Groundwater Model Conceptualisation

Transcription

Lower Robe River Groundwater
Model
FINAL 3
June 2010
Lower Robe River Groundwater Model
THIS PROJECT IS JOINTLY FUNDED BY THE DEPARTMENT OF WATER WESTERN
AUSTRALIA AND THE AUSTRALIAN GOVERNMENT’S WATER FOR THE FUTURE
INITIATIVE
FINAL 3
June 2010
Sinclair Knight Merz
ABN 37 001 024 095
33 Kerferd Street
Tatura, VIC, 3616 Australia
Tel: +61 3 5824 6400
Fax: +61 3 5824 6444
Web: www.skmconsulting.com
COPYRIGHT: The concepts and information contained in this document are the property of Sinclair
Knight Merz Pty Ltd. Use or copying of this document in whole or in part without the written
permission of Sinclair Knight Merz constitutes an infringement of copyright.
LIMITATION: This report has been prepared on behalf of and for the exclusive use of Sinclair
Knight Merz Pty Ltd’s Client, and is subject to and issued in connection with the provisions of the
agreement between Sinclair Knight Merz and its Client. Sinclair Knight Merz accepts no liability or
responsibility whatsoever for or in respect of any use of or reliance upon this report by any third
party.
The SKM logo trade mark is a registered trade mark of Sinclair Knight Merz Pty Ltd.
Contents
1. Executive Summary
6 2. Introduction
7 2.1. 2.2. 7 7 3. Hydrogeological Conceptualisation
3.1. 3.2. 3.3. 3.4. 3.5. 3.6. 3.7. 3.8. 3.9. 3.10. 3.11. 3.12. 4. Background to this project
Modelling study objectives
Available Data
Regional Setting
Regional Stratigraphy
Lower Robe River Alluvial Aquifer Extent
Hydraulic Conductivity
Rainfall Recharge
River Recharge
Evapotranspiration
Water Balance
Groundwater Dependent Ecosystems and Pools
Sea Water Intrusion
Schematic Diagrams
9 9 11 14 19 19 22 24 25 27 31 34 Model Description
35 4.1. 35 35 36 37 37 38 40 42 42 44 44 47 47 49 49 Modelling Approach
4.1.1. Modelling Software
4.1.2. Model Complexity
4.2. Model Domain
4.2.1. Finite Element Mesh Design
4.2.2. Digital Terrain Model
4.3. 4.4. Model Layers and Aquifers Represented
Model Boundaries
4.4.1. Saltwater-Freshwater Interface
4.5. River Representation (Surface Water-Groundwater Interactions)
4.5.1. River Flows
4.5.2. Groundwater Fed Pools
4.5.3. Flood plain inundation
4.6. 4.7. 5. 9 Rainfall Infiltration Recharge
Groundwater Evapotranspiration
Model Calibration
53 5.1. 53 53 Calibration Methodology
5.1.1. Time Steps
SINCLAIR KNIGHT MERZ
I:\VWES\Projects\VW04919\Deliverables\Robe_Model_Report_final.docx
PAGE i
5.2. 5.3. 5.4. 5.5. 6. Calibrated Model Mass Balance
Hydrograph Analysis (Qualitative model assessment)
Calibration Statistics (Quantitative Model Assessment)
Potentiometry and Depth to Watertable
54 55 59 60 Predictive Scenarios
62 6.1. 6.2. Methodology
Scenario Modelling Results
6.2.1. 6.2.2. 6.2.3. 6.2.4. 6.2.5. Pretext – Commentary on Scenario 3
Impact on the water balance
Impact on groundwater levels
Potential for impacts on GDE’s
Potential for Seawater Intrusion
62 65 65 65 66 70 73 7. Sensitivity Analysis
77 8. Conclusions
81 9. Model Limitations, Uncertainty and Recommendations
82 10. References
85 Appendix A Calibration Model Potentiometric Surface and Depth to
Watertable Maps
86 SINCLAIR KNIGHT MERZ
PAGE ii
Figures
Figure 1 Lower Robe Groundwater Model Extent
8 Figure 2 Typical vegetation outside the river channel
10 Figure 3 Vegetation along the main Robe River channel
10 Figure 4 Yarraloola Conglomerate
12 Figure 5 Aquifer saturated thickness (Commander, 1994)
15 Figure 6 Geological Sections (Commander, 1994) Section Locations shown in Figure 5 16 Figure 7 Interpolated Base of Alluvium Depth Based on Commander Bore Data and River
Alluvium Location
17 Figure 8 Interpolated Base of Alluvium Elevation Based on Commander Bore Data and River
Alluvium Location
18 Figure 9 Average annual number of tropical cyclones
20 Figure 10 Karratha Temperature and Rainfall
21 Figure 11 Monthly Rainfall Record at Fortescue River and Cumulative Deviation from the
Mean
22 Figure 12 Gravels in the Robe River bed
23 Figure 13 Pt Hedland Evaporation (BoM)
24 Figure 14 Average annual areal actual ET (BoM)
25 Figure 15 Robe River pools and riparian vegetation near road crossing
28 Figure 16 Selected hydrographs showing watertable change from level immediately
preceding cyclone ‘Chloe’ (Commander, 1994)
28 Figure 17 Robe River Pools
29 Figure 18 Robe River Bore Hydrographs and River Stage
30 Figure 19 Hourly Tide Data for Broome
31 Figure 20 EM survey (-10 m AHD) and low terrain
32 Figure 21 Seawater Interface Contour Lines
33 Figure 22 Schematic Diagram for Recharge during Wet Season
34 Figure 23 Schematic Diagram for Discharge during Dry Season
34 Figure 24 Robe model finite element mesh
37 Figure 25 Topography within the Lower Robe Groundwater Model extent
39 Figure 26 Robe model 3D block diagrams
41 Figure 27 Robe model boundary conditions
43 Figure 28 Inactivated area in layers 2 and 3 behind the halocline (saltwater-fresh water
interface)
44 Figure 29 Terrain image generated from Lidar DTM data at the southern end of the Lower
Robe Groundwater Model
45 SINCLAIR KNIGHT MERZ
PAGE iii
Figure 30 Example 2 years from the timeseries of Robe River flows and water levels
46 Figure 31 River Stage Record for Calibration Model Period at Yarraloola Pool.
47 Figure 32 Assumed area affected by flood plain infiltration (red)
48 Figure 33 Flooding history included in the Calibration Model
49 Figure 34 Groundwater evapotranspiration function (shown here with an extinction depth of
6m)
51 Figure 35 Groundwater evapotranspiration extinction depth
52 Figure 36 Bore and Stream flow Record Dates
53 Figure 37 Observation Bore Location
54 Figure 38 Mass Balance for Calibration Model
55 Figure 39 Calibration Model Hydrographs (continued overleaf)
57 Figure 40 Correlation between measured and predicted groundwater levels
59 Figure 41 Top – Production borefield (red) for scenarios 2 and 3; Bottom – Scenario 4
borefield (observation bores shown in yellow)
63 Figure 42 Top – Production borefield (red) for scenarios 5; Bottom – Scenario 6 borefield
(observation bores shown in yellow)
64 Figure 43 Scenario model hydrographs (Continued overleaf)
Figure 44 Maximum drawdown due to groundwater pumping of 5GL/yr after a prolonged dry
period (calculated as the difference in watertable elevation between Scenario 1 and Scenario
2 at scenario model time Dec-2028)
71 Figure 45 Maximum drawdown due to groundwater pumping of 12 GL/yr after a prolonged
dry period (calculated as the difference in watertable elevation between Scenario 1 and
Scenario 6 at scenario model time 2028)
72 Figure 46 Hydrographs generated immediately in front of the saltwater-freshwater interface
in Layers 2 and 3
74 Figure 47 Results of Scenarios in Alternative Boundary Model
76 Figure 48 Total Evapotranspiration as a percentage of calibration
78 68 SINCLAIR KNIGHT MERZ
PAGE iv
Document history and status
Revision
Date issued
Reviewed by
Approved by
Date approved
Revision type
Draft 1
12/2/2010
B. Barnett
B. Barnett
16/2/2010
Draft
Draft 2
14/04/2010
B. Barnett
B. Barnett
14/04/2010
Draft
FINAL
04/05/2010
B. Barnett
B. Barnett
06/05/2010
Final
FINAL 2
14 May 2010
B Barnett
14 May 2010
Sensitivity analysis and
conclusions added
FINAL 3
11 June 2010
B Barnett
11 June 2010
Review comments from N
Merrick addressed. Water
balance error corrected
B Barnett
Distribution of copies
Revision
Copy no
Quantity
Issued to
Draft 1
-
Electronic
H. Koomberi (Dept. of Water)
Draft 2
-
Electronic
FINAL
-
Electronic
FINAL 2
Electronic
FINAL 3
Electronic
Printed:
11 June 2010
Last saved:
11 June 2010 11:14 AM
File name:
Author:
Jarrah Muller, Hiro Toki, Anthony Goode
Project manager:
Brian Barnett
Name of organisation:
Department of Water
Name of project:
Pilbara Groundwater Models
Name of document:
Pilbara Groundwater Models
Document version:
Final 3
Project number:
VW04919
PAGE v
1.
Executive Summary
A numerical groundwater flow model of the alluvial aquifers of the Lower Robe River in the
Pilbara has been formulated in the FEFLOW finite element modelling code. The model
incorporates all of the important physical processes that control and influence the storage and
movement of groundwater in the alluvial sediments surrounding the river.
The model has been calibrated by matching model predicted groundwater behaviour to that which
has been observed and measured between 1984 and 2008. Calibration criteria include minimisation
of the normalised RMS error defining the differences between measured and predicted groundwater
levels while at the same time honouring the quantum of the conceptual mass balance fluxes. This
latter criterion was found to be necessary because improved RMS error statistics can be obtained
from models that include unreasonably high levels of recharge and evapotranspiration.
The calibrated model features hydrogeological parameters that are consistent with the
conceptualisation of the groundwater system. It relies heavily on recharge through the river bed
and flood plain in response to spasmodic river flow events that are largely controlled by cyclone
activity. The major discharge mechanism predicted by the model is evapotranspiration and this is
controlled to a large extent by the depth to water table and the presence of deep rooting vegetation.
In fact these two factors are linked in that deep rooting vegetation is located only in those areas
where the water table is close to the ground surface. In this regard the evapotranspiration flux in
the model is considered to be an indicator of water availability to groundwater dependent
vegetation.
A series of predictive model scenarios has been run and results reported. The predictions consider
a variety of groundwater extraction and potential future climate change assumptions. It was found
that groundwater can be extracted from production borefields constructed in the alluvial sediments.
The impact of future groundwater extraction regimes ranging from 5 to 12 GL/yr result in
drwawdown in groundwater levels in and around the borefield and a consequent reduction in
evapotranspiration of between 4 and 8 GL/yr. This loss of evapotranspiration represents a
reduction of between 6 and 11 % of the total water available for groundwater dependent
ecosystems.
Model predictions suggest that the drawdown cone associated with the potential future borefield
operations is unlikely to expand to the coast or to the existing saltwater-freshwater interface and as
such there is no apparent risk of further salt water intrusion.
Sensitivity analysis suggests that the model is highly sensitive to perturbations in key uncertain
parameters and as such the model predictions should be considered as being relatively uncertain.
PAGE 6
2.
Introduction
2.1.
Background to this project
The Department of Water in Western Australia is responsible for the management of the
groundwater resources of the Pilbara Region and is currently developing a regional water resource
management plan aimed at creating an equitable means of allocating water to key stake holders
including the environment. The overall objective of the work is to encourage responsible
development of the water resources of the Pilbara that will encourage economic development while
at the same time preserving the unique ecological communities and cultural features for which the
region is renowned. To this end it is important for the Department to be able to predict likely
impacts of groundwater extraction and other forms of development. The development of a
calibrated groundwater model will provide important predictive tools that the Department can use
in future to help ensure groundwater development of the area is undertaken in a sustainable
manner.
2.2.
Modelling study objectives
The modelling work forms an integral part of the Department of Water’s management plan for the
Pilbara. The project is aimed at quantifying the potential groundwater resources of the study area
and in particular in assessing the long term sustainable yield of the aquifer under various
assumptions of future groundwater extraction regimes and climate.
The Lower Robe River groundwater model will cover the region that is believed to contain the
alluvial aquifer associated with the Robe River, from upstream of the North West Coastal Highway
to the ocean. The model extent is shown in Figure 1, and the aquifer extent is discussed in
Section 3.4.
PAGE 7
Figure 1 Lower Robe Groundwater Model Extent
PAGE 8
3.
Hydrogeological Conceptualisation
3.1.
Available Data
At the commencement of the modelling project, the Department of Water supplied SKM with the
following data for constructing and calibrating a groundwater model for the Lower Robe River:
Daily stream flow measurements, as stage in m AHD, and flows in m3/s and ML/day from
1/4/1987 to 8/10/2009
Bore hydrographs for 11 bores installed by Commander with water level readings from 1983
to 2009
Bore water level readings for other groundwater bores in the study area, typically with one
reading in 1974 and one in 1996
Pool hydrographs for the highway pool and a database of pools and there level of permanency
along the river
ArcMap shapefiles showing bore locations, roads, geology and aquifer contours generated by
Commander (1994)
Aerial photography
Geophysical data, which unfortunately were not beneficial for delineating the alluvial aquifer
as the aquifer is shallow
DTM and LiDAR data
Daily rainfall data from 6/9/1988 to 1/4/2009 at Fortescue River
Bore logs for 87 bores
Several reports and articles discussing previous investigations (see reference list)
Additional climate data will be sourced from the Bureau of Meteorology as required.
3.2.
Regional Setting
The model lies within the Pilbara region of Western Australia. The major industry in the Pilbara is
mining, with the majority of Australia’s iron ore found in the region. The climate is tropical-arid,
with cyclones bringing heavy rains during the summer months and very little rainfall during winter
months. Grasses and low shrubs dominate the plains (Figure 2), with large trees dependent on
groundwater found along ephemeral and semi-permanent river channels (Figure 3). Rivers in the
Pilbara tend to flow only after summer storm events for a period of several weeks, then remain as a
series of groundwater dependent pools throughout the rest of the year.
PAGE 9
Figure 2 Typical vegetation outside the river channel
Figure 3 Vegetation along the main Robe River channel
PAGE 10
3.3.
Regional Stratigraphy
The primary water bearing formations in the Robe area are the alluvium deposits following the
modern path of the Robe river. These sediments consist of gravel and clay lenses. The deepest
gravel lens found to date is near the Yarraloola homestead, where the aquifer is 15 m thick (aquifer
bed ~22 m below ground surface). The gravel bed thins laterally and extends to a width of at least 6
km. The gravel bed also thins downstream of Yarraloola Homestead.
The water table in the alluvium tends to be close to the ground surface (5-10 m below ground
level). At some locations a calcrete layer has developed due to the rising and falling water table.
This calcareous alluvium is best counted as part of the main alluvial aquifer rather than as a
separate lithographic layer.
Beneath the alluvial sediments lies Tertiary Trealla Limeston and Robe Pisolite. These units are
aquitards and do not transport water except through fractures where the Limestone can be as
productive as the alluvial gravel. These units are underlain by siltstone, shale and Yarraloola
Conglomerate (Figure 4). The Yarraloola Conglomerate has high salinity and low permeability,
indicating little recharge and little potential for transporting water.
Bedrock in the Robe area is Ashburton Formation schist. Some weathered and fractured zones may
produce localised aquifers but generally the rock does not bear water.
The major stratigraphic layers are summarised in Table 1. For this project the base of the
groundwater model will be set to the base of the alluvial sediments.
PAGE 11
Figure 4 Yarraloola Conglomerate
PAGE 12
Table 1 Summary of Hydrostratigraphy (Haig, 2009)
Time Period
Cainozoic
(Quaternary)
Lithology
Alluvium (gravel, clay
lenses, calcrete close
to watertable)
Description
Deepest gravel section is near
Yarraloola Homestead, where it
is 15 m thick. Generally thins
away from the river and
becomes mixed with clay.
Sediments may be 3-6 km wide.
Aquifer Properties
Aquifer
Cainozoic
(Tertiary)
Alluvium, pisolite and
calcrete (Trealla
Limestone)
Trealla Limestone
Aquitard, except
where fissured
Cainozoic
(Tertiary)
Pisolitic iron stone,
silcrete, clay
Robe Pisolite, found along
current course of the Robe River
Aquitard
Cretaceous
Clay, claystone, or
marl
Toolonga Calcilutite
Aquitard
Cretaceous
Siltstone / Shale
Possibly Windalia Radiolite,
possibly Muderong Shale, up to
8 m thick
Aquitard
Cretaceous
Conglomerate, sand,
clay
Yarraloola Conglomerate, 3 – 22
m thick, outcropping on hills
Aquitard
Proterozoic
Schist
Basement consisting of
Ashburton Formation
Weathered water
bearing horizon in
some locations,
else non-water
bearing
PAGE 13
3.4. Lower Robe River Alluvial Aquifer Extent
The most complete description to date of the Robe aquifer extent is given by Commander (1994)
based on a series of drill holes, the locations of which are shown in Figure 5. Commander used
these drill holes to determine the stratigraphy of the drilled area, as presented Figure 6. Based on
this stratigraphy he produced a series of contours describing the saturated thickness of the gravel
aquifer near the Yarraloola Homestead, shown in Figure 5.
The Commander drill holes do not give a profile of the aquifer all the way to the coast. It can be
assumed that alluvium has been deposited by the river all the way to the coast, even if the aquifer
becomes too shallow to be of commercial benefit. This assumption is shored up by the presence of
ephemeral pools and phreatophytic vegetation along the river beyond the aquifer zone defined by
Commander; these pools and vegetation are groundwater dependent indicating the continued
presence of the aquifer.
Using the bore data (Figure 6) and assuming that the river has deposited alluvial material all the
way to the coast, Figure 7 was produced showing the assumed depth to the base of alluvium across
the model area. It was assumed that alluvial material was 15 m deep directly beneath the river,
thinning to 3 m deep at a distance 2 km from the river on each side. As the water table is typically
5-9 m below the surface, this gives a saturated thickness of 6-10 m beneath the river channel in the
regions not described by Commander. Figure 8 shows the assumed aquifer geometry in m AHD.
This geometry may be confirmed or disproved at a later date if a drilling program is undertaken
closer to the coast. Since this geometry does not assume any large gravel beds yet seems to explain
the presence of groundwater dependent vegetation, it is felt that this geometry is a conservative
estimate of the extent of the aquifer.
Electromagnetic survey results clearly delineate a sharp interface between fresh and saline water
some distance inland (refer for example to Figure 20). It is proposed that the model only extend to
the salt water interface and that the model boundary at this location be defined as a head dependent
boundary condition with the head set at 0 m AHD. The boundary condition will allow water to
enter the leave the model domain depending on predicted heads and gradients at the boundary
location. Any increase in flux into the model domain through the boundary will indicate the
movement of the salt water further inland from its current location.
PAGE 14
A’
B’
A
C’
B
C
D’
E’
D
F’
E
F
Figure 5 Aquifer saturated thickness (Commander, 1994)
PAGE 15
Figure 6 Geological Sections (Commander, 1994) Section Locations shown in Figure 5
PAGE 16
Figure 7 Interpolated Base of Alluvium Depth Based on Commander Bore Data and River Alluvium Location
PAGE 17
Figure 8 Interpolated Base of Alluvium Elevation Based on Commander Bore Data and River Alluvium Location
PAGE 18
3.5.
Based on pumping tests (Commander, 1994), the horizontal hydraulic conductivity of the gravel
has been estimated to be 400 m/day, except in the calcareous zone where it is estimated to be 150
m/day. Commander (1994) reports an average horizontal hydraulic conductivity of 250 m/day.
Table 2 Pump test results (Table 8 in Commander (1994))
3.6.
Rainfall Recharge
The Pilbara region climate is arid-tropical with hot dry conditions through most of the year.
Average maximum daily temperatures remain above 35°C between October and April, with daily
maximum temperatures dropping to 27°C during the months of June and July (Figure 10). Average
annual rainfall at Karratha is 280 mm, with most of this falling during summer months. Rainfall is
extremely variable as it is dependent on cyclones and storms (Figure 10), with an average of 0.8
cyclones per year impacting the model area (Figure 9). A cumulative deviation from the mean plot
for the nearby Bureau of Meteorology (BOM) rainfall site at Bilanoo Pool on the Fortescue River
(BOM Site #505046) since 1988 is presented in Figure 11. The figure suggests a relatively wet
period from 1994 to 2001 followed by dry conditions from 2001 to 2008.
Davidson (1974) determined that approximately 3% of rainfall infiltrates and recharges the De
Grey aquifer. Infiltration is likely to be similar in the Lower Robe River aquifer.
PAGE 19
Figure 9 Average annual number of tropical cyclones
PAGE 20
Figure 10 Karratha Temperature and Rainfall
PAGE 21
Rainfall 700
600
500
(mm)
400
300
200
100
0
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
Rainfall Cummulative Deviation from the Mean
800
600
400
(mm)
200
0
‐200
‐400
‐600
‐800
1988
1990
1992
1994
1996
1998 2000
Time (year)
2002
2004
2006
2008
Figure 11 Monthly Rainfall Record at Fortescue River and Cumulative Deviation from the
Mean
3.7.
River Recharge
The Robe River is an ephemeral river flowing for about 4 weeks after storms and cyclones, with 8
years during the 30 year record having no flow. Due to the gravel river bed (Figure 12), the
alluvium aquifer beneath the river is quickly recharged during river flow events.
PAGE 22
Commander (1994) reports that between 1972 and 1986 stream flow ranged from 8 – 160 GL/year,
with a mean flow of 51 GL and a median of 38 GL. However, Haig (2009) reports that the long
term average river flow is 87GL/year (based on 1973-2005 data). The differences are likely due to
the length of record used as the high variability in rainfall and stream flow cause statistics from
small datasets to be relatively fluid.
Only large river flows reach the ocean (Commander, 1994). Based on anecdotal evidence, flows
smaller than 50 GL/year are not sufficient to allow the river to flow all the way to the coast. From
water levels in observation bores, Commander estimated that in 1984 the aquifer was recharged by
24 GL, and in 1985 by 10 GL. This shows that the aquifer has the potential to absorb a significant
percentage of river flow.
Figure 12 Gravels in the Robe River bed
PAGE 23
3.8. Evapotranspiration
Evapotranspiration is the combined processes of evaporation and transpiration that act to remove
water from the aquifer. Although evapotranspiration processes are active in both the saturated and
unsaturated zones, it is the saturated zone component that is important in terms of numerical model
development.
Evapotranspiration is only active when the water table is close to the surface. In non-vegetated
regions evapotranspiration is effectively limited to those areas where the water table is within about
0.5 m of the surface. The presence of vegetation and the associated uptake of water through the
plants roots results in evapotranspiration being active to a greater depth (perhaps 10 m below the
surface near large trees). Water will also evaporate directly from the aquifer through the various
pools along the river.
Haig (2009) reports that transpiration could be taken as 80% of pan evaporation in vegetated areas.
Daily evaporation at Pt Hedland ranges between 11.5 mm and 6.4 mm (Figure 13) whilst areal
actual ET is expected to be in the range of 300-400 mm/yr (Figure 14).
Figure 13 Pt Hedland Evaporation (BoM)
PAGE 24
Figure 14 Average annual areal actual ET (BoM)
3.9.
Water Balance
The water levels in the Robe River aquifer do not remain constant over time. Some years the
aquifer is recharged to a greater extent, and there are extended dry periods during which the aquifer
is depleted. Generally the water is 5-9 m below ground surface, and remains accessible to deeply
rooted trees. Close to the river, the water table can fluctuate by up to 5 m /year due to
evapotranspiration and river recharge.
Table 3 shows an average yearly water balance created using data reported by Haig (2009) based
on modelling by Commander (1994). These data do not include a full accounting of outflows (pool
evaporation was not discussed) or an estimate of rainfall recharge, and so it is expected that the
water balance for the calibrated groundwater model will be different in some aspects from those
reported by Haig. The groundwater model also covers a significantly larger area than Commander’s
study area, subsequently modelled water balances can be expected to be in the order of 3-4 times
greater than those presented below. SKM’s estimates based on the larger model domain are also
shown in Table 3.
PAGE 25
Table 3 Water Balance (Haig, 2009)
Volume (after Haig)
(GL/yr)
Percent (After Haig)
SKM Estimate for Model
Domain (GL/yr)
River Recharge
8
62
30
Rainfall/flood
-
-
20
Aquifer Inflows
Lateral Inflow
5
38
5
TOTAL INFLOW
13
100
55
Aquifer Outflows
Volume (after Haig)
(GL/yr)
Percent (After Haig)
SKM Estimate for Model
Domain (GL/yr)
Transpiration
4
31
25
Pool Evaporation
4?
31
25
Lateral Outflow to Ocean
5
38
5
TOTAL OUTFLOW
13
100
55
Note: The Haig water balance was generated for an area significantly smaller than the model area, therefore
modelled water balances could be expected to be 3-4 times higher than shown here.
PAGE 26
3.10. Groundwater Dependent Ecosystems and Pools
The shallow alluvial aquifer supports both permanent and semi-permanent groundwater dependent
pools along the river bed during dry parts of the year, and supplies water to phreatophytic
vegetation such as Eucalyptus Camaldulensis (River Red Gum).
Figure 17 shows the locations of surveyed pools, while Figure 15 shows aerial photography of the
pools close to the highway bridge.
Figure 18 shows comparative hydrographs of pool level measured in the Yarraloola Gauging
Station at the upstream extremity of the model domain together with groundwater hydrographs
measured in groundwater observation wells. Note that the same river hydrograph is plotted in all
cases (this is the only river gauging station in the model domain) so that peaks in groundwater
hydrographs can be compared against the timing of river flow events. Pool and bore hydrographs
suggest a strong connection between the river flow events and aquifer recharge. During flood
events, the aquifer is recharged through the pools as through the rest of the river bed (Figure 16).
During dry periods the pools are maintained by groundwater flows. As the aquifer is close to
ground surface, phreatophytic vegetation is found across much of the aquifer. These larger trees on
the river banks and plains provide habitat and a vegetation corridor that supports a diverse
ecosystem. Maintaining these ecosystems is an important consideration in current and future water
resource management in the Lower Robe River catchment. It is assumed that the groundwater
dependent ecosystems within and adjacent to the Robe River channel will be adversely affected by
future groundwater developments that cause a permanent decline in groundwater levels at these
locations.
PAGE 27
Figure 15 Robe River pools and riparian vegetation near road crossing
Bore Number (Distance from riverbank in kilometres) Figure 16 Selected hydrographs showing watertable change from level immediately
preceding cyclone ‘Chloe’ (Commander, 1994)
l.docx
PAGE 28
Figure 17 Robe River Pools
PAGE 29
Figure 18 Robe River Bore Hydrographs and River Stage
PAGE 30
3.11.
Sea Water Intrusion
The tide at Broome varies between ±4.9 m AHD, with larger tides occurring every two weeks in
phase with lunar cycles as shown in Figure 19. Digital Terrain Model (DTM) data show that the
ground elevation is above the high tide level (4.9 m AHD) at 4 km inland from the sea (Figure 20).
From this it can be inferred that seawater may travel a significant distance inland along the river
channel during high tide when there is no fresh water outflow. It is then likely that the sea water
will seep through the river bed and recharge the aquifer. This process is expected to result in the
aquifer containing saline water in the area within 4 km of the ocean. Seawater will also enter the
model area by permeating coastal mudflats. This is confirmed by Figure 20, which shows the -10 m
AHD EM survey, in which it can be seen that highly conductive seawater intrudes approximately 6
km inland. No aquifer can be seen in the EM survey, indicating that the aquifer must be less than
10 m deep. Figure 21 shows contour lines of the seawater interface at 10 m depth intervals.
Figure 19 Hourly Tide Data for Broome
PAGE 31
Figure 20 EM survey (-10 m AHD) and low terrain
PAGE 32
Figure 21 Seawater Interface Contour Lines
PAGE 33
3.12.
Schematic Diagrams
The processes of seawater intrusion, recharge and evapotranspiration are illustrated during periods
of groundwater recharge and discharge in Figure 22 and Figure 23 respectively.
Figure 22 Schematic Diagram for Recharge during Wet Season
Figure 23 Schematic Diagram for Discharge during Dry Season
PAGE 34
4.
Model Description
4.1. Modelling Approach
4.1.1. Modelling Software
There are two commonly used groundwater simulation packages that are suitable for application in
this project. The finite difference MODFLOW package has been the industry standard numerical
simulation code for many years. It was developed by the US Geological Survey and has been
widely used throughout all groundwater modelling projects. In recent years the FEFLOW finite
element simulation package has become a widely used tool for simulating groundwater flow and
solute transport. Each of these codes have specific advantages and disadvantages that should be
considered in choosing the modelling platform to be used for the current project.
The choice of modelling software package to use for a particular project depends on a number of
factors related to the aquifer that is being modelled and the current and possible future uses of the
model. In particular reference to the Robe River Model the following issues should be considered:
1.
Modflow has difficulty in dealing with cells that dry and re-wet as the water table is
predicted to fall below and then rise above the base of any cell. This is likely to be of concern in
the current model due to the fact that the aquifer is recharged on sporadic occasions as the river
flows during and shortly after cyclone events. As a result it is expected that water levels in the
aquifer will rise and fall dramatically during the course of model runs. FEFLOW does not have the
same problems with drying and re-wetting cells and as such has an advantage over Modflow in this
regard.
2.
Modflow utilises a regular rectangular grid of elements while the finite element
formulation of FEFLOW allows the use of cells of rectangular or triangular shape. This feature
provides additional flexibility in modelling complex geometries of the geology and/or aquifer
boundary conditions. In this case the geometry of the model domain is relatively simple and as
such there is no real advantage in using FEFLOW.
3.
Groundwater flow models developed in FEFLOW can be easily extended to incorporate
density dependent solute transport simulation. This feature is required if the model is to be used (in
the future) to explicitly model the migration of salt water through the aquifer. Although recent
versions of Modflow have similar capability (using the SEAWAT package), our experience with
this feature suggests that it is not a reliable package and appears to be inferior to that available in
FEFLOW.
4.
FEFLOW offers a powerful programming interface that enables the introduction of
complex relationships to help define and control boundary conditions. This feature is particularly
PAGE 35
useful in defining flood inundation fluxes and in controlling the river boundary conditions when a
river ceases to flow and the river bed dries.
Given the foregoing discussion the FEFLOW package has been used for the development of the
Robe River model.
4.1.2. Model Complexity
Model complexity is defined as the degree to which a model application resembles the physical
hydrogeological system(MDBC, 2000) and the following three classifications are suggested:
A “Basic Model” is of low complexity that can be used for preliminary quantitative
assessments and model results may need to be checked by further field measurement.
An “Impact Assessment Model” is of medium complexity and requires more data and
resources than a Basic Model and can be used to predict groundwater response to future stress
applications with a reasonable degree of confidence.
An “Aquifer Simulation Model” is a high complexity model requiring substantial investment
of time, funds and data. These models are expected to provide accurate and detailed
estimations of groundwater responses to a range of changes in hydrogeological conditions.
In determining the complexity of each model to be developed, it is necessary to assess the amount
and quality of hydrogeological data available to base the model on, the use to which the model will
be applied, and the time and financial resources available for the project.
There is a reasonable coverage of hydrogeological data for the area near the homestead in the
region described by Commander. However, there is no data for the region closer to the coast. There
is uncertainty in the number of river pools and their permanency, especially as the morphology of
the river bed can change over time. The full aquifer extent is not known with any certainty.
Despite the deficiencies in the available data it is understood that the model is to be used to assess
future aquifer response to extraction and accordingly it is proposed that the model be classified as
medium complexity, i.e. an “Impact Assessment Model”.
PAGE 36
4.2. Model Domain
4.2.1. Finite Element Mesh Design
The model grid was automatically generated by Feflow using a triangular mesh refined near points
of interest such as the river and river pools. Figure 24 shows the Robe River groundwater model
finite element mesh. Near the river the node to node distances (element lengths) are in the order of
75-150 m whilst near the model boundaries element lengths are in excess of 1 km.
Figure 24 Robe model finite element mesh
PAGE 37
4.2.2.
Digital Terrain Model
The ground surface in the model was defined using a combination of the statewide SRTM (coarse
resolution) data and the high resolution Lidar survey data. Where the Lidar data was available this
was used, where Lidar was not available the SRTM data was used. However, during import it was
noticed that where the merged surface changed from Lidar to SRTM there was a consistent step
change in elevation of approximately 2.6 m. Given the Lidar data is considered to be of
significantly higher accuracy than the SRTM, it was decided to shift all SRTM data down by a
uniform 2.66m. This correction proved to provide a near seamless join between the two data sets.
PAGE 38
Figure 25 Topography within the Lower Robe Groundwater Model extent
PAGE 39
4.3.
Model Layers and Aquifers Represented
The Robe River alluvial aquifer is split into two layers of equal thickness. Whilst this split is
arbitrary it allows the model to potentially differentiate (should more data become available)
between coarser and finer sections of the aquifer by depth as well as aerially.
The definition of the base of the alluvial aquifer was described in Section 3.4.
A third model layer has been included beneath and surrounding the alluvial channel. This third
layer allows the model to replicate the influence of the Trealla Limestone. It is conceptualised that
there is potential for flow across the Limestone/Alluvial boundary and that this may aid in
maintaining water levels in the aquifer.
3D block diagrams from the model are shown in Figure 26. The figure shows a typical example of
the layer structure with the alluvial aquifer in the top two layers and a basal layer representing the
Trealla Limestone and underlying units (all considered as aquitards). The blue shaded are in the
figure represents the mapped extent of the alluvial channel, whilst the red follows the present day
river bed.
The hydraulic conductivity and storage parameters applied to the various model regions (Robe
River Channel, Robe Alluvial Aquifer and Regional Area) are listed in Table 4. These calibrated
model parameters are considered to be broadly consistent with the conceptual model for the
aquifer.
PAGE 40
Robe River Channel (Red)
Robe Alluvial Area (Blue)
Regional Area
Figure 26 Robe model 3D block diagrams
Table 4 Hydraulic Parameters applied to the model domain
Kx, Ky [m/day]
Kz [m/day]
Specific Storage
Specific Yield
[ m-1 ]
[~]
-6
Robe River Gravels
100
10
5x10
0.05
Robe Alluvial Aquifer
50
5
5x10-6
0.05
-6
0.05
Regional Area (Aquitard)
0.5
0.05
5x10
l.docx
PAGE 41
4.4.
Model Boundaries
No boundary conditions are set on the eastern, western and southern extents of the mesh and
therefore no fluxes are allowed to cross these boundaries. The north-eastern boundary is set along
the coastline with a constant head boundary set at 0.2 m AHD. This elevation is favoured over 0
mAHD as seawater has a greater density than fresh water and thus a higher equivalent freshwater
peizometric head for the same elevation.
Behind the coastline all model nodes in the ocean are set at extremely low conductivity, effectively
inactivating this area
It was not deemed appropriate to attempt to model tidal influences at the coastline due to the
monthly stress period used in the model. The large tidal cycle occurs approximately on a
fortnightly basis (as per the lunar cycle) and therefore could not be represented on a monthly time
step. Model boundaries are shown in Figure 27.
4.4.1.
Saltwater-Freshwater Interface
Given the Lower Robe model did not take into account density affects, it was necessary to make
assumptions regarding the halocline (saltwater-freshwater interface). Geophysical data suggested
that at depth (below approximately 5m the interface was approximately vertical at the location
shown in Figure 28. Due to a lack of further data it was assumed that at this point there was
minimal to no groundwater flow and therefore the model assumes no flows beyond the interface.
Behind the halocline the model mesh is set at extremely low conductivities effectively inactivating
this area in model layers 2 and 3. In layer 1 it is assumed that freshwater can flow through the
shallow alluvium toward the coast and therefore the boundary is set at the coastline in layer 1.
PAGE 42
Inactivated area
Constant head boundary set along
the coastline at 0.2 mAHD
Head dependent “Transfer”
boundary (flux constrained) set
along the river channel. Refer
section 4.5 for further details.
Figure 27 Robe model boundary conditions
l.docx
PAGE 43
Inactivated area in
Layers 2 and 3.
Approximate location
of halocline (saltwaterfreshwater interface)
Figure 28 Inactivated area in layers 2 and 3 behind the halocline (saltwater-fresh water
interface)
4.5. River Representation (Surface Water-Groundwater Interactions)
4.5.1. River Flows
Figure 27 showed the alignment of the river through the model domain. The true river channel
alignment was defined using the Lidar DTM data provided by the Department. As shown in Figure
29, the Lidar data provides high resolution topographic relief that enables us to accurately identify
and model the main Robe River channel including pools, braids and tributaries.
l.docx
PAGE 44
Figure 29 Terrain image generated from Lidar DTM data at the southern end of the
Lower Robe Groundwater Model
Feflow’s linear interpolation function was used to define a monthly time-varying head dependent
‘transfer’ boundary condition along the main river channel. The linear interpolation occurred
between the gauged permanent pool beneath the highway bridge and the coastline. An example of a
segment of the gauged time series at the highway bridge pool is provided in Figure 30. A complete
time series plot of river stage at Yarraloola Pool is presented in Figure 31. The record includes a
series of variations with time in what appears to be minimum pool level. If this assumption is
correct then it raises some concern as to the accuracy of the record.
A transfer boundary is set, as opposed to a specified head boundary, to allow for the effects of river
bed sediments to be taken into account. Herein, the modeller can adjust the rate at which water is
transferred between the river and aquifer. In the case of the Robe River, the bed is mostly
comprised of river gravels and therefore a high transfer rate of 0.5 (1/day) has been set.
PAGE 45
Flux constraints on the river boundary conditions were used to define when the river was flowing
or dry. When there is no flow in the river the maximum flux constraint is set to zero and therefore
no flow can occur from the river to the aquifer. No minimum flux was set and therefore water was
always allowed to discharge to the river
54.2
1800
Average Discharge
Average Monthly Stage
54.0
53.8
1400
53.6
1200
53.4
1000
53.2
800
53.0
52.8
600
52.6
400
Gauged Water Level (mAHD)
Monthly Flow in the Robe River (ML)
1600
52.4
200
52.2
0
52.0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Figure 30 Example 2 years from the timeseries of Robe River flows and water levels
PAGE 46
River Stage
58.5
58.0
(mAHD)
57.5
57.0
56.5
56.0
55.5
55.0
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
Time (year)
Figure 31 River Stage Record for Calibration Model Period at Yarraloola Pool.
4.5.2. Groundwater Fed Pools
No specific inputs are required to model the various groundwater fed pools along the Robe River.
Along the entire length of the river the model is refined to a fine resolution (75-150 m) with river
bed elevations set by the high resolution Lidar data. Where groundwater levels are at, or above, the
ground surface (i.e. at a pool) the evapotranspiration function is activated such that evaporation will
occur from the pool at the defined maximum rate.
4.5.3. Flood plain inundation
Infiltration during flood plain inundation is assumed to occur across the entire alluvial aquifer
extent. This simplification was considered appropriate owing to the generally flat nature of the
surrounding landscape. The assumed area is shown in Figure 32. It is specified as a constant flux
rate at the model surface. The rate has been set at 0.002 m/d after testing through the calibration
process. Floods are assumed to occur whenever the monthly flow in the river is greater than 60
ML, approximately 2 ML/day. Flows less than this are assumed to be contained within the river
channel and therefore the flood plain infiltration flux is not triggered. This also corresponds to the
minimum flow rate at which river flows are anticipated to reach the ocean (Commander, 1994).
The history of flooding included in the calibration model is presented in Figure 33.
PAGE 47
Robe river
Flood plain
Model domain
Figure 32 Assumed area affected by flood plain infiltration (red)
l.docx
PAGE 48
Flooding (On : 1, Off : 0)
1
0
1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Time (year)
Figure 33 Flooding history included in the Calibration Model
4.6. Rainfall Infiltration Recharge
Rainfall infiltration recharge is conceptualised as a minor component of the overall water balance.
It has been set at a constant rate across the model domain as a percentage of rainfall (except for the
ocean where it is zero). Infiltration rates have been set at 0.1% of rainfall. This may be considered
very low, however it is conceptualised that only very high rainfall events will result in recharge and
smaller events will not penetrate the unsaturated zone. When the high rainfall events occur, this is
also highly likely to trigger a flood event in which case the flood inundation fluxes are triggered in
the model.
4.7. Groundwater Evapotranspiration
A maximum groundwater evapotranspiration rate is set as well as water table extinction depth
beneath which no evapotranspiration occurs. If the watertable is at or above the natural surface
level then evapotranspiration occurs at the maximum rate. As the depth to watertable increases the
evapotranspiration rate decreases exponentially until the water table reaches the extinction depth at
which point the evapotranspiration rate is zero.
The maximum evapotranspiration rate is set uniformly over the model domain (excluding the
ocean) and the rate is set equal to pan evaporation. The only exception to this is on model nodes
where the river is active in which case ET is set to zero.
The extinction depth however is not uniform over the model domain. Along the main river channel
the extinction depth is set at 6m but elsewhere is set to 1.0 m. This distribution is designed to
PAGE 49
approximate the distribution of dense vegetation along the river and the sparsely vegetated
surrounds. Herein it is conceptualised that deeper rooting trees and shrubs are present in and near
the main river channel and pools. The density and size of vegetation reduces away from the main
river channel to where the grasslands dominate the landscape outside the alluvial valley.
The equation of evapotranspiration is given in Equation 1 and graphically in Figure 34. A map of
the modelled extinction depth is provided in Figure 35.
·
Equation 1.
Where:
ETmax =
α
=
d
=
Pan Evapotranspiration (defined as a monthly timeseries)
alpha (a calibration parameter that defines the shape of the
exponential curve, set to 0.5 in the Robe model)
depth to groundwater
PAGE 50
ET Rate (proportion of Max.)
Figure 34 Groundwater evapotranspiration function (shown here with an extinction
depth of 6m)
l.docx
PAGE 51
Extinction Depth = 1m
(Green)
Extinction Depth = 6m
(Red along river)
Figure 35 Groundwater evapotranspiration extinction depth
l.docx
PAGE 52
5. Model Calibration
5.1.
Calibration Methodology
The model has been calibrated by matching model-predicted groundwater levels to a series of
measured groundwater time series in observation wells located throughout the model domain.
Calibration involved a manual trial-and-error refinement of hydrogeological parameters and other
input data sets in order to achieve an optimal match between measured and model-predicted
groundwater levels in the set of observation wells.
The calibration period is from Jan 1984 to Dec 2008 (Figure 36). The location of the observation
bores which are used for calibration is shown in Figure 37.
Figure 36 Bore and Stream flow Record Dates
5.1.1.
Time Steps
The calibration model has been formulated with a monthly stress period. To avoid confusion with
leap years and to simplify data inputs and outputs, all months are assumed to be 30.44 days (i.e.
365.25 / 12 days). All time varying data sets used to formulate the calibration model have been
discretised accordingly before being imported to the model to ensure consistent stress period
assignment.
The model was calibrated using data from Jan 1984 to Dec 2008, a total of 25 years or 300 months.
PAGE 53
7428 – (9A)
7425 – (6A)
7421 – (3A)
7419 – (1A)
7430 – (11A)
7426 – (7A)
7422 – (4A)
7420 – (2A)
Figure 37 Observation Bore Location
5.2.
Calibrated Model Mass Balance
The mass balance for the calibrated model is presented graphically in Figure 38. Inflow to the
aquifer is made up of fluxes from the rivers including flooding inundation recharge and a minor
component of rainfall infiltration recharge. The combined influx/recharge through flood events is
estimated at just under 60 GL/yr. This is considered a reasonable estimate given that Commander
(1994) estimated flood recharge for his study area (3-4 times smaller than the model area) at around
24 and 10 GL during the 1984 and 1985 floods respectively.
It is interesting to note that almost all of the water flowing out of the aquifer is through the process
of evapotranspiration. Fluxes from groundwater to the ocean and to the river are almost negligible
compared to the evapotranspiration flux.
l.docx
PAGE 54
This result is somewhat contradictory to the conceptual model and model design for the river. The
river is constrained so that recharge can only occur during recorded flow events, however discharge
is allowed to occur at any time. Therefore the model result suggests that groundwater levels are
almost always below the input river level. It is possible that our limited data on river and pool
levels is a reason for this and the simplification of the linear interpolation between the highway
pool and the coastline. Another explanation is the high ET rates along the banks of the river which
may effectively intercept any water flowing toward the river.
Storage
4%
Flow IN
Ocean
9%
Storage
0%
Ocean
0%
Recharge
27%
Flow OUT
River
0%
Evapotransp
iration
100%
River
60%
Flow IN
GL/year
Flow OUT
GL/year
Recharge (Flood + Rainfall)
18.6
Evapotranspiration
68.1
River Leakage **
41.0
River (Baseflow)
0.0
Ocean Influx
6.3
Ocean Outflow
0.2
Storage Change
3.1
Storage Change
0.0
** River Leakage occurs only during recorded flow events
Figure 38 Mass Balance for Calibration Model
5.3.
Hydrograph Analysis (Qualitative model assessment)
Model predicted heads and measured groundwater levels in the set of observation bores are
presented graphically in Figure 39.
The modelled hydrographs represent a good match to observed data, particularly in the first ten
years where the most frequent and reliable observation data is available. Of particular note is the
good match of the flood peaks and more importantly, the recession after each flood. The match of
the flood peak-recession curve was considered a high priority during calibration as this is a strong
indicator of the quality of the model in replicating interactions occurring between the river and the
aquifer. The shape of the recession curve is also strongly linked to the storage capacity of the
aquifer and the evapotranspiration processes occurring.
PAGE 55
At almost all of the calibration sites, a good representation of the flood peak-recession curve was
established. In some instances there appears to be a slight upward or downward shift in the
modelled response. This appears to be due predominantly to the modelled river elevations near
each site (i.e. inaccuracies due to the linear interpolation of the river).
From approximately 1996 onwards, observations become more sparse (temporally) and this also
appears to coincide with a datum shift in the observation data. The reason for this is not known and
therefore these later time observation are not considered to be of high quality.
PAGE 56
Reduced Water Level (mAHD)
Figure 39 Calibration Model Hydrographs (continued overleaf)
49
7419 ‐ (1A) ‐ Observation
47
7419 ‐ (1A) ‐ Calibration
45
43
41
39
37
35
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
49
47
45
43
41
39
37
35
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
44
42
40
38
36
34
32
30
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
44
42
40
38
36
34
32
30
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
PAGE 57
41
39
37
35
33
31
29
27
25
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
39
37
35
33
31
29
27
25
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
39
37
35
33
31
29
27
25
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
38
36
34
32
30
28
26
24
22
20
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
PAGE 58
5.4.
Calibration Statistics (Quantitative Model Assessment)
The match between observed and predicted groundwater levels has been quantified in a series of
parameters that describe the correlation between the observed and predicted data sets. This
correlation is shown schematically in Figure 40.
The required calibration accuracy was set in accordance with the model complexity as defined by
the MDBC Groundwater Flow Modelling Guidelines(MDBC, 2000). For a medium complexity
model “Impact Assessment Model” such as this a normalised RMS error of approximately 10% is
considered appropriate. The combined normalised RMS error for the model is 8%. This is
considered to represent a reasonable level of correlation between the two data sets.
Figure 40 Correlation between measured and predicted groundwater levels
PAGE 59
5.5.
Potentiometry and Depth to Watertable
A series of nine potentiometric surface maps and depth to watertable maps are presented
in Appendix A as follows:
1) Initial Watertable Elevation (Jan-1984)
2) Initial Depth to Watertable (Jan-1984)
3) Final Watertable Elevation (Dec-2008)
4) Change in Watertable Elevation (Jan-1984 to Dec-2008)
5) Watertable Elevation During Flood (Jun-1989)
6) Depth to Watertable During Flood (Jun-1989)
7) Watertable Elevation After Prolonged Dry Period (Feb-1992)
8) Depth to Watertable After Prolonged Dry Period (Feb-1992)
9) Difference in Watertable Elevation between Flood Peak and Dry Period
A few observations from these maps are presented below:
Throughout the calibration model there was a consistent water level rise of 0.2 to 2m within
the alluvial aquifer. This is more indicative of the time since the last flood event as opposed to
a trend over time.
Outside of the alluvial aquifer the change in watertable elevation during the calibration period
is quite variable spatially. This is likely to be a sign that the aquitard was not in an equilibrium
state at the start of the model run. Also given there were no calibration points outside of the
alluvial aquifer this can also lead to the variable results. These differences are not expected to
influence model accuracy or scenario model results.
When comparing the watertable elevation during a flood with a dry period, it is clear that there
is a change in the direction of flow. During a flood event, water can be seen recharging the
aquifer from the river. During the dry, water flows toward the river.
The difference in watertable elevation between a flood peak and dry period was shown to be
significant (in excess of 5m in places). This highlights the massive storage changes that occur
across a flood cycle. However, outside of the alluvial channel, the limestone did not display
any significant response.
During a dry period, the depth to watertable in the alluvial aquifer is typically greater than 5m
with the exception of small patches along the river where there are depressions in the
topography (i.e. likely pool locations). In contrast during a flood there are significant areas
with very shallow depth to water (<2m).
PAGE 60
In all the maps it is clear that the depth to water decreases towards the ocean. Whilst this is a
plausible result it is worth bearing in mind that there is no observation data in that half of the
model and therefore model uncertainty is high toward the coast.
PAGE 61
6.
Predictive Scenarios
6.1.
Methodology
Six model scenarios were formulated for assessment with the calibrated Lower Robe Groundwater
Model. Each scenario varied either the climate and/or the groundwater abstraction regime with the
aim of assessing their impact on groundwater levels, the water balance, and importantly, the
potential impact on groundwater dependent ecosystems.
Each scenario was run over a 50 year time period, approximately representing the period 2010 to
2060. The scenarios are presented below in Table 5. Aside from the changes listed in Table 5 the
only changes to the calibration model were the extension of necessary time series inputs (such as
river levels). For the purpose of extending the time series data a repeating cycle of the calibration
model data was used.
Table 5 Details of predictive scenario models
Scn
Climate
1
Historical
Abstraction
Dry
2
No
3
200 ML/a
S&D 4
5 GL/yr 5
7 GL/a 5
10 GL/a 5
12 GL/a 5
1
2
3
4
5
6
1. Historical Climate – Repeating cycle of the calibration period rainfall and evapotranspiration
data
2. Dry Climate – Reduce overall recharge by approximately 10%
3. No abstractions - No future abstractions
4. 200 ML/a S&D - Introduction of an additional 200 ML/annum of stock and domestic extraction
across the alluvial aquifer. This will be represented as a negative areal flux distributed evenly
across the aquifer.
5. X GL/yr Abstraction – Introduction of a total of 5-12 GL/yr of abstraction. Borefield locations
are shown in Figure 41 and Figure 42.
PAGE 62
7430-(11A)
7426-(7A)
7428-(9A)
7425-(6A)
7421-(3A)
7422-(4A)
7420-(2A)
7419-(1A)
7430-(11A)
7426-(7A)
7428-(9A)
7425-(6A)
7421-(3A)
7422-(4A)
7420-(2A)
7419-(1A)
Figure 41 Top – Production borefield (red) for scenarios 2 and 3; Bottom – Scenario 4
l.docx
PAGE 63
7430-(11A)
7426-(7A)
7428-(9A)
7425-(6A)
7421-(3A)
7422-(4A)
7420-(2A)
7419-(1A)
7430-(11A)
7426-(7A)
7428-(9A)
7425-(6A)
7421-(3A)
7422-(4A)
7420-(2A)
7419-(1A)
Figure 42 Top – Production borefield (red) for scenarios 5; Bottom – Scenario 6
l.docx
PAGE 64
6.2.
Scenario Modelling Results
6.2.1.
Pretext – Commentary on Scenario 3
In Scenario 3, the aim was to identify the consequences of a drier climate (approximately reducing
recharge by 10%). This reduction was easily achieved for both the rainfall and flood flux which
were specified in the model as aerial fluxes to the top of the model (in reality the flood flux rate
would probably not change but the frequency and duration of floods would, therefore this is a
simplification).
However, the biggest component of recharge to the aquifer is through the river bed. Without
undertaking a rainfall-runoff assessment to re-evaluate river levels, it is not possible to predict the
impact of the drier climate on river levels. Consequently, in Scenario 3 the river levels are identical
to the remaining scenarios.
In reality, the biggest impact of a drier climate would be the reduction in river flows (potentially
through a reduced frequency of flow events). This would have the obvious impact of reducing
recharge through the river. Such impacts are not represented in the Scenario 3 and therefore it is
recommended that all results presented for Scenario 3 are considered within this context.
6.2.2.
Impact on the water balance
The mass balance results for each of the scenarios are given in Table 6.
Conceptually, the impact of pumping in the alluvial aquifer is a lowering of the water table and this
can lead to:
Additional leakage of water from the river and the pools with the possible drying of some
pools that would otherwise be permanent features,
Freed-up storage within the aquifer. Lower water tables mean that when the next flood comes
through, there may be an increased volume of recharge due to the larger available storage.
Change in the volume of throughflow towards the ocean and a potential for the salt water fresh
water interface to migrate inland in response to depressed water levels in the fresh water
aquifer.
Given the above discussion, the important consideration in the water balance results is the changes
in water balance components that compensate for the pumped water. In other words the impact
that increased pumping has on the water balance and in particular the changes in fluxes of water
into or out of the model due to the groundwater extraction. Table 7 provides a simplified
breakdown of the changes to the water balance as a percentage of the total abstraction in that given
scenario.
PAGE 65
In all scenarios the biggest changes to the water balance was a decrease in ET. Excluding Scenario
3 (refer to discussion in Section 6.2.1) 68 – 87% of the extracted volume was accounted for by a
decreased in ET. The remainder of the pumped volume was predominantly increased river recharge
reflecting localised declines in the groundwater levels near the river.
It should also be noted that changes in mass balance in Scenario 3 are attributed to both the
groundwater extraction applied in this Scenario (5 GL/y) as well as a drier climate. For this reason
the magnitude of change in ET for Scenario 3 exceeds the extraction rate (111% as shown in Table
7).
Table 6 Annual Average mass balance for the scenario model runs GL/year
Scn1
Scn2
Scn3
Scn4
Scn5
Scn6
River In
54.4
55.3
55.8
55.7
57.1
58.0
Ocean In
6.0
5.9
5.9
5.8
5.9
6.0
Rain & Flood In
18.7
18.7
16.9
18.8
18.8
18.8
Storage In
0.0
0.0
0.0
0.0
0.0
0.0
River Out
0.02
0.02
0.02
0.02
0.02
0.02
Ocean Out
0.1
0.2
0.1
0.1
0.2
0.2
ET, S&D Out
69.9
65.6
64.4
64.7
62.3
61.7
Abstraction
0.0
5.0
5.0
7.0
10.0
12.0
Storage Out
9.0
9.3
9.0
8.3
9.2
8.9
Table 7 Impact of groundwater extractions
Scn1
(-)
Scn2
(5 GL/yr)
Scn3
(5 GL/yr)
Scn4
(7 GL/yr)
Scn5
(10 GL/yr)
Scn6
(12 GL/yr)
Increased River Recharge
-
19%
28%
18%
27%
30%
Decreased ET
-
87%
111%
74%
76%
68%
Storage Change
-
-6%
-1%
10%
-2%
1%
* Percentages are calculated as the change in flux divided by the abstraction volume for the given scenario
6.2.3.
Impact on groundwater levels
As discussed previously, the direct impact of groundwater pumping is a lowering of the watertable.
Hydrographs have been plotted for each scenario at each of the observation sites in the model to
illustrate this impact. The hydrographs are presented in Figure 43.
The impact observed at each individual observation bore is highly influenced by the distance from
the borefield. In the extreme example, observation bore 7430 (11A) shows very significant
drawdown (up to 14m) in the pumping scenarios compared with no pumping scenario. This is
PAGE 66
because this site falls within the modelled borefield (refer Figure 41 and Figure 42). All other sites
did not show the same drawdown over time.
A noticeable occurrence in sites 1A through 9A is that they all (almost) completely recover with
every flood peak. This is consistent with the conceptual model that the ‘freed’ storage capacity is
taken up when a flood event occurs.
Further to this it is also clear that the longer the dry period, the greater the impact of pumping.
Where floods are seen to occur only a year apart, the increased drawdown due to pumping could be
considered negligible, however after three or four years of dry the increased drawdown due to
pumping is evident in all of the hydrographs and is likely to be widespread across the alluvial
aquifer.
The results highlight the fact that it is during the prolonged dry periods between flooding events
that the impacts of pumping are most pronounced.
PAGE 67
Figure 43 Scenario model hydrographs (Continued overleaf)
2009
7419 ‐ (1A) ‐ Scenario 1
7419 ‐ (1A) ‐ Scenario 2
7419 ‐ (1A) ‐ Scenario 3
7419 ‐ (1A) ‐ Scenario 4
7419 ‐ (1A) ‐ Scenario 5
7419 ‐ (1A) ‐ Scenario 6
2014
2019
2024
2029
2044
2049
2054
7420 ‐ (2A) ‐ Scenario 2
7420 ‐ (2A) ‐ Scenario 3
7420 ‐ (2A) ‐ Scenario 4
7420 ‐ (2A) ‐ Scenario 5
7420 ‐ (2A) ‐ Scenario 6
2014
2019
2024
2029
2034
2039
2044
2049
2054
7421 ‐ (3A)
7421 ‐ (3A) ‐ Scenario 1
7421 ‐ (3A) ‐ Scenario 2
7421 ‐ (3A) ‐ Scenario 3
7421 ‐ (3A) ‐ Scenario 4
7421 ‐ (3A) ‐ Scenario 5
7421 ‐ (3A) ‐ Scenario 6
2014
2019
2024
2029
2034
2039
2044
2049
2054
7422 ‐ (4A)
42
41
40
39
38
37
36
35
34
33
32
2009
2039
7420 ‐ (2A) ‐ Scenario 1
42
41
40
39
38
37
36
35
34
33
32
2009
2034
7420‐ (2A)
46
45
44
43
42
41
40
39
38
37
36
2009
7419 ‐ (1A)
46
45
44
43
42
41
40
39
38
37
36
7422 ‐ (4A) ‐ Scenario 1
7422 ‐ (4A) ‐ Scenario 2
7422 ‐ (4A) ‐ Scenario 3
7422 ‐ (4A) ‐ Scenario 4
7422 ‐ (4A) ‐ Scenario 5
7422 ‐ (4A) ‐ Scenario 6
2014
2019
2024
2029
2034
2039
2044
2049
2054
PAGE 68
7425 ‐ (6A)
39
38
37
36
35
34
33
32
31
30
29
2009
7425 ‐ (6A) ‐ Scenario 1
7425 ‐ (6A) ‐ Scenario 2
7425 ‐ (6A) ‐ Scenario 3
7425 ‐ (6A) ‐ Scenario 4
7425 ‐ (6A) ‐ Scenario 5
7425 ‐ (6A) ‐ Scenario 6
2014
2019
2024
2029
2034
2039
2044
2049
2054
7426 ‐ (7A)
37
35
7426 ‐ (7A) ‐ Scenario 1
33
7426 ‐ (7A) ‐ Scenario 3
29
7426 ‐ (7A) ‐ Scenario 4
7426 ‐ (7A) ‐ Scenario 5
27
7426 ‐ (7A) ‐ Scenario 6
25
2009
7426 ‐ (7A) ‐ Scenario 2
31
2014
2019
2024
2029
2039
2044
2049
2054
7428 ‐ (9A)
34
33
32
31
30
29
28
27
26
25
24
2009
2034
7428 ‐ (9A) ‐ Scenario 1
7428 ‐ (9A) ‐ Scenario 2
7428 ‐ (9A) ‐ Scenario 3
7428 ‐ (9A) ‐ Scenario 4
7428 ‐ (9A) ‐ Scenario 5
7428 ‐ (9A) ‐ Scenario 6
2014
2019
2024
2029
2034
2039
2044
2049
2054
7430 ‐ (11A)
34
32
30
28
26
24
22
20
18
16
14
12
10
2009
7430 ‐ (11A) ‐ Scenario 1
7430 ‐ (11A) ‐ Scenario 2
7430 ‐ (11A) ‐ Scenario 3
7430 ‐ (11A) ‐ Scenario 4
7430 ‐ (11A) ‐ Scenario 5
7430 ‐ (11A) ‐ Scenario 6
2014
2019
2024
2029
2034
2039
2044
2049
2054
PAGE 69
6.2.4.
Potential for impacts on GDE’s
The scenario model water balance results indicated that 68-87% of the extracted volume
(groundwater pumping) is realised as a reduction in evapotranspiration. For example, a borefield
extracting 5GL/yr could be expected to cause approximately a 4GL/yr decrease in ET. Whilst the
model does not have the capacity to reconcile between evaporation and transpiration, it is safe to
assume that only a minor proportion of the reduced ET would be direct evaporative loss.
Therefore, a conservative approach would suggest that the decrease in ET can be directly attributed
to a loss of water availability to groundwater dependent vegetation.
The observation bore hydrographs provide us with an understanding of the drawdowns that can
occur with the aquifer due to groundwater pumping. However, possibly more importantly they
provided an insight into the timing of the drawdowns. Here it is shown that it is during prolonged
dry periods when the pumping induced drawdowns are the greatest and therefore pose the greatest
risk to groundwater dependent ecosystems.
Examples of the maximum impact of the groundwater pumping, after a prolonged dry period, are
provided in Figure 44 and Figure 45. These images show the potential area effected by reduced
groundwater levels caused by groundwater abstractions of 5 GL/yr and 12 GL/yr respectively.
The figures show that in the immediate vicinity of the borefield, drawdowns greater than 5m can be
anticipated. Under Scenario 6 (12 GL/yr) this covers a significant area and encompasses a number
of intermittent pools. Based on this mapping, it is reasonable to expect that significant impacts to
GDE’s in the area of the borefield could be expected.
Under the 5 GL/yr scenario, based on the modelled borefield configuration, none of the permanent
or semi-permanent pools are likely to be significantly affected. However, for an assumed extraction
of 12 GL/yr (Scenario 6), four semi-permanent and one permanent pool are predicted to have
increased drawdowns of between 0.5 and 1.5 m. At these levels of drawdown there is likely to be
significant implications for the degree of permanency with obvious flow on effects for GDE’s.
However, it is noted that these are maximum impacts observed through the 50 year scenario model
period.
PAGE 70
Figure 44 Maximum drawdown due to groundwater pumping of 5GL/yr after a prolonged
dry period (calculated as the difference in watertable elevation between Scenario 1 and
Scenario 2 at scenario model time Dec-2028)
PAGE 71
Figure 45 Maximum drawdown due to groundwater pumping of 12 GL/yr after a
prolonged dry period (calculated as the difference in watertable elevation between
Scenario 1 and Scenario 6 at scenario model time 2028)
PAGE 72
6.2.5.
Potential for Seawater Intrusion
Due to the scarcity of data at the coastal end of the model there is a large degree of uncertainty
surrounding any modelling conclusions as to the potential for increased seawater intrusion due to
groundwater pumping in the Lower Robe alluvium. Nevertheless, any decrease in groundwater
flow towards the coast will result in an increased potential for inland migration of the saltwaterfreshwater interface. The importance of this is that there are known areas of freshwater pools and
groundwater dependent vegetation that could be adversely impacted by inland migration of saline
water.
The geophysical survey has already highlighted that under the current natural state, saline waters
are expected to be located up to 6km from the coast line (refer Section 3.11). Despite the large
uncertainty the scenario modelling can give a broad understanding of whether groundwater
pumping from the alluvial aquifer is likely to present a risk of further inland migration of saline
water. The method used here is to look for signs of pumping induced drawdown near the saltwaterfreshwater interface.
In Figure 44 and Figure 45 the maximum pumping induced drawdown observed within the scenario
model period was presented. Under both the 5GL/yr and 12 GL/yr pumping scenarios, there is no
indication that there is any significant drawdown extending to the salt water-fresh water interface.
Note that there is some drawdown evident in the maps near the coast but these are modelling
artefacts and are unrelated to the drawdown cone extending from the borefield.
In order to confirm the results of the drawdown mapping, hydrographs were generated just inland
from the saltwater-freshwater interface (Figure 46). The hydrographs were generated on model
slice 2 and slice 3 to cover the full thickness of the alluvium. In both cases there is no discernable
change in the hydrographs between any of the scenarios. Therefore, the scenario models predict
that it is unlikely that there will be any additional seawater intrusion as a result of the pumping in
the alluvial aquifer.
PAGE 73
Saltwater‐Freshwater Interface (Layer 2)
9
8
7
6
5
4
3
2
1
0
Add_Sce01_L2
Add_Sce02_L2
Add_Sce03_L2
Add_Sce04_L2
Add_Sce05_L2
Add_Sce06_L2
2009
2014
2019
2024
2029
2034
2039
2044
2049
2054
Saltwater‐Freshwater Interface (Layer 3)
9
8
7
6
5
4
3
2
1
0
Add_Sce01_L3
Add_Sce02_L3
Add_Sce03_L3
Add_Sce04_L3
Add_Sce05_L3
Add_Sce06_L3
2009
2014
2019
2024
2029
2034
2039
2044
2049
2054
Figure 46 Hydrographs generated immediately in front of the saltwater-freshwater
interface in Layers 2 and 3
The potential for additional salt water intrusion was further investigated by adding a constant head
boundary condition at the existing location of the saltwater-freshwater interface and re-running the
scenarios. The heads defined at the interface were obtained from the heads predicted by the
calibration model at the end of calibration. The predicted flux at this boundary can then be
interrogated at any time during the scenario models to determine whether there has been any
change in flux and hence change in location of the interface. The average mass balance fluxes for
all scenario models are presented in Table 8 and in Figure 47.
The results suggest that there is almost no change in the mass exchanges fluxes at the salt water
fresh water interface. In other words the extractive scenarios considered in this study are unlikely
to affect the location of the saltwater-freshwater interface. Note that most of the flux into the
model from the ocean occurs in the top model layer near the coast where evapotranspiration causes
water levels to decline near the coast resulting in an inflow of sea water into the aquifer.
PAGE 74
Table 8 Mass Balances for all Scenarios with Constant Head Boundary Applied at the
Saltwater-Freshwater Interface
IN (GL/year)
1
River
Ocean
Recharge (Rain & Flood)
Net Storage Change
OUT (GL/year)
River
2
3
4
5
6
55.4
56.0
56.7
57.0
58.8
59.8
6.8
6.9
7.0
6.9
7.0
6.9
18.7
18.7
16.9
18.7
18.8
18.8
8.5
7.7
8.0
8.2
9.5
9.3
1
2
3
4
5
6
0.04
0.02
0.02
0.02
0.02
0.02
3.5
3.5
3.4
3.5
3.5
3.5
68.7
65.2
64.0
63.7
61.4
60.5
S&D
0.2
0.2
0.2
0.2
0.2
0.2
Wells
0.0
5.0
5.0
7.0
10.0
12.0
Ocean
Evapotranspiration
PAGE 75
Figure 47 Results of Scenarios with Constant Head Boundary Applied at the SaltwaterFreshwater Interface
Flow IN (GL/year)
70
River
60
Ocean
50
Recharge (Rain, Flood) 40
Well
30
Storage
20
10
0
Sce_01
Sce_02
Sce_03
Sce_04
Sce_05
Sce_06
Flow OUT (GL/year) 70
River
60
Ocean
50
Recharge (ET, S&D) 40
Well
30
Storage
20
10
0
Sce_01
Sce_02
Sce_03
Sce_04
Sce_05
Sce_06
PAGE 76
7.
Sensitivity Analysis
A sensitivity analysis has been carried out on some of the key model input parameters. The
calibration model was used for this analysis. The methodology applied in this case involved
running the model consecutively with one of the input parameters varied by different amounts. In
each case the normalised RMS error for the particular model is noted. Model runs are repeated
until the normalised RMS error reaches 12 % or the parameter value is varied outside a reasonable
range for that particular parameter, or until the model mass balance increases by a factor of two
compared to the calibration model. The rationale behind this methodology is to demonstrate the
parameter range that can result in a model that is calibrated. In this case it is assumed that a model
with an RMS error of 12% (ie about 50% more than the calibrated model) is still calibrated and that
a model with a normalised RMS error that is greater than 12% is not calibrated. During calibration
it was recognised that better RMS error statistics are obtained when the recharge rates are
increased. However there is a limit to the volumetric fluxes that can reasonably be expected
through the available recharge and discharge mechanisms defined in the hydrogeological
conceptualisation. A limit to the fluxes has therefore been set at twice that included in the
calibration model. These criteria are somewhat arbitrary, however the approach is aimed at only
considering sensitivity within reasonable bounds as defined by calibration criteria. In other words
varying a particular parameter to the point that the model is not calibrated does not provide a useful
understanding of sensitivity. In this manner, model sensitivity only considers the impacts of
uncertainty within the bounds set by reasonable calibration criteria.
In this case the sensitivity analysis has been undertaken on the following parameters:
Hydraulic Conductivity,
Specific Yield,
River Bed Transfer (Bed Conductance),
Recharge
Evapotranspiration
The procedure involves selecting a series of perturbation factors. Multiplying the selected
parameter by the perturbation factors running the models and reporting the RMS error and total
model evapotranspiration flux. The results in terms of the perturbation factor, parameter value,
RMS error and total evapotranspiration flux are then reported for the cases where the model
calibration criteria (RMS error or mass balance fluxes) are just breached. In this case the total
model evapotranspiration flux has been chosen to illustrate the potential impact on groundwater
dependent ecosystems. In this regard it is assumed that evapotranspiration flux illustrates the
amount of water available for use by vegetation.
Results of the sensitivity analysis are presented in Table 9.
PAGE 77
The results suggest that:
Model predicted evapotranspiration is sensitive to most parameters included in this
assessment.
The parameters that do not impact significantly on predicted evapotranspiration are
specific yield (cases 4 and 5), reduced rainfall recharge (case 6) and reduced flooding
recharge (case 8).
Sensitivity cases 10 (reduced river bed transfer rate) and 12 (reduced ET) have the most
significant impact on predicted evapotranspiration rates are result in substantial reductions
in the water available for plant use.
Sensitivity cases 12 and 13 relate to perturbations in the evapotranspiration rate and hence
directly control the level of evapotranspiration included in the model. In this regard it is
not surprising that they impact significantly on the predicted level of evapotranspiration.
Future use of this model to predict the amount of water available for plant uptake should
be viewed with caution given the apparent model sensitivity to some of the uncertain
model input parameters. In particular the magnitude of any predicted change in
evapotranspiration rate should be considered as a relatively uncertain outcome.
Figure 48 Total Evapotranspiration as a percentage of calibration
Total Evapotranspiration
200
180
160
% of Calibration
140
120
100
80
60
40
20
0
1
2
3
4
5
6
7
8
9
10
11
12
13
PAGE 78
Table 9 Sensitivity Analysis Results
No
Parameter
Perturbation
Factor
1
Calibration
-
2
Hydraulic
Conductivity
3
Parameter
Value
-
0.02
ET % of
Calibration
Evapotranspiration
Rate (GL/year)
Normalized
RMS Error
100
68
8.2
65
44
12.3
131
89
11.1
Variable *1
3.0
4
0.2
0.01
109
74
14.1
5
6.0
0.3
109
74
7.5 *4
6
0.1
0.01%
of Rainfall
95
65
8.5 *6
7
10
1.0%
of Rainfall
177
120
7.6*5
8
0.005
1.0e-5
(m/day)
88
60
8.7 *6
5.0
0.01
(m/day)
151
103
10.6
42
29
10.7
165
112
8.0 *5
Specific Yield (1/d)
Recharge
(Rainfall)
Recharge (Flood)
9
10
11
12
13
0.01
River Bed
Transfer Rate
Variable *2
100
Evapotranspiration
Exponential Factor
10-5
10 *3
39
26
11.1
0.2
*3
143
98
11.0
0.1
The following notes refer to footnotes in Table 9.
*1
Variable (Hydraulic Conductivity)
No
Robe River
Gravels
Parameter
Robe Alluvial
Aquifer
Regional Area
(Aquitard)
Kx, Ky (m/day)
0.2
0.1
0.001
Kz (m/day)
0.02
0.01
0.0001
Kx, Ky (m/day)
300
150
1.5
Kz (m/day)
30
15
0.15
2
3
*2
Variable (River Bed Transfer Rate)
No
IN (10-4 1/d)
OUT (10-4 1/d)
10
5
0.1
11
50000
1000
PAGE 79
*3
Parameter (α) in Evapotranspiration Equation
ET = ETmax × e-α*d
(The details are shown in Equation 1 in Section 3.7)
ETmax = Pan Evapotranspiration
α = Exponential Decay Factor
d = Depth to groundwater
*4 The normalized RMS error is smaller than the calibrated value however the modelled
groundwater levels do not reflect the fluctuations apparent in the observed hydrographs. *5 The
normalized RMS error is smaller than calibrated value however the mass balance
criteria is breached. *6
The normalized RMS error has not reached the defined limit (12%) however the parameter
value has reached an acceptable limit.
PAGE 80
8.
Conclusions
A series of predictive model scenarios has been run and the results reported. The predictions
consider a variety of groundwater extraction and potential future climate change assumptions as
shown in Table 5. A no-extraction model scenario was run which simulates continuation of current
conditions. This case represents the base case against which other scenarios can be compared to
provide estimates of relative impacts caused by the particular elements included in each scenario.
Other scenarios include various groundwater extraction assumptions from potential future
borefields and an assumed dry future climate.
It was found that groundwater can be extracted from production borefields constructed in the
alluvial sediments. The impact of future groundwater extraction regimes ranging from 5 to 12
GL/yr result in drwawdown in groundwater levels in and around the borefield and a consequent
reduction in evapotranspiration of between 4 and 8 GL/yr. This loss of evapotranspiration
represents a reduction of between 6 and 11 % of the total water available for groundwater
dependent ecosystems. An 10% reduction in rainfall and flooding recharge assumed for Scenario 3
resulted in only minor changes to groundwater levels and the mass balance components suggesting
that future groundwater behaviour is not particularly sensitive to future climate change assumptions.
Model predictions suggest that the drawdown cone associated with the potential future borefield
operations is unlikely to expand to the coast or to the existing saltwater-freshwater interface and as
such there is no apparent risk of further salt water intrusion.
Sensitivity analysis suggests that the model is highly sensitive to perturbations in key uncertain
parameters and as such the model predictions should be considered as being relatively uncertain.
PAGE 81
9.
Model Limitations, Uncertainty and Recommendations
The Lower Robe Groundwater model can be defined as a medium complexity “Impact Assessment” model, as per (MDBC, 2000)The
primary reason for this level of complexity is the constraints imposed on the model by data limitations. The data constraints have
significant implications for model certainty and the level of confidence in results. The key data constraints and their impact on model
certainty are discussed below in Table 10.
Table 10 Data constraints, their impact on model certainty and recommendations to improve model reliability
Data
Constraint
Description
Impact on Model Uncertainty
Recommendation for data acquisition to improve model
reliability
Extent and
depth of alluvial
aquifer
With the exception of the area studied by
Commander (1994), the extent and
depth of the alluvial aquifer is poorly
understood. The unknown portion of the
model covers more than half of the
model domain and extends to the
coastline.
There is significant uncertainty in the aquifer
storage capacity and aquifer parameters in
the northern half of the model. This has
significant potential to influence mass
balance estimates for the model.
The lack of data near the coastline means
that potential impacts relating to seawater
intrusion are highly uncertain and cannot be
clarified within this model.
A drilling program to identify the extent of the alluvial aquifer
between the Commander (1994) study area and the coastline.
Each bore should be drilled to a sufficient depth that intercepts
the base of the alluvial aquifer.
It is likely that the most efficient way to achieve this would be to
drill 3-4 transects perpendicular to the river each with 2-3 bores
either side of the river.
River and pool
levels
Only one permanent river gauging
station exists, located at the Highway
pool at the southern end of the model.
Between the highway and the coastline
there is no additional data on river and
pool levels, leaving a very long stretch of
river without data.
Modelling results suggested that the river
was the predominant recharge mechanism
for the alluvial aquifer. Accurate data on
river levels is vital for establishing hydraulic
gradients and therefore fluxes to and from
the river.
The linear interpolation (between the
highway and coastline) used in the model is
considered a reasonable approximation
given the relatively flat terrain. However the
addition 1 or 2 key monitoring sites would
greatly improve model certain with regard to
fluxes to and from the river.
Addition of at least one permanent monitoring site in a
permanent pool (or pool with high degree of permanency)
approximately evenly spaced between highway and the
coastline.
Placement of monitoring bore transects perpendicular pool
monitoring site would also be highly advantageous to quantify
hydraulic gradients to and from the river.
PAGE 82
Data
Constraint
Description
reliability
Groundwater
observation
data
Only eight sites at the southern
(highway) end of the model had records
of groundwater levels over time.
In addition, monitoring at these sites has
been at a low frequency since
approximately 1995 and there are
questions regarding the consistency of
bore elevation datums throughout the
timeseries.
The observation data at the southern end of
the model is considered adequate to provide
a good level of certainty at the highway end
of the model.
In the coastal half of the model however,
there is significant uncertainty in
groundwater levels. This flows on to cause
uncertainty in attempts to calculate net
throughflow in the aquifer.
As previously mentioned this also creates
significant uncertainty in attempts to model
the potential for seawater intrusion.
With regard to monitoring data post 1995,
this is unlikely to have affected model
uncertainty given a good length of record
was already available prior to this.
In conjunction with the drilling program to identify the extent and
depth of the alluvial aquifer, it is recommended that these bores
be constructed as monitoring sites.
Ideally, as a many as practical of these sites would be monitored
on a monthly basis for at least the first two years (or at least one
full flood-recession cycle).
Once a good level of baseline data was achieved, the
monitoring program could be scaled back.
No existing
long-term
pumping
Currently the Lower Robe Alluvium is a
largely untapped resource (with the
exception of stock and domestic bores).
The lack of previous long-term pumping
data makes it harder to define the likely
impacts of future pumping. Eg. Long-term
pumping data combined with monitoring
records would provide extremely useful
information regarding the available storage
within the aquifer and the aquifer response
to pumping over time.
If a production bore field is established, monitoring of the
pumping rates at each individual bore is preferable to provide
the best information available should the model be recalibrated
in the future.
Extent of
flooding
There were no formal datasets available
that provided indications to the likely
extent of flooding. In this modelling
exercise it was assumed that flood
events filled the entire alluvial aquifer
extent.
In the current model, recharge fluxes
through the floodplain were shown to be
less significant than fluxes directly through
the river bed. Based on these results the
extent of the floodplain is not likely to
significantly impact model uncertainty.
However, flood plain mapping may provide
further reliability to model estimates.
Floodplain mapping to show the extent of flooding during a
major flood event would aid model certainty. This could be
achieved through acquiring aerial photography or satellite
imagery at the time of flooding.
PAGE 83
Data
Constraint
Description
reliability
Rainfall-river
level correlation
In Scenario 3, the aim was to identify the
impact of a dry climate on the aquifer
resource. However, in groundwater
models, river levels are a fixed model
input. Therefore, fluxes to and from the
river in Scenario 3 are not representative
of a dry climate.
Unfortunately, river fluxes are a significant
component of the model water balance and
therefore the results of Scenario 3 are
considered to be of limited use with a very
high degree of uncertainty.
Establishment of a rainfall-runoff style model based on rainfall
records and river levels at the highway pool gauge. This could
then be used to define river heights for various climate driven
scenarios.
PAGE 84
10. References
Commander, D. P. (1994). Hydrogeology of the Robe River alluvium. Western Australian
Geological Survey , 101-124.
Davidson, W. A. (1974). Hydrogeology of the De Grey River Area. 13-21.
Department of Water. (2009). Environmental values and issues for the lower Robe Rive, Western
Australia. Perth: Government of Western Australia.
Haig, T. (2009). The Pilbara coast water study. Perth: Department of Water.
Loomes, R., & Braimbridge, M. (2010). Lower De Grey River: ecological values and issues. Perth:
Department of Water.
MDBC. (2000). Murray-Darling Basin Commission Groundwater Flow Modelling Guidelines.
WorleyParsons Services. (2005). De Grey Goundwater Resource: Bulgarene Borefield Model.
Perth: Water Corporation.
PAGE 85
Appendix A Calibration Model Potentiometric
Surface and Depth to Watertable
Maps
PAGE 86
380000
390000
7650000
370000
7650000
360000
Robe River Model Area
Initial Watertable Elevation
(Jan-1984)
8
10
NC
7640000
Kilometres
4
6
TO
AR
2
EV
1
TR
0
REE
Pindooral Camp
RE
CR
RC
ER
7620000
7610000
7610000
7620000
R
IV
RE
EK
K
EE
E
TE
7630000
7630000
K
NO
YA
M
B
R
O
PE
7640000
Mary Anne Group
AM
RR
WA
BO O
7600000
EE K
COPYRIGHT
The concepts and information contained in this document are the copyright
of Sinclair Knight Merz Pty. Ltd. Use or copying of the document in whole
or in part without the written permission of Sinclair Knight Merz Pty. Ltd.
constitutes an infringement of copyright.
Sinclair Knight Merz Pty. Ltd. does not warrant that this document is
definitive nor free of error and does not accept liability for any loss caused
or arising from reliance upon information provided herein.
7590000
7590000
< 45
40 - 45
35 - 40
30 - 35
25 - 30
20 - 25
15 - 20
10 - 15
5 - 10
<5
CR
7600000
Elevation (mAND)
Refer to Sinclair Knight Merz document;
I:\VWES\Projects\VW04919\Technical\Spatial\ArcGIS\
Robe_River_RWL_Initial.mxd
360000
370000
380000
390000
380000
390000
7650000
370000
7650000
360000
Initial Depth to Watertable
(Jan 1984)
8
10
NC
7640000
Kilometres
4
6
TO
AR
2
EV
1
TR
0
REE
Pindooral Camp
RE
CR
RC
ER
7620000
7610000
7610000
7620000
R
IV
RE
EK
K
EE
E
TE
7630000
7630000
K
NO
YA
M
B
R
O
PE
7640000
Mary Anne Group
AM
RR
WA
BO O
CR
7600000
> 50
20 - 50
10 - 20
5 - 10
2-5
<2
COPYRIGHT
7590000
7590000
7600000
EE K
Depth (m)
Robe_River_SWL_Initial.mxd
360000
370000
380000
390000
380000
390000
7650000
370000
7650000
360000
Final Watertable Elevation
(Dec-2008)
8
10
NC
7640000
Kilometres
4
6
TO
AR
2
EV
1
TR
0
REE
Pindooral Camp
RE
CR
RC
ER
7620000
7610000
7610000
7620000
R
IV
RE
EK
K
EE
E
TE
7630000
7630000
K
NO
YA
M
B
R
O
PE
7640000
Mary Anne Group
AM
RR
WA
BO O
7600000
EE K
COPYRIGHT
7590000
7590000
< 45
40 - 45
35 - 40
30 - 35
25 - 30
20 - 25
15 - 20
10 - 15
5 - 10
<5
CR
7600000
Elevation (mAND)
Robe_River_RWL_Final.mxd
360000
370000
380000
390000
380000
390000
7650000
370000
7650000
360000
Change in Watertable Elevation
(Jan-1984 to Dec-2008)
8
10
NC
7640000
Kilometres
4
6
TO
AR
2
EV
1
TR
0
REE
Pindooral Camp
RE
CR
RC
ER
7620000
7610000
7610000
7620000
R
IV
RE
EK
K
EE
E
TE
7630000
7630000
K
NO
YA
M
B
R
O
PE
7640000
Mary Anne Group
AM
RR
WA
BO O
7600000
- Rising
Falling
COPYRIGHT
7590000
7600000
>5
2-5
1-2
0.5 - 1
Steady
1 - 0.5
2-1
5-2
>5
EE K
7590000
CR
Watertable Change (m)
Robe_River_rwl_diff.mxd
360000
370000
380000
390000
380000
390000
7650000
370000
7650000
360000
Watertable Elevation During Flood
(Jun-1989)
8
10
NC
7640000
Kilometres
4
6
TO
AR
2
EV
1
TR
0
REE
Pindooral Camp
RE
CR
RC
ER
7620000
AM
RR
WA
> 50
45 - 50
40 - 45
35 - 40
30 - 35
25 - 30
20 - 25
15 - 20
10 - 15
5 - 10
<5
CR
7600000
EE K
COPYRIGHT
7590000
7600000
BO O
Elevation (mAND)
7590000
7610000
7610000
7620000
R
IV
RE
EK
K
EE
E
TE
7630000
7630000
K
NO
YA
M
B
R
O
PE
7640000
Mary Anne Group
Robe_River_RWL_FLOOD.mxd
360000
370000
380000
390000
380000
390000
7650000
370000
7650000
360000
Depth to Watertable During Flood
(Jun-1989)
8
10
NC
7640000
Kilometres
4
6
TO
AR
2
EV
1
TR
0
REE
Pindooral Camp
RE
CR
RC
ER
7620000
7610000
7610000
7620000
R
IV
RE
EK
K
EE
E
TE
7630000
7630000
K
NO
YA
M
B
R
O
PE
7640000
Mary Anne Group
AM
RR
WA
BO O
CR
7600000
> 50
20 - 50
10 - 20
5 - 10
2-5
<2
COPYRIGHT
7590000
7590000
7600000
EE K
Depth (m)
Robe_River_SWL_FLOOD.mxd
360000
370000
380000
390000
380000
390000
7650000
370000
7650000
360000
Watertable Elevation After Prolonged
Dry Period (Feb-1992)
8
10
NC
7640000
Kilometres
4
6
TO
AR
2
EV
1
TR
0
REE
Pindooral Camp
RE
CR
RC
ER
7620000
AM
RR
WA
> 50
45 - 50
40 - 45
35 - 40
30 - 35
25 - 30
20 - 25
15 - 20
10 - 15
5 - 10
<5
CR
7600000
EE K
COPYRIGHT
7590000
7600000
BO O
Elevation (mAND)
7590000
7610000
7610000
7620000
R
IV
RE
EK
K
EE
E
TE
7630000
7630000
K
NO
YA
M
B
R
O
PE
7640000
Mary Anne Group
Robe_River_RWL_DRY.mxd
360000
370000
380000
390000
380000
390000
7650000
370000
7650000
360000
Depth to Watertable After
Prolonged Dry Period (Feb-1992)
8
10
NC
7640000
Kilometres
4
6
TO
AR
2
EV
1
TR
0
REE
Pindooral Camp
RE
CR
RC
ER
7620000
7610000
7610000
7620000
R
IV
RE
EK
K
EE
E
TE
7630000
7630000
K
NO
YA
M
B
R
O
PE
7640000
Mary Anne Group
AM
RR
WA
BO O
CR
7600000
> 50
20 - 50
10 - 20
5 - 10
2-5
<2
COPYRIGHT
7590000
7590000
7600000
EE K
Depth (m)
Robe_River_SWL_DRY.mxd
360000
370000
380000
390000
380000
390000
7650000
370000
7650000
360000
Difference in Watertable Elevation
between Flood Peak and Dry Period
8
10
NC
7640000
Kilometres
4
6
TO
AR
2
EV
1
TR
0
REE
Pindooral Camp
RE
CR
RC
ER
7620000
7610000
7610000
7620000
R
IV
RE
EK
K
EE
E
TE
7630000
7630000
K
NO
YA
M
B
R
O
PE
7640000
Mary Anne Group
AM
RR
WA
BO O
7600000
- Rising
Falling
COPYRIGHT
7590000
7600000
>5
2-5
1-2
0.5 - 1
Steady
1 - 0.5
2-1
5-2
>5
EE K
7590000
CR
Watertable Change (m)
Robe_River_flood_dry_diff.mxd
360000
370000
380000
390000

Groundwater Model Conceptualisation

Transcription

Similar documents

aloura series - cloudfront.net

The Turoc 231 - GJ Gardner Homes

split contract - McDonald Jones Homes

Purple Robe Saxifrage

Physical Geography Water Resources Chapter 9

6 Baywood Drive, Black Head Quality, Location

Awesome Aquifers - Groundwater Foundation

The Water Cycle - UN

Earth Observation Techniques - Global Navigation Satellite System

JSEM Conference Submittal