Dual Instruments or
Needless Duplication?
Evaluating the Combined Use of
Environmental Flow and Salinity Targets
Lisa Lee and Tiho Ancev,
Agricultural and Resource Economics,
The University of Sydney
Background




Part of a larger PhD project.
The initial aim of that project was to determine
cost-effectiveness of alternative ways to reduce
salinity impact from irrigated cotton production.
An interim result was that salinity impact
reduction targets and the environmental flow
targets are interrelated.
The question is: should targets for salinity impact
reduction be imposed when there is already
reduction in surface water availability?
Introduction




Increasing focus on water resource management
in Australia in the last 15 years.
Over-allocation and under-pricing of water
drives inefficient use.
Public pressure for more effective management
of scarce water resources.
Water Reform Framework, National Water
Initiative, Living Murray First Step, and the most
recent Commonwealth Water Plan – aimed at
efficient and sustainable water use.
Water management situation

Water Sharing Plans (WSP) introduced for NSW
Catchments:
Extractive rules
 Environmental flows



Also Groundwater WSPs, addressing overallocated aquifers. Up to 90% reduction.
Salinity concerns. Based on past negative
experiences with irrigation induced salinity.
Irrigation induced salinity

Occurs as irrigation water leaks below the root zone
and deep-drains (or percolates) into groundwater
aquifers.

On its way it may mobilise salt, which can subsequently
result in increased stream and soil salinity.

This is a most serious problem along the main stem of
the River Murray in SA, but end-of-valley salinity
targets also introduced in NSW.
Case Study - The Mooki Basin
NT
AUSTRALIA
QLD
WA
SA
NSW
VIC
ST GEORGE
QLD
TAS
GW Y
D
B ARW ON
Study Area
IN
RL
DA
G
ER
RIV
WAL GETT
IV ER
SARA R
COONABARABRAN
QUIRIN DI
WARREN
GIL GANDRA
MA
CQ
UA
RIE
DUBBO
RI V
ER
WELLIN GTON
NSW
LA
CH
L
AN
TURO
ORANGE R
R MU
MU
R
RRA
Y
MUR
R
"
MURRAY BRIDGE
AY R
IVER
ED
WA
RD
N RIV
RA
Y
COOTAMUNDRA
RI
VE
R
ER
HO
PK
IN
S
RIV
LA TROBE RIVE R
IV ER
KS
EE
CR
R
KING
N
VE
SE
MELBOURNE
RIVE R
YA"RRA
WA
KIE
R
WAGGA WAGGA
BAR
NA
RD
R
RI
VE
R
"
IVE
R
RIV
EEK
ER
CR
KO
YAN
ER
IV E
R
ER
WEST WYALONG
HAY
IV
DENILIQUIN
RO
YR
RUM
BID
GEE
GRIFFITH
R
MU
R
FI T
Z
CO
LO
IV
ER
RIV
ER
IV E
R
HU N
TE R
CLY D
E RI V
ER
RIVE
IVE
R
GUN NEDAH
NYNGAN
BROKEN HILL
D
EE
INVEREL L
PE
EL
R
NARRABRI
SA
TW
ER
IV
IR R
IV ER
NA
MOREE
MO
IR
IVE
R
RIVER
COONAMBLE
MUR
RA Y
MO
LE
R
GOONDIWINDI
R
CANBERRA
"
COOMA
GE
N
OA
RI
VE
R
IV E
R
RIV E
R
" NEWCASTLE
SYDNEY
The Mooki


Mainly cotton growers – high salt tolerance of
1,700 µS/cm.
EC reading for Mooki at Breeza is 534 µS/cm




(342kg salt per ML deep drainage).
Within Catchment Blueprint limit of 550
µS/cm.
Salinity damage not a significant issue in the
catchment.
No private incentives to internalise the
externalities caused by salt loading.
Potential salinity impacts

Main concern is downstream impact on the BarwonDarling system.

End-of-valley salt load target for Namoi is 127,600 t/yr.

Mooki at Ruvigne had a reading of 3,000t of salt in
2003/04. An unusually low reading caused by well
below average flow.

1% of the whole Namoi basin area contributes 2.3% salt load
Objectives

Compare economic and environmental
outcomes under surface water reduction targets
and under deep-drainage targets.

Assess the usefulness of a dual instrument to
control joined “pollution”.

Determine which instrument is less costly?
Previous studies

Caswell (1991); Heaney and Beare (2001)


Highlight the negative and positive role of drainage
on downstream users (quantity and quality)
Ancev et al. (2004); Caswell et al. (1990);
Khanna et al. (2000)

Proposed drainage taxes to encourage ‘cleaner’
technology and practices.
Previous studies

Legras and Lifran (2006)


Decoupled policy instruments separately for water
quantity and for salinity impact. Found inefficient
when the two are related.
Whitten et al. (2005); Weinberg et al. (1993)
Significant gap in hydrological understanding
 Water instrument in own right is sufficient

Analytical framework:
A stylised story

Catchment manager imposes reductions in surface
water allocations to ensure environmental flows.
The manager is not worried about salinity impacts.

The catchment manager may come under pressure
from the manager of the whole basin, to impose
salinity impact reduction targets in order to ensure
that the end-of-valley target is not breached.

How can the catchment manager evaluate this
“dual” instrument?
Analytical framework:
An optimisation model
Objective Function:

1  J
Max F ( S, G, J , Z )  
 (t )  AJ it  PwWdit  PwWsit 
t  R
Sijt ,Gijt , Jit , Zit
n1 t 1 (1  r )  j 1

N
S.t.
0
T
J
 WA
j 1
ijt
J
 Ai  J it  X it  G it
 ALit   (WAijt  W ijt )  DD it
j 1

Decision variables:
Crop choice
 Source of water
 Crop acreage
 Irrigation system
 Water trading
 All indexed over space and time, and simulated over
10 years

Biophysical Model



Biophysical model – Soil and Water Assessment Tool
(SWAT).
Catchment divided into sub-basins, based on GIS data.
Sub-basins further divided into Hydrological Response
Units (HRUs).


Homogenous land units with specific soil type and land use
We focus only on irrigated cotton HRUs, comprising
397km2 .(out of around 900km2 for the whole
catchment.
Biophysical Model

HRUs simulated under various landuses:
Irrigated cotton;
 Dryland cotton;
 Dryland wheat;
 Dryland sorghum.



Irrigated cotton simulated using furrow, pivot,
and drip irrigation.
Also sourcing from surface and groundwater.
Biophysical Model

Biophysical information on crop yield, water
use, and deep drainage obtained through SWAT
simulations.

Net revenue for each HRU calculated.

This information was used as input to create
activities in each HRU, which were subsequently
entered in a programming model.
Methodology

Two scenarios simulated:

Base Case – Comparison point
Water trade
 Surface and groundwater availability according to WSPs


Scenario One – Water Cap
Water trade
 Gradually tightening surface water caps (water availability
constraint)


Scenario Two – Deep Drainage Cap
Water trade
 Gradually tightening deep-drainage caps (DD constraint)

Results

Base Case – Comparison Point
NPV
($, 7%, 10yrs)
Deep
Drainage
(ML/yr)
Surface water
use (ML/yr)
286,898,465
22,913
59,000

Ground-water Total water use
use
(ML/yr)
(ML/yr)
49,883
108,883
Deep-drainage of 22,913ML = 7,836tonnes of salt load per
year

EC of 534 µS/cm = 342kg salt per ML drainage.
Results

Scenario One – Water Caps.
NPV
($, 7%, 10yrs)
Deep
Drainage
(ML/yr)
Surface water
use (ML/yr)
Ground-water
use
(ML/yr)
Total water
use
(ML/yr)
286,898,465
22,033
55,000
49,883
104,883
284,184,274
19,833
45,000
49,883
94,883
282,260,786
17,636
35,000
49,883
84,883
280,149,538
15,365
25,000
49,883
74,883
Results

Scenario One – Water Caps (cont.)
Activities Water Const. 55,000ML - 10,000ML (trade)
25
20
Area ('000 ha)
55,000ML
45,000ML
15
35,000ML
25,000ML
10
15,000ML
10,000ML
5
1
2
Surface Ground
Furrow Furrow
3
4
5
Surface Ground Surface
Activities
Pivot
Pivot
Drip
6
7
8
9
Ground Dryland Dryland Dryland
Drip
Wheat Sorghum Cotton
Results

Scenario Two – Deep Drainage Caps.
NPV
($, 7%, 10yrs)
Deep
Drainage
(ML/yr)
Surface water
use (ML/yr)
Ground-water
use
(ML/yr)
Total water
use
(ML/yr)
284,985,289
20,000
59,000
37,492
96,492
283,500,553
18,000
59,000
28,904
87,904
281,795,813
16,000
50,125
28,700
78,826
280,049,656
14,000
41,034
28,700
69,735
Results

Scenario Two – Drainage Caps (cont.)
Activities DD Cap. 24,000ML - 6,000ML (trade)
25
20
24,000ML
Area ('000 ha)
20,000ML
15
16,000ML
12,000ML
10,000ML
8,000ML
10
5
1
2
Surface Ground
Furrow Furrow
3
4
5
Surface Ground Activity
Surface
Pivot
Pivot
Drip
6
7
8
9
Ground Dryland Dryland Dryland
Drip
Wheat Sorghum Cotton
Performance of DD reduction
targets vs. surface water reduction
targets
Reduction in
Extra cost
of DD cap
Extra envi.
flow
Extra DD
reduction
ML
AUD mill.
ML
ML
t/yr
22,913
-0.31
0
33
11
22,000
0.2
-4,000
933
319
20,000
0.72
-9,000
1,833
627
18,000
1.47
-14,000
2,733
935
16,000
2.21
-10,125
3,636
1,244
14,000
2.96
-6,035
4,506
1,541
12,000
3.75
-2,691
5,365
1,835
10,000
4.55
1,077
6,225
2,129
8,000
5.35
4,844
7,084
2,423
DD cap
expected salt
load
Results
Comparison of cost curves for DD reduction
Cost Curves for Deep Drainage
1,800
1,600
1,400
1,200
1,000
800
600
cost/yr $('000)

400
200
25
23
21
19
17
DD/yr ('000ML)
15
13
11
9
DD cap
Water cap
Results
Comparison of cost curves for environmental flows
Cost Curves for Additional Envi. Flows
16,000
14,000
12,000
10,000
8,000
6,000
cost/yr. $('000)

4,000
2,000
0
60
50
40
30
Envi.flow ('000 ML)
20
10
0
Water
Cap
DD Cap
Discussion

When there is a reduction of surface water availability,
the costs of attaining deep-drainage reduction without
imposing an explicit deep-drainage target are very
similar to the costs of attaining deep-drainage
reduction when explicit targets are imposed.

Reduction of surface water occurs when explicit deepdrainage reduction targets are imposed. The costs of
attaining reduction in surface water use are much
greater under deep-drainage cap, then under surface
water reduction rules.
Conclusion

Irrigation induced salinity has been a very serious
problem in the Murray-Darling Basin system in
Australia.

Based on past experience with irrigation induced
salinity, deep-drainage reduction targets have been
imposed, or are currently being considered in many
catchments in NSW.

In the circumstances were surface water availability
declines, deep-drainage targets are not necessary and
may impose high cost on irrigators that are already
under financial stress.