prediction of the impact of increasing frequency of bushfire on the water resources of the forested...
DESCRIPTION
A scoping study of model options and linkages for a whole ecosystem model. The report examines how a single virtual model can be assembled from existing models, including vegetation dynamics, hydrology and biogeochemistry process models.TRANSCRIPT
Prediction of the
Impact of Increasing
Frequency of Bushfire
on the Water Resources
of the Forested Upland
Catchments of the
Murray Basin
A scoping study of model options and
linkages for a whole ecosystem model
TECHNICAL REPORT 09/1
August 2009
Page i
Produced for the Murray-Darling Basin Authority by:
Page ii
Final Report – August 2009
ISBN: 978-0-9806387-0-7
© Monash Sustainability Institute, 2009
Authors:
Terence Chan
John Langford
Ralph Mac Nally
Philip Wallis
Contributors:
David Abramson
Patrick Baker
Jason Beringer
Nick Bond
Jo Brown
Tim Cavagnaro
Colin Enticott
Dave Griggs
Christian Jakob
Phil Jordan
Nick Marsh
Kirsten Shelly
Mike Stewardson
Ross Thompson
Monash Sustainability Institute
Building 74, Clayton Campus
Wellington Road, Clayton
Monash University
VIC 3800 Australia
Tel: +61 3 990 59323
Fax number +61 3 990 59348
Email: [email protected]
Web: www.monash.edu.au/research/sustainability-institute/
DISCLAIMER:
Monash University disclaims all liability for any error, loss or consequence which may arise from you relying
on any information in this publication.
Page iii
Contents
Summary ...................................................................................................................................................... 1 1. Introduction .......................................................................................................................................... 2
1.1. Objectives of this scoping study .................................................................................................... 3 1.2. Our approach ................................................................................................................................. 4
2. Modelling capacity ............................................................................................................................... 5 2.1. Climate and weather models ....................................................................................................... 10
2.1.1. SEACI .................................................................................................................................. 10 2.1.2. ACCESS .............................................................................................................................. 10 2.1.3. Summary ............................................................................................................................. 10
2.2. Hydrology models ........................................................................................................................ 12 2.2.1. Simple rainfall-runoff models ............................................................................................... 12 2.2.2. Physically based hydrological process models ................................................................... 13 2.2.3. Modular systems.................................................................................................................. 13 2.2.4. Summary ............................................................................................................................. 14
2.3. Biogeochemistry models ............................................................................................................. 15 2.3.1. Export models ...................................................................................................................... 15 2.3.2. Biogeochemical cycling models ........................................................................................... 16 2.3.3. Summary ............................................................................................................................. 16
2.4. Vegetation models ....................................................................................................................... 18 2.4.1. Vegetation distribution models ............................................................................................ 18 2.4.2. Stand growth models ........................................................................................................... 18 2.4.3. Summary ............................................................................................................................. 19
2.5. Terrestrial biodiversity models ..................................................................................................... 20 2.5.1. Summary ............................................................................................................................. 20
2.6. Aquatic biodiversity models ......................................................................................................... 21 2.6.1. Process-based ecosystem models ...................................................................................... 21 2.6.2. Aquatic biodiversity models ................................................................................................. 21 2.6.3. Population models ............................................................................................................... 22 2.6.4. Summary ............................................................................................................................. 22
3. Linking models together ................................................................................................................... 23 3.1. Modelling systems ....................................................................................................................... 23
3.1.1. The Nimrod toolkit................................................................................................................ 23 3.1.2. Kepler .................................................................................................................................. 24 3.1.3. Interactive Component Modelling System ........................................................................... 24 3.1.4. Catchment Management Framework .................................................................................. 24 3.1.5. Ecological Modeller ............................................................................................................. 25
3.2. Data management systems ......................................................................................................... 25 3.2.1. National Data Grid Demonstrator Project (formerly PEMS) ................................................ 25
4. A conceptual framework for linking models ................................................................................... 26 4.1. Processes represented ................................................................................................................ 27
4.1.1. Climate and weather ............................................................................................................ 27 4.1.2. Hydrology ............................................................................................................................. 27 4.1.3. Biogeochemistry .................................................................................................................. 28 4.1.4. Vegetation ............................................................................................................................ 28 4.1.5. Terrestrial Biodiversity ......................................................................................................... 29 4.1.6. Aquatic Biodiversity ............................................................................................................. 29
4.2. Limitations .................................................................................................................................... 29 4.3. Issues of scale ............................................................................................................................. 30 4.4. Issues of uncertainty .................................................................................................................... 31 4.5. Issues of model integration .......................................................................................................... 32
5. Program of work ................................................................................................................................. 33 5.1. Recommended approach ............................................................................................................ 33
5.1.1. Define specific research questions and goals ..................................................................... 33 5.1.2. Select a case study catchment. .......................................................................................... 34 5.1.3. Identify data requirements ................................................................................................... 35 5.1.4. Obtain access to component models .................................................................................. 35 5.1.5. Implement grid workflows .................................................................................................... 36 5.1.6. Calibration and parameter optimization ............................................................................... 36 5.1.7. Validation, analysis and scenarios ...................................................................................... 36 5.1.8. Iterative model development ............................................................................................... 36
6. Conclusions ........................................................................................................................................ 37
Page iv
7. Glossary .............................................................................................................................................. 40 8. References .......................................................................................................................................... 41 Appendix 1 – Detailed model comparison tables ................................................................................... 47
Page 1
Summary
Comprehensive understanding of climate change and its consequences for water resources and quality at a
river basin or large catchment scale is vital to planning the future management of the Murray Darling Basin.
Changes in Australia‟s climate are causing increased uncertainty about the likely impacts at a river basin
scale, of events such as repeated bushfires, on whole ecosystems and the consequences for water resource
availability and quality. Climate change will simultaneously impact on the vegetation, biogeochemistry,
terrestrial and aquatic ecology and the hydrology of catchments. A new approach is therefore required that
takes a whole ecosystem view in understanding and predicting the impacts of climate change on large
catchments over long time periods.
In this report we outline how a whole ecosystem model can be assembled from existing component
models, including vegetation dynamics, hydrology and biogeochemistry process models, into a single virtual
model. Such a model could be driven by stochastic weather projections derived from downscaled global
climate models to make predictions about the effects of climate change on ecosystems and consequently on
available water resources. Model outputs, including predictions of habitat distribution, could be used to drive
statistical models of terrestrial and aquatic biodiversity. This approach would be capable of being used to
assess the whole ecosystem consequences of major impacts, such as large-scale bushfires, for water
resources across a large catchment.
This report presents the first steps in thinking about model choice within each component, and the
use of the latest computing techniques to link component models for the purpose of building a whole
ecosystem model. We identified six components that were considered necessary to describe a whole
ecosystem, including:
climate and weather (data as driving inputs);
hydrology;
biogeochemistry;
vegetation dynamics;
terrestrial biodiversity; and
aquatic biodiversity.
An overview of modelling is given for each component, as well as an assessment of the required
process representations. The review of component models was limited to an assessment of modelling
options, as time constraints precluded a more in-depth analysis. We do not present any recommendations
for choosing a specific model as this will depend on a more in-depth analysis of the aims of the modelling
task.
The approach to linking each of the modelling components outlined in this report utilises grid
workflows technology that can link different software models together and stream data into and out of each
component. This approach has some key advantages over other modular modelling systems, in that it can
link models written in different code together by wrapping them in scripts that control inputs, outputs and
parameters. However, while grid workflows have been demonstrated in a range of high performance
computing applications, the technology has not been applied to an ecological modelling task of this
magnitude and would require further development.
The development of a linked modelling system that can represent whole ecosystems over long time
periods in order to inform catchment-scale natural resources management is a challenging task; but one that
is feasible and has the potential to redefine the way that natural systems are understood. This approach
would be highly valuable to the agencies that manage natural resources on a river basin scale.
Page 2
1. Introduction
Since 1997, the forested upland catchments of the Murray Basin have experienced a shift to a drier and
hotter weather pattern (Murphy and Timbal 2008). In turn, these changed climate and weather patterns have
created ideal conditions for the spread of major bushfires (CSIRO 2007; Howe et al. 2005). Widespread fires
have occurred in the 2002/03, 2006/07 and 2009 fire seasons covering most of the upland catchments
feeding the Murray River (Figure 1). Indeed some areas have been burnt more than once within a short
period of time with potentially profound implications for the vegetation cover on these catchments, and
consequently the water resources derived from them.
Figure 1 Bushfire impacted areas in south-eastern Australia (inset: annual average rainfall in the
Murray-Darling Basin) (Sources: DSE, Geosciences Australia, Bureau of Meteorology).
Page 3
Repeated fires in the wet eucalypt forests could eliminate mountain and alpine ash forests if high-
intensity fires recur before the re-growth is old enough to seed (McCarthy et al. 1999). Major fires in both the
2002/03 and 2006/07 fire seasons have severely impacted succession vegetation where repeated burns
have occurred. In addition to fire frequency, a hotter and drier climate will progressively diminish the habitat
of the wet eucalypt forests and they will contract. The dry mixed species eucalypt forests in the upland
catchments will also suffer from the consequences of a hotter, drier climate. The mature trees could be
weakened by the repeated fires and loss of nutrients from the ecosystem with potentially significant
consequences for the succession vegetation, and its hydrological balance and the availability of water
resources.
In a world of changing climate, simply modelling the hydrology of these forests as they age after
infrequent bushfires will no longer be sufficient to predict the impact on water resources. Previous studies of
bushfire impacts on water yield and water quality in south-eastern Australia have focused separately on
water quality (Feikema et al. 2008) or on hydrological variables and the potential effects of fire on catchment
evapotranspiration and streamflow (Lane et al. 2007). Integrating and extending these studies and looking at
the impact of repeated events is essential, given the potential for increasing fire risk from climate change
(Howe et al. 2005).
Modelling whole ecosystems as one integrated system presents a number of significant advantages
for predicting the ecological effects of a range of climate change scenarios over large catchments for long
time periods. It will be necessary to consider time periods of over 100 years to describe the changes in
vegetation as the climate changes and the impact of repeated bushfires accumulates.
A whole system approach is also essential to understanding the effects of climate change and
consequent increased frequency and severity of bushfires on catchment hydrology aquatic ecosystems and
river water quality. Reduced water flowing to aquatic ecosystems in the Murray River, as a result of over-
allocation of water resources and extremely dry conditions, have already resulted in algal blooms, black
water events and acid drainage (Baker et al. 2000; Hall et al. 2006; Howitt et al. 2007). Climate change and
more frequent bushfires will exacerbate these problems unless steps are taken. A whole system ecological
model will allow future scenarios to be developed to inform future river basin planning on the likely availability
of water resources and the management of water quality and river health.
1.1. Objectives of this scoping study
To scope the feasibility and work involved in building a whole ecosystem model suitable for prediction of
large-scale impacts (in this case, bushfires in the Victorian uplands) on water quality, water yields and
aquatic ecology over large catchments in the southern Murray Darling Basin for long time periods.
To review the availability and utility of component models that describe a whole ecosystem
(comprising six modules: hydrology, biogeochemistry, climate and weather, terrestrial biodiversity and
aquatic biodiversity), and consequently define gaps in current modelling capability.
To review the feasibility of interconnecting component models by leveraging the existing code and
using contemporary approaches to computer workflows and model coupling. Grid Workflow systems enable
the coupling of component models into a single virtual model.
To build a conceptual model that shows which model elements are typically present in each
component (based on the six modules listed above) and to identify where linkages can be made between
common elements.
Page 4
1.2. Our approach
In this report, we present a feasibility study of the program of work necessary to build a whole ecosystem
model capable of predicting the implications of climate change and an increasing frequency of fire on water
quality, water yield and aquatic ecology of the Murray Basin. In addition, we review and report on the
availability of models covering the six components of a whole ecosystem model, the gaps in modelling
capacity, and a review of the feasibility of interconnecting the models.
We review ecosystem component models within a framework that we believe is necessary to assess
whole-of-ecosystem consequences of impacts, such as large-scale bushfires. These components include
vegetation models, biogeochemistry models, climate and weather models, hydrological models and aquatic
and terrestrial biodiversity models. We generally focus on models which attempt to dynamically represent
and simulate real physical, chemical, biological and ecosystem processes, as these will be required to
provide projections of impacts under conditions not previously observed. Note, however, that the models
considered are often hybrids, including empirical descriptions where needed for simplification or because of
data limitations. It should also be noted that where the current state of understanding is limited (and
particularly where this intersects with large natural variability, e.g. in biodiversity), and process-based models
are not available or feasible, statistical models are also considered.
We next consider the technologies available to link these component models together into a single
virtual model that allows an integrated assessment of catchment-scale impacts (e.g. large-scale bushfires)
without the need to re-write a single modelling framework from scratch. We also report on the capabilities of
grid workflow systems to manage computational load across computing grids.
Finally, we present a conceptual framework for linking each essential component model. We show
how these connect together conceptually, as well as the program of work required to link them
computationally. From this analysis emerges an understanding of model shortcomings and opportunities to
better integrate each component and finally to reach conclusions about the feasibility of developing a linked
model of a catchment ecosystem.
Page 5
2. Modelling capacity
The capacity to model ecosystem processes varies significantly across ecological disciplines. Models have
only recently begun to cross traditional disciplinary boundaries, but are still most readily classified according
to ecosystem components. We believe a minimum of six components are required to describe the
interactions necessary to create a whole ecosystem model of a large catchment capable of modelling over
long time scales. These components are listed below and are visually represented in an integrated
framework.
1. Climate and weather
2. Hydrology
3. Biogeochemistry
4. Vegetation dynamics
5. Terrestrial biodiversity
6. Aquatic biodiversity
To our knowledge, no whole ecosystem model exists that adequately combines the elements of
these six ecosystem components. To construct a new model that includes all of these elements would be
both an enormous investment of resources and would only duplicate the existing capacity to model within
each component. The modelling challenge is therefore to link existing models from each component into a
single virtual model that can be used to assess whole ecosystem impacts.
In this section (and associated tables in Appendix 1), we assess the modelling capacity in each of
the six ecosystem components listed above. This assessment includes querying model availability, capability
(i.e. what variables and processes are represented), quality (e.g. peer review, documentation availability),
spatial and temporal resolution and data requirements.
In evaluating each model under consideration we have attempted to cover some main points, such
as whether a model has been validated by reproduction and/or approximation of observed results, the
degree to which transparent testing and reporting of models has occurred, and whether a detailed
description of model structure and parameters exists (Jakeman et al. 2006). We identified a large number of
models in some modules and it was not possible to review them all. A shortlist of models for each component
was compiled for more detailed review by an expert panel, according to personal experience, known rigour,
plausibility according to previous applications and peer review, local and/or regional application within south-
eastern Australia and availability of personnel with relevant experience. The detailed criteria considered for
each shortlist are presented in Appendix 1. The following table contains a summary some of the models
assessed within each component (Error! Reference source not found.).
Data
storage
Hydrology
module
Vegetation
dynamicsmodule
Biogeo-
chemistrymodule
Aquatic
biodiversitymodule
Terrestrial
biodiversitymodule
Climate
and weathermodule
Statistical
data mining
Parameter
optimisation
Model
coupling
Page 6
Table 1 Summary of component models
Mo
del
Acro
nym
/ M
od
el
Nam
e
Pu
rpo
se
Develo
per
/ O
wn
er
CLIMATE & WEATHER
AC
CE
SS
A
ustr
alia
n C
om
munity C
limate
Change E
art
h S
yste
m S
imula
tor
Glo
bal clim
ate
model curr
ently u
nder
develo
pm
ent fo
r th
e 5
th
assessm
ent of th
e IP
CC
.
CS
IRO
; B
OM
MM
5
WR
F
The F
ifth
-Genera
tion N
CA
R /
Penn S
tate
Mesoscale
Model
(MM
5)
whic
h b
ecam
e the W
eath
er
Researc
h &
Fore
casting M
odel
Sim
ula
te a
nd p
redic
t m
esoscale
and r
egio
nal-
scale
atm
ospheric c
ircula
tion a
nd p
redic
t w
eath
er.
National C
ente
r fo
r A
tmospheric R
esearc
h (
NC
AR
), (
Lo
et
al. 2
008)
SE
AC
I
do
wn
scalin
g o
f
IPC
C 4
th
As
ses
sm
en
t
mo
dels
Inte
rgovern
menta
l P
anel on
Clim
ate
Change m
odels
modifie
d
by S
outh
East
Austr
alia
n C
limate
Initia
tive d
ow
nscalin
g
Dow
nscale
s the IP
CC
model outp
uts
for
hig
h r
esolu
tion
regio
nal (c
atc
hm
ent-
scale
) hydro
logic
assessm
ent
(rain
fall,
tem
pera
ture
, evapora
tion).
CS
IRO
; M
DB
A; B
OM
; D
CC
HYDROLOGY
Wate
rCA
ST
W
ate
r C
onta
min
ant
Analy
sis
and
Sim
ula
tion T
ool
Fle
xib
le m
odel fo
r both
quantity
and q
ualit
y o
f w
ate
r fr
om
(non
-
urb
an)
catc
hm
ents
to r
eceiv
ing w
ate
rs. U
ses S
IMH
YD
or
AW
BM
for
the h
ydro
logic
al com
ponent.
eW
ate
r C
RC
, (A
rgent et al. 2
009);
Repla
cem
ent fo
r E
2
SIM
HY
D
Sim
plif
ied H
YD
RO
LO
G m
odel
Daily
conceptu
al ra
infa
ll-ru
noff m
odel
(Chie
w e
t al. 2
002; K
and
el et
al. 2
005)
MS
M-B
IGM
OD
M
onth
ly S
imula
tion M
odel -
BIG
MO
D
Month
ly t
ime-s
tep w
ate
r bala
nce m
odelli
ng, fe
edin
g into
a f
low
and s
alin
ity m
odel fr
om
Hum
e d
am
to t
he M
urr
ay m
outh
.
MD
BA
AW
BM
A
ustr
alia
n W
ate
r B
ala
nce M
odel
Catc
hm
ent
wate
r bala
nce m
odel w
ith h
ourly/d
aily
rain
fall-
runoff
sim
ula
tion.
(Boughto
n 2
004)
Macaq
ue
M
odelli
ng c
atc
hm
ent
hydro
logy,
part
icula
rly a
fter
vegeta
tion
impacts
. D
evelo
ped a
nd a
pplie
d locally
.
(Peel et al. 2
003; W
ats
on 1
99
9; W
ats
on e
t al. 1
999)
PE
RF
EC
T (
in
CA
T)
Pro
du
cti
vit
y,
Ero
sio
n a
nd
Ru
no
ff F
un
cti
on
s t
o E
valu
ate
Co
nserv
ati
on
Tech
niq
ues (
in
the C
atc
hm
en
t A
naly
sis
To
ol)
Hydro
logic
al m
odel th
at help
s t
o d
efine t
he s
urf
ace a
nd
subsurf
ace m
ovem
ent of
wate
r and n
utr
ients
in a
catc
hm
ent,
and e
valu
ate
the im
pact of
diffe
rent
farm
ing s
yste
ms a
nd land
managem
ent str
ate
gie
s o
n v
egeta
tive g
row
th a
nd p
roductivity,
str
eam
qualit
y, str
eam
flow
s a
nd g
roundw
ate
r.
DP
I V
icto
ria; (L
ittleboy e
t al. 1
992; W
eeks e
t al. 2
008)
Page 7
M
od
el
Acro
nym
/ M
od
el
Nam
e
Pu
rpo
se
Develo
per
/ O
wn
er
BIOGEOCHEMISTRY C
AS
A (
to b
e
co
up
led
wit
h
CA
BL
E)
Carn
eg
ie A
mes S
tan
ford
Ap
pro
ach
Bio
sp
here
mo
del
Sim
ula
tes t
err
estr
ial ecosyste
m p
roduction a
nd s
oil
mic
robia
l
respiration. In
clu
des g
lob
al
so
il e
mis
sio
ns o
f n
itro
us o
xid
e
an
d c
arb
on
dio
xid
e. R
ecen
t vers
ion
s a
re c
ou
ple
d t
o a
Dyn
am
ic G
lob
al V
eg
eta
tio
n M
od
el
(DG
VM
).
NA
SA
; (P
otter
et al. 2
001; P
otter
and K
looste
r 1999;
Pott
er
et
al. 1
993)
CE
NT
UR
Y (
v4)
D
AY
CE
NT
CE
NT
UR
Y S
oil
Org
anic
Matt
er
Model E
nvironm
ent
modifie
d f
or
a
daily
tim
e s
tep
Sim
ula
te p
lant-
soil
carb
on a
nd n
utr
ient
dynam
ics f
or
diffe
rent
types o
f ecosyste
ms inclu
din
g g
rassla
nds, agricultura
l la
nds,
fore
sts
and s
avannas, capable
of sim
ula
ting d
eta
iled d
aily
soil
wate
r and t
em
pera
ture
dynam
ics a
nd t
race g
as flu
xes (
CH
4,
N2O
, N
Ox a
nd N
2).
Colo
rado S
tate
Univ
ers
ity;
US
DA
-AR
S; (P
art
on e
t al.
1988)
CM
SS
C
atc
hm
ent
Managem
ent
Support
Syste
m
Pre
dic
ts im
pacts
of
nutr
ient m
anagem
ent
on w
ate
r qualit
y.
eW
ate
r, (
Davis
et
al. 1
991),
ww
w.t
oolk
it.n
et.au/c
mss
Wate
rCA
ST
W
ate
r and C
onta
min
ant
Analy
sis
Sim
ula
tion T
ool
Support
s c
onstitu
ent genera
tion for
sedim
ent, n
itro
gen,
phosphoru
s a
nd litte
r.
eW
ate
r C
RC
,(A
rgent
et
al. 2
009);
Repla
cem
ent fo
r E
2
EM
SS
E
nvironm
enta
l M
anagem
ent
Support
Syste
m
Pre
dic
ts r
unoff a
nd t
ota
l suspended s
edim
ent
on a
daily
tim
e-
ste
p
(Vert
essy e
t al. 2
001)
Catc
hM
OD
S
Catc
hm
ent S
cale
Managem
ent
Of
Diffu
se S
ourc
es M
odel
Modelli
ng f
ram
ew
ork
that in
tegra
tes S
edN
et, e
nablin
g
calc
ula
tion o
f avera
ge a
nnual sedim
ent
and n
utr
ient lo
ads.
iCA
M,
icam
.anu.e
du.a
u/p
roducts
/catc
hm
ods.h
tml,
(Ne
wham
et
al. 2
002)
Sed
Net
R
egio
nal sedim
ent
and n
utr
ient budgets
for
river
netw
ork
s.
Spatially
accounts
for
sedim
ent and n
utr
ient sto
res, sourc
es
and f
luxes.
eW
ate
r C
RC
, C
SIR
O L
and a
nd W
ate
r, (
Pro
sser
et al.
2001a),
ww
w.t
oolk
it.n
et.
au/T
ools
/SedN
et
LA
SC
AM
L
arg
e S
cale
Catc
hm
ent
Model
Hydro
logic
al salt, sedim
ent and n
utr
ients
tra
nsport
model.
CW
R, U
niv
ers
ity o
f W
este
rn A
ustr
alia
; (S
ivapala
n e
t al.
1996a;
Siv
apala
n e
t al. 1
996b; S
ivapala
n e
t al. 1
996c)
Ro
thC
R
oth
am
ste
d C
arb
on M
odel
Sim
ula
tes t
urn
over
of org
anic
carb
on in s
oils
. C
alc
ula
tes t
ota
l
org
anic
carb
on, m
icro
bia
l bio
mass c
arb
on a
nd Δ
14C
over
tim
escale
s u
p t
o c
entu
ries.
(Cole
man a
nd J
enkin
son 1
999)
DN
DC
D
eN
itrification-D
eC
om
positio
n
Model
Sim
ula
tes c
arb
on a
nd n
itro
gen b
iogeochem
istr
y in a
gricultura
l
syste
ms.
(Li et
al. 1
992a; b)
SW
AT
(in
CA
T)
Soil
& W
ate
r A
ssessm
ent
Tool
(the n
utr
ient subm
odel in
Catc
hm
ent A
naly
sis
Tool)
Prim
arily
a n
itro
gen c
yclin
g a
nd d
istr
ibution m
od
el, a
lso m
odels
hydro
logy a
nd c
hannel ro
uting, sedim
enta
tion, cro
p g
row
th.
DP
I V
icto
ria; (W
eeks e
t al. 2
008)
(origin
ally
US
DA
)
Page 8
M
od
el
Acro
nym
/ M
od
el
Nam
e
Pu
rpo
se
Develo
per
/ O
wn
er
VEGETATION
3-P
G (
an
d
CA
BA
LA
)
Physio
logic
al P
rincip
les P
redic
ting
Gro
wth
(and t
he C
Arb
on
BA
LA
nce m
odel based o
n t
his
)
Genera
lised s
tand m
odel, f
or
rela
tively
even
-aged h
om
ogenous
fore
st or
pla
nta
tions.
3-P
G c
alc
ula
tes the r
adia
nt
energ
y
absorb
ed b
y f
ore
st canopie
s a
nd c
onvert
s it
into
bio
mass
pro
duction, m
odels
wate
r and b
iom
ass/c
arb
on (
modifie
d for
modelli
ng c
arb
on b
ala
nce s
pecific
ally
).
(Batt
aglia
et al. 2
004;
Landsberg
and W
aring 1
997)
CA
BL
E
CS
IRO
Atm
osphere
Bio
sphere
Land E
xchange
Calc
ula
tes c
arb
on,
wate
r and h
eat exchanges b
etw
een t
he land
surf
ace a
nd a
tmosphere
and is s
uitable
for
use in c
limate
models
and in the form
of
a o
ne
-dim
ensio
nal sta
nd
-alo
ne m
ode.
(Kow
alc
zyk e
t al. 2
006)
FV
S
Fore
st V
egeta
tion S
imula
tor
Com
petition-b
ased g
row
th m
odel (g
row
th d
ependent
on s
ize
and d
ista
nce o
f com
petito
r tr
ees),
inclu
des a
random
mort
alit
y
ftn, re
cru
itm
ent/new
tre
es n
eed to b
e s
pecifie
d/t
old
to o
ccur,
although c
an b
e lin
ked to a
Leaf A
rea I
ndex (
LA
I) t
hre
shold
(Cro
oksto
n a
nd D
ixon 2
005)
LA
ND
IS
S
patial fo
rest la
ndscape d
istu
rbance a
nd
successio
n m
odel.
(Mla
denoff
2004)
JA
BO
WA
Janak-B
otk
in-W
alli
s
Cell-
based indiv
idual tr
ee f
ore
st gap m
odel.
(Botk
in e
t al. 1
972)
SO
RT
IE
In
div
idual tr
ee
-based f
ore
st
gap m
odel.
(Pacala
et
al. 1
996; P
acala
et
al. 1
993)
LP
J
Lund-P
ots
dam
-Jena
A D
ynam
ic G
lobal V
egeta
tion M
odel (D
VG
M)
P
ots
dam
PIK
(should
have s
ourc
e f
or
vers
ions a
lso)
OR
CH
IDE
E
Org
aniz
ing C
arb
on a
nd H
ydro
logy
in D
ynam
ical E
cosyste
ms
A D
ynam
ic G
lobal V
egeta
tion M
odel (D
VG
M)
(K
rinner
et
al. 2
005)
Page 9
M
od
el
Acro
nym
/ M
od
el
Nam
e
Pu
rpo
se
Develo
per
/ O
wn
er
TERRESTRIAL BIODIVERSITY S
pecie
s-s
pecif
ic
an
d “
gu
ild
-
based
”
dis
trib
uti
on
mo
dels
P
urp
ose-b
uilt
sta
tistical dis
trib
utional m
odels
based o
n
bio
clim
atic e
nvelo
pes, to
pogra
phy,
and h
abitat chara
cte
ristics
(e.g
. tr
ee s
pecie
s, habitat str
uctu
re [
tree s
pacin
g, siz
e])
Many a
uth
ors
in A
ustr
alia
, and w
orld
-wid
e
VO
RT
EX
,
MA
RX
AN
,
RA
MA
S,
CIR
CU
ITS
CA
PE
Spatially
explic
it d
ynam
ic
dem
ogra
phic
models
Applic
ation o
f birth
-death
-em
igra
tion-im
mig
ration s
imula
tions t
o
specie
s‟ popula
tion d
ynam
ics, in
clu
din
g s
patially
explic
it
variation in p
opula
tion p
ara
mete
rs
Many a
uth
ors
in A
ustr
alia
, and w
orld
-wid
e
AQUATIC BIODIVERSITY
AU
SR
IVA
S
Au
str
alia
n R
iver
Assessm
ent
Schem
e
Rapid
pre
dic
tion s
yste
m u
sed t
o a
ssess the b
iolo
gic
al health o
f
Austr
alia
n r
ivers
. F
ocused o
n p
hysic
al assessm
ent
and
bio
assessm
ent of str
eam
s. In
clu
des p
redic
tive m
ode
lling
softw
are
for
macro
invert
ebra
tes.
eW
ate
r C
RC
; D
EW
HA
; S
tate
govern
ment depart
ments
Eco
log
ical
Mo
deller
S
tatistical m
odelli
ng lin
kin
g e
Wate
r hydro
logic
al to
ol outp
uts
to
ecolo
gic
al pro
cesses (
e.g
. fish s
paw
nin
g).
In d
evelo
pm
ent.
eW
ate
r C
RC
FIL
TE
RS
An a
quatic b
io-a
ssessm
ent
pre
dic
tive m
odel fo
r derivin
g
refe
rence c
onditio
ns w
ithout th
e u
se o
f re
fere
nce s
ites. M
ore
suitable
for
use in d
istu
rbed s
ites than A
US
RIV
AS
.
MD
BA
/SR
A;
(Chessm
an a
nd R
oyal 2004)
CA
ED
YM
C
om
puta
tional A
quatic E
cosyste
m
Dynam
ics M
odel
Generic a
quatic e
colo
gic
al m
odel desig
ned t
o b
e lin
ked to
hydro
dynam
ic m
odels
(e.g
. E
LC
OM
, th
e E
stu
arine a
nd L
ake
CO
mpute
r M
odel),
inclu
des b
iogeochem
ical cyclin
g a
nd
phyto
pla
nkto
n. O
ptions for
limited o
ther
bio
logic
al options.
CW
R, U
niv
ers
ity o
f W
este
rn A
ustr
alia
Page 10
2.1. Climate and weather models
The climate data and predictions most commonly utilised by the natural resources management community
in Australia are model outputs published in the 2007 Fourth Assessment Report on Climate Change by the
Intergovernmental Panel on Climate Change (IPCC). Since the release of the Fourth Assessment Report,
much work has been done on downscaling model outputs to produce regional climate and weather data.
2.1.1. SEACI
In its first operational phase (2006 – 2009), the South Eastern Australian Climate Initiative (SEACI)
addressed a range of policy-relevant climate and weather research questions, pertaining to climate variability
and drivers of climate change, in order to develop improved regional climate information for south-eastern
Australia. SEACI is managed by the Murray-Darling Basin Authority and involves the Victorian Department of
Sustainability and Environment, Department of Climate Change, Managing Climate Variability Program,
CSIRO and the Bureau of Meteorology. SEACI utilised six models from the IPCC Fourth Assessment report
as the basis of its major climate forecasting projects, selected for their ability to accurately model historical
records (Howden et al. 2008; Timbal and Jones 2008).
Among the many outputs of this program, the initiative produced daily 110-year long time series data
using statistical downscaling, based on four emissions scenarios. These were used to generate high
resolution rainfall, temperature, evaporation and water balance projections for the whole Murray-Darling
Basin, as well as specific water balance projections for catchments in the southern Murray-Darling Basin.
SEACI has also produced improved techniques for seasonal forecasting in the Murray-Darling Basin.
These techniques are used to produce probabilistic forecasts of seasonal rainfall and temperature, with
some work done to integrate these forecasts with management.
The independent mid-term review of SEACI identified that the individual project components did not
effectively interact, such that several projects researched global climate model downscaling using different
methods.
2.1.2. ACCESS
Currently under development, the Australian Community Climate and Earth-System Simulator (ACCESS) will
couple both climate and earth-system simulation to enable improved meteorological forecasting and
prediction of climate scenarios over a 50+ year timeframe. The first phase of ACCESS is to enable
meteorological forecasting for Australia. The second phase is to complete a physical global climate model
(GCM) that includes earth-system simulation; with the aim of including model outputs in the IPCC Fifth
Assessment Report. Once developed, ACCESS (and the IPCC Fifth Assessment database) with be the
model of choice for obtaining climate forecasts for south-eastern Australia.
There is potential to use ACCESS global climate outputs using downscaling models such as the
Weather Research and Forecasting (WRF) model (Lo et al. 2008), however in the first iteration of this
project, given the IPCC Fifth Assessment modelling is still being developed, we will use the output from the
IPCC Fourth review, which has already been downscaled as part of SEACI.
2.1.3. Summary
The statistically downscaled regional climate data and seasonal forecasts produced by SEACI are ideal
inputs to a whole-of-ecosystem model, as described in this report. The large research effort put into SEACI,
as well as the peer-review of project outputs gives strong incentive for utilising this climate data in this
project.
Page 11
The seasonal forecasts produced by the Bureau of Meteorology can provide up to nine months of
weather projections (rainfall and temperature) across south-east Australia with a higher degree of certainty
than downscaled global climate models.
With regard to producing a whole ecosystem simulation model, using SEACI data has the
disadvantage that there is no opportunity to input into the climate and weather forecasts. For example,
changes in water vapour flux and albedo from disturbance of forested areas, as would occur from fire, have
the potential to impact significantly on regional climate.
Page 12
2.2. Hydrology models
A large number of hydrological models have been developed for simulating the flow of water through a
catchment over the past 40+ years. A number of these have been specifically developed for Australian
conditions, and are in wide use here, with these models having more of a focus on water yield, as opposed
to the flood prediction focus of much of the rest of the world. The subset of Australian models alone remains
unwieldy, with at least 100 models developed (Boughton 2005; Ranatunga et al. 2008). However a number
of reviews (Boughton 2005; Boughton 1988; Croke and Jakeman 2001; Marston et al. 2002) have begun to
indicate that hydrological modelling is mature, and that there is a consensus that input data (rainfall
distribution) is a more significant factor than the specific hydrological model which is used, producing similar
output from a range of models (Boughton 2005), however, although a number of hydrological models have
been applied in the Murray-Darling region, a specific comparison of the results has not been made.
Three main approaches to hydrological modelling have been identified: 1) simple rainfall-runoff
modelling; 2) physically-based hydrological process modelling; and 3) modular systems. An example of each
type is discussed in more detail. The models discussed in this section are currently under continuous use
and development by the Australian hydrological modelling community.
2.2.1. Simple rainfall-runoff models
The simplest hydrological models are rainfall-runoff models that typically have only a few key input
parameters, such as precipitation, evaporation and some form of catchment parameter (e.g. permeability),
and can simulate surface runoff and baseflow (Boughton 2005). More advanced models can simulate
storages for soil moisture and groundwater, as well as interflow between stores.
WaterCAST (previously known as E2, applications and validation of E2 are thus considered here) was
developed by the eWater CRC for predicting and managing the quantity and quality of water resources in a
catchment (Argent et al. 2009). WaterCAST can use SIMHYD or AWBM for the hydrological component
(Chiew et al. 2002). SIMHYD utilises seven input parameters to simulate surface runoff, baseflow and
interflow and has previously been used to investigate climate change impacts on runoff (Boughton 2005;
Jones et al. 2006). However, these are not physically-based parameters and therefore need extensive
calibration data. The Australian Water Balance Model (AWBM) is a simple three parameter catchment water
balance model that can calculate runoff from rainfall, evaporation and a baseflow index (Boughton 2004).
Coupling WaterCAST to models outside the eWater Toolkit (described at www.toolkit.net.au)
requires rescaling of „upstream‟ weather from the spatial grid used to the subcatchment units (the basic
functional spatial unit used in WaterCAST). WaterCAST already includes a pre-processing plug-in to perform
a similar rescaling for SILO (the Australian historical meteorological database) data, and this could be used
for other weather projections (e.g. SEACI) once appropriately formatted.
The WaterCAST soil moisture component has not been reliably validated and output for the
vegetation module would need to be carefully considered. To fully couple these modules, additional code
would be needed to insert the vegetation model evapotranspiration output to replace the WaterCAST
evapotranspiration component (which relies on a simple calculation based on potential maximum
evapotranspiration and the current soil moisture). Again, rescaling would be required. Sediment (constituent)
generation from the sub-catchments is simply and empirically defined, requiring data on sediment erosion
rates and nutrient concentration in eroded soil and calibration.
Unlike most other hydrology models available, WaterCAST includes a riparian buffer filtering (of
particulates and nitrogen) component, which would be potentially useful. Additionally, catchment vegetation
models are also not concerned with this aspect of a catchment, and significant work would have be required
to adapt any vegetation models to the higher resolutions and additional processes of interest in riparian
zones. Note however, this component is largely untested.
WaterCAST (E2) has previously been used to examine the impact of fire on water quality (Feikema
et al. 2005). The 2003 pre- and post-bushfire water quality data was used to calibrate constituent generation
rates for each subcatchment unit, and these were then used to predict the long term average changes
Page 13
(increases) in loads (TN, TP and TSS) at specific points of interest. Subcatchments of 20-50km2 were used,
and detailed subcatchment rainfall data was used, but note this model application was not process-based,
and changes in runoff generation are not accounted for, nor are gradual changes such as recovery of the
catchment. Extending this application and addressing some of these limitations (in particular the lack of
changes in runoff, static catchment vegetation, etc.) would be of significant benefit to a more comprehensive
assessment.
MSM-BIGMOD (a Monthly Simulation Model with a daily timestep modification) is another key
example of a simple flow and salinity routing model that has been applied to the Murray-Darling Basin.
However, it does not model catchment runoff processes, which would be required for full interaction with the
biogeochemistry and vegetation components (Ravalico et al. 2007). Given its development and application in
the region of interest, some of the previous modelling is likely to be of use in parameterizing and validating
any integrated process-based models. Similarly, other Murray Flow Assessment Tools for floodplain and
wetland vegetation, fish, birds and algal risk are also relatively simple flow preference curves (Young et al.
2003), but some of the information used may be of use in parameterizing the process-based models
proposed here.
2.2.2. Physically based hydrological process models
More complex hydrological models include detailed representations of biophysical processes, but require
greater validation of model parameters. Such physically-based hydrological models are suited for predicting
the hydrological consequences of disturbances such as deforestation and bushfire (Lane et al. 2007).
Macaque is an example of a physically-based model, which was developed specifically to look at
water yield impacts in forested Victoria catchments, where parameterization is based on direct
measurements of catchment parameters (Lane et al. 2007; Watson 1999). Macaque is particularly suited to
the upland catchments of the southern Murray-Darling Basin where mountain ash forests dominate, as the
model was developed based on the observed changes water yield seen in the Victorian central highlands
after the 1939 bushfires (Lane et al. 2007). Macaque has been used to examine the long-term (250 year)
impact of the 2002-2003 bushfires on catchment water yield, however, this application did not look at
sediment or nutrients (Watson et al. 1999).
One of the core parameters represented in Macaque is leaf area index (LAI), which is a measure of
leaf surface area for a given area of ground surface and is used to determine retention of rainfall and
potential evapotranspiration by forests of different age structure. LAI can be physically measured using plant
canopy analysers, hemispherical photography or remotely using satellites, in order to validate this model
parameter (Watson et al. 1999). LAI would be a primary intersection when coupling hydrology with the
vegetation module.
2.2.3. Modular systems
The Catchment Analysis Tool (CAT) links individual component models into a single hydrological model
(Weeks et al. 2008). CAT has subsequently been utilised as the basis of the Catchment Modelling
Framework (CMF), which is described later in section 3.1.4. The processes represented in CAT
predominantly include land use (crop growth, forest growth, grazing) and hydrology (water balance,
groundwater).
The hydrology component of CAT is based on the Productivity Erosion Runoff Functions to Evaluate
Conservation Techniques (PERFECT) (Littleboy et al. 1992), with additional water balance improvements
adapted from the Soil Water and Assessment Tool (SWAT). CAT also includes an option to link surface and
subsurface flow modelling with a spatially distributed groundwater model, MODFLOW (McDonald and
Harbaugh 1988; Weeks et al. 2008).
The authors of CAT note that the performance of the model (and this type of landscape model) is
constrained by the availability of suitable validation data (Weeks et al. 2008). This is a serious concern for
the approach described in this report, and is discussed in more detail in section 4.2.
Page 14
The Catchment Modelling Toolkit is another collection of individual models with some potential for
linkage, with a strong hydrological component, including WaterCAST as described in section 2.2.1 (Argent et
al. 2009).
2.2.4. Summary
The hydrological models described use core hydrological parameters such as rainfall and evaporation in
order to model runoff for a given catchment area. Beyond these basic functions, for which many of the
models are likely to produce similar outputs, some models represent more complex physically-based water
balance concepts for different forest types or for agricultural applications. Of the three models described,
WaterCAST (E2) has the most published applications in Australia. Additionally, the previous application of
WaterCAST in examining fire impacts provides an analogue for large scale modelling of bushfire impacts on
hydrological parameters. Although Macaque has been applied for longer term water yield impacts, overall it
has less testing and general validation. Additionally, integration of WaterCAST with vegetation models will
allow similar modelling to the Macaque study to be performed. CAT may be the most difficult of the models to
evaluate, although many of its components are based on well validated models, the overall system does not
appear to have been peer reviewed. The fact that it already integrates a number of modules of interest
(hydrology, biogeochemistry, vegetation) could be a significant benefit and is discussed further in the section
on component-based modelling systems.
Page 15
2.3. Biogeochemistry models
Most models do not solely simulate biogeochemistry (Ciais et al. 2001; Moldan 1994). Models including
biogeochemistry are usually 1) part of hydrological models, and focused on nutrient transport and export to
the waterways (e.g. E2/WaterCAST), or 2) vegetation models focused on yield/growth of
vegetation/agriculture/forests (e.g. CENTURY). Most vegetation models include at least one potentially
limiting nutrient, and many of the hydrology models discussed in Section 2.2 include transport of nutrients
through the catchment. Some of these models are discussed in more detail below, with additional details in
Table 6 and Table 7.
Although there is a general consensus that carbon and nitrogen are the minimum requirements for
examining ecosystem nutrient impacts, phosphorus is also increasingly regarded as necessary, particularly
when the impact on aquatic systems is considered (Townsend et al. 2008). The level of detail needed to
represent these nutrients at an appropriate level also needs to be considered, and depends on the aims of
the modelling exercise. In terms of a whole-of-ecosystem model, it is appropriate to model total N and P only
(as some models do), given the importance of inorganic forms to primary production in the catchment and in
the waterways. There remain many models which examine the breakdown of organic matter and cycling of
nitrate/ammonium/phosphate. Although not intended to immediately couple fully with the climate model,
nitrous oxide and methane generation may also be of interest in this module.
Our understanding of biogeochemical cycling in the soil is quite well developed (Townsend et al.
2008), but different models place their emphasis on different parts of this cycling, representing processes at
different levels of detail. Weathering of bedrock and soil sediments is a primary source of phosphorous, while
atmospheric carbon dioxide and nitrogen fixed by plants and the primary source of carbon and nitrogen in
terrestrial ecosystems. Further cycling within the system occurs as organic matter (mainly plant material) on
and in the soil decomposes. This organic matter is generally taken as being composed of 1) easily decayed
matter (typically labelled as labile, fast, active, etc.), and 2) matter which is less easily broken down (typically
labelled as resistant, refractory, slow or passive). These decompose at different rates according to first order
kinetics, and turn into CO2, inorganic nutrients (primarily NH4, NO3 and PO4), microbial biomass, humus, and
very resistant organic residue (inert/passive). Temperature, moisture and soil texture affect soil nutrient
processes. However, quantification of these processes is difficult (not least because of heterogeneity).
Nutrients are rarely modelled conservatively, and uptake and partitioning of nutrients by vegetation is usually
quite simply and empirically represented.
Quantification and modelling of the link from nutrients in the catchment to waterways is especially
poor, particularly of dissolved nutrient (and sediment) removal in riparian zones (Drewry et al. 2006).
2.3.1. Export models
There are a number of models which look at export of nutrients from the catchment to the
waterways. These include: CMSS (the Catchment Management Support System), which is essentially a
database of nutrient generation rates for different land uses and calculates nutrient loads from the entire
catchment (Marston et al. 1995). It is an empirical model and does not model hydrology or nutrient cycling
processes. CatchMODS (Catchment scale Management Of Diffuse Sources) has a finer spatial resolution,
capable of resolving subcatchments, but it simulates TN and TP only, and has some significant
limitations/assumptions (e.g. TP is only transported adsorbed to sediment), and a coarse temporal scale
(annual only) (Newham et al. 2002). EMSS (the Environmental Management Support System), models daily
loads at a subcatchment scale, however, there are apparently problems at this temporal resolution, and
aggregating the daily output into monthly loads is recommended (Drewry et al. 2006).
The E2/WaterCAST “constituent generation” component uses empirical relationships to predict
nutrient loads based on past data and generation relationships. It should also be noted that WaterCAST
does include a component for nutrient/sediment removal by riparian buffers, but this is untested. Drewry et
al. 2006 discuss some of the complications associated with modelling riparian processes from a
biogeochemical perspective (e.g. limited decreasing effectiveness with time/lifespans, dissolved vs.
suspended forms, N vs. P).
Page 16
SedNet is another model in the Catchment Modelling Toolkit. It was developed by CSIRO Land and
Water and supported by the CRC for Catchment Hydrology. It creates nutrient (nitrogen and phosphorus)
and sediment budgets from catchment to waterway (Prosser et al. 2001b), explicitly representing catchment
erosion processes (e.g. hillslope erosion vs. gullying) and also accounting for sediment deposition on
floodplains and within the stream network. However, although daily loads can be disaggregated from the
mean annual timestep (and annual budgets produced), the developers actually recommend budgets are
averaged over long time periods (20 years) to identify spatial patterns and long term trends and impacts, so
daily outputs would need careful validation and interpretation before passing to another model.
2.3.2. Biogeochemical cycling models
RothC, the Rothamsted Carbon model, was originally developed for arable soils, but it has now been
applied to grasslands and forests, however this focuses on carbon cycling only and uses an annual timestep,
which is not at an appropriate temporal resolution for simulation of whole ecosystem processes (Coleman
and Jenkinson 1999). DNDC is a denitrification-decomposition model, which models in detail the carbon and
nitrogen cycles, however it does not include phosphorus (Miehle et al. 2009).
The Soil and Water Assessment Tool (SWAT) is in use by the DSE, and is the basis for the
biogeochemistry module in CAT. Carbon fixing via primary production is modelled for different crops,
pasture, forest and native vegetation. Carbon allocation occurs to live, senescing, dead, litter, above and
below ground pools. Four primary nitrogen processes are modelled, mineralisation, nitrification, volatilization
and denitrification. Two inorganic pools (nitrate and ammonium) and three organic pools (fresh, active humic,
stable humic) are used. Phosphorus is not currently modelled. Additionally, as with most vegetation models,
growth does not appear to be linked to explicit uptake of nutrient content in the soil and instead uses a „soil
fertility‟ parameter which needs to be calibrated for different sites and vegetation types (e.g. in the 3PG
submodel).
CENTURY is a general plant-soil-nutrient dynamics model with components for soil organic matter,
the water budget, grassland/crop production and forest production, and was developed by the Natural
Resources Ecology Laboratory at Colorado State University (Parton et al. 1988). It is freely available
(downloadable online), and has been used widely (including sites in Victoria, South Australia and
Queensland) for different soil and vegetation types. It simulates C, N and P (as well as S), and a newer
version, DAYCENT, has been adapted to use a daily rather than original monthly timestep. It has been
applied at a range of resolutions, down to 1kmx1km and daily timesteps). However, the quite detailed
process representation requires a large number of parameters to be defined.
2.3.3. Summary
Conceptually, biogeochemical models take inputs (from atmosphere and bedrock), cycling (via weathering,
leaching, uptake and return by plants/organisms in the soil) and provide outputs (partitions of nutrients within
the system and export back to the atmosphere and to the waterways) of nutrients from catchment soils, as
affected by parameters such as moisture and temperature. The models differ with regard to which cycles are
regarded as important and are thus represented, how empirically they are represented and the amount of
detail used. A particular difference is the detail with which vegetation is modelled, having significant impact
on nutrient inputs (fixing) from the atmosphere, uptake, and cycling.
It should be noted however, that reviews by (Drewry et al. 2006) and (Letcher et al. 2002) indicate
that modelling of the physics of nutrient export from catchments may be inappropriate for Australian
catchments where data is more sparse. Some of the more complex models require significant
parameterization and calibration (e.g. LASCAM, requiring 18 parameters for N alone, 11 for P, and 6 for
sediment). Simpler empirical models of export may produce more accurate results, however this may be of
less use when examining future scenarios (outside the observations of historical data), and in research into
the processes of interest for management.
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Additionally, many of the more sophisticated developments in modelling of nutrient processing in
catchments are not yet incorporated into the general models, despite showing significant improvements e.g.
for catchment DIN uptake (Wang and Linker 2006).
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2.4. Vegetation models
A large range of vegetation models have been developed, with the primary objectives of: 1) modelling
projections of vegetation distribution spatially; and 2) modelling growth behaviour within vegetation stands,
aspatially (Porté and Bartelink 2002). Many of these models have been developed for plantation industries to
predict vegetation growth/yield for productive forestry.
The models under consideration are generally “hybrid” models, being primarily process-based, but
combined with empirical representations (Miehle et al. 2009). This is generally necessary due to measured
data being very limited for many of the detailed processes of interest (e.g. with allocation of biomass to
different parts of each plant).
Recent modelling has also combined a process-based stand model with a mechanistic model of fire
spread, but this requires a large amount of parameterization (Perry and Enright 2006) and significant
computational power (He and Mladenoff 1999) which would only be exacerbated with further model coupling.
Modelling fire impacts on vegetation as a series of „disturbances‟ impacting forest composition and age
structure, as has been done for forests in China and the USA, may be more practical (He et al. 2008). Areas
that are not yet well addressed by modelling include the impacts of fire on understorey vegetation, and also
where low- and moderate-intensity fires inflict varying degrees of damage to general vegetation processes
involving water and carbon (Beringer et al. 2002).
2.4.1. Vegetation distribution models
Vegetation distribution can be modelled at either a broad landscape scale or at a finer plot scale (Perry and
Enright 2006). We discuss one landscape-scale model, LANDIS, and two plot-scale models, JABOWA and
SORTIE.
The LANDIS model represents vegetation disturbance and succession on a landscape scale, using a
cell-based grid with each cell containing information on tree species age classes, as well as spatial process
such as seed dispersal and fire (Mladenoff 2004).
JABOWA (acronym derived from author‟s names) is a spatial forest simulation model, originally
designed for use in the Hubbard Brook Ecosystem Study, now with many derivatives for local forest
simulation applications (Botkin et al. 1972; Bugmann 2001). JABOWA contains three main sub-routines for
modelling tree growth, death and establishment, which use cell-based „plots‟ as the spatial unit. While the
model represents stands of trees spatially across a landscape, there is no interaction between the patches
(Bugmann 2001).
SORTIE is a spatial model (derived from JABOWA) that represents long-term dynamics of forest
communities using mechanistic sub-models of individual tree growth and competition (Pacala et al. 1996;
Pacala et al. 1993). Sub-models include growth, mortality, recruitment and resources, which together make
up a population dynamic model. SORTIE differs from JABOWA in that individual trees each have a unique
spatial location, rather than using spatial cells. This is important, as one of the main driving factors of
SORTIE is competition for light; however, this comes at a high computational cost (Bugmann 2001).
2.4.2. Stand growth models
Stand growth models represent forest growth dynamics aspatially within stands. Heavy calibration may be
required for different forest types; for example, lots of work has been previously done on Eucalyptus globulus
(Miehle et al. 2009), while there has been relatively little done on remnant forest. A review of forest
succession models provides useful background information on the models described in this section (Taylor
et al. 2009).
The Physiological Principles Predicting Growth (3PG) model is a generalised stand model, which
can be used to estimate carbon production from photosynthetically active radiation (PAR) received at the
forest canopy, stand age, soil moisture and atmospheric vapour pressure (Landsberg and Waring 1997).
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3PG needs to be parameterised for specific species and is intended for relatively even-aged homogenous
forests or plantations (Landsberg and Waring 1997; Nightingale et al. 2008). It is included as part of the
Catchment Assessment Tool (other components of CAT are described in other sections).
The Carbon Balance model (CABALA) is a modification of 3PG developed by CSIRO, used to
estimate the tree growth and carbon sequestration in plantations and managed forests. Model inputs include
rainfall, temperature, salinity, water table depth and tree species data, which CABALA can use to estimate
biomass production, carbon sequestration, nitrogen content and canopy height of trees in plantations and
forests (Battaglia et al. 2004).
The Lund-Potsdam-Jena (LPJ) model is a dynamic global vegetation model that represents large-
scale vegetation dynamics and land-atmosphere exchange of carbon and water (Sitch et al. 2003). LPJ uses
a modular framework to link vegetation dynamics with land-atmosphere interactions. LPJ defines plant
functional types to generically represent the different vegetation types found globally.
The CSIRO Atmosphere Biosphere Land Exchange (CABLE) model is a land surface simulation
model that can be used independently „offline‟ or in conjunction with a GCM „online‟ (Kowalczyk et al. 2006).
CABLE represents soil, vegetation and atmosphere interactions and can be used to calculate carbon, water
and heat exchange and will eventually be implemented in ACCESS.
The Forest Vegetation Simulator (FVS) is a stand-based vegetation model that can be used to
predict individual tree growth and mortality after harvesting (Crookston and Dixon 2005).
The Organizing Carbon and Hydrology in Dynamical Ecosystems (ORCHIDEE) model is a dynamic
global vegetation model used to simulate surface-vegetation-atmosphere interactions (Gerten et al. 2008;
Krinner et al. 2005).
2.4.3. Summary
The available vegetation models cover a wide variety of spatial scales and processes, many of which could
potentially be used in an integrated modelling framework. Spatially explicit models are of most interest for
landscape-scale modelling and models exist that can do this (e.g. LANDIS). However, many of the stand
growth models can be interfaced with geographic information systems (GIS) to produce spatial vegetation
models that can, at least, represent vegetation processes within cell-based landscape models. Individual
tree-based models, such as JABOWA and SORTIE, while designed for small plot sizes, could potentially be
used on larger scales if high performance computing resources are applied to the task.
At the other end of the scale, vegetation models designed to model carbon dynamics and biomass
production on global scales, such as CABLE, can be applied to landscape-scale simulation to produce
coarse predictions of vegetation distribution and composition. These have the advantage that they are tightly
coupled to global climate models, thus ideal for climate change research. However, the coarse-scale outputs
of such models would not be of much use in modelling terrestrial biodiversity (as habitat input data) or for
catchment-scale hydrological modelling using models like Macaque.
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2.5. Terrestrial biodiversity models
As with the other modules, there are many models available for examining terrestrial ecology (and
specifically biodiversity), but as with the vegetation modelling (or even more so), there is a lack of
convergence of accepted techniques and process-representation for simulating wildlife dynamics (Shifley et
al. 2009).
There are two major forms of terrestrial (animal) biodiversity modelling. The first deals with modelling
of species‟ distributions based on bioclimatic envelopes, topography, soil types (as a surrogate for site
productivity) and habitat characteristics (Ferrier et al. 2002). There are literally thousands of such models,
but little overall consensus about which statistical methods should be used and how validation is to be
conducted (Elith et al. 2006; Fleishman and Mac Nally 2007). A systematic approach to this is needed
(Thomson et al. 2009). Some work uses guilds – species that use similar resources (food, nesting sites, etc.)
– as the basis for models because often there are few data for many species of conservation concern (Mac
Nally et al. 2008).
The alternative approach is one that focuses on building spatially explicit demographic models.
These typically aim to identify whether a particular species, usually represented as a metapopulation, is likely
to persist given the spatial pattern of habitats of various value to the species, birth and death rates, and rates
of movement across the landscape. Additional variables include simulated threats (e.g. habitat degradation,
hunting) and species support (e.g. augmented food). Models include VORTEX, MARXAN and RAMAS. A
combination of the two approaches is one that uses raster-based GIS data to infer metapopulation
persistence (Drielsma and Ferrier 2009), although whether such models can be validated is at this time
unclear.
Being more specific about the goals of this module is essential for the suggested model
development. We envisage attempting to make general predictions about change in biodiversity over
decadal timescales on the catchment scale and gain insight into the theoretical basis and representation of
system processes, to more solidly define what we understand about the system and identify the gaps and
uncertainties in that knowledge, and finally to gain a better idea of the key requirements for long term
monitoring to improve our knowledge in this area.
Incorporation of fire impacts is likely to require the more process-based explicit demographic models
to incorporate repeated disturbances and resetting of populations, superimposed on habitat suitability.
2.5.1. Summary
Terrestrial biodiversity models take inputs (from the climate module, temperature and rainfall), from
the biogeochemistry module (for soil nutrients as a proxy for vegetation quality), from the vegetation module
for vegetation characteristics (e.g. stand area/extent, age structure, size of trees, connectivity, etc.) to give as
outputs, abundance of indicator species or a measure of biodiversity. There is currently little consensus in
this area, and some development of the goals of this module and the modelling required to meet these will
be necessary. A thorough review of current terrestrial biodiversity modelling is recommended as a first step.
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2.6. Aquatic biodiversity models
Certain parts of the state of modelling in aquatic ecology are relatively advanced. Previous discipline-based
models in the fields of hydrodynamics, nutrient cycling and eutrophication (Reckhow and Chapra 1999), and
biology (in particular, primary production and algal processes), have given way to relatively sophisticated
models integrating these processes within aquatic ecosystems (Robson and Hamilton 2004). However,
modelling of higher ecosystem trophic levels remains limited.
In this section we look at two approaches: 1) the process-based integrated biophysical approach; 2)
a statistical approach looking at measures of biodiversity; and 3) population models.
As mentioned in §2.3, although sources of nutrients from the catchment to the waterways are known
(e.g. P input to waterways is often primarily from bank and gully erosion (Drewry et al. 2006)), quantification
is difficult and modelling of nutrient inputs can be poor. Both inorganic N and inorganic P can be the limiting
nutrient in freshwater/estuarine systems. Land use associated with nutrient loss to waterways is summarised
in the Nutrient Data Book (Marston et al. 1995).
2.6.1. Process-based ecosystem models
The process-based integrated biophysical approach is used by many models such as PROTECH (Reynolds
et al. 2001), CE-QUAL (Wlosinski and Collins 1985), SALMO (Recknagel et al. 1995), and ELCOM-
CAEDYM (Robson and Hamilton 2004). We use ELCOM-CAEDYM (the Estuarine and Lake Computer
Model with the Computational Aquatic Ecosystem Dynamics Model) as an example of this approach, having
been developed in Australia, and incorporating a high level of ecosystem detail. Some additional details on
ELCOM-CAEDYM are included in Table 11.
Hydrodynamics, nutrient cycling and phytoplankton dynamics are simulated, with additional options
for zooplankton, macroalgae, and sediment biogeochemical interactions, however higher trophic levels and
populations of interest such as macroinvertebrates and fish are not simulated. As noted with detailed models
in other modules, the level of detail involved requires a large amount of parameterization and calibration, and
relatively large amounts of processing power. It should also be noted that the instream hydrodynamic part of
the modelling (ELCOM) is modular and CAEDYM has previously been coupled with a range of 1-, 2-, and 3-
dimensional hydrodynamic applications up to very high resolution spatial scales (metre scales). It should be
possible to couple simpler river routing models already specifically applied to the Murray-Darling Basin such
as MSM-BIGMOD (Monthly Simulation Model with a daily timestep modification). Alternatively, such previous
modelling may be of use for comparison and validation of flows or salinity transport.
2.6.2. Aquatic biodiversity models
A primary aquatic biodiversity approach used in Australia is AusRivAS, the Australian River Assessment
Scheme (Reynoldson et al. 1997). This scheme compares the modelled distribution of invertebrates
calibrated in so-called pristine conditions with the distributions in equivalent disturbed environments. This
approach contains some difficulties in lowland areas of interest where pristine reference sites are often not
available. There are also problems in using this as a predictive tool, either under a climate change scenario
where reference sites will also be affected, or for fire impact scenarios, where the areas affected may be
skewed toward the pristine reference sites. More recent invertebrate distribution models, “Filters”, have been
developed for south-east Australia to address some of these difficulties (Chessman et al. 2008; Chessman
and Royal 2004). These models use the tolerances or preferences for specific environmental factors (e.g.
climatic, geomorphological and hydrological factors) as filters for the potential macroinvertebrate taxa
inhabiting a certain site, identifying the range of taxa which might be expected at the site under natural
conditions.
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2.6.3. Population models
Stochastic population models for species of interest (or indicator species) such as the Trout Cod
(Maccullochella macquariensis) and Murray Cod (Maccullochella peelii peelii) have also been developed,
using limited ecological data and understanding of temperature preferences, fecundity, spawning behaviour,
age-specific survival and density dependence given variations over time (Todd et al. 2004; Todd et al. 2005).
However in models such as this, there is usually limited validation of the faunal abundances due to limited
data, although (as for the cod models) there can be a rigorous analysis of model behaviour/sensitivity
including plausibility of outcomes.
2.6.4. Summary
The aquatic ecology models described examine inputs from the climate module (temperature and rainfall),
from the combined hydrology, biogeochemistry and vegetation modules (for runoff, evaporation, sediment,
nutrient and organic matter inputs) to give as outputs: aquatic variables (flow, nutrients, sediment), risk of
eutrophication/algal blooms, abundance of indicator species or a measure of biodiversity. However, there is
a significant gap between the process-based simulation of aquatic conditions and ecological/biodiversity
measures of interest that is currently spanned by statistical/empirical relationships. Two models may be
required in this module to cover the range of impacts in the aquatic system.
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3. Linking models together
The ability to link separate models together into a single „virtual‟ model is crucial to the development of a
whole ecosystem model. There is increasing development of “land surface models” covering aspects of
hydrology, biogeochemistry and vegetation, as these are strongly linked components of the system
(Abramowitz et al. 2008). In this section, we outline a number of systems for model integration, as well as
data management systems.
3.1. Modelling systems
3.1.1. The Nimrod toolkit
The Nimrod toolkit is a parametric modelling system, which can automate the running of software models by
collecting and staging data, running and monitoring experiments and collecting outputs. The scheduling
features of Nimrod allow models to be run on local computer networks and distributed across global
computer grids (Abramson et al. 2000).
The Nimrod toolkit was initially developed by the Distributed Systems Technology Centre (funded
through the Australian Research Council‟s Cooperative Research Centre program) and its continued
development is led through Monash University‟s Faculty of Information Technology.
The Nimrod toolkit can perform parameter sweeps (Nimrod/G), search the parameter domain using
non-linear optimization algorithms (Nimrod/O) or can enact fractional factorial design (Nimrod/E). The most
relevant tool for this assessment is Nimrod/K, based on the Kepler system (described in the next section),
which can link different models together, optimize model parameters and spread computational load across
global computing grids.
3.1.1.1. Nimrod/G
Nimrod/G is a version of the Nimrod parametric modelling system that can take advantage of grid computing
resources. Essentially, this version can distribute modelling tasks across multiple high performance computer
resources across the global computing grid.
3.1.1.2. Nimrod/O
Nimrod/O is a part of the Nimrod toolkit that uses a range of parameter optimization algorithms, which can be
used to find parameter sets that give rise to a series of observed results (Abramson et al. 2001; Abramson et
al. 2006). Parameter optimization can be computationally intensive, as a large combination of parameters
can result from just a few model variables. As Nimrod/O is grid-enabled, different sets of parameter sweeps
can be sent to clusters of processors on the grid, meaning that parameter optimization can be performed
much more rapidly. The parameter domain (i.e. the full range of available parameters) can be specified and
constraints can be set to define „soft‟ or „hard‟ limits to the range of parameters. A range of optimization
algorithms are used to cover the parameter domain in different ways. Some will sample throughout the whole
domain at a given resolution, while others will sample an iteratively finer grid around the best point from
previous sweeps (Abramson et al. 2006). Other search methods include non-linear techniques and genetic
algorithms, with the potential to add custom search algorithms. The user can also prioritize the model output
of interest, so that the parameter set can best describe certain model outputs.
3.1.1.3. Nimrod/E
When running a set of models using a large number of input variables, covering the full set of possible
combinations (i.e. full factorial design) becomes impractical. Nimrod/E uses a fractional factorial design,
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which reveals the most important interactions between parameters by using a subset of the full set of
parameters. This means that a good approximation of the information that would be derived from a full
factorial design can be achieved using a more practical number of model runs.
3.1.1.4. Nimrod/K
Nimrod/K represents the grid workflow component of the Nimrod toolkit. The workflow engine on which
Nimrod/K is built (Kepler) is described in the section below. Essentially, this grid workflow system enables
different software models to be linked together into a single „virtual‟ model, with the ability to run locally on a
desktop computer, or on a computing grid ranging from a grid of locally networked computers all the way up
to a global computing grid. The Nimrod/K prototype has the ability to run models on the grid in a way that is
parallel, distributed and dynamic. In other words, different software models can be run at the same time, from
different locations and using data stored either locally or at another location on the grid, while still
dynamically feeding model results back into new model runs.
Nimrod/K can wrap around models to feed in inputs and take off outputs and stream them around to
other models or statistical analysis tools. This can be conceptualised as „model plumbing‟, but in reality the
system is more sophisticated, for example, enabling archiving of model connections for future reference.
3.1.2. Kepler
Kepler is a software application for retrieving input data from locally or remotely stored files and executing
component models and performing statistical analyses on the retrieved data. Users can capture workflows,
so they can be easily exchanged, archived, versioned, and executed.
The workflow system is ideal for scientists with little background in computer science. The graphical
interface allows components (i.e. models or data sources) to be dragged and dropped onto a Workflow
canvas for connection and execution.
Kepler supports foreign language interfaces via the Java Native Interface (JNI), so that component
models written in different programming languages can be integrated. For example, Kepler includes the
ability to execute Matlab scripts and R code. Kepler is thus used to tie together diverse computational
systems into a unified framework.
The flow of data from one analytical step to another is captured in a formal workflow language.
Component models can either be loosely coupled, where each model runs on a single batch of data and the
outputs are transferred to another model, or more tightly coupled, where a continuous stream of data outputs
are fed into another model, allowing feedback mechanisms to occur at a high temporal resolution.
3.1.3. Interactive Component Modelling System
Developed by CSIRO Land and Water, Australian National University and Land & Water Australia, the
Interactive Component Modelling System (ICMS) is a software modelling system targeted for use by
catchment managers. It includes a number of simple models for rainfall-runoff, flow routing, crop selection
and management, salinity and nutrients, which can be linked and executed with a graphical interface called
the ICMSBuilder. ICMS has been designed for simple catchment representations and is not suitable for
complex examinations and detailed spatially explicit modelling (Newham et al. 2004).
3.1.4. Catchment Management Framework
The Catchment Management Framework (CMF) is a modelling framework for connecting modelling tasks
across different scientific disciplines. The primary model involved is the Catchment Analysis Tool (CAT),
developed by the Victorian Department of Primary Industries, originally for farming systems, and
incorporating and adapting a number of submodels such as PERFECT (Productivity, Erosion, Runoff
Page 25
Functions to Evaluate Conservation Techniques, a crop production model), SWAT (the Soil and Water
Assessment Tool), CERES-Wheat and CERES-Maize (Crop Environment Resource Synthesis), GRASP (a
dynamic pasture model), 3PG (Physiological Principles Predicting Growth) amongst others. CMF enables
coupling of CAT with models such as MODFLOW (MODular three-dimensional finite-difference ground-water
FLOW model) and 2CSalt. CMF also includes a number of other modelling tools for data analysis and
visualisation.
3.1.5. Ecological Modeller
Currently being developed by the eWater CRC, the Catchment Planning Tool is intended to be the interface
between WaterCAST (discussed in section 2.2.1) and Ecological Modeller (previously called the Ecological
Response Modelling tool). Ecological Modeller is a database system for running a range of ecological
models using time series data of habitat (e.g. streamflow). Ecological Modeller allows the user to run
different biodiversity models using common time series data for habitat conditions. For example, Ecological
Modeller could model the range of a fish species in a lowland river given a set of hydrological conditions over
time. Ecological Modeller is not a model in itself, simply a tool for writing and collating models or
relationships, allowing easy comparison of output.
3.2. Data management systems
3.2.1. National Data Grid Demonstrator Project (formerly PEMS)
The National Data Grid (NDG) Demonstrator Project refers to the Cooperative Research Centre for Spatial
information (CRC SI) project that builds on the earlier Platform for Environmental Modelling Support (PEMS)
Demonstrator Project. The NDG is a system for managing grid-based spatial data, which can integrate
different data sources into a shared, online database, using standard grids and projection systems. The NDG
Demonstrator is proposed to be operational by the end of 2009.
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4. A conceptual framework for linking models
In this section, we present a generic conceptual framework for linking component models together that would
be suitably flexible to address a range of natural resource management questions. The conceptual model
presented details how different model variables connect to each other across discipline boundaries (Figure
2). Note that this is a generic diagram, and many of the models reviewed may cover only part of what is
shown here (e.g. biogeochemistry models may not include phosphorus) or may simulate additional
components not detailed (e.g. vegetation fragmentation effects on terrestrial ecology, more specific
biogeochemistry compartments for cycling and species). More specific versions of this diagram should be
constructed once specific requirements are detailed (e.g. we are interested in denitrification to nitrous oxide,
or transport of phosphate to waterways), and again once specific models are selected for each module. The
explicit process names for the linking arrows are not given in the diagram, but are described in the next
section (4.1). However, this initial representation of the linkages between the six model components is the
first major step in building a whole ecosystem model. To construct a conceptual model as a blueprint for
building a whole ecosystem model would require a larger investment of resources.
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Figure 2 A conceptual model of integrated modelling across ecological disciplines
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4.1. Processes represented
The conceptual model aids in understanding the processes that can be represented within each component
and how these processes interact. In this section we list the specific ecosystem processes we think should
be represented in a whole-of-catchment integrated ecosystem model. These processes describe many of the
links (arrows) in Figure 2, although some are not explicitly represented in the diagram. The more detailed
models discussed in each module cover many of the processes listed below, however no single model in
each module may cover all the processes mentioned. Additionally, the italicised entries are often not
considered as important and are more rarely simulated. In combination with Figure 2, this list may be of use
in model selection.
4.1.1. Climate and weather
Climate and weather parameters can be derived from data of solar radiation, albedo of land and sea
surfaces, greenhouse gas concentrations and fluxes and land topography. The following parameters can be
modelled:
1. Heat exchange/temperature 2. Rainfall generation 3. Air pressure (from heat exchange, Coriolis force, friction, topography wind) 4. Humidity generation (from rainfall, evapotranspiration, water balance) 5. Cloud formation (from humidity)
4.1.2. Hydrology
Hydrological parameters are mainly derived from rainfall and temperature (evaporation) data, with more
complex models incorporating vegetation to calculate more accurate values for evapotranspiration, and land
use to calculate permeability more accurately. The following parameters can be modelled:
1. Interception (from rainfall and leaf area) 2. Infiltration (from interception and rainfall and soil chars) 3. Saturation excess (from infiltration and rainfall) 4. Surface flow (from saturation excess + runon from upslope) 5. Soil moisture (from infiltration) 6. Subsurface flow (from infiltration and soil moisture) 7. Soil evaporation (from temperature and soil moisture) 8. Plant uptake (from temperature and soil moisture and vegetation chars) 9. Plant transpiration (from temperature and wind and vegetation chars) 10. Drainage to groundwater (from soil moisture) 11. Groundwater flow 12. Erosion/transport (nutrients/sediment)
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4.1.3. Biogeochemistry
Biogeochemistry parameters, for both soil and hydrological components, can be defined by the element of
interest; mainly carbon, nitrogen and phosphorus. In addition, temperature and soil moisture are important
for modelling plant uptake and nutrient cycling processes. Transport of particles and dissolved salts is
considered in the „aquatic biodiversity‟ section (4.1.6). The following parameters can be modelled:
Carbon cycling 1. Fixing by plants 2. Breakdown of plant material/organic matter (potentially to labile and refractory, with further
processing/decay for further breakdown of refractory into labile and refractory, etc., alternatively to structural and metabolic, active and passive)
3. Mineralisation 4. Leaching 5. Respiration 6. Fermentation to methane
Nitrogen cycling
1. Fixation 2. Breakdown of organic matter 3. Mineralization (to ammonium) 4. Nitrification (to nitrite then nitrate) 5. Uptake by plants 6. Denitrification (to N2 and N2O) 7. Ammonification 8. Volatilization of ammonia 9. Adsorption of ammonia 10. Leaching
Phosphorus cycling
1. Weathering of bedrock/sediment 2. Breakdown of organic matter 3. Mineralisation 4. Uptake by plants 5. Adsorption 6. Leaching
4.1.4. Vegetation
Stand growth models typically utilize light (photosynthetically active radiation or PAR) to drive tree growth,
which can be constrained by parameters describing soil moisture, rainfall and stand age. Spatially distributed
models take into account competition for light to differentiate tree growth for individual trees. Some of the
parameters that can be represented by models are:
1. Photosynthesis 2. Growth leaf, stem, root allocation (giving outputs for stem size/basal area, crown height, stem
density) 3. Competition for light 4. Limitation by nutrients/temperature/salinity/conditions 5. Litterfall 6. Mortality 7. Disturbance (including harvesting/logging, disease/pathogens and fire – often impacts only
modelled, not actual disturbance) 8. Seeding 9. Recruitment
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4.1.5. Terrestrial Biodiversity
Models of terrestrial wildlife dynamics based on bioclimatic envelopes can include a number of parameters
related to habitat suitability. Demographic models can represent species birth and death rates. A summary of
parameters that can potentially be modelled are as follows:
1. Habitat suitability and quality from climate (temp, rainfall, etc.), soil (nutrients, texture, etc.), topography (elevation, aspect, etc), vegetation (type, extent/patch sizes, tree size distribution, etc.) (statistical relationship for) extent/area suitable
2. Species (statistical relationship for) estimate of biodiversity 3. Species birth and death rates 4. Rates of species movement through landscape 5. Metapopulation persistence
4.1.6. Aquatic Biodiversity
Aquatic biodiversity (macroinvertebrates, fish, algae, etc…) has strong conceptual links to water flow and
quality, as well as productive factors such as light, oxygen, nutrients and carbon. Some of the parameters
represented in aquatic biodiversity models include:
1. Physical flow/circulation (and effects on transport, nutrient distribution/processing) 2. Suspended sediment and sedimentation 3. Nutrient processing (largely as for biogeochemistry, including uptake for primary productivity) 4. Oxygen dynamics (respiration/photosynthesis) 5. Photosynthesis/growth (for phytoplankton from nutrients, light, temp) 6. Photosynthesis (for macroalgae, macrophytes) 7. (statistical relationship for) macroinvertebrates/other biodiversity.
4.2. Limitations
Obviously, there are major limitations on the degree to which some biophysical processes can be
represented, either because of a lack of knowledge about the process, lack of data to parameterize the
process, or purely because of limits to computing power and the time available. The ability to perform
integrated modelling for a particular location depends on data availability.
1. Data limitations occur for initial conditions, for calibration of parameters, and for validation. These
limitations may occur for a variety of reasons, including the long-term scale of interest, because
we‟re interested in scenarios such as climate change which we are not able to experiment with, or
because some of the data which would be of use in validation is difficult/expensive to collect (e.g.
physiological tree data, rare fauna surveys),
2. Natural variability can be immense. Spatial and temporal heterogeneity of physical factors such as
soil characteristics across a catchment and sub-daily rainfall events would be easy to omit or
misrepresent. There is possibly even greater variability in biological factors such as the growth or
behaviour between different species and even of individuals within a single species, which may be
represented by an „average‟ behaviour for a limited number of modelled types.
3. Traditional validation approaches may not be possible due to the large spatiotemporal extents under
consideration (Oreskes et al. 1994). Lack of data is one of the primary motivations in building a
large-scale, long-term integrated model (He et al. 2008). Techniques to analyse time series of
spatially explicit data are also currently lacking (Perry and Enright 2006). It may be more appropriate
to evaluate the integrated model in terms of how well (or plausibly) ecological processes are
represented, and how useful the model is for hypothesis testing and learning about the modelled
system (Perry and Enright 2006; Shifley et al. 2009).
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As with any simulation model, the proposed integrated model needs specific management questions
to frame modelling tasks around. Given the specific management questions, the general approach outlined
in this report is to make the integrated model as flexible as possible to address a large combination of NRM
questions.
Where models have been developed and applied only by a particular group, access to the model
and the expertise needed to apply the model may be a limitation. Most of the models discussed in this report
are not proprietary, or are available at minimal cost. An extended range of commercial simulation models are
available (e.g. MIKE), but do not necessarily provide greater modelling capacity than those reported.
It should be noted that the primary focus of this report is the long-term impact of fires, and does not
look at modelling of fire or fire risk. Overall, fire modelling is a relatively recent field that is still under
development and has some difficulties, particularly at the catchment scale (McKenzie et al. 1996), however
there is the possibility of eventually coupling the integrated ecological model with some recent limited fire
focused models which may contribute to our understanding of fire risk. For example, a recent process-based
model (developed in Australia) could be linked to weather (precipitation, temperature) and outputs from the
vegetation module (litter) to be used as inputs to simulate wetting and drying and fire risk to the fuel load
(Matthews 2006).
4.3. Issues of scale
There are a number of issues of scale which will need to be considered in integrating models for each
module. Specifically, the „boundary conditions‟ between models will need to be matched; a task that might be
made easier with the development of the National Data Grid. The output of each module will need to be
matched (and possibly up- or down-scaled) to match the input of the coupled models. Specifically, the
anticipated rescaling required includes:
1. Use of climate data that has already been downscaled spatially and temporally to weather for the
region;
2. Rescaling the weather data to the selected hydrological model spatial resolution (this may involve
rescaling grid to sub-catchment units);
3. Rescaling of the hydrology output data (soil moisture where available) for the vegetation model in
space (from sub-catchment or grid to „patch‟ or EVC) and from daily hydrological temporal scale to
the vegetation model time-step (which may be monthly or yearly);
4. Up-scaling the vegetation output data spatially if trees are modelled individually to stand/patch or to
larger units (possibly whole-of-catchment-scale) for terrestrial ecology;
5. Rescaling the hydrology, vegetation and biogeochemistry patches and/or grids for the boundaries of
the aquatic ecology model to provide runoff, sediment, organic matter and nutrient inputs.
There is also a disjunct between the landscape vegetation scale (km scale) and riparian buffer zones
(scale of metres), which may present problems for incorporating these important river-side zones into
vegetation simulations. Rescaling and inter/extrapolation will be required for collected field data (e.g. weather
stations, soil sampling) to match resolution required for model input data.
There are also issues with the resolution of the temporal scale. Modelling of long-term forest growth
often uses a time-step of more than a year (Crookston and Dixon 2005), while runoff is usually modelled on
a daily time-step. However, many studies indicate that nutrient inputs into waterways can be significantly
affected by storm events which may require a sub-daily resolution (Chessman 1986; Drewry et al. 2006).
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4.4. Issues of uncertainty
Any single simulation run of a model (an “instantiation”) is subject to many sources of variance. Uncertainties
arise from these sources:
Initial conditions and values of all variables, which can have profound effects (Lundberg et al. 2000;
May 1976)
Model functional relationships (what are the actual shapes of functional relationships between
response and predictor variables?)
Response and predictors may have different scaling in space and time, making their functional forms
change at different scales
Imperfect knowledge of functional relationships (variation explained in known relationships is usually
much less than 100%)
Model structure (are the important relationships included?)
“Non-stationarity” – relationships may change through time – some relationships may become
unimportant while others emerge.
These points have stimulated some ecologists to seek an alternative approach, which involves
specifications of “scenarios,” imagined futures, for which key measures are assessed (Carpenter 2002).
However, such approaches involve little incentive for on-going learning about knowledge gaps and
refinement of important relationships. As we have seen with climate change modelling, it is crucial that
numerical values be associated with forward projections not merely comparatively vague futures. Moreover,
scenario methods cannot provide a pathway for informing management and policy about going from “the
present” to “the future” because specific pathways need to be developed to do so.
These comments indicate why we favour an approach in which we will build the complete system
model, propagate uncertainties, and run the model many thousands of time to produce probability
distributions for variables about which we are interested. This is linkable to risk-based assessment of various
options that might be envisaged by any stakeholder group. Without a probability distribution, one cannot
assess the likelihood that undesirable outcomes may emerge with higher-than-acceptable chances. We think
that ensuring systems are managed so that they are “bounded away” from catastrophic results is a critical
lesson to convey, but this needs the many-instantiations approach to make judgements.
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4.5. Issues of model integration
Linking models together is a challenging task, and although coupling of two models has been done relatively
widely (Lynch et al. 2007; Robson and Hamilton 2004; Sherman et al. 2007), and there is some theory
developed as to re-using, coupling and integrating models (Brandmeyer and Karimi 2000; Parker et al. 2002;
Rizzoli et al. 1998) and conceptual work on whole-of-system integration (Gentile et al. 2001; Ogden et al.
2005), the larger scale of the project proposed here means the actual effort required, cannot be known until it
is attempted. Some of the issues that may potentially arise in a model coupling exercise include:
Data format – spatial resolution, file formats, boundaries
Model programming language
Model licensing
Increased processing and runtime requirements
The above points are certainly challenging, but are not in any way fatal to this exercise. The grid
workflows technology, notably Kepler, includes actors that can reformat data streams into a large number of
common file formats, with the capacity to also use custom formats. Kepler can also deal with programs
written in different programming languages, as in many cases it launches the modelling software in its own
environment. Programming languages only limit the degree to which any particular model can be altered in
Kepler. Scripts written in Matlab or R can be natively implemented in Kepler. Licensing of models can be an
issue, particularly where a licence is held on a USB memory stick. However, many of the models reviewed in
this report do not have such strict licensing requirements.
Distributed computing and increasing availability and access to supercomputer facilities as well as
sophisticated data scheduling and management will help address the potential problems in passing large
arrays of data back and forth between models.
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5. Program of work
The program of work required to build and run an integrated ecological model that can operate on a
catchment scale is considered in this section. A number of important factors would firstly need to be
addressed regarding the purpose and scope of the model. The framework proposed in this report, of using
six model components to describe a whole ecosystem, provides flexibility to make specific model
interconnections in order to address individual management and research questions and scenarios.
An understanding of the feasibility of linking models and identification of modelling gaps would
emerge from the production of a simplified draft model, which would be a useful first step towards a
comprehensive whole system model.
5.1. Recommended approach
We recommend the following logical approach to scoping, designing and implementing the modelling
framework outlined in this report, using the case study of upland bushfires and their impacts on catchment-
scale ecological processes.
5.1.1. Define specific research questions and goals
In the first instance, specific queries related to more general management goals need to be formulated to
input into the model. In the case of studying bushfire impacts, specific queries may be produced from more
general management questions as shown in the flowchart in Figure 3. For example, many vegetation models
do not directly simulate fire, but can simulate what will happen following a disturbance that removes mature
vegetation, such as logging or fire.
Large scale f ires in
uplands
Impacts of forest
disturbance
Forest
regrowth
C production Impacts on
water yield
Habitat
availability
Multiple f ires Water quality
Erosion
Community
composition
Aquatic
biodiversity
Figure 3 Model query flowchart
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5.1.2. Select a case study catchment.
Applying a linked ecological model to specific catchments within the Murray-Darling Basin is the most
practical way to implement this assessment. Operating an ecological model at a catchment scale provides a
number of advantages, including: data availability, consistent boundaries for input data, alignment with
waterways and alignment with existing management boundaries. For example, more than two decades of
ecological research has taken place within the boundaries of the Goulburn-Broken Catchment in Victoria.
Figure 4 shows the geographic distribution of published ecological research projects within the boundary of
the Goulburn-Broken Catchment. As can be seen from the map, a large amount of aquatic ecology research
has been conducted in the fire-affected Acheron River catchment.
Figure 4 Location of published ecological study sites in the Goulburn-Broken Catchment
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5.1.3. Identify data requirements
A review of ecological studies within a catchment is a useful first step for identifying potential sources of
data. Information on ecosystem processes can be derived from these studies and used to parameterize an
ecosystem model. Figure 5 shows the breakdown of ecological research themes within the Goulburn-Broken
Catchment.
In addition, input data requirements will need to be established for each individual model. Input data
usually needs to be formatted, which the Kepler system can be setup to perform automatically through data
filters. For some models, extensive parameterization and validation may be required.
Freshwater
Disturbance
Carbon cycling
Samplingmethods and classification
Habitat-fauna interactions
Life cycle
Restoration (4)
Flooding (6)
Habitat Fragmentation(6)
Riparian zone (1)
Woodlands (13)
Floodplain (8)
Woodland fauna (3)
Forests (3)
Effect of riverregulation (8)
Colonisation (14)
Morphology and sedimentation (9)
Effect of restoration(3)
In-stream (6)
Macroinvertebrates(18)
Fish (6)
Platypus (1)
Macroinvertebrates(4)
Fish (8)
Stream condition (1)
Fish (7)
Terrestrial
Figure 5 Themes of published ecological research undertaken in the Goulburn-Broken Catchment:
(number of studies in each theme).
5.1.4. Obtain access to component models
In most instances, the component models discussed are freely available. However, in a few cases the
publicly available model version is an executable, and access to the raw code would be of use in
development, additionally some models are under continued development by small research groups, and in
these instances further access may need to be negotiated.
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5.1.5. Implement grid workflows
In the first instance, wrapping workflows around a single model so that input data can be formatted and
routed to the model should be tested. This would involve setting file locations for input data and model
outputs and producing variable strings to be tested by the model. A workflow actor would implement the
model script or program, and other actors would handle file fetching, data formatting, writing outputs to file,
setting file locations and model variables.
Following this, a second model could be coupled to the first by wrapping the model in the same way
as described above, but adding workflow actors that can re-format data to be compatible with the first model.
A recommended order for model coupling would be 1. climate/weather with hydrology, 2. adding vegetation,
3. then biogeochemistry, 4. aquatic ecology, 5. terrestrial ecology (parallel development of the model for this
module is likely to occur during the previous stages). Consideration of rescaling input/output data will be
required for each coupling. Additionally, consideration of runtimes should occur in this phase, with
adjustment of model resolutions or investigation of additional processing power to ensure practical
application in subsequent phases.
5.1.6. Calibration and parameter optimization
As each model is implemented for the case study catchment, empirical and catchment-specific parameters
will need to be calibrated. As each additional module is coupled, an additional check will be required to see if
cross-cutting calibrated parameters maintain expected behaviour. Note that as discussed in section 3.1.1,
there are specific parameter optimization capabilities in the software integration systems proposed.
An examination of which parameters may be tuned at the scale of the entire Murray-Darling Basin
should be made (i.e. parameters which should not vary much between catchments/sub-catchments). This
requires review of current measured data, which may not be available across the basin, although nearby
studies should also be of use in identifying variability. There will still be some parameters which will be
catchment/sub-catchment specific, however it may be possible to produce a set of best-guess or default
parameter values for use at the basin scale, or in catchments where no specific data has been collected.
5.1.7. Validation, analysis and scenarios
As discussed in section 4.2, traditional validation approaches may not be possible for the fully integrated
model. However, where long term monitoring of certain variables or parameters is feasible, it may be
possible to verify and validate certain process representations within the model, particularly if the model is
used to guide future monitoring efforts. This approach is widely recommended for addressing knowledge
gaps and improving our understanding and modelling of the system (Shifley et al. 2009).
Multiple instantiations of the integrated model will provide a probability distribution for variables of
interest and estimates of uncertainty in the model outputs. Analysis of the ecological processes significant in
different outcomes can then occur, allowing identification of management practices favourable to desired
outcomes. Examination of specific scenarios within the distributions can also occur.
5.1.8. Iterative model development
There are several areas that existing models address poorly, such as process-based export of dissolved
catchment nutrients to waterways and changes or movement of vegetation functional types. Development in
these areas would add significantly to this research effort into a whole-of-catchment ecological model.
Additionally, depending on the final model selection, a number of potential gaps in ecological representation
may occur, given choice of model in each module will not solely be based on processes represented, but will
also take into account model quality (e.g. how appropriately the included processes are represented) as well
as practical implementation issues, access, and available expertise.
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It is also important to keep in mind that any model, no matter how complex, is a simplification of
reality, and as model development and application proceeds and we learn more about the system which we
are modelling, improvements can be made. In using the model to guide management decisions and
monitoring of outcomes, the processes modelled can be better represented and the model improved for
future management decisions. This iterative approach to model development can feed directly and usefully
into the on-ground adaptive management cycle.
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6. Conclusions
Two broad approaches to modelling a whole ecosystem can be identified; 1) build a single, highly
parameterized model from the ground-up and attempt to represent as many ecosystem processes as
possible in one framework; or 2) assemble a group of existing component models, validated and known to
perform well for their defined modelling task (with a mind to their limitations), linking inputs and outputs
across disciplinary boundaries. Where necessary, purpose-built models for components not well developed
yet can be linked into the second approach. The latter has significant advantages, in that it is built on proven
modelling techniques and is a way of integrating models from different disciplines without the need to
completely re-write a modelling framework. This represents an enormous saving of human and financial
resources that would be required to build and operate a single „does-it-all‟ model, at the expense of
intrinsically linked ground-up model integration.
We found that modelling capacity differs substantially among the six components: climate and
weather, hydrology, biogeochemistry, vegetation, terrestrial biodiversity and aquatic biodiversity. We have
identified that climate, and in particular weather, data should be a driver of ecosystem processes and not a
functional process component itself. High resolution, statistically downscaled climate data already exist for
south-eastern Australia, produced by SEACI, thus eliminating the need to undertake additional downscaling.
Furthermore, without the ability to tightly couple models to global climate models, such as occurs with some
global carbon or vegetation models, climate data becomes a „one-way‟ driver of ecosystem processes. As
the next iteration of global climate modelling (ACCESS) is developed, the next iteration of our integrated
ecosystem model can incorporate appropriate feedback loops, in particular for carbon and water.
Hydrology models are capable of modelling water balance and groundwater flows at landscape
scales over long time periods, with the potential to incorporate physical processes, such as leaf area index.
The major limitation of hydrology models in this context is the availability of high-quality data to appropriately
drive and validate models. The data might be appropriate for use in water resource planning, but may not be
of sufficient resolution, temporally or spatially, for modelling ecological processes within catchments of
intermediate sizes, which are nevertheless ecologically significant. However, a certain amount of essential
on-ground sampling would be undertaken in the modelling phase of this project to ensure that the models
are validated.
Biogeochemistry models differ with regard to which cycles are regarded as important and are thus
represented. Some of the more complex models require significant parameterization and calibration. Simpler
empirical models of export are available but are probably not appropriate.
Vegetation models range in scale from individual trees to global vegetation dynamics coupled to
global climate models. Landscape-scale models that can represent spatial processes, such as fire, would be
most suitable for a whole ecosystem model. The transfer and feedback of water and nutrients between the
vegetation, biogeochemistry and hydrology modules is a key component of integration.
Models of both terrestrial and aquatic (animal) biodiversity deal with modelling of species‟
distributions based on bioclimatic envelopes, topography, soil types and habitat characteristics. A more
systematic approach to this is needed. An alternative approach is one that focuses on building spatially
explicit demographic models. These typically aim to identify whether a particular species, usually
represented as a metapopulation, is likely to persist given the spatial pattern of habitats of various value to
the species, birth and death rates, and rates of movement across the landscape.
The availability of sophisticated model-linking software and the increasing computational power
offered by distributed systems makes it feasible to couple multiple models from the different modules.
Advances in eResearch provide opportunities for collaborating with other ecological research groups
internationally. One example is the Open Wildland Fire Modelling e-Community (www.openwfm.org), which
provides a portal for sharing modelling software, data and expertise.
The approach to constructing a whole ecosystem model outlined in this report is feasible and can be
implemented immediately using a suite of some of the existing models described. The conceptual framework
for linking models presented in this report, while not a blueprint, is a valuable device for formulating research
questions that can be used to query a whole of system model. We envisage that in implementing the next
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stage of this approach, by actually constructing a whole ecosystem model, the conceptual framework for
linking models will grow in size and detail in parallel with our understanding of ecological processes and our
ability to represent them with models.
An investment in this modelling approach is critical for initiating the next phase of natural resources
modelling in Australia. A linked, whole ecosystem model of this type will be highly compatible with other
modelling initiatives and will enhance, rather than compete with, other modelling systems being developed in
Australia and internationally. This approach is arguably the most promising method for modelling whole
ecosystems at catchment scales and, eventually, on the scale of the whole Murray-Darling Basin.
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7. Glossary
3-PG Physiological Principles Predicting Growth
ACCESS Australian Community Climate and Earth-System Simulator
AUSRIVAS Australian River Assessment Scheme
AWBM Australian Water Balance Model
CABALA CArbon BALAnce
CABLE CSIRO Atmosphere Biosphere Land Exchange
CAEDYM Computational Aquatic Ecosystem Dynamics Model
CASA Carnegie Ames Stanford Approach Biosphere model
CatchMODS Catchment Scale Management Of Diffuse Sources Model
CMSS Catchment Management Support System
DNDC DeNitrification-DeComposition Model
ELCOM Estuarine and Lake COmputer Model
EMSS Environmental Management Support System
FVS Forest Vegetation Simulator
Grid A computational grid, consisting of a distributed network of computers
ICMS Integrated Catchment Management System
IPCC Intergovernmental Panel on Climate Change
JABOWA Janak-Botkin-Wallis
LASCAM Large Scale Catchment Model
LPJ Lund-Potsdam-Jena
Model A computational model, consisting of a computer program that simulates a natural system
ORCHIDEE Organizing Carbon and Hydrology in Dynamical Ecosystems
PAR Photosynthetically active radiation
PERFECT Productivity, Erosion and Runoff Functions to Evaluate Conservation Techniques
R A programming language and software environment for statistical computing and graphics
RothC Rothamsted Carbon Model
SEACI South Eastern Australian Climate Initiative
SWAT Soil & Water Assessment Tool
WaterCAST Water Contaminant Analysis and Simulation Tool
Workflow A sequence of operations automated by a software application
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Page 47
Appendix 1 – Detailed model comparison tables
Table 2 Climate (and Weather) Module Models Comparison Matrix
M
od
el
Ac
ron
ym
Av
ail
ab
ilit
y
Ou
tpu
ts (
va
ria
ble
s/f
eatu
res/c
ap
ab
ilit
y)
Po
ten
tia
l R
eso
luti
on
Q
ua
lity
Rainfall
Temperature
Humidity
Cloud cover
Potential
Evapotranspiration
Wind
Spatial Resolution
Temporal Resolution
Previous rescaling
Peer Reviewed
Used by more than
developer
Previously coupled
to other models
CLIMATE
AC
CE
SS
C
urr
ently in
deve
lopm
ent
IPC
C 4
–
SE
AC
I
do
wn
sca
lin
g
for
we
ath
er
?
?1
?1
1x1
km
D
aily
WR
F
1x1
km
D
aily
1 T
hese m
ay n
ot
have b
een
arc
hiv
ed
.
Page 48
Table 3 Mixed/Overlapping Modules Models Comparison Matrix
Hydro
log
y
Veg
eta
tio
n
Bio
ge
ochem
istr
y
Resolu
tio
n
Qualit
y
M
od
el
Ac
ron
ym
Availability
Rainfall-runoff-
evapotranspiration
Soil/sub-surface
water
Sediment
Salinity
Vegetation
Forest stands
Carbon
Nitrogen
Phosphorus
Spatial resolution
Temporal resolution
Peer Reviewed
Used by more than
developer
Manual acceptable
LAND SURFACE (COMBINED)
MODELS
MODELS
CA
T
20x2
0m
daily
~
~
CA
BL
E
?
~
ICM
S
sub-
catc
hm
ent
annu
al
?
EW
ate
r
To
olk
it
sub-
catc
hm
ent
daily
Page 49
Table 4 Hydrology Module Models General Comparison Matrix
Ou
tpu
ts (
va
ria
ble
s/f
eatu
res/c
ap
ab
ilit
y)
Re
so
luti
on
Q
ua
lity
M
od
el
Ac
ron
ym
Availability
Rainfall-
runoff
Surface
runoff
Subsurface
runoff
Soil Water
Groundwater
Sediment
transport
Salinity
transport
Min spatial
resolution
Soil layers
Min temporal
resolution
Length of
simulations
Peer
Reviewed
Used by more
than
developer Manual
acceptable
HYDROLOGY
PE
RF
EC
T a
nd
MO
DF
LO
W (
in
CA
T)
20x2
0m
3+
d
aily
>
deca
d
al
~
~
?
Ma
ca
qu
e
S
lop
es
(>10
00
)
2
daily
>
25
0
ye
ars
?
SIM
HY
D (
in
E2
/Wa
terC
AS
T)
su
bca
tch
me
nts
(>1
00
s)
1
daily
>
10
0
ye
ars
Page 50
Table 5 Hydrology Module Models Comparison Matrix
INP
UT
S
PR
OC
ES
SE
S
OU
TP
UT
S
M
od
el
Ac
ron
ym
Topopgraphy (DEM)
Soil type
Vegetation/crop types
Rainfall
Temperature
Evapotranspiration
Land use
Crop rotation
Management strategy
Gauged streamflow
Bore data
Soil evaporation
Evapotranspiration
Rainfall-runoff
Saturation
Interception
Constituent generation
Transport
Runoff
Soil erosion/loss
Evaporation
Soil Water
Groundwater
Drainage
Crop growth
Salinity transport
Nutrient transport
HYDROLOGY
PE
RF
EC
T
an
d
MO
DF
LO
W (
in C
AT
)
Ma
ca
qu
e
SIM
HY
D
(in
E2
/Wa
terC
AS
T)
Page 51
Table 6 Biogeochemistry Module Models Comparison Matrix
O
utp
uts
(v
ari
ab
les/f
eatu
res/c
ap
ab
ilit
y)
Re
so
luti
on
Q
ua
lity
M
od
el
Ac
ron
ym
Availability
Carbon
Nitrogen
Phosphorus
Species
(N2O?)
Nutrient
Processing
Sediment
Soil layers
Spatial
resolution
Temporal
resolution
Length of
simulation
Peer
Reviewed
Used by more
than
developer
Manual
BIOGEOCHEMISTRY
SW
AT
an
d
DA
ISY
(in
CA
T)
3+
2
0x2
0
m
Da
ily
CA
SA
?
Se
dN
et
S
ubca
tch
me
nts
~D
aily
E2
/CM
SS
(eW
ate
r
To
olk
it)
1
Su
bca
tch
me
nts
Da
ily
CE
NT
UR
Y/
DA
YC
EN
T
4+
,
1cm
reso
lu
tio
n
<1
x1
k
m
Da
ily
Page 52
Table 7 Biogeochemistry Module Models Comparison Matrix
INP
UT
S
PR
OC
ES
SE
S
OU
TP
UT
S
M
od
el
Ac
ron
ym
Rainfall
Temperature
Soil characteristics
(e.g.
clay/sand
Initial concentrations
Atmospheric inputs
Plant lignin content
Fertilizer inputs
Gauged nutrients
Fixation
Plant uptake
Denitrification
Leaching
Volatilisation
Erosion
Phosphotase
production Mineralization
Sediment
SOC
Organic N
Organic P
DIN
PO4
TN
TP
CH4
CO2
N2O
NOx
BIOGEOCHEMISTRY
SW
AT
an
d
DA
ISY
(CA
T)
CA
SA
Se
dN
et
E2
/CM
SS
(eW
ate
r
To
olk
it)
CE
NT
UR
Y/
DA
YC
EN
T
Page 53
Table 8 Vegetation Module Models Comparison Matrix
M
od
el
Ac
ron
ym
Availability
Stand
Representation
Individual Tree
Representation
Age structure
Species
succession
EVCs
Riparian
Vegetation
Stand Health
Water
Carbon
Spatial
resolution
Temporal
resolution
Length of
simulations
Peer Reviewed
Used by more
than developer
Australian
Applications
Previous
coupling
VEGETATION
CA
T –
cro
p,
pa
stu
re,
fore
st
mo
dels
(in
clu
de
s 3
PG
)
?
20 x 20m
Crops daily, Forests
monthly
Decadal
FV
S
000s of
stands
5 years
> 100s
X
CE
NT
UR
Y/D
AY
-
CE
NT
1 yr-daily
> 100s
LP
J
> 100s
Page 54
Table 9 Vegetation Module Models Detailed Inputs/Outputs Comparison Matrix
INP
UT
S
PR
OC
ES
SE
S
OU
TP
UT
S
M
od
el
Ac
ron
ym
Rainfall
Temperature
Solar radiation
Vapour Pressure Deficit
CO2
Slope/aspect/DEM
Elevation
Soil chars (e.g. texture, water
capacity)
Initial stem number
Initial mass fractions: stem, foliage,
roots „Soil Fertility Ratio‟
Litterfall Rate
Maximum Stomatal Conductance
Canopy Quantum Efficiency
Net Primary Production
Biomass allocation
Water Use
Soil Water Balance
Nutrient uptake
Stem Mortality
Litterfall
Root turnover
Establishment/recruitment
Species specific traits
Biomass pools (stems, foliage,
roots) Tree density
Biomass fixed (growth)
Water Use (Soil Water left)
Evapotranspiration
Stand Relative Age
Stem Diameter Distn (Basal Area)
Leaf Area Index
Stand Health
Riparian Vegetation
EVC/Species Succession
VEGETATION
3P
G (
in
CA
T)
FV
S
~
~
~
LP
J
DA
YC
EN
T
UN
CE
RT
AIN
TIE
S:
Ro
ot d
ep
th,
so
il h
ete
rog
en
eity,
“fe
rtili
ty m
easu
res”
not
nece
ssa
rily
exp
licitly
nu
trie
nt re
late
d (
Lan
dsb
erg
et a
l. 2
00
3),
PR
OB
LE
MS
: S
ca
le issu
es/m
ultip
le d
istu
rba
nce
s,
va
lida
tio
n/la
ck o
f d
ata
(lo
ng
te
rm s
low
ph
eno
me
na
), m
ixe
s o
f tr
ee
sp
ecie
s n
ot
add
itiv
e, eff
ects
on s
oil
ch
em
/nutr
ients
(L
an
dsb
erg
et a
l. 2
003
), p
oo
r n
utr
ient
cyclin
g (
Mie
hle
et
al. 2
00
9),
poo
r e
sta
blis
hm
ent/
recru
itm
ent m
ode
llin
g (
Cro
oksto
n a
nd
Dix
on 2
00
5;
Po
rté
an
d
Ba
rte
link 2
00
2).
Mo
st
tre
e m
ode
ls g
row
th/y
ield
fo
cu
se
d –
be
tte
r g
row
th p
red
ictio
ns,
bu
t n
ot
ve
ry g
oo
d f
or
su
cce
ssio
n/d
yn
am
ics.
Ga
p m
od
els
(p
atc
hes o
f fo
rest w
ith
list
of
sp
ecie
s)
app
ear
be
st
for
su
cce
ssio
n m
ode
llin
g, e
sp
ecia
lly w
he
re h
ete
rog
en
eo
us/m
ixe
d s
pecie
s f
ore
sts
are
of
inte
rest
(Po
rté
an
d B
art
elin
k 2
00
2).
Larg
e g
ap
in
“Sta
nd
He
alth
”. S
till
a v
ery
active
are
a o
f re
se
arc
h a
nd
de
ve
lopm
ent
– n
o c
onse
nsu
s o
r co
nve
rge
nce
of
mo
de
ls (
He
et
al. 2
00
8).
Page 55
Table 10. Terrestrial Ecology Module Models Comparison Matrix
M
od
el
Availability
Process-
based
Statistical
Indicator
species
Biodiversity
Spatial
resolution
Temporal
resolution
Peer
Reviewed
Used by
more than
developer
TERRESTRIAL ECOLOGY
Sp
ecie
s
sp
ecif
ic
mo
dels
(e.g
.
Po
pu
lati
on
mo
dels
)
?
Vari
able
V
ari
able
Dem
og
rap
hic
sp
ati
all
y
dyn
am
ic
mo
dels
V
ari
able
V
ari
able
Page56
Table 11 Aquatic Ecology Module Models Comparison Matrix
M
od
el
Ac
ron
ym
Process-based
Statistical
Spatial resolution
(less than catchment)
Temporal resolution
(less than daily)
Sediment
Carbon
Nitrogen
Phosphorus
Algae
Higher ecology
Biodiversity
Peer-reviewed
Used by more than
developer
AQUATIC ECOLOGY
AU
SR
IVA
S
R
each
Eco
log
ical
Mo
deller
R
each
CA
ED
YM
arb
itra
ry
< 1
x1m
arb
itra
ry
< 1
hr
~
Mo
dels
sim
ila
r to
CA
ED
YM
–
CE
QU
AL
,
PR
OT
EC
H,
AQ
UA
MO
D
~