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Predicting Biogeochemical Ecosystem Processes in the Columbia River Basin Using the BGC Model
Robert E. Keane Intermountain Research Station Intermountain Fire Sciences Laboratory P.O. Box 8089 Missoula, MT
59807
DG: B.KEANE:S22L01A, Phone: 406-329-4846, FAX: 406-329-4877, email: fswa/S=B.KEANE/
[email protected]
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INTRODUCTION
Mechanistic ecosystem process modeling can provide insight into the causal mechanisms that control landscape dynamics.
An assessment of ecosystem
productivity and water, carbon and nutrient cycling can provide a means for determining ecosystem health and integrity.
Mapping the spatial distribution
of fundamental ecosystem processes such as photosynthesis, transpiration and respiration can identify areas that act as sinks for carbon, have high productivity or are at risk from insect and disease attacks.
In May 1994, Dr. Steven Running, University of Montana, Missoula, Montana, entered into a Research Joint Venture Agreement with the Intermountain Fire Sciences Lab, Intermountain Research Station, Missoula, Montana to simulate trends in ecosystem processes as a result of alternative, broad-scale land management strategies for the Interior Columbia River Basin (ICRB) scientific assessment under the guidance of the Landscape Ecology Group.
Dr. Running and
his staff of Joseph White and Peter Thornton then developed a strategy to use mechanistic process model called BGC (A biome BioGeoChemical process model) (Running and Hunt 1993) to model elemental ecosystem processes on the ICRB landscape.
The objective of this modeling effort was to characterize coarse-
scale changes in fundamental ecosystem processes as a consequence of various management scenarios.
Results from the simulations would be integrated into
the Environmental Impact Statement (EIS) for the entire ICRB.
This paper contains three sections.
A description of the procedure used to
simulate ecosystem process relationships using BGC for the entire ICRB is
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detailed in the first section.
The second section contrasts Biome-BGC
simulation results for the historical and current ICRB landscape to describe consequences of management policies for the last 90 years.
The last section
describes, in general, the results of the application of the BGC model to predictions of future landscapes as generated by the CRBSUM succession model (Keane and others 1996).
The BGC Model
The BGC is a mechanistic, ecosystem process model that simulates the fundamental relationships that govern vegetation dynamics.
It is a carbon,
nitrogen and water cycling model using daily and annual timesteps to simulate these flows across ecosystem compartments.
A detailed discussion of the BGC
model and its inputs is contained in Thornton and others (1996), which was used to develop this chapter.
BGC recognizes several compartments where
carbon, nitrogen and water can be transported including the leaf, root, stem, soil and air compartments.
The model simulates the flow of carbon, nitrogen
and water across these compartments using physiological process relationships. For example, carbon is transferred from the air to the leaves, stems and roots via photosynthesis.
Carbon lost from leaves, stems and roots is
transferred to the soil and air via litterfall and decomposition.
The driving
variables used to simulate the physiological relationships are primarily the weather variables of precipitation, temperature and radiation.
Carbon is
fixed daily using photosynthetic equations that are based on temperature, water and humidity factors.
Carbon is released to the air from autotrophic
(live plant) and heterotrophic (microbial) respirational processes that are
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primarily dependent on temperature.
Water is taken into the plants based on
soil water potential and released to the air via transpiration which is mostly governed by humidity, temperature and available soil water.
BGC has its roots in a single-tree, single-year, daily water balance model called H2OTRANS.
Then a stand-level, big-leaf model called Forest-BGC was
developed from H2OTRANS logic (Running and Coughlan 1988, Running and Gower 1991).
A spatial, landscape-level, multi-year application of Forest-BGC was
implemented in the model RESSys (Regional Ecosystem Simulation System). However, these three models were for only forested ecosystems. developed to simulate ecosystem processes across all biomes.
BGC was BGC has some of
the original Forest-BGC logic, but most algorithms have been refined to incorporate recent research findings.
The logical foundations of the CRB application of the BGC model are unchanged from its predecessor models.
The spatial dimension is resolved as discrete
units of area, with no explicit scaling of processes for horizontal spatial scale.
A regular Cartesian grid with gridcell spacing of two kilometers was
used for all simulations referenced here.
The model is implemented
independently over each of these discrete units of area, with no interactions occurring between units.
The vertical dimension in the canopy is described as
a Abig leaf@ model, with a single estimate of daily fluxes for the entire canopy compartment.
The vertical dimension below-ground is modeled as a
single unit of homogeneous soil with a specified depth and uniform rooting density.
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The basic temporal resolution is one day (24 hours), and for a given unit there is an explicit time dependency established by the maintenance of state variables between daily timesteps.
As an example, the soil moisture of two
adjacent gridcells are independent of one another, but for a single gridcell the initial soil moisture for a given timestep is transmitted as the soil moisture diagnosed at the end of the preceding timestep.
In addition to the
daily estimation of such processes as transpiration, maintenance respiration, and photosynthesis, the Forest- and BGC descriptions include the maintenance of state variables for the assessment of carbon allocation and nutrient dynamics at an annual time-step.
This dual-timestep logic is implicit to the
CRB-BGC implementation insofar as all simulations have been performed for one year, with prescribed initial values for the annual state variables.
Carbon enters into a carbon budget, when a canopy is present, as the result of photosynthetic assimilation.
Daily estimates of assimilation are based on a
modified version of the Farquhar model of photosynthetic biochemical pathways (Farquhar and others, 1980).
Principal factors controlling assimilation are
canopy absorbed radiation, leaf nitrogen concentration, leaf nitrogen allocation to the RuBisCO enzyme, leaf maintenance respiration rate, and conductances to CO2 at the leaf and canopy scale. each day to a photosynthate pool.
Assimilated carbon is added
Leaf, stem and root carbon pools were
specified as constant for the one-year duration of the simulations.
Daily estimates of maintenance respiration from leaves, stems, coarse roots, and fine roots are based on an exponential function of daily average temperature, using coefficients which specify the proportion of living biomass
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which would be respired in one day at a reference temperature.
Maintenance
respiration is totalled over a simulated year, and subtracted from the photosynthate pool at the end of the year to estimate the annual net primary productivity.
It is possible for this value to be negative, indicating a net
loss of photosynthate.
Plants which have carbon storage reserves maintained
between growing seasons could be expected to withstand a limited negative productivity.
For plants without significant storage of photosynthate between
seasons, this condition most likely indicates mortality.
Because the simulations were limited to a duration of one year, growth respiration and turnover of leaf, stem and root compartments were not modeled. Soil and litter carbon pools were not employed, in part because of the difficulty of parameterizing these pools without resorting to prohibitively time-demanding multi-decadal model initialization runs.
As a result, soil and
litter decomposition were not explicitly modeled. In order to provide a surrogate estimate of the nutrient availability which would result from organic matter decomposition, scalars which are typically used to estimate the decomposition of soil and litter organic matter were calculated (modification from Biome-BGC).
These scalars describe the modeled influence of temperature
and soil moisture on the rates of decomposition in the litter and soil.
APPLICATION OF BGC TO THE ICRB
Input Data Sources
BGC needs several data files and spatial data layers to quantify
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ecophysiological parameters and initialize the model.
Listed below is a
general description of the methods and data sources used to create or modify the various data files and data layers required for a BGC simulation.
A
detailed discussion of these tasks is provided in Thornton and others (1996).
Vegetation characterization--The CRBSUM model was the main tool used to predict vegetation dynamics and future ICRB landscapes as a consequence of management actions at a 1 km pixel resolution (Keane and others 1996).
CRBSUM
was parameterized using the Vegetation Dynamics Development Tool (VDDT) model developed by Beukuma and Kurtz (1995).
CRBSUM generated various tables and
spatial data layers at selected years during a 100 year simulation (10, 50 and 100 years).
Landscape changes were characterized using simulated cover type
and structural stage digital map layers.
Forest cover type naming convention
follows Eyre (1980), and rangeland cover types follow Shiftlet (1994).
Cover
type and structural stage data layers for the current and historical ICRB landscape, as well as those generated by CRBSUM, were imported into BGC for each of the simulation years (i.e., 10, 50, 100) and then BGC was executed for only one year.
In addition, all input parameters related to vegetation were
stratified by the cover type and structural stage categories.
The BGC
modeling effort reduced the structural stages into the categories of Table 1 and it reduced cover types to the categories presented in Table 2.
The vegetation type descriptions from the CRB vegetation type data layers were aggregated into modeling types based on drought tolerance (Appendix 1) (SAF, 1984; Lassoie and others 1985).
The rationale for this aggregation is that
most of the Upper CRB vegetation communities are water-limited, as opposed to
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light limited, and therefore can be described by water limitation gradients. This is also a functional grouping as most physiological responses of these communities are water-stress adaptations.
Some difficulties arose in the aggregation process because the groupings were also based on a definition of lifeform (e.g. tree, shrub, grass).
Some
community type descriptions naturally fell into two or more of these categories such as the juniper/big sage/bluebunch wheat grass.
The CRB is
designed at present to model single life form types because there are assumptions about the homogeneity of canopy conditions, carbon and nitrogen pools, and other specific physiological processes.
Where the vegetation type
contained more than one lifeform type, grouping was based on the dominance of respiring biomass.
In mixed classes, if woody vegetation was present, then
the class was aggregated as a forest or shrub type.
The initial structural stage definitions included definitions for forests, woodlands, and shrub communities.
For simplicity, these were aggregated into
early, middle, and late series (Appendix 2).
Also, shrub
development/structure definitions were aggregated into a single definition because no ecophysiological evidence could be found to support modeling more specific shrub categories.
Weather Data--The following meteorological variables are the principal driving inputs for CRB-BGC: daily maximum and minimum temperatures, daytime average temperature, daily average soil temperature, daily total precipitation, daytime average vapor pressure deficit, daytime average radiative flux
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density, and daylength.
These variables are provided as daily estimates
interpolated from surface observations onto a regular grid.
For the
simulations described here, three complete years of these daily gridded surfaces were employed, 1982, 1988, and 1989, corresponding to a relatively cool and wet year, a relatively warm and dry year, and a normal year, respectively.
In addition, the results of an assessment of various numerical
predictions of a likely future climate under conditions of twice current atmospheric [CO2] (Ferguson 1996) were used to construct a fourth year of daily meteorological variables which represented a likely normal year under projected future climate conditions.
The spatial daily weather data was generated using the MTCLIM-3D computer application (Thornton and others 1996).
This method involved compiling base
station data for the three weather years from approximately 800 National Weather Service weather stations in and around the ICRB.
The local
contribution from each base station was delineated using topography and climatic trends.
The daily base station data was then interpolated to a 2 km
grid layer using MTCLIM-3D logic.
Ecophysiological Parameters--BGC requires an extensive set of ecophysiological parameters to simulate photosynthesis, respiration and the other basic ecological processes such as decomposition, evaporation and transpiration. The value of each parameter depends on ecosystem condition.
A set of
ecophysiological parameters were developed for each combination of structural stage and cover type that occurred historically, presently or in the future on the ICRB landscape.
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The approach to modeling the difference between vegetation types was to reduce the classification scheme employed at the level of the CRBSUM routines (Keane and others 1996) to a smaller number of classes with similar ecophysiological characteristics. herbaceous types.
The most general division was between tree, shrub, and Each type was then divided again, first based on leaf
morphology (needle vs. broadleaf) and finally on drought tolerance classes (Appendix 1).
A set of parameters were developed based on the processes of
interest in this analysis and the ecophysiological variation represented by the vegetation types in the study area.
Four classes of such parameters
(referred to here as ecophysiological constants) were used in this analysis: canopy constants, conductance constants, phenological constants, and maintenance respiration constants.
A variety of ecophysiological parameters are needed to constrain various subcomponents of the BGC model.
These constants define leaf and plant
physiognomy, photosynthetic capacity, leaf conductance to water vapor and carbon dioxide, phenology, and cellular metabolism rates.
These Aconstants@
are actually average values that vary more or less constantly across vegetation types and species associations with some sensitivity to shade and drought tolerance.
For the CRB-BGC model, constant values were defined from
literature sources for the selected 14 modeled vegetation types (Appendix 3). In some instances, absolute constant values could not be derived from the literature, in which case, values were scaled linearly across vegetation types based on drought tolerance associations.
Certain key ecophysiological parameters were varied across cover type,
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structural stage and Leaf Area Index (LAI).
For example, specific leaf area
(SLA, m2 kg-1) was assigned for each cover class based on the relationship of LAI to SLA (Pierce and others 1994).
Likewise, the proportion of living stem
tissue (Pdl) was calculated from LAI based on cross-sectional water conducting tissue as inferred from cover type and structural stage (Waring and Schlesinger 1985).
SLA and stem tissue values were used to estimate the
proportion of live carbon to the total carbon for a pixel.
This parameter is
important because it dictates the amount of maintenance respiration needed to sustain all living tissue on the pixel.
High Pdl values usually result in
higher maintenance respiration rates to keep the cells alive.
This parameter
changes mostly because of structural stage and biome type.
Canopy constants included specific leaf area, or all sided leaf area per unit of leaf mass (m2 kg-1); the ratio between all sided and projected leaf area; the water interception coefficient, maximum mass of water per unit of leaf area per day that can be intercepted and held on the canopy; the light extinction coefficient (employed in the Beer=s law representation of radiation attenuation); and the proportion of leaf nitrogen which is incorporated in the RuBisCO enzyme.
Conductance constants are actually a sub-category of the canopy constants, and are required in the estimation of canopy-scale conductances to water vapor and sensible heat transport.
These include: a shape parameter describing the
relationship between photosynthetically active photon flux density and stomatal conductance; optimum and maximum temperatures for stomatal conductance, as well as a coefficient describing the shape of the temperature-
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conductance response curve; leaf water potentials for initial and complete stomatal closure in response to soil water stress; vapor pressure deficits for initial and complete stomatal closure in response to atmospheric humidity; a value for maximum stomatal conductance under optimal conditions; an estimate of leaf-scale cuticular conductance; and an estimate of leaf-scale boundary layer conductance.
Phenological constants consist simply of yeardays for leaf-on, leaf-off, fine root-on, and fine root-off.
Maintenance respiration constants include;
coefficients representing proportions of biomass respired in one day under reference temperature conditions for leaf, stem, coarse root, and fine root; and a Q10 coefficient describing the response of all these proportions to changes in temperature from the reference state.
Soil Parameters--Soil parameters required by BGC were taken mostly from the STATSGO spatial data layer developed by the Natural Resource Conservation Service (SCS 1991).
Soil depth, texture and water holding capacity input
values were estimated from the STATSCO data layer using available literature and computer modeling.
Soil water holding characteristics were derived from
STATSGO textural classes using techniques presented by Cosby and others (1984).
Soil depth was calculated for the CRB study area by extracting the maximum depth per soil sequence from the STATSGO database and weighting these values by area for a maximum average soil polygon depth.
A TSI value (Equation 1)
for the CRB area was calculated by first calculating the upslope drainage area
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and then the slope.
The TSI value was calculated using the above formula and
the derived topographic data layers. this process.
TSI values ranged from 0.1 to 18.0 from
The STATSGO maximum depth data layer and the TSI data were
merged using a rule-base where for each STATSGO polygon, the minimum and maximum TSI values were found.
The maximum TSI depth value was equal to the
polygon (i) depth value and depth values for smaller TSI values were found by a logarithmic scaling: 0.5
Soil Depthi = STATSGO Depth TSI i Max TSI i
where the coefficient 0.5 was derived empirically by looking at the shift in frequency plots of the resultant depth values.
The result of this process was
a new map of soil depth in meters.
The texture information required by the BGC model includes percent coarse fragment, sand, silt, and clay.
These attributes are not directly defined in
the STATSGO attribute list; however, information exists in the database that make them extractable with some manipulation.
Three STATSGO attributes were
used to define all textural categories: 1) percent by weight particles passing No. 10 sieve, 2) percent by weight particles passing No. 200 sieve, and 3) percent clay content.
Site soil texture is used to calculate the soil water holding capacity and the soil water potential (? soil) at various water contents.
Physical soil
characteristic information was obtained primarily from the STATSGO database (SCS, 1991).
This database is composed of digitized polygons from 1:250,000
scale state soils maps.
Soil attributes of each polygon are based on field
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surveys with no spatial reference.
Polygons are therefore a loose
conglomerate of various types with the polygonal structure derived from climatic, vegetation, and topographic features.
Leaf Area Index--LAI was estimated by first dividing the ICRB landscape into various biomes (e.g., conifers, broadleaf, shrublands, herblands, rock) using the current ICRB cover type and structural stage data layers.
Then, LAI
values for each pixel was computed by biome from the Normalized Difference Vegetation Index (NDVI) using equations taken from the literature.
The NDVI
data layer was computed from a May 1990 AVHRR scene acquired from EROS (Spanner and others 1990, Pierce and others 1993, Asrar and others 1984).
The minimum and maximum projected LAI values from this study were 1.0 and 13.0 for the evergreen, 1.0 and 8.0 for the deciduous, and 0.5 and 3.0 for the grass groups.
These values were converted into total LAI using the all-sided
to projected ratios described previously.
To simplify model calculation, LAI
values were averaged by modeling class and structural stage (Appendix 1 and 2).
The purpose of this summarization was to provide a methodology for
predicting LAI values in the non-current scenarios.
Analysis of LAI frequency
plots by modeling class and structural stage showed that the NDVI-LAI transformation process yielded extreme and average values that were somewhat higher than values expected for the CRB region (Waring and Franklin, 1979). LAI values were lowered calculating correction coefficients that were expected LAI values over the average NDVI-LAI values by modeling class.
Corrected LAI
values were calculated by multiplying the NDVI-LAIs coefficients across all structural stages (Appendix 4).
In the case of missing LAI values by
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structural class, modeling class average values were used to interpolate. Structural variance was assumed to only influence LAI in the coniferous and deciduous forest communities and, therefore, non-forest types were given uniform LAIs across structural stages.
These new LAI values were used in all
modeling scenarios using vegetation cover and structural classes as indexes for LAI assignment.
Stem carbon--Modelling class specific coefficients were derived for estimating living sapwood carbon from LAI.
Coefficient values were calculated based on
maximum LAI, species LAI to sapwood are ratios, maximum height, density of mature community, specific gravity of wood, and proportion living cell volume of total sapwood tissue.
This was a reasonable approximation given that a)
the amount of sapwood volume supported by a species is linearly related to diameter at breast height (dbh) and b) dbh is logrithmically related to sapwood cross-sectional area (Snell and Brown, 1978).
Maximum LAI values were
found for each modeling class in the CRB from the NDVI regression.
LAI to
sapwood are ratios were found in various literature sources (Snell and Brown, 1978; Kaufmann and Troendle, 1981; Waring and others 1982; Waring and Schlesinger, 1985; Gower and others 1987).
Maximum height values were
extracted from site index curves for the study area using the height value of stands with a site index value of 80 at year 160 (McArdle and Meyer, 1930; Haig, 1932; Meyer, 1938; Barnes, 1962; Jones, 1967; Milner, 1992).
Density
values were used to estimate tree/shrub level sapwood volume (Peet, 1988; Keeley and Keeley, 1988).
Specific gravity values were used to convert
sapwood volume to sapwood mass (Fahey, 1976) on kg C given a dry weight to carbon conversion factor of 0.5.
Proportion of living tissue of total sapwood
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volume were taken from Waring and Schlesinger (1985).
The final coefficient values from this process indicated an increase in sapwood mass per unit leaf area across a drought tolerance gradient (Appendix 5).
However, because sapwood hydraulic conductivity decreases with age
(Whitehead and others 1984) the amount of sapwood mass per unit LAI was reduced linearly for all classes across using structural class as a proxy for age.
The impact of this scaling was to reduce the respiration load on younger
stands.
Living sapwood (kg C m-2) was calculated for each modeling unit by
multiplying the coefficients by the appropriate LAI value and coefficient specific for each modeling class and structural stage.
Non-woody vegetation
types were assigned a minimum sapwood tissue as a substitute for reproductive and storage compartments not defined in the model.
BGC Outputs
The purpose of the CRB-BGC simulations were to provide a broad summary of both the productivity and the ecophysiological robustness for historic, current, and projected future vegetation over a large area under a variety of climatic conditions.
Because of the broad scale of the analysis and the large data
volume associated with spatial outputs, a limited number of the many potential output variables were selected for inclusion in further analysis.
These raw
outputs included: gross photosynthesis, maintenance respiration for each carbon compartment, evaporation, transpiration, hydrologic outflow, and the scalars controlling soil and litter decomposition.
Annual totals for each of
these variables, for each climate year, and for each vegetation scenario, were
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generated for each simulation gridcell, resulting in a collection of annual summary maps.
These summary maps were used to generate the final output
products, each of which is discussed in a following section.
BGC generates several spatial data layers describing the state of ICRB ecosystem processes for each simulation run.
To reduce complexity and
emphasize brevity, only four of these data layers will be discussed in this chapter.
The ICRB EIS team posed several questions they wanted answered from ecosystem process modeling:
1.
What is the productivity across the ICRB, how has it changed from
historical conditions, and how will it change as a consequence of our current and future management actions?
2.
Where are the risks for increased insect, disease or fire
disturbances under various management strategies?
How do we quantify
ecosystem "health" or integrity?
3.
What areas are at risk for declines in long-term site productivity?
Where do we have high nutrient availability?
White and others (1996) developed four indices that can be used to answer these questions.
Table 3 presents these indices and provides general
statistics about these indices across the ICRB.
The following is a detailed
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description of each variable.
Net primary production--From annual summaries of gross photosynthesis (GPSN) and maintenance respiration for all carbon compartments (3MR), net primary
NPP = GPSN - ∑ MR production (NPP) was estimated as: As noted above, growth respiration was not estimated, and our estimates of NPP should therefore be reduced by 20-40% for comparison to field estimates. general NPP indicates how fast the vegetation is growing.
In
This number is a
good index to describe the growth potential of an ecosystem.
Carbon stress index--In order to summarize the influence of respiration costs on the productive potential of the vegetation, a ratio of maintenance respiration to gross photosynthesis was calculated called the carbon stress index (CSI).
This index describes the balance between annual gross
productivity and maintenance costs.
Values close to 1.0 indicate that most of
the annual production of photosynthate is being consumed in the maintenance of living tissue, while values close to 0.0 indicate that a large surplus of the annual photosynthate production remains after maintenance costs are met, allowing, for example, allocation to new growth, production of protective compounds or reproduction.
This index is designed as an estimator of ecophysiological health and relative susceptibility to disease or insect attack.
It is more diagnostic in these
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respects than, for example, NPP, because it places vegetation types with different productive potentials on the same scale for the comparison of relative robustness or, alternatively, susceptibility.
For example, in the
comparison of a mature forest type with a shrub/regeneration type, NPP estimates might be higher for the forest than for the shrub, with the potential interpretation that the forest is more robust or less susceptible to disease or drought.
Depending on the amount of live tissue being supported in
each of these communities, the CSI might reveal that the forest uses 80 percent of its annual photosynthate production in maintenance costs, while the shrubs are using only 40 percent, providing an alternative interpretation of the relative health of the two communities.
CSI values of greater than 80
percent indicate high carbon stress and poor ecosystem health.
CSI values of
60-80 percent indicate moderate stress and values less than 60 percent describe healthy, unstressed ecosystems.
Strong carbon balances (CSI < 60%)
have plenty of carbon available for growth, reproduction and defensive chemistry.
Weak carbon balances will have less energy allocated to the carbon
sinks.
There are regions in the CSI outputs which have values greater than 1.0, indicating maintenance respiration costs for the year were higher than the gross production of photosynthate.
Perennial vegetation types with storage
reserves can be expected to withstand such conditions for a limited time.
For
example, a mature forest may be able to withstand a particularly dry year or an insect defoliation event which results in a net loss of photosynthate by using carbohydrate reserves stored in stem and root tissue, but these reserves are reduced and successive years with low production or high maintenance costs
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are more likely to cause mortality.
Vegetation without adequate reserves,
annual plants or tree saplings for example, would not be expected to withstand these conditions for even a single year.
Because the vegetation types and
structural stages for the CRB-BGC simulations were determined externally, with no mechanistic feedbacks between vegetation type and climate or soils, some instances of CSI > 1.0 are likely to be the result of a mismatch between estimated site conditions and designated vegetation type or structural stage. In other cases the spatial and temporal variability of meteorological conditions and vegetation communities could result in accurate predictions of high values for the CSI. Without a mechanistic interaction between meteorology, soils, and vegetation type or structural stage or biomass, it is difficult to discern between these cases.
Water stress index--Water availability is a key limiting factor for much of the study region, and this limitation is summarized in terms of the ratio of total annual evapotranspiration (ET) to total annual precipitation, the water stress index (WSI).
Values close to 1.0 are generally indicative of arid
conditions, with low soil moisture content and very likely limitations to production imposed by water stress.
Values close to 1.0 can also indicate
regions of substantial precipitation and high evaporative demand, which would not typically be considered arid, but which make water stress susceptibility for vegetation with high leaf area a consideration.
Low values are indicative
of a water surplus, which under annual soil water equilibrium conditions is manifested as hydrologic outflow.
Very low values of the WSI are
characteristic of upper elevation sites with abundant winter precipitation, and sparsely vegetated sites at lower elevations that receive precipitation in
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excess of bare soil evaporation.
Low WSI values (85 percent) indicate water is limiting to plant growth and most precipitation is being used by the vegetation or lost through evaporation, and there is little to no hydrologic outflow or groundwater recharge.
Nutrient availability index--Although the current analysis lacks many of the components required for a realistic estimation of nutrient availability, a few of the most relevant factors were summarized into the nutrient availability index (NAI).
Note that the term "nutrient" means principally nitrogen,
although some of what follows is applicable to other nutrients as well.
The
decomposition of organic matter in the litter and in the soil is the principal nutrient source for vegetation which lacks the ability to fix atmospheric N2. Described above is the estimation of a composite decomposition scalar, which integrates the effects of both soil moisture and temperature on the relative decomposition rate.
The most important pool acted upon by this scalar is the
litter organic matter, since its high N:C ratio provides a good substrate for the heterotrophs that do the work of decomposition.
The soil organic carbon
pool, although typically many times larger than the litter organic carbon pool, is of a poorer quality (lower N:C) and so decomposes more slowly and is of less importance in the release of nutrients to the plant available pools. The single best estimate of inputs to the litter organic matter pool, from the choices available under the constraints of these simulations, is NPP.
This
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argument rests on an assumption of equilibrium, that on an annual basis all the organic matter resulting from NPP will be returned to the litter, either as leaf, stem, or root turnover.
This is clearly not entirely accurate, since
conditions of net growth and net decline of live biomass are very common. However, it is likely that these non-equilibrium components (growth and decline) will be correlated with NPP, and so as a relative scalar of potential decomposition, NPP seems a logical choice.
By multiplying the annual average
decomposition scalar by the annual NPP, a value for the NAI was created which is comparable across vegetation types.
This index is a crude approximation of the potential nutrient availability from the rate of decomposition and productivity.
NAI is defined as a
decomposition scalar (ds) multiplied by NPP as defined above.
The
decomposition scalar is a function of daily soil temperature and soil water content summed over the year (Running and Hunt 1993).
High values are
associated with wet, warm soils and low values can indicate cold and/or dry soils.
The decomposition scalar represents the potential rate of
decomposition on a pixel.
The actual rate of decomposition is dependent on
the supply of litter and other organic material to the forest floor.
A
reasonably good representation of the potential organic material supply can be portrayed by NPP since NPP is directly proportional to litter production.
The
product of NPP and decomposition scalar describes the likely amount of decomposition for that year.
The connection between this index and nutrient
availability is a function of litter quality or lignin content. was made to integrate lignin interactions into NAI.
No attempt
Low values (>0.05)
signify low rates of decomposition and high amounts of nutrients tied up in
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the forest floor and plant parts.
High values (10% decline) occurred in the Late Seral Intolerant Multistrata Forests, Mid Seral Intolerant Forests, Riparian Herb and Woodland Upland. These NPP declines were the result of land area losses and conversions to other PVG's.
The major increases in carbon stress (CSI) are in Riparian Herb
and most late seral forests.
Water availability increased (decrease in WSI)
in all but Riparian Herb and Mid Seral Tolerant Forests, with the major increases in early seral types presumably because of lower leaf areas.
SPG's
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with the biggest declines in long-term site productivity are the two herblands (Upland and Riparian) and Mid Serial Intolerant Forests.
Most SPG's (10 of
17) had declines in nutrient availability.
SPG and ownership summary--There is very little difference between ownerships for most SPG (Appendix 9).
Landscape Shifts
This section deals the consequences of the conversion of one SPG to another within a PVG.
Only the major shifts will be evaluated using Appendix 10.
Most Cold Forests went from late seral conditions to early seral conditions resulting in declines of NPP, increases in CSI, higher water availability and higher decomposition rates.
Cool Shrub PVG's mostly experienced changes from
herb or shrublands to woodlands that caused a decrease in NPP, higher carbon stress, less water and less nutrient cycling.
Dry Forest PVG went mostly from
fire-maintained grass/shrub to early seral forests and from early seral forests to late seral forests with multiple strata.
This resulted in minimal
gains in productivity but increases in carbon stress and water stress and decreases in nutrient availability.
Dry Shrub PVG stayed mostly the same with
most changes to grasslands or exotics.
However, changes from herblands to
shrublands caused a high increase in NPP and decrease in CSI.
Moist Forest
PVG had advances in succession usually causing some increase in NPP but higher carbon stress and lower cycling potential.
Riparian Woodland PVG made great
gains from herblands to woodlands resulting in increases of NPP and decreases in carbon stress and NAI.
Keane--p.30
SUMMARY
General Results Statements -- Historical to Current
The following set of statements summarize the BGC simulation results contrasting current and historical conditions.
These statements were derived
from results shown in Appendix 11 and Appendix 12.
Concerning Productivity (NPP):
1.
Intolerant forest SPG's are less productive than tolerant SPG's
probably because of lower leaf areas and high stem respiration costs.
2.
Early seral forests are more productive than late seral forests and
mid seral forests are often very productive.
3.
Riparian communities are much more productive than upland sites.
4.
Exotics have less productivity than native vegetation and also have
higher carbon stresses and lower nutrient turnover.
5.
Ecosystem processes simulated for Water, Urban and Agriculture do
not compare well with the other vascular plant dominated ecosystems because of the broad assumptions used to quantify the parameters that describe these land classifications.
Keane--p.31
6.
In general, productivity across PVG's seems to follow conventional
wisdom with NPP values lowest in the driest or coldest ranges of a biome and increasing as moisture or temperature increases.
However,
grasslands, shrublands and forests have comparable NPP values.
7.
Conversion of native grass and shrublands to agriculture has
generally increased productivity but that increase is mainly due to fertilization and irrigation influencing the high leaf areas on agricultural lands.
8.
Riparian ecosystems have high productivity but high carbon and water
stress because BGC does not simulated subsurface water use by plants. The riparian ecophysiologic parameters have high leaf area indices but the weather data does not support this much leaf area.
As a
consequence, there are much higher WSI values due to the high water usage.
Concerning Carbon Stress (CSI):
1.
The highest carbon stressed PVG's occur in the driest areas.
The
productive moist forests, however, have some of the lowest CSI values, indicating less stress.
2.
Intolerant forests have higher carbon stress than tolerant forests
because tolerant forests have lower respiring live stem tissue.
Keane--p.32
3.
Upland shrub and herblands have higher CSI values than riparian
sites.
Concerning Water Stress (WSI):
1.
Water stress usually increases with successional status, with more
water available in early seral communities probably due to the lower leaf areas.
2.
The highest water stress values are in riparian environments because
of high leaf areas.
Concerning Long-Term Site Productivity (NAI):
1.
In general, nutrients are more available in early to mid seral
forest settings.
2.
Single-layer forests have higher rates of decomposition than
multistrata forests.
3.
Herb and shrublands have higher nutrient turnover than forests.
BGC Attributes for ICRB Vegetation Types A synthesis of the four BGC output variables for all PVG's and SPG's is presented by forested and non-forested environments in Tables 5 to 12.
These
Keane--p.33
tables provide a means for extrapolating BGC results from this effort to the interpretation of management alternatives in the development of the EIS.
Keane--p.34
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Estimating absorbed
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Bailey, Robert G. 1995. Description of the ecoregions of the United States. U.S. Department of Agriculture, Forest Service Misc. Publ. No. 1391. 108 p.
Barnes, G.H. 1962. Yield of even-aged stands of western hemlock.
U.S.
Department of Agriculture, Forest Service, Technical Bulletin No. 1273.
Beukema, S.J.; Kurtz, W.A. 1995. Vegetation dynamics development tool user's guide. Report prepared by ESSA Technologies Ltd., Vancouver, B.C., Canada. 51 p.
Cosby, B.J.; Hornberger, G.M.; Clapp, R.B.; Ginn, T.R. 1984. A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resources Research. 20:682-690.
Eyre, F.H. Editor. 1980. Forest cover types of the United States and Canada. Society of American Foresters, 5400 Grosvenor Lane, Washington D.C. 147 p.
Fahey, M.D. 1976. Some characteristics of western hemlock wood. In: Western Hemlock Management Conference. Atkinson, W.A.; Zasoski, R.J., eds. College of Forest Resources, University of Washington, May, 1976.
Keane--p.35
Farquhar, G.D.; von Caemmerer, S.; Berry, J.A. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta. 149:78-90.
Ferguson, Sue A. 1996. A climate-change scenario for the Columbia River Basin. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Seattle, WA.
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Allometric relations
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Haig, I.T. 1932. Second-growth yield, stand, and volume tables for the western white pine type. U.S. Department of Agriculture, Forest Service, Technical Bulletin No. 323.
Hardy, C.; Burgan, R.; Menakis, J.P. 1995. Development of a current vegetation map for the Interior Columbia River Basin Scientific Assessment.
Manuscript
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Jones, J.R. 1967. Aspen site index in the Rocky Mountains.
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Kaufmann, M.R.; Troendle, C.A. 1981. The relationship of leaf area and foliage biomass to sapwood conducting area in four subalpine tree species. Forest Science. 27(3):477-482.
Keane--p.36
Keane, R.E.; Menakis, J.P.; Long, D.; and others 1996. Simulating coarse scale vegetation dynamics with the Columbia River Basin Succession Model--CRBSUM. In press as a U.S. Department of Agriculture, Forest Service, General Technical Report.
Keeley, J.E.; Keeley, S.C. 1988. Chaparral. In: North American Terrestrial Vegetation. Barabour, M.G.; Billings, W.D., eds. Cambridge University Press, Cambridge: 391-420.
Lassoie, J.P.; Hinckley, T.M.; Grier, C.C. 1985. Coniferous forests of the Pacific Northwest. In: Physiological ecology of North American plant communities Chabot, B.F.; Mooney, H.A., eds. Chapman and Hall, New York. 351 p.
Losensky, B.J. 1994. Historical vegetation types of the Interior Columbia River Basin. Report on file at the Intermountain Fire Sciences Laboratory, Missoula, MT, for completion of RJVA INT-94951-RJVA. 102 p.
Loveland, Thomas R.; Ohlen, Donald O. 1993. Experimental AVHRR land data sets for environmental monitoring and modeling. In: Goodchild, M.F., Parks, B.O. and Steyaert, L.T. Environmental Modelling with GIS. Oxford University Press. New York: 379-385.
Loveland,R.; Merchant, J.M.; Ohlen, Donald O.; Brown, J.F. 1991. Development of a land-cover characteristics database for the conterminous U.S. Photogrammetric Engineering and Remote Sensing.
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Keane--p.37
McArdle, R.E.; Meyer, W.H. 1930. The yield of Douglas fir in the Pacific Northwest. U.S. Department of Agriculture, Forest Service, Technical Bulletin No. 201.
Meyer, W.H. 1938. Yield of even-aged stands of ponderosa pine.
U.S.
Department of Agriculture, Forest Service, Technical Bulletin No. 630.
Milner, K.S. 1992. Site index and height growth curves for ponderosa pine, western larch, lodgepole pine, and Douglas-fir in western Montana. Western Journal of Applied Forestry. 7(1):9-14.
Peet, R.K. 1988. Forest of the Rocky Mountains. In: North American Terrestrial Vegetation. Barabour, M.G.; Billings, W.D., eds. Cambridge University Press, Cambridge: 391-420.
Pierce, L.L.; Running, S.W.; Walker, J. 1994. Regional-scale relationships of leaf area index to specific leaf area and leaf nitrogen content. Ecological Applications. 4(2):313-321.
Pierce, L.L.; Walker, J.; Dowling, T.I.; and others. 1993.
Ecohydrological
changes in the Murray-Darling Basin III. a simulation of regional hydrological changes. Journal of Applied Ecology. 30:283-294.
Running, S.W.; Coughlan, J.C. 1988. A general model of forest ecosystem processes for regional applications. I. Hydrologic balance, canopy gas exchange, and primary production processes. Ecological Modelling. 42:125-154.
Keane--p.38
Running, S.W.; Gower, S.T. 1991. FOREST-BGC, a general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets. Tree Physiology. 9:147-160.
Running, S.W.; Hunt, E.R., Jr. 1993. Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. In: Scaling physiological processes: leaf to globe. Ehleringer, J.R. ; Field, C.B. eds. Academic Press, San Diego, CA.
Shiflet, T.N., ed. 1994. Rangeland cover types of the United States. Society of Range Management, 1839 York Street, Denver, CO, USA. 151 p.
Snell, J.A.K.; Brown, J.K. 1978. Comparison of tree biomass estimators-dbh and sapwood area. Forest Science. 24(4):455-457.
Society of American Foresters (SAF). 1984. Forestry Handbook. Wegner, K.F. ed. John Wiley & Sons, New York. 1335 p.
Soil Conservation Service (SCS)(presently Natural Resource Conservation Service). 1991. State Soil Geographic Database (STATSGO): Data Users Guide, U.S. Department of Agriculture, Soil Conservation Service, Misc. Pub. No. 1492.
Spanner, M.A.; Pierce L.L.; Peterson, D.L.; Running, S.W. 1990. Remote sensing of temperate coniferous forest leaf area index: the influence of canopy closure, understory vegetation, and background reflectance. International
Keane--p.39
Journal of Remote Sensing. 11(1):95-111.
Thornton, P.E; Running, S.W.; White, M.A. 1996. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology (In press).
Waring, R.H.; Franklin, J.F. 1979. The evergreen coniferous forest of the Pacific Northwest. Science, 204:1380-1386.
Waring, R.H.; Schroeder, P.E.; Oren, R. 1982. Application of the pipe model theory to predict canopy leaf area. Canadian Journal of Forest Research. 12:556-560.
Waring, R.H.; Schlesinger, W.H. 1985. Forest Ecosystems. New York: Academic Press. 340 p.
Whitehead, D.; Edwards, W.R.N.; Jarvis, P.G. 1984. Conducting sapwood area, foliage area, and permeability in mature trees of Picea sitchensis and Pinus contorta. Canadian Journal of Forest Research 14:940-947.
Keane--p.40
Table Captions Table 1--
Cross-reference of Biome-BGC forest structural stages with those
used in the ICRB scientific assessment.
Table 2--
Cross-reference of Biome-BGC cover types with those used in the
ICRB scientific assessment.
Table 3--
Description of Biome-BGC output indices used to describe ecosystem
trends across the ICRB.
Statistics are for current vegetation conditions for
the normal weather year (1989).
Table 4-CRBSUM.
General description of the four management futures simulated by These futures were designed by altering disturbance probabilities.
Table 5--
Average net primary productivity (NPP) estimates for forested
ecosystems.
Table 6--
Average net primary productivity (NPP) estimates for forested
ecosystems.
Table 7--
Average estimates for carbon stress index (CSI) in the forested
environments.
Table 8-ecosystems.
Average carbon stress index (CSI) estimates for non-forested
Keane--p.41
Table 9--
Average water stress (WSI) estimates for forested ecosystems.
Table 10--
Average water stress index (WSI) for non-forest environments.
Table 11--
Average nutrient availablity index (NAI) estimates for forested
environments.
Table 12--
Average nutrient availability index (WAI) estimates for non-
forested ecosystems.
Keane--p.42
Appendix Captions
Appendix 1--
Aggregation of the CRB Vegetation Types into Modeling Types
Based on Drought Tolerance.
Appendix 2--
Aggregation of the CRB Structural Stages into Simple Modelling
Stages.
Appendix 3--
Ecophysiological Constants Used for the CRB BGC Analysis.
Appendix 4--
NDVI derived LAI Values, Corrected LAI values, and Correction
Coefficients.
Appendix 5--
Sapwwod Mass to Total Leaf Area Coefficients.
Appendix 6--
Change Between Historic and Current by PVG.
Appendix 7--
Change Between Historic and Current by Ownership and
Physiognomic Type.
Appendix 8--
Change Between Historic and Current by Physiognomic Type.
Appendix 9--
Percent Change Between Historic and Current by Ownership and
Physiognomic Type.
Appendix 10--
Percent Change Between Historic and Current by PVG and
Keane--p.43
Physiognomic Types.
Appendix 11--
Current Conditions (1989 Weather Year) -- Using Current PVG and
Physiognomic Type.
Appendix 12--
Historic Conditions (1989 Weather Year) -- Using Current PVG
and Historic Physiognomic Type.
Appendix 1--Aggregation of the CRB Vegetation Types into Modeling Types Based on Drought Tolerance
Model Vegetation Types & Drought Tolerance (Abbreviation)
CRB Vegetation Types (GIS #)
Coniferous Forest Intolerant (CFI)
Western redcedar/western hemlock (29)
Coniferous Forest Moderate (CFM)
Pacific silver fir/mt. hemlock (5), Mixed conifer woodlands (33), Grand fir/white fir (13), Red fir (15), Mt. hemlock (20), Englemann spruce/subalpine fir (21), Interior Douglas fir (23), Western larch (24), Western white pine (25), Sierra Nevada mixed conifer (33)
Coniferous Forest Tolerant (CFT)
White bark pine/alpine larch (14), White bark pine (22), Lodgepole pine (27), Limber pine (28), Interior ponderosa pine (32), Pacific ponderosa pine (34)
Coniferous Forest Very Tolerant (CFVT)
Juniper woodlands (6), Juniper/big sage/bluebunch wheatgrass (8), Western juniper Juniper/big sage/bluebunch wheatgrass (37)
Deciduous Forest Intolerant (DFI)
Cottonwood/willow (31)
Deciduous Forest Moderate (DFM)
Aspen (26)
Deciduous Forest Tolerant (DFT)
Oregon white oak (30)
Shrub Intolerant (SI)
Herbaceous wetland/shrub (9)
Shrub Moderate (SM)
Seral shrub-regen (1), Mountain mahogany (39), Mountain shrub (45)
Shrub Intolerant (SI)
Antelope bitterbrush/bluebunch wheatgrass (36), Basin big sage (40), Mountain big sagebrush (41), Wyoming big sagebrush (42), Low sage (43), Salt desert shrub (44)
Grassland/Forbs (G/F)
Open grassland (10), Native forb (11), Exotics (12), Urban (18), Bluebunch wheatgrass (35), Idaho fesue/bluebunch wheatgrass (38)
Herbaceous wetland (W)
Herbaceous wetlands (4)
Alpine Tundra (T)
Alpine tundra (2)
Agriculture (A)
Irrigated cropland and pasture (16), Dry cropland and pasture (17)
Appendix 2--Aggregation of the CRB Structural Stages into Simple Modeling Stages
Modeling Stage
CRB Structual Stage (GIS #)
Early
Stand initiation (1), Woodland stand initiation (11)
Middle
Stem exclusion open canopy (2), Stem exclusion closed canopy (3), Understory reinitiation (4), Young forest multi-strata (5), Woodland stem exclusion (12), woodland understory reinitiation (13), Young woodland multi-strata (14)
Late
Old forest multi-strata (6), Old-forest single-strata (7), Old woodland multi-strata (15), Old woodland single-strata (16)
Shrub
Closed low shrub (23), Open low shrub (24), Open mid shrub (25), Closed mid shrub (26), Open tall shrub (27), Closed tall shrub (28)
Appendix 3--Ecophysiological Constants Used for the CRB BGC Analysis Constant
CFI
CFM
CFT
CFVT
DFI
DFM
DFT
SI
SM
ST
G/F
W
T
A
SLA (kg C m -2 )
35.0
25.0
15.0
10.0
45.0
45.0
40.0
45.0
35.0
25.0
45.0
45.0
30.0
45.0
2.2
2.2
2.2
2.2
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
% of Leaf N in RuBisCo
0.12
0.10
0.08
0.06
0.15
0.15
0.12
0.15
0.08
0.08
0.10
0.15
0.10
0.15
Shape parameter for PPFD curve
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
To p t for leaf conductance (EC)
25.0
25.0
25.0
25.0
25.0
25.0
25.0
25.0
25.0
25.0
25.0
25.0
25.0
25.0
Tmax for leaf conductance (EC)
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
Temperature coefficient
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
? leaf at start of conductance reduction (MPa)
-0.2
-0.3
-0.4
-0.5
-0.2
-0.4
-0.4
-0.2
-0.3
-0.5
-0.3
-0.2
-0.5
-0.5
? leaf at stomatal closure (MPa)
-1.5
-2.0
-2.5
-3.0
-1.5
-2.0
-2.0
-2.0
-2.5
-3.0
-2.0
-1.5
-3.0
-1.5
VPD at start of conductance reduction (Pa)
500.0
500.0
500.0
500.0
500.0
500.0
500.0
500.0
500.0
500.0
500.0
500.0
500.0
500.0
VPD at stomatal closure (Pa)
2000.0
2000.0
2000.0
2000.0
4000.0
2000.0
2000.0
4000.0
2000.0
2000.0
2000.0
4000.0
2000.0
2000.0
Maximum stomatal gleaf (m sec -1 )
0.008
0.006
0.006
0.004
0.010
0.008
0.006
0.010
0.008
0.006
0.008
0.010
0.006
0.010
0.00005
0.00005
0.00005
0.00005
0.00005
0.00005
0.00005
0.00005
0.00005
0.00005
0.00005
0.00005
0.00005
0.00005
Boundary layer conductance gleaf (m sec )
0.08
0.08
0.08
0.08
0.02
0.04
0.04
0.01
0.02
0.04
0.04
0.01
0.03
0.02
Leaves on/off
0/365
0/365
0/365
0/365
110/300
110/300
110/300
110/300
110/300
0/365
110/300
110/300
110/300
110/300
Total/projected Leaf area Precipitation interception (kg H2 O m Light extinction coefficient (sec m
-2
-2
-1
d ) -1
µmol )
Cuticular gleaf (m sec -1 ) -1
Fine roots on/off
0/365
0/365
0/365
0/365
110/300
110/300
110/300
110/300
110/300
0/365
110/300
110/300
110/300
110/300
-1
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
-1
Stem Rm coefficient at 20 EC (day )
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
Coarse root Rm coefficient at 20 EC (day -1 )
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
Fine root Rm coefficient at 20 EC (day -1 )
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
0.010
Q10 for all Rm
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
Leaf Rm coefficient at 20 EC (day )
Appendix 4--NDVI derived LAI Values, Corrected LAI values, and Correction Coefficients
CFI
CFM
CFT
CFVT
DFI
DFM
DFT
SI
SM
ST
G/F
W
T
A
Early
NDVI-LAI
20.7
9.6
6.9
4.9
NA
8.4
7.6
2.0
6.0
1.5
2.2
2.2
1.6
2.3
Early
Corr-LAI
12.0
6.0
4.0
1.0
2.0
2.0
2.0
2.0
1.5
1.5
1.5
2.2
0..5
2.3
Middle
NDVI-LAI
22.7
13.7
8.7
3.3
8.2
8.6
7.7
2.0
6.0
1.5
2.2
2.2
1.6
2.3
Middle
Corr-LAI
14.0
9.0
6.0
2.3
3.0
3.0
3.0
2.0
1.5
1.5
1.5
2.2
0..5
2.3
Late
NDVI-LAI
25.4
12.1
7.2
NA
8.8
NA
8.4
2.0
6.0
1.5
2.2
2.2
1.6
2.3
Late
Corr-LAI
15.5
7.0
5.0
2.0
3.5
3.5
3.5
2.0
1.5
1.5
1.5
2.2
0..5
2.3
Ratio
0.62
0.66
0.69
0.70
0.37
0.35
0.39
1.0
0.25
1.0
0.68
1.0
0.31
1.0
Appendix 5--Sapwwod Mass to Total Leaf Area Coefficients
Stage
CFI
CFM
CFT
CFVT
DFI
DFM
DFT
SI
SM
ST
G/F
W
T
A
Early
0.00752
0.00930
0.01044
0.02027
0.01525
0.01304
0.01353
0.00500
0.09740
0.00600
0.00001
0.00001
0.00001
0.00001
Middle
0.00986
0.01315
0.01438
0.02658
0.02778
0.02140
0.02271
0.00500
0.09740
0.00600
0.00001
0.00001
0.00001
0.00001
Late
0.01420
0.02263
0.02609
0.05217
0.04348
0.06030
0.07135
0.00500
0.09740
0.00600
0.00001
0.00001
0.00001
0.00001
APPENDIX 6--Change Between Historic and Current by PVG OBS 1 2 3 4 5 6 7 8 9 10 11 12 13 14
CUR_PVG
HIS_KM
CUR_KM
PERCHKM
AGRICULT ALPINE COLD FOR COOL SHB DRY FOR DRY GRS DRY SHR MOIST FOR RIP SHR ROCK URBAN WATER WOODLAND RIP WDLND
118363 3727 99153 56952 146615 41059 209953 112104 5500 2299 1143 7550 4748 12194
118363 3727 99153 56952 146615 41059 209953 112104 5500 2299 1143 7550 4748 12194
0 0 0 0 0 0 0 0 0 0 0 0 0 0
NPP_SUM
NPP_AVG
CSI_AVG
WSI_AVG
NAI_AVG
5.55 -7.36 2.12 -6.00 4.21 -4.41 -4.84 -2.14 -3.82 -5.26 -18.04 3.95 8.09 13.48
5.22 -7.03 2.80 -4.22 4.78 -6.54 -2.16 -2.21 -3.29 -5.71 -23.03 4.74 10.54 13.15
8.54 -0.41 -5.34 7.95 4.01 3.05 4.00 1.25 2.00 -3.63 39.81 -2.02 -2.39 -9.60
12.94 -1.88 0.30 -0.32 4.88 1.70 0.48 1.55 3.60 -2.61 9.51 2.80 1.60 3.18
-18.21 -4.49 3.05 -5.38 -6.14 -9.11 -2.51 -5.51 -14.83 -0.71 -32.78 0.40 10.63 3.10
APPENDIX 7--Change
OBS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
OWN BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS Other Other Other Other Other Other Other Other Other Other Other Other Other Other
Between Historic and Current by Ownership and Physiognomic Type. CUR_PVG AGRICULT ALPINE COLD FOR COOL SHB DRY FOR DRY GRS DRY SHR MOIST FOR RIP SHR ROCK URBAN WATER WOODLAND RIP WDLND AGRICULT ALPINE COLD FOR COOL SHB DRY FOR DRY GRS DRY SHR MOIST FOR RIP SHR ROCK URBAN WATER WOODLAND RIP WDLND
KM
NPP_SUM
NPP_AVG
CSI_AVG
WSI_AVG
NAI_AVG
2188 3209 80076 34759 77257 13657 134521 68765 2806 2050 2 1214 2144 5976 116175 518 19077 22193 69358 27402 75432 43339 2694 249 1141 6336 2604 6218
-11.55 -9.24 1.96 -5.58 3.22 -14.91 -5.92 -3.14 -5.19 -5.91 10.65 2.19 -3.93 10.38 6.21 0.89 2.80 -6.51 5.54 2.57 -3.32 -0.11 -2.59 -2.01 -18.11 4.76 18.36 16.83
-12.18 -8.05 2.56 -3.75 3.79 -18.99 -3.33 -3.14 -5.27 -6.55 10.65 3.62 -4.60 8.52 5.51 -2.44 3.90 -5.02 6.04 0.11 -0.12 -0.32 -1.47 -0.19 -23.05 5.20 21.18 18.15
19.10 -0.11 -5.72 5.89 -0.96 0.23 5.57 -0.17 2.07 -3.24 39.22 -4.63 6.22 -7.51 8.34 -1.82 -3.69 11.08 9.87 4.60 1.33 3.35 2.01 -6.33 39.84 -1.13 -9.47 -11.36
7.92 -2.10 0.00 -0.50 1.45 -2.85 0.41 0.11 3.81 -2.52 17.31 -1.35 0.08 2.49 13.03 -0.62 1.47 -0.02 8.28 3.57 0.60 3.34 3.37 -2.87 9.38 3.83 2.67 3.67
-31.44 -4.64 3.41 -3.79 1.24 -11.23 -3.77 -4.20 -19.63 -1.85 -31.25 7.98 -3.82 1.48 -18.02 -4.34 1.76 -7.90 -12.66 -8.37 -0.50 -7.77 -10.45 5.17 -32.70 -1.95 18.90 4.55
APPENDIX 8--Change Between Historic and Current by Physiognomic Type OBS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
PHYSG Agricultural Exotics Rock/Barren Urban Water Alpine Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Herb Riparian Shrub Riparian Woodlnd Woodland Upland
HIS_KM . . 2299 . 7561 3729 122962 311641 72024 36992 44987 97756 16809 20609 4873 44620 1999 5309 8972 18218
CUR_KM 118363 11378 2299 1143 7550 3727 46156 236839 49849 16654 25863 110603 21301 18986 7496 100006 1316 2906 17283 21642
PERCHKM . . 0.00 . -0.15 -0.05 -62.46 -24.00 -30.79 -54.98 -42.51 13.14 26.72 -7.88 53.83 124.13 -34.17 -45.26 92.63 18.79
NPP_SUM . . -5.26 . 3.75 -7.42 -59.65 -32.78 -31.06 -63.61 -40.28 -1.12 20.47 -17.76 31.70 117.95 -37.63 -40.69 148.06 -10.12
NPP_AVG . . -5.71 . 4.74 -7.06 -9.00 -5.26 -3.30 -13.35 8.01 -14.84 3.90 -9.34 1.31 -0.89 -29.21 -6.80 8.84 -21.96
CSI_AVG . . -3.63 . -1.98 -0.48 6.33 5.34 -8.19 4.05 -6.58 6.05 -12.48 7.28 1.49 3.47 15.26 -2.95 -1.88 1.61
WSI_AVG . . -2.61 . 2.85 -1.94 -5.02 -1.53 -19.76 3.56 -16.39 17.10 -19.08 8.19 -6.26 20.10 11.99 -3.64 -3.19 8.20
NAI_AVG . . -0.71 . 0.37 -4.55 -31.73 -9.63 -1.37 -11.34 9.24 -16.31 1.80 -0.04 33.10 3.15 -46.66 -10.50 1.50 11.77
APPENDIX 9--Percent Change Between Historic and Current by Ownership and Physiognomic Type OBS
OWN
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS BLM/FS Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other
7
PHYSG
HIS_KM
CUR_KM
PERCHKM
NPP_SUM
NPP_AVG
CSI_AVG
WSI_AVG
NAI_AVG
Agricultural Exotics Rock/Barren Urban Water Alpine Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Herb Riparian Shrub Riparian Woodlnd Woodland Upland Agricultural Exotics Rock/Barren Urban Water Alpine Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Herb
. . 2050 . 1214 3209 20833 163376 38608 22458 25770 65378 13316 15944 3455 35338 118 910 5395 11252 . . 249 . 6347 520 102129 148265 33416 14534 19217 32378 3493 4665 1418 9282 1881
2188 7532 2050 2 1214 3209 18303 152386 33563 12084 16865 60968 17650 14435 4242 60469 626 1426 10622 8790 116175 3846 249 1141 6336 518 27853 84453 16286 4570 8998 49635 3651 4551 3254 39537 690
. . 0.00 . 0.00 0.00 -12.14 -6.73 -13.07 -46.19 -34.56 -6.75 32.55 -9.46 22.78 71.12 430.51 56.70 96.89 -21.88 . . 0.00 . -0.17 -0.38 -72.73 -43.04 -51.26 -68.56 -53.18 53.30 4.52 -2.44 129.48 325.95 -63.32
. . -5.91 . 2.19 -9.24 -8.16 -15.35 -21.18 -57.69 -36.25 -11.89 24.71 -17.91 10.54 80.08 429.69 38.47 167.68 -43.73 . . -2.01 . 4.46 0.52 -70.58 -47.58 -45.80 -73.85 -46.37 22.99 4.55 -17.25 85.20 259.05 -58.87
. . -6.55 . 3.62 -8.05 3.61 -1.58 -10.14 -15.21 -0.22 -9.46 2.61 -7.67 3.48 4.00 7.19 1.71 9.24 -24.84 . . -0.19 . 5.17 -2.53 -4.60 -7.98 6.95 -12.48 21.53 -19.20 9.98 -14.72 -0.43 -7.39 -13.72
. . -3.24 . -4.63 -0.11 2.57 2.35 -8.70 5.97 -4.56 4.69 -11.99 7.00 0.87 0.90 -22.08 -12.52 -5.74 0.48 . . -6.33 . -1.10 -2.22 3.46 9.43 -8.51 2.40 -8.39 5.63 -13.97 7.94 0.41 5.43 1.59
. . -2.52 . -1.35 -2.10 -5.24 -0.24 -15.16 5.81 -17.56 9.69 -19.23 10.47 -7.02 12.82 27.48 -1.09 -3.96 8.74 . . -2.87 . 3.89 -0.96 0.59 -1.72 -18.57 4.42 -12.00 20.59 -14.80 1.12 -9.42 19.83 1.47
. . -1.85 . 7.98 -4.64 -9.62 -1.99 -0.11 -10.95 -1.49 -14.32 1.47 2.24 37.09 5.91 43.23 4.42 3.02 15.87 . . 5.17 . -2.00 -4.52 -25.94 -14.23 3.17 -11.32 29.24 -18.96 6.06 -7.07 22.50 -7.19 -34.98
38 39 40
Other Other Other
Riparian Shrub Riparian Woodlnd Woodland Upland
4399 3577 6966
1480 6661 12852
-66.36 86.22 84.50
-63.09 118.01 40.33
-16.12 8.07 -21.19
9.83 4.28 3.91
-4.56 -1.36 3.20
-19.99 -0.29 0.58
APPENDIX 10--Percent Change Between Historic and Current by PVG and Physiognomic Types Eq: (((CUR_NPP - HIS_NPP) / HIS_NPP) * 100) OBS
CUR_PVG
HIS_PHY
CUR_PHY
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT ALPINE COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR
Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Herb Riparian Shrub Riparian Woodlnd Woodland Upland Alpine EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor
Agricultural Agricultural Agricultural Agricultural Agricultural Agricultural Agricultural Agricultural Agricultural Agricultural Agricultural Agricultural Agricultural Agricultural Alpine EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL MidSerTolFor EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor
KM_SUM
NPP_SUM
NPP_AVG
CSI_AVG
WSI_AVG
NAI_AVG
45718 59745 2728 902 1438 1517 98 205 102 289 1335 3325 45 916 3727 3304 280 1861 4485 1346 263 30 5614 709 1379 386 464 2388 691 130 9 1579 494 207 82 5756 370 423 97 444 5560 842 1162 11278
40.49 -23.26 26.13 40.92 56.01 50.14 -1.55 4.15 8.19 3.85 10.34 -18.92 3.42 3.18 -7.36 1.81 -1.44 8.35 -1.67 25.80 23.64 5.95 23.57 27.33 2.02 1.66 -0.79 -0.30 24.51 30.45 26.44 27.08 36.62 -4.13 -1.83 -3.51 -0.07 23.81 25.19 23.97 0.85 1.92 -0.81 1.00
47.26 -24.43 28.45 41.46 62.56 54.11 0.17 5.91 9.10 4.99 4.17 -23.65 4.63 3.71 -7.03 2.34 -0.83 12.16 -1.69 27.81 27.20 4.48 25.65 30.51 2.59 1.60 -0.49 -0.51 26.03 29.44 26.44 28.45 38.21 -3.46 -1.04 -3.08 -0.67 27.90 25.23 25.37 1.58 1.72 -1.38 0.54
-24.21 55.24 -23.00 -39.48 -39.95 -41.49 -27.25 -31.80 -35.94 -30.86 7.53 54.75 5.24 -29.55 -0.41 -5.48 10.72 19.57 14.78 -14.91 -4.74 30.51 11.12 -43.55 -20.09 -3.72 -0.79 1.35 -25.09 -15.36 -22.91 -12.45 -58.96 -12.75 -2.61 -1.28 2.35 -24.93 -18.84 -9.72 -21.77 -5.37 -2.39 0.33
7.42 20.33 4.95 -10.18 -7.87 -9.36 -8.71 -10.88 -13.36 -10.85 1.81 -0.16 6.98 -1.78 -1.88 -0.94 3.98 6.31 6.23 6.22 7.81 10.14 14.13 1.73 -6.95 -1.64 0.39 1.48 1.25 4.70 3.59 9.14 -1.16 -6.02 -1.62 -1.30 2.18 3.15 2.70 15.05 -8.45 -2.97 -1.65 1.28
22.38 -50.17 16.94 77.26 104.15 99.98 28.74 42.27 44.84 39.69 3.04 -18.86 -0.55 11.58 -4.49 4.29 -9.20 -1.05 -14.10 21.16 16.67 -35.16 -5.60 29.46 16.54 4.60 -0.53 -3.19 26.56 25.58 23.40 17.65 54.42 4.77 1.65 -0.35 -4.93 26.98 26.09 13.78 18.73 7.22 -0.37 -2.17
45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93
COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COLD COOL COOL COOL COOL
FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR SHB SHB SHB SHB
MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor Riparian Woodlnd Riparian Woodlnd Riparian Woodlnd Riparian Woodlnd Riparian Woodlnd Upland Herb Upland Herb Upland Herb Upland Herb
EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd EarSerIntFor LatSerIntFoMultL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd EarSerIntFor MidSerIntFor LatSerTolFoSingL MidSerTolFor Riparian Woodlnd Exotics Upland Herb Upland Shrub Woodland Upland
2957 698 26 8370 1711 1163 310 8 1146 713 125 11 1513 407 1942 107 27 1695 1289 218 23 3008 483 397 42 351 59 16 16 372 320 3278 920 53 4207 2276 553 35 4875 1255 4 13 15 187 326 558 406 5824 1142
25.41 22.11 28.49 22.51 30.67 -24.76 -24.76 -26.08 -24.46 0.83 -6.10 -0.34 -3.63 6.16 -25.65 -24.20 -19.42 -21.47 2.09 -5.64 5.32 2.61 11.65 -19.15 -21.76 -21.21 2.87 -8.93 0.78 -1.69 11.45 -24.30 -24.40 -19.89 -22.76 -0.29 -7.85 2.14 -1.69 9.42 -26.16 -2.70 -18.62 -11.00 9.13 18.46 8.41 52.33 55.09
28.32 23.73 29.95 25.93 36.31 -25.67 -25.31 -26.08 -25.33 0.86 -6.36 -0.30 -3.63 6.03 -27.78 -24.54 -20.11 -22.15 2.56 -5.68 5.05 3.28 12.62 -19.60 -21.92 -21.30 2.81 -8.27 0.75 -1.47 12.50 -25.80 -25.29 -20.65 -24.74 -0.38 -7.64 1.88 -1.54 10.53 -29.46 -6.36 -16.66 -15.21 7.61 16.18 7.25 54.51 68.12
-25.72 -16.96 -25.83 -11.67 -55.03 -2.17 17.64 5.11 20.82 -9.00 1.72 7.23 9.11 -46.87 -10.18 12.30 8.52 15.67 -12.67 -2.83 -9.23 1.10 -50.52 -7.98 13.19 13.39 -7.88 -2.96 -5.71 2.28 -54.91 -9.90 9.73 4.86 14.33 -12.66 -3.30 -7.03 1.47 -51.85 98.52 42.82 78.96 96.75 -18.28 -0.90 -0.57 -37.08 -15.71
0.14 1.63 0.69 7.26 -1.53 -12.79 -5.27 -6.21 -2.11 -4.05 -1.98 2.74 3.80 -5.90 -12.26 -5.90 -9.39 -3.63 -4.60 -4.08 0.00 3.07 -5.77 -13.27 -5.71 -3.11 -3.08 -4.16 -0.11 1.84 -7.21 -14.60 -10.37 -10.24 -5.12 -6.95 -5.85 -0.50 0.68 -8.38 -0.85 4.65 5.29 5.74 1.16 1.11 1.53 -13.61 -5.89
29.67 21.35 33.58 15.57 52.77 -10.25 -18.31 -13.89 -24.72 4.72 -4.52 -13.56 -9.20 26.18 -11.48 -13.50 -8.66 -20.09 5.50 -1.27 12.82 -0.95 38.09 -2.29 -13.77 -20.42 10.31 1.22 4.88 -4.68 44.85 -6.86 -13.62 -5.15 -20.11 6.34 0.07 9.14 -2.87 41.18 -36.84 -15.69 -47.56 -39.28 8.27 6.17 -0.05 101.03 103.82
94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142
COOL SHB COOL SHB COOL SHB COOL SHB COOL SHB COOL SHB COOL SHB COOL SHB DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR
Upland Shrub Upland Shrub Upland Shrub Upland Shrub Woodland Upland Woodland Upland Woodland Upland Woodland Upland Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL
Exotics Upland Herb Upland Shrub Woodland Upland Exotics Upland Herb Upland Shrub Woodland Upland Exotics Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Exotics Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Exotics Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Exotics Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor
841 1740 31650 8608 77 145 761 5200 28 146 90 3447 1029 1376 10106 204 90 23 5137 2 4 6 134 107 268 3273 98 21 283 1660 34 62 37 5198 1190 1401 5390 495 1103 711 6280 99 110 19 902 1238 3495 4196 155
-36.67 -41.80 -3.62 -26.90 -0.21 -40.34 -13.34 -2.28 -0.05 6.93 59.97 24.85 16.11 19.41 3.82 74.26 37.33 66.10 43.72 -27.92 -24.57 -11.42 -16.96 -24.22 -9.74 -45.60 55.78 40.01 2.14 4.11 -13.10 -16.90 -26.61 -0.49 -4.23 -5.33 -12.79 35.30 28.44 32.37 16.77 5.48 -4.48 -20.67 4.86 3.58 2.85 -4.70 36.64
-41.84 -44.07 -2.14 -28.54 -3.41 -42.08 -18.72 -0.10 -3.94 5.28 61.39 25.55 15.49 20.54 1.12 88.37 40.06 85.91 50.64 -27.92 -24.57 -11.42 -11.01 -22.70 -9.09 -51.65 76.96 42.95 6.14 4.83 -13.75 -16.31 -27.01 0.08 -5.62 -5.58 -15.29 38.43 29.27 36.77 18.16 4.29 -5.94 -16.27 3.87 3.36 2.15 -5.09 37.46
78.75 74.88 4.04 69.96 2.29 33.42 -33.20 3.28 15.37 5.26 -37.50 -6.55 32.28 30.43 33.52 -0.31 23.73 11.15 26.36 35.24 91.74 17.73 38.66 83.31 87.03 101.70 44.73 75.17 114.76 102.03 -19.87 -1.83 -35.46 0.55 13.78 19.96 22.88 -0.39 8.78 15.03 18.07 -24.06 -12.96 -35.84 -24.14 -1.29 -1.48 2.35 -21.58
14.26 12.82 0.99 3.47 16.90 8.10 -29.22 2.11 10.77 5.26 -3.58 2.79 24.84 28.37 13.49 17.71 24.60 51.31 16.83 34.15 44.44 4.39 18.72 39.65 56.68 32.18 34.07 35.41 37.01 31.29 -16.05 -2.33 -30.99 1.29 7.94 12.70 10.48 13.26 17.54 34.50 14.57 -15.49 -11.18 -24.35 -13.90 0.08 -0.59 1.84 1.25
-57.46 -57.01 -3.13 -33.55 -24.45 -54.95 33.53 -2.24 -24.38 -4.24 58.57 20.70 -22.80 -14.69 -29.97 32.36 -9.63 31.35 -9.51 -50.00 -41.18 -17.65 -26.05 -56.53 -48.26 -70.90 8.13 -22.93 -49.50 -53.37 10.91 -14.25 -2.88 -0.92 -16.49 -24.41 -32.96 12.34 -3.80 -1.25 -16.66 33.66 12.59 7.22 32.04 3.47 3.82 -8.86 32.95
143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191
DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY
FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR FOR
LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL
LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Exotics Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Exotics Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Upland Herb EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Upland Herb EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Upland Herb EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor
1518 622 1831 121 161 13 1346 3063 3903 8405 293 3497 827 4094 158 194 88 2601 3036 3914 10809 514 2916 1129 6288 9 776 73 50 500 492 323 165 2045 13 857 64 407 401 456 833 474 1877 6 503 41 13 277 173
34.10 29.00 23.41 3.43 5.86 -19.92 5.13 4.14 2.99 -2.89 39.89 31.55 23.66 30.88 0.96 4.29 -24.18 -0.01 3.46 4.29 -1.73 32.93 31.80 24.73 22.17 -26.77 -27.20 -32.59 -33.67 -32.84 3.60 -7.00 -10.41 -6.36 -18.08 -20.47 -28.28 -21.29 -26.18 6.29 -3.97 -4.83 -1.80 8.62 -22.81 -28.37 -28.31 -29.27 8.13
34.83 30.14 24.25 4.01 9.10 -1.64 5.31 3.20 2.33 -4.51 40.46 33.00 24.90 32.49 1.68 3.18 -26.97 -1.26 3.83 4.75 -1.92 33.65 33.02 25.56 23.44 -24.37 -27.96 -32.15 -34.02 -33.56 3.26 -6.91 -10.77 -6.87 -17.06 -20.81 -28.35 -24.18 -26.72 6.57 -3.82 -4.36 -1.82 8.62 -23.33 -28.65 -28.31 -29.90 8.09
-14.56 -14.72 -7.28 -21.42 -18.17 -39.86 -25.11 -1.08 -1.17 2.56 -21.57 -14.16 -12.64 -9.41 -26.81 -20.10 -34.68 -25.52 -2.78 -2.74 1.02 -21.68 -14.72 -14.73 -8.35 -7.92 -9.82 15.66 19.21 23.64 -9.64 2.31 3.48 10.25 -24.08 -16.81 10.75 11.58 15.47 -14.71 -1.81 -2.37 3.96 -1.66 -15.83 13.69 11.57 14.97 -13.38
3.03 2.04 5.96 -12.32 -10.98 -20.95 -12.64 0.19 -0.33 2.40 1.93 3.16 2.89 6.00 -17.34 -11.80 -31.77 -15.55 -0.92 -1.33 1.01 1.79 2.45 1.18 5.15 -10.22 -14.34 -5.49 -6.16 -1.47 -2.52 -1.11 -3.01 2.76 -20.72 -16.65 -6.42 -5.62 -2.17 -3.91 -2.93 -4.30 2.40 4.04 -16.90 -4.00 -11.21 -2.66 -2.71
26.61 25.08 8.15 33.84 36.12 62.86 34.38 3.73 2.97 -9.95 35.09 23.94 16.87 16.26 42.07 37.12 8.14 29.86 6.97 8.28 -3.57 33.19 27.08 22.24 9.07 -8.33 0.64 -22.60 -24.53 -32.50 7.07 -6.93 -9.92 -13.52 15.96 10.45 -14.76 -14.28 -23.11 12.77 0.87 -0.88 -7.77 14.04 4.93 -19.89 -13.19 -24.20 14.60
192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY GRS DRY GRS DRY GRS DRY GRS DRY GRS DRY GRS DRY SHR DRY SHR DRY SHR DRY SHR DRY SHR DRY SHR DRY SHR DRY SHR DRY SHR DRY SHR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR
LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor Upland Herb Upland Herb Upland Herb Woodland Upland Woodland Upland Woodland Upland Upland Herb Upland Herb Upland Herb Upland Herb Upland Shrub Upland Shrub Upland Shrub Upland Shrub Woodland Upland Woodland Upland EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL
LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Upland Herb EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Exotics Upland Herb Woodland Upland Exotics Upland Herb Woodland Upland Exotics Upland Herb Upland Shrub Woodland Upland Exotics Upland Herb Upland Shrub Woodland Upland Upland Shrub Woodland Upland EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor
256 246 1030 36 2498 239 361 1568 1013 1190 803 5559 1359 28311 3233 208 7267 681 415 795 9148 4 7170 6483 184514 252 854 318 3864 309 75 11114 1240 581 174 10807 1885 482 175 5970 419 397 206 4843 755 573 504 4373 174
-3.19 -6.19 -4.47 -24.52 -22.07 -27.38 -18.97 -27.85 6.02 -4.18 -3.42 -1.76 4.53 2.77 24.14 -32.60 -26.56 -1.62 0.50 17.20 107.25 0.00 -50.92 -46.19 -2.93 -6.12 18.59 -2.22 -8.59 -2.71 3.82 -11.42 18.82 16.89 14.04 4.05 -6.50 0.36 2.06 -9.93 12.01 27.22 25.09 0.12 18.36 3.05 4.59 -3.50 44.00
-2.90 -5.62 -4.61 -27.11 -23.89 -27.88 -19.14 -30.00 5.86 -4.23 -2.64 -1.68 3.85 0.45 29.56 -37.08 -32.76 -0.96 2.82 13.66 138.25 0.00 -57.08 -51.13 -1.52 -13.04 27.01 -7.14 -8.11 -3.23 5.51 -13.43 21.09 17.35 17.90 4.85 -6.67 0.70 1.71 -10.20 12.32 28.00 25.56 0.15 19.68 2.75 3.99 -4.32 44.77
-3.33 -2.89 5.65 -19.00 -16.04 9.74 6.53 14.96 -15.31 -4.21 -5.76 1.92 2.57 5.18 21.34 -16.07 -11.08 0.17 1.30 -6.53 -46.92 0.00 107.51 85.18 2.51 55.98 -35.61 -7.86 -8.36 11.60 12.47 19.52 -6.04 1.49 6.24 14.67 -18.62 -1.89 -2.20 6.47 -13.80 -11.17 -12.64 1.87 -21.47 -0.90 -2.43 2.10 -18.21
-3.55 -5.52 2.56 -20.16 -15.67 -7.18 -9.23 -2.85 -5.28 -5.04 -5.96 1.05 3.18 2.80 21.49 -23.99 -11.70 1.48 0.12 -0.29 -9.14 0.00 9.39 8.22 0.41 3.63 -2.54 -12.65 -6.08 8.29 10.42 6.47 8.15 12.28 19.20 10.80 -14.91 -0.42 -0.57 1.05 -1.75 4.12 3.03 4.28 -10.31 0.57 -0.01 2.27 2.00
1.82 -0.87 -11.27 8.95 10.32 -12.48 -6.73 -23.43 14.66 3.52 5.48 -4.46 -3.54 -8.38 -19.10 -12.50 -8.77 -2.22 -1.94 8.99 227.36 0.00 -67.06 -59.49 -2.30 -24.23 35.02 -9.77 3.01 -15.56 -8.33 -26.93 7.01 -2.81 -5.97 -19.11 22.85 1.92 3.52 -13.36 17.53 21.78 22.32 -8.42 49.40 1.08 4.82 -10.47 42.20
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289
MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR RIP SHR RIP SHR RIP SHR RIP SHR RIP SHR RIP SHR RIP SHR
LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor Upland Shrub Upland Shrub Upland Shrub Riparian Herb Riparian Herb Riparian Herb Riparian Herb
LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor EarSerIntFor LatSerIntFoMultL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Upland Shrub Riparian Herb Riparian Shrub Exotics Upland Shrub Riparian Herb Riparian Shrub
758 542 2754 4124 914 429 12227 1254 1247 482 11087 1073 178 25 1470 1073 528 143 2391 775 456 28 1648 847 397 156 1920 58 5 66 35 43 64 382 1813 688 90 2874 2612 1188 281 4059 1057 377 1623 31 146 447 31
39.69 34.63 28.33 -5.56 3.36 4.75 -6.05 19.08 21.76 29.14 7.12 -21.36 -25.43 -30.97 -17.62 1.42 -10.52 -9.31 -6.26 -13.80 -21.09 -19.59 -12.76 4.72 -5.36 -5.99 -1.64 -21.90 -2.79 -15.27 0.33 -1.04 -0.81 -2.48 -15.02 -20.23 -19.42 -15.12 3.48 -2.73 -2.86 -2.51 -1.56 -25.42 -0.34 -18.18 55.40 7.96 40.90
42.04 36.95 29.86 -6.33 3.96 5.60 -6.19 19.94 20.54 30.94 8.74 -23.51 -26.63 -31.42 -18.73 1.36 -11.50 -11.48 -6.76 -14.35 -22.25 -21.35 -13.18 5.83 -4.88 -5.98 -1.47 -22.38 -2.79 -17.13 1.16 -1.02 -0.77 -2.56 -15.81 -21.89 -19.53 -16.39 4.04 -2.83 -2.52 -2.28 -1.13 -25.47 -1.58 -27.76 62.52 7.37 32.99
-13.57 -13.55 -7.57 -22.61 -3.62 -4.14 3.54 -20.15 -11.85 -14.41 -3.02 -6.50 18.10 20.94 19.76 -8.10 5.50 10.10 10.30 -14.15 11.42 12.82 12.30 -13.84 -0.12 4.22 4.01 -26.62 -2.31 12.13 -3.59 -5.23 -1.69 3.50 -14.37 7.63 1.29 11.00 -15.49 -4.44 0.65 2.51 0.13 31.45 -5.03 16.26 -4.82 -5.88 -15.74
4.90 5.47 6.25 -17.23 -0.83 -1.05 0.93 -1.91 1.67 3.37 4.92 -16.87 -5.15 -7.06 0.45 -4.52 0.10 12.77 5.16 -16.41 -5.86 -7.54 -0.38 -8.83 -2.01 5.65 3.91 -24.27 -9.44 0.10 -1.39 -1.73 -2.01 1.89 -17.09 -8.66 -10.68 -2.28 -11.88 -5.06 3.67 1.84 -0.08 7.01 9.94 -7.04 -3.56 3.02 -3.07
36.14 29.91 13.85 23.83 5.84 9.50 -9.05 25.45 20.25 29.27 -0.65 -5.26 -22.65 -26.46 -21.58 5.18 -12.77 -19.91 -14.29 7.88 -14.72 -16.38 -14.57 15.17 -1.79 -10.07 -7.30 24.04 0.00 -21.17 3.48 9.87 2.19 -7.31 11.02 -11.97 -2.23 -14.91 13.44 3.73 -4.31 -6.01 -1.72 -44.61 -25.43 -24.09 84.85 1.88 29.56
290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
RIP SHR RIP SHR RIP SHR ROCK URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN WATER WOODLAND WOODLAND WOODLAND WOODLAND WOODLAND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND
Riparian Shrub Riparian Shrub Riparian Shrub Rock/Barren Water Alpine Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL MidSerTolFor Riparian Herb Riparian Shrub Riparian Woodlnd Woodland Upland Water Upland Herb Upland Herb Upland Shrub EarSerIntFor Woodland Upland Upland Herb Upland Herb Upland Herb Upland Herb EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor LatSerIntFoMultL MidSerIntFor MidSerIntFor LatSerTolFoMultL MidSerTolFor Riparian Shrub Riparian Shrub Riparian Shrub Riparian Shrub Riparian Shrub Riparian Woodlnd Riparian Woodlnd Riparian Woodlnd Riparian Woodlnd
Upland Shrub Riparian Herb Riparian Shrub Rock/Barren Urban Urban Urban Urban Urban Urban Urban Urban Urban Urban Urban Urban Urban Urban Urban Water Upland Shrub Woodland Upland Upland Shrub Woodland Upland Woodland Upland Exotics Upland Herb EarSerIntFor Riparian Woodlnd Exotics Upland Herb EarSerIntFor Riparian Woodlnd Riparian Woodlnd Exotics Riparian Woodlnd Riparian Woodlnd Riparian Shrub Exotics LatSerIntFoSingL MidSerIntFor Riparian Shrub Riparian Woodlnd Exotics MidSerIntFor Riparian Shrub Riparian Woodlnd
88 492 1208 2299 11 2 382 520 126 7 14 19 1 2 6 9 41 2 1 7550 1339 88 1205 326 1790 12 68 1 2483 51 200 5 631 1 191 14 1 1 21 18 1 9 106 2 2 34 8342
13.48 -12.58 -3.91 -5.26 138.68 0.00 14.08 -46.44 -10.92 -1.90 155.54 18.75 -31.36 9.18 7.93 -28.18 -40.46 -27.76 0.00 3.95 30.27 62.91 -12.71 49.28 2.34 15.89 12.38 1.17 66.49 -13.78 -13.56 9.05 28.74 -30.96 -17.69 6.53 19.16 0.00 -22.48 -78.05 0.00 7.48 -40.44 -63.88 -56.80 78.59 3.44
18.27 -12.35 -2.67 -5.71 273.73 0.00 10.82 -50.11 -14.37 -4.23 185.49 16.19 -31.36 9.18 9.55 -39.58 -42.87 -27.76 0.00 4.74 28.93 75.36 -12.39 58.15 4.37 14.61 10.89 1.17 79.09 -16.77 -18.00 6.74 34.72 -30.96 -20.74 8.26 19.16 0.00 -29.50 -84.35 0.00 9.64 -41.13 -63.88 -56.80 74.35 1.68
0.87 21.62 2.18 -3.63 290.29 0.00 -0.17 101.67 11.88 -21.20 -35.76 -19.21 -16.42 -33.77 -14.88 113.38 71.81 180.95 0.00 -2.02 -12.67 -19.10 21.93 -8.83 -5.47 7.24 3.31 76.32 -39.89 24.73 13.32 6.45 -22.55 -26.53 -34.41 -29.27 -47.95 0.00 47.79 139.00 0.00 16.59 69.20 96.88 161.54 -27.91 4.68
-0.30 -0.86 0.24 -2.61 290.48 0.00 2.79 17.36 3.19 -8.58 -8.61 -6.10 -28.57 -25.49 -7.14 -8.29 0.11 12.40 0.00 2.80 0.92 -5.08 4.40 23.51 -2.11 4.13 2.94 11.84 5.16 4.69 0.81 2.82 8.74 -33.33 -11.30 -1.14 -4.08 0.00 -3.66 27.61 0.00 17.35 4.59 -10.00 0.00 0.58 2.45
34.85 -8.92 -5.20 -0.71 337.50 0.00 3.26 -64.30 -19.06 22.47 264.86 29.03 -9.09 125.00 47.06 -32.51 -32.73 -48.39 0.00 0.40 22.94 131.92 -18.51 40.98 13.13 -3.03 -0.34 -54.55 48.77 -29.34 -20.94 -2.38 11.49 0.00 10.47 20.27 100.00 0.00 -26.92 -86.09 0.00 -10.88 -54.33 -50.00 -75.00 100.00 -6.40
Percent Change Between Historic and Current by Physiognomic Types Eq: (((CUR_NPP - HIS_NPP) / HIS_NPP) * 100) OBS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
4
HIS_PHY
CUR_PHY
KM_SUM
NPP_SUM
NPP_AVG
CSI_AVG
WSI_AVG
NAI_AVG
Rock/Barren Water Water Alpine Alpine Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Herb Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub Upland Shrub EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor
Rock/Barren Urban Water Urban Alpine Agricultural Exotics Urban Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd Woodland Upland Agricultural Exotics Urban Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Herb Riparian Shrub Woodland Upland Agricultural Exotics Urban Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL
2299 11 7550 2 3727 45718 2372 382 29726 16401 3448 1029 1376 10106 204 90 23 5137 2483 4467 59745 8013 520 8227 218432 134 107 268 3273 98 21 283 1660 377 1623 8860 2728 85 126 262 37 12371 1779 3337
-5.26 138.68 3.95 0.00 -7.36 40.49 5.91 14.08 3.28 73.32 24.83 16.11 19.41 3.82 74.26 37.33 66.10 43.72 66.49 30.16 -23.26 -49.05 -46.44 -45.22 -3.15 -16.96 -24.22 -9.74 -45.60 55.78 40.01 2.14 4.11 -25.42 -0.34 -26.18 26.13 -13.46 -10.92 -14.68 -26.61 -3.19 -3.48 2.60
-5.71 273.73 4.74 0.00 -7.03 47.26 5.38 10.82 0.81 88.24 25.54 15.49 20.54 1.12 88.37 40.06 85.91 50.64 79.09 37.12 -24.43 -55.45 -50.11 -49.65 -1.67 -11.01 -22.70 -9.09 -51.65 76.96 42.95 6.14 4.83 -25.47 -1.58 -28.02 28.45 -15.50 -14.37 -17.65 -27.01 -2.55 -4.46 4.89
-3.63 290.29 -2.02 0.00 -0.41 -24.21 1.46 -0.17 4.70 -41.54 -6.53 32.28 30.43 33.52 -0.31 23.73 11.15 26.36 -39.89 9.50 55.24 104.35 101.67 82.95 2.81 38.66 83.31 87.03 101.70 44.73 75.17 114.76 102.03 31.45 -5.03 69.59 -23.00 2.59 11.88 8.66 -35.46 -3.90 12.94 19.59
-2.61 290.48 2.80 0.00 -1.88 7.42 2.13 2.79 2.70 -9.77 2.80 24.84 28.37 13.49 17.71 24.60 51.31 16.83 5.16 13.91 20.33 9.81 17.36 9.07 0.50 18.72 39.65 56.68 32.18 34.07 35.41 37.01 31.29 7.01 9.94 3.47 4.95 -3.15 3.19 0.07 -30.99 -1.43 7.47 9.53
-0.71 337.50 0.40 0.00 -4.49 22.38 -2.75 3.26 -7.96 146.82 20.67 -22.80 -14.69 -29.97 32.36 -9.63 31.35 -9.51 48.77 3.79 -50.17 -66.02 -64.30 -58.99 -2.51 -26.05 -56.53 -48.26 -70.90 8.13 -22.93 -49.50 -53.37 -44.61 -25.43 -33.17 16.94 -11.66 -19.06 -19.63 -2.88 1.73 -15.49 -13.42
45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93
EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor EarSerIntFor LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoMultL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL LatSerIntFoSingL MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor MidSerIntFor EarSerTolFor
MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd Woodland Upland Agricultural Exotics Urban Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd Agricultural Exotics Urban Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Agricultural Exotics Urban Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd Agricultural
20989 3081 1947 915 22701 1340 326 902 99 7 110 19 4166 2106 4134 12554 1265 2045 837 8253 495 1438 121 14 161 13 2308 3718 10163 13148 890 4352 1369 7292 1517 349 19 194 88 12285 4792 5505 34314 4725 4861 1637 25745 1725 98
-9.41 24.24 23.83 25.94 11.97 27.91 49.28 40.92 5.48 -1.90 -4.48 -20.67 -1.53 2.33 2.05 -6.66 21.47 32.43 27.96 10.04 36.35 56.01 3.43 155.54 5.86 -19.92 7.13 3.76 -0.56 -2.96 32.37 32.65 27.35 29.24 50.14 -8.93 18.75 4.29 -24.18 -1.90 3.14 2.74 -2.75 24.24 27.57 26.06 15.15 30.45 -1.55
-11.26 26.54 25.21 31.30 13.24 32.45 58.15 41.46 4.29 -4.23 -5.94 -16.27 -1.72 2.30 1.68 -7.05 22.18 33.12 28.98 9.87 37.97 62.56 4.01 185.49 9.10 -1.64 7.92 3.02 -0.96 -4.29 34.44 34.10 29.08 30.96 54.11 -12.98 16.19 3.18 -26.97 -1.91 3.45 2.93 -2.77 26.45 28.28 27.06 17.61 36.11 0.17
19.44 -9.25 4.70 13.82 14.76 -34.60 -8.83 -39.48 -24.06 -21.20 -12.96 -35.84 -20.36 -1.84 -1.45 4.05 -20.88 -13.94 -14.29 -2.92 -58.91 -39.95 -21.42 -35.76 -18.17 -39.86 -22.87 -1.09 -1.29 2.40 -22.22 -14.14 -13.01 -8.70 -41.49 -30.62 -19.21 -20.10 -34.68 -22.86 -3.37 -2.78 1.69 -23.78 -14.27 -14.79 -7.08 -54.83 -27.25
7.56 8.47 15.07 30.61 12.70 5.00 23.51 -10.18 -15.49 -8.58 -11.18 -24.35 -12.31 -0.31 -0.52 1.43 0.10 3.30 2.28 5.51 -1.22 -7.87 -12.32 -8.61 -10.98 -20.95 -11.53 0.21 -0.77 2.36 2.35 3.43 3.81 6.39 -9.36 -14.35 -6.10 -11.80 -31.77 -13.15 -1.19 -1.35 1.06 -0.20 2.19 1.75 5.69 -1.53 -8.71
-26.32 13.19 -1.41 -2.73 -15.60 20.22 40.98 77.26 33.66 22.47 12.59 7.22 23.21 3.26 3.19 -10.61 23.68 25.63 24.44 -1.22 54.06 104.15 33.84 264.86 36.12 62.86 35.51 3.27 1.43 -9.97 32.36 25.76 21.19 15.24 99.98 25.15 29.03 37.12 8.14 23.50 6.79 6.10 -5.51 28.73 24.46 24.12 6.12 52.55 28.74
94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142
EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor EarSerTolFor LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoMultL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL LatSerTolFoSingL MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor MidSerTolFor Riparian Herb Riparian Herb
Urban Upland Herb EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd Agricultural Urban Upland Herb EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd Agricultural Upland Herb EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd Agricultural Urban Upland Herb EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Shrub Riparian Woodlnd Agricultural Exotics
1 9 3012 561 83 3116 2278 976 319 5949 407 205 2 13 3574 627 462 3744 2592 1448 653 6805 484 102 6 958 88 13 694 267 315 326 1784 320 289 6 36 7589 1847 504 8649 5901 2931 1119 14493 1 1255 1335 31
-31.36 -26.77 -24.09 -25.91 -32.16 -22.26 1.73 -8.58 -9.69 -5.58 6.16 4.15 9.18 -18.08 -21.35 -22.53 -20.96 -17.81 3.76 -4.69 -4.75 0.05 11.66 8.19 8.62 -21.21 -23.45 -28.31 -23.69 6.04 -3.14 -4.95 -3.47 11.45 3.85 7.93 -24.52 -21.21 -23.19 -19.15 -20.75 2.43 -4.38 -3.10 -1.96 0.00 9.42 10.34 -18.18
-31.36 -24.37 -25.35 -26.45 -32.50 -23.10 1.63 -9.15 -10.84 -5.97 6.03 5.91 9.18 -17.06 -23.02 -23.22 -23.69 -18.24 4.29 -4.41 -4.46 0.60 12.63 9.10 8.62 -21.69 -23.62 -28.31 -24.10 6.12 -2.89 -4.61 -3.51 12.50 4.99 9.55 -27.11 -22.67 -24.36 -19.36 -22.63 2.61 -4.41 -2.49 -1.80 0.00 10.53 4.17 -27.76
-16.42 -7.92 -5.70 17.51 18.44 20.83 -8.70 4.05 6.60 9.99 -46.87 -31.80 -33.77 -24.08 -12.77 11.50 11.50 14.15 -13.43 -1.47 -0.98 2.77 -50.51 -35.94 -1.66 -13.33 12.40 11.57 13.92 -10.81 -3.57 -2.77 4.52 -54.91 -30.86 -14.88 -19.00 -13.06 8.94 5.44 13.35 -14.42 -4.15 -4.15 1.94 0.00 -51.85 7.53 16.26
-28.57 -10.22 -14.60 -5.26 -6.47 -0.93 -3.87 -0.60 3.13 3.82 -5.90 -10.88 -25.49 -20.72 -14.30 -5.94 -5.97 -2.10 -5.64 -2.86 -2.11 3.06 -5.77 -13.36 4.04 -16.06 -4.97 -11.21 -2.67 -2.58 -3.34 -4.73 2.28 -7.21 -10.85 -7.14 -20.16 -15.57 -9.27 -9.61 -3.74 -8.42 -5.20 -3.74 1.13 0.00 -8.38 1.81 -7.04
-9.09 -8.33 -5.67 -19.88 -24.60 -24.00 5.44 -9.85 -14.44 -12.83 26.18 42.27 125.00 15.96 -0.72 -14.55 -14.09 -17.37 10.10 -0.14 -2.88 -4.88 38.13 44.84 14.04 3.22 -15.20 -13.19 -21.89 12.18 2.68 -0.29 -9.17 44.85 39.69 47.06 8.95 3.41 -12.96 -5.85 -18.52 11.01 2.97 3.20 -4.45 0.00 41.18 3.04 -24.09
143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170
Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Riparian Woodland Woodland Woodland Woodland Woodland Woodland
Herb Herb Herb Herb Shrub Shrub Shrub Shrub Shrub Shrub Shrub Shrub Shrub Woodlnd Woodlnd Woodlnd Woodlnd Woodlnd Woodlnd Woodlnd Woodlnd Woodlnd Upland Upland Upland Upland Upland Upland
Urban Upland Shrub Riparian Herb Riparian Shrub Agricultural Exotics Urban Upland Shrub LatSerIntFoSingL MidSerIntFor Riparian Herb Riparian Shrub Riparian Woodlnd Agricultural Exotics Urban EarSerIntFor MidSerIntFor LatSerTolFoSingL MidSerTolFor Riparian Shrub Riparian Woodlnd Agricultural Exotics Urban Upland Herb Upland Shrub Woodland Upland
9 146 447 31 3325 21 41 88 18 1 492 1217 106 45 2 2 4 15 15 187 34 8668 916 285 1 7412 1615 7989
-28.18 55.40 7.96 40.90 -18.92 -22.48 -40.46 13.48 -78.05 0.00 -12.58 -3.76 -40.44 3.42 -63.88 -27.76 -26.16 -10.76 -18.62 -11.00 78.59 3.72 3.18 -26.98 0.00 -26.77 -0.98 -0.79
-39.58 62.52 7.37 32.99 -23.65 -29.50 -42.87 18.27 -84.35 0.00 -12.35 -2.55 -41.13 4.63 -63.88 -27.76 -29.46 -12.65 -16.66 -15.21 74.35 1.91 3.71 -31.79 0.00 -32.86 -1.55 0.66
113.38 -4.82 -5.88 -15.74 54.75 47.79 71.81 0.87 139.00 0.00 21.62 2.24 69.20 5.24 96.88 180.95 98.52 58.35 78.96 96.75 -27.91 3.80 -29.55 -11.01 0.00 -10.32 -34.38 0.64
-8.29 -3.56 3.02 -3.07 -0.16 -3.66 0.11 -0.30 27.61 0.00 -0.86 0.34 4.59 6.98 -10.00 12.40 -0.85 3.68 5.29 5.74 0.58 2.41 -1.78 -11.65 0.00 -11.39 -17.95 0.53
-32.51 84.85 1.88 29.56 -18.86 -26.92 -32.73 34.85 -86.09 0.00 -8.92 -5.28 -54.33 -0.55 -50.00 -48.39 -36.84 -23.73 -47.56 -39.28 100.00 -5.89 11.58 -15.46 0.00 -9.70 34.29 1.36
APPENDIX 11--Current Conditions (1989 Weather Year) -- Using Current PVG and PHYSG
OBS PVG 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
AGRICULT ALPINE COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COOL SHB COOL SHB COOL SHB COOL SHB DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY GRS DRY GRS DRY GRS DRY SHR DRY SHR DRY SHR DRY SHR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR RIP SHR
PHYSG Agricultural Alpine EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd Exotics Upland Herb Upland Shrub Woodland Upland Exotics Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Exotics Upland Herb Woodland Upland Exotics Upland Herb Upland Shrub Woodland Upland EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Exotics
NPP_SUM NPP_AVG CSI_AVG WSI_AVG NAI_AVG KM_SUM 53255080 665978 7930696 1357714 4316935 12584748 6528530 1209695 80406 16787481 3710463 307882 473054 14843939 4554315 141762 218101 112198 7812641 3847611 5465482 14789662 2670139 6517188 3073173 20233509 550238 11761149 1491873 1230834 1351614 72508881 241848 7428221 1569622 691195 18748626 5039667 2625111 1148093 22227694 12594
450.21 179.65 462.54 462.75 462.69 486.44 672.07 579.91 487.31 647.62 651.19 208.59 206.48 388.66 304.76 322.19 296.74 443.47 428.65 381.71 361.45 329.62 686.06 556.45 586.15 566.05 351.81 332.13 382.43 162.29 185.79 373.01 421.34 518.95 435.64 521.26 472.81 666.27 511.42 561.96 582.58 406.26
44.34 36.92 48.68 63.85 57.52 62.80 44.88 53.44 55.93 54.54 26.13 66.33 64.41 34.52 57.02 55.54 59.87 37.19 49.41 71.31 72.86 73.81 46.93 59.99 58.92 61.48 50.95 52.74 68.13 71.04 66.70 37.03 48.94 49.32 67.59 64.91 68.30 48.91 62.53 62.06 62.31 46.13
80.58 22.08 37.82 40.78 35.48 50.56 34.19 38.82 54.78 52.15 48.85 67.86 57.96 56.84 63.72 47.88 49.44 36.87 49.06 57.91 54.29 70.11 45.92 54.80 51.89 67.59 59.34 65.29 77.80 72.53 68.88 66.28 51.26 38.49 45.75 45.68 52.77 33.33 43.15 44.03 53.51 57.48
11.09 2.20 6.42 6.56 5.47 5.41 8.24 7.27 5.35 7.19 9.01 3.88 3.99 10.83 8.78 7.96 6.23 11.24 8.62 6.06 6.40 5.16 8.81 8.00 10.72 7.39 8.00 6.79 6.46 3.25 4.18 10.54 13.45 10.16 6.28 9.84 6.65 9.63 7.25 9.27 8.00 5.39
118363 3727 17234 2969 9331 25933 9754 2100 165 25962 5705 1476 2291 38235 14950 442 741 253 18262 10080 15188 44925 3893 11747 5283 35801 1567 35578 3914 7585 7278 194516 574 14347 3605 1326 39742 7654 5139 2048 38243 31
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
RIP SHR RIP SHR RIP SHR ROCK URBAN WATER WOODLAND WOODLAND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND
Upland Shrub Riparian Herb Riparian Shrub Rock/Barren Urban Water Upland Shrub Woodland Upland Exotics Upland Herb EarSerIntFor LatSerIntFoSingL MidSerIntFor Riparian Shrub Riparian Woodlnd
230378 557100 1122830 392501 380677 818108 998690 946087 106892 87943 2771 947 1276 12786 6644247
178.45 423.97 392.74 171.85 333.63 108.44 392.57 436.59 385.89 328.15 461.83 52.61 425.33 290.59 574.46
70.12 40.68 35.64 17.24 57.32 12.77 46.78 50.43 43.62 45.66 49.67 90.56 73.33 49.32 30.78
61.57 77.68 78.53 21.20 82.39 15.04 72.73 57.51 45.20 59.43 68.83 75.50 89.67 91.48 62.69
4.59 9.03 8.09 2.14 9.37 2.44 9.35 12.35 5.82 5.66 7.67 1.17 4.33 4.61 8.40
1291 1316 2862 2299 1143 7550 2544 2204 277 268 6 18 3 44 11578
Current(89) -- Using Current PVG OBS 1 2 3 4 5 6 7 8 9 10 11 12 13 14
PVG AGRICULT ALPINE COLD FOR COOL SHB DRY FOR DRY GRS DRY SHR MOIST FOR RIP SHR ROCK URBAN WATER WOODLAND RIP WDLND
NPP_SUM
NPP_AVG
53255080 665978 54506668 20179190 64881466 13803260 75333177 59478229 1922902 392501 380677 818108 1944777 6856862
450.21 179.65 551.32 354.62 443.43 337.69 359.03 532.05 349.94 171.85 333.63 108.44 412.82 562.87
2
CSI_AVG
WSI_AVG
NAI_AVG
KM_SUM
44.34 36.92 53.63 42.46 64.95 54.14 39.32 62.07 45.01 17.24 57.32 12.77 48.46 31.57
80.58 22.08 45.10 58.98 61.65 66.25 66.55 48.97 74.22 21.20 82.39 15.04 65.73 62.36
11.09 2.20 6.62 9.84 6.87 6.81 10.06 7.87 7.48 2.14 9.37 2.44 10.73 8.25
118363 3727 99153 56952 146615 41059 209953 112104 5500 2299 1143 7550 4748 12194
Current(89) -- Using Current PHYSG OBS 1 2 3 4 5 6
PHYSG Agricultural Exotics Rock/Barren Urban Water Alpine
NPP_SUM 53255080 2350202 392501 380677 818108 665978
NPP_AVG 450.21 206.67 171.85 333.63 108.44 179.65
CSI_AVG 44.34 66.33 17.24 57.32 12.77 36.92
WSI_AVG 80.58 68.45 21.20 82.39 15.04 22.08
NAI_AVG 11.09 4.24 2.14 9.37 2.44 2.20
KM_SUM 118363 11378 2299 1143 7550 3727
7 8 9 10 11 12 13 14 15 16 17 18 19 20
Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Herb Riparian Shrub Riparian Woodlnd Woodland Upland
13891861 88694086 23174329 6774947 10474559 46124312 14238336 10351994 4301672 59248684 557100 1135616 10354710 7234123
302.13 374.76 466.36 407.71 406.07 417.80 672.57 546.83 577.33 593.55 423.97 391.19 599.79 335.13
55.61 36.91 49.14 69.19 66.92 69.25 46.70 59.95 59.72 60.00 40.68 35.85 29.24 58.15
65.20 64.77 42.14 52.25 47.06 59.30 36.04 49.88 49.80 58.20 77.68 78.73 58.12 65.31
6.23 10.54 8.30 6.19 6.24 5.76 8.84 7.71 10.20 7.57 9.03 8.03 8.60 8.84
46156 236839 49849 16654 25863 110603 21301 18986 7496 100006 1316 2906 17283 21642
APPENDIX 12--Historic Conditions (1989 Weather Year) -- Using Current PVG and Historic PHYSG OBS PVG 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT AGRICULT ALPINE COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COLD FOR COOL SHB COOL SHB COOL SHB DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY FOR DRY GRS DRY GRS DRY SHR DRY SHR DRY SHR
PHYSG Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Herb Riparian Shrub Riparian Woodlnd Woodland Upland Alpine EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Woodlnd Upland Herb Upland Shrub Woodland Upland Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Upland Herb Woodland Upland Upland Herb Upland Shrub Woodland Upland
NPP_SUM NPP_AVG CSI_AVG WSI_AVG NAI_AVG KM_SUM 17892056 27042636 1182148 343431 454648 538649 55644 107854 52946 146779 915873 1424421 20196 437906 716785 8495832 3808262 3460895 16641920 3438468 5343056 889455 10634144 303098 1769822 17255481 2047650 7288523 2362556 9304159 5325979 9374095 13405884 2743179 3072160 1446686 7587369 10553576 4218910 1749909 74870764 376428
391.59 452.82 433.66 383.29 316.39 356.25 567.80 526.12 519.08 511.43 686.05 428.53 448.80 480.16 193.36 475.88 507.77 469.27 512.09 637.82 608.55 567.98 612.64 556.14 223.91 402.97 331.39 337.07 403.72 425.86 375.86 364.61 424.18 620.21 573.49 573.63 574.50 322.26 519.12 169.09 377.56 321.18
48.75 32.17 51.47 72.41 75.95 74.80 57.73 62.49 63.36 62.91 29.27 36.09 41.58 57.75 37.06 51.35 60.13 58.89 61.01 52.18 52.85 56.05 55.71 31.00 59.20 33.23 56.10 51.80 35.63 54.25 72.41 73.44 70.10 54.90 58.63 59.69 59.50 50.92 59.06 69.19 36.07 54.75
71.69 71.35 64.04 57.33 62.85 63.72 63.30 66.06 64.73 66.47 71.67 84.37 54.47 73.26 22.50 47.13 43.20 36.74 44.92 43.05 45.69 48.48 46.60 57.79 69.00 57.42 58.59 65.20 55.61 55.78 55.53 62.17 58.66 54.60 51.60 55.53 56.66 65.76 62.69 70.64 66.13 44.92
12.68 14.99 11.12 7.44 5.97 6.50 9.62 8.74 8.83 8.08 20.68 9.42 12.07 9.12 2.30 6.62 6.13 5.25 6.05 7.83 6.76 6.56 6.86 7.98 4.27 11.77 8.71 8.29 12.11 8.39 5.96 5.90 6.58 8.16 7.94 8.24 7.16 7.78 6.33 3.74 10.67 9.23
45718 59745 2728 902 1438 1517 98 205 102 289 1335 3325 45 916 3727 17892 7520 7379 32604 5396 8792 1573 17452 545 7930 42839 6183 21676 5856 21901 14185 25723 31647 4433 5382 2545 13267 32903 8156 10362 198419 1172
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82
MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR MOIST FOR RIP SHR RIP SHR RIP SHR ROCK URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN URBAN WATER WOODLAND WOODLAND WOODLAND WOODLAND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND RIP WDLND
EarSerIntFor 15112331 LatSerIntFoMultL 7911884 LatSerIntFoSingL 3611200 MidSerIntFor 17154442 EarSerTolFor 4617172 LatSerTolFoMultL 3857392 LatSerTolFoSingL 367869 MidSerTolFor 8186000 Upland Shrub 993411 Riparian Herb 273381 Riparian Shrub 722081 Rock/Barren 416017 Water 1104 Alpine 596 Upland Herb 152908 Upland Shrub 233529 EarSerIntFor 61870 LatSerIntFoMultL 3887 LatSerIntFoSingL 1813 MidSerIntFor 6801 EarSerTolFor 440 LatSerTolFoMultL 610 MidSerTolFor 1801 Riparian Herb 7343 Riparian Shrub 20150 Riparian Woodlnd 1185 Woodland Upland 297 Water 781113 Upland Herb 483831 Upland Shrub 448551 EarSerIntFor 106113 Woodland Upland 724240 Upland Herb 871726 EarSerIntFor 406215 LatSerIntFoMultL 885 MidSerIntFor 105762 LatSerTolFoMultL 287 MidSerTolFor 496 Riparian Shrub 60233 Riparian Woodl 4114686
537.54 550.70 346.23 541.10 672.96 624.38 565.08 606.95 324.96 418.65 404.53 182.14 100.36 298.00 400.28 449.09 491.03 647.83 129.50 377.83 440.00 305.00 300.17 815.89 491.46 592.50 297.00 103.54 339.05 372.24 341.20 409.64 340.39 457.97 885.00 515.91 287.00 496.00 388.60 551.27
55.01 63.57 75.59 64.49 53.25 57.52 61.57 59.31 46.56 47.77 38.57 17.90 9.36 81.00 48.50 32.59 45.62 56.50 89.29 67.61 67.00 77.00 76.17 15.78 37.46 21.00 81.00 13.04 51.67 40.88 49.36 53.83 47.91 40.85 49.00 60.58 73.00 38.00 36.20 29.67
52.28 48.73 54.75 47.93 38.78 41.25 53.09 42.57 69.70 64.75 77.50 21.76 9.55 47.00 77.27 76.70 71.86 49.50 73.86 61.61 56.00 51.00 65.33 61.67 85.59 64.50 99.00 14.64 78.98 61.78 41.80 59.24 64.67 51.45 51.00 46.86 49.00 76.00 71.94 60.22
9.29 28164 8.43 14377 5.54 10433 8.06 31764 9.68 6881 8.85 6227 7.82 653 8.09 13605 9.08 3057 9.21 655 8.11 1788 2.16 2299 2.91 11 5.00 2 13.27 382 15.18 520 14.49 126 13.67 7 2.64 14 8.67 19 11.00 1 4.00 2 5.67 6 22.56 9 12.15 41 15.50 2 4.00 1 2.43 7550 8.54 1427 9.30 1205 9.86 326 10.95 1790 6.45 2564 8.39 887 21.00 1 5.67 205 2.00 1 12.00 1 8.55 155 8.48 8380
Historic(89) -- Using Current PVG OBS
PVG
NPP_SUM
NPP_AVG
CSI_AVG
WSI_AVG
3 NAI_AVG
KM_SUM
1 2 3 4 5 6 7 8 9 10 11 12 13 14
AGRICULT ALPINE COLD FOR COOL SHB DRY FOR DRY GRS DRY SHR MOIST FOR RIP SHR ROCK URBAN WATER WOODLAND RIP WDLND
50615187 716785 53015130 21072953 61910590 14772486 76997101 60818290 1988873 416017 494334 781113 1762735 6060290
427.89 193.36 536.23 370.32 423.13 361.40 366.96 544.04 361.94 182.14 433.25 103.54 374.17 497.48
40.85 37.06 56.66 39.32 62.45 52.54 37.81 61.31 44.10 17.90 40.99 13.04 49.57 34.92
71.35 22.50 44.97 59.16 58.77 65.15 66.24 48.21 71.65 21.76 75.31 14.64 64.72 60.44
Historic(89) -- Using Historic PHYSG OBS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
PHYSG Rock/Barren Water Alpine Upland Herb Upland Shrub EarSerIntFor LatSerIntFoMultL LatSerIntFoSingL MidSerIntFor EarSerTolFor LatSerTolFoMultL LatSerTolFoSingL MidSerTolFor Riparian Herb Riparian Shrub Riparian Woodlnd Woodland Upland
NPP_SUM 416017 782217 717381 40762351 123206928 34668668 17394328 16902651 47853458 10854903 12381359 2756956 26556589 1196597 2226885 4939165 7805431
NPP_AVG 182.14 103.54 193.42 332.25 395.56 482.41 470.88 375.90 490.60 647.13 603.29 569.50 598.85 599.20 419.77 551.06 429.84
CSI_AVG 17.90 13.03 37.08 52.28 35.04 53.52 66.48 71.64 65.30 53.37 55.86 58.84 57.98 35.25 36.94 29.80 57.20
WSI_AVG 21.76 14.63 22.51 68.66 65.77 52.49 50.42 56.30 50.65 44.47 46.10 53.11 48.50 69.37 81.71 60.04 60.35
13.56 2.30 6.42 10.40 7.32 7.49 10.32 8.33 8.78 2.16 13.93 2.43 9.72 8.00
118363 3727 99153 56952 146615 41059 209953 112104 5500 2299 1143 7550 4748 12194
4 NAI_AVG KM_SUM 2.16 2299 2.43 7561 2.30 3729 9.13 122962 11.66 311641 8.42 72024 6.99 36992 5.71 44987 6.88 97756 8.68 16809 7.72 20609 7.65 4873 7.33 44620 16.94 1999 8.98 5309 8.47 8972 7.92 18218
