application of two water quality models as a decision
TRANSCRIPT
Application of two water quality models as a Decision Support System (DSS) within the Douglas Shire A Report to the Douglas Shire Council and the Department of the Environment and Heritage, Milestones 5.5, 5.10 and 5.15
Tim Ellis(1), Rebecca Bartley(2), Joel Rahman(1), Tony Weber(3), Anne Henderson(4), Charles Magee(5), Jenet Austin(1), Peter Hairsine(1), Sam Davies(1), Sue Cuddy(1) and Jake Macmullin(1) (1) CSIRO Land and Water, Canberra (2) CSIRO Land and Water, Atherton (3) WBM Oceanics, Brisbane (4) CSIRO Land and Water, Townsville (5) Australian National University, Canberra CSIRO Land and Water, Client Report April 2005
2
Copyright
© 2005 CSIRO and the Douglas Shire Council. This work is copyright. It may be reproduced subject to the inclusion of an acknowledgement of the source.
Important Disclaimer
CSIRO Land and Water and the Douglas Shire Council advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO Land and Water and the Douglas Shire Council (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.
Acknowledgments
This study was commissioned by the Douglas Shire Council using funds obtained from the Coastal Catchments Initiative Program within the Australian Government’s Natural Heritage Trust (NHT). This study presents the final results of the load modelling and Decision Support System development for the Douglas Shire Project. The modelling project would not have been possible without the support from numerous people including Scott Wilkinson and Ron DeRose (CSIRO).
We acknowledge the hydrological data supplied by Neale Searle and Darren Alston (Mareeba) and Alan Hooper and Morgain Sinclair (South Johnstone) of the Queensland Department of Natural Resources and Mines hydrological section. Data was also supplied from John Russell (Queensland Department of Primary Industries), Allan Stafford (Mossman Agricultural Services), Brynn Mathews and Terry Webb (Queensland Environment and Protection Agency) and Peter Bradley (Douglas Shire Council).
We also thank Kit Rutherford (CSIRO) and Albert Van Dijk (CSIRO) who provided valuable comments on an earlier draft of this document.
3
Executive Summary
This report
This report describes:
1. a water quality decision support system (DSS) developed for the Douglas Shire catchments which comprises two models (EMSS and SedNet);
2. preliminary comparison of results from the two models with the 1st year of measured loads;
3. application of the 2 models to provide an indication of the likely effectiveness of 17 potential on-ground changes (comprising 13 management actions and 4 hypothetical land use changes) to reduce pollutant export.
The sub catchments used for modelling and reporting pollutant loads within the Douglas catchment are described and aligned to represent the stream and landscape groupings determined for environmental values and pollutant management (Bennett, 2003).
The 13 management actions for the reduction of pollutant export (TSS, TN and TP) from the Douglas catchment have been proposed by the Douglas Shire Council following consultation with stakeholders and scientists. The 4 hypothetical land use changes have been modelled to test sensitivity of the model to potential future demographic changes.
The aim of this study is to provide the best advice possible at this time regarding the likely effectiveness of these on-ground changes, to help make choices and agree upon best management practice (BMP) scenarios for the future. We provide the outputs of a decision support system (DSS) for use by the Douglas Shire Council for evaluating the likely effect of on-ground changes intended to reduce the export of pollutants TSS (total suspended solids), TN (total nitrogen) and TP (total phosphorus) from the catchment.
The models
The DSS for the Douglas Shire comprises preliminary applications of two models (EMSS and SedNet).
• EMSS (Chiew et al., 2002; Vertessy et al., 2001; Watson et al., 2001) is a distributed parameter, daily time step model, which was calibrated to local conditions (measured pollutant concentrations and stream flow);
• SedNet (Prosser et al., 2001; Newham et al., 2003) is a process-based annual average model based on the Revised Universal Soil Loss Equation RUSLE (Renard et al., 1997) which models hillslope, bank and gully/drain erosion as separate erosion processes and also describes stream bed and flood plain deposition.
The two models were included in the DSS for two reasons. First, some on-ground changes can be described by one model but not the other. Second, it allows a choice of the model most appropriate for application within different terrains and land uses in the catchment.
Observed and simulated results
At the time of this analysis, monitoring was only completed for a single wet season (1/12/03 to 30/6/04) and there are gaps in these data (McJannet et al. 2005). Consequently there are currently insufficient data available to accurately calibrate and test the EMSS and SedNet models or to estimate annual loads, event mean and base flow concentrations for the sub-catchments within the Douglas Shire, notably for the Daintree National Park sub-catchments. Nevertheless, attempts are made to estimate annual loads and the effects of potential on-ground changes.
Average annual loads were simulated for each of the 15 sub catchments, using both models. Following comparison of these predictions with the few observed data available, we chose the most appropriate
4
model to represent each sub catchment, also considering the relative merits of the models and their ability to predict the effects of potential on-ground changes. For example, EMSS was calibrated to local observed stream flow and pollutant loads from Daintree and Mossman National Park areas and was therefore thought to provide the best simulations for natural forest. SedNet, however, following showed reasonable agreement with measured loads from agricultural land uses and describes channel and flood plain processes, so it was used to describe the flood plain and agricultural areas.
Simulation results are reported in terms of change in pollutant [total suspended solids (TSS), total nitrogen (TN) and total phosphorus (TP)] load relative to current conditions. The effect of each management action on the load is expressed as a percentage change relative to pollutant loads from the whole catchment and from each sub catchment.
Simulations of drain repair potential on-ground changes produced the largest reduction in TSS and TP export (24-35% and 7-8%, respectively) from the whole Douglas catchment. Drain repair warrants further investigation and field study to confirm the model predictions and more rigorously quantify benefits.
Simulations of a 75% reduction in point source pollutants indicated 1% and 8% reductions in TN and TP, respectively, from the whole catchment.
Improving riparian vegetation throughout the catchment was predicted to reduce TSS and TP loads by 1-4% but to be less effective in reducing TN loads. In Cassowary Creek, a small sub catchment of Mossman developed sub catchment, riparian management was predicted to even more effective (13% and 50% for TSS and TP respectively). Both EMSS and SedNet were used to simulate riparian zones but they represent the effects of riparian vegetation in different ways. We do not yet know which riparian sediment generation and deposition processes operate in which parts of the Douglas Shire and, therefore, where each model is most suitable. Further field surveys/experiments are required to allow a more accurate estimation of the effects of riparian vegetation management for reducing pollutant delivery.
Uncertainty
When this report was written, there was insufficient data from the monitoring program to provide a firm estimate of the uncertainty associated with measured loads, model calibration and simulated loads. By comparing simulated loads with the small amount of observed loads available, we recommend a factor of uncertainty factor of 2-3 be applied to the loads reported in this study. However, we expect that, as more pollutant observations are collected from the catchment, better model calibration and thorough, local, evaluation of the models will allow more confident use of simulations.
Decision support interface
A Decision Support System (DSS) interface was developed to describe the implementation of the two models in the Douglas catchments and to provide guidance for their future use. We provide a brief description of the DSS structure and function, and this is described more fully by Ellis et al. (in preparation).
5
Table of Contents Copyright.................................................................................................................................................................. 2 Important Disclaimer................................................................................................................................................ 2 Acknowledgments.................................................................................................................................................... 2
Executive Summary .................................................................................................................. 3 This report................................................................................................................................................................ 3 The models .............................................................................................................................................................. 3 Observed and simulated results .............................................................................................................................. 3 Uncertainty............................................................................................................................................................... 4 Decision support interface ....................................................................................................................................... 4
Table of Contents ...................................................................................................................... 5
Introduction................................................................................................................................ 6 Rationale for this study ............................................................................................................................................ 6 Aims ......................................................................................................................................................................... 6 Milestones................................................................................................................................................................ 6
5.5 DSS initiated.................................................................................................................................................. 7 5.10 DSS finalised............................................................................................................................................... 7 5.15 DSS evaluated and revised......................................................................................................................... 7
Background................................................................................................................................ 8 Characteristics of Douglas Shire ............................................................................................................................. 8 Sub catchments ....................................................................................................................................................... 8 Proposed on-ground changes for reducing pollutant export ................................................................................... 8
Methods.................................................................................................................................... 15 The water quality models....................................................................................................................................... 15
EMSS ................................................................................................................................................................ 15 SedNet (and ANNEX)........................................................................................................................................ 15
Application and evaluation of the water quality models......................................................................................... 16 The Decision Support System (DSS) interface ..................................................................................................... 16
Results and discussion........................................................................................................... 17 Comparison of simulated and observed data ........................................................................................................ 17 Uncertainty............................................................................................................................................................. 20 Rationale for the use of SedNet and EMSS results within the Douglas Shire ...................................................... 22 Simulated effects of potential on-ground changes ................................................................................................ 23
Conclusions............................................................................................................................. 30
References ............................................................................................................................... 32
Appendix A –Riparian vegetation and water quality monitoring sites................................ 34
Appendix B - Implementation of land use, land management, riparian and drain treatments within EMSS and SedNet ..................................................................................... 36
Drain erosion potential on-ground changes 2 and 14 - SedNet ............................................................................ 36 Riparian potential on-ground changes 6, 7, 16 and 17 – EMSS ........................................................................... 36 Minimum tillage/legume rotation potential on-ground changes 3 and 15 - SedNet............................................... 37
Appendix C – comparison of pollutant loads simulated by EMSS and SedNet ................. 38
Appendix D – Comparison of the rainfall periods considered by EMSS and SedNet ....... 40
Appendix E - Rainfall runoff calibration of EMSS................................................................. 41 Rainfall-runoff calibration ....................................................................................................................................... 41
Appendix F –Pollutant parameters used in EMSS and SedNet ........................................... 46
Appendix G – Point source pollutants used in EMSS .......................................................... 48
Appendix H - Riparian model used in EMSS......................................................................... 50 Sediment delivery ratio ...................................................................................................................................... 50 Nutrient (N and P) delivery ratio ........................................................................................................................ 51
Appendix I – Structural layout of DSS interface ................................................................... 52
6
Introduction
Rationale for this study
In 2003, the Douglas Shire Water Quality Improvement Project was initiated within the Douglas Shire, as the first of several studies aimed at monitoring, modelling and reducing the export of pollutants TSS (total suspended solids), TN (total nitrogen) and TP (total phosphorus) from the catchment.
These studies form the basis for the development of the Water Quality Improvement Plan, as part of the Queensland Coastal Catchments Initiative (CCI) program, undertaken by the Department of Environment and Heritage (DEH). The following paragraph and list of eight key points has been modified from the DEH CCI, web site (see http://www.deh.gov.au/coasts/pollution/cci/).
“The [CCI] Framework builds upon key elements of the National Water Quality Management Strategy (NWQMS) and the National Principles for the Provision of Water for Ecosystems. The key features of the Framework include:
1. the environmental values of the coastal water;
2. the catchment that discharges to that coastal water;
3. the water quality issues and subsequent water quality objectives;
4. the load reductions of pollutant/s to be achieved to attain and maintain the water quality objectives;
5. the setting of the maximum load of pollutant/s against diffuse and point sources of pollution;
6. the river flow objectives to protect identified environmental values, having regard for matters such as natural low flows, flow variability, floodplain inundation, interactions with water quality and the maintenance of estuarine processes and habitats;
7. management measures, timelines and costs in implementing the plan; and
8. the grounds for a "reasonable assurance" from jurisdictions to provide security for investments to achieve and maintain the specified pollutant load reduction and environmental flow targets.”
This study describes the implementation of two catchment scale water quality models (EMSS; Chiew et al., 2002 and SedNet; Prosser et al., 2001) within the Douglas Shire and their use as a decision Support System (DSS) for the management of water-borne pollutants, in the above context. The DSS allows simulation of potential on-ground changes for assessment of their affects on pollutant export from the catchment.
Aims
We provide the outputs of a decision support system (DSS) for use by the Douglas Shire Council for evaluating the likely effect of potential on-ground changes intended to reduce the export of pollutants TSS (total suspended solids), TN (total nitrogen) and TP (total phosphorus) from the catchment.
Milestones
This report represents the three following milestones associated with the Douglas Water Quality Project:
• 5.5 Decision Support System (DSS) initiated;
• 5.10 Decision Support System (DSS) finalised;
• 5.15 Decision Support System (DSS) evaluated and revised.
7
The exact nature of these milestones has evolved during the execution of the project and can now be defined as follows:
5.5 DSS initiated
• Definition of the terms of reference of the models comprising the DSS and manner in which they would be applied within the Douglas catchment.
• Outline and plan for the DSS interface developed to link the models comprising the DSS and facilitate their use.
5.10 DSS finalised
• Definition of the sub catchments used to describe the Douglas catchment for environmental value and water quality monitoring (i.e., the contributing areas associated with the stream reaches identified by Bennet (2003)).
• Agreement regarding the potential on-ground changes to be simulated by the models and the necessary GIS and data pre-processing required to constrain and implement the potential on-ground changes within the models.
5.15 DSS evaluated and revised
• Rationale and decision regarding the appropriate model for the representation of each management action and/or part of the catchment.
• Simulation of potential on-ground changes, running of models and presentation of results.
• Evaluation of simulated results by comparison with observed pollutant loads.
• Estimation of the likely uncertainty associated with the simulated results.
• Indication of the relative effects of each of the potential on-ground changes for reducing pollutant export from the Douglas catchment, and each of the sub catchments.
8
Background
Characteristics of Douglas Shire
The climate, land use and terrain of the Douglas shire are described in detail in Bartley et al. (2004). Features of the region pertinent to this study include:
1. highly seasonal (monsoonal) nature of the climate;
2. highly episodic, high intensity rainfall events;
3. large spatial variability of individual rainfall events and average annual rainfall (D. McJannet, unpublished data);
4. high average annual rainfall (between 2000 and 5000 m);
5. under the above conditions, the large proportion of annual pollutants fluxes that occur during a small number of large runoff events during the ‘wet season’ (McJannet et al., 2005);
6. large area of the natural forest (approximately 87% of the Douglas Shire; Table 1, Bartley et al., 2004; see also land use map Appendix A).
Our ability to understand and model pollutant generation and transport processes occurring within any catchment is limited by the data available to characterise the terrain, land use, stream flow and climate. Of critical importance with respect to spatially and temporally explicit models, is the spatial and temporal representativeness of climate, stream flow and pollutant loads. There is a paucity of all these data types for the Douglas catchments. The most serious limitation to this study is the lack of a dense spatial coverage of rainfall time series.
Sub catchments
Bartley et al. (2004) divided the Douglas Shire into four main catchments: Daintree, Saltwater Creek, Mossman and Mowbray. For the purposes of this study we have further divided these into 15 sub catchments (Table 1; Figure 1). Thirteen of these are associated with the stream links identified by Bennett, J. (2003) for the identification, evaluation and modelling of environmental values and water quality within the Douglas Shire. The Coastal Strip was added as a separate management unit and includes the relatively flat littoral terrain, which could not be described in greater detail due to the resolution of the digital elevation model (DEM) available. In this report, we refer specifically to sub catchments Daintree National Park and Mossman National Park, which were previously referred to as Upper Daintree and Upper Mossman, respectively, by McJannet et al. (2005), but will be referred to as Daintree National Park and Mossman National Park from here on.
Proposed on-ground changes for reducing pollutant export
Table 2 lists the 17 proposed on-ground changes for reducing pollutant export from the Douglas Shire. These were assembled by Douglas Shire Council following stakeholder consultation and following the initial study of Bartley et al. (2004) in which the model SedNet was applied to provide initial estimates of diffuse pollutant generation. In this study, we provide simulations of the likely effect of these changes on pollutant export from the 15 Douglas Shire sub catchments (Figure 1). On-ground changes 1 to 7 represent those proposed for adoption in 7 years time; on-ground changes 12 to 17 represent those proposed for adoption in 25 years time. Numbers 8 to 11 are hypothetical and their adoption would be subject to influences external to this project.
9
Tab
le 1
Cat
chm
ent a
nd s
ub c
atch
men
t hie
rarc
hy
and
asso
ciat
ed a
reas
with
in th
e D
ougl
as S
hire
. Lan
d us
e ar
eas
with
in e
ach
sub
catc
hmen
t are
sho
wn.
D
ain
tree
cat
chm
ent
Mo
ssm
an
catc
hm
ent
Mo
wb
ray
catc
hm
ent
Sal
twat
er C
k ca
tch
men
t
Land use
Area (ha)
Coastal strip
Daintree – developed
Daintree – estuarine
Daintree – National Park
Daintree – Stewart Creek
Daintree South Arm – estuarine
Mossman –developed
Mossman –estuarine
Mossman – National Park
Mowbray – developed
Mowbray –estuarine
Mowbray –National Park
Saltwater –developed
Saltwater –estuarine
Saltwater –National Park
Rai
nfo
rest
11
6235
16
93
7939
67
10
5270
1 16
432
372
4516
88
11
128
359
3 56
87
4177
28
7 41
43
Wet
Scl
ero
ph
yll
1374
1 16
78
41
284
7300
0
4 92
5 15
0
85
21
2361
74
4 24
7 35
D
ry S
cler
op
hyl
l 30
765
2 0
0 30
674
0 0
0 0
0 0
0 90
0
0 0
Gra
zin
g
5705
41
11
95
1547
3
1473
0
0 0
0 11
30
0 19
2 12
3 0
0 C
lear
ed
287
181
0 22
0
0 0
11
0 0
0 1
53
5 1
14
Reg
row
th
275
9 0
38
0 0
2 0
2 0
0 0
0 27
18
17
9 P
rod
uct
ion
Fo
rest
ry
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
Tre
e C
rop
53
22
0
26
0 0
0 0
0 0
0 0
0 5
1 0
Co
asta
l Mo
saic
40
54
1812
0
785
0 0
1178
0
77
0 0
55
13
0 13
6 0
Du
nes
69
0 42
2 0
0 0
0 26
3 0
0 0
0 5
0 0
0 0
Mel
aleu
ca/T
ran
siti
on
al
400
61
0 26
7 0
0 54
0
1 0
0 0
0 0
17
0 A
qu
acu
ltu
re
95
68
0 0
0 0
27
0 0
0 0
0 0
0 0
0 O
ther
13
88
338
0 94
0
0 72
34
7 15
0
1 11
69
28
2 13
4 25
H
ead
lan
d
634
163
0 61
0
0 30
15
7 27
0
0 10
13
83
88
2
Su
gar
can
e 81
32
1950
0
655
0 0
467
2083
37
6 0
9 80
21
9 11
87
1083
24
R
ura
l Res
iden
tial
92
2 31
1 7
145
0 19
66
18
4 6
0 4
2 84
51
22
20
U
rban
61
2 44
1 0
24
0 0
0 12
4 10
0
0 0
3 4
6 0
Ind
ust
rial
29
0
0 0
0 0
0 29
0
0 0
0 0
0 0
0 R
oad
17
7 51
0
13
0 0
7 50
5
0 0
2 9
27
14
1 T
ou
rist
18
2 17
5 0
7 0
0 0
0 0
0 0
0 0
0 0
0 Q
uar
ry
19
1 0
0 0
0 6
12
0 0
0 0
0 0
0 0
Wat
er
1064
14
5 10
2 50
5 33
7
206
0 25
0
0 16
5
0 20
0
TO
TA
L
1854
62
9565
92
84
1118
2 90
711
1793
1 27
55
8437
64
6 11
128
1589
20
5 87
98
6714
20
73
4442
10
Figure 1 The 15 sub catchments which comprise the four major catchments (inset) and land uses within the
Douglas Shire. The sub catchments were grouped to represent the stream reaches identified by Bennet (2003) for the assessment and management of environmental values and water quality. NOTE: we recommend that this figure be printed at A3 size.
11
Tab
le 2
: S
even
teen
pot
entia
l on-
grou
nd c
hang
es f
or r
educ
ing
pollu
tant
exp
ort
for
Dou
glas
Shi
re in
: O
n-gr
ound
cha
nges
1 t
o 7
repr
esen
t th
ose
prop
osed
for
ado
ptio
n in
7
year
s tim
e; o
n-gr
ound
cha
nges
12
to 1
7 re
pres
ent t
hose
pro
pose
d fo
r ad
optio
n in
25
year
s tim
e. N
umbe
rs 8
to 1
1 ar
e h
ypot
hetic
al a
nd th
eir
adop
tion
subj
ect t
o in
fluen
ces
exte
rnal
to th
is p
roje
ct. N
/A m
eans
‘not
app
licab
le’.
No
. D
escr
ipti
on
R
atio
nal
e Im
ple
men
tati
on
in S
edN
et
Imp
lem
enta
tio
n in
EM
SS
1 (7 y
r)
Red
uce
N lo
sses
from
sug
ar
cane
by
35%
T
his
repr
esen
ts th
e ap
plic
atio
n of
ap
prop
riate
bes
t m
anag
emen
t pra
ctic
es,
new
pro
duct
s an
d te
chno
log
y
35%
red
uctio
n of
N p
ollu
tant
co
ncen
trat
ion
asso
ciat
ed w
ith
suga
r ca
ne a
reas
35%
red
uctio
n of
N p
ollu
tant
co
ncen
trat
ion
asso
ciat
ed w
ith s
ugar
can
e ar
eas
2 (7 y
r)
Rep
air/
mod
ify d
rain
s by
65%
[p
ropo
rtio
ned
to 4
0% s
wal
e,
25%
dee
p dr
ain]
(se
e A
ppen
dix
B fo
r m
ore
deta
il)
Obs
erva
tions
and
initi
al
mod
el r
esul
ts fr
om
Sed
Net
indi
cate
d th
at
drai
ns c
ould
be
a si
gnifi
cant
sed
imen
t so
urce
. Thi
s re
pres
ents
th
e pr
opos
ed le
vel o
f dr
ain
repa
ir ac
com
plis
hed
in 7
yea
rs
time.
Met
hods
dev
elop
ed fo
r de
scrib
ing
drai
n re
pair
wer
e de
velo
ped
from
stu
dies
in
othe
r Q
ueen
slan
d co
asta
l ca
tchm
ents
(se
e A
ppen
dix
B)
N/A
3 (7 y
r)
960
ha o
f the
cur
rent
are
a un
der
conv
entio
nal s
ugar
ca
ne m
anag
ed u
sing
m
inim
um ti
llage
/legu
me
rota
tion
prac
tices
Thi
s is
the
area
of
pred
icte
d ad
optio
n of
m
inim
um ti
llage
/legu
me
fallo
w p
ract
ices
in 7
ye
ars
time.
Are
a w
eigh
ted
adju
stm
ent o
f co
ver
fact
or (
see
App
endi
x B
N/A
4 (7 y
r)
Mai
ntai
n ye
arly
ave
rage
co
ver
of 9
5% o
n sl
opes
>8%
sl
ope
on 1
00%
of g
razi
ng
land
.
Red
uce
the
eros
ion
haza
rd o
n st
eep
slop
es
For
slo
pes
>8%
on
graz
ing
area
s th
e C
fact
or in
the
US
LE
grid
was
cha
nged
to 0
.01
N/A
5 80
% im
prov
emen
t in
ripar
ian
vege
tatio
n in
the
Dai
ntre
e N
eed
to a
sses
s th
e lik
ely
effic
acy
of
impr
ovin
g rip
aria
n
Rip
aria
n ve
geta
tion
cove
r in
crea
sed
to 8
0%
Ful
l app
licat
ion
of r
ipar
ian
trap
ping
mod
el
(App
endi
x H
) to
sel
ecte
d sm
all s
ub
12
(7 y
r)
graz
ing
area
s
vege
tatio
n
catc
hmen
ts (
see
App
endi
x B
)
6 (7 y
r)
80%
impr
ovem
ent o
f rip
aria
n ve
geta
tion
in C
asso
war
y (M
ossm
an)
sub
catc
hmen
t
Nee
d to
ass
ess
the
likel
y ef
ficac
y of
im
prov
ing
ripar
ian
vege
tatio
n
Rip
aria
n ve
geta
tion
cove
r w
as
incr
ease
d to
80%
Rip
aria
n tr
appi
ng m
odel
(se
e A
ppen
dix
H)
was
app
lied
to 8
0% o
f all
stre
ams
bord
erin
g ag
ricul
tura
l lan
d.
7 (7 y
r)
50%
impr
ovem
ent o
f rip
aria
n ve
geta
tion
in o
ther
(al
l) ca
tchm
ents
Nee
d to
ass
ess
the
likel
y ef
ficac
y of
im
prov
ing
ripar
ian
vege
tatio
n
Rip
aria
n ve
geta
tion
cove
r w
as
incr
ease
d to
50%
Ful
l app
licat
ion
of r
ipar
ian
trap
ping
mod
el
(App
endi
x H
) to
sel
ecte
d sm
all s
ub
catc
hmen
ts (
see
App
endi
x B
)
8
Con
vert
20%
of s
ugar
can
e pr
esen
tly g
row
n on
3-1
0%
slop
e to
rur
al r
esid
entia
l.
Pro
ject
ed d
emog
raph
ic
chan
ges
Are
a w
eigh
ted
adju
stm
ent o
f co
ver
fact
or (
see
App
endi
x B
)
The
pro
port
ion
of s
ugar
can
e oc
curr
ing
on 3
-10%
slo
pe w
as d
eter
min
ed. T
his
was
red
uced
by
20%
and
rur
al r
esid
entia
l la
nd u
se w
as in
crea
sed
by
the
equi
vale
nt
area
.
9
Con
vert
20%
of s
ugar
can
e pr
esen
tly g
row
n on
3-6
%
slop
e to
farm
fore
stry
.
Red
uctio
n of
ero
sion
ha
zard
on
stee
p sl
opes
A
rea
wei
ghte
d ad
just
men
t of
cove
r fa
ctor
(se
e A
ppen
dix
B)
The
pro
port
ion
of s
ugar
can
e oc
curr
ing
on 3
-6%
slo
pe w
as d
eter
min
ed. T
his
was
re
duce
d by
20%
and
farm
fore
stry
was
in
crea
sed
by
the
equi
vale
nt a
rea.
10
Con
vert
20%
of s
ugar
can
e pr
esen
tly g
row
n on
3-6
%
slop
e to
gra
zing
.
Red
uctio
n of
ero
sion
ha
zard
on
stee
p sl
opes
A
rea
wei
ghte
d ad
just
men
t of
cove
r fa
ctor
(se
e A
ppen
dix
B)
The
pro
port
ion
of s
ugar
can
e oc
curr
ing
on 3
-6%
slo
pe w
as d
eter
min
ed. T
his
was
re
duce
d by
20%
and
gra
zing
was
in
crea
sed
by
the
equi
vale
nt a
rea.
11
Con
vert
100
% o
f gra
zing
on
slop
es >
8% s
lope
to n
atur
al
rain
fore
st.
With
vie
w to
red
ucin
g th
e er
osio
n ha
zard
on
stee
p sl
opes
For
sce
nario
11,
all
of th
e gr
azin
g ar
ea o
n >
8% s
lope
w
as c
onve
rted
to r
ainf
ores
t (i.
e. C
fact
or o
f 0.0
06).
The
pro
port
ion
of g
razi
ng o
ccur
ring
on
>8%
slo
pe w
as d
eter
min
ed. T
his
was
re
duce
d by
20%
and
nat
ive
bush
by
the
equi
vale
nt a
rea.
12
Red
uce
expo
rt o
f all
pollu
tant
s fr
om p
oint
sou
rces
by
75%
Inte
ntio
n to
app
ly m
ore
stric
t con
trol
s on
the
man
agem
ent a
nd
N/A
T
N a
nd T
P p
oint
sou
rce
expo
rt r
educ
ed
by 7
5%.
13
(25
yr)
trea
tmen
t of p
oint
so
urce
pol
lutio
n
13
(25
yr)
Red
uce
N lo
sses
from
sug
ar
cane
by
53%
T
his
repr
esen
ts th
e ap
plic
atio
n of
ap
prop
riate
bes
t m
anag
emen
t pra
ctic
es,
new
pro
duct
s an
d te
chno
log
y
N e
xpor
t fro
m s
ugar
can
e w
as
redu
ced
by 5
3%.
Pol
luta
nt N
exp
ort f
rom
sug
ar c
ane
was
re
duce
d by
53%
.
14
(25
yr)
Rep
air/
mod
ify d
rain
s by
95%
[p
ropo
rtio
ned
to 6
0% s
wal
e,
35%
dee
p dr
ain]
(se
e A
ppen
dix
B fo
r m
ore
deta
il)
Obs
erva
tions
and
initi
al
mod
el r
esul
ts fr
om
Sed
Net
indi
cate
d th
at
drai
ns c
ould
be
a si
gnifi
cant
sed
imen
t so
urce
. Thi
s re
pres
ents
th
e le
vel o
f dra
in r
epai
r ap
plie
d in
25
year
s tim
e.
Met
hods
dev
elop
ed fo
r de
scrib
ing
drai
n re
pair
wer
e de
velo
ped
from
stu
dies
in
othe
r Q
ueen
slan
d co
asta
l ca
tchm
ents
(se
e A
ppen
dix
B).
N/A
15
(25
yr)
Cha
nge
1600
ha
of th
e pr
esen
t sug
ar c
ane
area
un
der
min
imum
til
lage
/legu
me
rota
tion
prac
tices
.
Pre
dict
ed g
reat
er
adop
tion
of m
inim
um
tilla
ge/le
gum
e fa
llow
pr
actic
es
Are
a w
eigh
ted
adju
stm
ent o
f co
ver
fact
or (
see
App
endi
x B
).
N/A
16
(25
yr)
95%
impr
ovem
ent o
f rip
aria
n ve
geta
tion
in C
asso
war
y (M
ossm
an)
sub
catc
hmen
t
Nee
d to
ass
ess
the
likel
y ef
fect
of i
mpr
ovin
g rip
aria
n ve
geta
tion.
Rip
aria
n ve
geta
tion
cove
r w
as
incr
ease
d to
95%
R
ipar
ian
trap
ping
mod
el (
see
App
endi
x H
) w
as a
pplie
d to
80%
of a
ll st
ream
s bo
rder
ing
agric
ultu
ral l
and.
17
(25
yr)
95%
impr
ovem
ent o
f rip
aria
n ve
geta
tion
in o
ther
(al
l) ca
tchm
ents
Nee
d to
ass
ess
the
likel
y ef
ficac
y of
im
prov
ing
ripar
ian
vege
tatio
n
Rip
aria
n ve
geta
tion
cove
r w
as
incr
ease
d to
95%
Ful
l app
licat
ion
of r
ipar
ian
trap
ping
mod
el
(App
endi
x H
) to
sel
ecte
d sm
all s
ub
catc
hmen
ts (
se A
ppen
dix
B)
14
15
Methods
The water quality models
Two water quality models (EMSS and SedNet) were applied within this project to simulate the effects of the 17 potential on-ground changes listed in Table 2. These two models have been applied to provide greater flexibility for the description of pollutant generation and transport processes within the catchment. This has allowed a choice of the model most appropriate for describing different land use types (e.g. natural forest, agriculture). In some cases, potential on-ground changes could only be described by one of the models. EMSS and SedNet (and the associated ANNEX model) are briefly described below but for more detailed descriptions we refer readers to the references cited in the following sections.
EMSS
The Environmental Management Support System (EMSS) (see Chiew et al., 2002) is a spatially explicit model for estimating pollutant (total suspended solids, TSS; total nitrogen, TN; and total phosphorus TP) delivery from catchments and investigating the effects of land use or management changes within them. The modelling environment and an overview of the EMSS model components are described in Watson et al. (2001) and Vertessy et al. (2001) respectively. Details of the application and use of EMSS can be found in Murray et al. (2005). EMSS is a hydrologic based, semi-distributed parameter, regional water quality model. It has the capacity to predict daily runoff, and daily loads of total suspended sediment, total nitrogen and total phosphorus from sub-catchments, interlinked within a river network. Users of the EMSS can manipulate climate, land use, land management, riparian management and point source inputs to the model to ascertain their effect on runoff and pollutant loads.
EMSS is classified as a ‘distributed parameter’ model, calibrated to local conditions. It requires an input data layer of elevation to produce a node-link network of sub catchments and streams. Data layers of rainfall and potential evapotranspiration time series provide climatic inputs to the rainfall runoff component (SIMHYD; see Chiew et al., 2002), which is calibrated to observed flow (see Appendix D). The stream flow concentration of pollutants, derived from the various land use types, is calibrated to observed values of event mean concentration (EMC) for runoff events, and dry weather concentrations (DWC) for base flow conditions (see Appendices E and F). These are spatially distributed across the landscape according to a land use map, and are adjusted with respect to an ‘erosion hazard index’ derived from the Universal Soil Loss Equation (RUSLE; Renard et al., 1997) parameters. Riparian vegetation is described as a pollutant sink, thereby reducing sediment loads in streams.
SedNet (and ANNEX)
SedNet (the Sediment River Network model) and the associated ANNEX (Annual Network Nutrient Export), are a set of modelling programs that were developed to construct catchment sediment and nutrient budgets (Prosser et al., 2001; Newham et al., 2003). Compared to EMSS, these models aim more towards describing the physical processes associated with pollutant sources and sinks on the land surface and within streams. The models report average annual pollutant export, driven by a representation of annual climate derived from a long-term record (typically 100 years; in the case of this study, up to 90 years). The sources of sediment described are soil erosion by hillslope surface processes (RUSLE; Renard et al., 1997), gully/drain erosion and riverbank erosion. We have described drains as a modified gully (see Appendix B). The application of ANNEX within the Douglas Shire is described in detail by Bartley et al. (2004). Pollutant sinks described by these models include channel and flood plains where sediment is deposited as flow velocities decrease. The model is also capable of assessing the implications of changes in land management practices on downstream water quality using an annual time step. Large scale application of the SedNet and ANNEX models to all of the Great Barrier Reef Catchments are described in McKergow et al., (2005 a and b).
16
Application and evaluation of the water quality models
For each management action, the model used to perform the simulation is listed in Table 2. In most cases, both models were able to represent the potential on-ground changes. However, only SedNet was suitable for representing drain repair (potential on-ground changes 2 and 14), minimum tillage/legume rotation (potential on-ground changes 3 and 15) and maintenance of cover on steep grazing land (potential on-ground change 4). Conversely, only EMSS was able to represent the reduction in point source pollutant export.
SedNet used a long-term (up to 90 years) climate record but, due to computational demands, our application of EMSS used only a 9.5 year climate record. We investigated the characteristics of the 9.5 year record used and found that it was a representative sample of the long-term climate period (Appendix D). The calibration of (SIMHYD) the rainfall runoff component of EMSS is described in Appendix E. The relevant diffuse and point source parameters used by the models are described in Appendix F and G, respectively.
Few data were available for calibration of EMSS, evaluation of both models and estimation of uncertainty in the simulated pollutant loads. Nevertheless uncertainty analysis helped provide a rationale for the presentation of the results of only one model for each management action, within each sub catchment. In this analysis we also considered the suitability of each model for the representation of the various land uses and pollutant transport processes. We expect that, as the monitoring program continues, a more complete data set will be available for a calibration and evaluation of the models in 1 to 3 years time.
The Decision Support System (DSS) interface
To provide a common platform for EMSS and SedNet applications to the Douglas Shire, a software interface has been developed in a web-style modelling environment. This interface also functions as a repository for the large amount of background, specifications and rationale surrounding the Douglas Shire Water Quality Project. Information is presented in a form to assist with the navigation of the information and familiarisation with the catchment and the models. Applications of the models are provided to describe the scenarios (comprising selected potential on-ground changes described in this report) composed for the management of pollutants within the Douglas Shire. The models can be altered by users of the interface to investigate the likely effects of other changes in land use or management. A structure of the interface is shown in Figure 15, Appendix I, and a detailed description of the interface will be provided in Ellis et al. (in preparation).
17
Results and discussion
Comparison of simulated and observed data
In this section we present and compare simulated pollutant loads and the small set of observed data available at the time that this report was compiled. We emphasise that this is not a rigorous comparison but provides a basis to choose the most appropriate model for different parts of the catchment and an estimation of the uncertainty associated with simulated results.
We have presented in Table 3 observed and simulated (EMSS and/or SedNet) pollutant loads from Daintree National Park and Mossman National Park sub catchments. In addition, we have listed observed pollutant loads from a recent field study of sugar cane in the Douglas Shire (McJannet et al., 2005) for comparison with SedNet predictions from this land use. EMSS reports pollutant loads delivered from each sub catchment, but does not report the composition of these loads with respect to the individual land uses within the sub catchments. To augment these comparisons, we also list pollutant loads measured by Hunter et al. (2001) from a six year study undertaken in the Johnstone catchment in the Queensland wet tropics.
When comparing simulated results with observed data we considered:
1. observed loads from Daintree National Park and Mossman National Park were calculated from relatively short measurement periods (1/12/03 to 30/3/04 see McJannet et al., 2005);
2. Daintree National Park comprises rainforest, wet sclerophyll and dry sclerophyll whereas Mossman National Park is almost entirely rainforest;
3. the observation periods were unusually wet: approximately 1.5 and 0.9 times the mean annual flow was recorded over 7 months, for Daintree National Park and Mossman National Park, respectively;
4. EMSS was calibrated to local flow and pollutant load observations, where available (see Appendices E and F, respectively) whereas SedNet was not calibrated to local flow or pollutant load observations.
McJannet et al. (2005) indicate that it is difficult to accurately determine pollutant loads from the dataset obtained from the Daintree National Park sub catchment because of the paucity of pollutant concentration data for the flow measurement period. However, the values calculated for Mossman National Park are likely to be a reasonable representation of seasonal load because of a good spread of pollutant concentration measurements throughout the flow measurement period representation.
Reasonable stream flow calibrations were achieved within EMSS for a sub catchment of Saltwater Creek National Park (Whyanbeel) and the Daintree National Park, but the calibration for Mossman National Park was poor (see Appendix E). This was thought to be due, in part, to missing flow data and an inability to adequately scale the rainfall data to simulate the suspected high spatial variation in this catchment. Mass balance calculations undertaken during the calibration procedure indicated that the rainfall surfaces available for the Douglas Shire (particularly the National Park areas) did not represent sufficient rainfall to produce the measured stream flows. We suspect that this is due to poor representation of the strong rainfall gradients thought to occur in these areas (McJannet – unpublished data). To satisfy the mass balance requirements of the calibration procedure, it was necessary to impose a scaling regime on the monthly rainfall values (see Appendix E).
Event mean concentrations (EMCs) are required within EMSS to describe pollutant export from each land use. Reliable data to calculate EMC values for natural forest were available from the Mossman National Park but not from the Daintree National Park. We suspect that the Mossman
18
data may underestimate EMC values for the Daintree because of differences in local geology and vegetation types. This is discussed in detail by McJannet et al. (2005).
SedNet simulates the hillslope generation of TSS using RUSLE (Renard et al., 1997), a modified universal soil loss equation. ANNEX links the generation of TN and TP to the TSS load, We expect that these approaches are well-suited to representing pollutant generation from agricultural land uses and this is supported by pollutant loads measured from field experiments from sugar cane areas. However, the suitability of the RUSLE for tropical forest is unknown and hence the uncertainty is high in the SedNet and ANNEX predictions for the Daintree and Mossman National Parks.
19
Tab
le 3
: O
bser
ved
and
sim
ulat
ed p
ollu
tant
load
s fr
om D
ougl
as s
ub c
atch
men
ts a
nd o
bser
ved
load
s fr
om t
he s
imila
r w
et t
ropi
cal J
ohns
tone
cat
chm
ent.
The
m
easu
red
data
for
the
Dou
glas
Shi
re w
as s
ourc
ed f
rom
McJ
anne
t et
al.,
(20
05)
and
repr
esen
ts a
wet
sea
son
with
hig
her
than
ave
rage
rai
nfal
l. S
edN
et s
imul
atio
ns r
epre
sent
the
long
-ter
m a
nnua
l ave
rage
(up
to
90 y
ears
). E
MS
S s
imul
atio
ns r
epre
sent
ann
ual a
vera
ges
over
a 9
.5 y
ear
perio
d fr
om a
cal
ibra
ted
mod
el. J
ohns
tone
was
dat
a so
urce
d fr
om H
unte
r et
al.,
(20
01),
col
lect
ed o
ver
a si
x ye
ar p
erio
d.
T
SS
(t/
ha/
yr)
TN
(kg
/ha/
yr)
TP
(kg
/ha/
yr)
S
imu
late
d
(Do
ug
las)
M
easu
red
(D
ou
gla
s)
Mea
sure
d
(Jo
hn
sto
ne)
S
imu
late
d
(Do
ug
las)
M
easu
red
(D
ou
gla
s)
Mea
sure
d
(Jo
hn
sto
ne)
S
imu
late
d
(Do
ug
las)
M
easu
red
(D
ou
gla
s)
Mea
sure
d
(Jo
hn
sto
ne)
L
and
use
Sed
Net
EM
SS
Sed
Net
EM
SS
Sed
Net
EM
SS
Fo
rest
1.
18a
0.21
a 2.
48a
6.8a
8.7a
39a
1.2a
0.3a
2.65
a
1.
20b
0.18
b 0.
33b
1.2
7.7b
6.7b
17
8.9
0.9b
0.2b
0.17
b
2.3
Su
gar
0.
63
0.
53c
3.9
25.4
3
11.5
8c 38
.1
1.83
2.46
c 6.
6
Ban
anas
4.
92
4.0
13.5
5
42
.2
2.54
6.
8
Pas
ture
1.
2
1.
2 45
.13
8.3
7.42
2.
4
a =
Dai
ntre
e N
atio
nal P
ark
b =
Mos
sman
Nat
iona
l Par
k c
= r
esul
ts fr
om fe
rtili
zer
tria
ls
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
___
20
Uncertainty
Table 3 shows some large differences between simulated and observed loads. To decide if there is a statistically significant difference between observed and predicted load estimates, one needs to know the uncertainties of both estimates. These cannot yet been quantified accurately. Nevertheless, a semi-quantitative discussion of uncertainty is attempted here.
The measured loads in the Daintree have a high uncertainty because only a single season has been monitored and there are large gaps in this record (McJannet et al., 2005). Consequently, we suggest that an uncertainty factor of 3-5 be ascribed to current load estimates from the Daintree. The measured loads in the Mossman have a lower uncertainty because more samples were collected. However, there were problems matching simulated flows to the observed flow record. We suggest that an uncertainty factor of 2-3 be ascribed to current load estimates from the Mossman. The measured loads are for a single year, which was significantly wetter than average. Consequently, load estimates may not be representative of a ‘typical’ year. We currently have insufficient flow and concentration data to accurately estimate the year-to-year variability in load.
The uncertainty in predicted loads is also high because of uncertainty in calibration of the flow and EMC components of EMSS. SedNet has not been calibrated locally and we rely upon the accuracy of its earlier calibration and testing (Prosser et al., 2001).
EMSS appears to underestimate the measured TSS load in both Daintree (by a factor of 10) and Mossman (by a factor of 2) National Parks. Three factors may contribute to this:
• in Mossman National Park – an under prediction of flow due to the poor calibration allowed by the available data;
• in Daintree National Park – the pollutant concentration data for this site reported by McJannet et al. (2005) did not allow reliable estimates of EMCs. Therefore, EMCs for natural forest were calculated from Mossman National Park data (all rainforest), which are likely to underestimate the EMCs from Daintree National Park (sclerophyll and rainforest);
• the measurement periods of McJannet et al. (2005) represent much wetter than average season. Based on Mean Annual Flow it was the 6th highest discharge year on record. The March 2004 events had the 3rd highest stage height on record (pers. comm., Gary Drake, NRM Mareeba). EMSS predictions are the average for a 9.5 year period considered to have a ‘typical’ climate;
EMSS estimates of TSS load are lower (by a factor of 6) than the observations by Hunter et al. (2001) from the Johnstone catchment. However, the latter represent six year period and, although it is also a wet tropical catchment, it is remote from Douglas Shire catchments and is not necessarily representative of local conditions.
Overall, we suspect that the current EMSS model underestimates TSS and TP loads from the natural forest catchments, because it probably underestimates the EMC for TSS and TP from the Daintree. There is qualitative evidence to support this with photographs during floods suggesting significantly higher TSS concentrations in water draining from the Daintree than from the Mossman (Dave McJannet pers. comm.). It is likely, with 1-2 years of additional observations, it will be possible to refine and improve the EMSS model calibration. In the meantime, we suggest that the EMSS model predictions for the natural forests be ascribed an uncertainty factor of 2-3.
SedNet simulated long term average annual TSS and TP loads are approximately half of the seasonal load observed for Daintree National Park but approximately four times those observed for Mossman National Park. There are two possible reasons for these differences:
• the unknown performance of the RUSLE (Renard et al., 1997) approach for simulating TSS generation in tropical forest hillsopes. This method could possibly overestimate TSS generation because of the sensitivity of the model to the high slopes and intense rainfall events within tropical environments. This could also account for high TSS and TP loads from Mossman National Park simulated by SedNet;
21
• the very wet season recorded at Daintree National Park
We, therefore, suggest that the SedNet predictions of TSS and TP load is ascribed an uncertainty of 2-3. We favour EMSS over SedNet for predicting pollutant loads from natural forest because we have the ability to re-calibrate EMSS as more monitoring data becomes available, whereas SedNet was not intended for this type of adjustment. However, the RUSLE approach within SedNet was developed for application in agricultural areas and is therefore likely to better represent pollutant loads from these land uses. Table 3 shows that SedNet simulations are close to observed pollutant loads for sugar cane areas in the Douglas catchment and sugar, bananas and pasture in the Johnstone catchment (Hunter et al., 2001). These comparisons, while useful, are considered ‘a rough guide’ due to:
• the Johnstone catchment, although similar in nature to the Douglas catchment, is remote and may not suitably represent local conditions;
• the small spatial scale and short temporal scale of the flume observations of land use effects made within the Douglas catchment.
Observed TN loads in the Daintree and Mossman catchments are 3-6 times higher than predicted by SedNet and EMSS. We consider there are two possible explanations for these discrepancies for the Douglas Shire forested areas.
• Firstly, 2003/04 was a very wet year. This large flow was followed by two of the driest years on record. When large flow years follow dry years, the build up of organic nutrient matter from leaf litter may be mobilised, thus resulting in the very high measured TN loads from the forested areas. These measured TN loads in Table 3 probably represent the upper limit of TN values for the forested areas. Based on this result, it may be that the models have actually represented the long term mean TN values reasonably well, but further measured data will be required to validate this;
• Secondly, the models may not represent nitrogen transport very well in forested systems. More data will be required to rectify this within the modelling frameworks used, particularly so EMSS is better able to represent stream flow from Mossman National Park.
Overall, we suggest that the uncertainty in load estimates averages approximately a factor of 2-3. We expect that with one or two more years of data collection within the Douglas Shire, it will be possible to make more informed judgements regarding the accuracy of the simulated loads using EMSS and SedNet.
22
Rationale for the use of SedNet and EMSS results within the Douglas Shire
From the comparisons in the previous section, we recommend the two water quality models be used within the Douglas Shire as follows:
1. EMSS to describe pollutant export from the (mainly) forested catchments: Daintree National Park, Daintree Developed, Stewart Creek, Saltwater National Park, Mossman National Park and Mowbray National Park. Our rationale is that EMSS has been roughly calibrated to local observed flow and pollutant concentrations (EMCs) for these areas, and can be re-calibrated as more monitoring information becomes available.
2. SedNet to describe pollutant export from the remaining catchments, which includes most of the less steep and agricultural land. Our rationale is that SedNet has been found to give reasonable load predictions in these areas, and is better suited than EMSS for examining the proposed control measures.
We note that at the present time EMSS is likely to underestimate pollutant loads from Mossman National Park (due to flow calibration problems) and Daintree National Park (because of sparse concentration data).
Simulated annual loads for current conditions and the model of origin of TSS, TN and TP for the 15 sub catchments of the Douglas Shire are listed in Table 4.
Table 4 Simulated annual pollutant export for current land use and management conditions, from the 15 sub catchments of the Douglas Shire. For each case, results are shown from the model (either EMSS or SedNet) judged to be the best for describing local conditions within the sub catchment. Note the negative value for Mossman estuarine represents SedNet calculation of a net sediment sink in this sub catchment, due to channel and flood plain deposition.
Pollutant (t/yr)
Sub catchment Model used TSS TN TP
Coastal strip SedNet 30,685 135 35.0
Daintree - developed EMSS 8,430 194 12.0
Daintree - estuarine SedNet 14,645 150 30.0
Daintree - National Park EMSS 21,359 1,052 35.7
Daintree - Stewart Creek EMSS 10,208 262 14.9
Daintree - South Arm estuarine SedNet 2,600 30 5.0
Mossman - developed SedNet 23,275 90 10.0
Mossman - estuarine SedNet -6,845 10 5.0
Mossman - National Park EMSS 2,047 97 3.3
Mowbray - developed SedNet 1,190 5 0.8
Mowbray - estuarine SedNet 680 3 0.1
Mowbray - National Park EMSS 1,931 34 1.9
Saltwater - developed SedNet 14,755 90 15.0
Saltwater - estuarine SedNet 3,610 40 5.0
Saltwater - National Park EMSS 1,962 60 2.8
TOTAL 130,531 2,252 177
Annual loads from the areas predominated by National Park (Daintree National Park, Daintree Developed, Stewart Creek, Saltwater National Park, Mossman National Park and Mowbray National Park) are significantly lower that those reported by Bartley et al. (2004). This is due to our use of EMSS simulations for these catchments, as discussed in the previous section. The total TSS in Table 4 is approximately 60% of that reported by Bartley et al. (2004) for the whole catchment. This difference illustrates the high uncertainty in the load estimates available at this time and is part of the reason we recommend ascribing load estimates an uncertainty factor of 2-3. At this stage it is not clear whether
23
SedNet simulations over-estimate loads from these areas, or whether the EMSS simulations underestimate EMC (and hence loads) especially from the Daintree National Park.
Table 4 shows annual average TSS load from Mossman estuarine is as a negative number. SedNet simulates the total pollutant generation from hillslopes plus drain/gully/riverbank erosion, minus any channel and/or flood plain deposition (i.e., net export). In the case of the ‘Mossman estuarine’ sub catchment, sediment deposition in the channel and/or floodplain is greater sediment generation from the land surfaces and drains/gullies. The net export from the Mossman estuarine sub catchment is therefore negative.
Simulated effects of potential on-ground changes
The simulated effects of the 17 potential on-ground changes for reducing pollutant export (Table 2) is given in the following four tables. Table 5 and Table 6 give the relative reduction in annual load of each pollutant from each sub catchment, expressed as a percentage of total catchment loads, for potential on-ground changes 1 to 9 and 10 to 17, respectively (see Table 2 for description of treatments). Table 7 and Table 8 give the relative reduction in annual load of each pollutant from each sub catchment, expressed as a percentage of sub catchment loads, for on-ground changes 1 to 9 and 10 to 17, respectively.
24
Table 5 Change in annual pollutant export from each sub catchment as a percent of total export from the whole Douglas Shire for treatments 1 to 9 (Table 2). EMSS results were reported for the National Park sub catchments and Stewart Ck; SedNet was used for all other sub catchments. Dark shaded areas indicate where only SedNet could represent the on-ground change (e.g. drains) and light shaded areas indicate where only EMSS could represent the on-ground change (e.g. point source pollutants).
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Percent change in pollutant export relative to TOTAL export
Tre
atm
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Po
lluta
nt
Sed
Net
EM
SS
Sed
Net
EM
SS
EM
SS
Sed
Net
Sed
Net
Sed
Net
EM
SS
Sed
Net
Sed
Net
EM
SS
Sed
Net
Sed
Net
EM
SS
TO
TA
L
TSS N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
1 TN 1 N/A 0 N/A N/A 0 1 0 N/A 0 0 0 1 0 0 3
TP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
TSS 11 N/A 1 N/A N/A 1 9 -3 N/A 0 0 0 4 1 0 24
2 TN 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
TP 2 N/A 0 N/A N/A 1 1 0 N/A 0 0 0 2 1 0 7
TSS 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
3 TN 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
TP 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
TSS 0 0 0 N/A 0 0 0 0 N/A 0 0 0 0 0 0 0
4 TN 0 0 0 N/A 0 0 0 0 N/A 0 0 0 0 0 0 0
TP 0 2 0 N/A 0 0 0 0 N/A 0 0 0 0 0 0 2
TSS N/A 0 0 N/A 0 0 N/A N/A N/A N/A N/A N/A N/A N/A N/A 0
5 TN N/A 0 0 N/A 0 0 N/A N/A N/A N/A N/A N/A N/A N/A N/A 0
TP N/A 0 0 N/A 0 0 N/A N/A N/A N/A N/A N/A N/A N/A N/A 0
TSS N/A N/A N/A N/A N/A N/A 0 N/A N/A N/A N/A N/A N/A N/A N/A 0
6 TN N/A N/A N/A N/A N/A N/A 0 N/A N/A N/A N/A N/A N/A N/A N/A 0
TP N/A N/A N/A N/A N/A N/A 0 N/A N/A N/A N/A N/A N/A N/A N/A 0
TSS 0 0 0 N/A 0 0 0 0 N/A 0 0 0 0 0 0 0
7 TN 0 0 0 N/A 0 0 0 0 N/A 0 0 0 0 0 0 0
TP 0 0 0 N/A 0 0 0 0 N/A 0 0 0 0 0 0 0
TSS 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
8 TN 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
TP 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
TSS 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
9 TN 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
TP 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
25
Table 6 Change in annual pollutant export from each sub catchment as a percent of total export from the whole Douglas Shire for treatments 10 to 17 (Table 2). EMSS results were reported for the National Park sub catchments and Stewart Ck; SedNet was used for all other sub catchments. Dark shaded areas indicate where only SedNet could represent the on-ground change (e.g. drains) and light shaded areas indicate where only EMSS could represent the on-ground change (e.g. point source pollutants).
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Percent change in pollutant export relative to TOTAL export
Tre
atm
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Po
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nt
Sed
Net
EM
SS
Sed
Net
EM
SS
EM
SS
Sed
Net
Sed
Net
Sed
Net
EM
SS
Sed
Net
Sed
Net
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SS
Sed
Net
Sed
Net
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SS
TO
TA
L
TSS 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
10 TN 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
TP 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
TSS 0 1 0 N/A 2 0 0 0 N/A 0 0 0 0 0 0 3
11 TN 1 0 0 N/A 1 0 0 0 N/A 0 0 0 0 0 0 2
TP 0 2 0 N/A 2 0 0 0 N/A 0 0 0 0 0 0 4
TSS N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
12 TN 1 N/A 0 N/A N/A 0 0 0 N/A N/A N/A N/A 0 0 0 1
TP 7 N/A 0 N/A N/A 0 0 1 N/A N/A N/A N/A 0 0 0 8
TSS N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
13 TN 1 N/A 1 N/A N/A 0 1 0 N/A 0 0 0 1 1 0 5
TP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
TSS 16 N/A 2 N/A N/A 1 12 -5 N/A 0 1 0 6 2 0 35
14 TN 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
TP 2 N/A 0 N/A N/A 1 2 0 N/A 0 0 0 2 1 0 8
TSS 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
15 TN 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
TP 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0 0
TSS N/A N/A N/A N/A N/A N/A 2 N/A N/A N/A N/A N/A N/A N/A N/A 2
16 TN N/A N/A N/A N/A N/A N/A 0 N/A N/A N/A N/A N/A N/A N/A N/A 0
TP N/A N/A N/A N/A N/A N/A 3 N/A N/A N/A N/A N/A N/A N/A N/A 3
TSS 0 0 0 N/A 0 0 1 0 N/A 0 0 0 0 0 0 1
17 TN 0 0 0 N/A 0 0 0 0 N/A 0 0 0 0 0 0 0
TP 0 0 1 N/A 0 0 0 0 N/A 0 0 0 0 0 0 1
26
Table 7 Change in annual pollutant export from each sub catchment as a percent of total export from the sub catchment for treatments 1 to 9 (Table 2). EMSS results were reported for the National Park sub catchments and Stewart Ck; SedNet was used for all other sub catchments. Dark shaded areas indicate where only SedNet could represent the on-ground change (e.g. drains) and light shaded areas indicate where only EMSS could represent the on-ground change (e.g. point source pollutants).
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Percent change in pollutant export from the SUB CATCHMENT
Tre
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Po
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Sed
Net
EM
SS
Sed
Net
EM
SS
EM
SS
Sed
Net
Sed
Net
Sed
Net
EM
SS
Sed
Net
Sed
Net
EM
SS
Sed
Net
Sed
Net
EM
SS
TSS N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
1 TN 13 N/A 4 N/A N/A 14 21 33 N/A 2 24 6 12 24 0
TP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
TSS 48 N/A 12 N/A N/A 40 50 63 N/A 5 71 0 38 47 N/A
2 TN 5 N/A 1 N/A N/A 3 6 10 N/A 0 17 0 4 7 N/A
TP 5 N/A 1 N/A N/A 8 15 6 N/A 1 45 0 6 18 N/A
TSS 1 N/A 0 N/A N/A 1 1 0 N/A 0 0 15 1 1 0
3 TN 4 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0
TP 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0
TSS 0 5 2 N/A N/A 0 0 0 N/A 21 0 0 0 0 2
4 TN 0 0 3 N/A N/A 0 0 0 N/A 0 0 0 0 0 3
TP 0 25 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0
TSS 0 5 1 N/A 5 0 0 0 N/A 0 0 0 0 0 1
5 TN 0 3 0 N/A 3 0 0 0 N/A 0 0 0 0 0 0
TP 0 4 0 N/A 0 0 0 0 N/A 0 0 0 0 0 0
TSS N/A N/A N/A N/A N/A N/A 1 N/A N/A N/A N/A N/A N/A N/A N/A
6 TN N/A N/A N/A N/A N/A N/A 0 N/A N/A N/A N/A N/A N/A N/A N/A
TP N/A N/A N/A N/A N/A N/A 0 N/A N/A N/A N/A N/A N/A N/A N/A
TSS 0 3 0 N/A 2 0 0 0 N/A 0 0 0 0 0 0
7 TN 0 2 0 N/A 1 0 0 0 N/A 0 0 0 0 0 0
TP 0 2 0 N/A 1 0 0 0 N/A 0 0 0 0 0 0
TSS 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 15 0 0 3
8 TN 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 1 0 0 0
TP 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 1 0 0 0
TSS 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 11 0 0 3
9 TN 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 1 0 0 0
TP 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 3 0 0 0
27
Table 8 Change in annual pollutant export from each sub catchment as a percent of total export from the sub catchment for treatments 10 to 17 (Table 2). EMSS results were reported for the National Park sub catchments and Stewart Ck; SedNet was used for all other sub catchments. Dark shaded areas indicate where only SedNet could represent the on-ground change (e.g. drains) and light shaded areas indicate where only EMSS could represent the on-ground change (e.g. point source pollutants).
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Percent change in pollutant export from the SUB CATCHMENT
Tre
atm
ent
Po
lluta
nt
Sed
Net
EM
SS
Sed
Net
EM
SS
EM
SS
Sed
Net
Sed
Net
Sed
Net
EM
SS
Sed
Net
Sed
Net
EM
SS
Sed
Net
Sed
Net
EM
SS
TSS 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 9 0 0 3
10 TN 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0
TP 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 1 0 0 0
TSS 0 23 4 N/A 27 0 0 0 N/A 36 0 5 1 0 0
11 TN 19 8 3 N/A 9 0 0 0 N/A 0 0 2 0 0 0
TP 0 20 0 N/A 25 0 0 0 N/A 0 0 7 0 0 0
TSS N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
12 TN 16 N/A 1 N/A N/A 2 3 22 N/A N/A N/A N/A 1 2 0
TP 37 N/A 3 N/A N/A 8 7 43 N/A N/A N/A N/A 3 6 0
TSS N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
13 TN 19 N/A 6 N/A N/A 21 31 51 N/A 2 36 9 18 36 1
TP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
TSS 69 N/A 17 N/A N/A 57 69 93 N/A 7 97 0 53 72 4
14 TN 7 N/A 2 N/A N/A 5 9 0 N/A 1 25 0 5 10 0
TP 7 N/A 2 N/A N/A 10 22 8 N/A 1 65 0 9 25 0
TSS 1 N/A 1 N/A N/A 2 1 0 N/A 0 0 0 0 2 0
15 TN 4 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0
TP 0 N/A 0 N/A N/A 0 0 0 N/A 0 0 0 0 0 0
TSS N/A N/A N/A N/A N/A N/A 13 N/A N/A N/A N/A N/A N/A N/A N/A
16 TN N/A N/A N/A N/A N/A N/A 6 N/A N/A N/A N/A N/A N/A N/A N/A
TP N/A N/A N/A N/A N/A N/A 50 N/A N/A N/A N/A N/A N/A N/A N/A
TSS 0 7 3 N/A 4 1 3 6 N/A 2 3 0 2 4 0
17 TN 0 4 0 N/A 2 0 0 0 N/A 0 0 0 0 0 0
TP 0 5 0 N/A 3 0 0 0 N/A 0 0 0 0 0 0
Typically, the changes relative to the whole catchment are small (Table 5 and Table 6), due to the relatively small area (areas under human management; 13% of the catchment) within which the potential on-ground changes could be applied. Many of the potential on-ground changes were applied only to a small subset of this area. For example, on-ground change 9 (converting 20% of sugar cane grown on 3 to 6% slopes) is relevant only to an area of 1160 ha (Table 9), and therefore had only a small effect on total loads. The other land use changes were similarly applied to a small total area and therefore only had small effects on total loads. Where these values were less than 1% we have reported them as zero.
28
The two drain repair actions (on-ground changes 2 and 14) are predicted to significantly reduce TSS export (by 24% and 35% of the total catchment load, respectively). Drain repair is also predicted to reduce TP export (by 7% and 8%) which is comparable with the effect of a 75% reduction in point source pollution (on-ground change 12; 8% TP reduction). Drain repair was simulated using SedNet (as described in detail in Appendix B). The modelling predicts large potential benefits from improved drain management. There are a number of reasons why the drain repair scenarios are so high:
• The erosion rate of 9 t/ha/yr used to represent drain erosion (based on the results in Roth et al., 2003), is considered to be the upper limit of drain erosion for the Wet Tropics;
• The drain erosion modelling process used within the SedNet model does not take sediment deposition within the drain system into account (see Appendix B);
• Due to the lack of local drain erosion measurements, there is considerable uncertainty in the estimates used.
We regard the drain erosion modelling performed in this study as a first approximation, which indicates drains may be a significant source of pollutants. This warrants a more detailed study to confirm the model predictions and to more accurately quantify associated benefits.
Table 9 Areal changes (ha) associated with hypothetical land use changes 3 and 8 to 11 (Table 2).
Land use Total area (ha) between
brackets as % of total Douglas
area
On-ground change
(see Table 2)
Area affected (ha)
between brackets as % of total Douglas area
No. 3. - 960 ha minimum till/legume rotation 960 (0.5)
No. 8. – convert 20% of sugar cane on 3-10% slope to rural residential
920 (0.5)
No. 9. – convert 20% sugar cane on 3-6% slope to farm forestry
1160 (0.6)
No 10. – convert 20% sugar cane on 3-6% slope to grazing
1160 (0.6)
Sugar cane 8132 (4.4)
No. 15. – 1600 ha minimum till/legume rotation 1600 (0.9)
Grazing 5705 (3.1) No. 11. – convert all grazing on slopes >8% to natural forest
2100 (1.1)
The simulated changes in pollutant export, relative to total the sub catchment (Table 7 and Table 8) allow prioritisation of potential on-ground changes within sub catchments. For example, converting grazing to forest on slopes >8% (on-ground change 11) was simulated to reduce TSS export by 23% from Daintree developed sub catchment. This is more than three times the simulated effect on TSS load reduction of 95% rehabilitation of riparian vegetation (on-ground change 17). However, there may be other benefits associated with the rehabilitation of riparian vegetation which have not been simulated in this study and may need to be considered by catchment managers.
The two simulated nitrogen fertilizer reduction potential on-ground changes (1 and 13) produced, respectively, 3 and 5% reduction in total TN export from the whole catchment, while a 1% TN reduction is associated with a 75% reduction in point source export (12).
The riparian potential on-ground changes 5, 6 and 7 were predicted to produce only small (<1%) reductions in total pollutant load (Table 5). Potential on-ground changes 16 and 17 were predicted to reduce TSS and TP loads by 1-3% but to be less effective in reducing TN loads (Table 6). In Cassowary Creek (a small sub catchment of Mossman developed) riparian management was predicted to reduce TSS and TP loads significantly (13% and 50% respectively) although it was predicted to be less effective
29
in other catchments (0-7%). Depending on the catchment, either SedNet or EMSS was used to predict the effects of riparian management. SedNet and EMSS represent the effects of riparian vegetation in different ways: SedNet simulates the effects of riparian vegetation in reducing stream bank erosion (which is most relevant to larger streams where stream power is large) while EMSS simulates the effect of riparian vegetation in reducing the delivery of sediment to streams (which is most important in small, headwater streams). We have not attempted to explicitly allocate the most appropriate riparian model to each sub catchment because we do not yet know which riparian sediment generation and deposition processes operate in which parts of the Douglas Shire. Obtaining such an understanding will require further field investigations. Nevertheless, we suggest that the effects of riparian vegetation listed in the above tables provide a useful first approximation of the likely benefits of improvement in riparian vegetation function.
30
Conclusions The application of the two water quality models EMSS (Chiew et al., 2002; Vertessy et al., 2001; Watson et al., 2001) and SedNet (Prosser et al., 2001; Newham et al., 2003; Bartley et al., 2004) in the Douglas Shire has provided a simulation of the likely relative effects on total pollutant loads (total suspended solids, TSS, total nitrogen TN and total phosphorus TP) of the 17 potential on-ground changes (Table 2) proposed by the Douglas Shire Councill for reducing pollutant export.
EMSS is a distributed parameter model, calibrated to local conditions (stream flow and pollutant concentration) whereas SedNet uses a non-calibrated process-based approach to the generation of pollutants from hillslopes, gullies/drains and river banks. SedNet also describes deposition processes occurring within stream channels and flood plains. These and other differences between the models, have allowed the representation of more potential on-ground changes than would be made possible by the application of only one of these models, and has also provided a choice of the model most appropriate for application in different parts of then catchment.
A first evaluation of the two models within the Douglas Shire was made possible using the few observed data reported by McJannet et al. (2005). On this basis, and with due consideration of the different methods used by the models to describe pollutant generation, transport and deposition, EMSS was judged as the most appropriate model for describing the forested areas of the catchment whereas SedNet has been judged more appropriate for the lower slopes and agricultural areas. However, we consider the EMSS simulations of pollutant loads in this report, from some forested areas (e.g. Daintree National Park), to be underestimates. As more pollutant load observations become available over the next few years, from the Douglas Shire water quality monitoring program, it will be necessary to revisit the evaluation of the models.
We have reported the simulated changes in pollutant load, associated with each of the potential on-ground changes, relative to total catchment loads (Table 5 and Table 6) and relative to the load from the sub catchment (Table 7 and Table 8). This allows prioritisation of potential on-ground changes at both the catchment and sub catchment scales.
By far the greatest simulated reduction in pollutant load was associated with 24% and 35% repair/modification of drains (respectively, potential on-ground changes 2 and 14). These appear to be large values and should be taken as preliminary estimates, which warrant more detailed investigation to rigorously quantify the potential benefits. Generally, the proposed land use changes produced only small reductions in simulated loads, principally due to the small areas over which they were applied (see Table 9). For example, where land use changes (say, sugar cane to farm forestry) were proposed for 20% of sugar cane areas, within a slope band (say 3 to 6%), these typically represented approximately 1,000 to 2,000 ha, a small percentage of the approximate 185,500 ha area of the whole catchment.
The riparian on-ground changes (numbers 5, 6, 7, 16 and 17) produced only very small reductions in pollutants, relative to total catchment loads (Table 5 and Table 6). Cassowary Creek (a small sub catchment of Mossman developed) was an exception to this and improvement in riparian vegetation function (potential on-ground changes 6 and 16) produced 13, 6 and 50% reductions, respectively, in TSS, TN and TP, relative to the whole catchment. This result warrants a more detailed investigation of the options for riparian rehabilitation in this area. In other areas, at the sub catchment level (Table 7 and Table 8) improvement in riparian vegetation function produced small reductions (between 0 and 7%) in TSS export.
Because of the different methods within EMSS and SedNet used to represent riparian vegetation function, the above results represent slightly different processes, depending on the sub catchment in question, and hence the model (either EMSS or SedNet) applied in that sub catchment. We have not, however, attempted to explicitly allocate the most appropriate riparian model to each sub catchment as we cannot justify this detailed level of model application as we do not yet completely understand the distribution of the riparian sediment generation and deposition processes with the Douglas Shire. Obtaining such an understanding will require further field investigations. However, we suggest that the effects of riparian vegetation listed in the above tables provide a useful first approximation of the likely benefits of improvement in riparian vegetation function.
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Simulation by EMSS of 75% reduction on point source pollution (on-ground change 12; Table 2) suggests this may be an effective method for reducing total catchment export of TP (by up to 8%). The greatest reductions in total TN export (3% and 5%) were associated with, respectively, 35% and 53% reductions in TN export from sugar cane (on-ground changes 1 and 13; Table 2).
Overall, the results of this modelling study are surrounded by considerable uncertainty because of a lack of data, but on-going data collection should help to substantially reduce this uncertainty in future.
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References Bartley, R., Henderson, A., Hotham, H., Hartcher, S. and Wilkinson, S. (2004) Using the SedNet model
for scenario analysis within the Douglas Shire Catchment: final results and evaluation of the model. Report to Douglas Shire Council and the Department of Environment and Heritage, Canberra. http://www.clw.csiro.au/publications/consultancy/2004/Patterns_of_Erosion_Sediment_Nutrient_Transport.pdf
Bennett, J. (2003) Towards the establishment of Environmental Values and Water Quality Objectives for Douglas Shire waterways. QEPA report on initial workshop 16th May 2003.
Chiew, F., Scanlon, P., Vertessy, R. and Watson, F. (2002) catchment scale modelling of runoff, sediment and nutrient loads for the southeast Queensland EMSS. Cooperative research Centre for Catchment hydrology, Technical Report 02/1, February 2002
DeRose, R.C., Prosser, I. and Weisse, M. (2004) Patterns of erosion and sediment transport in the Murray-Darling Basin. Sediment Transfer through the Fluvial System, Proceedings of the Moscow Symposium, August 2004, IAHS Publication no. 288.p 245-252.
Dillaha, T.A. Reneau RB, Motaghimi, S. and Lee D. (1989). Vegetative filter strips for agricultural non point source pollution control. Transactions ASAE 32: 513-519.
Ellis, T.W., Davies, S., Cuddy, S. and MacMullin, J. (in preparation) An interface for the application of two catchment scale water quality models (EMSS and SedNet) within the Douglas Shire. A report to the Douglas Shire Council and the department of Environment and Heritage.
Herron, N.F. and Hairsine P.B., (1998). A scheme for evaluating the effectiveness of riparian zones in reducing overland flow to streams. Aust Jnl Soil Res. 36(4) 683-98.
Hunter, H., Sologinkin, S., Choy., S., Hooper, A., Allen, W., Raymond, M. and Peeters, J. (2001) Water management in the Johnstone Basin. Queensland Department of natural Resources and Mines, Brisbane.
Karssies L.,and Prosser, I.P. (2001). Designing grass filter strips to trap sediment and attached nutrient. Proc of third Australian Stream Management Conf. CRC for Catchment Hydrology pp 349-353.
McJannet, D., Fitch, P., Henderson, B., Harch, B., Bartley, R., Henderson, A., Thomas, S., Davis, R. Armour, J. and Webster, T. (2005) Douglas Shire water Quality Monitoring Strategy. A report to the Douglas Shire Council and the Department of Environment and Heritage, Canberra.
McKergow, L.A. Prosser, I.P., Hughes, A.O. and Brodie, J. (2005a) Sources of sediment to the Great Barrier Reef World Heritage Area. Marine Pollution Bulletin, 51, 200-211.
McKergow, L.A. Prosser, I.P., Hughes, A.O. and Brodie, J. (2005b) Regional scale nutrient modeling: exports to the Great Barrier Reef World Heritage Area. Marine Pollution Bulletin, 51, 186-199.
Murray, N., Cuddy, S.M, Rahman, J., Hairsine, P., Chiew, F., Grayson, R. and Seaton, S. (2005) EMSS User Guide. Client Report for the Cooperative Research Centre for Catchment Hydrology. CSIRO Land and Water, Canberra.
Newham, L.T.H., Norton, J.P., Prosser, I.P., Croke, B.F.W., Jakeman, A.J. (2003) Sensitivity analysis for assessing the behaviour of a landscape based sediment source and transport model. Environmental Modelling and Software, 18, 741-751.
Prosser, I., Hughes, A., Rustomji, P.. and Moran, C. (2001) Predictions of the sediment regime of Australian rivers. In proceedings of the Third Australian Stream Management Conference, Rutherford, I., Bunn, S., Sheldon, F. and Kenyon, C. (Eds). Cooperative Research Centre for Catchment Hydrology, Melbourne. P. 529-533.
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Prosser, I.P., L. Karssies, R. Ogden and P.B. Hairsine. (1999). Using buffers to reduce sediment and nutrient delivery to streams.. Chapter in Price, P. and S. Lovett (eds) 1999. Riparian Land Management Technical Guidelines, Volume 2: On ground management tools and techniques, LWRRDC Canberra. http://www.rivers.gov.au/acrobat/techguidelines/tech_guide_vol2_chapd.pdf
Renard, K.G., Foster, G. A., Weesies, D.K., McCool, D.K., and Yoder, D.C. (1997) Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation. Agriculture Handbook 703, United States department of Agriculture, Washington DC.
Roth, C. H., Visser, F., Wasson, B., Prosser, I., and Reghenzani, J. (2003) Quantifying and managing sources of sediment and nutrients in low-lying canelands. CSIRO Land and Water Technical Report 52/03. http://www.clw.csiro.au/publications/technical2003/tr52-03.pdf
Russell, D.J., McDougall, A.J. and Kistle, S.E. (1998) Fish resources and stream habitat of the Daintree, Saltwater, Mossman and Mowbray catchments. Report No. QI98062, Queensland Department of Primary Industries.
Vertessy, R., Watson, F., Rahman, J., Cuddy, S., Chiew, F., Scanlon, P., Seaton, S. and Marston, F. (2001). New software to help manage river water quality in the catchments of the south-east Queensland region. In: Rutherfurd, I., Sheldon, F., Brierly, G. & Kenyon, C. (eds). Proc. 3rd Australian Stream Management Conference, Brisbane 27-29 August, 2001, pp. 611-616.
Visser (2003) Sediment budget for cane land on the Lower Herbert River floodplain, North Queensland, Australia. Unpublished PhD Thesis, Australian National University, Canberra.
Watson, F., Rahman, J., and Seaton, S. (2001). Deploying environmental software using the Tarsier modelling framework. In: Rutherfurd, I., Sheldon, F., Brierly, G. & Kenyon, C. (eds). Proc. 3rd Australian Stream Management Conference, Brisbane 27-29 August, 2001, pp. 631-637.
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Appendix A –Riparian vegetation and water quality monitoring sites
This section provides some detail regarding the land use categories used within EMSS and SedNet, the distribution of riparian vegetation within the Douglas Shire and the location of water quality monitoring sites. The land use categories used by EMSS and SedNet are listed in Table 10.
Table 10 The land use categories used in EMSS as they represent the 20 land use categories used in SedNet.
Land use category in EMSS Comprising in SedNet Water Water Suburban Urban, industrial, road, tourism Dense urban N/A Future residential N/A Intensive agriculture Sugar cane, headland, tree crops Broad acre agriculture N/A Native bush Rainforest, wet sclerophyll, dry sclerophyll,
coastal mosaic, dunes, melaluca, transitional Grazing Grazing Plantations Production forestry Managed forest N/A Conservation area As with native bush, above
Riparian vegetation % cover is shown in Figure 2 and the stream flow water quality monitoring sites used from which pollutant concentration data were gathered are shown in Figure 3.
Figure 2: Riparian vegetation cover (from Bartley et al., 2004; adapted from Russell et al., 1998).
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Figure 3: Stream flow and water quality monitoring in the Douglas Shire. The yellow/orange dots represent the location of the stations that were installed in October 2003. These sites are described in detail by McJannet et al. (2005).
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Appendix B - Implementation of land use, land management, riparian and drain treatments within EMSS and SedNet Note: this section covers the methods not already described in Table 2, Background.
Drain erosion potential on-ground changes 2 and 14 - SedNet
A drainage map for the sugar cane areas in Saltwater Creek was provided to CSIRO from Mossman Agricultural Services (MAS). This map was a preliminary map of the ‘major’ drains in Saltwater Creek (note minor drains were not included). This map was the only drain information available for the Shire. The mapping suggested that the drain density for the catchment was 7.8 m/ha. There were no drain erosion data available for the mapped areas, so an erosion rate of 9 t/ha/yr was used based on the detailed drain erosion studies presented in Roth et al., (2003). These data were then used to determine an average cross-section area for the drains, and the relationship between drainage density, drain cross-section area, upstream catchment area and sediment bulk density was used to determine the relative contribution coming from drains in those catchments covered by sugar cane. There are a number of issues that need to be considered when evaluating the results from the cane drain modelling:
• the drain erosion rate used (i.e. 9 t/ha/yr) is considered to be at the upper limits for drain erosion in the Wet Tropics due to the highly erosive soils of the Ripple Creek area and the relatively high rainfall years over which the data were collected (pers. comm. Christian Roth), however, given that no other erosion data is currently available, the 9 t/ha/yr rate was applied in the DSC project.
• It is worth noting that deposition is NOT taken into consideration within the major drains. It is expected that there may be considerable deposition in the drain network, however, without appropriate data, it was not possible to implement this within the model. It is worth noting that although Roth and Visser (2003) report that there was an average erosion rate of 9 t/ha/yr for the ‘major’ drains in the Ripple Creek area (Herbert River Catchment), the ‘minor’ drains actually experienced 35 t/ha/yr of deposition over the same measurement period. ‘Minor’ drains were not considered within the DSC study, hence, deposition was not included.
It was highlighted in Bartley et al., (2004) that this approach was preliminary, and more data on both the drainage density, spatial location of different types of drains and erosion and deposition rates are required. During 2004/05 staff from MAS have done some more investigation on the drains in Saltwater Creek and have found that there are ~ 56 m of major drains and ~100 m of minor drains per hectare of sugar cane (pers. comm. Daryl Parker). This total length of drain network (156 m/ha) is considerably higher than the original density of 7.8 m/ha used in Bartley et al., (2004a). It is considered inappropriate at this stage to revise the drainage density data (from 7.8 m/ha to 156 m/ha) as this will have large implications on the sediment budget for the entire Douglas Shire area, which will also affect the results of the scenario analysis. It is also not considered appropriate to implement the new drain density estimates without the following information:
• Digital mapping of drain location for all sugar areas in Douglas Shire, not just Saltwater Creek;
• The drains need to be classified into ‘major’, ‘minor’ and ‘swale or spoon’ drain types;
• Erosion rates associated with each of the drain types need to be measured or estimated;
• Estimates of deposition occurring in the different drain types are also important, so that a ‘drain budget’ component can be included within the models.
Riparian potential on-ground changes 6, 7, 16 and 17 – EMSS
For flow and pollutant load calculations within EMSS, the Douglas Shire was divided into 206 ‘small’ sub catchments (note: these were grouped to form the 15 ‘reporting’ sub catchments listed in Table 1). All land use and management changes within EMSS need to be implemented at this level. To determine the relative response of small sub catchments to riparian treatment, full riparian treatment (as described in Appendix H) was first applied to all streams bordering agricultural land. Small sub catchments were identified for final riparian treatment by:
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1. determining the pollutant load reduction D from the riparian treatment, compared to current conditions, for each small sub catchment;
2. accumulating D in descending order of magnitude until ∑D = β%.
where β% = 50% or 95%, depending on the treatment.
Minimum tillage/legume rotation potential on-ground changes 3 and 15 - SedNet
The hillslope sediment generation procedure used by SedNet (Prosser et al., 2001) is based on the Revised Universal Soil Loss Equation RUSLE (Renard et al., 1997), which describes the protection afforded to exposed soil by vegetation/residue using a ‘cover’ factor, C. For the minimum tillage/legume rotation management action, the cover factor C for sugar cane land use was adjusted as follows:
⎥⎥⎦
⎤
⎢⎢⎣
⎡×−= sugar
sugar
rotationsugarnewsugar C
A
ACC )( (2)
Where Arotation is the area representing the minimum tillage/legume rotation, Asugar is the total area of sugar cane, Csugar is 0.056 (see Table 11).
Table 11 C factor and average slope values used in the RUSLE calculations for the Douglas Shire Project. Average slope values for each land use were obtained from the DEM. N/A means ‘not applicable’.
Land use C factors used in this study Average hillslope gradient (%)
Quarry 0.07 1.14
Fruit trees (e.g. bananas)
0.056 9.94
Sugarcane 0.056 1.71
Cleared 0.025 5.08
Headland 0.017 as for sugarcane
Grazing 0.016 9.20
Regrowth 0.015 8.10
Rural Residential 0.015 12.14
Production Forestry 0.012 N/A, area < 1%
Dry Sclerophyll 0.01 25.48
Wet Sclerophyll 0.008 25.48
Rainforest 0.006 28.39
Road 0.003 2.82
Tourism areas 0.003 4.62
Urban 0.003 2.86
Industrial 0.003 8.39
Other 0.003 1.14
Aquaculture 0.003 as for ‘other’
Dunes 0.001 as for ‘other’
Coastal Mozaic 0.001 as for ‘other’
Melaleuca/Transitional 0.001 as for ‘other’
Water 0.001 as for ‘other’
.
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Appendix C – comparison of pollutant loads simulated by EMSS and SedNet This section illustrates the patterns of agreement and disagreement between the two water quality models (EMSS and SedNet) applied within the Douglas catchment. Figure 4, Figure 5 and Figure 6 give an indication of the relationship between EMSS and SedNet simulations within Douglas Shire for TSS, TN and TP, respectively.
0
20000
40000
60000
80000
100000
120000
0 20000 40000 60000 80000 100000 120000
SedNet simulated TSS (t/yr)
EM
SS
sim
ula
ted
T
SS
(t/
yr)
Non-National park
1:1 line
National Park
Figure 4 Comparison of annual TSS loads simulated by EMSS and SedNet, for each of the 15 sub catchments. Major disagreement occurs only in National Park sub catchments (Daintree National Park, Mossman National Park, Saltwater National Park and Mowbray National Park).
0
200
400
600
800
1000
1200
0 200 400 600 800 1000 1200
SedNet simulated TN (t/yr)
EM
SS
sim
ula
ted
T
N (
t/yr
)
1:1 line
Daintree National Park
Figure 5 Comparison of annual TN loads simulated by EMSS and SedNet, for each of the 15 sub catchments. This shows the largest disagreement which occurred for the Daintree National Park sub catchment.
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0
20
40
60
80
100
120
0 20 40 60 80 100 120
SedNet simulated TP (t/yr)
EM
SS
sim
ula
ted
T
P (
t/yr
)
1:1 line
Daintree National Park
Figure 6 Comparison of annual TP loads simulated by EMSS and SedNet, for each of the 15 sub catchments, showing a similar pattern to that of TSS, due to the association between suspended sediment and phosphorus. As with TSS, the largest disagreement between the two models occurs with the representation of Daintree National Park.
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Mossman Daily Rainfall Comparison to 1994-2003 to 1910-2003 records
Probability of excedence
0.001 0.01 0.1 1 10 30 50 70 90 99 99.9 99.99
Dai
ly r
ainf
all (
mm
)
0
100
200
300
400
500
600
700
1910 to 20031994 to 2003
Appendix D – Comparison of the rainfall periods considered by EMSS and SedNet Peter Hairsine 14 February 2005
For computational reasons we chose to use the simulation period for EMSS from 2 January 1994 to 31 July 2003 as a representative rainfall period for all simulations. To check that this period has similar rainfall characteristics to the historical record (used by SedNet) we compared the daily rainfall record of the Mossman station (record available 10 February 1910 to 31 July 2003) to the representative period. The two sets of data are presented in Figure 7.
The number of no rainfall days in each record are identical at 70 percent. Also the daily rainfall totals of the events with a probability of excedence greater than 0.1 percent are near identical. The only significant divergence between the two distributions occurs for the very infrequent events that are exceeded <0.1 percent of days. In the longer record there are five days with rainfall totals greater than 420 mm that are not present in the sample record. The largest of these is 742.7 mm that occurred on 31 March 1911. In the sample record the highest daily rainfall was 420.6 mm that occurred on the 6 March 1996.
Overall it is judged that the 10-year sample used adequately represents the historical record. It is possible to further check this adequacy by comparing key outcomes of varying the rainfall record input.
Figure 7 Comparison of probability of excedence of the rainfall events used as long-term input to SedNet (1910 to 2003) and the shorter term (1994 to 2003) used for EMSS.
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Appendix E - Rainfall runoff calibration of EMSS
Rainfall-runoff calibration
The characteristics and calibration SIMHYD, the rainfall runoff model used EMSS, are described in detail by Chiew et al. (2002). The application of SIMHYD within EMSS requires calibration for ‘forest’ and ‘non forest’ land use groups. The data required for calibration of SIMHYD includes
• catchment area
• long term (years to decades) daily flow record
• spatial coverage of daily rainfall
• spatial coverage of daily potential evapotranspiration
Long-term Flow records were available for (see Figure 3; see also Table 3, Bartley et al., 2004)
• Daintree@Bairds
• Mossman@Mossman
• Whyanbeel@ upstream of Little Falls Creek
• Saltwater Creek@ Donoghue
All these sub catchments were located in areas of natural forest, in upland terrain where rainfall is thought to be highly spatially variable. No data was available for calibration of SIMHYD in the ‘non forest’ land use group. Daily rainfall surfaces were obtained from the Queensland Government, Natural Resources and Mines (QNRM). These surfaces were interpolated using local records, which included only six sites within the Douglas catchment, principally on the coastal plain. Monthly potential evapotranspiration surfaces were obtained from the National Land and Water Audit (http://www.nlwra.gov.au/). At all calibration sites, the area-weighted rainfall totals were insufficient to account for the observed runoff. This mass imbalance was thought to be consistent with a poor spatial representation of rainfall within these sub catchments and rainfall was therefore scaled upwards to address this. To achieve a reasonable calibration for Whyanbeel@ upstream of Little Falls Creek (Figure 8), rainfall was scaled as shown in Table 12.
Table 12 Rainfall scaling factors applied to calibrate SIMHYD rainfall run off model for the ‘forest’ land use group, within Region 1 (see Figure 12)
Month Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Scaling factor
1.8 1.5 1.3 1.2 1.0 1.0 1.0 1.0 1.2 1.3 1.5 1.8
This scheme also produced a reasonable calibration for Daintree@Bairds (Figure 9), for which the contributing area was Daintree National Park sub catchment, however, calibrations for the Mossman@Mossman (Figure 10) and Saltwater Creek@Donoghue (Figure 11) sub catchments were poor. For the latter, there was a significant gap in observed flow for the calibration period. Generally, the quality of the calibrations was severely limited by the available data, particularly rainfall.
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0
5000
10000
15000
20000
25000
31/1/1993 15/6/1994 28/10/1995 11/3/1997 24/7/1998 6/12/1999 19/4/2001 1/9/2002 14/1/2004 28/5/2005
Date
Mo
nth
ly f
low
(M
L)
Observed flowSimulated flow
Figure 8 Monthly observed flow and monthly flow simulated by SIMHYD for Whyanbeel@ upstream of Little Falls Creek.
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
1000000
Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02 Jan-04 May-05
Date
Mo
nth
ly f
low
(M
L)
Observed flowSimulated flow
Figure 9 Monthly observed flow and monthly flow simulated by SIMHYD for Daintree@Bairds.
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0
20000
40000
60000
80000
100000
120000
140000
160000
180000
Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02 Jan-04 May-05
Date
Mo
nth
ly f
low
(M
L) Observed flow
Simulated flow
Figure 10 Monthly observed flow and monthly flow simulated by SIMHYD for Mossman@Mossman.
0
5000
10000
15000
20000
25000
30000
35000
40000
31/1/1993 15/6/1994 28/10/1995 11/3/1997 24/7/1998 6/12/1999 19/4/2001 1/9/2002 14/1/2004 28/5/2005
Date
Mo
nth
ly f
low
(M
L)
Observed flowSimulated flow
Figure 11 Monthly observed flow and monthly flow simulated by SIMHYD for Saltwater Creek@Donoghue.
Within EMSS, therefore, two hydrologic regions were identified (Figure 12), broadly divided between the uplands (Region 1; almost entirely forest land use group) and the coastal plains (Region 2; comprising some ‘forest’ and all ‘non forest’ land use group). Within Region 1, the QNRM rainfall surface was scaled
44
as indicated in Table 12; within Region 2, the QNRM rainfall was used unscaled. The sub catchments contained within these two regions are listed in Table 13.
Figure 12 A map of the Douglas Shire catchments showing hydrologic Region 1 (white) and Region 2 (blue) used
in EMSS and boundaries of the 15 sub catchments.
Table 13 Sub catchments within the two hydrologic regions within the Douglas Shire, relating to the rainfall scaling (Table 12) calibration and the SIMHYD parameters for ‘forest’ and ‘non forest land use groups (Table 14).
Hydrologic regions used in EMSS
Region 1 Region 2
Daintree National Park Daintree estuarine
Daintree developed Daintree South Arm
Stewarts Creek Saltwater Creek developed
Saltwater National Park Saltwater Creek estuarine
Mossman National Park Mossman developed
Mossman estuarine
Mowbray developed
Mowbray National Park
Mowbray estuarine
Coastal Strip
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Table 14 shows the SIMHYD parameters listed by hydrologic region and land use group as applied within EMSS to describe runoff within the Douglas Shire. We note that this is a very simplistic description of the Douglas Shire hydrology but represents the best possible representation within the limits of data available at the time of this study.
Table 14 The SIMHYD parameters applied within the 2 hydrologic regions used within the EMSS model.
Region Group INSC COEFF SQ SMSC SUB CRAK RK 1 Forest 3.3 364 0.8 52 0.535 0.64 0.009
1 Non
forest 2 200 1.5 200 0.35 0.7 0.3 2 Forest 5 200 1.5 273 0.35 0.7 0.3
2 Non
forest 2 200 1.5 200 0.35 0.7 0.3
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Appendix F –Pollutant parameters used in EMSS and SedNet EMSS requires specification regarding the event mean concentration (EMC) and dry weather concentration (DWC) for each pollutant, for each land use, and total annual loads of each pollutant from each point source in the catchment. The values used in the Douglas Shire implementation of EMSS are listed in Table 15. Values for ‘Conservation areas’ and ‘Native bush’ were calculated from data collected from the Mossman National Park sub catchment (see McJannet et al., 2005; ‘Appendix E, Upper Mossman); values for ‘Grazing’, ‘Broadacre agriculture and ‘Intensive agriculture’ were derived from the Wivenhoe (central Brisbane) catchment (Tony Weber, unpublished data). The remaining values are derived from Brisbane catchment, as described in Chiew et al. (2002).
Table 15 Event mean concentrations (EMC) and dry weather concentrations (DWC), for each land use category used in the model EMSS, expressed in mgl-1.
Land use TSS DWC (mgl-1)
TSS EMC (mgl-1)
TN DWC (mgl-1)
TN EMC (mgl-1)
TP DWC (mgl-1)
TP EMC (mgl-1)
Conservation areas Lower 0.0001 3.92 0.043 0.29 0.003 0.003 Median 2 21.27 0.091 1.144 0.006 0.036 Upper 3.5 92.1 0.285 2.238 0.017 0.089 Managed forest Lower 3 8 0.3 0.4 0.02 0.05 Median 7 20 0.4 0.8 0.03 0.2 Upper 14 90 0.5 2 0.06 0.4 Plantation Lower 3 8 0.3 0.4 0.02 0.05 Median 7 20 0.4 0.8 0.03 0.2 Upper 14 90 0.5 2 0.06 0.4 Native bush Lower 0.0001 3.92 0.043 0.29 0.003 0.003 Median 2 21.27 0.091 1.144 0.006 0.036 Upper 3.5 92.1 0.285 2.238 0.017 0.089 Grazing Lower 5 110 0.5 1.17 0.03 0.128 Median 10 260 0.7 2.08 0.07 0.3 Upper 23 700 0.9 5.98 0.14 0.77 Broadacre agriculture Lower 5 80 0.5 0.9 0.03 0.107 Median 10 300 0.7 1.95 0.07 0.321 Upper 23 800 0.9 5.2 0.14 0.803 Intensive agriculture Lower 5 1000 0.5 0.9 0.03 0.12 Median 10 1000 0.7 2.1 0.07 0.36 Upper 23 1000 0.9 5.9 0.14 1.1 Rural residential Lower 5 40 0.5 0.9 0.05 0.12 Median 10 140 0.7 1.6 0.11 0.28 Upper 23 380 0.9 4.6 0.28 0.72 Future urban Lower 5 40 0.9 0.9 0.05 0.12 Median 7 140 1.5 1.6 0.11 0.28 Upper 27 380 2.8 4.6 0.28 0.72 Suburban Lower 5 40 0.9 0.9 0.05 0.12 Median 7 140 1.5 1.6 0.11 0.28 Upper 27 380 2.8 4.6 0.28 0.72 Dense urban Lower 5 40 0.9 0.9 0.05 0.12 Median 7 140 1.5 1.6 0.11 0.28 Upper 27 380 2.8 4.6 0.28 0.72
47
The SedNet simulations used the concentrations of dissolved inorganic nitrogen DIN, dissolved organic nitrogen DON, filtered reactive phosphorus FRP and dissolved organic phosphorus DOP specified in Table 16.
Table 16: Estimated average concentrations of dissolved inorganic nitrogen DIN, dissolved organic nitrogen DON, filtered reactive phosphorus FRP and dissolved organic phosphorus DOP in runoff from land uses Wet Tropics Regions (after Brodie et al., 2003).
Land use DIN (μg/L)
DON (μg/L)
FRP (μg/L)
DOP (μg/L)
Rainforest 40 150 10 10 Ungrazed savannah/ woodland 100 100 20 10 Grazing 200 250 50 12 Sugar cane 1100 350 40 30 Horticulture 500 200 30 20 Forestry 150 150 8 8
48
Ap
pen
dix
G –
Po
int
sou
rce
po
lluta
nts
use
d in
EM
SS
T
his
sect
ion
prov
ides
the
annu
al p
oint
sou
rce
pollu
tant
load
s us
ed in
EM
SS
for
12 lo
catio
ns w
ithin
the
Dou
glas
Shi
re a
nd th
e ra
tiona
le b
ehin
d th
e es
timat
ion
of th
e lo
ads
Tab
le 1
7. W
here
pol
luta
nt d
ata
was
not
ava
ilabl
e, th
e nu
mbe
r of
sew
erag
e cl
eani
ng c
harg
es (
2,33
0) w
ithin
the
Dou
glas
Shi
re w
as
used
to e
stim
ate
volu
mes
of e
fflue
nt a
nd w
aste
wat
er a
ssoc
iate
d w
ith th
ese
resi
denc
es. I
t was
ass
umed
that
eac
h re
side
nce
repr
esen
ted
2.5
equi
vale
nt
pers
ons
beds
. One
equ
ival
ent p
erso
n w
as e
stim
ated
to p
rodu
ce 2
50 li
tres
/day
of l
iqui
d w
aste
, with
6.9
kg/
yr o
f N a
nd 3
.2 k
g/yr
of P
, bas
ed o
n 20
litr
es/d
ay
of e
fflue
nt a
nd w
aste
wat
er w
ith T
N a
nd T
P c
once
ntra
tions
35m
g/l a
nd 1
0mg/
l, re
spec
tivel
y (B
rynn
Mat
hew
s (E
PA
), p
ers.
com
.).
Tab
le 1
7: L
ocat
ions
and
est
imat
ed a
nnua
l poi
nt s
ourc
e po
lluta
nt lo
ads
with
in th
e D
ougl
as S
hire
.
Set
tlem
ent
Are
a (k
m2 )
Nu
mb
er o
f P
rop
erti
es
Cle
ansi
ng
ch
arg
e V
acan
t L
ots
O
ther
P
(k
g/y
r)
N
(kg
/yr)
E
asti
ng
/No
rth
ing
C
atch
men
t C
ooya
B
each
34
219
1,75
2 3,
778
3297
57/8
1812
45
Mos
sman
D
aint
ree
Vill
age
25
60
12
in
clud
ing
5 ea
ting
prem
ises
, 48
0 1,
035
3204
51/8
2025
73
Dai
ntre
e
O
ne S
tate
Sch
ool
- 14
pup
ils**
* 1
2
1
play
ing
field
,
2
publ
ic
acce
ssed
to
ilets
,
T
OT
AL
481
1,03
7
&
cara
van
park
w
ith
unit
acco
mm
odat
ion
Mia
llo
10
47
resi
denc
e,
1 fa
rm
mac
hine
ry
376
811
3265
48/8
1870
49
Sal
twat
er
O
ne S
tate
sch
ool
- 11
6 pu
pils
***,
5
16
W
orks
hop
T
OT
AL
381
827
New
ell
Bea
ch
24
16
0
1,
280
2,76
0 32
9704
/818
3045
M
ossm
an
Roc
ky
Poi
nt
41
63
11
in
clud
ing
1 ac
com
mod
atio
n 50
4 1,
087
3297
02/8
1870
29
Sal
twat
er
Som
mer
set
27
37
4
29
6 63
8 32
6267
/818
4682
S
altw
ater
Sou
th A
rm
51
52
6
41
6 89
7 33
1257
/819
4945
D
aint
ree
Won
ga
Bea
ch
95
30
6
2,
448
5,27
9 33
0430
/819
1652
D
aint
ree
O
ne S
tate
Sch
ool –
12
4 pu
pils
***
5 17
30
7 25
9 68
5
5,
480
11,8
16
49
T
OT
AL
7,93
3 17
,112
M
ossm
an
stp
87
9 15
8 32
6574
/818
0206
M
ossm
an
Sea
farm
P
ty L
td
33
0 1,
022
3337
27/8
1747
63
Mow
bra
y P
ort
Dou
glas
45
4 33
5505
/817
4209
M
owbr
ay
50
Appendix H - Riparian model used in EMSS This section describes the basis for the method used to simulate the effect of riparian vegetation on pollutant delivery within EMSS, modified from notes by Peter Hairsine, 2001. It describes the trapping of pollutants by riparian vegetation acting as a filter to pollutant fluxes from hillslope to stream using the following assumptions:
1. The delivery ratios are for the riparian zone only. Sediment loading rates to the riparian zone already contain the delivery ratios associated with the flow path above the zone.
2. The model is daily though most of the concepts come from studies of event and simulation studies.
3. (from 2.) The daily time step implies that only one event is considered per day.
4. The understanding summarised in this approach is based around studies where the dominant path of sediment and nutrient delivery is via overland flow from the hillslope to the stream. Other mechanisms of sediment generation (e.g. gully and streambank erosion) and delivery (e.g. subsurface delivery of nutrients in soluble form) do exist. These mechanisms can also be influenced by the presence of riparian vegetation. For instance riparian forests reduce stream bank erosion and may also result in denitrification of shallow subsurface flows. As a result the following proposed model is conceptual and taken to represent a combination of all sediment and nutrient delivery processes.
Sediment delivery ratio
The riparian model is summarised by Figure 13.
Sediment loading rate (t/km/d)
Sedi
men
t del
iver
y ra
tio, S
DR
Sediment loading rate threshold(SLRT)
1.0
Sediment loading at sill
(SLRS)
Figure 13 Showing a discontinuous linear relationship which describes the relationship between sediment loading rate (t/km/d) applied to a riparian zone and the resulting sediment delivery ratio.
This model was formulated to be compatible with loading functions and specification of stream network but with minimal parameterisation. It is consistent with observed behaviour and caprures some of the behaviour of different buffer models including:
• Threshold model (e.g. Herron and Hairsine, 1996) where overland flow is subject to enhance infiltration in riparian zone so that small events have zero delivery and there is a threshold behaviour
51
• Capture of sediment by settling (e.g. Dillaha et al. 1989) where sediment is trapped according to sediment size class so that SDR is a funtion of incoming sediment velocity distribution, incoming sediment concentration and the incoming water discharge rate per unit width of flow.
• Finite storage model (e.g. Prosser et al., 1999) where the ripairan zone has a finite storage of sediment which when filled results in SDR approaching 1.
Current estimates of thresholds are SLRT=0.1 t/km/d and SLRS=10 t/km/d.
Nutrient (N and P) delivery ratio
A relationship for the delivery ratio of nutrients N and P was derived from a conceptual relationship between pollutant concentrations of eroded soil and sediment loading rate (Figure 14). When the sediment loading rate is low then the majority of nutrient is trapped in the riparian zone. This prediction is observed in environment where the riparian zone acts to reduce overland flow rates so that fine sediment and attached nutrients are trapped. As sediment loading rates increase the capacity of the riparian zone to store overland flow, fine sediment and related nutrients is more quickly reduced so that more nutrients leave the riparian zone and enter the stream. When the sediment loading rate is very high then the capacity of the riparian zone to stores overland flow, fine and coarse sediment and associated nutrients is quickly overwhelmed resulting in very little buffering of the stream from nutrient inflow by the riparian zone.
Sediment loading rate (t/km/yr)
0 2 4 6 8 10 12
Nutrient delivery ratio
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Figure 14 The relationship derived (Peter Hairsine, unpublished notes) between sediment loading rate and nutrient (N and P) delivery ratio.
52
Ap
pen
dix
I –
Str
uct
ura
l lay
ou
t o
f D
SS
inte
rfac
e
Fig
ure
15 S
truc
tura
l dia
gram
of t
he d
ecis
ion
Sup
port
Sys
tem
(D
SS
) in
terf
ace
whi
ch p
rovi
des
guid
ance
as
to th
e us
e of
the
EM
SS
and
Sed
Net
mod
els
with
in th
e D
ougl
as
catc
hmen
ts, a
nd a
rep
osito
ry fo
r in
form
atio
n re
gard
ing
the
Dou
glas
Shi
re a
nd th
e w
ater
Qua
lity
Impr
ovem
ent P
lan.
The
inte
rfac
e is
des
crib
ed in
det
ail i
n a
sepa
rate
d re
port
(E
llis
et a
l., in
pre
para
tion)
.