects on forest dynamics: scaling 
           across a southern boreal/northern hardwood landscape
                                     
                                     
                                     
                                     
                                     
                                     
       John F. Weishampel1,*, Robert G. Knox2 and Elissa R. Levine2

                                     
1Department of Biology, University of Central Florida, Orlando, FL  32816-2368
2Code 923 Biospheric Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD 
  20771

Keywords: aggregation, biomass, drainage class, GIS, soil maps, succession model,
waterlogging

*author for correspondence.
  Fax: (407) 823-5769
  Phone: (407) 823-6634
  e-mail: jweisham@pegasus.cc.ucf.edu
Abstract

Simulations of forest response to climatic change at the southern boreal/northern hardwood
transition zone need to incorporate landscape-level patterns of soil heterogeneity.  The
duration and extent of soil moisture saturation are critical edaphic properties that determine
the present distribution and structure of forest communities of this region.  To capture this
relationship for 
soil series found in east-central Maine, we parameterized a forest gap model using soil
moisture attributes derived from a physical model of soil moisture and heat flux.  Simulated
average soil moisture and depth to an apparent water table were highly dependent on soil
series attributes, e.g., horizon thicknesses, saturated hydraulic conductivity, porosity, and clay
mass fraction.  Differences among the series yielded a variety of communities comprised of
species with various tolerances for soil waterlogging.  Aboveground biomass accumulated
more slowly in poorer draining soils, which suggests that climatically drier conditions may
increase the capacity of patches, that presently consist of forested wetlands, to function as
carbon sinks.  When these predictions were scaled to a 600 ha landscape based on local-
(1:12,000), county- (1:120,000) and state- (1:250,000) scale soil maps, the removal of the
waterlogging effect caused a 20-60% increase in biomass accumulation during the first 50
years of simulation.  However, this increase and the maximum level of biomass accumulation
over a 200 year period varied as much as 40% depending on the source of geographic
information which provided the composition of the soil series.  Thus, simulated effects of soil
heterogeneity depended on the scale of the soil data provided, suggesting caution when
scaling up effects of potential climate change with forest 
patch models.     

1. Introduction
 
Individual-based forest patch models can address questions at larger spatial scales by using
stratified sampling to simulate patches representing different environmental conditions and
aggregating the area-weighted results to represent a landscape mosaic (Shugart et al. 1992). 
This form of direct extrapolation (King 1992) assumes conditions within a patch to be
homogeneous and typically ignores patch to patch interactions which may be related to the
spatial configuration of the landscape.  Although individual-based forest models have depicted
the trajectories of patches that comprise a contiguous, heterogeneous landscape (e.g.,
Pearlstine et al. 1985, Shao 
et al. 1994), aggregated, landscape-level responses from these models have not been reported. 
Here, we report aggregated responses for a landscape in east-central Maine.
     The complex patterning of communities located near the transition zone between the
southern boreal and northern hardwood forests is often determined by edaphic influences,
expressed largely by soil moisture regimes which may favor the development of forested 
and unforested bogs (Glebov and Korzukhin 1992, Pastor and Mladenoff 1992).  Though 
the formation of broad scale wetlands is primarily dependent on climatic variables, e.g.,
evapotranspiration and precipitation, finer scale mosaic patterning of forested wetlands is 
a function of water table levels which are dependent on drainage and local soil properties
(Solomon 1992).  Because of the overriding importance of hydrological properties of the
underlying soil, vegetation patches located near this ecotone are often coarsely classified
accordingly, i.e., into wet, mesic, and dry categories (Bonan and Shugart 1989).  The 
recognition of the importance of soil saturation in determining forest structure near this
ecotone between northern hardwood and boreal forests has led to the inclusion of related
hydrological controls in one series of succession models (e.g., Botkin and Levitan 1977,
Botkin et al. 
1989, Botkin and Nisbet 1992a,b, Botkin 1993).  For more northern latitudes where there is 
a permafrost layer, a depth to seasonal thaw growth multiplier was formulated (Bonan 1989), 
which in effect, mimics the waterlogging multiplier as permafrost impedes infiltration and
yields elevated soil moisture levels during the growing season (Bonan and Shugart 1989). 
However, other simulation efforts for this same general geographic region (e.g., Solomon
1986, Pastor and Post 1986, Pastor and Post 1988, Martin 1992, Bugmann and Solomon
1995) have concentrated 
on the dynamics of more upland forests and thus neglected saturation effects.
     This inconsistency may be due in part to the difficulty in measuring soil moisture
regimes across this heterogenous landscape.  Because the number of observation wells needed
to adequately describe the saturation level varies with the complexity of the soil profile
(USDA 
SCS 1971) and horizontal and vertical properties of soils which were subject to glacial
activity 
are highly variable over relatively short distances (Levine et al. 1994, Levine and Knox, in
press), many wells would need to be installed at a variety of depths for each soil series. 
Moreover, temperature fluxes of northern soils yield freeze/thaw periods and spring snow
melt and subsequent drawdown produced by vegetative growth that create significant seasonal
variation 
of depths to water table.  These temporal patterns require wells to be frequently monitored
over 
many years to account for interannual changes in precipitation and temperature.
     To estimate soil moisture conditions for a variety of soil types, we used a soil physics
model (Levine and Knox, in press) which simulates water and energy dynamics in a soil
column given site-specific edaphic and climatic parameters.  The primary objective of the
present study was to evaluate the sensitivity of an individual-based forest succession model to
model-derived soil moisture parameters for soil series comprising a range of drainage classes
found at the northern hardwood/southern boreal transition zone in east-central Maine.  The
secondary objectives were to aggregate the output from the succession model for the different
soil series 
to calculate and compare effects of waterlogging for a specific 600 ha landscape based on 
edaphic information obtained from local (1:12,000), county (1:120,000), and state (1:250,000)
soil maps.

 
2. Methods

2.1 Study area 
  
International Paper's Northern Experimental Forest (NEF) in east-central Maine (4515' N, 
6845' W) has served as the site of an intensive field and multisensor aircraft campaign that
emphasized the use of remote sensing to study forested landscapes (Goward et al. 1994).  The
NEF comprises ~7000 ha consisting primarily of hemlock-spruce-fir (i.e., primarily Tsuga
canadensis, Picea rubens, P. mariana, and Abies balsamea), aspen-birch (i.e., primarily
Populus tremuloides, P. grandidentata, Betula papyrifera, and B. allegeniensis) and hemlock-
hardwood mixtures.          
     The terrain of the NEF is relatively flat, possessing a maximum elevation change of 
135 m over 10 km, which decreases the rate of lateral drainage and promotes ponding during 
spring melting.  The snow pack is typically continuous from December to March.  Due to the
region's glacial history and more recent alluvial events, soil drainage classes, defined as the
rate at which water is removed from the soil, drastically vary from somewhat excessively
drained eskers to very poorly drained topographic depressions that possess a compacted,
impermeable horizon.  Trees in excessively drained soils may suffer from drought effects,
whereas trees in poorly drained soils are subject to anoxic conditions in the rooting zone. 
Because of the differential sensitivity of tree species to different drainage patterns, the NEF
consists of a mosaic of stands of different species composition, biomass levels, and stem
densities which are to a certain extent correlated to spectral reflectance, i.e., NDVI (Levine et
al. 1994), and radar backscatter (Ranson and Sun 1994a, 1994b) patterns exhibited across the
landscape.

2. 2 Soil moisture relationships in a forest model
 
The Forest Ecosystem Dynamics modeling environment (Levine et al. 1993), conceived as a
network of individual biotic and abiotic models to simulate remotely sensed patterns
associated with forest succession, included a gap model, ZELIG (Urban 1990).  Though for
the most part employing the same methodology from early models developed over 25 years
ago, this class of individual-based models is believed to offer a reasonable approach to predict
long-term responses of forest vegetation to environmental change (Shugart et al. 1992,
Malanson 1993a, Hinckley et al. 1994).   
     ZELIG represents a generic form of gap model which was designed to serve as a 
readily adaptable framework for cross-site comparisons of forested systems.  In its original
published form (Urban 1990), ZELIG included only a positive effect of increasing soil
moisture 
to reduce drought stress.  On a monthly basis, the model estimates available soil moisture by
calculating actual (AET) and potential evapotranspiration (PET) for a site for given edaphic
conditions, i.e., field capacity (FC = cm water at 0.1 MPa) and permanent wilting point (PWP
= cm of water at 1.5 MPa ), and climatic regimes, i.e., latitudinally related radiation,
temperature, and precipitation (Thornthwaite and Mather 1957, Pastor and Post 1986).  From
FC and accumulated potential monthly water loss, the amount of water remaining in the soil-
water column is determined.  If PET > precipitation, AET is equal to precipitation and the
loss of the water remaining from the previous month.  To predict the effects of drought stress,
a drought-day index, i.e., the proportion of days during the growing season in which soil
moisture is below the PWP, is calculated annually.  This index is incorporated into a growth
multiplier which relates basal area growth to a given species-specific tolerance class (Pastor
and Post 1986, Urban 1990).
     For the situation where PET < precipitation, AET = PET.  Excess water is added to
the 
soil-water column.  If water depth exceeds the FC, it is treated as run-off.  However, the
terrain of the NEF is relatively flat and drainage tends to be slow.  As a result, some soils
remain saturated for periods exceeding the monthly time step.  Such saturation promotes
anaerobic 
conditions which can affect root respiration, pH, decomposition of organic matter, and for less
tolerant tree species, inhibit establishment and growth and increase mortality rates.  Following
this Thornthwaite bucket model approach and with only a positive relationship between soil
moisture and growth, ZELIG would have been incapable of reproducing the heterogeneity in
forest composition and structure caused by soil saturation common to the NEF.
     To incorporate water saturation effects, we modified and incorporated a simple
waterlogging growth multiplier described in Botkin (1993) to the gap model.  The original
equation depicts a site wetness factor for a species (WeFi) as:
 
     WeFi = max [0, 1 - (DTmini/DT)]                                  (1)
 
where DTmini = minimum distance to the water table tolerable for species i and DT is the
average growing season depth to the water table.  Because we believed that waterlogging-
sensitive species were excessively penalized at depths to the water table well below the
rooting zone, the growth multiplier was slightly modified to:

     WeFi = {0 if Dtmini > DT,                                   
     
            (DT - DTmini)2/[DTmini2 + (DT - DTmini)2], otherwise}.         (2)  
 
This change maintains the general form of the growth multiplier, but reduces the differences
between tolerance classes at high DT's (Fig. 1).  With this multiplier, there is a range of
responses for the species pool available at the NEF (Table 1).  Species can be grouped into
waterlogging tolerance classes based on their DTmin's.  Tolerant species with low DTmin's at
the NEF include larch (Larix laricina) and northern white cedar (Thuja occidentalis). 
Intolerant species with high DTmin's include eastern white pine (Pinus strobus) and quaking
aspen (Populus tremuloides).

2.3 Derivation of soil moisture parameters
 
To derive soil hydrology parameters necessary for the forest succession simulations, we used
a soil process model FroST (Frozen Soil Temperatures).  The FroST model (Levine and
Knox, 
in press) was developed as a modification of the "Residue" model described by Bristow et al.
(1986) and Bidlake et al. (1992).  FroST (Fig. 2) is similar to Residue in that it simulates the
dynamic aspects of mass and energy transfer in a soil-vegetation-atmosphere system using
numerical methods to describe fundamental physical processes.  It differs from other models
which predict permafrost presence in soil in a number of ways.  Modifications to Residue
present in the FroST model included changing all input climate requirements from daily to
hourly, and the addition of algorithms to simulate snow dynamics, transpiration, and forest
canopy conditions.  Climatic input requirements include global short-wave radiation, air
temperature, average wind speed, and precipitation.  The model simulates short-wave and
long-wave radiative transfer, soil and canopy temperature, canopy vapor density, rainfall
interception by the canopy, infiltration redistribution, evaporation, freezing, thawing, and
drainage.
     FroST is unique from ecosystem models which predict soil moisture or temperature
flux such as TCX (Bonan 1991) or EXE (Martin 1992) in that it predicts hourly soil moisture,
heat, and ice content in individual horizons of a given soil profile.  Specific soil properties are
defined to represent differences in thickness, particle size distribution, hydraulic conductivity,
water holding capacity, air entry matric potential, and bulk density in each horizon.  Mass and
energy move through soil layers designated by nodes at specific depths.  A canopy model is
included in which the top three nodes represent forest overstory, and the bottom three
represent litter.  Radiative and convective transfer is described for each layer.  Solutions to
the mass and energy balance equations use the Newton Raphson technique to solve for
temperature and matric potential of the soil and temperature and vapor density of the air in
the canopy at the end of each hourly time step (Campbell 1985).  Using these techniques, the
model has been used to determine 

soil heat and water budgets and is especially useful for cold climates in which continuous or
discontinuous ice is present (Levine and Knox, in press).
     For this study, data for soil series (see Table 2a) were obtained and an input file
containing the relevant information for nodes at specific depths within horizons was created. 
Nodes were chosen either as midpoints within a soil horizon as described in the soil profile
description or to represent a specific depth.  Data for each soil node included: node depth
(m), distance to next upper node (m), thickness of the soil layer (m), air-entry matric potential
(J/kg), bulk density (Mg/m3), slope of the log-transformed moisture characteristic, saturated
hydraulic conductivity (kg.s/m3), particle density (Mg/m3), clay mass fraction (%), initial soil
temperature (C), and initial volumetric water content (%).  A saturated zone was designated
at a specific depth for each soil based on the hydrologic group classification given in the soil
survey.  The groups are defined by the Soil Survey Division Staff (1993) according to the
saturated hydraulic conductivity and the depth to internal "free water" (water table), where
group A has the highest conductivity and deepest occurrence of internal free water, and group
D has the lowest conductivity and the shallowest occurrence of internal free water.  For
purposes of setting up the starting conditions for FroST, soils classified as group A were
given an initial saturated horizon of 12 m, group B at 2.5 m, group C at 1.0 m, and group D
at the soil surface (see Table 2b).
     Input data required for the canopy nodes were reflectivity, zero plane displacement
height (m), and momentum roughness length (m), which were obtained from structural
characteristics 
of the vegetation at the NEF.  Other site characteristic inputs included degrees north latitude
and hour of solar noon.  Climate data for the years 1988-1992 collected at the NEF were used
to drive the model using inputs of: day of year, time of day, total incoming solar radiation
(W/m2), atmospheric air temperature (C), atmospheric relative humidity (%), wind speed
(m/s), and daily precipitation (cm).

2.4 Simulations and scaling procedures
 
Site characteristics (e.g., monthly values of temperature and precipitation) and autecological
parameters (e.g., tree age, height and diameter maxima; growth  and sapling establishment
rates; light, water, fertility, and temperature tolerances) were derived from empirical data and
published sources (e.g., Pastor and Post 1985, Botkin 1993, Weishampel et al. 1997) for the
species found at the NEF.  Because the model contains stochastic elements (e.g., weather,
mortality, and recruitment) replicates with different random number seeds were averaged to
estimate the system's behavior.  Thirty runs, each consisting of nine, 10 x 10 m plots, were
simulated starting from bare ground conditions for 14 soils series which comprise
approximately 95% of the NEF (Table 2).  To isolate soil moisture effects, differences in
nutrient status of the soil series were not included.  For comparison, half of the simulations
did not include waterlogging effects and used laboratory estimates of FC, the other half
included waterlogging effects and used estimates of depth to water table and effective FC
predicted with FroST.  The laboratory estimates of FC and PWP were derived from
measurements of soil moisture at 0.03 MPa and 1.5 MPa, respectively.  These were obtained
by pressure-plate extraction techniques (USDA NRCS 1996) for soil samples of the
predominant soil series found at the NEF (USDA SCS 1990).  
     Aboveground productivity from the simulations for the different soil types were
aggregated for a 600 ha area extracted from local (1:12,000), county (1:120,000), and state
(1:250,000) soil maps.  The local map was created as a soil survey of part of the NEF in
1990 
by the USDA Soil Conservation Service in Orono, ME (USDA SCS 1990).  The soil series
distributions at the county scale were obtained by digitizing the soil maps associated with the
study area from a 1963 USDA county soil survey of Penobscot County, Maine (Goodman et
al. 1963).  Additional information regarding the local and county soil maps is available at the
Forest Ecosystem Dynamics world-wide website (Fifer 1996).  At the state level, soil polygon
data were obtained from the State Soil Geographic data base (STATSGO) map for Maine
(USDA SCS 1993).
     Because land area represented by a given map increases as the scale decreases, less 
detail is depicted on smaller scale maps, i.e., maps with coarser resolution.  Thus, generalized
maps of counties or states rarely consist of mapping units comprised of a single soil series. 
More often, these mapping units represent associations of more than one soil series or other
taxonomic unit (e.g., soil family, subgroup, or great group).  The 600 ha map at the local
scale consisted 
of eight soil associations consisting of twelve soil series; at the county scale, five soil series
comprised four associations; and at the state scale, thirteen soil series comprised a single
association (Fig. 3).  The contribution of each association to the overall productivity of the
landscape was weighted according to its areal extent.  Within each association, the
contribution 
of each series was weighted based on its percent composition.  Table 2a gives the taxonomic
classification, drainage class, and parent material of all the soil series used at each scale in
this study.  Because soil series not present in the local map existed in the smaller scale maps,
analogs from the local map (Table 3) were substituted when appropriate.  Three associations
that comprised a small fraction (<1.25%) of the local and county scale data (and for which
there was insufficient information to parameterize FroST) were excluded in the weighted
aggregation.  In the case of the STATSGO data, two lithic (shallow to bedrock) soils, Lyman
and Monson, which together comprise 15% of the soil association, did not have an
appropriate analog at the local scale.  In order to include these soils in the scaling exercise,
new soil hydrology parameters were estimated with FroST based on their description.   

3. Results

3. 1 Predictions of soil water conditions

For the 13 predominant soil series at the NEF (excluding the lithic soils), the sum of
laboratory field capacities was correlated with average moisture levels in FroST simulations
(R2 = 0.52, 
P < 0.01), but many soils retained more moisture in the multilayer dynamic simulations than
would be expected from simple pressure plate measurements on isolated soil samples (Fig. 4). 
The somewhat excessively drained soil, Adams, and the very poorly drained soil, Biddeford,
possessed the lowest and the highest moisture levels, respectively, in both the summed
laboratory FC and the average simulated profile water content estimates.  In general, the
better drained soils tended to exhibit lower field capacities than the more poorly drained soils
and poorly drained soils had the larger excesses over the expected from laboratory FC with
free drainage.
     FroST yielded a range of depths to water table for the soil series (Table 2b).  To a
certain extent, these predictions corresponded to drainage class, i.e., well drained soils had
deeper DT's than more poorly drained soils.  The somewhat excessively to moderately well
drained soils, were on average 2.8 m deeper than the more poorly drained soils.  Though the
average depth to water table for the somewhat poorly drained class was greater than the
averages for the poorly and the very poorly drained classes as expected, the average for the
poorly drained class was less deep than the average for the very poorly drained class. 
Laboratory data for the Kinsman series were from a somewhat poorly drained profile rather
than the very poorly drained "taxadjunct" Kinsman soils mapped at the NEF.  Peacham soils
occur in depressions with better drained series and may be difficult to simulate correctly
without lateral water movement.
 
3.2 Waterlogging effects on species composition
 
When running the baseline ZELIG without the waterlogging multiplier, edaphic responses are
a product of drought from FC and PWP estimates.  However, given the cold, humid (80-100
cm precipitation/year), continental climate of the NEF, typically AET = PET, thus species
trajectories were very similar for the different soil drainage classes (Fig. 5a).  Intermediately
waterlogging tolerant species, primarily red spruce (Picea rubens) and paper birch (Betula
papyrifera), dominated the stand throughout the 200 year simulation.  Waterlogging intolerant
species 
outcompeted tolerant species during the early stages of succession, but were supplanted by
waterlogging tolerant species after ~75 years.  Overall basal area on the somewhat excessively
drained soil was reduced probably due to slight drought effects.
     When including the waterlogging multiplier, species trajectories varied based on the
drainage class of the soil (Fig. 5b) as found with earlier simulations (Botkin et al. 1989,
Botkin and Nisbet 1992a,b, Botkin 1993) based on the Botkin and Levitan (1977) multiplier. 
The well drained soils revealed little difference from those in the previous slide without
waterlogging.  
But as drainage and depth to water table decreased, the species trajectories changed so that
the importance of more waterlogging tolerant species increased.  For the somewhat poorly
drained soil, tolerant species accounted for roughly a third of the entire basal area after 100
years.  In the scenario with the very poorly drained soil, tolerant species, primarily northern
white cedar (Thuja occidentalis), were the only ones which persisted which is consistent with
field data collected from the NEF (Levine et al. 1994).

3.3 Waterlogging effects on aboveground biomass

Without waterlogging, the 14 soils at the NEF all exhibited a similar sigmoidal pattern of
above ground biomass accumulation (Fig. 6a).  Though the biomass trajectories were similar,
levelling off at ~100 years, the amount of biomass at 200 years ranged between 100 and 200
Mg/ha.  These differing biomass levels are primarily related to evapotranspirative-induced
drought effects.  The correlation coefficient between the average biomass levels between 50
and 200 yr and the differences between FC and PWP was 0.86 (P<0.001).  The difference
between FC and PWP, however, does not necessarily correspond to drainage class.  For
example, the difference in FC and PWP for the Plaisted series, although it is well drained due
to its gravelly loam texture, is comparable to a poorly drained soil series such as Monarda
which is classified as a silt loam.

     With waterlogging, drought effects were reduced in most soils as biomass trajectories
segregated according to drainage classes.  More poorly drained soils were less productive than 
better drained soils during the first 100 years of succession (Fig. 6b).  One very poorly
drained soil series, Biddeford, which corresponds to open bogs in the NEF landscape,
possessed a depth to water table estimated at 0 m and yielded no tree growth as expected
with the waterlogging function.
 
3.4 Scaling effects
  
While the general pattern from waterlogging was consistent for the data from the different
scaled maps, the effect was more exaggerated for projections based on the local and state soil
maps which had overall lower levels of aboveground biomass than projections based on the
county soil map (Fig. 7).  When we aggregated simulations based on the areal extent of soil
series across the 600 ha landscape, the effect of waterlogging amounted up to a ~40%
reduction for the local-scale and state-scale projections and up to a ~20% reduction for the
county level projections in aboveground biomass during the first 50 years.  However, after
125 years, the aboveground biomass from the simulations with waterlogging converged with
the simulations without waterlogging.  This general waterlogging effect was fairly consistent
among the various scales.  The absolute levels of biomass varied substantially, maintaining up
to a ~40% difference between the projections based on state and county data.  The soil
composition information from the county map (1:120,000 scale) yielded the highest biomass
levels, nearly 200 Mg/ha after 200 years.  The soil composition information from the state
map (1:240,000 scale) yielded the lowest biomass levels, approximately 120 Mg/ha at 200
years.  The biomass level derived with the information from the local map approached 160
Mg/ha at 200 years. 

4. Discussion

Successional pathways of southern boreal-northern hardwood forests are highly dependent on
edaphic conditions which tend to be fairly heterogeneous (Pastor and Mladenoff 1992, Levine
et al. 1994).  Most global warming scenarios predict increased temperatures and evaporation
of free water for this transition zone which should lead to warmer substrates and lowered
water tables (Price and Apps 1993).  Thus, the inclusion of water table dynamics may be
critical to how forests occupying poorer draining soils respond.  Although changes in depth to
water table were not included as part of the climate change scenarios (Botkin et al. 1989,
Botkin and Nisbet 1992a, Botkin 1993), a sensitivity analysis of a gap model by Botkin and
Nisbet (1992b) showed slight changes (10%) in DT produced little change in forest structure
for a given soil.  The parameters derived from the soil physics model for the variety of soils
found at the NEF were substantially different and yielded dramatic differences in species
composition and aboveground biomass levels.  The differences in depths to the water table
from the somewhat excessively drained, Adams, to the very poorly drained, Biddeford, as
well as among most other soil series were much larger than the 2-6 cm differences tested by
Botkin and Nisbet (1992b).  Based on this study, it would seem that if depths to water table
were to decrease to levels found in drier soil series, denizen forests would exhibit a change in
species composition which corresponds to an increase in aboveground productivity during
early stages of succession.  Hence, more poorly drained regions may better function as
aboveground carbon sinks with increasing temperatures and drier conditions.  
     One of the major criticisms raised by the use of gap models to predict effects of 
climate change of forests is the unrealistic mechanism for simulating species specific
temperature responses (Urban and Shugart 1989).  Like the temperature relationship, species
responses to waterlogging stress has been to use crude parabolic (Phipps 1979, Pearlstine et
al. 1985) or sigmoidal relationships (Botkin and Levitan 1977, Botkin and Nisbet 1992) that
relates to the optimal depth to water table for a species.  However, as with all the growth
multipliers, each species probably possesses their own uniquely shaped soil saturation
response curve (Malanson 1993b).  Furthermore, field measurements of forested wetlands in
Minnesota found aboveground net primary productivity and biomass to be more sensitive to
fluctuations in groundwater than 
to average depth to the water table (Grigal and Homann 1994).  Thus, the general
waterlogging growth multiplier approach using an annual time increment and an average DT
may be less appropriate than a seasonally or monthly changing water table.  In order to fully
account for changes in evapotranspiration, a more complete coupling of the hydrological and
ecological dynamics would be needed to adjust for shifts in AET associated with successional
changes in the forest composition.  When an evapotranspirative feedback was included from a
gap model 
to a biophysical atmospheric model (Martin 1992), it was found that soil drying increased
dramatically causing a shift from forests to grasslands.  This implies that water table
drawdown 
by evapotranspiration could potentially promote a change from waterlogging tolerant to 
less tolerant species as found on the better drained soils.  Thus, there is a need to create
physiologically tractable growth relationships (Bonan 1993, Bugmann and Martin 1995) in 
this class of individual based model perhaps along the line of Friend et al. (1993) which
relate 
to soil saturation effects.   
     There was no a priori expected result from this scaling exercise, other than it was not
expected that "scaled-up" results would be consistent as the mapping resolution changed. 
Certain local areas may be wetter or drier on average than others and such fine-scale
heterogeneity may not be captured with coarser mapping units.  Based on the aggregated
landscape, regardless of scale, the effects of waterlogging on aboveground biomass were most
apparent during the early periods of succession, i.e., up to 100 years.  (This model did not
include differences in fertility among these soils.)  Afterwhich, aboveground biomass
trajectories from the aggregate landscape that included waterlogging effects converged with
that which did not.  This consistency was surprising as the edaphic information regarding the
areal extent of drainage classes from the three different soil maps varied considerably (Table
3).  At the local scale, poorly and very poorly draining soils comprised <50% of the 600 ha
NEF area.  Compared to the local soil map, the state map overestimated somewhat
excessively drained soils for the 600 ha landscape.  And compared to the local soil map, the
county map underestimated and overestimated the extent of poorly and well-drained soils,
respectively
     In this exercise, it was found that the choice of which geospatial information was used 
was more critical than whether or not soil saturation effects were included.  As a result of
how soils for an arbitrarily chosen 600 ha area from the NEF were classified, the model
predictions based on data from county soil map overestimated and predictions based on the
state soil map underestimated productivity levels when compared to predictions based on the
more detailed 
local soil map.  This 40% range in productivity predictions demonstrates how aggregation
error from decreasing map scale is propagated by simulations models which rely on the map
data.  Furthermore, the fact that biomass levels increased as map resolution decreased from
1:12,000 to 1:120,000 then decreased as map resolution decreased from to 1:120,000 to
1:250,000 suggests that there are no simple scaling rules for soil heterogeneity which could
be applied for this area.  

Acknowledgments

Research was supported in part by an National Research Council (NRC) Research
Associateship and by funding for the Forest Ecosystem Dynamics (FED) project from
NASA's Terrestrial Ecosystem and Modeling Program.  The authors would like to thank Steve
Fifer for assisting with the geographical analysis and Dean Urban with help in estimating tree
species parameters and providing the ZELIG source code.

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Table 1. Tree species parameters used in implementation of ZELIG for the NEF.

Scientific name(Common name)AgeDBHHGRGDD-GDD+DTminLMNAbies balsamea(Balsam fir)2005015006925024040.211113Acer pensylvanicum(Striped maple)751550015088955000.567233Acer rubrum(Red maple)1501003000176126066010.322233Acer saccharum(Sugar maple)300150300089120432000.567122Betula allegeniensis(Yellow birch)250753000106142030840.600322Betula papyrifera(Paper birch)140100250016070025000.544433Betula populifolia(Gray birch)25025100037100728801.000523Fagus grandifolia(American beech)366100300072132755560.489132Larix laricina(Tamarack)3357525006628026600.156533Picea mariana(Black spruce)2504020007026519290.156233Picea rubens(Red spruce)30010030008950025800.489223Pinus strobus(Eastern white pine)450150350068150031831.000333Populus grandidentata(Bigtooth aspen)70752500316110031690.400532Populus tremuloides(Quaking aspen)12575220015888955560.700532Thuja occidentalis(Northern white cedar)400100240055100021880.100233Tsuga canadensis(Eastern hemlock)650150350047132431000.489123
Variables are as follows: Age=maxium age (yr); DBH (cm); H=height (cm); GR=Growth rate
(dimensionless); GDD- and GDD+=minimum and maximum growing degree-days (5.56C
base); DTmin (m); L=shade tolerance (rank 1=very tolerant); M=drought tolerance (1=least
tolerant); N=nutrient stress tolerance (1=least tolerant)
Table 2a. Dominant soil series found at the NEF at local, county and state scales.

Series                       Drainage Class      Parent Material
Adams1 (Typic Haplorthod)         Somewhat Excessively     Outwash
Biddeford2 (Histic Humaquept)     Very Poorly              Marine Sediments
Boothbay1 (Aquic Eutrochrept)     Somewhat Poorly          Lacustrine and
Marine Sediments
Brayton3,4 (Aeric Haplaquod)      Poorly                   Till
Burnham2,3 (Typic Haplaquept)     Somewhat Poorly          Till
Buxton3,4 (Dystric Eutrochrept)   Somewhat Poorly          Lacustrine and
Marine Sediments
Colonel1 (Aquic Haplorthod)       Somewhat Poorly          Till
Croghan1 (Aquic Haplorthod)       Somewhat Poorly          Outwash
Dixfield1 (Typic Haplorthod)      Moderately Well          Till
Hermon3,4 (Typic Haplorthod) Somewhat Excessively     Outwash
Howland2,3 (Aquic Haplorthod)     Somewhat Poorly          Till
Kinsman1 (Aquic Haplorthod)  Very Poorly              Outwash
Lyman4 (Lithic Haplorthod)        Somewhat Excessively     Bedrock
Marlow1 (Typic Haplorthod)        Well                Till
Masardis3,4 (Typic Haplorthod)    Somewhat Excessively     Outwash
Medomak5 (unknown)      Very Poorly              Alluvium
Monarda2 (Aeric Haplaquod)        Poorly                   Till
Monson4 (Lithic Dystrochrept)     Somewhat Excessively     Bedrock
Peacham1 (Histic Humaquept)  Very Poorly              Till
Plaisted2 (Typic Haplorthod)      Well                Till
Roundabout5 (unknown)        Poorly Drained      Alluvium
Scantic1 (Typic Haplaquept)       Poorly                   Lacustrine
and Marine Sediments
Tunbridge3,4 (Entic Haplorthod)   Moderately Well          Till
Westbury1 (Aeric Haplaquod)       Poorly                   Till
Wonsqueak5 (unknown)         Very Poorly              Organic Materials
1Data derived from Soil Survey of NEF (USDA SCS 1990)
2Data dervied from Penobscot County Soil Survey Report (Goodman et al. 1963)
3Used input file from similar soil (see Tables 3b and 3c)
4Data derived from STASGO data base (USDA SCS 1993)
5Not included in this study

Table 2b. Hydrologic soil grouping, measured PWP and predicted DT for selected soil series
used as inputs to FroST.

Series Hydrologic
GroupSummed
Laboratory
PWP (cm) Modeled
DT (m) AdamsA4.84.00BiddefordD22.10.00BoothbayC12.80.35ColonelC9.61.23CroghanB10.31.05DixfieldC8.13.4KinsmanC10.80.84MarlowC8.73.3MonardaC6.30.34PeachamD15.00.62PlaistedA3.83.0ScanticC18.20.25WestburyC5.70.71Lithic*A2.910.00
*Category included to account for Lyman and Monson series identified at the state level   
  in the STATSGO database without an analog at local or county levels. 

Table 3a. Composition of 6 ha NEF area using information from the 1:12,000 local soil map
(USDA SCS, 1990).

Soil AssociationPercent
composition 
of landscape 
Series comprising 
soil associationPercent
composition of
association**Colonel-Westbury50.61Colonel50Westbury50Colonel-Dixfield21.59Colonel50Dixfield50Peacham-Westbury8.08Peacham70.6Westbury29.4Boothbay-Scantic6.05Boothbay50Scantic50
Croghan-Kinsman5.22Croghan56.3Kinsman43.7Colonel-Dixfield-Marlow4.83Colonel17.6Dixfield47.1Marlow35.3Biddeford2.45Biddeford100Bucksport-Wonsqueak*0.93Bucksport60Wonsqueak40Roundabout-Medomak*0.24Roundabout35Medomak         65             *not included in aggregation
**does not account for inclusions

            
            
            
            
            
            
            
            
            
            
            Table 3b. Composition of 6 ha NEF area using information from the 1:120,000 county soil
map (Goodman et al. 1963).  Analogs for series are in parentheses.

            
            
            Soil Association    Percent
         composition 
      of landscape       
            Series comprising 
   soil association    Percent
         composition of
     association**                                                      PrC           58.41          Plaisted        100                                                           MrB           32.46          Monarda         50                                   Burnham (Colonel)    50                                                           HvB           8.60          Howland (Croghan)    100                                                           Mu*           0.54          Muck          100             *not included in aggregation**does not account for inclusions

            
            
            
            
            
            
            
            
            
            
            
            
            
            
            Table 3c. Composition of 6 ha NEF area using information from the 1:250,000 state soil map
(USDA SCS 1993).  Analogs for series are in parentheses.

            
            
            Soil Association    Percent
         composition 
      of landscape       
            Series comprising 
   soil association    Percent
         composition of
     association**                                                      ME008          100           Brayton (Westbury)   20                                   Dixfield         12                                   Hermon (Adams)     11                                   Lyman (Lithic)     11                                   Peacham         11                                   Biddeford        8                                    Colonel         7                                    Marlow         6                                    Monson (Lithic)     4                                    Buxton (Boothbay)    3                                    Scantic         3                                    Masardis (Adams)    2                                    Tunbridge (Dixfield)  2            **does not account for inclusions

            Figure Legends
     Fig. 1. Comparison of species growth responses for a) Botkin (1993) and b) modified
waterlogging growth multipliers.

            Fig. 2. Schematic of fluxes in the soil physics model FroST version 1.1.

            Fig. 3.  Spatial distribution of NEF soil associations for a 600 ha area from the a) local
(1:12,000), b) county (1,120,000), and c) state (1:125,000) maps.  The gray shades within
each 
          2 x 3 km rectangle correspond to associations listed in Table 3.

            Fig. 4. Comparison between measured and modeled field capacities for the predominant soil
series (excluding the lithic soils) at the NEF.
 
            Fig. 5. Average species trajectories for three drainage classes for simulations a) without 
and b) with the waterlogging growth multiplier.  The tolerant class includes all NEF tree
species with DTmin = 0.3 m; for intermediate, 0.6 m > DTmin > 0.3 m; for intolerant,
DTmin = 0.6 m. 
     
            Fig. 6. Average aboveground biomass trajectories for the 14 predominant soil series a)
without and b) with the waterlogging growth multiplier. 

            Fig. 7. Aboveground biomass trajectories aggregated to a 600 ha area based on soil
information from maps of the local (1:12,000), county (1,120,000), and state (1:125,000)
scales for simulations with and without waterlogging.