AN ABSTRACT OF THE DISSERTATION OF
Wendy Peterman for the degree of Doctor of Philosophy in Forest Engineering
presented on June 4, 2014.
Title: Using Soil Data to Enhance Modeling of Forest Responses to Climate Change.
Abstract approved: ______________________________________________________
Richard H. Waring
Paul W. Adams
ABSTRACT
The over-arching theme of this work is that soil data affect the performance and realism
of vegetation models with particular focus on their ability to predict or explain disturbances such
as fire or disease. We tested the sensitivity of the Excel version of the 3-PG model to soil
properties and applied this information to understanding bark beetle attacks in drought-stressed
forests. We tested the sensitivity of the MC2 model to soil depth with a particular focus on how
soils affect the biogeochemistry and fire modules of the Dynamic Global Vegetation Model
(DGVM). We found in these sensitivity analyses, soil depth, soil water storage capacity (ASW)
and soil texture are among the most important soil factors to map and include in models of forest
productivity. Chapter 2 shows how soil available water storage capacity has an effect on where
pinyon pines are most likely to be stressed during a drought that lasts longer than one year.
Chapter 3 shows how ponderosa pines in western Montana are more vulnerable to disturbance on
fine-textured soils during years when precipitation is significantly less than in previous years.
Finally, Chapter 4 shows that changes in soil depth affect model simulations of productivity and
hydrology across the landscape, but fire simulations are only affected in areas with lower
precipitation. The value of this research is the knowledge that soil characteristics such as depth,
ASW, soil fertility and soil texture can be used in hindcasts of forest disturbances to determine
thresholds where forests may become more vulnerable to disturbance in response to rising
temperatures or changes in precipitation.
©Copyright by Wendy L. Peterman
June 4, 2014
All Rights Reserved
Using Soil Data to Enhance Modeling of Forest Responses to Climate Change
by
Wendy L. Peterman
A DISSERTATION
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Presented June 4, 2014
Commencement June, 2014
Doctor of Philosophy dissertation of Wendy L. Peterman presented on June 4, 2014
APPROVED:
Co-Major Professor representing, Forest Engineering
Co-Major Professor representing, Forest Engineering
Head of the Department of Forest Engineering, Resources, and Management
Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection of Oregon State
University libraries. My signature below authorizes release of my dissertation to any reader
upon request.
Wendy L. Peterman, Author
ACKNOWLEDGEMENTS
The author expresses sincere appreciation to her major advisor Richard H. Waring for
listening with a keen ear, speaking with clarity, modeling good ethics, mentoring calmly and
faithfully and always being willing to use the mind of a beginner, despite his ample wisdom and
knowledge. She also gives thanks to her secondary advisors Thomas Hilker and Paul Adams.
Dr. Hilker is inspiring, dedicated and fun to work with, and Dr. Adams is sincere, caring and
shares the author’s curiosity for what makes nature tick. She thanks Dr. Dominique Bachelet of
Conservation Biology Institute for encouraging her to pursue her dream of getting a PhD and
collaborating with her on a project. She thanks Dr. Ron Reuter for coming all the way to
Corvallis from the Cascades Campus to serve on her committee with his positive attitude and soil
expertise. She sends warm thoughts and appreciation to her employers, Jim Strittholt and Pam
Frost of Conservation Biology Institute. She gives the most sincere her partner Stacey
Bartholomew-Peterman, her parents and her children for supporting her in pursuing her
education and career goals through thick and thin.
CONTRIBUTION OF AUTHORS
Dr. Richard H. Waring re-ran models for the confirmation of results for Chapter 2 and
contributed text to Chapters 2 and 3. Trent Seager ran 3-PG and made graphs for Chapter
2.William Pollock wrote background text for Chapter 2. Dr. Dominique Bachelet contributed
text and acted as Primary Investigator for the sensitivity analysis in Chapter 4. Ken Ferschweiler
contributed data and wrote computer software to calculate Pearson’s coefficients for Chapter 4.
Tim Sheehan collaborated on the production of MC2 data for Chapter 4. Brendan Ward
contributed a Python script that was used to aggregate spatial and tabular soil data for use in
Chapters 2 through 4.
TABLE OF CONTENTS
Page
Chapter 1 ………………………………………………………………………………………..1
Introduction ………………………….………………………………………………..2
Background…………………………………………………..…………….….2
What is available soil water storage capacity?.........................................5
Use of ASW in models ………..………………………………..……7
Why model? …………………………………….……..……………..8
Objectives of this dissertation …….…………………..……………...9
References…………………………………………………………………..11
Chapter 2……………………………………………………………………………………….13
Soil properties affect pinyon pine – juniper response to drought……………………14
Abstract ……………………………………………………………………15
Introduction …………….…………………………………………………16
Methods ……………………………………………………………………18
Gis analysis of pinyon-juniper mortality and soil
characteristics….…………………………………………………18
Site description ………………………………………………….19
Processing of climatic data………….………………………21
Process-based growth model…………….………………….22
Parameterization of the 3-PG model…………………..…………24
Simulations with the 3-PG model ……………………………….25
Results …………………………………………………………………….26
TABLE OF CONTENTS (continued)
Page
GISanalysis………………………………………………………..26
3-PG model simulations …...……………………………………..28
Discussion…………………………………………………………………32
Drought duration, frequency and intensity……………………….32
Variation in vapor pressure deficits and reduction in frost-free
period …………………………………………………………… 34
Value of modeling climatic variation preceding observations
of mortality………………………………………………………34
Increasing role of remote sensing………………………………..35
Conclusions ………………………………………………………………35
Acknowledgments ……………………………………………………….36
References ……………………………………………………………….37
Chapter 3……………………………………..…………………………………………………..42
Overshoot in leaf development of ponderosa pine in wet years
leads to bark beetle outbreaks on fine-textured soils in drier years…………………. 43
Abstract ………………………………………………………………….44
Introduction ……………………………………………………………...45
Methods ………………………………………………………………….49
Study areas…………………………………………………….49
Climatic conditions……………………………………………51
GIS analysis………………………………………………...…52
Remote sensing……………………………………………….52
TABLE OF CONTENTS (continued)
Page
3-PG process model …………………………………………..53
Model parameterization and assumptions…………………….54
Results………………………………………………………………….56
Soil sensitivity analysis………………………………………56
Discussion……………………………………………………………..61
Simulated sensitivity of ponderosa pine growth to
soil and climate...…………………………………………….61
Simulated effects of interannual variations in precipitation…65
Data and model limitations ……….…………………………67
Conclusion……………………………………………………………69
Acknowledgments……………………………………………………70
References …………………………………………………………...71
Chapter 4…………………………………………………………………………………………79
Soil depth affects simulated carbon and water in the MC2 dynamic global
vegetation model…………………………………………………………………….80
Abstract…………………………………………………………….81
Introduction ……………………………………………………….82
Background and model description ………………..…….83
Methods……………………………………………………………86
Study area…………………………………………………86
Soil depth in the MC2 model……………………………..88
Model sensitivity analysis ……………………………….89
TABLE OF CONTENTS (continued)
Page
Results……………..……………………………………………..91
Discussion………………………………………………………..97
Conclusion ……………………………………………………… 99
Acknowledgments………………………………………………100
Chapter 5………………………………………………………………………………………..105
Conclusion…………………………………………………………………………..106
Bibliography……………………………………………………………………………………110
Appendices……………………………………………………………………………………..126
Appendix I…………………………………………………………………………127
Chapter 3 additional graphs……………………………………..127
Appendix II………………………………………………………………………..134
Methods addendum……………………………………………..134
3-PG (Physiological Principles to Predict Growth) model.…….134
Methods for aggregating soil data for the study area
(used in Chapters 2-4)……………………………………..……137
Methods for filling data gaps in soil maps (used in Chapter 4)...139
Methods for making soil datasets (used in Chapters 2-4)…...….139
Methods for intersecting soil datasets with forest
mortality data…………………………………………………...140
Methods for MC2 soil sensitivity analysis……………...………140
LIST OF FIGURES
Figure
Page
1.1 Jay Stratton Noller, OSU soil science professor stands next
to his five-panel painting in the pool room of theAllyson Inn, Newberg,
OR. The painting was done with acrylics and soil from the building site
of the inn. (photo by Lisa Wells)………………………………………………… 3
2.1. A. Map of the area where pinyon pine and juniper species
occur together (green) indicate that mortality since the turn of the
century was highest in the period 2003-2004 (us forest service, 2008).
B. Most of the mortality recorded in 2003-2004 occurred on soils classified
with available water holding capacities (ASW) <100 mm
(NRCS, SSURGO 2011)………………………………………………………...27
2.2 Graph of the relationship between six mapped classes of
available soil water holding capacity (ASW) and the percentage of
total area recorded with mortality in the period 2003-2004 (see table 2). ………28
2.3 Simulations with repeated climatic sequence (1985-2005)
show leaf area index (lai) increasing with stand age to peak at 1.5 at Cortez,
Colorado with available soil water storage set at 50 mm. Interannual
variation in gross photosynthesis, here expressed as a percent of maximum,
showed minimum variation until lai > 1.2………………………………………29
2.4 Simulations with the 3-PG model for mature stands of pinyon
pine and juniper over the period from 1985-2005 indicate that gross
photosynthesis decreases as the soil water storage capacity drops below
100 mm in years with consistently below average precipitation at assumed
equilibrium LAI. Cortez, the coldest of the four sites, recorded its greatest
decrease in photosynthesis between 1985-1990, whereas the period since 2000
was more stressful at the other sites. The Cortez site, during non-drought years,
also recorded much more variation in photosynthesis, which we attribute mainly
to interannual variation in the growing season vapor pressure deficit (see text). .30
2.5 Coarse-textured soils hold water in a more readily available form
than finer textured soils. As a result, the latter impose progressively more
constraints on photosynthesis and transpiration as water is withdrawn from the
soil profile (Landsberg and Waring, 1997). …………………………………….31
3.1 Map showing two study sites in western Montana with black dots
showing locations of meteorological stations used for climate data……………..50
LIST OF FIGURES (continued)
Figure
Page
3.2 Modeled gross photosynthesis with four different soil textures.
Modeled results………………………………………………………………….57
3.3 Simulated LAI between 1997 and 2010…………………….………………58
3.4 Leaf area index (LAI), estimated from correlations with
monthly greenness between 1997 and 2010……………………………………59
3.5 Growth efficiency (GE) normalized over the modeled average for the time
period for (A) site I and (B) site II. 60
3.6 Precipitation on siteI between 1997 and 2010……………………………....61
3.7 Average annual minimum and maximum (A) temperatures and (B)
precipitation for site i and site ii between the years 1985 and 2013…………… 62
3.8 Percent clay content of the soil versus the percent of the total area
of pines killed on each site. ……………………………………………………..64
4.1. Graphical representation of MC2 DGVM including the biogeography
component, using rules derived from the static biogeography model MAPSS
(Neilson 1995), the biogeochemistry component using algorithms from a
modified version of the biogeochemical model Century (Parton et al. 1987) and
the dynamic fire component including fire occurrence and effects (Lenihan et al.
1998). 85
4.2. Map of the study area for the continental portion of the North Pacific
Landscape Conservation Cooperative (shaded area)……………………………87
4.3. Flow diagram of the century hydrology model used in mc2 where
“nlayer” is the total number of soil layers, “nlaypg” the number of rooted
soil layers, “avh20” the available soil water, “tr” the transpiration flow
fraction, “aglivc” the aboveground grass biomass and “rleavc” the tree
leaf carbon pool. (Parton et al., 1987) …………………………………………..89
4.4. Percent change in 30 year means of a subset of mc2 simulated ecosystem
processes with strong positive correlations (0.7 to 1.0) with the difference in
soil depth: a) total ecosystem carbon, b) forest carbon, c) vegetation carbon,
d) net primary productivity and e) actual evapotranspiration……………………93
LIST OF FIGURES (continued)
Figure
Page
4.5 Difference (g c m-2) in 30-year mean carbon consumed by wildfire between
the two MC2 simulations, one using Kern (1994, 1995) and the other using
Peterman (this paper) soil depth…………………………………………………94
4.6 Thirty-year modal (1981 to 2011) vegetation types simulated by MC2
with a) Kern’s soil depth (1994, 1995), b) Peterman’s soil depth (this paper). …95
4.7 Soil depth (left-a) from Jeff Kern derived from statsgo data
(Kern 1994, 1995) and soil depth derived from ssurgo data (right-b)
by Wendy Peterman (this paper). ……………………………………………….96
4.8. A graph of elevation vs. Mean soil depth, shows the differences between
STATSGO-based soil depth data provided by Kern and the SSURGO-based soil
depth data provided by Peterman………………………………………………..97
LIST OF TABLES
Table
Page
2.1 Location and climatic summaries (1985-2005) for meteorological
stations obtained from the western regional climate center……………………..20
2.2 Parameterization of the 3-PG stand growth model for mixed
stands of pinyon pine and juniper……………………………………………….25
2.3 Area and percent of total dieback recorded from aerial surveys in
2003-2004 in reference to classification of available soil water storage capacity
(ASW). SSURGO error assumed to be 3 ha. (this study, US Forest Service, 2008,
NRCS SSURGO, 2011)…………………………………………………………27
3.1 Summary of parameter settings used in the 3-pg simulations for
ponderosa pine…………………………………………………………………..56
4.1. Correlations between the differences in soil depth and the difference
in 30-year means for a subset of ecosystem processes simulated by aMC2 with the
two soil depths datasets (see text)……………………………………………….92
1
Chapter 1
2
Introduction
BACKGROUND
I first became interested the subject of how soil data could be used to test and improve
vegetation and forest growth models when I read a 2010 article by Craig Allen and 19 other
scientists about the unprecedented levels of forest die-off around the world. The group identified
seven gaps in knowledge and decision-support tools that the scientific community could address
to improve the ability of models to predict the timing and locations of forest mortality. My
response to this article was to wonder how I could use my skills as a soil scientist and GIS
analyst to help fill some of these gaps. Of the seven identified gaps, there were three I felt I could
address. The first was to contribute to documenting die-offs. I worked with Dr. Joerg Steinkamp
of the Biodiversity and Climate Research Centre in Frankfurt, Germany to make a spatial dataset
of the die-offs documented in Allen et al. 2010, and I uploaded this to Data Basin
(http://databasin.org/maps/1ad980b9bcb04884b44c8ba72df6a184) for public use.
Another gap was related to filling what the paper termed the “black box” of belowground processes in models of forest mortality. The paper discussed the emphasis modelers often
put on above-ground processes without a great deal of representation of belowground processes.
For me, this brought to mind a painting done by my MS major advisor Dr. Jay S. Noller (Fig. 1).
The painting includes five 16-foot panels showing the development of a soil profile in both
seasonal and geologic time. Dr. Noller depicted the aboveground world as simple, shallow and
two-dimensional and the world below the surface as deep, colorful and full of complexity. His
explanation for this was that he felt most artists and scientists depict the aboveground world with
rich detail, color and complexity, while hardly giving a nod to the world beneath the surface as if
life stops at the base of a plant. Life not only doesn’t stop at the base of a plant, but all life is
3
dependent on what lies below the plant - the soil that absorbs water, heat and organic matter and
provides minerals from its very essence as nourishment to the plants.
Figure 1.1 Jay Stratton Noller, OSU soil science professor stands next to his five-panel painting
in the pool room of the Allyson Inn, Newberg, OR. The painting was done with acrylics and soil
from the building site of the inn. (Photo by Lisa Wells)
Soil is the primary water storage container on the landscape. Soil properties govern the
acceptance, storage and redistribution of water to plants, streams and the atmosphere. Soil is
dynamic and ever-changing, yet it is considered one of the more stable systems in the
environment. Soil morphology in an undisturbed system does tend to stay the same during a
human’s lifetime, but the composition of the soil is constantly changing due to changes in
4
nutrient cycling, weathering, adsorption and desorption, leaching and moisture content (Smeck et
al., 1983). Soil is an open system, and as such, it does not reach equilibrium, but according to
some conceptual models may approach steady state as long as external inputs to the system
exceed out puts from system.
The third gap identified by Allen et al. (2010) was in understanding the feedbacks
between tree physiological stress or mortality and insects, diseases and climate. As I studied the
literature on diseases and insect attacks, I noticed that soil moisture and soil temperature were
often mentioned as vectors for the spread of root diseases (eg. Brasier, 1996), and water stress
was often a culprit in insect susceptibility (Negrón, 1988; Berg et al., 2006; Raffa et al., 2008;
Fettig et al., 2007). I wondered if the physical and chemical composition of the underlying soils
was related to whether trees could defend themselves against insects during drier periods, since
soils determine how water enters and transmits in the soil and how much water can be stored for
later use.
At the suggestion of my colleague, Dominique Bachelet, I first began exploring what
connections I could see between soil factors and beetle-infested pinyon pine dieoff in the
southwestern USA during the 2003-2004 drought. Using STATSGO soils data (NRCS) and
USFS forest damage data from 1997 to 2010 (USDA), I observed through GIS analysis that there
were some clear patterns in soil characteristics and pinyon pine mortality. Five characteristics
appeared as particularly related: soil order, soil moisture regime, soil temperature regime, soil
texture and calcium carbonate concentrations (Peterman, unpublished data). Essentially, lessdeveloped soils in areas where temporary changes in precipitation and temperature would have a
significant impact were the most common covariates with pinyon die-off. An example would be
Entisols in an Ustic moisture regime, where there was usually just enough rain in the growing
5
season to support vegetation. This led me to ask: What happened when there wasn’t enough rain
during the growing season to support this vegetation?
I also learned that calcium carbonate (CaCO3) concentrations were high in aridland soils
and the combination of their high solubility and low levels of intermittent rainfall caused
accumulations and concretions that could limit water passage through the soil, especially during
drier periods
(file:///C:/Users/wendybrd/Downloads/Saline%20and%20Sodic%20Soils%20%20(1).pdf). Since
that time, my work with other organizations (BLM, US Reclamation, DRECP and CA LCC)
conducting soil vulnerability analyses has reinforced the importance of calcium carbonates and
other salts in drought-sensitive soils. These salts not only make soils brittle and impermeable in
dry conditions, they also change the osmotic potential of the soils, redirecting water from a path
that might otherwise be determined by soil structure
(file:///C:/Users/wendybrd/Downloads/Saline%20and%20Sodic%20Soils%20%20(1).pdf). As
seen below, calcium carbonate concentrations are important in calculating available soil water
storage capacity.
What is available soil water storage capacity?
Available soil water storage capacity, often represented with “ASW,” is an
approximation of the volume of water expected to be available for plant roots in a given quantity
and type of soil. ASW is calculated using the difference between “field capacity” and “wilting
point.” These terms refer to the amount of water that is held in the soil at two different tensions,
1/3 bar and 15 bars, respectively (USDA NRCS, 2013). A more qualitative way of explaining
these values is: 1) “field capacity” is the moisture content of the soil following a precipitation or
6
snowmelt event after the excess water has drained through gravity flow, and 2) “wilting point” is
the moisture content of the soil when the tension required for plant roots to extract the water
from the soil is too great for their survival. It is important to recognize that these values (1/3 bar
and 15 bars) are derived for farm crops that have fine, shallow root systems. In reality, plants
and trees have a wide range of wilting points that are not individually represented in models of
vegetation water balances.
There is a great deal of spatial variability in ASW, since it is influenced by local soil
texture, depth, the percent and porosity of rock fragments, and organic matter and calcium
carbonate concentrations. Some of this heterogeneity is evident in soil survey maps, but the true
diversity of soil characteristics across the landscape is impossible to capture at that scale. Soil
maps used by the Natural Resource Conservation Service (NRCS) are created at the 1:20,000 to
1:24,000 minimum scale. Soil map units may therefore be hundreds of acres in area. For this
reason, there will be a great deal of variability in characteristics within a soil map unit. Soil
components are given minimum and maximum estimates for each map unit and a representative
value for the entire map unit (USDA NRCS, 2013). When available, calculations are based on
measured and observed data for numerous pedons within a map unit, but lacking these data, soil
characteristics are compared to known soils with similar attributes, and values are estimated or
predicted based on these inferences.
Soils with higher clay content but similar depths will have higher available water storage
capacities, but soil texture will determine the rate at which the water is released to plants. When
large quantities of water enter the soil and pass rapidly through the macropores, the majority of
precipitation passes to streams through subsurface flow (Neary et al., 2009). Forest soils with
high infiltration rates and high water storage capacities moderate baseflows and peakflows.
7
Differences in infiltration may, however, vary with landscape position, influencing the amount,
distribution and routing of overland flow following precipitation events (Sauer et al., 2005). This
non-uniform infiltration within a watershed leads to differences in plant available water within
the rooting zone as well as partitioning between water that evaporates directly from the soil
versus water transpired by plants (Sauer et al., 2005). A given landscape usually has some areas
where runoff occurs when the precipitation rate exceeds the infiltration rate and others where
runoff occurs due to soil saturation (Sauer et al, 2005).
Use of ASW in models
In this dissertation research, maximum ASW is used as a limit to the amount of water that
can be received, stored and redistributed in a soil profile. The models used in this dissertation
define ASW, but the methods of obtaining and using ASW values are different for the two
models. 3-PG uses ASW values that have either been observed in the field or recorded in soil
surveys, but MC2 uses separate inputs for mineral soil depth and bulk density to calculate ASW
for each cell in a spatial grid (Bachelet et al., 2001). In the 3-PG water balance, precipitation
entering the soil infiltrates until the soil water content meets the available water storage capacity.
Excess water is allowed to run off or contributes to deep drainage (Landsberg and Waring,
1997). Water that enters the soil profile but is not used in that month’s evapotranspiration is
added to the water balance for the following month, so that precipitation use is averaged over the
entire year (Landsberg and Waring, 1997). In MC2, soil depth does not influence runoff (set at a
constant 55% of precipitation when rainfall exceeds 50 mm), but it does influence streamflow
and baseflow (Bachelet et al., 2001). Monthly precipitation drains through the soil profile until it
reaches bedrock, and then it is lost through belowground flow (Bachelet et al., 2001).
8
It is important to note that ASW doesn’t change within the time period of a model
simulation. What changes and is affected by ASW is the amount of water stored in the soil
profile after precipitation events. Shallow soils will have low ASW. If the model is using this
correctly, shallow soils with low ASW should lead to runoff or streamflow in high precipitation
events and greater constraints on vegetation evapotranspiration during drought periods. Deeper
soils with higher ASW should have the opposite effect on runoff and streamflow.
Why model?
Although some field and laboratory experiments have been performed to determine the
potential vulnerability of forest ecosystems to drought and increased temperatures, these are
limited in scope and spatial extent. As an alternative to manipulative experiments, it may be
advisable to make preliminary evaluations with mechanistic models, developed and validated
using field observations. In essence, a model contains within it a hypothesis and a simulation to
test it experimentally (Smeck at al., 1983). A good model provokes scientists to improve,
redefine or expand it to enhance the understanding of the system they are modeling.
Furthermore, the value of models is not only to highlight the phenomena being described
in a system, but also to expose what is being left out that would add more meaning (Smeck et al.,
1983). Inherent in all models is a degree of uncertainty, which must be appreciated when
interpreting model projections. These uncertainties arise from incomplete knowledge or
understanding of the system as well as from unknown behaviors of the external environment.
Examples of uncertainties in this dissertation are 1) the margin of error in soil maps (herein
estimated at +/- 3 ha), 2) the margin of error in climate data, 3) errors inherent in remote sensing
data from random noise and atmospheric interference, and 4) random error in model simulations.
9
Uncertainties by no means negate the value of modeling exercises, but it is always important to
use caution when interpreting and applying model results to real world systems. As my major
professor, Dr. Waring says, “Trust, but verify.” Using our best knowledge and tools and availing
ourselves of the best available data, scientists can simulate natural phenomena in simple ways.
This may not lead to concrete answers about what exactly is occurring in nature, but it does help
us to sharpen the questions we are asking by illuminating the possibilities of what is occurring
the system of interest.
Objectives of this dissertation
Three wonderful things emerged from my initial interest in adding to the scientific
knowledge of how soils could be better utilized in vegetation modeling : 1) Dr. Bachelet and I
received a grant from the North Pacific Landscape Conservation Cooperative to study soil
relationships with forest mortality in the Pacific Northwest USA and to test the sensitivity of the
MC2 model to soil inputs, 2) I received a grant from the Southern Rockies Landscape
Conservation Cooperative to develop a soil vulnerability index for the Southern Rockies
Landscape Conservation Cooperative, and 3) I met Dr. Richard H. Waring, who mentored me on
using process-based models to test my hypotheses about the interactions of soils and climate with
tree physiology.
The following manuscripts present some of the findings from these three projects.
Chapter 2 shows how soil available water storage capacity has an effect on where pinyon pines
are most likely to be stressed during a drought that lasts longer than one year. Chapter 3 shows
10
how ponderosa pines in western Montana are more vulnerable to disturbance on fine-textured
soils during years when precipitation is significantly less than in previous years. Finally, Chapter
4 shows that changes in soil depth affect model simulations of productivity and hydrology across
the landscape, but fire simulations are only affected in areas with lower precipitation.
The over-arching theme is how soil data affect the performance and realism of vegetation
models with particular focus on their ability to predict or explain disturbances such as fire or
disease. For the first two papers, I tested the sensitivity of the Excel version of the 3-PG model to
soil properties and applied this information to understanding bark beetle attacks in droughtstressed forests. In the third paper, I tested the sensitivity of the MC2 model to soil depth with a
particular focus on how soils affect the biogeochemistry and fire modules of the Dynamic Global
Vegetation Model (DGVM).
11
REFERENCES
Allen, C.D., Macalady, A.K. Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M.,
Kitzberger, T., Rigling, A., Breshears, D.D., Hogg, E.H., Gonzalez, P., Fensham, R.,
Zhang, Z., Castro, J., Demidova, N., Lim, J-H, Allard, G., Running, S.W., Semerci, A.,
and Cobb, N. 2010. A global overview of drought and heat-induced tree mortality reveals
emerging climate change risks for forests. Forest Ecology and Management 259: 660–
684.
Bachelet, D., Neilson, R. P., Lenihan, J. M., and Drapek, R. J. 2001. Climate change effects on
vegetation distribution and carbon budget in the US. Ecosystems 4: 164–185.
Berg, E.E., Henry, J.D., Fastie, C.L., DeVolder, A.D., and Matsuoka, S.M. 2006.
Spruce beetle outbreaks on the Kenai Peninsula, Alaska, and Kluane National Park and
Reserve, Yukon Territory: Relationship to summer temperatures
and regional differences in disturbance regimes. Forest Ecology and
Management 227: 219–232.
Brasier, C.M. 1996. Phytophthera cinnamomi and oak decline in southern Europe.
Environmental constraints including climate change. Annals of Forest Science 53: 347358.
Fettig, C.J., Klepsig, K.D., Billings, R.F., Munson, A.S., Nebeker, T.E., Negron, J.F., and
Nowak, J.T.. 2007. The Effectiveness of Vegetation Management Practices for
Prevention and Control of Bark Beetle Infestations in Coniferous Forests of the
Western and Southern United States. Forest Ecology and Management 238.1-3
: 24–53.
Neary, D.G., Ice, G.G. and Jackson, C.R.. 2009. Linkages Between Forest Soils
and Water Quality and Quantity. Forest Ecology and Management:
2269–2281.
Negron, J.F. 1988. Probability of infestation and extent of mortality associated with the
Douglas-fir betle in the Colorado Front Range. Forest Ecology and Management
107: 71-85.
Raffa, K.F., Aukema, B.H., Bentz, B.J., Carroll, A.L., Hicke, J.A., Turner, M.G., and
Romme, W.H.. 2008. Cross-scale drivers of natural disturbances prone to anthropogenic
amplification: the dynamics of bark beetle eruptions. BioScience, 58: 501-517.
Sauer, T.J., Logsdon, S.D., Brahana, J.V., and Murdoch, J.F. 2005. Variation in
infiltration with landscape position: Implications for forest productivity and surface water
quality. Forest Ecology and Management 220:118-127.
12
Smeck, N.E., Runge, E.C.A and McKintosh, E.E. 1983. Dynamics and genetic modelling of soil
systems. In Pedogenesis and Soil Taxonomy I. Concepts and Interactions, (ed)
L.P. Wilding, N.E. Smeck and G.F. Hall pp. 51-81.
USDA NRCS. 2013. U.S. Department of Agriculture, Natural Resources Conservation Service.
National soil survey handbook, title 430-VI. Available online at
http://soils.usda.gov/technical/handbook/, Amendment 23, February, 2013, pgs 618 A.2 –
618 A.8. Accessed [04/15/2013].
13
Chapter 2
14
Soil Properties affect pinyon pine – juniper response to drought
Wendy Peterman, Richard H. Waring, Trent Seager, and William L. Pollock
Ecohydrology
Address
Issue 6, 2013
15
ABSTRACT
Since the turn of the century, drought-driven dieback has affected more than a million
hectares of pinyon pine-juniper woodlands in the Southwest, U.S.A. From annual analysis of
aerial surveys by the US Forest Service and soil survey data, it appears that most of the mortality
occurred in the period 2003-2004, and that 70% was restricted to soils mapped as having
available water storage capacities (ASW) < 100 mm. We conducted a more refined GIS analysis
and found as ASW increased in increments of 50 mm up to 300 mm, the distribution of areas with
observed mortality decreased exponentially from 42% to 3% (n = 6 classes, r2= 0.93). We used
this information in a process-based stand growth model, 3-PG, to assess year to year variation in
gross photosynthesis between 1985 and 2005 with climatic data acquired at monthly intervals
from four weather stations located where pinyon-juniper woodlands were confirmed to be present
from satellite imagery. A sensitivity analysis identified sustained periods of drought and
supported field observations that once canopy leaf area approaches a maximum value, the
majority of mortality should be restricted to soils with ASW values < 100 mm. Additional
analyses indicated that differences in soil texture played a small part (<10%) in accounting for
variation in gross photosynthesis and that consecutive years of drought may have a cumulative
effect on pinyon pine vulnerability to bark beetle attack. Disturbances that reduce the canopy LAI
should result in less pine mortality in the future, although conversion to shrub and grassland may
occur if climate conditions continue to become less favorable.
16
INTRODUCTION
Pinyon pine (Pinus edulis), in association with juniper (Juniperus osteosperma and
Juniperus scopulorum), covers approximately 15 million acres in Utah, Colorado, New Mexico,
and Arizona with sporadic occurrences in Texas, Oklahoma, Wyoming, California, and Mexico.
Over the areas that these species occupy, elevations range between 1,370 and 2,440 m, and mean
precipitation varies from 250–560 mm annually (Burns and Honkala, 1990). Studies of pollen
fossils from the 13th and 14th centuries indicate that pinyon pine expanded its range in response
to increases in precipitation and contracted its distribution following prolonged periods of
drought on approximately decadal intervals (Gray et al., 2006). The explanation for the shift in
dominance from juniper to pinyon pine reflects differences in tree physiology. Pinyon pine has
higher photosynthetic rates than juniper when water is more available, while juniper continues to
photosynthesize longer with increasing water stress (Lajtha and Barnes, 1991; Schwinning and
Ehlerlinger, 2001; Williams and Snyder, 2003).
More recently, between 1998 and 2010, scientists in the southwestern United States have
reported extensive dieback in pinyon-juniper associated with multiple-year drought (Shaw et al.,
2005; Breshears et al., 2008; Floyd et al., 2009). Research is underway to clarify the exact cause
of mortality (Gray et al., 2006; McDowell et al., 2008; Barger et al., 2009; Allen et al., 2010).
Under prolonged drought, a lower photosynthetic rate may prevent pine from producing
sufficient oleoresins, making trees more susceptible to bark beetles, fungi, and other biotic agents
(Cobb et al., 1997; McDowell et al., 2008). Juniper, while not attacked by bark beetles, may
continue to transpire until the vascular system becomes inoperative (McDowell et al., 2008).
17
Mature stands of pinyon pine growing with juniper normally exhibit tree mortality rates
of < 0.5% annually (Shaw et al., 2005). Stand growth models, using a -3/2 power function
between tree number and stand biomass, predict similar low rates of mortality under stable
climatic conditions (Landsberg and Waring, 1997). Under variable climatic conditions, mortality
rates may vary considerably from the norm. Regional observations indicate that recent pinyon
pine mortality, while wide spread, varies considerably year to year and place to place (Shaw et
al., 2005). Ogle et al. (2000) compared growth rates of trees over a range of sites and found that
those that died showed more interannual variation in ring-widths during the previous decade than
those that survived.
Tree physiologists refined the correlation by noting that tree susceptibility to insect
attack is better related to wood production per unit of leaf area than to diameter growth alone
(Larsson et al., 1983; Mitchell et al., 1983; Waring and Pitman, 1983). Betancourt et al. (1993)
suggested that further insights might be gained by using process-based models to investigate
when drought differentially affects pinyon pine and juniper. West et al. (2008) developed such a
model, but in considering competition among species, extensive data requirements limit its
application.
In this paper, we simplify the data requirements for process modeling by combining the
properties of pinyon pine and juniper into a generic woodland type. We hypothesize that once
equilibrium leaf area for a site is reached, the spatial variation in recent tree mortality will be
accounted for by interannual climatic variation combined with the soil water-holding capacity.
To test this hypothesis, we first collected maps of areas within the pinyon-juniper woodland type
in Utah, Arizona, Colorado and New Mexico that have shown significant spatial and temporal
variation in mortality. Next, we overlaid maps classifying differences in soil available water-
18
holding capacity (ASW) and tallied the corresponding areas with observed mortality to seek a
predictive relationship. Finally, to evaluate the importance of soil water storage capacity versus
soil texture in explaining the observed patterns of mortality, we utilized a simplified processbased growth model to analyze and interpret relative variations in gross photosynthesis between
1985 and 2005 at representative sites in the four southwestern states.
METHODS
GIS analysis of pinyon-juniper mortality and soil characteristics
The pinyon-juniper dieback data were developed by the Merriam-Powell Center for
Environmental Research (MPCER) at Northern Arizona University from an aerial mapping
project of the USDA Forest Service, Forest Health Technology Enterprise Team (FHTET).
MPCER selected individual species from the USFS original dataset, which used aerial detection
surveys to map insect, disease and abiotic damage to forested areas in the USA. The USFS aerial
surveys combine low-level flights (315 to 630 m above ground level) and USGS paper maps
(1:100,000 scale) as well as a digital sketchmap system (GPS and GIS database) to record
disturbance on an annual basis. Due to the size of the polygons and the patchy nature of forest
insect activity, some polygons may contain unaffected areas. For the sake of recording new
damage, only trees with yellow, brown or red foliage or some defoliation were mapped as part of
the aerial survey. Dead trees with no foliage were assumed to be recorded in a previous year’s
study. The MPCER dataset refined the FHTET dataset to remove the effects of past dieback and
focus the data on individual species.
19
We aggregated the tree dieback dataset for all study years (2000 to 2007) to create one
large dieback dataset and intersected this with the NRCS Soil Geographic Database (SSURGO)
for the region. SSURGO soil surveys are usually 1:20,000 to 1:24,000 in scale and reported on
the county level. We used a Python script to aggregate all soil survey data available for Arizona,
New Mexico, Colorado and Utah and attached the relevant fields from the National Soil
Information System database to the map units as attributes. There were some gaps in the
SSURGO coverage of the region, especially in Utah, but most of these did not overlap areas
where tree mortality was recorded. The total difference between the tree mortality dataset and the
SSURGO soils data within the dieback polygons was only approximately 1%. The US General
Soil Map (STATSGO2, 1:250,000) was far too coarse to improve the soils dataset by filling in
the minor gaps. The intersected SSURGO soil map units with similar values were aggregated to
create two new datasets representing available water storage capacity and texture. X-Tools Pro
(Data East) was used in ArcMap (ESRI) to calculate the areas where mortality was extensive.
The results of the GIS analysis were used in the 3-PG model to learn where, why and when
drought and soil available water affect pinyon-juniper woodlands.
Site description
The climate throughout southwestern North America is notably dry with most of the
precipitation falling from late June through September (Sheppard et al., 2002). The El NinoSouthern Oscillation (ENSO) cycles cause extreme variation in precipitation. Weiss et al. (2004)
documented that such variation is reflected in a consistent manner from satellite-derived
estimates of greenness across six broad types of vegetation, including pinyon-juniper woodlands.
Ogle et al. (2000) confirmed that interannual variation in precipitation is reflected in tree-ring
20
records. Although the general climatic patterns are wide spread, the effect of drought may differ
depending on the density of vegetation and local climatic conditions. We downloaded monthly
temperature and precipitation records from sites in Arizona, Colorado, New Mexico, and Utah,
available from the Western Climate Center (http://www.wrcc.dri.edu). The weather stations,
their location, and climatic summaries for the period 1985-2005 are presented in Table 2.1.
Included in the summaries is an assessment of the relative decrease in the number of subfreezing
days over the years analyzed, which varied from 5% to 24% over the two decades.
Station
Lat
(decimal
degrees)
Long
(decimal
degrees)
Elev (m)
Mean Max
Monthly
Temp (°C)
Mean Min
Monthly
Temp (°C)
Mean
Ann.
Ppt
(mm)
Mean
Ann.
Frost
Days
% decrease
frost days 20 yr period
Bowie,
AZ
32.32647
-109.487
1147
36.3
-0.1
313
41
5%
Socorro,
NM
34.06167
-106.8994
1442
34.3
-5.4
250
98
8%
Blanding,
UT
37.62333
-109.4789
1899
32.9
-5.8
353
102
20%
Cortez,
CO
37.34917
-108.5792
1893
33.3
-10
335
154
24%
Table 2.1 Location and climatic summaries (1985-2005) for meteorological stations obtained
from the Western Regional Climate Center
21
Processing of climatic data
To understand and model how climatic conditions affect tree growth requires additional
information beyond that recorded at most weather stations. The upper limits on photosynthesis
are set by the amount of visible light intercepted by leaves, and further constrained by frost, the
atmospheric vapor pressure deficit, and the net radiation. Fortunately, these additional variables
can be derived from monthly mean temperatures along with knowledge of the location, calendar
date, physiography, and canopy leaf area.
To estimate mean monthly daytime vapor pressure deficits (VPD), we assumed that the
water vapor concentration present throughout the day was equivalent to that held at the mean
minimum temperature. Because humidity deficits in the summer often exceed a threshold limit
(in the model) where stomata close (i.e., >3.0 kPa), no adjustments were made to compensate
for conditions where the humidity was less than 100% at night (Kimball et al., 1977). The
maximum VPD was calculated each month as the difference between the saturated vapor
pressure at the mean maximum and minimum temperatures. Mean daytime VPD was calculated
at half of the maximum value.
The number of days per month with subfreezing temperatures ( 2oC) was estimated
from empirical equations with mean minimum temperature (Coops et al., 1998). In modeling,
we did not attempt to account for extremes in temperature that might directly kill trees, because
both live pinyon and juniper exceeding 300 years in age were reported in the region (Gray et al.,
2006).
22
Monthly estimates of total incoming short-wave radiation were calculated using an
approach detailed by Coops et al. (2000) where the potential radiation for any latitude and
physiographic setting was first calculated and then reduced, based on the clarity (transmissivity)
of the atmosphere. Changes in the atmospheric transmissivity were mirrored in temperature
extremes (Bristow and Campbell, 1984). The modeling approach, when compared with direct
measurements, predicted both the direct and diffuse components of mean monthly incoming
radiation with 93 - 99% accuracy on flat surfaces with a mean error < 2 MJ m-2 day-1, conditions
which we assumed generally applied for this modeling exercise. The visible portion of shortwave radiation was about half of the total (Landsberg and Waring 1997).
Process-based growth model
There are a variety of physiologically-based process models available, but only a few
have been designed to scale projections of photosynthesis, growth, and mortality across
landscapes (see reviews by Mäkelä et al., 2000; Landsberg, 2003; Nightingale et al., 2004).
Among the most widely used is 3-PG (Physiological Principles Predicting Growth) developed by
Landsberg and Waring (1997). The model is based on a number of established biophysical
relationships and constants and incorporates simplifications that have emerged from studies
conducted over a wide range of forests (Landsberg et al., 2003). 3-PG has been successful in
predicting gross photosynthesis and water balances in deciduous and evergreen forests (Waring
et al., 1995; Soares and Almeida, 2001 Landsberg and Waring, 2004; Law et al., 2000; Coops et
al., 2007).
23
The model simplifications include the following assumptions: a) that monthly time-steps
in climatic data are adequate to capture major trends, b) that knowledge of the most limiting
variable constraining photosynthesis each month is sufficient, c) that autotrophic respiration
(Ra) and net primary production (Pnet) are approximately equal fractions of gross photosynthesis
(Pg), and d) that the proportion of photosynthate allocated to roots increases with drought and
decreases with nutrient availability. The model incorporates the -3/2 power function which
results, under stable climatic conditions, in generating mortality rates at canopy closure similar
to those recorded in forestry yield tables (Waring and McDowell, 2002). In addition, the model
includes an age-related decrease in photosynthesis, which is associated with a progressive
increase in hydrologic resistance to water movement as stems approach their maximum
extension for a site (Koch et al., 2004).
The 3-PG model calculates gross photosynthesis, transpiration, growth allocation and
litter production at monthly intervals, and takes into account deficiencies in precipitation in
previous months and years by sequentially updating a soil water balance. A monthly time-step
excludes accurate calculation of evaporation from the canopy and soil. Similarly, the model is
unable to compute an accurate snow water balance, although it may be assumed that most of the
precipitation in months with average temperatures below freezing is in the form of snow.
Subfreezing temperatures at night are known to halt photosynthesis the following day (Hadley,
2000), thus the number of frost days per month needs to be estimated, along with mean monthly
temperature extremes, humidity deficits, solar radiation, and precipitation.
24
In regard to biological properties, two sets of allometric equations are required; one to
describe the relationship between growth in tree diameter (at breast height, 1.37m), and the
other, the relationship between tree diameter and the growth of new foliage. The annual rate of
foliage turnover also must be estimated to calculate net change in canopy leaf area within and
between years.
Parameterization of the 3-PG model
We parameterized the model using a variety of sources to encompass a range in projected
leaf area index (LAI) from 0.5 to 1.5. The values for leaf-longevity and turnover were
established for the woodland type by simulations (Table 2.2). Similarly, allometric relations
between stem diameter and leaf mass (and area) were derived to produce stable values of LAI
under stable climatic conditions. Other values reported in Table 2.2 were constrained to limit
maximum transpirations rates to <30 mm/month and stand basal area to ~20 and 40 m2/ha at 100
years for the range in LAI values assessed based on detailed information acquired at a field site
near Los Alamos, New Mexico (Pangle et al., 2012; Plaut et al., 2012). To minimize structural
changes in stand properties that influence tree mortality, the initial stocking was set low at 100
trees per ha to prevent self-thinning, and the age-related algorithm constraining photosynthesis
was turned off. This allowed detailed analyses of interannual variation in relative gross
photosynthesis in response to variation in environmental conditions alone. The effect of soil
texture on water availability was assessed through comparisons of soils with the same water
holding capacities but significantly different water release curves, which are incorporated in 3PG (Landsberg and Waring, 1997).
25
Variable
Quantum Efficiency
Soil Fertility Ranking (0-1 scale)
Temperature: Min., opt., and max.
Fraction of Leaf turnover annually
Available soil water storage
capacity (ASW)
Soil texture
Growth in Foliage biomass (kg)
Value or Function
0.025 mol C mol
photon
0.05 to 0.3
-2oC, 24oC, 40oC
1/10th
Range: 50 -200 mm
Range :Sand to Clay
Foliage Biomass =
0.0483(dia2.365)
Growth in stemwood biomass (kg)
Stem Biomass =
0.0411(dia2.4192)
Maximum canopy conductance, m/s 0.0.008
Source
This study
This study
This study
Lajtha and
Getz (1993),
This study
This study
This study
This study
Jenkins et al.
(2003)
This study
Table 2.2 Parameterization of the 3-PG stand growth model for mixed stands of pinyon pine and
juniper.
Simulations with the 3-PG model
We began modeling with the assumption that all four sites had similar equilibrium LAI
values of 1.0, although satellite imagery indicated that the Bowie, Arizona site might support a
slightly lower LAI and the Cortez, Colorado site a higher LAI. By varying estimates of soil
fertility, which changes the partitioning of photosynthate, we increased and decreased LAI from
the default value (1.0) by ± 0.5 LAI to evaluate when differences first appear in annual Pg as
ASW was allowed to vary in 50 mm intervals from 50 to 200 mm. Over the narrow range in
simulated LAI values, maximum daily rates in transpiration were simulated to increase linearly
from 0.35 to 1.0 mm day-1. As in previous studies, we also performed a series of sensitivity
analyses to evaluate the extent that interannual variation in the number of subfreezing days and
26
monthly variation in vapor pressure deficits influenced the relative patterns in gross
photosynthesis (Waring and McDowell, 2002; Waring et al., 2008).
RESULTS
GIS analysis
GIS analysis demonstrated that 84% of the pinyon-juniper mortality recorded in 2003 and
2004 (Figure 2.1A) occurred on soils mapped with an available soil water capacity (ASW) of
<150 mm (Figure 2.1B), with 70% on soils with <100 mm (Table 2.3). For the six classes of
ASW, a negative power function described the percentage decrease in the area on which mortality
was recorded between 2003-2004 (Figure 2.2). It was not possible to assess spatial variation in
soil texture because that classification was incomplete, but considerable variation was noted in
areas with similar values of ASW.
A
27
B
Figure 2.1. A. Map of the area where pinyon pine and juniper species occur together (green)
indicate that mortality since the turn of the century was highest in the period 2003-2004 (US
Forest Service, 2008). B. Most of the mortality recorded in 2003-2004 occurred on soils
classified with available water holding capacities (ASW) <100 mm (NRCS, SSURGO, 2011).
ASW
0 - 50 mm
50 - 100 mm
100 - 150 mm
150 - 200 mm
200 - 250 mm
250 - 300 mm
Total
Area of Mortality % of Total
(ha)
Mortality
535,877
42.4
352,991
27.9
167,959
13.3
90,808
7.2
79,739
6.3
37,000
2.9
1,264,374
100
Table 2.3 Area and percent of total dieback recorded from aerial surveys in 2003-2004 in
reference to classification of available soil water storage capacity (ASW). SSURGO error
assumed to be 3 ha. (This study, US Forest Service, 2008, NRCS SSURGO, 2011).
28
Mortality, % of total area (2003-2004)
50
y = 55.541x-1.453
R² = 0.93
40
30
20
10
0
Available Soil Water Storage Capacity, mm
Figure 2.2 Graph of the relationship between six mapped classes of available soil water holding
capacity (ASW) and the percentage of total area recorded with mortality in the period 2003-2004
(See Table 2).
3-PG Model Simulations
The extent that variation in the available soil water holding capacity affected the
interannual patterns in Pg depended on the site, the equilibrium LAI, and monthly variation in
precipitation. In general, when the equilibrium LAI was set at 0.5, variation in Ac had little
effect. At Cortez, the site with the highest equilibrium LAI, simulations of stand development
showed that the sequentially repeated 20 year climatic patterns have little impact on Pg , even
when ASW was set at 50 mm, until LAI approached 80% of the maximum value (Figure 3).
29
1.6
1.4
100
1.2
80
1.0
60
0.8
0.6
40
LAI
% Max Photosynthesis
120
% Max Photosynthesis
LAI
0.4
20
0.2
0
0.0
0
20
40
60
80
Stand Age, Years
100
Figure 2.3 Simulations with repeated climatic sequence (1985-2005) show leaf area
index (LAI) increasing with stand age to peak at 1.5 at Cortez, Colorado with available
soil water storage set at 50 mm. Interannual variation in gross photosynthesis, here
expressed as a percent of maximum, showed minimum variation until LAI > 1.2.
When the equilibrium LAI was set at 1.0 (or 1.5 at Cortez), the patterns in Pg showed
considerable sensitivity at ASW ≤ 100 mm. Figure 2.4 indicates that under climatic conditions at
Socorro, New Mexico, proportionally more interannual variation in gross photosynthesis
occurred over the 20 year period as ASW decreased from 150 mm to 50 mm. These results are
representative of soils with a texture of clay loam. Sandy soils, as shown in Figure 2.5, release
water to roots far more easily than do clay soils. This difference in water release appears to
become important only during sustained periods of drought on soils with ASW < 100mm. Under
such condition, clay soils reduced modeled gross photosynthesis by 4% more than sandy soils
when ASW= 100 mm, and by 10% when ASW = 50 mm (simulations not shown).
30
Figure 2.4 Simulations with the 3-PG model for mature stands of pinyon pine and juniper over
the period from 1985-2005 indicate that gross photosynthesis decreases as the soil water storage
capacity drops below 100 mm in years with consistently below average precipitation at assumed
equilibrium LAI. Cortez, the coldest of the four sites, recorded its greatest decrease in
photosynthesis between 1985-1990, whereas the period since 2000 was more stressful at the
other sites. The Cortez site, during non-drought years, also recorded much more variation in
photosynthesis, which we attribute mainly to interannual variation in the growing season vapor
pressure deficit (See text).
Fractional reduction in Gross
Photosynthesis
31
1.0
0.8
0.6
0.4
Sand
Sandy-loam
0.2
Clay-loam
Clay
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Fraction of storage capacity
Figure 2.5 Coarse-textured soils hold water in a more readily available form than finer
textured soils. As a result, the latter impose progressively more constraints on
photosynthesis and transpiration as water is withdrawn from the soil profile (Landsberg
and Waring, 1997).
The impact of subfreezing conditions differed among the four sites in proportion to the
number of days recorded below freezing, which range, on average, from 41 days annually at
Bowie, Arizona to 153 days at Cortez, Colorado (Table 2.1). The rate that a rise in minimum
temperatures reduced subfreezing days over the period varied in parallel, from 5% at Bowie to
24% at Cortez, which has implications of bark beetle population build up (discussed in the next
section). During the winter, temperatures were suboptimal and solar radiation less than half that
in the summer months; as a result, the maximum reduction in annual Pg at Cortez, the coldest
site, averaged only 17% less with frost, at equilibrium LAI and ASW =200 mm, than without. In
contrast, at Bowie, the difference was only 10%. A comparative analysis showed that interannual
32
patterns in Pg with and without frost restrictions, were highly correlated (r2>0.9, results not
shown).
Although temperatures during drought years tended to average somewhat higher than in
more normal years, with a maximum temperature set at 40oC for the model, little response to
variation in monthly mean temperatures was observed. This is in part because, at very high
temperatures, the humidity deficit often exceeded 3.0 KPa during the day, which limited stomata
conductance to very low values.
In sensitivity tests where average monthly vapor pressure deficits for the twenty year
period were substituted for recorded values at all sites, annual Pg peaked near 100% of maximum
at ASW > 150 mm. From these analyses, we attributed the large interannual variation in Pg
simulated at the Cortez site to seasonal variation in vapor pressure deficit (VPD) during the
growing season that matched or exceeded periodic constraints from limitations in soil storage
capacity.
DISCUSSION
Drought duration, frequency and intensity
Breshears et al. (2009) demonstrated that pinyon pine is able to survive short periods of
drought that are sufficient to cause complete stomatal closure and thereby halt all photosynthesis.
When such conditions are extended beyond 8 months, however, trees become highly susceptible
to bark beetle (Ips confusus) attack. Similar observations have been made following experiments
that artificially reduced precipitation by 50% on sites where the equilibrium leaf area index was
estimated at 1.0 (Monica Lisa Gaylord, Northern Arizona University and Nate McDowell, Los
Alamos National Laboratory, personal communications). These observations may explain why a
33
simulated drop to 50% of maximum Pg at Socorro in 1990 did not result in wide-spread
mortality, whereas less extreme, multiple-year drought recorded around the turn of the century
did (Figure 2.4).
From experiments conducted on other species, Christiansen et al. (1987) showed that the
a tree’s ability to stop the spread of blue-stain fungus introduced by bark beetles is associated
with a shift from wood to resin production at the sites of infection. In mature stands of pinyon
pine, a relative reduction in maximum gross photosynthesis represents a proportional reduction
in wood production per unit of leaf area. If trees were much older than a century, we would
expect them to be more vulnerable to drought than younger trees because, as trees approach
maximum height on a given site, hydraulic restrictions to water transport increase and further
limit photosynthesis (Koch et al., 2004). Pathogens also can weaken trees, causing them to be
more susceptible to beetle attack during a drought (Negrón and Wilson, 2003).
Some of the observed mortality on soils classified with ASW values >200 mm (Figure
2.1B) may indicate the presence of older age classes of pine, or shallower soils interspersed
among deeper soils (Greenwood and Weisberg, 2008). Similarly, the absence of mortality on
some soils classified with less than 100 mm of water storage capacities may reflect recently
disturbed woodlands where young trees face less competition for water because canopy leaf area
has yet to reach its maximum (Figure 2.3). In the future, if climatic conditions continue to be
less favorable for pinyon-juniper, conversion to other plant types may occur (Floyd et al., 2009).
34
Variation in vapor pressure deficits and reduction in frost-free period
Although frequent and sustained periods of drought create favorable conditions for bark
beetle attacks on pinyon pine and xylem failure in the more resistant juniper, other factors can
also play a role. We believe that Cortez, Colorado, the coldest site among the four analyzed
(Table 2.1), differs in three important ways: (1) the simulations indicated a major decrease in Pg
between 1985-1990 not evident at the other sites (2) the effects of variation in growing season
VPD on Pg were much more pronounced than at the other sites, and (3), the reduction in
subfreezing days on Pg between 1985-2005 was larger than at the other sites.
The greater sensitivity to variation in vapor pressure deficit (VPD) during growing season
at Cortez caused variation in Pg similar to that attributed to drought. Combined with the rapid
warming trend recorded at Cortez over the 20 year period, conditions also became more
favorable for bark beetle reproduction (Safranyik et al., 2010). It is this latter point that may
explain why dieback of pinyon pine has mainly been observed locally in 2003-2004 (Personal
Communication with R. Carter, Stone Free Farm, Cortez, Colorado).
Value of Modeling Climatic Variation Preceding Observations of Mortality
The empirical relation between ASW and tree mortality shown in Figure 2.2 only applies
widely if pinyon-juniper woodland are known to have reached equilibrium LAI, as demonstrated
through modeling. Process-based modeling also provides an interpretation of the extent that
variation in other variables, such as a reduction in subfreezing conditions and growing season
vapor pressure deficits affect tree vigor prior to observations of wide-spread mortality. Coops
and Waring (2011) developed a general approach to assess the effects of recent climatic variation
across the Pacific Northwest, but lacked adequate information on soil properties to include in
35
their analyses. Evidence that soil properties account for differential mortality of pinyon pine
makes the development of methods to improve soil classifications a high priority.
Increasing role of remote sensing
In this paper, we took advantage of multi-year remote-sensing imagery from the U.S.
Forest Service to document when and where tree mortality has occurred between 2000 and 2007
in the southwestern USA. We also utilized Google Earth imagery to confirm the presence of
pinyon pine-juniper woodlands near four representative weather stations. Kennedy et al. (2007)
and Huang et al. (2010) have analyzed historical patterns of disturbance more broadly using
Landsat imagery acquired since the 1970s. These analyses include automated protocols to
distinguish different types of disturbances (Kennedy et al., 2007). Although differences in the
background soil colors in desert environments create special challenges in estimating LAI by
remote sensing, laser altimetry offers considerable promise to improve this situation (White et
al., 2000).
CONCLUSIONS
We developed an empirical model that predicted tree mortality associated with soil
properties that accounted for approximately 93% of the observed variation recorded on aerial
photographs in the period 2003-2004 (Figure 2.2). Simulations with a process-based model
provided an explanation for the observed patterns and identified in which years the empirical
model could be applied. The modeling also provided an assessment of the critically important
equilibrium LAI. On soils with ASW values < 100 mm, at equilibrium LAI, a notable decrease in
gross photosynthesis was predicted at four woodland sites around the turn of the century that
36
presumably reduces mean tree vigor (indexed as wood production per unit of leaf area) below a
threshold where bark beetles could successfully attack and kill pinyon pine. Differences in soil
texture played only a minor role. Death caused by drought alone would likely be limited to soils
with very low available soil water storage, where photosynthesis would approach zero for an
entire year. The simulations also indicated that interannual variation in vapor pressure deficits
and a warming trend in minimum temperatures can reduce tree vigor, creating conditions more
favorable for beetle outbreaks at higher elevation sites, possibly without extreme soil drought. If
climatic conditions were to continue to become less favorable for pinyon-juniper, we would
expect a reduction in equilibrium LAI and conversion to other types of vegetation.
ACKNOWLEDGMENTS
This paper owes its origin to Nate McDowell, who invited Richard Waring to participate
in a workshop on the topic of modeling stress-induced mortality in pinyon pine and juniper
woodlands. At the same time, Waring had agreed to offer a modeling tutorial to the other
authors, who were enrolled in an Oregon State University graduate class on woody plant
physiology taught by Drs. Kate McCulloh and Barbara Bond. Brendan Ward at the Conservation
Biology Institute deserves special recognition for developing a computer program that assembles
large soil data sets in an efficient matter for map display. We are also grateful to Robbie Hember
and Nicholas Coops at the University of British Columbia for providing a copy of a report in
which they did some initial modeling with 3-PG for pinyon pine and juniper. The work reported
in this paper was partially supported by a grant (NNX11A029G) to Waring from the National
Aeronautics and Space Administration’s program for biodiversity and ecological forecasting.
37
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42
Chapter 3
43
Overshoot in leaf development of ponderosa pine in wet years leads to bark beetle
outbreaks on fine-textured soils in drier years
Wendy Peterman, Richard H. Waring
44
ABSTRACT
Frequent outbreaks of insects and diseases have been recorded in the native forests of
western North America during the last few decades, but the distribution of these outbreaks has
been far from uniform. In some cases, recent climatic variations may explain some of this spatial
variation along with the presence or absence of expansive forests composed of dense, older trees.
Forest managers and policy makers would benefit if areas especially prone to disturbance could
be recognized so that mitigating actions could be taken. In this paper, we use two ponderosa
pine-dominated sites in western Montana, U.S.A. to apply a modeling approach that couples
information acquired via remote sensing, soil surveys, and local weather stations to assess where
bark beetle outbreaks might first occur and why. Although there was a general downward trend
in precipitation for both sites over the period between 1998 and 2010 (slope = -1.3 R2
= 0.08), interannual variability was high, with some years showing large increases followed by
sharp decreases. Both sites had similar topography and fire histories, but bark beetle activity
occurred earlier (circa 2000 to 2001) and more severely on one site than on the other. The initial
canopy density of the two sites was also similar, with leaf area indices derived via Landsat
imagery ranging between 1.7- 2.0 m2 m-2. We wondered if the difference in bark beetle activity
might be related to soils that were fine-textured at site I and coarse-textured at site II. To assess
this possibility, we applied a process-based stand growth model (3-PG) to analyze the data and
evaluate the hypotheses. We found that when wet years were followed by drier years, the
simulated annual wood production per unit of leaf area, a measure of tree vigor, dropped below a
critical threshold on site I but not on site II. We concluded that the difference in vulnerability of
the two stands to beetle outbreaks can be explained largely by differences in gross
45
photosynthesis attributed to the fact that an equivalent amount of stored water in the rooting zone
(100 mm) is extracted less efficiently from fine-textured soils than from coarse-textured ones.
INTRODUCTION
In the past few decades, a world-wide forest decline has been reported in association with
increases in temperature and decreases in precipitation (Allen et al., 2010). Throughout forests in
western North America the extent of disturbances caused by fire, insects and disease is
unprecedented (Bentz et al., Raffa, 2005; Raffa, 2008; 2009). It is well known that drought
reduces tree defenses, enabling insects to surpass a population threshold required for an outbreak
(Berg et al., 2006; Rouault et al., 2006). The impact of biotic agents is, however, expected to be
spatially patchy (Dale et al., 2001; Allen et al., 2010), because density-dependent processes
between the host organism and its attackers differ with local variations in soils and topography as
well as with the physiological status of the host (Christiansen et al., 1987; Allen et al., 2010).
Satellite-derived records of disturbance confirm that tree mortality is not uniformly distributed
and that some of the variation can be attributed to differences in soil (Yoshiko and MuellerDombois, 1995; Brasier, 1996; Turner and Lambert, 2005; Bigler et al., 2006; Hogg et al., 2008;
Harper et al., 2009). In general, outbreaks are more prevalent on poorer quality sites and where
there are large populations of overly-dense, older trees (Fettig et al., 2007). In arid ecosystems,
however, outbreaks during periods of multi-year drought are more likely on soils with very low
water storage capacities (Peterman et al., 2013).
The mechanisms by which beetles recognize, attack, and kill trees is well-described
(Horntvedt et al., 1983; Christiansen et al., 1987; Franceschi et al., 2005). Beetles bore into the
phloem of the trees, create tunnels in the secondary phloem and deposit their eggs,
46
simultaneously allowing the entry of mutualistic fungi, which can disrupt the vascular system of
the trees with their hyphae, leading to tree morality (Horntveldt et al., 1983; Franceschi et al.,
2005; Raffa et al., 2008). In healthy trees, a rapid flush of resin expels the initial beetle attack
(Erbilgin et al., 2003), and a barrier of dead or hardened tissues forms around the area of fungal
infection to prevent it from spreading (Horntveldt et al., 1985).
Because the beetles have co-evolved with their host tree species, they have developed
mechanisms for overcoming tree defenses (Franceschi et al., 2005). It is widely accepted that
aggressive beetle species overwhelm the trees’ physiological ability to produce defensive tissues
and chemicals by recruiting threshold numbers of individuals (Christiansen, 1985; Wallin and
Raffa, 2000, 2004; Huber et al., 2004; Franceschi et al., 2005). Production of bark, resin ducts
and toxins is very costly and dependent on trees’ ability to partition resources from local storage
and photosynthesis to defense systems (Bryant and Julkunen-Tiitto, 1995; Franceschi et al.,
2005). The ability to create defensive mechanisms is compromised by water stress, temperature
stress and pollution (Franceschi et al., 2005; Raffa et al., 2008).
Since the start of the 21st century, there are many places in the western United States
where long-term patterns in precipitation have changed from past conditions (Allen et al., 2010).
The northern Rocky Mountain region is one of those places, as mapped by Waring et al. (2011).
In western Montana, outbreaks of bark beetles on ponderosa pine have been reported at lower
elevations, but the pattern is not uniform (USFS FHTET, 2010). Such spatial variation in tree
mortality offers an opportunity to test a number of hypotheses. The simplest would be to
evaluate whether there has been a progressive downward trend in precipitation over the last few
decades that would stress trees more on shallow soils than would be the case for trees growing
on deeper soils. Alternatively, large, multi-year variations in precipitation can lead to an
47
imbalance between the production of leaves and the availability of water from one year to
another, causing a reduction in tree vigor. The latter situation occurred in plantations of
Monterrey pine in Australia, resulting in major dieback during an extended drought (Linder et
al., 1987). Why, however, would there be so much spatial variation in tree mortality under
similar climate conditions? We hypothesize that soil texture plays a role. Specifically, tree
photosynthesis should be more constrained for trees growing on high clay soils than low clay
soils soils under similar climate conditions, and if trees have less photosynthate available to
produce defensive compounds, they will be more vulnerable to bark beetle attacks (Waring and
Pitman, 1985).
We can discern climatic trends from published records and obtain maps of soil properties
for areas in western Montana. It is difficult, however, to obtain ground measurements of plant
water stress retrospectively or to assess subtle changes in canopy leaf area. Some researchers
have correlated climatic variation from tree ring analyses (Garfinkel and Brubaker, 1980; Briffa
et al., 1990; Villalba et al., 1990) and from the analysis of variations in the stable isotopes of
carbon (Panek and Waring, 1997). These types of analyses are spatially restricted and require a
non-random approach to defining areas for sampling. In this paper, we propose an alternative
approach by utilizing a process-based stand growth model driven by climatic variables that
incorporates important soil properties. Such models simulate the dynamics of trees as they shift
their growth between leaves, stems and roots in response to seasonal and annual climatic and soil
conditions.
In a stable environment, a forest canopy can be expected to reach a maximum leaf area
index (LAI) that can be supported by available soil moisture and fertility (Waring, 1983). In
western coniferous forests, LAI varies from < 1 to 12 m2 m-2 (Runyon et al., 1994; Waring et al.,
48
2014). If growing conditions become less favorable, the LAI will decrease, often abruptly (Pook,
1984), which can result in increased tree mortality. Field experiments (Christiansen et al., 1987)
have shown that the ratio of annual wood production to LAI, termed “growth efficiency” (GE),
has proven useful as a general index of tree vigor and specifically to identifying the vulnerability
of pine stands to attacks from mountain pine beetles (Coops et al., 2009).
We chose to use the 3-PG process-based growth model to simulate forest responses to
differences in the climate and soil conditions that affect tree vigor and forest vulnerability to
disturbances such as bark beetles. Process-based growth models use mathematical
representations of biotic and abiotic processes to simulate components of both the water and
carbon balances. We chose the 3-PG process-based model (Physiological Principles Predicting
Growth), because it is widely used to represent the multiple interactions between climate, soils
and forests with its main focus on predicting growth as measured by foresters (i.e. tree number,
mean diameter, basal area, volume, with thinning and defoliation subroutines). Process-based
hydrological models, such as RHESSys (Tague et al., 2004) and WEPP (Flanagan, 1985) include
representations of soils, plants and the atmosphere, but the focus is on the influences of these
variables on erosion and streamflow rather than forest growth and tree vigor.
Historically, fires, diseases and insect outbreaks were valuable agents in maintaining
ecosystem structure and function in forests, but fire suppression and other human activities have
altered stand densities and thus the dynamics of these disturbances (Gosheen and Hansen, 1993;
Parker et al., 2006). Foresters have recognized that carefully thinning stands in drought-prone
areas can reduce competition for resources and mitigate the vulnerability of residual trees to bark
beetle attacks (Sartwell and Stevens, 1975; Kolb et al., 1998). In terms of management, it would
be desirable to identify locations where trees are particularly vulnerable to disturbances such as
49
bark beetle outbreaks and to monitor or thin those stands in the hope of minimizing an outbreak
with the potential to affect thousands of hectares of trees with possible consequences for regional
energy and water flows, nutrient cycling and carbon sequestration (Anderegg et al., 2013;
Mikkelson et al., 2013; Bearup et al., 2014).
METHODS
Study areas
Using PRISM climate data in a GIS environment, we selected a study area in western
Montana where climate conditions were consistent, but there were uneven distributions of bark
beetle activity. The area selected lies within the Lewis and Clark National Forest (Latitude:
47.1833, Longitude: - 111.4500) and extends over an elevation range of 1400 m to 2900 m. The
dominant vegetation is a mixture of evergreen conifers, subalpine fir (Abies lasiocarpa),
Douglas- ir (Pseudotsuga menziesii), lodgepole pine (Pinus contorta), Englemann spruce (Picea
engelmannii), and ponderosa pine (Pinus ponderosa). The climate is continental with minimum
winter temperatures dropping to - 120C and summer maximum reaching 240C. Precipitation
averages between 90 and 100 cm yr-1 with more in the spring than summer or winter. Both sites
selected are classified as wilderness and are not used for timber production (Figure 3.1). Sixtyfour percent of the ponderosa pine forest is considered “highly stocked” and susceptible to
disease and insect attack (DeBlander, 2002). For our research, we selected a 24-year period
between 1998 to 2010 when two major outbreaks of mountain pine beetle (Dendroctonus
ponderosae) occurred, one circa 2001 to 2004, the other circa 2006 to 2010.
50
Figure 3.1 Map showing two study sites in western Montana with black dots showing
locations of meteorological stations used for climate data.
We chose two sites where beetle outbreaks were recorded and mapped by the USFS
aerial surveys (Figure 3.1). The presence of ponderosa pines at these locations was verified
using Forest Inventory and Analysis (FIA) plots
(http://databasin.org/datasets/49a4de7e6d0f4e08a87f94f07f40304e) and Little’s tree range maps
(http://databasin.org/galleries/5e449f3c91304f498a96299a9d5460a3). A meteorological station
was situated near site (I) at an elevation of 1844 m., where the mortality of ponderosa pine
averaged 750 trees ha-1 the first year of the beetle outbreak one circa 2000 to 2001 and 950 trees
ha-1 at the beginning of the second outbreak circa 2006 to 2007. Another meteorological station
located at an elevation of 2469 m was near site (II) where tree mortality was 400 ha-1 trees circa
51
2000 to 2001 and 1300 ha-1 circa 2006 to 2007. Forest canopies at both sites were fairly open
with maximum leaf area indices ranging between 1.7 to 2.0 m2m-2. The boundaries around site II
were irregular in shape to include the full range of elevations at which ponderosa pine was
recorded and where soil survey data were available. Soils on site I are Mollisols, Entisols,
Inceptisols and Vertisols developed on carbonate, alluvium and fine to medium-grained
sedimentary and clastic parent materials. On site II, soils are Mollisols, Inceptisols, Alfisols and
Entisols formed on carbonate, quartzite and coarse-grained volcanic parent materials.
Climatic conditions
Monthly meteorological data for local weather stations (Crystal Lake and Spur Park, MT)
for the years 1985 to 2013 were downloaded from the National Oceanic and Atmospheric
Administration (NOAA) Climate Data Online website (NOAA, 2014). Some gaps and errors in
the temperature and precipitation data were filled using correlations with the station at
Lewistown, MT and the first-order automated surface observation station (ASOS) at the Helena,
MT airport (Appendix). Since solar radiation data were not available for these sites, we
calculated monthly mean values, using differences between temperature extremes following
procedures outlined by Coops et al. (2000). At each site, the correlation between calculated
averaged solar radiation for each month of the year and that measured at Helena, MT was 0.99
(Appendix). Similarly, the evaporative demand, expressed as mean day-time vapor pressure
deficit (VPD) each month and the number of frost days (< -2oC) were calculated from monthly
mean temperature extremes using the 3-PG function tools (3PGpjs version 2.7, 3-PG version 1,
September 2010).
52
GIS analysis
Initial comparisons of forest mortality and soil characteristics were made by intersecting
polygons in the Soil Survey Geographic database (SSURGO) soil data (NRCS, min. 1:24,000)
with polygons in the annual forest die-off data (USFS FHTET, min. 1:100,000) from 1997 to
2010 to join the attributes from the soil map unit polygons to the tree mortality polygons. Soil
mapping units were aggregated to generalize common properties and to minimize differences
associated with variable spatial scales. Soil characteristics between the two areas were compared
to seek possible differences among pine forests where tree mortality was recorded between 1998
and 2010. GIS analysis was performed by overlaying datasets representing ponderosa pines
killed by mountain pine beetles at the two sites between 1997 and 2010 (Meddens et al., 2012,
http://databasin.org/datasets/fd3445f4435c4de2bd1d99d0c7ab4746) along with SSURGO soils
data.
Remote sensing
Landsat graphs of monthly greenness (NDVI) values for the years 1997 to 2010 were
downloaded from the Glovis Data Viewer (Tile IDs: LT50380271998231PAC00, path38, row
27, product: ETM + L1T and LT50390271999193XXX02 path 39, row 27, product: ETM +
L1T) (USGS, 2013). These were used to compare trends in the normalized difference vegetation
indices (NDVI) [Eq. 1] for both sites and to calculate estimates for maximum annual leaf area
index (LAImax). Estimates were made from a correlation established between NDVI and LAI
reported by Turner et al. (1999):
NDVI = 0.5724 + 0.0989x – 0.0114x2 + 0.0004x3, R2 = 0.74, SEE = 0.14
(1)
53
This method was chosen due to the similarity in the range of NDVI and LAI values between
their sites and ours, and the testing of their correlations over a broad range of temperate sites
using Landsat 5 surface reflectance data with atmospheric correction gave us confidence that it
would be applicable in our study area.
3-PG process model
The 3-PG model uses monthly averages of climatic variables to calculate a water balance,
carbon budget and energy balance that is summed for the year. At a minimum, precipitation and
maximum and minimum air temperature must be provided from meteorological stations. Other
climate variables can be calculated using the 3-PG function tools and verified using correlations
with nearby weather station records. Four soil variables are also included in the model: minimum
and maximum available soil water storage capacity (ASW) in the top 1500 mm of soil, soil
fertility (FR) and soil texture. The soil receives a fraction of the precipitation for each month, and
the soil water that is not released through evapotranspiration is added to the next month’s water
balance or allowed to run off (Landsberg and Waring, 1997; Waring et al., 2014). ASW is
defined as the difference between field capacity (soil moisture held at 1/3 bar tension) and
wilting point (soil moisture held at 15 bar tension). FR in the model is a rating on a scale of 0 to
1, with 0 being the lowest and 1 being the highest. For this study, we fixed the photosynthetic
capacity at 0.05 mol C per mol photon (2.75 g C/MJ of absorbed photosynthetically-active
radiation). This value has been measured and used in other ponderosa pine growth modeling
exercises (Waring et al., 2014; Wei et al., 2014), and sensitivity analysis of the model showed
that lower photosynthetic capacity settings require unrealistically high FR rankings. In the
model, photosynthesis is restricted by frost, high vapor pressure deficit, suboptimal temperatures
54
and soil water deficits. Each month, the upper limits on gross photosynthesis are set by the
amount of light (PAR) absorbed by the LAI. About half the total gross photosynthesis is assumed
to be lost through plant respiration, and the rest is available for growth. Growth is partitioned
above- and below-ground as a function of soil fertility and to a lesser extent, soil water deficit
(Landsberg and Waring, 1997)
Model parameterization and assumptions
For this modeling exercise, we used the Excel version of 3-PG with growth parameters
for ponderosa pine based on available literature and Landsat-derived LAI (Table 3.1). The model
parameters can be set to accommodate selected silvicultural treatments such as thinning and
fertilization, but these options were not used here. Sensitivity analyses for three soil variables,
maximum ASW, FR and soil texture were performed by running the model with two soil
variables held constant while the third was changed incrementally. Annual outputs for gross
photosynthesis or gross primary productivity (GPP), LAI and Penman-Monteith transpiration
were compared to evaluate the effects of each soil variable on key physiological responses. Once
realistic values for soil parameters and LAI were determined through GIS, remote sensing and
sensitivity analyses, soil characteristics were set to constrain simulated LAI to the estimated
maximum value (1.7 to 2 m2/m2), and model output was compared at annual intervals to interpret
the simulated effects of climatic variation on stand growth and water balances for each site.
55
3-PG Parameter
Units
Biomass partitioning and turnover
Allometric relationships & partitioning
Foliage:stem partitioning ratio @ D=2 cm
Foliage:stem partitioning ratio @ D=20 cm
Constant in the stem mass v. diam. relationship
Power in the stem mass v. diam. relationship
Maximum fraction of NPP to roots
Minimum fraction of NPP to roots
Litterfall & root turnover
Maximum litterfall rate
1/month
Litterfall rate at t = 0
1/month
Age at which litterfall rate has median value
months
Average monthly root turnover rate
1/month
NPP & conductance modifiers
Temperature modifier (fT)
Minimum temperature for growth
deg. C
Optimum temperature for growth
deg. C
Maximum temperature for growth
deg. C
Frost modifier (fFRost)
Days production lost per frost day
days
Soil water modifier (fSW)
Moisture ratio deficit for fq = 0.5
Power of moisture ratio deficit
Canopy structure and processes
Specific leaf area
Specific leaf area at age 0
m2/kg
Specific leaf area
for mature leaves
m2/kg
Age at which specific leaf area =
(SLA0+SLA1)/2
years
Light interception
Extinction coefficient for absorption of PAR by
canopy
Age at canopy cover
years
Maximum proportion of rainfall evaporated
from canopy
LAI for maximum rainfall interception
Production and respiration
Canopy
photosynthetic capacity
molC/molPAR
ponderosa
pine
1.328
0.75
0.0046
2.97
0.8
0.25
0.021
0.001
36
0.015
-7
18
40
1
0.7
9
3.1
3.1
2.5
0.5
15
0.1
5
0.05
56
Ratio NPP/GPP
-
0.47
m/s
m/s
1/mBar
m/s
0
0.016
5
0.05
0.14
Conductance
Minimum canopy conductance
Maximum canopy conductance
LAI for maximum canopy conductance
Defines stomatal response to VPD
Canopy boundary layer conductance
Table 3.1 Summary of parameter settings used in the 3-PG simulations for ponderosa pine.
It was assumed for this exercise that ponderosa pines attacked by bark beetles were more
than 60 years old (Cole and Amman, 1969; Amman, 1978; Wellner, 1978; Coops et al., 2009)
and that stand densities exceeded 18 m2 ha-1 (Schmid and Mata, 1992, 2005; Negrón and Popp,
2004), a threshold above which ponderosa pine stands in the Rocky Mountains are highly
susceptible to beetle attacks (Negrón and Popp, 2004; Zausen et al., 2005).
RESULTS
Soil sensitivity analysis
The relative influence of soil water storage, soil fertility, and soil texture varied in their
effects on GPP at the two sites. On both sites, values of ASW ≤ 100 mm caused a reduction in
simulated productivity in years with below average precipitation (Appendix). Likewise on both
sites, modeled increases in soil fertility (FR) caused an increase in GPP, resulting in the
production of more leaf area, but as FR increased above 0.5, the relative effects of increasing FR
decreased. During periods of lower precipitation, the modeled difference in FR effect was less
57
(Appendix). The influence of soil texture on modeled tree growth was most evident on site I,
particularly between the years 2000-2004 and 2006-2007 (Figure 3.2).
35
30
GPP tDM ha-1
25
20
clay
15
clay loam
sandy loam
10
sand
5
0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Year
Figure 3.2 Modeled gross photosynthesis with four different soil textures.
Modeled results
Inter-annual variation in simulated LAI showed large increases corresponding with wetter
periods and decreases during drier periods (Figure 3.3). On site I, there was a higher LAI
between 2003 and 2006, followed by a reduction in LAI circa 2007 to 2010 (not including the
thinning attributed to beetle-caused mortality). On site II, simulated LAI steadily increased
between 2000 and 2005 to a peak of 1.8 m2/m2 and then decreased between 2005 and 2010.
58
A
1.7
LAI m2 m-2
1.65
1.6
1.55
Series1
1.5
1.45
1.4
1995
2000
2005
2010
2015
Year
B
1.85
LAI m2 m-2
1.8
1.75
1.7
LAI
1.65
1.6
1.55
1995
2000
2005
2010
2015
Year
Figure 3.3 Simulated LAI between 1997 and 2010.
As in our simulation, inspection of Landsat monthly greenness graphs for the years 1997
to 2010 showed an increase in evergreen conifer greenness at the end the 1990’s in the tile
containing site I , followed by a decrease that lasted from 2000 to 2002 and an increase in
greenness in 2005 (Figure 3.4). On site II, they show a return to the average LAI after 2006, but
on site I, they do not show a decrease between 2007 and 2010. For the tile containing site II,
greenness stays fairly consistent, except for a one-year dip near the turn of the century and a
59
temporary spike in 2005 with a return to average the following years.
3
2.5
LAI (m2m-2)
2
1.5
site I LAI
site II LAI
1
0.5
0
1995
2000
2005
2010
2015
Year
Figure 3.4 Leaf area index (LAI), estimated from correlations with monthly greenness
between 1997 and 2010.
Figure 3.5 shows growth efficiency (GE = stem growth/LAI * 100), normalized over the
entire modeled time period. On site I, modeled GE was 10% below average during outbreak one,
and it was approximately 25% below average during outbreak two. Between outbreaks, however,
GE was approximately 20% above average. On site II, modeled GE was above average
preceding outbreak one, at which point it dropped to the average, and then went above average
until outbreak two, when it dropped to approximately 15% below the average. Another
difference between the two graphs was that the variance of GE on site I was 0.014, and on site II,
it was 0.008, reflecting larger departures from the mean on site I. A graph of precipitation for the
region showed below-average rainfall corresponding to the dips in GE at outbreaks one and two
on site I (Figure 3.6).
60
A
1.2
Percent of Max GE
1
0.8
Annual
0.6
Average
Series1
0.4
Series2
0.2
0
1995
2000
2005
2010
2015
Year
B
1.2
Percent of Max GE
1
0.8
Annual
0.6
Averag
e
0.4
Series1
Series2
0.2
0
1995
2000
2005
2010
2015
Year
Figure 3.5 Growth efficiency (GE) normalized over the modeled average for the time
period for (A) site I and (B) site II.
61
200
180
Annual ppt (cm)
160
140
120
100
Series1
80
Series2
60
40
20
0
1995
2000
2005
2010
2015
Year
Figure 3.6 Precipitation on site I between 1997 and 2010.
DISCUSSION
Simulated sensitivity of ponderosa pine growth to soil and climate
Warmer, drier trends in climate over the twenty-four year period of this study signaled
that atmospheric conditions were becoming more conducive to forest vulnerability to water stress
and disturbances (Figure 3.7). Temperature trends were upward on both sites, with warmer
conditions recorded near site I than site II (Figure 3.7 A). Monthly precipitation, when averaged
for each year, showed a slight decrease over the twenty-four year period from 1989 to 2013
(Figure 3.7 B).
62
Figure 3.7 Average annual minimum and maximum (A) temperatures and (B)
precipitation for site I and site II between the years 1985 and 2013.
63
In addition to climate conditions, soil characteristics such as water storage capacity,
fertility and texture are well known to limit forest productivity (Vierek et al., 1983; Reich et al.,
1997; Powers, 2005; Xiao-niu et al., 2008; Puhlick et al., 2012). As our previous research
indicated (Peterman et al., 2013), during periods of below-average precipitation, even widely
spaced trees with low LAI were stressed when soil water storage was below 100 mm (Appendix
I). As expected, increases in soil fertility (FR) increased modeled GPP at both sites (Appendix
I). Johnson et al. (2007) showed that increased soil fertility caused a greater proportion of growth
to be shifted above ground to leaves, resulting in more interception of light and increased
photosynthesis. In this sensitivity analysis, as FR was increased above 0.5, the advantages of this
increased foliage began to diminish, resulting in little change in simulated GPP. In our
simulations, during periods of lower precipitation, the difference in the modeled FR effect was
less, apparently because there was not enough water available for the trees to use the available
soil nutrients effectively (Raison and Meyers, 1992).
GIS analysis of soil characteristics showed that soil water storage capacities varied
considerably across the landscape. Most soils in the region could store between 50 and 150 mm
of water. Sand content was generally higher on site II, but the main difference between the soils
was the clay content. Clay content on site I ranged between 22 and 60%, with 80% of the total
area containing soils with between 27 to 37% clay content. The clay content of soils at site II was
between 0 and 28%, and for 90% of the area, it was 18% to 25%. A graph of clay content versus
the percent of the area of trees killed by mountain pine beetles showed a close relationship
between the area affected and the clay content of the soil (Figure 3.8). The percent of the total
area of beetle-killed trees on soils with >20% clay content was approximately 100% on site I and
65% on site II.
64
100
Percent of total area killed
90
80
70
60
50
40
Series1
30
Series2
20
10
0
<10 % 11 to 15 16 to 20 21 to 25
%
%
%
% clay content
>25%
Figure 3.8 Percent clay content of the soil versus the percent of the total area of pines
killed on each site.
In the sensitivity analysis, simulated effects of soil texture on GPP were evident on both
sites at the end of the study period, but the importance of soil texture was particularly
pronounced on site I between 2001 to 2005 and 2006 to 2009. Therefore, it may be that the
amount of rain or snowmelt that actually reached the available root-zone water supply during
drier periods, was lower at site I than site II, because of differences in percolation rates between
coarse and fine-textured soils. Wu and Chen (2012) showed that inter-annual decreases in soil
moisture had more influence on forest productivity than did increased summer temperatures, and
65
Nambiar and Sands (1992) showed that the high bulk density and the increased tension per unit
of water extracted from clayey soils limited transpiration. Our modeling exercise suggested that
even with the same available soil water storage capacity, coarse-textured soils provided less
constraint on simulated GPP and tree growth than fine-textured soils.
In ponderosa pine ecosystems, soil texture is an important factor in forest water stress
during lower precipitation years and post-disturbance regeneration (Scianna, 2011). Other
ecologists have observed greater pine productivity on coarse-textured soils than fine-textured
soils (Franklin and Dyrness, 1969; Hoffman and Alexander, 1976), and post-disturbance
ponderosa pine regeneration is more dense on coarser soils (Ffolliott and Baker, 1997; Goodwin,
2004). Puhlick et al. (2012) showed that the survival of ponderosa pine seedlings in the
southwestern USA was highly dependent on soil texture and parent material. In basalt-derived
soils, the water available for ponderosa pine use becomes limited at a moisture content of 10%
(Heidmann and King, 1992), whereas in coarse-textured soils, developed from sedimentary
rocks, water isn’t limiting to ponderosa pines until it reaches 1.5% moisture content.
Simulated effects of interannual variations in precipitation
An interesting result of this study is the simulated response of trees to the inter-annual
variations in precipitation. In the simulations, the trees added foliage and increased LAI during
the years with above-average precipitation (Figure 3.3). This response can have adverse
consequences if the following year has below-average precipitation. Samuelson et al. (2004)
showed a lag effect in the response of foliage to large changes in precipitation by correlating
current growth of loblolly pine with the previous year’s LAI. In western Montana, an overallocation to foliage in wetter years may have stressed the trees and reduced their resistance to
66
beetles during drier years. In our simulations, when precipitation fell below average, the trees
quickly shed foliage to adapt to the drier conditions, but tree vigor was negatively affected by the
sudden drop in leaf area as indicated by the decrease in GE. Wright et al. (1984, 1979) reported
that severe decreases in foliage can make forests more vulnerable to disease. Furthermore,
previous studies have shown when GE drops below a threshold level, tree resistance to disease is
greatly compromised (Larsson et al., 1983; Waring and Pitman, 1983). In this study, simulated
GE did not drop below average during outbreak 1 on site II, but it did drop below average during
outbreak 2. On site I, however, simulated GE dropped below average during both outbreaks. The
fact that both sites did have some level of beetle activity throughout the study period is evidence
that mountain pine beetles are a natural a part of the forest environment and their activity is
influenced by local variations in stand dynamics, soil characteristics and micro-climates.
Furthermore, this modeling exercise not only points to the combination of reduced
precipitation and available soil moisture in tree water stress, but it also raises the question of
whether delayed affects from a decrease in precipitation may still be at play during a subsequent
drought, making trees more susceptible to disturbance. A large decrease in precipitation and
concurrent increases in temperature on both sites circa 2007 was concurrent with a spike in
beetle-killed trees on both sites, suggesting that the intensity of that drought, combined with the
effects of earlier droughts may have overwhelmed the trees’ defense systems. Several studies
have shown that the cumulative effects of multi-year droughts can affect the resilience of trees
that are well-adapted to dry conditions (Mueller et al., 2005; Gaylord et al., 2013), and others
have shown a lag time between a prolonged drought, which weakened, but did not kill trees and
a second disturbance from which the stressed trees were unable to permanently recover (Tainter,
1984; Jenkins and Pallardy, 1995; Bigler, 2006; Bigler, 2007; Dorman et al., 2013).
67
Madrigal-Gonzalez and Zavala (2014) showed that the frequency of droughts may
ultimately have a greater impact on forest growth than either intense droughts or overall
temperature or precipitation changes, and that stand structure (age/size distributions and
competition) can enhance these effects to varying extents across the landscape. Younger trees
competing for limited resources are especially sensitive to drought, however, very tall trees also
show increased sensitivity to drought due to the distance water must travel against gravity
constraining photosynthesis for leaves high off the ground (Ryan et al., 1997; Koch et al., 2004).
While young trees may be the most sensitive to drought, older trees with lower vigor can be
more susceptible to being killed during beetle attacks under drought conditions (Christiansen et
al., 1987; Becker and Schröter, 2000). These factors make good arguments for maintaining stand
heterogeneity in terms of sizes and ages and to maintain stand densities that decrease competition
in areas where periodic droughts occur, especially on fine-texture soils.
Data and model limitations
All data are subject to human error and limitations on their ability to accurately and
realistically represent nature based on the spatial scale and precision of measurements taken
(Xhu et al., 2001). According to the USDA NRCS (1995), the US General soil map (STATSGO)
is made by generalizing more detailed maps and is not suitable for local planning or productivity
analyses. Semi-detailed maps like SSURGO, are based on field measurements, aerial
photography and visual inspection of landscape features. They are considered suitable for
determining differences and similarities in the soil characteristics of land areas greater than
approximately 1 ha and can be used for planning on the level of farms and forests. They are also
recommended for use in modeling with other environmental datasets representing geology,
68
vegetation, elevation and climate. SSURGO soil maps are not meant to be used in place of
onsite sampling for specific locations and uses. Soils grouped into one map unit will have a great
deal of variation in reality, and land use practices will alter soil characteristics from their
originally mapped condition (USDA NRCS, 1993). Therefore, it is imperative to make detailed
measurements on specific plots for field research studies and monitoring programs.
There are also considerations when using climate data. As with soil data, the area
represented by a given weather station is also subject to spatial variation due to changes in local
topography. Equipment from meteorological stations are subject to malfunction and operational
differences that bias measurements (NCAR, 2014 https://climatedataguide.ucar.edu/). For
example, intense rainfall and strong winds can affect the amount of precipitation measured in
collectors. Comparing monthly values with nearby stations to check for correlations can help to
adjust for errors and missing values.
Remote sensing satellites are limited by spatial, temporal and spectral resolutions,
dynamic range and interference by clouds, snow or aerosols (NAS, 2001). Remote sensing data
may not be useful in estimating LAImax for densely-forested regions, because satellite sensors
saturate from the high reflectance (Zhao et al., 2012; Waring, 2014). Comparing greenness
values for several years can help analysts estimate reasonable values for LAImax for model
calibration, but disturbances may be difficult to ascertain due to coarse spatial or spectral
resolution or cloud interference (Hilker et al., 2009). Atmospheric interference such as cloud
cover causes more visible light to be reflected back to the sensor than would be the case if the
light reached the surface and was absorbed by vegetation. When this occurs, the difference
between the infrared reflectance and the visible red reflectance, used in calculation NDVI, is
smaller than it would be if the emitted radiation were intercepting vegetation. This can result in
69
unrealistic values of NDVI and thus calculated LAI. In the event of an equipment malfunction,
data may be missing for multiple months (http://landsat.usgs.gov/).
In addition to awareness of the limitations of data used in modeling, acknowledgment of
the limitations of the models themselves is also important. In this exercise, we used the Excel
version of 3-PG with climate data from local weather stations, and thus the simulation results
only apply locally. New simulations would need to be run to apply this approach to other sites
with appropriate soil and climate values. In addition, 3-PG is run on a monthly time-step, so it
estimates general trends in annual carbon, energy and water balances, and it does not have
specific algorithms to incorporate the effects of snowmelt dynamics on tree physiology. There is
a general assumption that precipitation falling on days with temperatures below -2°C is received
as snow (Waring, 2014). Although, 3-PG has some subroutines to simulate the effects of disease
on forest productivity, it does not simulate the population dynamics of beetle populations or their
responses to climate change.
CONCLUSION
Outbreaks of native insects and diseases are expected to occur in response to future
warming temperatures and a drier environment, but tree mortality is not likely to be uniformly
distributed across the landscape. Forest managers and policy makers can benefit from ways of
identifying where forests are most susceptible to disturbance, so they might ameliorate
conditions where and when it will do the most good. Retrospective analyses as performed in this
modeling exercise suggest that three specific site variables would be most useful to map: interannual variations in canopy leaf area index, soil texture and soil available water holding capacity.
70
ACKNOWLEDGMENTS
We would like to thank Thomas Hilker (Oregon State University) and Dominique
Bachelet (Conservation Biology Institute) for their careful and insightful editing and feedback in
writing this article. We thank Brendan Ward (Conservation Biology Insitute) for the use of his
Python script to help extract and aggregate soil map units and their attributes from the NRCS soil
survey maps.
71
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Chapter 4
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Soil depth affects simulated carbon and water in the MC2 dynamic global vegetation model
Wendy Peterman, Dominique Bachelet, Ken Ferschweiler, Timothy Sheehan
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ABSTRACT
Climate change has significant effects on critical ecosystem functions such as carbon and
water cycling. Vegetation and especially forest ecosystems play an important role in the carbon
and hydrological cycles. Vegetation models that include detailed belowground processes require
accurate soil data to decrease uncertainty and increase realism the in their simulations. The MC2
DGVM uses three modules to simulate biogeography, biogeochemistry and fire effects, all three
of which use soil data either directly or indirectly. This study includes a sensitivity analysis of
the MC2 model to soil depth by comparing a subset of the model’s carbon and hydrological
outputs using soil depth data of different scales and qualities. The results showed that the model
is very sensitive to soil depth in simulating carbon and hydrological variables, but competing
algorithms make the fire module less sensitive to changes in soil depth. Simulated historic actual
evapotranspiration and net primary productivity showed the strongest positive correlations (both
had correlation coefficients of 0.82). The strongest negative correlation was streamflow (-0.82).
Ecosystem carbon, vegetation carbon and forest carbon showed the next strongest correlations
(0.78, 0.74 and 0.74 respectively). Carbon consumed by forest fires and the part of each grid cell
burned showed only weak negative correlations (-0.24 and -0.0013 respectively). This study
shows that the biogeochemistry module of MC2 is highly sensitive to changes in soil depth data,
but that the fire module is not very sensitive to changes in soil depth.
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INTRODUCTION
Climate change is an important driver of forest dieback and species migration with
increases in drought, early snow melt, reduced snow depth, pest outbreaks, and fire risk
(McKenzie et al. 2004, Mote et al. 2005, van Mantgem et al. 2009, Allen et al. 2010). In the
North Pacific landscape of the USA, precipitation as rainfall is projected to increase in winter
and spring, and decrease in summer, while temperatures rise from 2° to 5° C by 2080 (Mote and
Salathe, 2010). Vegetation models suggest that forest cover may increase at high elevations and
latitudes in response to wetter winters, and dramatically decrease at lower elevations and
latitudes due to severe competition for water from shrubs and grasses, even without
consideration of future water needs from human land use (CIG, 2011). However, some
vegetation models suggest possible vegetation shifts to lower elevations where water might be
more readily available as higher elevations become drier (Crimmins et al., 2011).
Climate-related stress can also affect forests indirectly by increasing their vulnerability to
pests and pathogens. Littell et al. (2009) projected a reduction of climate suitability for Douglas
fir in the Puget Trough as well as increases in wildfires and mountain pine beetle outbreaks,
which would affect tree growth and survival in the region. Lodgepole pines in British Columbia,
Oregon, Washington and California have also shown increased vulnerability to climate change in
recent decades and been subject to well-documented beetle attacks (e.g. Raffa et al., 2008).
Vegetation models indicate that lodgepole may disappear from most of its current range by the
end of this century (Coops and Waring, 2011). Further North, Alaskan Yellow Cedar decline in
southeast Alaska and portions of British Columbia has also been connected to warming air that
melts snow and exposes roots to lethal subfreezing temperatures (D’Amore and Hennon, 2006).
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Changes in available soil moisture are increaseing tree vulnerability across many systems
and thresholds in maximum available water storage capacity (ASW) have now been documented
beyond which forest decline starts to occur (Peterman et al., 2013; Mathys et al., 2013). Soil
physical characteristics are important for assessing ASW, which influences ecosystem functions
including carbon and nutrient cycling, as well as succession through seedling establishment in
post-disturbance forests (USDA NRCS, 1993; Neilsen and Drapek, 1998; Dale et al., 2001;
Allen et al., 2010, Puhlick, 2012). Simulation results from vegetation models are used in global
and regional assessments in an attempt to forecast ecosystem responses to climate change
(Cramer et al., 2001; IPCC,2007, Handler et al., 2013). The complex interactions between plants
and pests or pathogens, driven by ASW can only be simulated if reliable soil data are available.
Soil data have historically been a primary source of uncertainty for modelers who simulate
belowground-processes such as root growth and decomposition as well as hydrological processes
(Allen et al., 2010; Coops et al., 2012). In this paper, we report results from a sensitivity analysis
of a dynamic global vegetation model (MC2) that demonstrates the importance of soil inputs in
simulations of vegetation dynamics, growth and mortality in the 21st century.
Background and model description
The MC1 dynamic global vegetation model (DGVM) was developed for the
Vegetation/Ecosystem Modeling and Analysis (VEMAP) project (Bachelet et al., 2001). It
consists of three component modules (Figure 4.1): 1) a biogeography module derived from the
static biogeography model MAPSS (Neilson, 1995), 2) a biogeochemistry module, derived from
the CENTURY model (Parton et al., 1987) and 3) a dynamic fire model (Lenihan et al., 1998).
The MAPSS model is used solely to determine the potential life forms and vegetation types
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present on the landscape, using a twelve-month long-term average climate to characterize each
grid cell during the equilibrium phase of the model (Bachelet et al., 2001). A modified version of
the CENTURY model is then called to simulate the carbon and nitrogen pools associated with
the potential vegetation types (Bachelet et al., 2001). These initial conditions are used to start the
“spinup” phase during which the full DGVM simulates 1) biogeography, using a set of climate
and biomass threshold rules, 2) carbon and nitrogen cycling, using a modified version of
CENTURY version 4 and 3) fire occurrence and effects, using the dynamic fire module. The
DGVM is run iteratively for 600 years using a de-trended historical monthly climate until net
ecosystem productivity nears zero and the fire return interval (FRI) nears historical estimates
(Leenhouts, 1998). Once this “spinup” phase is completed, the DGVM is run with historical
climate and future climate projections.
In the MC2 DGVM, the hydrology algorithms (a simple bucket-type model) from
CENTURY are used to calculate hydrological flows. The model uses soil depth, texture, rock
fragment content and bulk density to estimate monthly available soil moisture. Because the MC2
DGVM uses these soil characteristics to regulate the water fluxes that directly affect plant
growth and decomposition, we expect it to be sensitive to these inputs. However, to date, no
formal sensitivity analysis has been performed.
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Figure 4.1. Graphical representation of MC2 DGVM including the biogeography
component, using rules derived from the static biogeography model MAPSS (Neilson
1995), the biogeochemistry component using algorithms from a modified version of the
biogeochemical model CENTURY (Parton et al. 1987) and the dynamic fire component
including fire occurrence and effects (Lenihan et al. 1998).
Conklin (2009) used MC1 to simulate vegetation shifts in Yosemite National Park and
observed that the model was over-estimating carbon pools and simulating closed-canopy forests
at the top of the Sierras. He found that the STASGO-based US soils map that had been used (e.g.
Bachelet et al., 2008, Lenihan et al., 2008) included overestimated soil depths, especially at high
elevations (Conklin, 2009). In the original data, he found deep soils at the top of Half Dome,
where there should be no soil or vegetation. He used a modified soil dataset based on expert
opinion for Yosemite and simulated the more realistic bare rock at the crest of the Sierras.
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The NATSGO soil dataset (1:7.5 Million scale), originally used in MC1, was replaced by
the STATSGO (1:250,000 scale) national soil dataset for the USA (personal communication
Kern, 2014). Since then, the finer scale State Soil Geodatabase (SSURGO - average 1:24,000
scale) has been expanded to cover large areas at the state and county level (NRCS, 2014). For
this paper, we conducted our sensitivity analysis using the MC2 model, the most recent C++
version of MC1, to evaluate whether substituting the soil depth layer from STATSGO with
SSURGO data would result in significant changes in carbon cycling and hydrological flows.
METHODS
Study area
Our study area is the portion of the North Pacific Landscape Conservation Cooperative
(http://www.northpacificlcc.org/) within the conterminous United States. This includes the states
of Washington and Oregon, west of the Cascade mountains, and a fraction of northern
California, west of the Sierra Nevada mountains (Figure 4.2), between the latitudes 39° to 49°.
The climate is maritime with moderate winter temperatures and precipitation falling mostly as
rain during winter and spring seasons. The region is largely forested with some grasslands and
oak savannas in lowland regions, reflecting the “rain-shadow effect” of the Coast Range to the
west and the high Cascades to the east. Soils range from multi-colored Spodosols on the coast to
ancient Alfisols and Ultisols in the foothills, and fertile Mollisols in the valleys. High, steep
mountain slopes may have less developed Inceptisols and Entisols or even bare rock outcrops.
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Figure 4.2. Map of the study area for the continental portion of the North Pacific
Landscape Conservation Cooperative (shaded area).
Soil data preparation
To generate the most reliable soil dataset, we attempted to maximize the use of state soil
data. We downloaded SSURGO and STATSGO data from the NRCS Geospatial Data Gateway
website (http://datagateway.nrcs.usda.gov/) in spatial and tabular forms. First, all of the spatial
data were aggregated from the county and national soil datasets into a single soil map covering
the entire study area. Then, for every map unit, a Python script was used to extract: 1) attributes
directly from the map unit tables of SSURGO and STATSGO databases, 2) attributes for the
dominant component within the map unit from the “component” table (the component with the
highest representative percent of the map unit), and 3) attributes for the horizons for all recorded
components. For each component, the arithmetic mean of all non-zero values across all horizons
was recorded. Components within each map unit were combined using an area-weighted
algorithm based on the normalized percent of each component relative to the others.
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The soil datasets include various gaps and errors. While we prefer to use SSURGO soil
data because of their finer spatial scale, they often include gaps over large landscapes. Where
SSURGO data were not available, STATSGO data were used to fill these gaps. Secondly, we
used the “select by attributes” tool in ArcMap to locate water bodies, marshes and dams and
gave those units a "missing value" of -9999. We also selected rock outcrops, badlands and urban
lands, and set the depths to zero. Thirdly, where there were NULL values for minimum depth,
we used the map unit name(s) to look up the official soil series descriptions on the NRCS
website (https://soilseries.sc.egov.usda.gov/osdname.asp). For associations of several soil series
within one map unit, we entered the most commonly occurring dominate soil series value for
minimum depth to bedrock. After processing all map units this way, there were 8,008 total map
units with 4,606 null values replaced by estimated soil depth values ranging from 0 to 3860 mm.
Where the values could not be estimated ("missing value"), the value was set to -9999.
Soil depth in the MC2 model
MC2, the latest C++ version of the MC1 dynamic global vegetation model, uses static
soil and monthly climate inputs to simulate plant dynamics, carbon pools and fluxes as well as
fire occurrence and effects over large regions. Climate variables include precipitation, minimum
and maximum temperatures, vapor pressure and wind speed. Other inputs include elevation and
soil characteristics: bulk density, depth to bedrock, sand and clay content, as well as rock
fragment content. Soil texture is originally provided for 3 soil layers that are later refined on soil
depth. For this sensitivity analysis, we only modified the depth to bedrock variable. Depth is
used to determine the total number of soil layers (Figure 4.3), including rooted layers, and is
therefore critical to simulating available soil moisture content and hydrological fluxes.
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Figure 4.3. Flow diagram of the CENTURY hydrology model used in MC2 where
“nlayer” is the total number of soil layers, “nlaypg” the number of rooted soil layers,
“avh20” the available soil water, “tr” the transpiration flow fraction, “aglivc” the
aboveground grass biomass and “rleavc” the tree leaf carbon pool. (Parton et al., 1987)
Model sensitivity analysis
For the LandCarbon Project (USGS, 2011), MC2 was run for the conterminous USA
under nine climate and emissions scenarios at 30 arc-seconds. Since large runs of MC2 are
processor-intensive and time-consuming, "strided" runs using subsamples of the input datasets
were first used to calibrate the model for the historical time period (1895 to 2005). To create the
"strided" data, we resampled the data for every tenth row and column of the gridded datasets. To
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quickly perform our sensitivity analysis, we decided to use the same method, so we generated a
new strided soil data set and ran the model with strided 30 arc-second climate data for our study
area.
Previous MC1 and MC2 simulations used soil data for the conterminous USA originally
provided by Jeff Kern for the VEMAP project (Kern et al. 1994, 1995). VEMAP Members
(1995) projected and resampled the soils data to a finer scale at 10-km, based on a gridded Soil
Conservation Service national level (NATSGO) database. Cluster analysis was used to group the
10-km subgrid elements into 1-4 dominant ("modal") soil types for each 0.5o cell. Using this
approach, cell soil properties were represented by one or more dominant versus average soil
profile that might not correspond to realistic soil characteristics in the region.
For the sensitivity analysis, we substituted the standard soil depth that had also been used
for the LandCarbon runs with a new depth layer developed specifically for this study and left the
other 10 soil variables (representing bulk density and soil texture) unchanged. We ran MC2 with
strided input data for our study area for the historical period using the same configuration
settings used in the LandCarbon project. Once the runs were completed, we correlated the
differences in depths between the two soil input datasets (Kern depth – Peterman depth) with
differences in carbon, fire and hydrology model results (LandCarbon 30-year mean – this study’s
30-year mean) using Pearson’s correlation coefficients. The model was run for 111 years (18952005), but we used the average of the most recent thirty years in our comparisons. Differences
between any two variables were calculated using the “ncdiff” command in the NCO toolbox
(Zender, 2014). Correlation coefficients were calculated using a Python script that eliminated
outliers greater than three standard deviations from the mean for each variable’s thirty-year
average differences. Vegetation cover is a discrete variable, so we used the modal value of the
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last thirty years of the historical period to visually compare differences. Since vegetation types
are categorical, we did not calculate any correlation with depth differences.
RESULTS
All but three of the simulated variables chosen for this study showed strong correlations
to differences in mineral soil depth (Table 4.1). Ecosystem carbon, net primary production, forest
carbon, actual evapotranspiration and vegetation carbon (Figure 4.4) all showed strong positive
correlations (0.7 to 1.0) with changes in depth, while carbon consumed by wildfire only showed
a weak positive correlation (0 to 0.3). Correlations were most evident in northern California
(Figure 6). Stream flow values showed a strong negative correlation (-1.0 to -0.7) with
differences in mineral depth, and the difference in the fraction of the grid cell burned by wildfire
showed a weak negative correlation (-0.3 to 0) (Table 4.1).
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Variable
Correlation
Ecosystem Carbon
0.7835
NPP
0.8170
Stream Flow
-0.8166
Forest Carbon
0.7442
Carbon Consumed by Fire
0.2389
Part of Cell Burned by
-0.0013
Fire
Actual Evapotranspiration
0.8161
Vegetation Carbon
0.7442
Table 4.1. Correlations between the differences in soil depth and the difference in 30-year
means for a subset of ecosystem processes simulated by MC2 with the two soil depths
datasets (see text).
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Figure 4.4. Percent change in 30 year means of a subset of MC2 simulated ecosystem
processes with strong positive correlations (0.7 to 1.0) with the difference in soil depth:
A) total ecosystem carbon, B) forest carbon, C) vegetation carbon, D) net primary
productivity and E) actual evapotranspiration.
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Figure 4.5 Difference (g C m-2) in 30-year mean carbon consumed by wildfire between
the two MC2 simulations, one using Kern (1994, 1995) and the other using Peterman
(this paper) soil depth.
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We found little difference between the two vegetation maps simulated with the 2 soil
datasets (Figure 4.6) at this scale. Each vegetation type occupied the same total area, but the
spatial distribution of forests and woodlands is somewhat different in northern California.
Figure 4.6 Thirty-year modal (1981 to 2011) vegetation types simulated by MC2 with A)
Kern’s soil depth (1994, 1995), B) Peterman’s soil depth (this paper).
Differences in soil depth between the Kern dataset and our dataset (Figure 4.7) ranged
from - 1530 mm to + 1700 mm. A comparison between soil depth and elevation for both soil
datasets (Figure 4.8) illustrates that the coarse resolution STATSGO-based depth dataset (Kern)
shows less overall variation (standard deviation = 161 mm) than the finer-scale SSURGO-based
depth dataset (Peterman, standard deviation = 247 mm). The ‘Kern’ soils are on average 394 mm
deeper than the ‘Peterman’ soils. Both depth datasets show a general downward trend in depth
96
with increasing elevation, however the slope for Kern depth versus elevation is -0.03 (R2=0.018),
and the slope for the Peterman depth versus elevation is -0.23 (R2=0.35).
Figure 4.7 Soil depth (left-A) from Jeff Kern derived from STATSGO data (Kern 1994,
1995) and soil depth derived from SSURGO data (right-B) by Wendy Peterman (this
paper).
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1800
1600
depth (mm)
1400
1200
1000
800
600
400
200
0
0
500
1000
1500
2000
2500
3000
3500
elevation (m)
depth Peterman
depth Kern
Linear (depth Peterman)
Linear (depth Kern)
Figure 4.8. A graph of elevation vs. mean soil depth, shows the differences
between STATSGO-based soil depth data provided by Kern and the SSURGObased soil depth data provided by Peterman.
DISCUSSION
We expected that soil depth would affect MC2 carbon simulations, since its three
modules each use soil depth either directly or indirectly. Biogeography rules use biomass
thresholds that are indirectly linked to soil depth through soil water availability for plant growth.
The biogeochemistry algorithms from CENTURY use soil depth to calculate water availability
and nutrient cycling. The fire module of MC2 uses soil moisture as a proxy for fuel moisture,
which is used as one of the thresholds for fire risk. The strong correlations between the change
in soil depth and resulting differences in carbon pools and streamflow show that the
biogeochemistry module is sensitive to soil depth. Decreases in soil depth lead to decreases in
98
net primary productivity and ecosystem, forest and vegetation carbon as expected, since it
reduces overall soil water availability. The strong positive correlation to simulated actual
evapotranspiration shows that vegetation in the model is able to respond to the greater or lesser
amount of available soil water provided by the change in soil depth to meet the evaporative
demand. The strong negative correlation to stream flow indicated that the shallower soils were
less rooted than deeper soils. The related transpiration fluxes, calculated for each soil layer, were
better able to use the available soil moisture and thus reduce the amount of water draining
through the profile.
The MC2 fire module uses soil moisture as a proxy to determine coarse fuel moisture.
Below a set fuel moisture threshold conditions are met to start a fire in a given cell as long as
other conditions (fuel load and climate) allow it. A shallower soil depth reduces soil moisture
available to plant growth thereby limiting fuel production and thus limiting fire risk. Such
combination of competing thresholds (shallow soil depth means low fuel moisture but also low
fuel load) may explain the low correlation between soil depth and the amount of biomass
consumed by wildfire, and even lower for area burned. Another possible explanation for
insensitivity in the fire module is that the soil depths are rounded to either 600 or 900 mm, so soil
depths that are very different between the two input layers could yield the same results, if they
were rounded to the same value (David King, personal communication). Conversely, two soil
depths that are close to one another might be rounded to values that are very far apart.
99
CONCLUSION
Concerns that MC2 was simulating closed-canopy forests on the peaks of Yosemite
National Park raised questions about the model's sensitivity to soil characteristics. Conklin
(2009) found that the problem was attributed to inaccuracies in the soil input data. In that case,
by revising the soil dataset with local knowledge, the problem was solved. To better evaluate the
model's sensitivity to soil depth, we performed a sensitivity analysis over the North Pacific LCC
domain evaluating the response of a subset of model results to two soil datasets, one used
previously and originally created for the VEMAP project at coarse resolution and a new dataset
created with finer scale soil information more relevant for regional analysis.
Correlations between changes in soil depth and resulting differences in simulated carbon
and water fluxes were strong and demonstrated the expected sensitivity of the model to soil
depth. Because the model simply calculated runoff as a fraction of precipitation, no correlation
was possible with this variable. Weak correlations between changes in soil depth and changes in
fire effects reflect competing drivers of fire occurrence and effects. Fire occurrence and effects
depend on high fuel loads driven by high plant productivity and high soil water availability as
well as by dryness of fuels driven by low soil water associated with shallow depth. The small
change in simulated vegetation cover reflects the climatic variability of the study area subject to
very dry summers and very wet winters, causing vegetation types to be well adapted to wide
variations in soil water availability. Only in northern California, where winter precipitation is
less abundant than in western Oregon and Washington, does soil depth affect the potential
vegetation type simulated by the model.
100
This modeling exercise demonstrates the need for detailed and accurate soils data for
more realistic simulations of ecosystem processes. Carbon sequestration potential and the
amount and timing of water provision are highly valued ecosystem functions that can only be
projected with the best possible soil data available. Therefore, efforts to map soils with better
accuracy and precision are essential to decreasing uncertainty and increasing the “realism” of the
climate change impacts models.
ACKNOWLEDGMENTS
We would like to acknowledge the North Pacific Landscape Conservation Cooperative
and the US Department of the Interior, for providing funding for this research. In addition, we
would like to thank individuals who provided technical support and expertise. Brendan Ward
(Conservation Biology Institute) created the Python script for extracting and aggregating soils
data from the NRCS soils database. David Conklin (Common Futures), Myrica McCune
(Institute for Natural Resources), Jeff Kern (Oregon Department of Fish and Wildlife) and
Raymond Drapek (USDA Forest Service) provided background on previous methods and uses of
soil data and MC1/2. David King provided insight about the relationships between soil depth
data and the MC2 fire module.
101
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Chapter 5
106
Conclusion
My exploration into the world of vegetation and forest growth simulation models has led
to some useful insights about how models incorporate soil data and what questions can be asked
about plant, soil and atmosphere interactions, using these models. I learned that soils have such a
strong influence on forest productivity and responses to climate change that simple factors like
soil depth and texture can have profound effects on simulated growth and water stress and that
the models I used are sensitive enough to these factors to be affected by changes in the scale and
quality of the available soil data. This is both important and exciting, because it means that the
models are able to represent some key features of the soil that contribute to forest vulnerability or
resilience to changes in precipitation and temperature.
The models also have some limitations in representing forest responses to climate
change. For example, 3-PG uses soil fertility very effectively in the model to limit photosynthetic
capacity, but this isn’t a value isn’t directly reflected in soil maps. This value is currently
estimated as a rank from 0 to 1 and then calibrated, using on-the-ground or remote sensing
estimates of leaf area index (LAI). As shown by Coops et al. (2012), inverting the model might
be the best way available to estimate soil fertility at the ecoregion scale, using measured LAI
values. As part of my investigations, I looked at soil pH as a possible proxy for soil fertility,
since it is a value included in soil map attribute tables. In statistical models and GIS overlays, it
often appeared as an explanatory variable for tree mortality, but it was affected by soil water
holding capacity (Peterman, unpublished data). It makes sense, because soil pH affects cation
exchange capacity and thus soil ability to store and release nutrients to plants (Cronan and
Grigal, 1995). Other studies of soil factors and forest productivity are finding a link between soil
pH and tree growth (Cronan and Grigal, 1995; Ware, 1990; Schulze, 1989). This brings to mind
107
Leipig’s Law of the Minimum, used in creating forest site indices, which states that the most
limiting factor will determine site performance (Locatelli et al., 2008). When there is enough soil
water to meet the evaporative demand of the atmosphere, soil nutrition becomes the limiting
factor (Raison and Meyers, 1992). When there isn’t enough soil water to meet the evaporative
demand, soil fertility becomes less important to forest productivity (Raison and Meyers, 1992).
This raises the point that soil fertility is an important factor not only to include in models
of forest growth, but for soil scientists to find a way map that is easily incorporated into
simulation models already in use. It is a difficult factor to measure and quantify. Does it mean
the amount of carbon in the soil? Not exactly, because the contribution of soil carbon is
dependent on soil nitrogen and soil microbial activity (Harvey et al., 1996). For similar reasons,
it doesn’t necessarily mean the amount of nitrogen in the soil. In addition, high nitrogen
concentrations can exceed the forest and soils’ ability to use the available nitrogen and lead to
cation leaching (Aber et al., 1989). These cycles require complex subroutines in process models.
Century (the biogeochemical model in MC2) includes both carbon and nitrogen cycles, but it
doesn’t use soil carbon or nitrogen as inputs to the model, and they are tightly coupled in a way
that can hinder the performance of the nitrogen simulations (Parton et al., 1987). Maybe a
ranking system could be developed like the 0 to 1 scale used in 3-PG, where soil scientists rank
the general soil fertility based on carbon, nitrogen and cation concentrations (like Ca+) as well as
soil pH and land use history. This would be somewhat subjective, and may depend on whether it
was being rated for forests or agriculture, but it could be structured somewhat like the NRCS soil
hydrologic group rating system. The Coops et al. (2012) soil fertility map might be a good place
to start with predictive mapping and subsequent refinement through ground-truthing.
108
As we found in these sensitivity analyses, soil depth, soil water storage capacity (ASW)
and soil texture are among the most important soil factors to map and include in models of forest
productivity. While soil depth and soil texture are used in calculations of ASW (USDA NRCS,
1993), these factors are important for reasons other than water storage. Soil depth limits the
extent that roots can penetrate to access water and nutrients. Although some trees can penetrate
rocks and access water (Matthes-Sears and Larson, 1995; Schwinning, 2010), soil depth is a
good general estimator of limitations on root allocation and thus evapotranspiration and nutrient
cycling as mentioned in Chapter 4 of this dissertation regarding MC2 sensitivity to soil depth.
In addition to quality soil maps of useful soil characteristics, realistic estimates of LAI
are essential to modeling forest growth. During my time at OSU, I learned that remote sensing
can be used both to estimate maximum LAI and to validate models that simulate LAI. I found
that challenges arose with combining remote sensing products with climate data and GIS maps of
differing projections, spatial resolutions and data formats.
In summary, the value of this research is the knowledge that soil characteristics such as
soil depth, ASW, soil fertility and soil texture can be used in hindcasts of forest disturbances to
determine thresholds where forests may become more vulnerable in response to rising
temperatures or changes in precipitation. This knowledge can enhance the understanding of what
soil characteristics are important to map in more detail for more realistic simulations in
vegetation and forest growth models. Running simulations to determine what question arise
about the combined effects of climate and soils on vegetation growth can give scientific
managers an idea of where it is most important to map available soil water content, soil fertility
and soil texture as trees may become more vulnerable on these sites during drought periods.
109
Some things this dissertation has contributed to science are: 1) approaches to linking
observable patterns in available soil data with forest disturbance, 2) approaches to using these
patterns observed in spatial data to simulate the effects of these conditions on tree physiology to
a) ascertain if models are sensitive enough to soil processes to show an effect of changes in these
soil characteristics and b) learn how the combination of climate and soil characteristics may have
influenced forest vulnerability to disturbance in a past time period. An original idea that emerged
from Chapter 3 of this dissertation is that interannual variation in precipitation may lead to
overshoot in leaf area in wetter years, detrimentally affecting growth efficiency in drier years,
making trees more vulnerable to disease.
Some interesting projects that could develop from this research are: 1) greater
development of the role of soils in the MC2 hydrology algorithms, especially the calculation of
runoff, 2) more exploration into the details of how soil is used the MC2 fire module and how
this can be improved, 3) Expansion of the MC2 soil sensitivity analysis to include sensitivity to
soil texture inputs, and 4) field experiments using dendrochronology and carbon isotopes to learn
how stem growth was affected in years surrounding bark beetle outbreaks to see if this supports
the hypothesis that interannual variation in precipitation and thus leaf area affects growth
efficiency in drier years.
110
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Information Sciences XXXIX-B1: 77-81.
126
Appendices
127
Appendix I
CHAPTER 3 ADDITIONAL GRAPHS
A
Helena tmax (°C)
30
-20
y = 1.7551x + 24.987
R² = 0.8167
20
Site I monthly
maximum
temperature for one
year
10
0
-15
-10
-5
0
-10
Linear (Site I monthly
maximum
temperature for one
year)
-20
Site I tmax (°C)
B
25
y = 1.575x + 19.233
R² = 0.8781
Helena tmax (°C)
20
-20
Site II monthly
maximum
temperature for one
year
15
10
5
0
-15
-10
-5
-5
-10
Site II max (°C)
-15
0
Linear (Site II monthly
maximum
temperature for one
year)
128
C
10
y = 1.4321x + 20.434
R² = 0.9374
Helena tmin (°C)
5
-30
0
-20
-10
0
-5
Site I monthly
minimum temperature
for one year
Linear (Site I monthly
minimum temperature
for one year)
-10
-15
-20
Site 1 tmin (°C)
D
10
y = 1.3297x + 17.575
R² = 0.8617
Helena tmin (°C)
5
-30
0
-20
-10
0
-5
-10
-15
Site II tmin (°C)
Site II monthly
minimum temperature
for one year
Linear (Site II monthly
minimum temperature
for one year)
-20
Figures 3.A-D Linear relationships between climate data at the two study sites and
climate data from Helena, MT, used to fill gaps and repair errors in local temperature
data.
129
E
Crystal Lake ppt (mm)
300
250
y = 1.0497x + 39.262
R² = 0.6408
200
150
Series1
100
Linear (Series1)
50
0
0
50
100
150
200
Lewistsown ppt (mm)
Spur Park ppt (mm)
F
180
160
140
120
100
80
60
40
20
0
y = 0.8071x + 32.941
R² = 0.5922
Series1
Linear (Series1)
0
50
100
150
200
Lewistown ppt (mm)
Figures 3.E and F Linear relationships between climate data at the two study sites and
climate data from Lewistown, MT, used to fill gaps and repair errors in local precipitation
data.
130
G
Calculated solar rad
(MJ m-2 mo-1)
1000
y = 1.2227x - 82.951
R² = 0.9881
800
600
calculated MJ m-2 mo1
400
200
Linear (calculated MJ
m-2 mo-1)
0
0
200
400
600
800
Helena measured solar rad
(MJ m-2 mo-1)
H
calculated solar rad
(MJ m-2 mo-1)
1000
y = 1.0195x - 22.598
R² = 0.9996
800
600
calculated MJ m-2 mo-1
400
200
Linear (calculated MJ m2 mo-1)
0
0
500
1000
Helena measured solar rad
(MJ m-2 mo-1)
Figures 3.G and H Graphs of calculated solar radiation versus measured solar radiation
from Helena Montana for (G) site I and (H) site II.
131
I
GPP tDM ha-1 yr-1
35
30
25
FR .5
20
FR .4
15
FR .3
10
FR .2
5
0
J
GPP tDM ha-1 yr--1
35
30
25
20
15
10
FR .5
FR .4
FR .3
FR .2
5
0
Figures 3.I and J Graphs of sensitivity analysis of GPP to soil fertility (FR) for (I) stie I
and (J) site II.
132
K
GPP tDM ha-1 yr-1
35
30
25
50 mm
20
100 mm
15
150 mm
10
200 mm
5
0
L
GPP tDM ha-1 yr-1
35
30
25
20
15
10
50 mm
100 mm
150 mm
200 mm
5
0
Figures 3.K and L Graphs of sensitivity analysis of GPP to ASW for (K) site I and (L) site II.
133
M
35
GPP tDM ha-1
30
25
20
15
clay
clay loam
sandy loam
10
sandy
5
0
Figure 3.M Graph of sensitivity analysis of GPP to soil texture on site II.
134
Appendix II
METHODS ADDENDUM
3-PG (Physiological Principles to Predict Growth) Model:
The Physiological Principles to Predict Growth (3PG, Figure II.1) is a simple processbased model of forest growth developed by Landsberg and Waring (1997) in which individual
species may be represented using species-specific parameters characterizing light, water and
nutrient use as well as above and below ground biomass allocation. It was created to combine
measurement-based growth and yield models with process-based carbon and energy-balance
models. The model has been widely applied to simulate tree growth under different
environmental conditions around the world (Sands, 2004). There are two versions of the model:
1) an Excel spreadsheet-based version in which all data are entered in worksheets within the
same Excel file, and parameters are set with fixed values for the entire stand for each model run,
and 2) a C++ spatial version that can be run at a resolution of 1 km. Because we were asking
questions at the local level, we used the Excel spreadsheet version of 3-PG with climate data
from nearby meteorological stations and soil values extracted from SSURGO soil maps.
3-PG uses available site and climatic data as inputs to simulate stand development on a
monthly basis. The climate data used in the model are monthly averages of total daily total solar
radiation, minimum and maximum air temperature, daytime atmospheric vapor pressure deficit
(VPD), monthly rainfall and frost days. The model can be run using either monthly weather data
for each year or long-term averages of the monthly data (Sands, 2004). Soil inputs include a site
fertility rating (based on a relative 0 to 1 ranking scale), maximum available soil water, and soil
texture (Landsberg and Waring, 1997).
135
Figure II.1 A flow chart of the 3-PG model, relating climatic and soil variables to forest
water, energy and carbon balances.
“Quantum efficiency” a factor representing the proportion of intercepted
photosynthetically active radiation (PAR) used by the tree species in gross photosynthesis is a
direct function of soil fertility (Richard Waring, personal communication). Light use efficiency is
constrainted by the effects of frost and VPD on stomatal conductance. Available soil water,
VPD, mean air temperature, frost days per month, soil fertility and stand age are all used in
calculating In the model, net primary productivity is set at approximately half of the gross
primary productivity (Waring et al., 1998). According to Sands (2004), Available soil water,
136
VPD, mean air temperature, frost days per month, soil fertility and stand age are all used in
calculating Simulated photosynthate allocation to roots is mechanistically controlled by available
soil water, VPD and site fertility. The carbon allocated to roots increases under conditions of low
FR and/or ASW (Sands, 2004). Simulated allocation to stems and foliage is age-dependent and
determined by stem diameter (DBH). There is an inverse relationship between stand age and
allocation to foliage and a positive relationship between age and stem allocation (Sands 2004).
The model includes algorithms representing monthly age-related mortality, self-thinning and
anthropogenic thinning. Mortality can be affected by stress factors such as water stress.
The 3-PG water balance uses the Penman-Monteith equation, which uses monthly
precipitation, solar radiation, VPD and canopy conductance to calculate monthly
evapotranspiration. Our analyses included a multiplicative relationship between VPD, ASW and
stand age (Jarvis, 1976), rather than using the most limiting of VPD or ASW with stand age. The
equation governing the effect of this modification on gross photosynthesis is:
GPPactual =GPPmax * APAR *f(T)*f(frost)*f(VPD)*f(SW)*f(FR)*f(age)
APAR = photosynthetically active radiation
T = temperature
Frost = the number of frost days per month
VPD = vapor pressure deficit
SW = soil water content
FR = soil fertility rating
Age = stand age
137
Canopy interception and conductance are determined by leaf area index (LAI)
(Landsberg and Waring, 1997), and a species-specific stomatal conductance is constrained by
VPD, ASW and stand age (Sands, 2004). In the event that precipitation exceeds the ASW
setting, the excess water is lost from the system as runoff (Landsberg and Waring, 1997). In the
model, trees transpire during the day, the length of which is determined by the month and
latitude setting (Sands, 2004).
Realistic simulations of stand dynamics are limited by the available data for site
conditions and characteristics of individual populations of tree species. Whenever possible,
measured values for species-specific parameter should be used in the model. Where these data
are not available, similar species may be used to estimate parameters and the model may be
calibrated to fit simulations to real data. A protocol for estimating unknown parameters and
validating model output with observed data is described in Sands (2004).
Methods for aggregating soil data for the study area (used in Chapters 2-4):
SSURGO (county surveys) and STATSGO (national survey) soil data were downloaded
from the NRCS Soil DataMart website in spatial and tabular forms. First, all of the spatial data
were aggregated from the county and national soil datasets into one soil map covering the entire
study area.
For every map unit (coded with mukey), a Python script was used to extract:
1) attributes directly from the SSURGO (1:20,000 scale) or STATSGO (1:250,000
scale) “muaggatt” table from the NRCS database
138
2) attributes for the dominant component within the map unit from the “component”
table. The dominant component was determined using the component with the highest
representative percent of the map unit (comppct_r).
3) attributes for the horizons in all recording components. For each component, for each
attribute of interest, values for the attribute were averaged across all horizons with
non-zero values (the arithmetic average). An area-weighted algorithm was used to
combine the components together, based on the normalized percent of each
component with respect to the other components with values recorded for that
attribute.
Example: A map unit contains three components: 40% is A, 35% is B and 25% is C. For
the attribute of interest, maybe only A and C have values recorded. In this case, re-normalized
percents were calculated with A(62%) and C(38%) as multipliers for the attribute of interest.
For this study, the following SSURGO and STATSGO soil attributes were extracted:
Map Unit Symbol, Map Unit Name, minimum bedrock depth, available water storage (AWS) at
25, 50, 100 and 150 cm, drainage class, hydrologic group, runoff, hydric condition, hydric rating,
taxonomy, particle size, moisture regime, temperature regime, landform, saturated coefficient of
conductivity, available water capacity, calcium carbonate concentration, gypsum concentration,
sodium absorption ratio, pH, water and wind erodibility factors.
139
Methods for filling data gaps in soil maps (used in Chapter 4):
Where SSURGO data were not available, STATSGO data were used to fill gaps. We
encountered a difficulty with inconsistently recorded values of NULL and zero, so we used the
“select by attributes” tool in ArcMap to locate water bodies, marshes and dams and gave those
units a value of -9999. We also selected rock outcrops, badlands and urban lands, and set the
values to zero. Where there were NULL values for minimum depth and taxonomy, we used the
map unit name(s) to look up the official soil series descriptions on the NRCS website
(https://soilseries.sc.egov.usda.gov/osdname.asp). For associations of several soil series within
one map unit, we entered the most commonly occurring value for minimum depth to bedrock in
both inches and centimeters. After dissolving them to similar soil series, there were 8,008 total
map units with 4,606 null values replaced by estimated soil depth values ranging from 0 to 386
cm. Where the values could not be estimated, the value was set to a “null” value of -9999.
Methods for making soil datasets (used in Chapters 2-4):
After aggregating all map units and entering attributes where available, the ArcMap
“dissolve” tool was used to create a separate dataset for each attribute of interest. For the sake of
size, each dataset was clipped by state. To create the ASW dataset, the ASW for 150 cm was
converted to mm and made an integer to remove decimal values. Then, the dataset was dissolved
once again to combine polygons containing equal ASW values.
140
Methods for intersecting soil datasets with forest mortality data:
The mortality data, which were polygons digitized from aerial photos, were intersected
with the dissolved ASW dataset in Arc GIS. This linked the ASW values from the soil dataset
polygons to the tree mortality polygons. This dataset was dissolved by ASW to again connect all
polygons with the same ASW value into one entry in the attribute table. Areas of mortality for
each ASW value were calculated in Arc GIS.
Methods for MC2 soil sensitivity analysis:
MC2 was run for historical vegetation for the time period 1971 to 2000, using 8K strided
data for the Landcarbon project. This run used the soil data for the contiguous USA provided in
(2003) by Jeff Kern of the Oregon Department of Fish and Wildlife. We substituted the depth
layer in band 0 of the soil netcdf dataset with the new depth dataset developed for this study. We
left the other 10 soil variables as they were in the Landcarbon runs. Once the runs were
completed, we took the difference in depths between the two soil datasets and compared them
with the differences in 30 year averages of several carbon and hydrology outputs from the model.