prediction of the impact of increasing frequency of bushfire on the water resources of the forested...

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

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.

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).

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.

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.

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

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)

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

)

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)

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

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.

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.

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

(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.

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.

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).

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.

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).

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).

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.

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.

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.

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.

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,

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

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.

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.

CARBON

CL

IMA

TE

(D

OW

NS

CA

LE

D

WE

AT

HE

R)

HY

DR

OL

OG

YV

EG

ETA

TIO

N

TERRESTRIAL

ECOLOGY AQ

UA

TIC

EC

OL

OG

Y

RAINFALL TEMPERATURE

WIND

HUMIDITY

SURFACE

RUNOFF

EVAPORATION

FUNCTIONAL

TYPE/EVC

EXTENT

STAND

STRUCTURE

Inorganic C

Organic C

Humic OM

ORGANIC

INPUTS

CLOUD

COVER

P.A.R.

EVAPOTRANSPIRATION

LEAF

AREA

INDEX

Organic NInorganic N

Organic PInorganic P

Adsorbed P

HABITAT

SUITABILITY

METAPOPULATION

DYNAMICS/PERSISTENCE

ALGAEMACROINVERTEBRATES

EROSION

GROUNDWATER

N2O

CH4

CO2INTERCEPTION

SUSPENDED

SEDIMENT

FLOW

PRIMARY

PRODUCTIVITY

& GROWTH

NITROGEN

PHOSPHORUS

STAND

HEALTH

NUTRIENTS

IN-STREAM

P.A.R.

IN-STREAM

TEMP

VEGETATION

NO3- NH4

+NO2-

Passive

Organic

Matter

Labile OM

Resistant OM

RIPARIAN

VEGETATION

Organic N

Organic P

Inorganic N

Inorganic P

Organic C

SPECIES X

POPULATION

SPECIES Y

POPULATION

SOIL

TYPE

FOOD

AIR PRESSURE

Inorganic C

SUBSURFACE

RUNOFF

SOIL

MOISTURESOIL

TEMP

NUTRIENTS

Figure 2 A conceptual model of integrated modelling across ecological disciplines

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)

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

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).

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).

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.

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.

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

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

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.

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.

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.

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

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.

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

8. References

Abramowitz G., Leuning R., Clark M. & Pitman A. (2008) Evaluating the Performance of Land Surface Models. J. Clim. 21, 5468-81.

Abramson D., Giddy J. & Kotler L. (2000) High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid? In: International Parallel and Distributed Processing Symposium (IPDPS) pp. 520-8, Cancun, Mexico.

Abramson D., Lewis A., Peachey T. & Fletcher C. (2001) An Automatic Design Optimization Tool and its Application to Computational Fluid Dynamics. In: SuperComputing 2001, Denver.

Abramson D., Peachey T. & Lewis A. (2006) Model Optimization and Parameter Estimation with Nimrod/O. In: The International Conference on Computational Science, University of Reading, UK.

Argent R. M., Perraud J. M., Rahman J. M., Grayson R. B. & Podger G. M. (2009) A new approach to water quality modelling and environmental decision support systems. Environmental Modelling & Software 24, 809-18.

Battaglia M., Sands P., White D. & Mummery D. (2004) CABALA: a linked carbon, water and nitrogen model of forest growth for silvicultural decision support. Forest Ecology and Management 193, 251-82.

Beringer J., Hutley L. B., Tapper N. J., Coutts A., Kerley A. & O'Grady A. P. (2002) Fire impacts on surface heat, moisture and carbon fluxes from a tropical savanna in northern Australia. pp. 333-40. C S I R O Publishing, Darwin, Australia.

Botkin D. B., Janak J. F. & Wallis J. R. (1972) Some Ecological Consequences of a Computer Model of Forest Growth. Journal of Ecology 60, 849-72.

Boughton W. (2004) The Australian water balance model. Environmental Modelling & Software 19, 943-56.

Boughton W. (2005) Catchment water balance modelling in Australia 1960-2004. Agric. Water Manage. 71, 91-116.

Boughton W. C. (1988) Partitioning streamflow by computer. Transactions of the Institution of Engineers, Australia. Civil engineering CE30, 285-91.

Brandmeyer J. E. & Karimi H. A. (2000) Coupling methodologies for environmental models. Environmental Modelling and Software 15, 479-88.

Bugmann H. (2001) A Review of Forest Gap Models. Climatic Change 51, 259-305.

Carpenter S. R. (2002) Ecological futures: Building an ecology of the long now. Ecology 83, 2069-83.

Chessman B. C. (1986) Impact of the 1983 wildfires on river water quality in East Gippsland, Victoria. Australian Journal of Marine and Freshwater Research 37, 399-420.

Chessman B. C., Muschal M. & Royal M. J. (2008) Comparing apples with apples: Use of limiting environmental differences to match reference and stressor-exposure sites for bioassessment of streams. River Res. Appl. 24, 103-17.

Chessman B. C. & Royal M. J. (2004) Bioassessment without reference sites: use of environmental filters to predict natural assemblages of river macroinvertebrates. Journal of the North American Benthological Society 23, 599-615.

Chiew F. H., Peel M. C. & Western A. W. (2002) Application and testing of the simple rainfall-runoff model

SIMHYD. In: Mathematical Models of Small Watershed Hydrology (ed V. P. Singh, Frevert, S.K.) pp. 335–67. Water Resources Publications, Littleton, Colorado.

Ciais P., Friedlingstein P., Friend A. & Schimel D. S. (2001) Integrating Global Models of Terrestrial Primary Productivity. pp. 449-79. Academic Press.

Coleman K. & Jenkinson D. S. (1999) A model for the turnover of carbon in soil. Model description and windows users guide. Rothamsted Research, Harpenden, Herts.

Croke B. F. W. & Jakeman A. J. (2001) Predictions in catchment hydrology: an Australian perspective. Mar. Freshw. Res. 52, 65-79.

Crookston N. L. & Dixon G. E. (2005) The forest vegetation simulator: A review of its structure, content, and applications. Comput. Electron. Agric. 49, 60-80.

CSIRO. (2007) Climate change in Australia. p. 152. Commonwealth Scientific and Industrial Research Organisation, Australia.

Davis J. R., Nanninga P. M., Biggins J. & Laut P. (1991) Prototype Decision Support System for Analyzing Impact of Catchment Policies. Journal of Water Resources Planning and Management 117, 399-414.

Drewry J. J., Newham L. T. H., Greene R. S. B., Jakeman A. J. & Croke B. F. W. (2006) A review of nitrogen and phosphorus export to waterways: context for catchment modelling. Marine and Freshwater Research 57, 757-74.

Drielsma M. & Ferrier S. (2009) Rapid evaluation of metapopulation persistence in highly variegated landscapes. Biol. Cons. 142, 529-40.

Elith J., Graham C. H., Anderson R. P., Dudik M., Ferrier S., Guisan A., Hijmans R. J., Huettmann F., Leathwick J. R., Lehmann A., Li J., Lohmann L. G., Loiselle B. A., Manion G., Moritz C., Nakamura M., Nakazawa Y., Overton J. M., Peterson A. T., Phillips S. J., Richardson K., Scachetti-Pereira R., Schapire R. E., Soberon J., Williams S., Wisz M. S. & Zimmermann N. E. (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29, 129-51.

Feikema P., Lane P. & Sherwin C. (2008) Hydrological studies into the impact of timber harvesting on water yield in state forests supplying water to Melbourne - Part 2 of Hydrological studies (Climate Change and Bushfire). School of Forest and Ecosystem Science, The University of Melbourne for the Victorian Government, Melbourne.

Feikema P. M., Sheridan G. J., Argent R. M., Lane P. N. J. & Grayson R. B. (2005) Using E2 To Model The Impacts Of Bushfires On Water Quality In South-Eastern Australia. In: MODSIM 05 International Congress on Modelling and Simulation (eds A. Zerger and R. M. Argent) pp. 1126-32. Modelling and Simulation Society of Australia and New Zealand, Melbourne, Australia.

Ferrier S., Drielsma M., Manion G. & Watson G. (2002) Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling. Biod. & Cons. 11, 2309-38.

Fleishman E. & Mac Nally R. (2007) Contemporary drivers of fragmentation and measurement of their effects on animal diversity. Can. J. Zool. 85, 1080-90.

Gentile J. H., Harwell M. A., Cropper W., Harwell C. C., DeAngelis D., Davis S., Ogden J. C. & Lirman D. (2001) Ecological conceptual models: a framework and case study on ecosystem management for South Florida sustainability. Sci. Total Environ. 274, 231-53.

Gerten D., Luo Y., Le Maire G., Parton W. J., Keough C., Weng E., Beier C., Ciais P., Cramer W., Dukes J. S., Hanson P. J., Knapp A. A. K., Linder S., Nepstad D., Rustad L. & Sowerby A. (2008) Modelled effects of precipitation on ecosystem carbon and water dynamics in different climatic zones. Global Change Biology 14, 2365-79.

He H. S., Keane R. E. & Iverson L. R. (2008) Forest landscape models, a tool for understanding the effect of the large-scale and long-term landscape processes. Forest Ecology and Management 254, 371-4.

He H. S. & Mladenoff D. J. (1999) Spatially explicit and stochastic simulation of forest-landscape fire disturbance and succession. Ecology 80, 81-99.

Howden M., Carberry P. & Hayman P. (2008) SEACI technical report for Milestone 3.1.6. SEACI.

Howe C., Jones R. N., Maheepala S. & Rhodes B. (2005) Melbourne Water Climate Change Study: Implications of potential climate change for Melbourne's water resources. p. 26. CSIRO, Melbourne.

Jakeman A. J., Letcher R. A. & Norton J. P. (2006) Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software 21, 602-14.

Jones R. N., Chiew F. H. S., Boughton W. C. & Zhang L. (2006) Estimating the sensitivity of mean annual runoff to climate change using selected hydrological models. Advances in Water Resources 29, 1419-29.

Kandel D., Chiew F. & Grayson R. (2005) A Tool for Mapping and Forecasting Soil Moisture Deficit over Australia. Cooperative Research Centre for Catchment Hydrology.

Kowalczyk E. A., Wang Y. P., Law R. M., Davies H. L., McGregor J. L. & Abramowitz G. (2006) The CSIRO Atmosphere Biosphere Land Exchange (CABLE) model for use in climate models and as an offline model. CSIRO Marine and Atmospheric Research, Aspendale, Victoria.

Krinner G., Viovy N., de Noblet-DucoudrÃ© N., OgÃ©e J. r. m., Polcher J., Friedlingstein P., Ciais P., Sitch S. & Prentice I. C. (2005) A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochem. Cycles 19.

Landsberg J. J. & Waring R. H. (1997) A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management 95, 209-28.

Landsberg J. J., Waring R. H. & Coops N. C. (2003) Performance of the forest productivity model 3-PG applied to a wide range of forest types. Forest Ecology and Management 172, 199-214.

Lane P., Feikema P. M., Sherwin C., Peel M. C. & Freebairn D. M. (2007) Physically-based prediction of water yield from disturbed forested water supply catchments. In: MODSIM 2007 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (eds L. Oxley and D. Kulasiri) pp. 2953-9. MODSIM, Christchurch, New Zealand.

Letcher R. A., Jakeman A. J., Calfas M., Linforth S., Baginska B. & Lawrence I. (2002) A comparison of catchment water quality models and direct estimation techniques. Environmental Modelling & Software 17, 77-85.

Li C., Frolking S. & Frolking T. A. (1992a) A Model of Nitrous Oxide Evolution From Soil Driven by Rainfall Events: 1. Model Structure and Sensitivity. J. Geophys. Res. 97.

Li C., Frolking S. & Frolking T. A. (1992b) A Model of Nitrous Oxide Evolution From Soil Driven by Rainfall Events: 2. Model Applications. J. Geophys. Res. 97.

Littleboy M., Silburn D. M., Freebairn D. M., Woodruff D. R., Hammer G. L. & Leslie J. K. (1992) Impact of Soil-Erosion on Production in Cropping Systems .1. Development and Validation of a Simulation-Model. Australian Journal of Soil Research 30, 757-74.

Lo J. C.-F., Yang Z.-L. & Pielke R. A., Sr. (2008) Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J. Geophys. Res. 113.

Lundberg P., Ranta E., Ripa J. & Kaitala V. (2000) Population variability in space and time. Trends in Ecology & Evolution 15, 460-4.

Lynch A. H., Abramson D., Gorgen K., Beringer J. & Uotila P. (2007) Influence of savanna fire on Australian monsoon season precipitation and circulation as simulated using a distributed computing environment. Geophys. Res. Lett. 34, 5.

Mac Nally R., Fleishman E., Thomson J. & Dobkin D. S. (2008) Use of guilds for modeling avian responses to vegetation change in the Intermountain West (U.S.A.). Global Ecology and Biodiversity 17, 758-69.

Marston F., Argent R., Vertessy R., Cuddy S. & Rahman J. (2002) The Status of Catchment Modelling in Australia. Technical Report 02/4. Cooperative Research Centre for Catchment Hydrology, Canberra.

Marston F. M., Young W. & Davis R. (1995) Nutrient Data Book: Nutrient generation rates data book. CSIRO Division of Water Resources, LWRRDC, SWB, Canberra.

Matthews S. (2006) A process-based model of fine fuel moisture. Int. J. Wildland Fire 15, 155-68.

May R. M. (1976) Simple mathematical-models with very complicated dynamics. Nature 261, 459-67.

McCarthy M. A., Malcolm Gill A. & Lindenmayer D. B. (1999) Fire regimes in mountain ash forest: evidence from forest age structure, extinction models and wildlife habitat. Forest Ecology and Management 124, 193-203.

McDonald M. G. & Harbaugh A. W. (1988) A modular three-dimensional finite-difference ground-water flow model. In: Techniques of Water-Resource Investigation p. 586.

McKenzie D., Peterson D. L. & Alvarado E. (1996) Extrapolation problems in modeling fire effects at large spatial scales: A review. Int. J. Wildland Fire 6, 165-76.

Miehle P., Battaglia M., Sands P. J., Forrester D. I., Feikema P. M., Livesley S. J., Morris J. D. & Arndt S. K. (2009) A comparison of four process-based models and a statistical regression model to predict growth of Eucalyptus globulus plantations. Ecological Modelling 220, 734-46.

Mladenoff D. J. (2004) LANDIS and forest landscape models. Ecological Modelling 180, 7-19.

Moldan B., Černý, J. (1994) Small catchment research. In: Biogeochemistry of small catchments, a tool for environmental research. (ed B. Moldan, Černý, J.) pp. 1-30. John Wiley & Sons, Chichester, UK.

Murphy B. F. & Timbal B. (2008) A review of recent climate variability and climate change in southeastern Australia. International Journal of Climatology 28, 859-79.

Newham L. T. H., Letcher R. A., Jakeman A. J. & Kobayashi T. (2002) A framework for integrated hydrologic, sediment and nutrient export modelling for catchment-scale management. pp. 1029-38. Elsevier Sci Ltd, Lugano, SWITZERLAND.

Newham L. T. H., Letcher R. A., Jakeman A. J. & Kobayashi T. (2004) A framework for integrated hydrologic, sediment and nutrient export modelling for catchment-scale management. Environmental Modelling & Software 19, 1029-38.

Nightingale J. M., Hill M. J., Phinn S. R., Davies I. D., Held A. A. & Erskine P. D. (2008) Use of 3-PG and 3-PGS to simulate forest growth dynamics of Australian tropical rainforests: I. Parameterisation and calibration for old-growth, regenerating and plantation forests. Forest Ecology and Management 254, 107-21.

Ogden J. C., Davis S. M., Barnes T. K., Jacobs K. J. & Gentile J. H. (2005) Total System Conceptual Ecological Model. Wetlands 25, 955-79.

Oreskes N., Shraderfrechette K. & Belitz K. (1994) Verification, Validation, and Confirmation of Numerical-Models in the Earth-Sciences. Science 263, 641-6.

Pacala S. W., Canham C. D., Saponara J., Silander Jr. J. A., Kobe R. K. & Ribbens E. (1996) Forest Models Defined by Field Measurements: Estimation, Error Analysis and Dynamics. Ecological Monographs 66, 1-43.

Pacala S. W., Canham C. D. & Silander J. A. J. (1993) Forest models defined by field measurements: I. The design of a northeastern forest simulator. Can. J. For. Res. 23, 1980-8.

Parker P., Letcher R., Jakeman A., Beck M. B., Harris G., Argent R. M., Hare M., Pahl-Wostl C., Voinov A., Janssen M., Sullivan P., Scoccimarro M., Friend A., Sonnenshein M., Baker D., Matejicek L., Odulaja D., Deadman P., Lim K., Larocque G., Tarikhi P., Fletcher C., Put A., Maxwell T., Charles A., Breeze H., Nakatani N., Mudgal S., Naito W., Osidele O., Eriksson I., Kautsky U., Kautsky E., Naeslund B., Kumblad L., Park R., Maltagliati S., Girardin P., Rizzoli A., Mauriello D., Hoch R., Pelletier D., Reilly J., Olafsdottir R. & Bin S. (2002) Progress in integrated assessment and modelling. Environmental Modelling & Software 17, 209-17.

Parton W. J., Stewart J. W. B. & Cole C. V. (1988) Dynamics of C, N, P and S in Grassland Soils: A Model. Biogeochemistry 5, 109-31.

Peel M. C., Watson F. G. R. & Vertessy R. A. (2003) Modelling impacts of land use change on low flows in the North Esk river using the macaque model. pp. 849-54. Univ Western Australia, Townsville, AUSTRALIA.

Perry G. L. W. & Enright N. J. (2006) Spatial modelling of vegetation change in dynamic landscapes: a review of methods and applications. Progress in Physical Geography 30, 47-72.

Porté A. & Bartelink H. H. (2002) Modelling mixed forest growth: a review of models for forest management. Ecological Modelling 150, 141-88.

Potter C. S., Alexander S. E., Coughlan J. C. & Klooster S. A. (2001) Modeling biogenic emissions of isoprene: exploration of model drivers, climate control algorithms, and use of global satellite observations. Atmospheric Environment 35, 6151-65.

Potter C. S. & Klooster S. A. (1999) Dynamic global vegetation modelling for prediction of plant functional types and biogenic trace gas fluxes. Global Ecology and Biogeography 8, 473-88.

Potter C. S., Randerson J. T., Field C. B., Matson P. A., Vitousek P. M., Mooney H. A. & Klooster S. A. (1993) Terrestrial Ecosystem Production - a Process Model-Based on Global Satellite and Surface Data. Global Biogeochemical Cycles 7, 811-41.

Prosser I., Rustomji P., Young B., Moran C. & Hughes A. (2001a) Constructing River Basin Sediment Budgets for the National Land and Water Resources Audit. CSIRO Land and Water, Canberra.

Prosser I. P., Rutherfurd I. D., Olley J. M., Young W. J., Wallbrink P. J. & Moran C. J. (2001b) Large-scale patterns of erosion and sediment transport in river networks, with examples from Australia. Marine and Freshwater Research 52, 81-99.

Ranatunga K., Nation E. R. & Barratt D. G. (2008) Review of soil water models and their applications in Australia. Environmental Modelling & Software 23, 1182-206.

Ravalico J. K., Dandy G. C. & Maier H. R. (2007) More sensitivity analysis of the MSM-BIGMOD River Murray flow and salinity model. In: MODSIM 2007 International Congress on Modelling and Simulation (ed L. O. a. D. Kulasiri) pp. 2754-60. Modelling and Simulation Society of Australia and New Zealand, Christchurch, New Zealand.

Reckhow K. H. & Chapra S. C. (1999) Modeling excessive nutrient loading in the environment. Environmental Pollution 100, 197-207.

Recknagel F., Hosomi M., Fukushima T. & Kong D.-S. (1995) Short- and long-term control of external and internal phosphorus loads in lakes--A scenario analysis. Water Research 29, 1767-79.

Reynolds C. S., Irish A. E. & Elliott J. A. (2001) The ecological basis for simulating phytoplankton responses to environmental change (PROTECH). Ecological Modelling 140, 271-91.

Reynoldson T. B., Norris R. H., Resh V. H., Day K. E. & Rosenberg D. M. (1997) The reference condition: a comparison of multimetric and multivariate approaches to assess water-quality impairment using benthic macroinvertebrates. Journal of the North American Benthological Society 16, 833-52.

Rizzoli A. E., Davis J. R. & Abel D. J. (1998) Model and data integration and re-use in environmental decision support systems. Decis. Support Syst. 24, 127-44.

Robson B. J. & Hamilton D. P. (2004) Three-dimensional modelling of a Microcystis bloom event in the Swan River estuary, Western Australia. Ecological Modelling 174, 203-22.

Sherman B., Todd C. R., Koehn J. D. & Ryan T. (2007) Modelling the impact and potential mitigation of cold water pollution on Murray cod populations downstream of Hume Dam, Australia. River Res. Appl. 23, 377-89.

Shifley S. R., Rittenhouse C. D. & Millspaugh J. J. (2009) Validation of Landscape-Scale Decision Support Models That Predict Vegetation and Wildlife Dynamics. In: Models for Planning Wildlife Conservation in Large Landscapes pp. 415-48. Academic Press, San Diego.

Sitch S., Smith B., Prentice I. C., Arneth A., Bondeau A., Cramer W., Kaplan J. O., Levis S., Lucht W., Sykes M. T., Thonicke K. & Venevsky S. (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology 9, 161-85.

Sivapalan M., Ruprecht J. K. & Viney N. R. (1996a) Water and salt balance modelling to predict the effects of land-use changes in forested catchments. 1. Small catchment water balance model. Hydrol. Process. 10, 393-411.

Sivapalan M., Viney N. R. & Jeevaraj C. G. (1996b) Water and salt balance modelling to predict the effects of land-use changes in forested catchments. 3. The large catchment model. Hydrol. Process. 10, 429-46.

Sivapalan M., Viney N. R. & Ruprecht J. K. (1996c) Water and salt balance modelling to predict the effects of land-use changes in forested catchments. 2. coupled model of water and salt balances. Hydrol. Process. 10, 413-28.

Taylor A. R., Chen H. Y. H. & VanDamme L. (2009) A Review of Forest Succession Models and Their Suitability for Forest Management Planning. For. Sci. 55, 23-36.

Thomson J. R., Moilanen A., Vesk P., Bennett A. F. & Mac Nally R. (2009) Where and when to revegetate: a quantitative method for scheduling landscape reconstruction. Ecol. Appl. 19, 817-28.

Timbal B. & Jones D. (2008) Future projections of winter rainfall in southeast Australia using a statistical downscaling technique. Climatic Change 86, 165-87.

Todd C. R., Nicol S. J. & Koehn J. D. (2004) Density-dependence uncertainty in population models for the conservation management of trout cod, Maccullochella macquariensis. Ecological Modelling 171, 359-80.

Todd C. R., Ryan T., Nicol S. J. & Bearlin A. R. (2005) The impact of cold water releases on the critical period of post-spawning survival and its implications for Murray cod (Maccullochella peelii peelii): A case study of the Mitta Mitta River, southeastern Australia. River Res. Appl. 21, 1035-52.

Townsend C. R., Begon M. & Harper J. L. (2008) Essentials of Ecology. Wiley-Blackwell, Oxford.

Vertessy R. A., Watson F. G. R., Rahman J. M., Cuddy S. M., Seaton S. P., Chiew F. H., Scanlon P. J., Marston F. M., Lymburner L., Jeannelle S. & Verbunt M. (2001) New software to aid water quality management in the catchments and waterways of the south-east Queensland region. In: Third Stream Management Conference: The Value of Healthy Streams (eds I. Rutherfurd, F. Sheldon, G. Brierley and C. Kenyon) pp. 611-6. Cooperative Research Centre for Catchment Hydrology, Brisbane.

Wang P. & Linker L. C. (2006) A correction of DIN uptake simulation by Michaelis-Menton saturation kinetics in HSPF watershed model to improve DIN export simulation. Environmental Modelling & Software 21, 45-60.

Watson F. G. R. (1999) Large scale, long term modelling of the effects of land cover change on forest water yield. In: Department of Civil and Environmental Engineering p. 443. University of Melbourne, Melbourne.

Watson F. G. R., Vertessy R. A. & Grayson R. B. (1999) Large-scale modelling of forest hydrological processes and their long-term effect on water yield. Hydrological Processes 13, 689-700.

Weeks A., Christy B., Lowell K. & Beverly C. (2008) The Catchment Analysis Tool: Demonstrating the Benefits of Interconnected Biophysical Models. In: Landscape Analysis and Visualisation pp. 49-71.

Wlosinski J. H. & Collins C. D. (1985) Evaluation of a water quality model (CE-QUAL-R1) using data from a small Wisconsin reservoir. Ecological Modelling 29, 303-13.

Young W. J., Scott A. C., Cuddy S. M. & Rennie B. A. (2003) Murray Flow Assessment Tool. A Technical Description (based on MFAT version 1.4). p. 81. CSIRO Land and Water, Canberra.

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

.

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

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

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)

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

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

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

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).

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

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

~

prediction of the impact of increasing frequency of bushfire on the water resources of the forested...

Documents

weather models

hydrology models

biogeochemistry models

clayton monash university

murray basin

page i

simple rainfallrunoff

contents summary