>
Chapter 4 Challenges and Needs in Fire
Management: A Landscape Simulation
Modeling Perspective
Robert E. Keane', Geoffrey J. Cary and lvlike D. Flannigan
Abstract
Fire management will face many challenges in the future from global
climate change to protecting people, communities, and values at risk.
Simulation modeling will be a vital tool for addressing these challenges
but the next generation of simulation models must be spatially explicit to
address critical landscape ecology relationships and they must use mechanistic approaches to model novel climates. This chapter summarizes important issues that will be critical for wildland fire management in the
future and then identifies the role that simulation modeling can have in
tackling these issues. The challenges of simulation modeling include: (i)
spatial representation, (ii) uncertainty, (iii) complexity, (iv) parameterization, (v) initialization, (vi) testing and validation. The LANDFlRE
project is presented as an example on how simulation modeling is used
to support current fire management issues. Research and management
needs for successful wildland fire-related simulation modeling projects
will need (i) exteusive mechanistic research programs, (ii) comprehensive databases, (iii) statistical validation methods and protocols, (iv)
software and hardware research, (v) modeling science explorations, and
(vi) extensive training. Models will continue to play an integral role in
fire management but only if the science keeps pace and managers are
poised to take advantage of advances in modeling.
• Robert E. Keane: USDA Forest Service, Rocky Mountain Research Station, Fire Sciences
~boralory. 5775 Hwy 10 West r..tissoula, Montana 59808 USA. E-mail: rkeaneOf.s.fed.us
e
S..Government's right to retain a non·exclusive, royalty-rree licence in and to any
ooPYTIght IS acknowledged.
l!.
.....
4.2
76
Chapter 4
Keywords
Ecological modeling, spatial dynamics, landscape ecology, mechanistic
simulation, parameterization.
4.1
Simulation modeling in fire management
7;
Landscape Simulation in Fire Management
els ca~l also be ~Ised to update broad-scale digital maps and dcsi n futur
sampltng
change . Fire
behavior alld e"
gil
. t . strategies
t d ' for assessing
.
.
nec t S .moc
e scane
e
Ul
egra
e
to
simulate
WIldfire
s!Jread
and
resllitan'
"
.
b . .f .11"
' oJre seventy to deter
IUlne I .WI .("res are providing ecological b ene"IS
" (I( eane and Karau 2010):\ Iec.
landscape
models can be used t 0 exp
. 1Ole
. t
.:.
'
.
halllstIc
,
.
ule.
clImate
and
veg •
. ..
etatlon mteractIons and to quantify fire regimes in S Jace
et a!. 2003. i\eilson el a!. 2005). :'IIost importantly,
model,s can help precilct potential fire dynamics in future el·llllates t
.I
6em 1 g
. ."
I'
.
. 0 pro VIC C
r
UI a emelll cutIca lI1formatlOn to mitigate adverse ef'eets (~Icl(
. t
a1. 2011).
"
"
enzle e
~echa~~~ti~'~i~l1~~~el~~~
Introduction
Fire management in the United States faces a number of challenges in the
next century. Seventy years of fire exclusion policies implemented under successful fire suppression programs have resulted in areas with increased canopy
and surface fuels, especially those that historically experienced frequent fire,
that now require extensive fuel treatments to reduce fire hazard and restore
ecosystems (Ferry et a1. 1995). In some areas, extractive land management
practices, such as grazing and timber harvest, along with exotic species invasions have created novel ecosystem and fuels characteristics that may also require innovative pro-active fuel and ecosystem treatments. ?"1eanwhile l hwnan
development is expanding into the nation's wildland extending the wildland
urban interface thereby making fire fighting difficult, heightening the risk of
loss of property or life from wildfire, and increasing the need for intensive fuel
treatments (Radeloff et a1. 2005). Fire suppression costs are spiraling upwards,
along with the ecooomic and social costs of treating fuels to reduce high fire
severity. New fuel treatment technologies, such as mastication, are finding
favor in fire management because they are less risky, easier, and cheaper to
implement but their impacts on ecosystems remain unknown (Agee and Skinner 2005). Above all, future climates are predicted to be wanner and drier
resulting in substantial increases in fire size severity, intensity, and frequency
(Cary 2002, Running 2006, Westerling et a1. 2006). Curiously, these same fireprone forests are being proposed for storage of carbon from the atmosphere
even though they will probably burn long before they can be effective carbon sinks (Sampson and Clark 1995, Tilman et a1. 2000). Fire management
will need to develop new policies , strategies, and tools to meet these future
challenges and ensure the sustained health of US landscapes (GAO 2007).
Simulation modeling will be one of the most important tools for fire management in the challenging future by providing an effective, standard, and objective context to evaluate management actions and ecological change (Lauenroth et a1. 1998). Models can be used to simulate effects of alternative treatments to determine the most effective fuel reduction or ecosystem restoration
strategy (Miller 2000). 'ovel treatments can be simulated to determine resultant short- and long-term effects on a diverse array of ecosystem elements
(Ryu et al. 2006). Fire hazard and risk can be simulated to prioritize areas
for treatment and to design the most effective treatment prescriptions (Keane
et a!. 2008). Simulation can also be used to approximate historical landscape
conditions that can then be used as reference for ecologically based landscape
prioritization and planning (Wimberly et a1. 2000). Predictive landscape lUodI
This chapter discusses
III a d eI·mg .
.
d the
. challenges of using simulnlioll
<
111"
"re
DIana1gement. An llltro uctlOn to simulation
n
ad
I·
.
(
1
e mg IS presented that will
lay 11t Ie groundwork
most
'In tl·
f . for
.understanding
.
. material
<
lIS CIlapter. Th en the
cha enges_0 usmg
sImulation
in
fire
manalTement
ap
J..•
.'
pled t·lOns are dIscussed
I
.
(0
an d an examp c of the lise of slIllUlation in III
r.:. e management IS
. presented
future
direction. and possible sol u t·Ions are StUllmanzed
. .
_
.
Last, researcI1 needs.
to
ensure
t
Iat
sllllulatlon
modeling
becomes
an
a
t',
c.
.
. I f 1
ell€C 1\ e lire management tool
III t le .
uture because
'traditional fire management appl·oaclles WI·U b e severely
tes t ed III a warmer clImate (Flannigan et a1. 2008).
.
4.2 Simulation modeling in fire management
d~mul~:ionf i~ an important tool to predict fire behavior and effects for a wide
dlver~~
ue management applications. However. the lenninology used to
y::r
en e
e models can be confusing and inconsistent.
l
4.21 A simulation modeling primer
variabl·
Tltree
types
the cent
a1 oft'f
b .es al.e genera Ily used .1Il models: state variables describe
change t~e :;~:el~~ri e~g slm~~ted. flux .variabl:s represent the processes that
\"3!iables B
a €s. an 'l.1ltermedzate vanables are used to compute f1tLX
variable ;vOUolrdebxamIPle. trbee leaf carbon might be a state variable and a flux
are involved in s.e t ,eI car
t' all lost .ea ch year to I
eaf 'talL In general. four tasks
.
..
mg
values' tl UTIli a lOll. modelma-O' }",·t·
~ W t··lza t·tOn .Il1VO I ves asslgmng
start'a 'e state
vanabIes . p.
·
. concerns quantifying those
model parameters
that.
. marne t.e1'lzatzon
in10nnediate
. bl
ale used 111 algonthms that compute state flux and
vana es Vahdat'
t '1
.
'
.
rl~ull are realistic
and
. l~~ en a1 5 testmg the model to ensure that the
lanily (Rykiel 1996). L,;uantlf} 1I1g the accuracy of results to estimate uncerttT::o and alga .thru
stly, senszt1,1Jlty analysls 111\'0Ives modifying !Jarames to determ·me tlell
l · ·111 fl uence on model results (Cariboni
ef al. 2007). n
78
Chapter 4
Landscape Simulation in F'ire Management
i\lodel approaches can be described as empirical. mechanistic. stochastic,
and deterministic. Empirical models are created from extensiye data often
using statistical modeling techniques. Examples include t.he Australian fire
behavior model (~IcArthur 1967), the CONSUA1E fire effects model (Oltmar
et al. 1993), and the growth and yield model FVS-FFE coupled to a fire
and fuels extension (Beukema et al. 1997). Empirical models are accmate but
limited in application to the conditions represented by the data. Mechanistic
models simulate biophysical processes using universal physical and chemical
relationships} therefore mechanistic models are applicable to a wide range
of domains, but they are often complex} which often results in instability:
inaccw·acy. and difficulty in parameterization. Stochastic models contain numerical relationships that use probability distributions. which often require
repeated runs to quantify the variability in results. Deterministic models contain mathematical equations that represent important processes resulting in
output that often does not vary for a particular set of inputs. In reality. most
models contain a diverse mixture of these four approaches, especially longterm landscape models. For example a landscape fire succession model can
simulate fire using a mechanistic function) seed dispersal using stochastic al·
gorithm l tree growth using mechanistic biophysical equations. and fire effects
using deterministic decision trees (Keane et al. 2004).
Two groups of simulation models are used in fire management. Fi1"e behavior models simulate the physical combustion processes of wildland fire such
as spatial growth, rate of spread} firelil1e intensity} and flame length. These
are strategic models for real-time. operational usc under wildfire conditions
or planning applications to describe fire hazard. Examples of these models include the mechanistic fiTe model of Rothermel (1972) that is implemented into
the BEHAVE software for point evaluations and into FARS1TE for spatial applications, and the empirical McArthur (1967) Australian fire spread model.
Fi1"C effects models simulate direct and indirect effects of fire on ecosystems
(Reinhardt et al. 2001). Direct or first order fire effects include fuel consumption, tree mortaility, and smoke production (Ottmar et al. 1993, Reinhardt
et al. 1997), willie second order or indirect effects include vegetation development, erosion, and fire regimes (He et al. 2008). Fire effects models include
all those ecological simulation models that contain any representation of wildland fire from the stand-level gap models that simulate individual tree growth,
mortality} and regeneration to the landscape fire succession models that simulate ecological processes. such as fire regime, in a spatial domain (Keane et
al. 2004)" This chapter mainly covers landscape level fire effects simulatioll
modeling which nearly always contains embedded fire behavior models.
l
4.3
4.3
Technical challcllges in fire management modeling
79
Technical challenges in fire management modeling
There are assorted difficulties and dilemmas encountered b" 1110d I
tl
. d . .'
.
J e e r s as ley
bUll. ,·aJ lOllS computer programs for fire management tllat span from IlOW to
design a model to how to usc the model correctly.
4.3.1
Model design
The chief
challenge facing modelers is to build fire sim"lal'lon 1110 d eIs usmg
. a
. .
mechalllstic approach such that causal processes
are
"lllke,1
t
.
0 ecosystem responses so that new. unforeseen results can be generated (Paca Ia aileI T"II man
1994. Rastetter et al. 2003). Properlv designed mechanl'stl'c Inod e Is are qUi"te
robust 111 terms of scope and application so that
( thel'I' SI'lllulated consequences
slIch as responses to climate change, become emergent properties of the modei
rather
than predetermllled . results generated as a conseq'le"c
"
.
eo f parametenzat'OIl .(Peng 2000).
The
major
challenge
is
to
design
l'ob'ISt
fi"
d
i
d
.
Ie 1110 e s aroun
detlUled
algonthms
' I
..
. that ..use nux variables to represellt '1111 pol' t ant p I
lYSlca
relationships and Il1teractlons between dynamic, readily quantifiable inputs.
" pI
' I re IaUnfortunately.
.
. .
- research has quantied a fraction of the major
lYSlca
tlOnslllps In a handful of ecosystems) so simulation design compromises are
always made to account for the limited state of knmvledge (Keane et al. 2010).
F\lrthermore l those processes With IlltrmslC uncertainty. such as fire ignition.
may. always need to be modeled stochastically because of their inherent compleXIty lUld cross-scale influences. It is important that modelers identifY the
piau Ible extent .o~ mechanis~ic design using available literature and existing
models and exphcltly reeogmze these bounds in the results.
Future fire models must also be designed to be spatially explicit to adcomplex scale issues (Peters et al. 2004). This means that the mod. lees must ~plicItly Itlcorporate spatial relationships in model design and
(~Plementatron. One-dimensional (lD) or point models such as BEHAVE
lin~:ws and Bevins 1999) and FOFEM (Reinhardt et ~l. 1997), may have
aI
use III the future because they can represent fire behavior at only one
. tu:e fir~ behavior models, especially research-oriented mOdels must,
1.., e. Fu
ot mu ltl-dnnensl
I'
d "
,
- ona III space an tnne to ensure that those processes that
I)(~ur at one seal
d I'
.
!o< I
. e an ocatlon arc affectmg processes that OCcur at other
a""
and
10catlOus
(Gard nel. ea.
t I 1991) . TI'
for' I
liS approach has man'\! obstacles
Imp ementation' I d' 1 k f .",.
J
hil'h d
me U lllg ac 0 SluuClent data across appropriate scales
,
emand
for
comp
t
.
.
'
d
'
f
i
'
'
for s' I .
U CJ Iesources: I entl catIOn of the appropriate scales
IInll atlon (e
I t"
I'
.
<
hn' activity (:Ma .g., se ec IIlg t le nght ~lxel ~ize). spatial autocorrelation in
Howeve
-I gnussen 2008), and speCIficatIon of the proper spatial extent.
r, exp oratIOns of
t' I .
.
h-p}), ,,'
I
spa la lllteractions are the only wau to eomprehen" .Imu ate Iir b h '
d
J
e e aVlor an effects across landscapes (Keane et aJ. 2010):
•
:rcss
L
bO
Chapter -t
Lalldscape Simulation in Fire
~Ianagelllent
It is important that the spatial scales represented in the model are reCOllciled to the ecosystem processes that they represent and to the lime scales at
\yhich these proce:sses operale (\rarillg and Running 1998). Tree regeneration
d~·namics. for example. may require a spatiall,v explicit. anllual seed dispersal
model and a simulation of reproduction phenology at a daily time step to
properly reflect climate interactions of tree life history.
Another challenge is lhat future simulation lIlodels must be able to simulate the complex interactions of state and flux "ariables across scales (Rastetter et a1. 2003 1 Drban 2005). Dynamic feedbacks and cross-scale interactions
will allow the prediction of no\"{~l ecosystem responses and interactions betweell climate. vegetation. and disturbance which are likely to lead to nonlinear model bellm-ior and cause important phase transitions that are critical
for landscape management ('.IcKenzie et al. 201l[in press)). The trend and
magnitude of these interactions are Illostl~- unknown for man~' ecosystems and
thev are difficult to ~tudy outside of a sinmlatioll approach. One important
int;raction is the role th~t humans play in past (e.g.. "\"ative American burning). present (fire exclusion era). and future (enlightened fire management) on
landscape dynamics (Kay 2007). [Ilieractions can dictate important thre:;holds
and phase transitions of landscapes in changing climates so that management
can anticipate these changes and respond (Allen 2007). AIso important are
how multiple factor interactions. such as multiple disturbances. create novel
landscape conditions that may accelerate landscapes toward important thresholds and phase transitions, Ecosystem science has only scratched the surface
ill determining the sign and amplitude of most ecological interactions largely
because of the complexity in the nested scales of time and space involved
(Allen and Starr 1982. King and Pimm 1983).
One last challenge is balancing complexity with utility in model design.
In general. simulations are more difficult to conduct as model complexity increases because with complexity come additional parameterization, detailed
initializatioDs. higher computing demands. and complicated model behavior.
Developing a parsimonious list of important variables to model is critical
to efficiently simulating ecological processes. otherwise a model can become
O\-erly complex and difficult to parameterize because of lack of information.
It is also easy to oversimplify model design such that simulation results are
meaningless. Conversely. if too much detail is included. the intrinsic uncertainties associated with each modeled process may compound to produce equally
meaningless results (Rastetter et a!. 1991, illcKenzie ct al. 1996). Moreover,
manager:; may not have the time. expertise. and resources to operate ~nd
interpret highly complex fire models. Therefore. it is critical that simulatIOn
design is properly matched to the level of information required by managers
and this is effectiYel.\' accomplished by plainly stating the objeeth"cs of the
simulation efrort.
1.3
4.3.2
Tl.'Chnical challenges ill fire management modeling
81
7Ilodcl use
One of 'the. greatest challenges in modeling is to dearh'
s·
I
. articlliatn\,; .lmU8tion 0 bJcctlves to inform the simulatioll project. ""hi Ie seemingk ob .. .
."
'1 I
.J
\ lOllS.
thiS IS easl y t Ie ~~ast undcrstood concept in the design and implementHtion
of fire 1ll0~e1s, \\ Ilhout an explicit statemeut of simulation objectives. it is
prob~cmatlc .. and perhaps impossible. to build a comprchcnsh-e model that
prondes eaSily understandable results to address research aud maU8 crement
C~IICCl·~IS. A c1e~r mode~ing objective allows the modeler to easily identify the
(I) \'anablc~ to II1clude 111 the model structure. (ii) sequeuce of 5imulation for
selected vanables. (iii) input and output file structures. (h·) critical ecosystem
~rocesses to Slllllll~.te. (\-) ill1~ortal1t interactions 10 iuclude. (vi) timc St~ps to
lIn~l~menl. Rnd (\"11) appropnate spatial and temporal resolutions aBd extellt.
ft IS Important to state this objectivc so that the nlost appropriate models
are ~elected or built. the right pararneters arc selected or quantified. the simulatIons arc successfully completed in an acceptable time. and the results are
easily ulldersto?d. \Vhile. in general. additional objectives can be explored
~ the cO~~lplexlty of mechanistic models increases" it is unlikely that there
Will be a uber-model that addresses all objecth'es because there will llever be
sufficient science to support its development or computing reSOurces to conduct the simulation. Therefore~ it will always be imperative to focus model
development with a clear simulation objective.
. It seems logical t.hal .fire simulation models will be much more complex
m tI~e f~ture: and thIS will demand increased computer resources. higher expertls~ ~n model lise. and more extensive parameterization. Complex models
arc Cl:,tlcally needed because it is nearly intractable to design wildland fire
exp~rtments that explore the dynamic relationships of fire and ecosystem behaVior over. multiple time and space scales. Crown fires. for example. are
extremely difficult to study using empirical approaches because it is difficult
~nd costly to measure heat flux across the large spatial scales involved llSmg contemporary experimental cquipment (Albini 1999). The long temporal
scales II1vol\'oo In exploring dynamic fire regimes may preclude short-term anwers from intensive field surveys. which are undoubtedly invaluable in the
longer term. However. it is unlikch' that the fire manaucment would '~dOI>t
tbese ne
I·
d
I
.".
0
,
w comp lcate moe els 01 uecessanly afford the computers needed to
these models in an operational application. Therefore~ the challenge will
h to develop complex spatially explicit fire models for research purposes and
tben synthesize them and their results to create manalTcmcnt-oricnted models
tatma
tb
.
.
0
. ~ no e as 10bust as expected but will perform well in operational
apphcatlons becaus tl
.
.
ICY al e easy to parametenze. execute. and understand
(Keane and Finney e2003).
The input of cr t ·
. I·
.
atlOn models IS an increasing challenge facing modele
d lI11a
d e lIlto Slmu
.
tit t
rs an mo el users III the futnre (Keane et al. 2010). It is important
a models have a
I· ·t .
.
.
n exp ICI lepreSentatlOn of clImate across multiple scales
:0
pi
82
Chapter 4
Landscape Simulation ill Fire i\lanagelllent
to enSlU'C a realistic response of ecosystem dyuamics to climate. Phenology.
for example. may need a daily climate time step at 100-111 rcsolution. whereas
decomposition may need monthly time steps at i-kIll resolution (Edmonds
1991. White et al. 1997). Identification of the appropriate hierarchical scales
will be difficult. but the task of matching the ecosystem processes of disturbance and plant dynamics to the appropriate climate scales may be even
more challenging. Identifying the most parsimonious climate data stream to
input into models is also crucial given that too much weather data could
potentially complicate and slow fire model simulations. This is inherently difficult because each weather variable has an intrinsic scale (e.g.. micl'osite,
regional) and resolution (e.g.. vertical layers abo,·e ground. grid size). ~Iul­
tiple weather streams representing past. prescnt. and future projections of
climate are needed to determine potential climate effects on disturbance and
vegetation. And. multiple climate scenarios are needed to bracket the range of
potential effects and to identify important thresholds of ecosystem change. An
explicit simulation of atmospheric transport is also desirable for fire models.
\Vind speed and direction at various heights can feed disturbance proce es
(e.g.. windthrow. fire spotting, insect epidemics) and plant dynamics (e.g. l
seed dispersal) (Greene and Johnson 1995). Atmospheric transport can also
be llsed to simulate importallt feedbacks such as smoke dispersal. atmospheric
deposition. and radiation budgets.
A last challenge is quantifying the uncertainty im'olved in fire simulations
so that fire managers and researchers fully ullelerstand the impact and significance of the predictions and results (Bunnell 1989. Araujo et al. 2005).
This includes developing methods to present simulation results that contain
an estimate of error or degree of uncertainty (Bart 1995). This assessment
of uncertainty should account for the error in parameterization. initialization,
and model algorithms: as well as the error and variability in model predictions
(Brown and Kulasiri 1996). The IPCC (2007) report contains protocol and
classification that they propose that all modelers use to describe the uncertainty assessment for modelers. Results must also be synthesized into variahl
and formats that are commonly employed by fire management.
4.4
A fire management simulation example
An example of how diverse simulation modeling approaches can be integrated
together to create a viable management tool is presented to illustrate the use
of landscape modeling in fire management.
4.4
4.4.1
A fire management sirnulat.ion example
83
The LANDFIRE mapping project
The US Healthy. Forest Restoration Act and the .'1ational F,· I·e PI an 'C
.
s 0 heSlve
Strategy establIshed a national comntitment to reduce fire hazard and restore
fire to those ecosystems where it had been excluded for decades (L'
t
d
r li' . 2
)..
. .
. aver yan
II It ams
000.
rlus
conumtment
reqmred
detailed
multi-scale
spat·
I
d
t
. ...
I
la a a
f~r pnontlzlllg. p anuing. and designing fuel reduction and ecosystem restoratIOn treatments across the entire nation (GAO 2007). These spatial data layers
mu~t ~rovIde essent18l fuel. fire regime. and vegetation information critical for
dCSlgomg treatments and acthities at spatial scales compatible with effective
land management (Hann and Bunnell 2001). The Fire Regime Condition Class
(FRCC, an ordinal index with three categories that describe how rar the current landscape has departed fmm historical conditions) has been identified
as. ~n~ of the primary metrics to be used for distributing resources and priontlzlllg treatment areas to protect homes 1 save I,·ves : and restore d ec I·Illmg
.
fire-adapted ecosystems (Hann 2004). The LAXDFIRE project was initiated
111 2005 to create a sCIentifically credible and ecologically meaningful national
map of FReC. along With developing a number of supporting maps of vegetatIon, fuels. and biophYSical seLtmgs, at 30 m pixel resolution that could be
~sed a~ro~s mUltipl~ organizational scales. This project integrated mechanistIC ~tatlstlcal modellllg with landscape fire succession simulation to create the
desired products to serve as an example of how fire management might solve
the current challenges mentioned above.
. .All LANDFIRE methods and protocols were based on a data-driven, empIrIcal approach where the majority of mapped and simulated entities were
created from complex spatially explicit mechanistically based statistical modelmg (Keane et al. 2007) because managers required the LANDFIRE product~ to .b~ SCIentifically credible, repeatable I and accurate with a millimum of
SUbJectIVIty. To meet this challenge. the LAXDFIRE reference database was
created. by. collecting georeferenced data from thousands of plots obtained
from a vanety of sources. most importantly. the USDA Forest Service Forest Inventory and Analysis program (Caratti 2006) (Table 4.1. Table 4.2).
Th~ d~~a were used. ~or (i) ~eveloping training sites for imagery classificatIon, (n) parametenzmg. vahdating. and testing simulation models (iii)
.
'
developll
t t·
I ill·
. . I g vege a Ion c ass catIons. (IV) creating statistical models, (v) determJ~mg data layer attributes, (Vi) describing mapped categories} and (vii)
;:jmg the accuracy of maps. models, and classifications (Rollins and Frame
6: The concept of lustoncal range and variability (HRV) was used as the
ilr~mlse of all FRCC calculations (Landres et al. 1999: Swetnam et al. 1999).
V
,_ ds was defined as the quantification of temporal and spatial fluctuations of
",n cape comp·r
(
.
f
w t
OSI Ion portion a area by each vegetation map unit) prior to
Inn~ European-American settlemeut (Hann and Bunnell 2001). Historical
FRec (Plies were then compared to current landscape composition to compute
ann 2004).
...
i
4.4
84
Chapler <1
Table 4.1 The linked models used in the LA;\"DFIRE project.
r-.lodel
LA:"DSU~I
\\,XFlHE
Description
A landscape
succession
model
Purpose
Generate HR'·
A biophysical weather
extrapolation
model
time series
and map fire
regimes
Extrapolate
coarse scale
gTidded
weather to
Data
ources
\'egctation
studies. fire
history studies
Keane el al.
(2002). 1<eane
et al. (2006b)
CLI,IET gridded database,
Digiwl elevation models
Keane and
Holsinger
(2006)
30 meters,
compute
climatc variables
for
BGC
A biogeochemical ccos~·stem
model
II RVSTAT
A statistical analysis
program
FIRE~ION
DAY~IET
A fire managcment simulation cxamplc
85
Landscape Simulation in Fire )'lanagcmclIl
A database and
analysis system
for data management
A model to
create gridded
daily weather
across the S
Statistical
algorithms
for regression
vegetation
mapping
Compute
ecosystem process variables
fOT
vegetation
mapping
Computing
FRee
from
HRV-current
comparison
Create the
LANDFIRE
database
Create the
DAY~tET
database for
use in simula~
tion modeling
Create vcgeta·
tion and fuels
maps
WXFIRE.
CLI~IET
gridded database
Thornton
(1998). Thornton ct 01.
(2002)
LA:"DSU'1.
LANDFlRE
mapS
Steele et al.
(2006)
Legacy data
from univer~
siLy, government, and
private agen-
Lutcs et. al.
(2006)
cies
\\'eather st.a~
t.ion data
collected
throughout the
CS
Simulated data
from all models
Thornt.on et. al.
(1997)
Quinlan (2000)
tree empirical
modeling
'] LA!'\DFlRE protot)'pe project developed the methods and protocols
1 1e
.
.
J . . tegratlO n
sed to map FRee acrosS the United States llsmg a camp ex In
. al
~r several ~CologicaJ models (Table 4.2) (Rollins et al. 2006). The hiS::~~n.
spatial time series that. represented HRV were created from ~ands~la~~ :he US;
HOIl modelina since historical maps and data are absent fot m~c
d utput
the LA:\DSU:"I model was used to simulate landscape dynamIcs an 0
landscape composition over 5.000-year HRV simulations (Keane et al. 2006b:
Pratt et al. 2006). Historical fire regime and vegetation succession field data
collected from numerous studies were used to parameterize LAi\DSU:\l (Long
et al. 2006). LA"DSU~I stratifies these parameters by three \'egetation-based
classifications: (i) Potential Vegetation Type (PVT) defined by biophysical
setlings. (ii) coyer types described by dominant vegetation, and (iii) structural
stage described by \·ertical stand structure. The PVT approximates biophysical seLting by assuming that the unique nclima..( vegetation community that
would eventually develop in the absence of disturbance can be used to identify uniqne environmental conditions (Daubenmire 1966). The LANDFfRE
PVT classification is a biophysically based site classification that uses plant
species names as indicators of unique environmental conditions (Holsinger et
al. 2006). Cover types were named for the species with plurality of canopy
cover or basal area. while structural stage was based on canopy cover and
height (Zhu et al. 2006).
Table 4.2 Flow of LAXDFIRE tasks to create the fire regime condit.ion class
(FRee) and various fire management projects that use LAXDFIRE data (:\Iodels
are defined in Table 4.1).
Data
FIA data. research data.
legacy data
LANDF'JRE database. Simulated outputs,
LANDFIRE database, simll~
late<! outputs, satellite imagery
LA DFlRE dat.abase, litcrature, PVT map. covcr type
map, structural stage map
LANDFIRE database. litera·
ture, PVT map, cover type
map, structural stage map,
N1F~tID database
LAKOSUM output. PVT
map, coyer type map, structural stage map
Departure estimates
LA tDFIRE fuels maps
LANDFIRE fuels and vegeta.tion maps
Task
Flow of LANDF1RE tasks
Compile
LA:\'DFIRE
database (Caratti 2006)
Build PVT map (Holsinger et
al. 2006)
Create current cover typc and
structural stage maps (Zhu et
al. 2006)
Develop ancillary fucls data
layers (Keane et. al. 2006a)
~Iodels
FIRE~ION
IVXFIRE, BGC, See5
IVXFtRE, BGC, See5
\vXFIR8, BGG, See5
Simulate HRV historical t,ime
series (Prat.t. et a!. 2006)
LAl\DSU~1
Compute departure (Pratt et
al. 2006)
HRVSTr\1'
Compute FRCC (Steele et al.
2006)
Use of LANDFIRE data
Compute fire hazard and risk
(Kcane et al. 2010. keane and
karau 2010)
Compute potential fire severity (1<arau and I<eane 2010Iin
prep))
HRVSTAT
FIREHARM
PLEAT
""
'"
-_-_...
-"----------------_.
--. _------------,,,,_.
---------._----......-- . _<lII_
o
g-
~
B
A
~
t"'
•5.
•o
'C
....
t
t
~-
~-
~
!!1
3
"~
o'
"
"
."
...
-_.
@'
"
"J
'"
'0
. ..
~o
wv
'NY
....
..-::-
.•"
'"
.. ,. ,..
----~--.
3
'"
:-oc..-..-,~:-
•-"---~--.
..
..
I. . . .
D
t
~
:..
_.
-_.-,_.
_. -....
.,.
},"".
(\
. '. ' I .~
..
"',~ ':.
),
\.,!
_-'-
wv
'"
'.
--=-=-=-..
- ...
------".
-----
:>-
;;'"
3
•
wv
~
'"
~
-=-::-=_..
..
..
uo
I.
•
FRee fol' the northern H.ockies mapping zone
D, FRee map; E, fire behavior fuel model map. 19.
o
ntiaI vegetation type map; 13, cover type map; C, structural stage map;
.".
g'
3
Fig.4.1 Critical maps creat.ed by the LANDPIR8 project to compute
A, Pote·
~
•
.
3
'C
""
~
...
88
Chapter 4.
Landscape Simulation in Fire
~Ianagclllelit
\"egetation maps portraying current conditions were thcu needed to compare with the simulated HRV time series to compute FRCC (Table 4.2). The
three classifications used in the LA:'\DSU~j modeling (PVT. cover lype. structural stage) were mapped to describe current conditions so that simulated
HllV data matched existing conditions, The PVT map (Fig, <I.IA) was crcated using a gradient modeling approach where plot-based assessmeuls of
PVT were modeled from a plethora of biophysical variables t hal werc summarized from field data using regression tree (CART) statistical techuiques
(See5 model (QUinlan 2000)), The biophysical variables ,,'ere computed from
the biophysical \vXFlRE model (Keane and Holsinger 2006). simulated using the BGC model (Holsinger et a1. 2006), or taken from the DAY:IJET
gridded wealher database (Thornton et al. 1997) (Table 4,2), Resnltant empirical CART models \\·ere then used to map PVT across lhe region llsing
independent variables created from the same models (Holsinger et a1. 2006),
The biophysical PVT map was subsequently used with other biophysical "ariabies and Landsat 7 Thematic :\lapper satellite imagery to create the cover
type and structmc stage maps using a gradient modeling approach (Zhu et
a1. 2006) (Fig. 4,lB,C),
Two methods were used to calculate the rlcpartul'e statistic that quantitatively compares the existing condition to the many historical landscape compositions, Steele et al. (2006) developed the I-1RVSTAT model that computed
departure and statistical significance using advanced regression techniques
and Pratt et al. (2006) used a variation of the Sorenson's index based on
management-oriented FRCC methods (Hann 200<1, Barrett et al. 2006) (Fig,
4,ID). Both departure indexes ranged from zero to 100 with 100 being the
most departed, FRCC was finally created by making classes of the departure
statistic (Pratt et al. 2006),
Some products created fTom the LAl\DFIRE process (e,g.. biophysical
variables) were then used to create additional fuels and fire regime layers that
are critical in the eventual planning and implementation of fuel and restoration treatments at local scales (Fig, 4.1E), Fire regimes were taken frOIll the
LAl\DSU~1 model (Pratt et al, 2006) and described fire return interval and
probability of tlu'ee fire severity types, Canopy fuels maps were created using the gradient modeling approach where the same independent variabl
used for vegetation mapping were correlated to plot level fuels characteristics (Keane et al. 2006a). Surface fuel models were assigned to combinations
of categories in the three vegetation classifications (Fig, 4,IE), LANDFIRE
products have been extensively used by fire management for a variety of applications. Fire hazard and risk maps have been created for large regioWi
using the FIREHAR~j program (Hessburg et al. 2007: Keane et a1. 2008),
The FLEAT program is being used to estimate fire severity and quantify ec0logical benefits from wildfire using HRV simulations using LAKDFfRE input
data (Karau and Keane 2010 [in press)), The LAl\DFIRE fuels data layer,
are being used as inputs to FARSITE to simulate fire behavior on wildfire:;
4,5
Research and llwllagemcllt needs and solutions
89
tlu'oughout the US,
4.5
Research and management needs and solutions
IrrespecLive of design, the performance of any simulation llIodel .
d
f I
"
IS governe
bj ' tl 1e q ua It'
I} 0 tIe underlYlJlu SCIence the technl'cal
'.'
,.
f I
d I .
..
o·
ane CICatlve ablhty
o tIe .InO C eI to quantltauvcly implement this understandin
did
011 wInch the model' b
d TI , ' , "
g, illl t ,e ata
,
IS ase. lelclO1e. slIl1uluuon research nceds C) I . I
qualIty, rele"ant science on which to base future models (1'1') I' I I I ug ,I
'I t b 'Id
'
lIg I Y trame,
mot e ers 0 til ,apply, test. and teach these models and (ii') ·t
' d
I'
'
I ex enslve ata
'
,
,
to d eve Iop. t es.
t IIlltla lze~ and parameterize mOdels
The quality of the underlying science is deten;,ined b tl
I'
d'
t f I '
Y lC Cumu atl,'e
a \ancemen 0 p lyslcal. ecological, and climatic kno,,'ledge ' d b
'
.
b
'
pIne
yc~
~f1ment.atlOn, 0. sen'atlOn. and publication in peer-reviewcd journ' I TJ".
IlIformatlOn prOVides a mechanistic understanding f k .
a s. lIS
'
.
0
ey Plocesses that govern
ecosystem d ynamlCs as accepted br a broader s' t'fi
'
·
, '.
.
<
Clen I c community. Because
I future IS
tIe
so uucertalJ1 It IS vitally importal t tl ttl-' .
'
.1
.
'
.
1
la lele al e comprehenSive
rese81C 1 prog lamS.8Ullm g at understanding novel CCos\'stelns hlel
dOt'
'I '
I
.
con I lOllS
an d SOCIa Issues t I3t will evolve as climate are III ,'fi d I
' '
' .
oe Ie, JUmao populatIOns
IIlcrcase, and attitudes chanue. Since mechanist'lc' . I
.
•
,
_
0
uPpl oac les are suggested
It IS IlDportant that field researcll proJ'ects sho Id
I,
"
I I'
.
u ene eavOl to quantify the
causa J:e ~tlOnslll~S that govern fire and ecosystem dynamics so that these
l~lecha..n~t~c eqtlatIO~ls can. ~e developed for implementation in future simulation ~no e s. There IS a cnLJcal need for fundamental research into tb b '
phYSical processes that control fire behavior and subseqtle"t efl'ect F. e aSllc
Ii b 1 , ' .
S. '1I1C sea e
re .e la\ lOr o.utputs should leed detailed ecosystclllll1odels to mechanistically
pred~ct ecological response. This includes a new theory of wildland comb t'
~~I~ICS 8 nd ha more physiological approach to simulating vegetation rcs~~~:l~~
e an d t e subsequent development.
l
,J
•
Iin~Ood mOdelers. possessing comprehensive knowledge across diverse disci-
~irst 't~:ebr~e, .However.
~O·
even good modelers are limited by many reasons,
~c glotilld. knowledge. and experience of a modeler can limit the
~o \~~iC~ua~lty•.and com~lexity of model design, Second, there is an extent
exist"
e'~"lIlg ecologIcal understanding can be feasibly incorporated into
[ionslllogf IIl°t elstr~lctures based on the modeler's skills. There are also liluitaI a parsllnony d
'a
'I b'I'
"
binder tl .
. an aval a 1 Ity, and engmcenng restrictions. which
Ie lIlcorporatlOn of ne ' k
led'
"
('\"en the m
.
\\ now ge IIlto eXlstmg model structures by
peral and ost ,accomphshed mOdeler. ?\lore theoretical issues such as temei
spatIal scale reconciliation and representation (Urban 2005) a d
o-econollllC factors J
dd' ,
n
tnunit), TI
~
I a so pose a
ItlOnal challenges to the modeling com" lerelore ed
t'
ltlUSt empl'
J.
u~a Ion programs. particularly at the graduate level
dl ~landinl~~~~ a dlver~lty of modeling ,approaches and multi-disciplinary un~
g
Iture fil e models are gomg to possess the attributes needed in
90
Chapter 4
pi
'1.5
Landscape Simulation in Fire ~Ianagcment
the future. In addition. modeling projects must involve collaboration across
multiple disciplines to ensure that current science is appropriately integrated
into model algorithms.
At the center of future simulation research is a need for comprehensive
data to run and validate future models. The balance of data needs versus
model advancement reRects a critical imperati\"e for cross~fertilization between
field ecologists. who provide data and equations to modelers. and modelers}
who must then integrate that knowledge to provide descriptions of phenomena at different spatial and temporal scales. It is critical that extensive field
programs should be intimately integrated with simulation efforts to ensure
that sufficient parameter and validation data arc measured for model applications. Temporally deep. spatially explicit databases created from extensive
field measurements are needed to quantify input parameters. describe initial
conditions, and provide a reference for model testing and validation. especially as landscape fire models are ported acroSS large geographic areas and
to new ecosystems (Jenkins and Birdsey 1998), For example, Hessl et al.
(2004) compiled a number of ecophysiological parameters for use in mechanistiC ecosystem models, which has increased parameter standardization and
decreased the time modelers spend on parameterization. :\ew sampling methods and techniques for collecting the data are needed to ensure that the right
variables are being compared at the right scales. Field data useful in simulation modeling should be stored in standardized databases, such as F1RE\IOt\
(Lutes et a!. 2006), and stored on web sites so that thcy are easily accessible
for complex modeling tasks. Last. new instruments are needed to quantify
important simulation variables such as canopy bulk density: to initialize and
parametcrize firc behavior models (Keane et al. 2005),
Model validation research is also critically needed to ensure that future
models axe behaving realistically and accurately (Rykiel 1996; Gardner and
Urban 2003), There are many ways to validate models, The most preferable one is direct comparison of model results with field measurements in the
proper spatial and temporal context. :\ext. intermediate results from model
algoritlmls or modules can be compared against appropriate field data (Oderwald and Hans 1993), Results from complex sensitivity analyses can also be
used to evaluate model behavior and to compare behavior against measured
data, expert opinion, or modeler experience (Cariboni et al. 2007), Comparative modeling exercises or ensemble modeling is also another potential lool
for validation where several models are applied to the same area (e.g. stand
or landscape) under the same initial conditions with comparable parameterizations (Cary et a!. 2006), Results [TOm ensemble modeling can be used to
evaluate the sensitivity. accuracy, and validity of model results and to explore
new ecosystem rcsponses (Cary et nl. 2009), ilfodel outputs can also be evaluated by a panel of experts to estimate the degree of accuracy and realism
I
(Keane et a1. 1996),
New quantitative methods are also needed to evaluate the uncertainty
Research and management needs and solutions
91
around model predictiol1& (Gardner and rban 20
. .
analysis methods are needed to sup pOl' t va I'd'
03), StatIstIcal
and
I allon compansons
a dtests 't'
ity
that account for Sl)atial an d t empma
' I autocorrelatio n d sensl
, tests
'ft
t
'IVslgm cance (~Iayer and Butler 1993) _C ntlca
" I to testing
_ and \'a1idat'
n an estd,or
I
is an assess.ment of whether the internal com lexit' of t h ·
1l1~ m~ e 5
tually malllfested in results (Cary et a1. 200~ anito bot:model deSIgn IS acestnnate of uncertainty (Klcijnen et al, 1992) illodeler
d compleXIty m an
that compare the variance and trend of'
I't'
need
slmu a IOn resu ts to thestatistIcal
x
d tests
e pecte outcomes from model algorithms (O':\''-Il 1973) TI .
algorithms and software to test the I~odeI ovC!.. Its
' 1C) a.lso need both statistical
entIre range f
I' I 'I'
1"
't',
I
.
0 app 'Ica t')1 ity
and creale response surfaces for variolls
c
11 Icl coneI'ItiOns
paramet
and scenarios. There are also needs fa'I a rno d e I'lIlg SCIence
. ' resea)
enza IOns:
I
where new modeling approaches . met!1od s. an d protocols are devcl
rc 1 agenc
I ta
ensure
, It Ilat
d models are used correctlv
" by fi.Ie management Optim opec. 0
and
shapes
are
needed to define t
. .
lim SllllUatlon
an
scape
size
I
future simulation projects (Karau and l{ eane 2007) andheprope
spattal context
T b 'for
periods must be determined to ensure that
'
r eqlll I ratIon
' managers lllCorporate meaningful
results (Pratt et al 2006) Th
for stochastic mod~ls mus; be ~:~;:~~;,ate num, belr of simulation replicates
.
r
ong Wit 1 t 1e appropriate sin ul t'
time
lor
1 a Iond
' dspans
'
I creating fire regime ma ps (I{ eane et al ?OO?) ~Iethod
gul es ,or se eding the most appropriate m d I ~
,- -"
s an
are also needed.
0 e or a management application
,5
There is a need for future modelin u
d
.
code that is efficient. fast. and useful t 0 en eavOl s to create programming
many purposes and appli~ations Ther 0 ~anagemellt and o~her modelers for
provide challenges for
•
oPtil11all~odel Je:~;I~~~l:); ~:~I~;:;llllll1g concerns that
~~O~~~;'~~~~':I:t~~;i~:,~t~:~ity to compilc the modei on many machines
• ~~::ula~ tieSig~, 110del functions should be built in modular form with
.
co e so t lat modelers can modify coded algorithm r. .'
.
111 another model.
s 01 II1tegratlon
'
tloutput. Easy way to enter input and un
• de
Graphicat
t d User Inter'
Jace tnpU
rs an output.
• and
Openissource
anti
integrated
d
S
d
'
posted or bl' I d rco e. ource co e IS written in modular style
• At I .
pu IS 1e lor others to use.
l!
It-threaded
executions,
Abilit.)' t 0 nm on many processors across many
computers.
• clearly
Extensive
document' ab~on,
t"
All'
defined
written code is fully documented including
t varia es and associated Ul1lts, descriptions of modules
and Lheir .
User man:~Pu and output structure, and descriptions of all functions
lished and \m~del descriptions, and design descriptions should be pu~
• Sim1l.lat,' mh~ a- ata reco~'ded for all input/output parameters.
on lStOry
inform future
s' Iretentl
'
on. AbT
I Ity to remember past simulations to
II11U at Ions.
•
.----~--------
1
References
92
Chapter 4
. must be developed to ensure efficient modelNew software technologies
' ge modular sharing. and
.
r data accesS and
stOl a .
iug building. rapid exccu lon,
t
t 1-11101ofn.r might be needed to
.
bT . 5 New compu er ec
bJ
di,"erse debuggmg a 1tlle .
. fi. 111anagement to evolve a new
.
f · 'C This may reqmre [c
support thIS new so twal .
.~.
I fi e models of the future. sug.
. I
t run and mterpret t 1C r
.'
d
capaclt)' to Imp emen . '
.
f odelinu experts wlthm an
ucstin u specialized training and fOrilling tcams 0 III
0
c
c
acrosS agencies.
4.6 Summary
'11
t' l11e to delJCnd on computer simud research Wl con II
Fire management ~l~
Heations. I-lOWC\"cr. man~r aspects nUist
lation for many ploJecls and app fl' tl f ltll'C (Table ~.3). :'dodels Will
.'
d Is to be use U in le u
incorporated m new mo e
., d'
I t s'IITIulate physical processes
challlstIc eSH!TIS t 18
~
.
· .
I
need spatially exp ICIL me
. I
I C 'I arrement-oriented Illodels mnst
-'
t' saver lUUltIp e sea es. 1\ an (:)
.
and theu mterac lOn
I I . ated for rcsearch e"llloratIons.
. df
tl
TIj,lex fire moc e s CI e
be syntheslze rom lC C O l ·
t . llect data that is useful to pa.
I
nt ficld efforts mus co
t
.,
Research anc manageme
. ) . . . r. -'
(inventory). and vahdatlOn of
rameterization (research studIes 1 mltla IzatiOn
t:e
d f simulation in fire management and
Table 4.3 Challenges and researc I1 nee s or
research applications.
~tajor user
Challellgc
Data
coHeet,iolls
llesearch need
Need sampling methods and protocols
possible directions
Standardized
databases, map creation
procedures.
database managelllcnt
technologies
~lechanist.ic
design, balancing complexit.y
with utility
)..lore
93
Landscape Simulation ill Fire !\'lanagclllent
efficient
models
Ficld research in basic
physical process,
in-
)"lodel use by
managers
rc-
quantify
~lodel analyses and
validation procedures
and technology
Develop models that
are parsimonious but.
explanatory.
develop
effective training and
);ew
programming
software. open source
code
development.
modular code design
Ensemble
modeling.
mela-modeling.
novel field sampling
techniques
Expert cadres. centers
of excellcnce, extensive
training courses and
Agee JK and Skinner CN (2005) Basic principles of forest fuel reduction treatments.
Forest Ecology and Management 211: 83-96.
Albini FA (1999) Crown fire spread rate modeling. Progress Report RJVA RMRS99525, Fire Sciences Laboratory P.O. Box 8089} Missoula, MT
SA.
Allen CD (2007) Interactions across spatial scales among forest dieback, fire, and
erosion in northern New Mexico landscapes. Ecosystems 10: 797-808.
Allen TF and Starr TB (1982) Hierarchy: Perspectives for Ecological Complexity.
The University of Chicago Press, Chicago.
Andrews PL and Bevins CD (1999) BEHAVE Fire Modeling System: Redesign and
expansion. Fire Management Notes 59: 16-19.
Araujo MB, Whittaker RJ, Ladle RJ and Erhard M (2005) Reducing uncertainty
in projections of extinction risk from climate change. Global Ecology and Bio-
geography Letters 14: 529-538.
Research
Class (FRCC) mapping tooL In: PL Andrews and BW Bntler (eds) Fuels Management - How to Measure Success. USDA Forest Service Rocky Mountain
Research Station, Portland, OR, USA. 575-585.
Bart J (1995) Acceptance criteria for using individual-based models to make management decisions. Ecological Applications 5: 411-420.
Beukema SJ, Greenough JE, Robinson DC, Kurtz WA, Reinhardt ED, Crookston
NL, Brown JK, Hardy CC and Stage AR (1997) An introduction to the Fire
mechanistic
paramet.ers
lnnovat.ive software design, better computer
References
Barrett S\OV, DeMeo T, Jones JL, Zeiler JD} Hutter \lVC (2006) Assessing ecological departure from reference conditions with the Fire Regime Condition
ventofY systems that
resources
Accurate and
realistic models
Ecophysiological
scarch,
Hcsearch and
management
fire models. Software and hardware technologies need to be developed that
facilitate efficient and rapid simnlation. New test and validation statistical designs will be needed to evaluate the reliability and nncertainty iu simulation
resnlts. And, modeling science research will need to develop suitable gnidelines for using and interpreting models. To effectively use these advances in
modeling technology, management will need to train modeling specialists to
effectively ntilize these models and interpret their resnlts. Simnlation holds
an important role in the fntnre of fire management bnt it is up to research to
develop comprehensive models that predict and explain important ecological
phenomena, and it is up to fire management to understand these complex
models so they can be used effectively in common analysis tasks.
Research
and Fuels Extension to FVS. In: MMaJ A R Teck (ed) Proceedings: Forest VegResearch
and
etation Simulator Conference. United States Department of Agriculture, Forest
Service, Intermountain Forest and Range Experiment Station, Ft. Collins, CO
USA. 191-195.
management
Brown T t, Kulasiri D (1996) Validating models of complex, stochastic, biological
1lana.g:emenl
systems. Ecological Modelling 86: 129-134.
Bunnell FL (1989) Alchemy and nncertainty: What good are models? USDA Forest
Service General Technical Report P W-GTR-232.
C'ariboni J, Gatelli 0, Liska R, Saltelli A (2007) The role of sensitivity analysis in
C ecological modelling. Ecological Modelling 203: 167-182.
workshops
_______~al!?)p~l~ic~a~[~io~nc:'~·e:!:h~ic:!.les~-----------------
ar)' G, Flannigan MO, Keane RE, Bradstock R, Davies ID, Lenihan JL, Li C,
et aI. (2009) Relative importance of fuel management, ignition likelihood, and
weather to area burned: Evidence from five landscape fire succession models.
94
Chapter 4
Landscape Simulation ill Fire
~Ianagement
References
International Journal of \oVildland Fire 18: 147-156.
Cary GJ (2002) Importance of a changing climate for fire regimes in Australia.
In: Bradstock RA, AI\II Gill and JE Vv'illiams (cds) Flammable Australia: The
Fire Regimes and Biodiversity of a Continent. Cambridge University Press,
Cambridge, UK. 26-46.
Cary GJ, Keane RE, Gardner RH, et al. (2006) Comparison of the sensitivity of
landscape-fire-sliccession models to variation in terrain, fuel pattern and climate. Landscape Ecology 21: 121-137.
Daubcnmire R (1966) Vegetation: identification of typal communities. Science 151:
291-298.
Edmonds RL (1991) Organic matter decomposition in western United States forests.
In: Management and Productivity of \"'estern-montane Forest Soils. SDA Forest Service Intermountain Research Station General Technical Report, Boise,
10. 118-125.
Ferry G\OV, RG Clark, RE Montgomery, R\V Mutch, \tVP Leenhouts, and 01' Zimmerman (1995) Altered fire regimes within fire-adapted ecosystems. U.S Department of the Interior .ational Biological Service, '<\'ashington, DC.
Flannigan MD, BJ Stocks, ~IR Thretsky, and BM WoLton (2008) Impact of climate
change on fire activity and fire management in the circumboreal forest. Global
Change Biology 15: 549-560.
GAO (2007) \oVildland fire management: Better information and a systematic pre?
cess could improve agencies approach to allocating fuel reduction funds and selecting projects. GAO-07-1168, United States General Accounting Office, V\'ashington DC.
Gardner RH, MG Turner, RV O'Neill, and S Lavorel (1991) Simulation of the scale.
dependent effects of landscape boundaries on species persistence and dispersal.
In: MM Holland, PG Risser, and RJ Naiman (OOs) Ecotones: The Role of
Landscape Boundaries in the ~Ianagement and Restoration of Changing Envi·
ronments. Chapman and Hall, New York. 76-89.
Gardner RH, DL Urban (2003) i\l lodel validation and testing: Past lessons, present
concerns, future prospects. In: CD Canham, JC Cole, and \VI< Lauenroth (OOs)
Models in Ecosystem Science. Princeton Univ. Press, Princeton, NJ USA.
Greene DF and EA Johnson (1995) Long-distance wind dispersal of tree seeds. Can.
J. Bot. 73: 1036-1045.
Hann \<\'J (2004) Mapping fire regime condition class: A method for watershed
and project scale analysis. In: RT Engstrom, I<EM Galley, and WJ De Groot
(eds) 22nd Tall Timbers Fire Ecology Conference: Fire in Temprate, Boreal,and
Montane Ecosystems. Tall Timbers Research Station. 22-24.
Hann \tVJ and DL Bunnell (2001) Fire and land management planning and implementation across multiple scales. International Journal of Wildland Fire 10:
389-403.
He H, RE Keane, and L Iverson (2008) Forest landscape models, a tool for under·
standing the effect of the large-scale and long-term landscape processes. Forest
Ecology and Management 254: 371-374.
Hessburg PF, KM Reynolds, RE Keane, KM James, and RB Salter (2007) Evaluating wildland fire danger and prioritizing vegetation and fuels treatments. Forest
Ecology and Management 247: 1-17.
Hessl AE, C Milesi, MA Wbite, DL Peterson, and RE Keane (2004) Ecophysiological
parameters for Pacific Northwest trees. General Tecllllical Report PNW-G'TR·
618, USDA Forest Service Pacific Northwest Research Station, Portland, OR.
USA.
Holsinger L, RE Keane, R Parsons, and E Karau (2006) Development of bioph)
ical gradient layers. General Technical Report RMRS-GTR-175, USDA For~1
95
JPC~e(~~7)R~~kY
MOlclllltain Research Station, Fort Collins. CO USA
Imate lange 2007 - The Phy'c IS·
B.
_.
versity Press, few York ew Yo k USA I a clence aSlS. Cambridge Un iJenkins JC a,~d R Birdsey (J998) Vali;a~ion d~ta
'.
ground biomass and net primary productiVi~as~~~slln~latl~1lmo~lels: Ab~ve­
FIA data. General Technical Report NC-212 YU~DA ~estlJnatlol~ usmg easwlde
tl"al Research Station, Boise, 10. USA.'
orest SerVice, North CenKarau E and RE Keane (2009) (in prep) B
.
.
modeling and satellite imagery Inter t~lrn f~verlty mapPing using simulation
Karan EC and RE Keane (2007) Dete ~a. Ionia doufIlal of "Vildland Fire.
rmlnlllg an scape ext.ent ~
d ·IStUI" bance simluation modell·ng La
d
E
or Succession and
.
n scape . cology 22· 993-1006
Kay CE ( 200 7) Are lightning fires unnatural? A c
.'
.'
ning ignition rates in the United Stat
j. R~nTaflson of abonginal and lightProceedings of the 23rd Tall Timber:sp. n. E I' lasters and KEM Galley (eds)
land and Shrubland Ecosystems 'I1 Ii ~~e b co ogy Conference: Fire in GrassFL. 16-28.
. a
lin ers Research Station, Tallahassee.
Keane HE, G Cary, 10 Davies. ~fD Flanni an RH G
han, C Li, and TS Rupp (200 11) A Ig.il . ardner, S Lavorel, JM LenniasSI
models: Spatially explicit models otfi
c~tlOn of I?ndscape fire succession
Modelling 256: 3-27.
re an vegetatIOn dynamic. Ecological
Keane RE, GJ Cary, and R Parsons (2003) U .
.
.
An evaluation of approaches strategies s~nf sl.mu~atJon to map fire regimes:
of \Vildland Pire 12: 309-322.
,an ImitatIOns. International Journal
Keane RE, SA Drury, E [(arau PF Hessbur
d
for mapping fire hazard and risk across g, a',l . IJ<fv( Reynolds .(2008) A method
t
fire management. Ecological Modelling 2~~:u 2~f8e scales and its application in
Keane RE and l\fA Finney (2003) The simulation ~
.
ecosystem dynamics. In: TT Veblen WL Bak
landscape fire, chmate, and
nam (eels) Fire and Global Change'. ~
er,
Montenegro, and T\V SwetAmericas. Springer-Verlag I'ew Yor~nN emeerakteuES'cosystems of the \Vestern
Keane HE TL Fr·
'
, ew lor.
A. 32-68
,
escmo, MC Reeves, and J Lon (2006) I '
..
•
across large regions for the LANDFIRE
g
a . Mapping Wildland fuels
C Fhme (eds) The LANDFIRE Proto p:o~~pe proJect. In: MG Rollins and
Loc~lIy Relevant GeospatiaJ Data for \'~~Iand ~~~t: Nationally Co?sistent and
Service Rocky tl.Iountain Research Station. 367-3~~ Management. USDA Forest
Keane HE and L Holsinger (2006) S· I · .
.
modeling and ecncnrst
. IInu ~tll1g bIOphysical environment for gradient
-~J em mappmg usmg the WXFlRE
O1en~ation and application. Research Pa er Rlvl S
program: Model docuK Ser~oe Rocky Mountain Research Stati~n Fort ~;I~TR~68~~AUSDA Forest
ea~le " L Hols ll1ger, and SPratt (2006b) S' I '
~ns, . 0,
.
ICS using the landscape fire .
.
Inm atmg hlstoncal landscape dynamTecl .
succession model LANDSUM
. 40
llllcal Report RMRS-GTR-I71CD USD : verSion .. General
K Research Station, Fort Collins, CO USA
A Forest Service Rocky Mountain
.ane HE and EC Karau (2010) E I . .
integrating fire and ec
t
va uatmg the. ecological benefits of wildfire by
K.ane HE, C Miller E S osy~ em models. Ecologtcal Modelling 221: 1162-1172
~ressJ Represent'ing c~~tatIC~! ~ ~cI(enZie, D F~lk, .and L Kellogg (2011) 'fin
Simulation models G
e , IT'lslu~ ance, vegetation mteractions in landscape
p .
. enera j ~ec 1111Cal Repo r t P \V XXX
K 8Clfic orthwest Research Station P tl d 0 I
. US Forest Service
eane HE P M .
' or an, R USA.
ical'
organ, and SW Running (1996) FIRE-BGC
h. .
process model for simulatin fi
.
- a mee amstlc ecoIogof the northern Rocky 1\1
t. g ~ SUcceSSion Oil coniferous forest landscapes
Department of Agricultur~u~ alliS. S ~ch Paper INT-RP-484, United States
, orest ervlce Intermountain Forest and Range Ex-
J
96
Chapter 4
Landscape Simulation in Fire ~I{anagement
perimcnt Station, Ogden, UT USA.
(2002) Estimating historical range and
and P Hessburg
.'
I
Keane R E I R P arsons,
d ' . Limitations of the sllTIulatlon appraae 1.
variation of landscape patch ynarTIlcs.
Ecological Modelling 151: 29-49.
Gra '. and JJ Reardon (2005) Estimating
Keane RE. ED Reillhardt, ~ Scot.t. K. i;direct methods. Canadian Journal of
forest canopy bulk density usmg SiX
Forest Research 35: 724-739'
(2007) Using simulated historical time series to
Keane RE, ~lG Rollins, and Z Z h u
55 the United States: The LANDprioritize fuel treatn~ents on lan~scapesda~~o 204: 485-502.
FIRE prototype proJect. Ecologtcal Mo d~ ng't and stability' A reconciliation
King A\V, and SL Pimm (1~~3) Complexit)~ ~~~:~i~~n Naturalist '122: 229~239.
of theoretical and empmcal results.)T~~. es for sensitivity analysis of simKleijnen JP, Ham G, Rotmans J (19~2 h ec~1q~reenhouse effect. Simulation 58:
ulation models: A case study 0 t e
2 0
410-4l7.
.
9) Overview and use of natural variability
Landres PB, ~lorgan ~,SwansOl~ FJ (199 ms. Ecological Applications 9: 117~1l~.
concepts 111 managmg ecologlc~l ~yst~p et al. (1998) Simulation ffi.od.ellO.g III
Lauenroth \VI<, Canham CD, K111Zlg ·ptvi G ffman (eds) Successes, LnmtatlOns,
ecosystem science. In: ML Pace ~nd 'S:o ~ Verlag New York, New York l
and Frontiers in Ecosystem SCience. pnnger
,
SA. 404-415.
.
eopIe and sustaining resources in fire.
Laverty L, \~/illiams J (2000) Prot~ctmg Pt
Forest Service Response to GAO
t s - A cohesive stra egy.
C
adapted ecosys err:
SOA Forest Service, \~lashington 0 . .
Report GAO/RCED 99-65 ~ D (2006) Vegetation succession modehng for the
Long DG, Loscnsky J, Beduna 1.
Technical Report RMRS-GTR-175,
LANDIFRE Prototype ProJect. Gene; earch Station Fort Collins, CO USA.
USDA Forest Service Roc~y MountRl(20~~) FIREtV10N:\ Fire effects monitoring
Lutes DC, Keane RE, Carattl JF, e~:~i1lliCal Report'RMRS-GTR-164-CD, USDA
and lllventory system. Gener~l
. h Station Fort Collins, CO USA.
Forest Service Rock~ Mou~ta111 ~ese~I~
f an~ual area burned in Canadian
Magnussen S (2008) JOll1t regional ~lmu ~ Ion 0 l' 37-53
1
forest fires. The Open Forest S.cl~nc~v~r~~t~on' Ecolo~ical Modeling 68: 21-32.
Mayer DG, Butler DG (1993) Statlstlcaeucal pt for~sts. LeaOet Number 107, ComMcArthur AG (1967) Flr~ behavlOI JO nd Ti~ber Bureau. Sidney, Australia.
.
monwealth of Austraha Forestry ~ (1996) Extrapolation problems in modehng
McKenzie 0\ Peterson DL , ~lvarado. A
.
International Journal of 'Wildland
reView.
fire effects at large spatial scales.
Fire 6: 165-176.
. wetnam TW, Peterson DL, Littell JS (2009)
McKenzie 0, Sd~mol~t D, I<eane RE, ~ t bance interactions: Building ~ research
{in press} Chmatlc ch~nge and d1~~V_XXX US Forest Service Pacific Nortb,
agenda. General Techlllcal Report 1
west Research Station, Portland, ~R 'irSAt f fire management alternatives on
Miller C, Urban DL (2000) M~de:ing ~:e~:;i~al Applications 10: 85-94.
.
Sierra Nevada mIXed-com er ores . T han RJ Midgeley GF, Fragoso J~, L~
1 eilson RP, Pitelka LF, Solomon AM, Na~
. 'nal to global plant migration Ul
K (2005) Forecastmg reglO
H Thompson
chke
,.
B"
"5' 749-760.
.
response to clImate change.. IOSClenc~ ~.
els Itr OJ Nelson (ed) RadlonuO'Neili RV (1973) Error analysIs of ecologIcal m~d .' Springfield Virgioia, USA
elides in Ecosystems. Technical Information erVlce,
'
898-908.
RP (1993) Corroborating models with model properties. Forest
Oderwald RG, Hans
a!
Ecology and Management, 62: 271-2~D (1993) CO SUME users guide. Gener
Oltmar RD, Burns 'I, Hall IN, Hanson
References
97
Technical Report PNW-GTR-304, USDA Forest Service.
pacala S\-V, Tilman 0 (1994) Limiting similarity in mechanistic and spatial models
of plant competition in heterogeneous environments. The American Naturalist
143: 222-257.
Peng C (2000) From static biogeographical model to dynamic global vegetation
model: A global perspective on modelling vegetation dynamics. Ecological .\[odelling 135: 33-54.
Peters D. Herrick JE, Urban DL, Gardner RH, Breshears DD (2004) Strategies for
ecological extrapolation. Oikos 106: 627-663.
Pratt SO, Holsinger L, Keane RE (2006) ~lodcling historical reference conditions for
vegetation and fire regimes using simulation modeling. General Technical Report
RMRS-GTR-175, USDA Forest Service Rocky Mountain Research Station, Fort
Collins, CO USA.
Quinlan JR (2000) Data mining tools See5 and C5.0. http://www.rulequest.com/
see5-info.html.
Radeloff VC, Hammer BC, Stewart SI, Fried JS, Holcomb SS, ~lcKeefry JE (2005)
The wildland-urban interface in the United States. Ecological Applications 15:
799-805.
Rastetter EB, Aber JB, Peters D, Ojima D. Burke IC (2003) Using mechanistic
models to scale ecological processes across space and time. Bioscience 53: 6877Rastetter EB, Ryan MG\ Shaver GR, et al. (1991) A general biogeochemical model
describing the responses of the C and N cycles in terrestrial ecosystems to
changes in C02, climate, and N deposition. Tree Physiology 9: 101-126.
Reinhardt ED, Keane RE, Brown JK (1997) First Order Fire Effects Model: FOFE~1
4.0 User's Guide. General Technical Report INT-GTR-344, USDA Forest Service.
Reinhardt ED I Keane RE, Brown JK (2001) Modeling fire effects. International
Journal of Wildland Fire 10: 373-380.
Rollins MG, Keane RE, Zhu Z (2006) An overview of the LANDFJRE Prototype Project. General Technical Report RMRS-GTR-175\ USDA Forest Service
Rocky Mountain Research Station, Fort Collins, CO USA.
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland
fuels. Research Paper INT-1l5, United States Department of Agriculture\ Forest
Service, Intermountain Forest and Range Experiment Station, Ogden, Utah.
Running SW (2006) Is global warming causing more, larger wildfires? Science 313:
927-928.
R)'kiel EJ (1996) Testing ecological models: The meaning of validation. Ecological
Modeling 90: 229-244.
Ryu SR, Chen J, Zheng D, Bresee MK, Crow TR (2006) Simulating the effects of
prescribed burning on fuel loading and timber production (EcoFL) in managed
northern Wisconsin forests. Ecological Modelling 196: 395-406.
Sampson RN, Clark LR (1995) Wildfire and carbon emissions: A policy modeling
approach. Tn: American Forests, The Forest Policy Center, ,"Vashington DC
1-23.
teele BM, Reddy SK, Keane RE (2006) A methodology for assessing departure
of current plant communities from historical conditions over large landscapes.
Ecological Modelling 199: 53-63.
..et?am TW, CD Allen, aod JL Betancourt (1999) Applied historical ecology:
Th Usmg the past to maoage for the future. Ecological Applications 9: 1189-1206.
ornton P, Law B, Gholz HL, Clark KL, et al. (2002) Modeling and measuring
(he effects of disturbance history and climate on carbon and water budgets in
evergreen needleleaf forests. AgricuJtural and Forest ~leteorology 113: 185-222.
------=------98
Chapt.er 4
p:
Landscape Simulat.ion in Fire ;"lanagement.
Thornton PE, Running S\V·I'VhilC
~lA (1~9~mGp~~e~:~~:ftls~r~~~~SalO~fd~~~rl~11~~
orological variables over arge regions
0
C
.
190, 214-251.
. I·
C b·· 19 surface- aud
allon:
om lJm .
.
d
Th ornt on PE (1998) Regional ecosystemI· SlnlU
ka
b t 'ee terrestnal energy an
satellite-based observations to sl.U?~ In. ges e \\ 1 1 .
~IT USA.
. b d t PhD Dissertation. Umverslty of ~lontana. :-'1Jssoula,
'
mass u ge s.
1 P I A Vos E Peterson D. I< nops J
Tilman 0, Reich PB, Phillips B, ~lenton ~, ale,
'colo 81: 2680-2685.
(2000) Fires suppression and ecosystem carbon storage. E
gy 10 '86- 1996Urban DL (2005) Modeling ecological processes across scales. Eco
g)
Chapter 5 Using Landscape Disturbance and
Succession Models to Support Forest
Management
Eric J. Gustafson', Brian R. Sturtevant, Anatoly Z. Shvidenko
and Robert M. Scheller
.
2006 · I" R
. SW (1998) Forest Ecosystem" Analysis at ~lultiple Scales
\Vanng R "I, unnmg
.
CA USA
(2nd). Academic Press, Inc, San Dleg~" t 'm T\t\f (2006) \.\farming and earlier
\,Vesterling AL, Hidalgo HG, Cayan DR, ~~~fin.a ctivity Science 313: 940-943.
spring increase in western US forest WI He a
..
I henolo
model for
\Vhit.e MA, Thornton P, Running S\V.(1997) A ~OI;~llle~~ta, PiabilitY~YGlobal Biomonitoring vegetation responses t.o lllterannua c una
IC \81'
. geochemical ~Yc1e~11'L217.26j· Whitlock C (2000) Simulating historical vari\Vlmberly ~IC, pies
'fongll r' t· tl e Oregon Coast Range. ConservatIOn
ability in the amount 0
ores 10 1
°(
Zhu B~~I~~e\~";'~7J~8ghlen D, Kost J, Chen S, Tolk B, RollinS ~~~,~~~~k.~I:
ping existing vegetation composItIon and structure. ~eneral h Station ~ort
RMRSGTR-175, USDA Forest Service Rocky 110untam Researc
,
Collins, CO USA.
Abstract
Managers of forested landscapes must account for multiple, interacting ecological processes operating at broad spatial and temporal scales.
These interactions can be of such complexity that predictions of future
forest ecosystem states are beyond the analytical capability of the human
mind. Landscape disturbance and succession models (LDSM) are predictive and analytical tools that can integrate these processes and provide
critical decision support information. We briefly review the state of the
art of LDSMs and provide two case stuclies to illustrate the application
and utility of one LDSM, LANDIS. We conclude that LDSMs are able
to provide useful information to support management decisions for a
number of reasons: (i) they operate at scale that is relevant to many
forest management problems, (ii) they account for interactions anlOng
ecological and anthropogenic processes, (iii) they can produce objective
and comparable projections of alternative management options or various global change scenarios, (iv) LDSMs are based on current ecological
knowledge and theory, (v) LDSMs provide a vehicle for collaboration
among decision-makers, resource experts and scientists, (vi) LDSr-.1s are
the only feasible research tool that can be used to investigate long-term,
large area dynamics.
• Eric J. Gustafson: Institute for Applied Ecosystem Studies, USDA Forest Service, North-
Th Research Station, 5985 Highway K, Rhinelander, WI USA. E-mail: egustafson@fs.fed.us
e ~.S. ~overnment's right to retain a non-exclusive, royalty-free licence in and to any
IS acknowledged.
Olpynght.
Chao Li
Raffaele Lafortezza
Jiquan Chen
Landscape Ecology in
Forest Management and
Conservation
Challenges and Solutions for Global Change
With 73 figures
~ -;t 'fIt i ~ Pi. jJ.
•
HIGHER EDUCATION PRESS
~ Springer
Editors
Dr. Chao Li
Canadian Wood Fibre Centre
Canadian Forest Service
Natural Resource~ Canada
5320-122 Street Edmonlon
Alberta Canada T6H 355
E-mail: Chao.Li@NRCan-R.!\iCan.gc.ca
Dr. Raffaele Lafortezza
greenLab Dept. Scienze delle
Produzioni Vcgetali
Universita degli Studi di Bari
Via Amendola I65/A 70126 Bari, hal)'
E·mail: r.Jafonezza@.agr.uniba.it
Foreword
Prof. Jiquan Chen
Landscape Ecology & Ecos)stem Science (LEES)
Department of Environmental Sciences (DES)
Bowman-Odd)' Labor:llorie..;. ~lail Stop 6O-l
Uni\'crllit)' of Toledo. Toledo
OH 43606-3390_ USA
E-mail: Jiquan.Chen@Uloledo.edu
ISBN 978-7-04-029136-0
Higher Education Press, Beijing
ISBN 978-3-642-12753-3
c-ISBN 978-3-642-12754-0
Springer Heidelberg Dordrecht London New York
Library of Congress Conlrol Number: 2010925386
©.Highcr ~duca~ion Press. Beijing and Springer-Verlag Berlin Hcidelberg 2011
Tlll~ work IS SU?Jcct to cop~'right. All rights arc resen'cd. whether the whole or part of the material is
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liable to prosecution under the German Copyright Law.
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Springer is pan of Springer Science + Business Media (www.springer.com)
Like many others. my first exposure to the science of landscape ecolog
from the book entitled Landscape Ecology published by Richard Forma
~lichel Godron in 1986. For me. this was a new and exciting way for look
the world in which we live. It was obvious to me after reading this boo
the science of landscape ecology had much to offer natural resources Illao
But it is also important to recognize that a '"landscape perspective" ha:
around for a long time in a variety of sources and in a variety of placf'~
example is a book published in 1962 by Paul B. Sears. an early ecolol
the United States, entitled The Living Landscape. In this book writt
a general audience: Sears described with great elegance why a .Ilam
perspective" is relevant (page 162):
"Compared to the noblest work of human genius: the landscape ab
offers endless variety of interest and challenge. It is more than somet h
look at: it is something to comprehend and interpret. \Ve are insepar
part of it: and it is equally a part of us. Our destinies are linked) and
Nature will assuredly have the final judgment, modern man has the po
determine whether it will be thumbs up or down.:
Aside from the gender bias that was common to that period. mode
manity indeed will be making important choices that will profoundly
our children and many subsequent generations. Those choices should be
cated on the best available scientific knowledge. The current book edited
Lafortezza, and Chen is another valuable contribution to comprehendil
interpreting forested landscapes. It represents the latest work resultin.
the bi-annual meetings sponsored by the IUFRO Landscape Ecology \V
Party (08.01.02). The strength of this book is in the fact tbat it reflec
experience and knowledge gained by scientists in 15 different countries.
provides a rich source of international literature.
It would be naive. howc\·er. to think that all we need to cure our cit
ing environmental and human problems is to do good science. Humall
to recognize what Sears stated so well in his book - "\\"e are inseparably
of it, and it is equally a part of us,"
ntil this linkage is clearly estill
l