Bank Erosion in the Upper Brisbane River: Prioritising ...

Final Report Bank Erosion in the Upper Brisbane River: Prioritising management Options at the Burrows and Weildons Properties

Andrew Brooks, Graeme Curwen, John Spencer, Fabio Iwashita, Joe McMahon, Jon Olley Australian Rivers Institute - Griffith University 9/17/2014

Final Report– September 2014 Project Leader and Host Organisation Dr. Andrew Brooks Australian Rivers Institute, Griffith University Nathan Qld 4111

Project Team –John Spencer1, Graeme Curwen1, Fabio Iwashita1, Joe McMahon, Jon Olley1, Daniel Borombovits1 1 Australian

Rivers Institute, Griffith University, Nathan Qld 4111

Citation: Brooks, A.P. Curwen, G., Spencer, J., Iwashita, F., McMahon, J., Olley, J. (2014). Channel erosion in the upper Brisbane River: prioritising management options at the Burrows and Weildons Properties. Report to Seqwater, Griffith University, 135 pp.

Acknowledgements We thank Dan Garcia and Kate Smolders from Seqwater for initiating the project and providing feedback on an earlier draft. Thanks also to Leslie Burrows for her historical insights and photographs and to Terry Malone for the provision of the upper Brisbane modelled hydrology data.

Contents Citation: ............................................................................................................................................ 2 Acknowledgements .......................................................................................................................... 2 1

Executive Summary ................................................................................................................... 4

2

Recommendations ..................................................................................................................... 8

3

Project Objectives .................................................................................................................... 16

4

Background .............................................................................................................................. 17

5

Approach ................................................................................................................................. 18

5.1 5.2 5.3

Understanding Controls on recent Channel Erosion ............................................. 19 Predicting future Erosion ........................................................................................... 20 Study Area .................................................................................................................. 22

5.4

Upper Brisbane Macro-channel –type example ................................................. 24

5.3.1 5.4.1

6

6.1 6.2

Morphological Change Detection ........................................................................... 31 Repeat LiDAR Analysis .............................................................................................. 31

6.3

Geomorphic Unit Differentiation ............................................................................. 32

6.4

Hydraulic Analysis ..................................................................................................... 34

6.3.1

8

9

Land-use impacts......................................................................................................................................................................... 26

Methods ................................................................................................................................... 31 6.2.1

7

The Upper Brisbane Catchment ............................................................................................................................................... 22

Spatial Variation of Erosion ..................................................................................................................................................... 31 Geomorphic Unit Delineation within the Macro-channel ................................................................................................... 32

Results ..................................................................................................................................... 34

7.1 Analysis of Erosion across the upper Brisbane River between Cooyar Ck and Lake Wivenhoe ................................................................................................................................ 34 7.2 Sediment Production by Reach ................................................................................ 35 7.3 Analysis of Erosion by Geomorphic Unit................................................................ 39 7.4 Erosion Analysis in the Burrows and Weildon’s Reach ........................................ 40 7.5 Detailed Analysis of the recent changes in the Burrows-Weildons Reach ...... 42 7.6 Additional Channel Erosion between 2011 and 2013 at selected sites in the study reach 55 7.7 Costs of Erosion to Seqwater & Riparian Landholders ....................................... 57

Sub-reach Rehab Options ........................................................................................................ 59

8.1 8.2 8.3 8.4

Sub-reach 75-76 ....................................................................................................... 60 Sub-reach 77 .............................................................................................................. 63 Sub-reach 78 .............................................................................................................. 64 Sub-Reach 79 ............................................................................................................. 65

Summary and Cost –Risk Analysis .......................................................................................... 67

9.1

Rehabilitation Prioritisation ...................................................................................... 68

10

Summary ............................................................................................................................. 70

11

References ........................................................................................................................... 72

12

Appendices.......................................................................................................................... 76

1

Executive Summary • Analysis of channel erosion in the 85km reach of the upper Brisbane River above Wivenhoe dam between 2001 and 2011 shows that a total of 4.34 M m3 of sediment was eroded during this period (or 6.95 Mt), most of which occurred in the 2011 flood (Table E1). Over the same period 1.15M m3 (1.84 Mt) was deposited within the channel zone – most of which was bed material. Therefore, over this period there was net erosion from this 85km reach of 3.19 M m3 (5.1 Mt). This represents the minimum amount of sediment delivered to Wivenhoe dam over this period, because no account is made of the inputs from tributaries and the catchment above the study reach. The total area of high value alluvial flats eroded from the reach represents 100.7Ha.

Table E1 Summary table showing the net bed and bank erosion for the upper Brisbane River LiDAR reach between 2001 – 2011 as well as the corresponding changes in the study reach

Sub-reach 1-91 bed material only Sub-reach 1 – 91 all other erosion

erosion (m3)

erosion (t)

deposition (m3)

deposition (t)

net change (t)

-1,202,250

-1,923,599

905,243

1,448,389

-475,210

-3,142,064 -4,344,314

-5,027,303 -6,950,902

249,837 1,155,081

399,740 1,848,129

-4,627,563 -5,102,773

-290,598

-464,957

115474

184,758

-212,386

6.69%

6.69%

10.00%

10.00%

4.16%

sub-reach 75-79 % change rchs 75-79 cf 85 km rch

There was considerable spatial variability of erosion within the 85km reach surveyed, with 50% of all erosion coming from just 10 of 91 sub-reaches (or 19.7% of the total channel area). 75% of all erosion comes from just 25 of the 91 sub-reaches. The two sub-reaches in the vicinity of the Burrows property (sub-reaches 78 & 79) are within the top 15 sediment producing sub-reaches (Figure E1). 100.0%

Weildons Burrows

80.0% 60.0% 40.0%

77

52

51

76

84

43

45

38

11

31

66

68

15

23

74

78

63

57

64

32

59

79

72

65

54

62

55

56

0.0%

73

20.0% 58

cummulative proportion of all erosion contributed by sub-reach

•

total

Sub-reach number Figure E1 Cumulative contributions of the top 30 sediment producing sub-reaches to the total volume of sediment eroded within the 91 sub-reaches in the upper Brisbane River between 2001 and 2011.

•

Hydraulic analysis undertaken as part of a larger parallel project (Brooks et al., in prep)., highlights that when the percentage foliage cover (pfc) of woody vegetation (>5m in height) is > 20% within the macro-channel, that erosion per unit stream power

• •

•

is minimized (see Figure 43). Hence, it is proposed that a rehabilitation objective should be to increase woody vegetation pfc to a minimum of 20% and ideally >30%. At present the Burrows-Weildons reach has the lowest woody vegetation percent foliage cover (pfc) in the entire upper Brisbane River – with levels typically < 5%. The majority of the upper Brisbane River is characterized as a partially confined semialluvial river with much of the river length confined by bedrock or less erodible terraces that date to the last interglacial period > 20ka (at least). The channel form can predominantly be described as a complex “macro-channel” – whereby the majority of erodible deposits are inset floodplains and benches that occur within the bounds of the confining terraces or bedrock. 93% of all bank erosion occurred in the benches, vegetated islands and inset floodplains – rather than the more expansive floodplain units. The Burrows & Weildons study reaches reflect this broader pattern (Figure E2). Sub-reach erosion breakdown for the Burrows/Weildons study reach 2001-2011

120000

erosion (m3)

100000 80000 60000

bed_material+water zone colluvial terraces Inset flood plains benches VegebarsIslands

40000 20000 0 75

76

77 sub-reach number

78

79

Figure E2 Breakdown of erosion by geomorphic unit for the 5 sub-reaches encompassing the Weildon’s/Burrows study reach

• • •

•

• •

The ~5km reach that is the focus of this study adjacent to the Weildon’s and Burrow’s properties contributed 464 Kt of sediment from channel erosion, or 6.7% of the total contribution of erosion from the 85km reach surveyed A greater proportion of the total deposition (~10%) occurred within this reach, probably reflecting the fact that it is within the dam backwater zone when the dam is a 100% flood capacity (200% domestic water supply level) The Burrows and Weildons sub-reaches are a high priority for rehabilitation given that at the sub-reach scale (~ 1km channel length), 4 of the 5 sub-reaches in this vicinity fall within the top 30 (of 91) sub-reaches in the whole of the upper Brisbane catchment (Figure E1). The estimated total cost of this erosion (primarily associated with the 2011 flood which was a 4%AEP flood) was ~$108M or $33.80/m3 of eroded sediment. The costing includes: capital value of land lost; productive capacity of lost land; costs of water treatment; costs of lost water storage capacity (including dredging costs and equivalent bulk water value). Within the Burrows/Weildons study reach a total of 7.4 Ha of alluvial flats were lost between 2001 and 2011, and the equivalent total cost of the erosion from the reach is estimated to be $9.73M. At some sites (notably the outer bank at Burrows – sub-reach 78) significant additional erosion has occurred in the period 2011-2013, primarily associated with flood in 2013 (Table E1).

2

3

area eroded (m )

Volume eroded (m )

2001-2011

5819

20618

2011-2013

2769

9811

Table E1 The various rehabilitation strategies canvassed for implementation in the study reach and associated costings for each strategy. The codes of each strategy and associated colour are used in each of the tables

•

•

However, not all sites eroded between 2001-2011 will continue to erode at the equivalent rate as the preceding period (i.e. primarily associated with the 2011 flood) because much of the available erodible material has now gone. Furthermore, channel capacity has been enlarged in some areas to such an extent that the equivalent hydraulic conditions would not be generated at the peak flow in a flood equivalent to the 2011 event. The risk assessment approach undertaken for prioritizing the rehabilitation in the Burrows-Weildons reach takes qualitative account of this differential risk at the geomorphic unit scale – which we consider to be the most appropriate scale for designing a rehabilitation strategy. Rehabilitation strategies have been costed for each of the 5 sub-reaches in the 5km Burrows-Weildons reach that incorporate some or all of the nine rehabilitation strategies shown in Table E2. The unit costs in table E2 are based on figures supplied by Seqwater or the authors’ data. The costings are for a 5 year period only. Beyond 5 years there will be ongoing maintenance costs – but if vegetation is well established, these costs should taper off significantly.

Table E2 Costs associated with various Rehabilitation options

treatment a Engineered Log Jams/m x 5m b rock rip rap/linear m x 1m c bank battering/linear m x 6m high bank d reveg/Ha e primary weed control/ha f weed maintenance (assisted natural regen) /ha g new fence construction/m h fence maintenance i sand/gravel extraction

unit cost $800 $85 $500 $45,000 $5,000 $2,000 $15

Recurrent/yr

$15,000 $4,000* $5

-$100

* i.e. weed maintenance to be undertaken twice yearly. •

Given that different rehabilitation approaches are appropriate for each geomorphic unit it was not possible (nor appropriate) to assess too many permutations of the rehab strategies in each sub-reach (i.e. if you model 2 or 3 options per geo unit and there are ~ 7-10 geo-units per sub-reach - very quickly you end up with a matrix of 10s – 100s of possible permutations). Hence, the optimal rehabilitation strategy has been selected for each sub-reach and alternative options costed only where there was a real choice to be made.

• •

The full costings are included in Table E3. Different options are included for sub-reach 75/76 (bank battering with and without toe protection) and reach 78 (ELJs or Rock Rip rap). In each case the preferred option is indicated. The option of sand and gravel extraction has been suggested for sub-reach 78 only, because there is evidence that there has been substantial aggradation on the point bar in this sub-reach, which has been sustained over a period of several decades (based on photographic evidence provided by Mrs Burrows). Hence, a one-off opportunity exists here to extract sand and gravel as part of the rehabilitation strategy, and to use the proceeds of the sale of the sand and gravel to offset the rehabilitation costs for the rest of the reach.

Table E3. Summary of rehabilitation costs over 5 years broken down by geo-unit and sub-reach

2 Subreach Area (Ha) 23.4 23.4 15.7 29.8 29.8 49.2

3 Vegetated midchannel bar

4

5

6

Inset Flood Bed zone Bench Terrace Plain Sub-reach Number 75/76 option 1 $173,526 $82,010 $61,485 $458,374 $51,403 75/76 option 2* $173,526 $82,010 $61,485 $618,374 $51,403 77 $131,480 $55,095 $659,907 $40,540 78 option 1* $199,172 $17,183 $552,342 $1,469,538 $10,443 78 option 2 $199,172 $17,183 $566,342 $1,498,038 $10,443 79 $162,580 $45,455 $60,000 $592,035 $93,638 total preferred option 118.15 $666,758 $144,648 $728,922 $3,339,854 $196,023 * denotes preferred option Potential Cost Offset Sale of Sand and Gravel from sub-reach 78: 40,000 m3 @ net $50/m3

•

•

• • •

Misc fencing

total

$16,000 $16,000 $17,500 $17,500 $17,500 $17,500

$842,798 $1,002,798 $904,522 $2,266,177 $2,308,677 $971,208

$68,500

$5,144,703

$2,000,000

A risk assessment prioritization approach is proposed whereby the potential for achieving a more resilient channel (i.e. one which produces less sediment per unit imposed stream power) is analysed for each geomorphic unit within each sub-reach. The approach combines knowledge of the extent of past erosion from the geo-unit (and its associated cost), the likely risk of the unit continuing to erode if nothing further is done, coupled with the cost of the rehabilitation strategy required to increase the stability of the geo-unit. Not all geo-units are equivalent; actively eroding high banks on inset floodplains are more difficult to stabilize (requiring major engineering works) than a low bar which can be stabilized using a strategy of “assisted natural regeneration (ANR)” (i.e. the facilitation of natural regeneration by controlling competition from weeds and elimination/reduction of grazing pressure). The highest priority areas (and the most cost effective) are the extensive bed and bar areas that can be tackled with ANR. However, the benches and inset floodplains at key sites (as identified) present the greatest threat to ongoing sediment supply to the dam and therefore cannot be ignored despite the associated costs. Decisions regarding prioritizing between individual geo-units must, however, take into account that the strategy employed on one unit can have an impact on other adjacent units. (e.g. if ANR is undertaken on the bed and bars in one section of the channel -

but nothing is done with the adjacent bench or inset floodplain banks, this may increase the potential for erosion on the banks). Hence -it is critical to consider the whole reach strategy in its entirety. The sub-reach strategies have been devised with this principle in mind 2

Recommendations 1. The Burrows-Weildons reach, as identified in this study, represents a significant source of sediment supplied to Wivenhoe dam, and should be a high priority for undertaking a comprehensive rehabilitation strategy with the objective of increasing the resilience of the channel to future flood damage (and hence sediment supply to the dam). A detailed rehabilitation plan should be developed as a matter of priority. 2. A key rehabilitation objective should be to increase woody vegetation percentage foliage cover (pfc) to a minimum of 20% and ideally >30% within the macro-channel. 3. A feasibility study into the potential sale of sand & gravel from the point bar opposite Burrows should be undertaken as part of the detailed rehabilitation plan. 4. It is clear that there are some key river reaches further upstream that are even greater sources of sediment to Wivenhoe dam than the Burrows-Weildons reach, and it should be a high priority for Seqwater to engage with the appropriate stakeholders upstream to begin the process of rehabilitating some of the key upstream reaches identified in this report – particularly in the vicinity of Harlin. 5. The most fundamental component of any river rehabilitation strategy is the cessation of key disturbances that are currently contributing to the destabilization of the channel, and hence the ongoing production of elevated sediment yields to Wivenhoe Dam. Inchannel grazing and sand & gravel extraction within the channel of the upper Brisbane River are fundamentally at odds with the objective of developing a more resilient channel and reducing sediment pollution to Wivenhoe Dam. As a matter of the highest priority, Seqwater should exert all pressure that it can bring to bear to ensure the practices of in-channel grazing and sand & gravel extraction are eliminated from within the bounds of the macro-channel in the upper Brisbane River and its major tributaries. Such practices are incompatible with achieving more resilient channels in the stream network and hence reducing sediment delivery to Wivenhoe Dam. 6. This study has highlighted the immense value of repeat LiDAR data as a basis for understanding channel erosion dynamics and for prioritizing river management works. It is recommended that airborne LiDAR should be acquired on a regular basis (at least once every 5 years). Priority should be given to reflying areas already acquired, but the area of channel network should be expanded beyond that already covered – at least including the major tributaries.

List of Figures

Figure 1 Upper Brisbane River Catchment showing location of gauges and LiDAR extent (in dark blue) ........................................................................................................................................18 Figure 2 Comparison between SedNet predicted bank erosion rates (Prosser et al., 2003) and observed rates (2001-2011) in the Upper Brisbane River. Each point on the graph represents a ~ 1km section of the upper Brisbane River with the bank erosion rate derived from repeat LiDAR analysis (this study) plotted against the corresponding bank erosion rate predicted by the SedNet model for the same reach. ......................................18 Figure 3 Spot Image showing the LiDAR reach extending ~ 85km upstream from the top of Lake Wivenhoe and the locations of the study area around the Burrows and Weildons properties. Also shown are the numbered sub-reaches into which the erosion and deposition analyses have been determined .............................................................................21 Figure 4 Characteristic morphology of an underfit macro-channel, in which it can be seen that the channel zone is comprised of a complex suite of geomorphic units which are of Holocene age, inset within a broad laterally constrained meandering macro-channel that is inset within a Pleistocene Age floodplain. The image on the right shows the extent of erosion within the channel in a 1:25 yr flood (2011), with the majority occurring within the Holocene age inset features. The Pleistocene surface only erodes once the buttressing inset Holocene units have been eroded ................................................................24 Figure 5 Upper Brisbane River valley near Gregors Creek, looking upstream, August 2005 showing the Macro, meso – and low flow channel (Photo by QDNRW) .............................25 Figure 6 Schematic model showing a typical section of the upper Brisbane River with associated configuration of geomorphic units within the Macro-channel. ...........................25 Figure 7 Cattle grazing within the channel on the upper Brisbane River - sub-reach 68. .......26 Figure 8 The extent of woody vegetation within the macro-channel zone has been defined from the 2001 liDAR data./ These data were compared with the Spot Satellite imagery from 2009, which shows that it is a valid representation of the extent of woody vegetation at the time of the 2011 flood. The data shows the extent of vegetation varies considerably down the river, with the study area at Weildon’s property particularly, having the lowest vegetation cover in the whole upper Brisbane River.......27 Figure 9 Official data on volumes of sand and gravel extracted from the upper Brisbane River between 1984 and 2007 (from Shellberg and Brooks, 2007), showing the estimated additional extraction from both within the deltaic zone of Lake Wivenhoe (for the Western Pipeline project) in 2007 and 2008. Also shown on the graph are the DNRM estimates of the sustainable extraction volume and the pre-2007 mean annual extraction rates. .............................................................................................................................28 Figure 10 Aerial photograph showing sand and gravel extraction from the channel in subreach 58 ..........................................................................................................................................29 Figure 11 Aerial photograph showing sand and gravel extraction from the channel in subreach 58 ..........................................................................................................................................29 Figure 12 Maximum Daily Discharge at the Gregors Creek Gauge 1961 – 2013 from the DNRM website ................................................................................................................................30 Figure 13 Annual peak discharge derived from the data downloaded from the Gregors gauge (note the discrepancy between the DNRM derived plot and the Annual peak

data derived from the data downloaded from the DNRM site). The plot in Figure 12 suggests that the 2011 event was the gauged flood of record, whereas the data in this plot suggests that the 1999 flood was larger. ........................................................................31 Figure 14 Example from the study reach showing the mapped geomorphic units within the macro-channel – which is defined by the sub-reach masks. Also shown are the 5 subreaches (75-79) that have been analysed as a sub-set of the 91 sub-reaches in the LiDAR reach and the bank stratigraphy sites. ..........................................................................34 Figure 15 Cumulative contributions of the top 30 sediment producing sub-reaches to the total volume of sediment eroded within the 91 sub-reaches in the upper Brisbane River between 2001 and 2011. ...........................................................................................................35 Figure 16 Map showing the spatial distribution of total channel erosion by sub-reach. Purple markers represent the study area ..............................................................................................36 Figure 17 Upper Brisbane River Observed bank erosion 2001-2011 by geomorphic unit breakdown. Error bars represent the likely undetected erosion that was filtered out in the noise removal process (which used a 0.5m limit of detection (LOD)) or due to the ~ 0.5m water level variance between surveys. The error includes LOD values from 0.20.5m adjacent to erosion cells. ....................................................................................................37 Figure 18 Net change in bed material load (deposition – erosion) by sub-reach. Error bars represent the likely undetected erosion that was filtered out in the noise removal process (which used a 0.5m limit of detection (LOD)) or due to the ~ 0.5m water level variance between surveys. The error includes LOD values from 0.2-0.5m adjacent to erosion cells. An estimate of the erosion and deposition below water level has also been included with the volume calculated on an area proportion ratio of the erosion and deposition within the above water bed material zone. .........................................................................................38 Figure 19 Breakdown of bank erosion by geomorphic unit for the 90km reach of the upper Brisbane River represented by the repeat LiDAR data .........................................................39 Figure 20 Breakdown of erosion by geomorphic unit for the 5 sub-reaches encompassing the Weildon’s/Burrows study reach ..................................................................................................40 Figure 21 Map showing deposition within the study reach. Note that there is significant deposition on the point bar adjacent to the Burrow’s property. ..........................................41 Figure 22 Plot of absolute and net (deposition – erosion) bed material change for the 5 study area sub-reaches. Note the sub-reach 78 is one of the few sub-reaches showing significant net deposition, which from Figure 21can be seen to be focused on the large point bar opposite where most of the bank erosion is occurring. .........................................41 Figure 23- Sub-reach 75 in 2009 with 2011 erosion raster superimposed .................................43 Figure 24 Sub-reach 75/76 in 2011 with 2011 erosion raster superimposed ..........................44 Figure 25 Sub-reach 75/76 in 2013 with 2011 erosion raster superimposed. Note there is little evidence of further erosion at this site after the 2013 flood but some is evident on the right............................................................................................................................................45 Figure 26 Sub-reach 77 in 2009 with 2011 erosion raster superimposed ..................................46 Figure 27 Sub-reach 77 in 2011 with 2011 erosion raster superimposed. Note the mass failures along the left bank (top of picture) .............................................................................47 Figure 28 Sub-reach 77 in 2013 with 2011 erosion raster superimposed. Note that at the location of the mass failures the bank seems to have been battered .................................48 Figure 29 Sub-reach 78 in 2009 with the 2011 erosion raster superimposed ............................49

Figure 30 Sub-reach 78 in 2011 with the 2011 erosion raster superimposed ..........................50 Figure 31 Sub-reach 78 in 2013 with the 2011 erosion raster superimposed. Note that there has been considerable additional erosion at the bend apex associated with the 2013 flood. ................................................................................................................................................51 Figure 32 Sub-reach 79 in 2009 with the 2011 erosion raster superimposed ............................52 Figure 33 Sub-reach 79 in 2011 with 2011 erosion raster superimposed .................................53 Figure 34 Sub-reach 79 with 2011 erosion raster superimposed. There is little evidence of additional erosion this site following the 2013 flood. ............................................................54 Figure 35 Map showing the location of areas of detailed change analysis between 2011 and 2013 ................................................................................................................................................55 Figure 36 Detail area A from Figure 35 showing the additional erosion between 2011 and 2013 at the Burrows upstream of the tributary confluence. ..................................................55 Figure 37 Bank erosion along the bank downstream of tributary junction between 2001 and 2011 and additional erosion between 2011 and 2013 (area B Figure 35). ...................56 Figure 38 Upper Brisbane River - Observed area of alluvial land lost between 2001-2011 by geomorphic unit breakdown (total lost = 100.7 Ha) ........................................................58 Figure 39 Overview of sub-reaches 75 and 76 showing the geomorphic unit layout and the location of the section of bank for which battering has been costed. .................................61 Figure 40 Overview of sub-reach 77 showing the geomorphic unit layout and the location of the 340m section of bank for which bank stabilization work has been costed .................63 Figure 41 Overview of sub-reach 78 showing the geomorphic unit layout and the location of the three sections of bank for which bank stabilization work has been costed. Also shown are some areas where sand and gravel could be extracted ...............................................64 Figure 42 Overview of sub-reach 79 showing the geomorphic unit layout and the location of the three sections of bank for which bank stabilization work has been costed .................66 Figure 43 Relationship between erosion per unit streampower (thresholded at 10 Wm-2) and proportion of large woody vegetation (higher than 5 metres) canopy cover proportion within the macro-channel. The analysis shows that once woody vegetation cover exceeds (From McMahon et al., in prep)...................................................................................71 Figure 44 LiDAR hillshade of the Brisbane River macro-channel (flowing left to right) and Wallaby Creek (entering from bottom) near the town of Moore. .......................................77 Figure 45 LiDAR hillshade image of the Brisbane River macro-channel (flowing left to right) upstream and downstream of the Gregors Creek confluence and bridge. .......................77 Figure 46 Estimates of minimum annual total sand and gravel extraction from the Brisbane River and Ivory and Maronghi Creeks (1984-2007). Based on or contains data provided by the Queensland Department of Natural Resources, Mines and Energy, Queensland [2007] which gives no warranty in relation to the data (including accuracy, reliability, completeness or suitability) and accepts no liability (including without limitation, liability in negligence) for any loss, damage or costs (including consequential damage) relating to any use of the data. ................................................................................80 Figure 47 Locations of permits given to mine sand and gravel within the upper Brisbane River watercourse between 1971 to 2007 from the Water Entitlements Register Database (WERD). ........................................................................................................................81 Figure 48 The systematic filtering of ‘erosion' values from the difference layer in increments of 10cm did not show a step change the indicates a threshold value. .....................................84

Figure 49 Comparison of 2001 and 2011 LiDAR showing an obvious slump and different water surface ..................................................................................................................................85 Figure 50 Left picture has no raster values masked, right picture has raster values from 0 to 0.5 masked. Noise has been removed from the inset flood plain and the terrace. .........86 Figure 51 Left picture has values from 0 to 1.6 masked, some noise remains over the water. Right picture has values masked from 0 to 1.8, noise cleared from over water, bank erosion better identified. ..............................................................................................................86 Figure 52 Profile of left and right bank in 2001 and 2011 showing bank retreat and differences in water height. Profile is shown by blue line across the channel in figures 1, 2 and 3 ............................................................................................................................................86 Figure 53 Erosion of an island in the backwater of Lake Wivenhoe, showing differences in water level and island profile between 2001 and 2011 .....................................................87 Figure 54 Erosion of the bank toe at the downstream end of the LiDAR capture ......................87 Figure 55 Mass failure ...........................................................................................................................88 Figure 56 Orthophoto from 2011 shows erosion and deposition patches scoured in banks and bar, and locations where vegetation removal artefacts showed up as 'erosion' also......88 Figure 57 Red polygon in the hill shade LiDAR on the left appears to be area of erosion in the 2011 image. ...................................................................................................................................89 Figure 58 Red polygon in picture on left sits above vegetation, which is still present in 2011, seen in the right picture. This polygon of 'erosion' was an artefact of the vegetation removal process. ............................................................................................................................89 Figure 59 Chute Channel ........................................................................................................................92 Figure 60 Cobble and Open Riverbed ................................................................................................93 Figure 61 Point Bar ..................................................................................................................................93 Figure 62 Partially Vegetated Bars .....................................................................................................94 Figure 63 Bench ........................................................................................................................................94 Figure 64 Mid-Channel Bar – Vegetated............................................................................................95 Figure 65 Mid-Channel Bar - High Surface.........................................................................................95 Figure 66 Inset Flood Plain .....................................................................................................................96 Figure 67 High Terrace ...........................................................................................................................96 Figure 68 Colluvial Slope .......................................................................................................................97 Figure 69 Orthophoto and hillshade LiDAR from 2011 at Gregor's Crossing .............................97 Figure 70 Surfaces with slope less than 5 degrees in green shows historical bench surfaces. Cross section shows elevatin of benches above water surface. ............................................98 Figure 71 Vegetation highlighted with rendering of Canopy Height Raster ..............................98 Figure 72 Water surface elevation difference between 2001 – 2011. Negative values show water was higher in 2011 than 2001. Each of the 91 reaches may have more than one waterbody that was present in both 2001 and 2011 (hence the 201 polygons for the 91 sub-reaches – which are arranged from upstream (L)-downstream (R). Positive values around 1.5m show higher level of backwater in Lake Wivenhoe at the time of LiDAR capture in 2011. Selected pools with water higher in 2001 are investigated in next section. .......................................................................................................................................... 101

Figure 73 Difference in water height between 2001 and 2011 for 173 locations in distinct pools along 85km of Upper Brisbane River. Water level in 2011 was subtracted from 2001. ............................................................................................................................................ 101 Figure 74 Lowering of bed in a riffle sequence is the reason for 0.5m lower water height in 2011. Erosion patch seen in 2011 Hillshade LiDAR covers part of the polygon which was placed on area of water common to both 2001 and 2011. This should not have been possible, as the area of water in 2001 was used to mask out erosion values from the difference layer. ......................................................................................................................... 102 Figure 75 Orthophoto from 2011 shows a large tree over the water, which is visible in the 2001 hillshade LiDAR as a raised lump. Faulty vegetation removal is responsible in this instance for higher "0.5m water height" in 2001. ................................................................ 102 Figure 76 Polygon with blue highlight has water 0.3m higher in 2001, though the polygon to the left (upstream) has water 0.6m lower in 2001 and the polygon to the right has water 0.05m higher in 2001, a negligible amount. Hillshade LiDAR from 2001 shows a surface indicating LiDAR artefacts at that location. ............................................................. 103 Figure 77 This sample location at the upper most end of the LiDAR capture had water height as 0.3m higher in 2001. However, faulty vegetation removal was responsible for the result. Riparian vegetation can be seen in the 2009 Mosaic image. ............................... 103 Figure 78 Increase in area of water in each of the 91 reaches in the Upper Brisbane. ......... 104 Figure 79 Correlation between volume of erosion in each reach and the increase in area of water. ............................................................................................................................................ 105 Figure 80 Volume of erosion per reach estimated to be ‘unseen’ due to higher water level in 2011 ............................................................................................................................................. 106 Figure 81 Distribution of reaches where water height was higher in 2001 than 2011 .......... 106 Figure 82 Burrows site with detail of water area in 2001, 2011 and area where land was replaced with water at time of 2011 LiDAR capture. ......................................................... 107 Figure 83 Cross section at Burrows site with detail of erosion and changes in water height. Area of uncertainty has been highlighted. While it is not known if scouring of the channel bed occurred, it could be assumed that bank retreat occurred at least down to the water level of 2001. .............................................................................................................................. 107 Figure 84 Comparison between Observed bank erosion length on the O'Connell R and % reach fail analysis (as per the method applied in Simon et al. 2003, 2011). ............... 117 Figure 85– Model evaluation using the first method. Consists of a single prediction comparing with the measured values of the validation set of effective cohesion, friction angle and critical stress and erodibility (JO scour). ................................................................................. 119 Figure 86 Cross validation of soil geotechnical properties. The SOM produced unbiased estimation with excellent predicting power ........................................................................... 120 Figure 87 Analysis of bank erosion according to the “percent reach fail” methodology (Simon et al 2003) in the O’Connell River .......................................................................................... 127 Figure 88 Comparison between Observed bank erosion length on the O'Connell R using LiDAR differencing (2010-13) and % reach fail analysis for 2km reach segments as per the method proposed by Simon et al., 2011 ............................................................................... 128 Figure 90 Map showing the maximum extent of the 2011 flood (shown in red) in the vicinity of the study area (yellow markers). ........................................................................................ 129

Figure 90 Geomorphic zones are defined by spatial variation in 2011 flood width and proportion of vegetation higher than 5 metres canopy cover ........................................... 130 Figure 91 Relationship between erosion per unit streampower (thresholded at 10 Wm-2) and proportion of large woody vegetation (higher than 5 metres) canopy cover proportion ........................................................................................................................................................ 130

List of Tables

Table 1 Definitions of Geomorphic Units defined within the Macro-channel of the Upper Brisbane River (see also ..............................................................................................................32 Table 2 Simplified geomorphic units that have been used to undertake the erosion analysis. All change that occurs in Units 1 and 2 is considered to represent bed material erosion and deposition, whereas erosion of the remaining units is considered to represent “bank erosion” in its more traditional sense, which delivers a combination of suspended and bed-material load in varying proportions depending on the unit. ......................................33 Table 3 Summary table showing the net bed and bank erosion for the upper Brisbane River LiDAR reach between 2001 – 2011 as well as the corresponding changes in the study reach ................................................................................................................................................42 Table 4 Volume of erosion that occurred at the Burrows bend upstream of the tributary (Area A) between 2001-11 and 2011-13. .........................................................................................55 Table 5 Erosion below the tributary junction at the Burrows bend within the area defined by blowout box C (Figure 35)...........................................................................................................56 Table 6 Costings used to calculate losses associated with the 2011 flood .................................57 Table 7 Calculated losses associated with 2011 flood for the 85km reach of the upper Brisbane river analysed above Lake Wivenhoe .....................................................................58 Table 8 Calculated losses associated with 2011 flood for the 5-sub-reaches comprising the Burrows and Weildons study reaches ........................................................................................59 Table 9 The various rehabilitation strategies canvassed for implementation in the study reach and associated costings for each strategy. The codes of each strategy and associated colour are used in each of the tables.........................................................................................60 Table 10 Costings for rehabilitation scenarios by geo-unit for sub-reach 75 and 76.............61 Table 11 Costings used for calculating the engineering treatments at the five sites within the study area. ......................................................................................................................................62 Table 12 Costings for rehabilitation scenarios by geo-unit for sub-reach 77 ............................63 Table 13 Costings for rehabilitation scenarios but geo-unit for sub-reach 79 ...........................66 Table 14 Summary of rehabilitation costs over 5 years broken down by geo-unit and subreach ................................................................................................................................................67 Table 15 Summary table showing the volume of erosion sourced from each geo-unit in each subreach ..........................................................................................................................................69 Table 16 ratio of rehab cost (preferred option) /1:25 yr flood cost (the lower the number the better the value per m3 of sediment that is likely to be retained. .......................................69

Table 17 Estimated probability of the geo-unit experiencing on-going erosion under a “do nothing” management scenario. (1.0 = similar to 2011 event; 0 =highly unlikely)......69 Table 18 Risk analysis by geo-unit and sub-reach (i.e. probability x consequences of ongoing erosion) should nothing further be done. This is a relative index that is specific to the study reach) ....................................................................................................................................70 Table 19 Table showing the cost/risk ratio by sub-reach and geo-unit for the study reach (a low number = good value). Cost is based on the cost/unit volume of potential erosion mitigation .........................................................................................................................................70 Table 20 Estimated volumes of extractable sand and gravel within the “high banks” of the upper Brisbane River above Lake Wivenhoe (Waye 1997). ...............................................82 Table 21 Increments of masking for noise and values above and below threshold ..................85 Table 22 Definitions of Geomorphic Units defined within the Macro-channel of the Upper Brisbane River.................................................................................................................................91 Table 23 Simplified geomorphic units that have been used to undertake the erosion analysis. All change that occurs in Units 1 and 2 is considered to represent bed material erosion and deposition, whereas erosion of the remaining units is considered to represent “bank erosion” in its more traditional sense, which delivers a combination of suspended and bed-material load in varying proportions depending on the unit. ......................................92 Table 24 Possible combination of water and land from two time slices and a changing hydrology, with implications listed..............................................................................................99 Table 25 Area covered by increase of water height by 0.5m, modelled from 2001 relative bank height raster ...................................................................................................................... 104 Table 26 Area of water in 2001 and 2011 relative to macro channel area and bed load area ............................................................................................................................................... 105 Table 27 The average of predicted values through SOM for effective cohesion, friction angle, erodibility and critical stress. .................................................................................................... 118

3

Project Objectives

The project aim is to better understand the channel erosion dynamics at two locations (Weildons and Burrows properties – see Figure 1Figure 3) on the Upper Brisbane River and determine their investment priority for erosion remediation activities. The objectives are to: • •

• •

• • •

•

Estimate the volumes of soil lost from these reaches between 2001 and 2011 and predict soil loss up to 2018 expressed as a probability (for example there is a 90% probability of losing 200 tons of soil from this site in the next 4 years) Identify changes within the study reach from the LiDAR data (2001-2011). Analysis will include an assessment of the relative contributions from all geomorphic units contained within the macro-channel in the study reach, allowing the identification of those parts of the channel subject to what is normally regarded as ‘bank erosion’. Other (e.g. bed, bar and bench scour) will also be quantified but only the bank erosion components will be incorporated into the predictive modelling. Compare changes for the study reach with the changes over the 85km reach of the Upper Brisbane River mainstem channel for which repeat LiDAR data is available. Undertake bank erosion modelling using the GU revised version of the Bank Stability and Toe Erosion Model (BSTEM) to predict bank erosion (for the parts of the channel cross section identified as being subject to bank erosion). Model simulations will be completed for the following AEP event magnitudes: 99%, 90%, 80%, 50% ,20%, 5%, 1% Estimate the impact of this soil loss on Seqwater in terms of lost storage capacity, cost to dredge, and cost to remove suspended sediment at WTP’s. Review current economics of lost storage capacity and water treatment costs. Relate predicted sediment contributions from study reach and 90km section of the mainstem river. Identify and compare cost and efficiency of various options for stabilising these banks and reducing soil loss, Express efficiency of remediation options as a probability (e.g. option 1 is predicted to reduce soil loss by 90% and flood resilience; e.g. the probability of bank collapse following remediation using option 2 is estimated to be 5% chance per year). Review current costing’s for the range of management options to be canvassed in the model scenarios.

Complete risk analysis/cost benefit analysis for the different combinations of rehabilitation management scenarios. Management options to be reviewed include • • • • • • • •

Do nothing Assisted natural regeneration (i.e. de-stocking and weed management) Revegetation and fencing Battering Toe protection using rock Toe protection using ELJs Various combinations of the above Prioritise sections for remediation within the 1 km reaches.

4

Background

Previous research in the upper Brisbane River has highlighted that bank erosion is a key sediment source to Lake Wivenhoe (e.g. Prosser et al. 2003; Caitcheon et al., 2005), however, as pointed out by Shellberg and Brooks (2007), these catchment scale sediment budget and tracing exercises are at scale that is too coarse for management planning. This point is made more starkly from an analysis undertaken as part of this study, which compares the observed erosion rates (as outlined below), with the rates predicted by the previous SedNet modeling for the same reach (Figure 2). The plot demonstrates that the SedNet model has very poor predictive power at a resolution that is useful for designing a management strategy, explaining only 13.5% of the variability in bank erosion at the 1km reach scale. A detailed field reconnaissance and airphoto analysis of bank erosion conducted by Shellberg and Brooks (2007), confirmed that bank erosion was a major issue in the Upper Brisbane River, but also highlighted the significant uncertainty in the data, particularly as that analysis was restricted to airphoto observations, and was limited by the resolution of the airphotos. Sediment tracing studies using fallout radionuclides, have confirmed that the dominant source of sediment entering Lake Wivenhoe is channel erosion, and 50% of this sediment was shown to be sourced from the Esk Formation geology, through which the Upper Brisbane flows (Douglas et al. 2007, Olley et al. 2013). A large flood in 2011, and subsequent flood in 2013, have further highlighted the threat posed by bank erosion to SE Qld’s primary water storage reservoir, as well as to riparian landholders through the loss of valuable alluvial land. In this study we use repeat aerial LiDAR surveys collected 10 years apart, to provide a much more robust analysis of the extent of bank erosion that has occurred in the upper Brisbane River. Analysis of a range of geomorphic, hydrologic and hydraulic variables is then undertaken to explain the spatial variability of observed erosion – which can then inform analysis of the likelihood of the erosion continuing at any one site. While the primary focus of this study is on the upper Brisbane River adjacent to the two properties (Weildons and Burrows - Figure 1) located just upstream of the 100% domestic water storage level (i.e. 50% dam storage capacity), a key objective of the project was to place changes at these two properties within the broader context of changes in the upper Brisbane River as a means of informing Seqwater about options to invest resources in bank stabilisation works at these sites. To this end we have focused efforts on understanding the changes between 2001 and 2011 coincident with the time and extent of two LiDAR surveys acquired across a ~ 90km reach of the river (Figure 1, Figure 3). This section of the river encompasses the main alluvial sections of the Upper Brisbane River, an area that experienced significant erosion during the 2011 flood, the largest flood recorded during the 50 year operational life of the Gregors Creek gauge (stn. 143009A). As outlined in more detail below, the 2011 flood was significantly smaller than the projected 100 ARI event reported in Shellberg and Brooks (2007), and hence there is a real risk of even larger floods being experienced within this section of river. The Upper Brisbane River discharges into Lake Wivenhoe, which is the largest municipal water supply catchment in the State of Queensland, Australia (Shellberg and Brooks 2007).

Study area

Figure 1 Upper Brisbane River Catchment showing location of gauges and LiDAR extent (in dark blue)

SedNet predicted rate (m/yr)

1.0000

0.1000

0.0100

0.0010

0.0001 0.0001

y = 0.0241ln(x) + 0.1762 R² = 0.1352 0.001

0.01

0.1

1

observed rate (m/yr) Figure 2 Comparison between SedNet predicted bank erosion rates (Prosser et al., 2003) and observed rates (20012011) in the Upper Brisbane River. Each point on the graph represents a ~ 1km section of the upper Brisbane River with the bank erosion rate derived from repeat LiDAR analysis (this study) plotted against the corresponding bank erosion rate predicted by the SedNet model for the same reach.

5

Approach

Following the 2011 flood, a repeat airborne LiDAR survey was completed for a ~ 85km reach of the upper Brisbane River, for which LiDAR data had been collected in 2001 by the former Department of Natural Resources and Mines (DNRM) (Witte et al., 2000). The

approach adopted in this study has been to undertake a very detailed analysis of this repeat LiDAR dataset as a way of providing some context for the analysis of bank erosion in the two properties that are the primary focus of this study – the Burrows and Weildons properties. Full details of the methods used for delineating the geomorphic units and for undertaking the LiDAR analysis are included in Appendix B. The two properties around which the analysis is focused are owned by Seqwater and leased to the current land managers, and represent the most upstream riparian properties under the direct control of Seqwater. Significant erosion has occurred on these properties over recent years and the focus of this study is to provide some context for the erosion on these properties and to assess the likelihood of the erosion continuing, and hence whether major investment in rehabilitation works can be justified in light of the costs associated with the option of doing nothing. 5.1

Understanding Controls on recent Channel Erosion

Our ability to quantify the extent of channel erosion that has occurred in recent years has been greatly enhanced by the development of high resolution topographic surveys collected using airborne LiDAR. In this study we are extremely fortunate to have a baseline LiDAR dataset that was acquired in 2001, against which changes over the ensuing decade can be compared. We can also be reasonably confident that the vast majority of the changes that were observed over this period occurred during the 2011 flood, given that there were few other floods of any magnitude during this period. Analysis of the spatial variability of channel changes is far more complex than simply demonstrating where the erosion has occurred, but it is only through understanding the drivers of past changes that we can begin to understand how we can predict future erosion. To understand why channel erosion is occurring in the Upper Brisbane River, the following constraints need to be assessed: Hydrology: • •

Magnitude and frequency of the flows that have driven the observed changes Characteristics of individual flood hydrographs

Channel Hydraulics •

Flow velocity distribution within the macro-channel (see below for description of what is meant by the macro-channel) • Unit streampower distribution • Bend radius of curvature at different flow stages Ii.e. macro-channel full or mesochannel full condition) Channel Boundary Conditions • Complex geometry of the channel boundary/geomorphic unit architecture • Vegetative hydraulic roughness • Vegetative resistance to erosion (root cohesion) • Bed and bank material sedimentology (as a function of geomorphic units that comprise the “macro-channel”) The extent to which the spatial distribution of erosion is explained by the arrangement of geomorphic units within the macro-channel is also explored. Detailed mapping of the arrangement of geomorphic units within the channel was undertaken, and this detailed mapping provides the context for the analysis of the “DEM of Difference” between the two

LiDAR derived 1m resolution DEMs. The output of this analysis is a detailed volumetric analysis of both erosion and deposition between the two timeslices, broken down to the geomorphic unit within which the erosion or deposition occurred. For ease of analysis, the 85km reach was broken into a series of 91 sub-reaches, the boundaries of which were delineated by the points of inflexion between bends and straight reaches. This allowed for a single radius of curvature to be assigned to each sub-reach. The 91 sub-reaches have then been used as our basic unit of analysis for understanding the drivers of observed erosion across the 85km reach of the upper Brisbane River. 5.2

Predicting future Erosion

The science of predicting future erosion is far less developed (and more uncertain) than the science of explaining past erosion. The Bank Stability and Toe Erosion Model (BSTEM) (Simon et al., 2011) is one such model that is used for predicting erosion, but in addition to the problems associated with predicting future hydrology (i.e. a major factor in predicting erosion), there are a whole raft of uncertainties inherent within a model such as BSTEM which necessitates great caution being used when assessing the model predictions. So while we evaluate this model in this study as a tool for helping to evaluate rehabilitation scenarios, we urge great caution in taking the model predictions at face value. The BSTEM model is incapable of accurately predicting future bank erosion without extensive field work to fully explain the sedimentary characteristics of every section of bank where the model is to be applied. To do this requires detailed quantification of the three dimensional sedimentary architecture of the bank at regular intervals along the length of the channel in question (dependent on channel scale - but probably every 50m in the Upper Brisbane R) to have any faith in the model results, coupled with a detailed particle size analysis of each sedimentary strata. This is not feasible or practical, so the model can only be used at a site where the sedimentary architecture is well described, to undertake the evaluation of different rehabilitation scenarios. The analysis undertaken at the Burrows and Weildon’s properties is based on detailed sedimentological analysis of the each site (see Appendix D), coupled with an extensive dataset of relationships between a range of geotechnical parameters and particle size distribution data. A bootstrap statistical modeling approach (i.e. one which involves multiple re-sampling of the sample to improve model confidence) has then been used to derive the geotechnical characteristics for each bank based on the observed stratigraphy, as outlined in Appendix E. It is beyond the scope of this report to fully evaluate the BSTEM model uncertainties, particularly in the complex channels typically found in Queensland coastal rivers, but a brief summary of some of the issues is outlined in Appendix E-G. In summary though, the BSTEM model is not designed to operate in channels with a complex cross sectional geometry and sedimentology in which the inset geomorphic units are formed at a different time to the main floodplain units, and have different internal sedimentary structure. The model is not computationally up to the task of dealing with such scenarios, and yet these are the norm in the upper Brisbane River, and indeed many rivers in Queensland. Our analysis of the sites using this model is included in Appendix F, but having evaluated this approach, in our opinion greater accuracy can be gained by adopting a more empirical approach based on observations of past erosion and the likelihood of erosion continuing at a site given the local geomorphic, hydraulic and sedimentological constraints at the sites. The benefit-cost assessment we have outlined is based more on this approach, rather than the geotechnical model predictions, which we regard to be extremely unreliable.

Figure 3 Spot Image showing the LiDAR reach extending ~ 85km upstream from the top of Lake Wivenhoe and the locations of the study area around the Burrows and Weildons properties. Also shown are the numbered sub-reaches into which the erosion and deposition analyses have been determined

5.3 5.3.1

Study Area The Upper Brisbane Catchment

The climate and hydrology of the Upper Brisbane River is best described by its extremes rather than average conditions. Rainfall in the catchment is heavily influenced by atmospheric circulation patterns, such as the El Nino Southern Oscillation and the Inter-decadal Pacific Oscillation resulting in distinct multi-annual periods of average or below average rainfall and discharge and clusters of wet years with large flood events (Shellberg and Brooks 2007). The Upper Brisbane River has a large difference between common and rare flood magnitudes, extreme flash flood behaviour and large annual variability in peak discharge leading Brizga and Finlayson (1996) to classify it as hydrologically extreme compared to rivers around the world, and even other Australian rivers of similar size. A more recent analysis by Rustomji et al. (2009) confirms the earlier findings of Brizga and Finlayson (1996), indicating the extreme variability of the flood regime in this catchment. Annual flood frequency at the Gregors Creek gauge (143009A Figure 1) was calculated by Shellberg and Brooks (2007) using peak flow data between 1963 and 2005 and the Log-Pearson Type III distribution. They estimated the 50 year recurrence interval flood as approximately 10190 m3s-1 and the 100 year recurrence interval flood as approximately 16437 m3s-1. With the inclusion of the data since 2007, these estimates are now 11,700 m3s-1 and 19,800 m3s-1 respectively, which makes the 2011 event the equivalent of a 1:25 year flood (4% AEP). Morphological bankfull discharge of the macro-channel was estimated to occur for the 50 year recurrence interval flood (10190 m3s-1). Continuous instantaneous unit stream power was calculated at the gauge over the same period and found not to exceed 30 Wm-2 for a 1 in 2 year recurrence interval event, 100 Wm-2 for a 1 in 10 year event, and 200 Wm-2 for a 1 in 25 year event (Shellberg and Brooks 2007). These estimates put the river into the medium-energy non-cohesive floodplain category described by Nanson and Croke (1992)*, with the most appropriate sub-class being the high energy category (class A), which is typically appropriate for higher energy floodplain/channel categories. The 5476 km2 Upper Brisbane River catchment ranges from 540 metres elevation at the western edge of the catchment to 79 metres at the maximum flood storage of Lake Wivenhoe and has a major stream length of 2161 km (Johnson, 2004). Geological structure has a strong influence on the form of the river with the main stem flowing through the Esk Formation and bounded by the D’Aguilar Block to the east and the Yarraman Block to the west (Brizga and Finlayson 1996). A defining characteristic of the river is it’s incision into the surrounding geology forming a planform controlled mildly sinuous macro-channel with large meander wavelength, some irregular meanders and higher than average floodplain (QDPI 1995, Brizga and Finlayson 1996, Brennan and Gardiner 2004, Shellberg and Brooks 2007). The macro-channel planform geometry was found to have changed very little over the past fifty years by Brizga and Finlayson (1996). Using the empirical relationships relating meander wavelength to bankfull discharge of Dury (1965), Brizga and Finlayson (1996) estimated that a bankfull discharge of 7115 m3s-1 at the Gregors Creek gauge would be needed to result in the macro-channel meander wavelength. A smaller alluvial channel with better connected inset alluvial floodplains and benches and low sinuosity exists within the macro-channel (Brennan and Gardiner 2004, Shellberg and Brooks 2007). Due to lack of chrono-stratigraphic *

Medium –energy non-cohesive floodplain sub-classes (from Nanson and Croke, 1992)

Class A. High-energy non-cohesive floodplains (Specific stream power at bankfull: >300 W m2). These are disequilibrium landforms which erode, either completely or partially, as a result of infrequent extreme events. In some cases floodplains which are close to some threshold condition may erode as a result of a series of moderate events. Stream power is typically high because of their location within steep upland areas and bank erodibility is primarily a function of the linear relation between the size of sediment entrained and stream power. Despite their high energy, these channels are usually prevented from migrating laterally by very coarse alluvium or bedrock and their floodplains are dominated by relatively coarse vertical-accretion deposits.

evidence it’s unknown whether the inset features reflect the channel’s contemporary response to common flood events or are a historical reaction to increased sediment supply post European settlement (Rustomji and Pietsch 2007, Shellberg and Brooks 2007). The river has an imbricated gravel and cobble bed underlain by sand, which becomes noticeably sandier downstream of the Maronghi Creek confluence, while floodplains and terraces are composed of gravel, sand, silt and clay (Brizga and Finlayson 1996, Brennan and Gardiner 2004, Shellberg and Brooks 2007). Of the stream length surveyed for riparian vegetation by Johnson (2004), 46% was categorised as very poor and 26% was categorised as poor. The river has a relatively low gradient and therefore the stream power available for substantial geomorphic change only occurs in the largest flood events (Shellberg and Brooks 2007). During these events however, as the macro-channel confines the majority of the flow high energy conditions occur and lead to extensive erosion of the inset features (Nanson and Erskine 1986, Brizga and Finlayson 1996, Shellberg and Brooks 2007). Historical and contemporary land uses (such as clearing, grazing and sand/gravel extraction) which altered the resistance of the inset features are likely to exacerbate this stripping and hinder the recolonisation of vegetation (Brizga and Finlayson 1996). Given the hydrologic characteristics of the Upper Brisbane River, low frequency large events are likely to have the greatest influence on channel characteristics such as sediment transport, meander wavelength, channel width and valley width (Shellberg and Brooks 2007). The variability of the flood regime also predisposes the river to channel instability and catastrophic change when those low frequency flood events occur (Baker 1977, Brizga and Finlayson 1996). It is unknown whether the macrochannel is a response to these low frequency large events, however it is interesting to note that the discharge needed to create the macro-channel wavelength estimated by the relationships in Dury (1965) (7115 m3s-1 ) is close to the 50 year recurrence interval flood (10190 m3s-1) estimated by Shellberg and Brooks (2007), despite the Upper Brisbane River being partially bedrock confined. What is clear however is that all surfaces adjacent to the river are actively influenced by the contemporary hydrologic regime, even if the frequency of interaction between the channel and it’s floodplain has changed over time, and the interaction with the upper surface is rare (Shellberg and Brooks 2007). Evidence to counter the argument that the macro-channel is a function of the extreme variability of the flood regime is found from the fact that very similar channel morphologies can be found in rivers all the way up the east coast of Australia, from the Hunter Valley in NSW (Hoyle., et al., 2010), to the Brisbane River (this study) to the O’Connell River in central Qld, to the Normanby River in Cape York (the authors unpublished data). According to Rustomji et al., (2009), the Hunter and Brisbane Rivers have highly variable flood regimes, whereas the O’Connell and Normanby Rivers have relatively low inter-annual variability. Based on this evidence, it would appear likely that the characteristic macro-channel morphology that is found in many rivers up the east coast of Australia is a function of a persistence of fundamentally different hydrologic regime through the Holocene than was the case in the Pleistocene. Most importantly, it is likely that a fundamentally different sediment supply regime persisted through the Holocene (more supply limited) than existed during the last glacial maximum (LGM ~ 20Ka) and in the post LGM period. Hence, the current channel planform and complex three dimensional morphology is more a function of this evolutionary legacy than the present day hydrological regime. While these macro-channels have some of the superficial characteristics of incised channels (sensu Schumm et al, 1984, Simon and Hupp, 1987) they are NOT incised channels, and analysis of their behavior based on an assumption that they are incised will lead to erroneous conclusions being made.

5.4

Upper Brisbane Macro-channel –type example

Figure 4 Characteristic morphology of an underfit macro-channel, in which it can be seen that the channel zone is comprised of a complex suite of geomorphic units which are of Holocene age, inset within a broad laterally constrained meandering macro-channel that is inset within a Pleistocene Age floodplain. The image on the right shows the extent of erosion within the channel in a 1:25 yr flood (2011), with the majority occurring within the Holocene age inset features. The Pleistocene surface only erodes once the buttressing inset Holocene units have been eroded

Characteristics of typical underfit macro-channel •

• • • •

Channel laterally constrained by Pleistocene age terraces that are rarely inundated under contemporary flow regime (>1:50 yr flood). Whilst in cross section these channels may appear to have similar characteristics to incised channels as described by Schumm et al (1984), these are not incisional channels under the contemporary (Holocene) regime; they are relict (underfit) channels that reflect channel dimensions from prior climatic/flood regime. The active channel (Holocene floodplain) consists of complex sequences of benches, bar complexes, mid-channel islands and inset floodplains inset within the older channel boundary Terraces often show evidence of bank gullies, which are probably similar to the floodplain drainage features described by McCloskey (2012) Channel erosion tends to be confined to the inset features Given that the Holocene sediments (sedimentary deposits 5m 45% 40% 35% 30% 25%

Weildon/Burrows reach

20% 15% 10% 5% 0% 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 Figure 8 The extent of woody vegetation within the macro-channel zone has been defined from the 2001 liDAR data./ These data were compared with the Spot Satellite imagery from 2009, which shows that it is a valid representation of the extent of woody vegetation at the time of the 2011 flood. The data shows the extent of vegetation varies considerably down the river, with the study area at Weildon’s property particularly, having the lowest vegetation cover in the whole upper Brisbane River.

5.4.1.2 Sand and Gravel extraction Extensive sand and gravel extraction has also been undertaken throughout the upper Brisbane River, particularly in the mid-reaches around Harlin and in the vicinity of the Linville gauge. The intensity of extraction has increased fairly significantly in recent years, although accurate estimates of the precise extent of extraction are hard to determine as no records have been kept since 2007. A more detailed description of the extraction rates and the estimates of “sustainable” extraction rates are covered in Appendix 1. While a thorough analysis of the extent to which in-channel sand and gravel extraction is driving bank erosion is beyond the scope of this study, there is a strong circumstantial case that it is one of a number of key factors contributing to bank erosion through two key mechanisms. Firstly, the practice of quarrying sand and gravel from within the bed zone of the macrochannel (i.e. the geomorphic units that fall between the inset benches and the low flow wetted channel) has the effect of continually removing all vegetation from the surfaces being mined and loosening the bed material.. This means that when the next bed mobilizing flow occurs, the remaining bed material is more readily removed than it otherwise would be had the quarry activity not occurred. The destabilization of the bed and bars then makes it more likely that the adjacent benches and inset floodplains will be eroded, given that these bars act to buttress and protect the toe of these adjacent surfaces. A second mechanism by which excessive quarrying of bed material destabilizes the banks adjacent to, and upstream of, the extraction activity, is when the amount extracted exceeds the rate of natural replenishment of bed material. Leaving aside the first mechanism, whereby the direct disturbance of the channel destabilizes the bed and banks (irrespective of whether extraction occurs or not) if extraction occurs at a rate that is more than the replenishment rate, there is a high likelihood that net bed lowering will occur, even if only at a local scale. Bed lowering then significantly increases the likelihood of bank failure. As outlined in Figure 9, the

estimated mean annual bedload transport rate (which is sometimes taken to represent the upper limit of the potentially sustainable extraction rate) is around 22,300 t/yr. Yet, between 1984 and 2007 the estimated minimum annual average extraction rate had been in the order of 50,000 t/yr. While no official records of extraction rates have been kept since 2007, it is estimated that extraction rates are in the order of 150,000 m3 (~240 Kt) per year in recent years (Paul Martin, DNRM pers. comm.), which is approaching an order of magnitude more than the theoretically sustainable limit. It must be remembered though, that the upper theoretical sustainable limit (i.e. the mean annual bed load transport rate) is based on an assumption that new sand and gravel supply is in equilibrium with the contemporary flow and sediment transport dynamics (i.e. a river that is in geomorphic equilibrium sensu Wolman and Leopold (1957)). In this situation, we know this is not the case, because by definition a macrochannel morphology is a dis-equilibrium river form, where the contemporary morphology is a relict from a past hydrological and sediment supply regime. Hence, the contemporary bedmaterial load, rather than being in equilibrium with upstream supply, is being sourced from stores within the channel itself (i.e. features like that bed and bars that are being quarried). Hence the extraction of these stores is pushing the system further into dis-equilibrium than it already is, which on its own is likely to increase erosion of the channel boundary. When this is coupled with disturbance to the vegetation on these surfaces, which is the primary additional factor that is resisting the erosion of these features, the system is being pushed inextricably towards greater disequilibrium (i.e. more erosion than sediment supply). Evidence presented in section 7.1demonstrates that the upper Brisbane is a net erosional system between 20012011, with ~50,000 t/yr more erosion than deposition.

600,000 Brisbane River Mainstem Above Deltaic Zone Ivory Creek Sand and Gravel Extraction (Tonnes / Year)_ (Assume 1650 kg/m 3)

Maronghi Creek 500,000

2007-2008 Estimated Commercial Above Deltaic Zone 2007 Western Corridor Pipeline Within Deltaic Zone QDNRM Extraction Cap: Upper Brisbane River Average Annual Bed Material Load Transported By River (Joo 2007)

400,000

Pre-2007 Average Annual Extraction

? 300,000

200,000

100,000

Extrac tion Cap: 2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

0 1984

Average Annual Bed Material

+

Figure 9 Official data on volumes of sand and gravel extracted from the upper Brisbane River between 1984 and 2007 (from Shellberg and Brooks, 2007), showing the estimated additional extraction from both within the deltaic zone of Lake Wivenhoe (for the Western Pipeline project) in 2007 and 2008. Also shown on the graph are the DNRM estimates of the sustainable extraction volume and the pre-2007 mean annual extraction rates.

Figure 10 Aerial photograph showing sand and gravel extraction from the channel in sub-reach 58

Figure 11 Aerial photograph showing sand and gravel extraction from the channel in sub-reach 58

5.4.1.3 The 2011 Flood In the summer of 2010-2011, a strong La Nina event and elevated sea surface temperatures led to a series of massive storm cells across South East Queensland. This weather pattern culminated in the second-highest recorded flood in the last 100 years being recorded on the 13th of January 2011 at the Port Office gauge in central Brisbane, and the largest recorded flood since the construction of the Wivenhoe dam (Babister and Retallick 2011, BoM 2012). In the Upper Brisbane River the 2011 flood reached the highest maximum daily discharge over the gauge record (Figure 12). Flood peaks on Cooyar, Emu and Maronghi Creeks resulted in a maximum daily discharge of 7803 m3s-1 being reached on the main stem of the Upper Brisbane River at the Gregors Creek gauge (143009A) on the 9th of January although reassessment of the rating curve at Gregors Ck, has resulted in the peak discharge being downgraded to 6725 m3s-1 (making it slightly smaller than the 1999 event) (Figure 13), and equivalent to a 1:25 year event.

Figure 12 Maximum Daily Discharge at the Gregors Creek Gauge 1961 – 2013 from the DNRM website

7000 6000 5000 4000 3000 2000 1000 0

WY 1963 WY 1964 WY 1965 WY 1966 WY 1967 WY 1968 WY 1969 WY 1970 WY 1971 WY 1972 WY 1973 WY 1974 WY 1975 WY 1976 WY 1977 WY 1978 WY 1979 WY 1980 WY 1981 WY 1982 WY 1983 WY 1984 WY 1985 WY 1986 WY 1987 WY 1988 WY 1989 WY 1990 WY 1991 WY 1992 WY 1993 WY 1994 WY 1995 WY 1996 WY 1997 WY 1998 WY 1999 WY 2000 WY 2001 WY 2002 WY 2003 WY 2004 WY 2005 WY 2006 WY 2007 WY 2008 WY 2009 WY 2010 WY 2011 WY 2012 WY 2013 WY 2014

Annual Peak Discharge (cms)_

8000

Figure 13 Annual peak discharge derived from the data downloaded from the Gregors gauge (note the discrepancy between the DNRM derived plot and the Annual peak data derived from the data downloaded from the DNRM site). The plot in Figure 12 suggests that the 2011 event was the gauged flood of record, whereas the data in this plot suggests that the 1999 flood was larger.

6

Methods

6.1

Morphological Change Detection

This study uses repeat LiDAR surveys of the Upper Brisbane River to identify changes to fluvial form and infer dominant processes driving that change in form. The analysis of morphological change to the river is determined from comparison between two 1m resolution digital elevation models derived from airborne LiDAR data. LiDAR surfaces for approximately 85km of the mainstem of the Upper Brisbane River upstream of Lake Wivenhoe were captured in May 2001 and June 2011 (Figure 1). The 2001 LiDAR was captured by Gunn Resources Pty Ltd using an Optech Airborne Laser Terrain Mapper (ALTM) 1020 system. A flying height of 400m and a swathe width of 150m were used. The accuracy of the capture was ±0.15m in the z direction and ±0.4m in the x and y directions. The accuracy of the digital elevation model (DEM) was verified through survey data collected in the field. The results of this comparison showed that the DEM was accurate to within 30cm for at least 80% of the data. The 2011 LiDAR was captured using a Toposys Harrier 68i/G1 LiDAR system. A flying height of 700m and a swathe width of 730m were used. The accuracy of the capture was ±0.15m at one sigma (67% confidence level). In lieu of a ground survey, the 2001 DEM was used to test the LiDAR accuracy and provide additional adjustment values. OrthoPhotography was captured simultaneously with the 2011 LiDAR using the scanner’s integrated camera. 6.2 6.2.1

Repeat LiDAR Analysis Spatial Variation of Erosion

As outlined in section 5.1 the macro channel has been divided into a series of sub-reaches which have then been used as the fundamental unit of analysis for the whole 85km stretch of river channel. The macro-channel bank tops were digitised and the resulting polygon was

subdivided based on whether the channel was meandering or straight into ninety-one reaches. The LiDAR DEM of difference between the 2001 and 2011 DEM was then converted to a volume of erosion and deposition, and the net results reported for each of 91delineated subreaches. Each sub-reach was then further sub-divided into geomorphic units. 6.3

Geomorphic Unit Differentiation

Given that the upper Brisbane River is for the most part characterized as an underfit “macro channel” (sensu Dury, 1965, and Van NieKirk, 1993) the channel morphology is in many places quite complex, and understanding the channel dynamics must therefore be placed in this context. To enable us to understand which parts of the channel are eroding and why, the 85km study reach has been broken into a suite of geomorphic units so that the units most susceptible to erosion can be identified - and estimates made of the relative proportions of fine and coarse sediment that has been remobilised during the 2011 flood. The entire ~85km reach has been analysed to the same level of detailed as shown in Figure 14. 6.3.1

Geomorphic Unit Delineation within the Macro-channel

A detailed description of the process for undertaking the DEM of difference analysis and the geomorphic unit delineation is provided in Appendix B. A summary of the characteristics of the various geomorphic units and the criteria used for differentiating them is provided in Table 1. While there is inevitably some subjectivity in mapping any analogue feature within the landscape, a number of objective criteria were used to aid the process. A new DEM was derived that attributes every pixel with its elevation above the nearest point on the channel thalweg (lowest point in the channel cross section). The relative elevation of every point within the channel could then be classified into height (or depth) classes, and these then guided the geomorphic unit delineation. The extent of vegetation on each surface was also used to help discriminate some of the in-channel features, particularly active gravel bars from more stable vegetated –in-channel surfaces that may have similar elevation characteristics. Table 1 Definitions of Geomorphic Units defined within the Macro-channel of the Upper Brisbane River (see also

1.

Water and Wet Area (low flow channel)

Ponded water seen in LiDAR imagery and the lowest 0.4m of surfaces derived from the Bank Height Raster

2.

High Flow Chute Channel

Obvious high level distributary channel that would run as the main channel fills. An island would be formed between the chute channel and the main channel as the chute channel is activated

3.

Gravel bars and Open Riverbed

Adjacent to water, surface is cobbles, not vegetated, generally smooth surfaces, height threshold 0.4 to 1.5m above the adjacent thalweg

4.

Point Bars and extensive sculpted/partially vegetated lateral bars

Wide surface with obvious deposition zone on the inside of bends, with wave form deposits. OR a long, broad surface with tremendous sculpting adjacent to main channel.

5.

Mid-Channel Bar-Bench

Raised, level surface isolated from main channel banks, mostly vegetated. Seem as an “island” of perched material

in the channel bed. 6.

Mid-Channel Bar-High Surface

As for Mid-Channel Bar-Bench, but with elevation greater than 5m. Possibly an isolated fragment of flood plain.

7.

Bench

A long, relatively narrow feature running parallel to the river that is relatively level or only gently inclined and is bounded by distinctly steeper slopes above and below (i.e. it forms an intermediate alluvial feature between the elevation of the bed and the floodplain (or inset floodplain).

8.

Inset Floodplain

An extensive flatish surface formed at an intermediate level between the river bed/bars and the high terrace where accommodation space is available(and above the elevation of any benches). Inset floodplains would be inundated regularly (probably every 2-5 years on average)

9.

High Terrace

The upper, flattish surfaces seen in LiDAR. Usually higher than 12m. Often cultivated. Limited extent of LiDAR does not allow seeing if there is a higher surface beyond.

10. Colluvial Slope

Bedrock hillslopes that are impinging directly on the channel margin (i.e. any unconsolidated material that is found on these slopes is a function of accumulation as a result of downslope process rather than alluvial depositions associated with the river.

These 10 categories have been further simplified into just seven units for ease of analysis, most importantly in terms of differentiating the dominant processes of sediment transport and erosion operating in each zone. In this simplified schema, units 2 – 4 are considered to consist primarily of bed material load and as such erosion and deposition within these three units represents the contemporary bed material load of the river. The remaining units will contribute the bulk of the fine sediment (< fine sands) with varying degrees of bed material depending on the site specific sedimentology. Table 2 Simplified geomorphic units that have been used to undertake the erosion analysis. All change that occurs in Units 1 and 2 is considered to represent bed material erosion and deposition, whereas erosion of the remaining units is considered to represent “bank erosion” in its more traditional sense, which delivers a combination of suspended and bed-material load in varying proportions depending on the unit.

GeoUnit 1 2 (units 2-4) 3 (units 5-6) 4 5 6 7

Simplified Geomorphic Unit Description Water Bed material zone Vegetated mid-channel bars Bench Inset Flood Plain Terrace Colluvial Slope

Figure 14 Example from the study reach showing the mapped geomorphic units within the macro-channel – which is defined by the sub-reach masks. Also shown are the 5 sub-reaches (75-79) that have been analysed as a sub-set of the 91 sub-reaches in the LiDAR reach and the bank stratigraphy sites.

6.4

Hydraulic Analysis

The analysis of morphological change between the two LiDAR time series (i.e. channel erosion) has then been coupled with a detailed hydraulic analysis, using a HecRAS model setup with ~900 channel cross section across the reach. Channel roughness was calibrated using the 2011 flood extent layer (see Figure 90)and the rating curves for the two gauges within the reach (Linville Gauge - station 143007A, and Gregors Ck gauge - stn 143009A). This enabled the determination of unit stream power at each cross section and the the derivation of average velocity distribution throughout the river at different flow stages. A full description of the model setup and the hydraulic model results are outlined in Appendix G. 7 7.1

Results Analysis of Erosion across the upper Brisbane River between Cooyar Ck and Lake Wivenhoe

The following analysis shows the spatial distribution of channel erosion in the ~85km reach between Cooyar Ck and Lake Wivenhoe, which is the section of the mainstem upper Brisbane River that experienced major channel erosion during the 2011 flood. The distribution of gross channel erosion (i.e. undifferentiated between geomorphic units or bed and bank erosion) by sub-reach is shown in Figure 16, in which it can be seen that there several reaches from which a significant proportion of the total bed and bank erosion is derived.

100.0%

Weildons

90.0% 80.0%

Burrows

70.0% 60.0% 50.0% 40.0% 30.0% 20.0%

77

52

51

76

84

43

45

38

11

31

66

68

15

23

74

78

63

57

64

32

59

79

72

65

54

62

55

56

0.0%

73

10.0% 58

cummulative proportion of all erosion contributed by sub-reach

7.2 Sediment Production by Reach As can be seen in Figure 16 a large proportion of total erosion occurs within a relatively small number of river reaches across the upper Brisbane River. Indeed when the sub-reaches are arranged in rank order of net erosion (Figure 15) just four sub-reaches (58, 73, 56 and 55 – which represent 7.4% of the total macro-channel area ) contribute more the 25% of the total erosion within the 91 sub-reaches in the upper Brisbane River. A further six reaches (i.e. the 10 reaches in total representing 19.7% of the macro-channel area) contribute 50% of total erosion, while 27 reaches (44.7% of channel area) contribute 75% of all erosion. Hence it is readily apparent that there are some particular erosion hotspots within the upper Brisbane River that should be a priority for management.

Sub-reach number Figure 15 Cumulative contributions of the top 30 sediment producing sub-reaches to the total volume of sediment eroded within the 91 sub-reaches in the upper Brisbane River between 2001 and 2011.

Of the sub-reaches that are the focus of this study (75-79) only sub-reach 79 (i.e. below Burrow’s bend) makes it into the top 10 – contributing 2.3% of the total erosion, while subreach 78 comes in at number 15, contributing 1.7%. Reach 76 and 77 are the 27th and 30th highest contributing reaches, producing 1.2% and 1.0% of the total erosion respectively. Cumulatively, these five sub-reaches contribute 6.7% of the total erosion from the 92 reaches (Table 3).

Harlin

Figure 16 Map showing the spatial distribution of total channel erosion by sub-reach. Purple markers represent the study area

Cressbrook Creek 350000

Ivory-Maronghi Ck

250000

Emu Ck

Cooyar Ck colluvial

200000

terraces

Weildons Burrows

observed "bank" erosion m3

300000

Inset flood plains 150000

benches/ islands all bed erosion

100000

50000

0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 -50000

sub-reach number)

Figure 17 Upper Brisbane River Observed bank erosion 2001-2011 by geomorphic unit breakdown. Error bars represent the likely undetected erosion that was filtered out in the noise removal process (which used a 0.5m limit of detection (LOD)) or due to the ~ 0.5m water level variance between surveys. The error includes LOD values from 0.2-0.5m adjacent to erosion cells.

150000

Cressbrook Ck

Net change in bed materialvVolume 2001-2011 ( m3)

Burrows 100000

Cooyar Ck

200 % dam level 100% dam level

Emu Ck Ivory Maronghi Ck

50000

Weildons

0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 -50000

-100000

-150000

sub-reach number

Figure 18 Net change in bed material load (deposition – erosion) by sub-reach. Error bars represent the likely undetected erosion that was filtered out in the noise removal process (which used a 0.5m limit of detection (LOD)) or due to the ~ 0.5m water level variance between surveys. The error includes LOD values from 0.2-0.5m adjacent to erosion cells. An estimate of the erosion and deposition below water level has also been included with the volume calculated on an area proportion ratio of the erosion and deposition within the above water bed material zone.

7.3

Analysis of Erosion by Geomorphic Unit.

The spatial distribution of erosion by sub-reach tells an interesting story about the concentration of erosion in certain reaches and raises important questions about the explanation for such a concentration. Analysis of the relative importance of different drivers of erosion in certain areas is the subject of ongoing research, and is beyond the scope of this report. However, even greater insight into the sensitivity of some parts of the channel network to erosion is evident when each sub-reach is further sub-divided into the geomorphic units that comprise the macro-channel. This analysis also enables the erosion and deposition dynamics of the channel bed to be disaggregated from the erosion of what might be considered to be more traditional “bank erosion”. Figure 17 shows the downstream variability of erosion by sub-reach for the “bank” erosion component which has also been broken down into the geomorphic units that were extant prior to the 2011 flood, and hence were the sources of the erosion that occurred during the 2011 flood. Figure 18, on the other hand shows the breakdown by sub-reach of the net change in bed material load (erosion + deposition), which tells a different story again. From both Figure 17 and Figure 19 it is very apparent the great majority of erosion has been sourced from benches, followed by inset floodplains and mid-channel islands and vegetated bars. Insights at this scale provide a far more useful template for the development of rehabilitation plans, because different geomorphic units have variable sensitivity to erosion, and therefore require different treatments. Furthermore, given that in any one cross section there may be three or four different inset geomorphic surfaces, the erosion of the most distal surface requires the lower inset surface to be removed first, before the higher surface can be eroded. So the differential stability of each surface needs to be understood to gain a sense of the overall channel stability. Hence, this geomorphic unit template is used as the basis for understanding the past behavior of the study reach, the relative economic consequences of these changes, as well as the basis for developing a rehabilitation plan.

21100, 1% 197000, 6% 516100, 17%

VegebarsIslands benches

762000, 24%

Inset flood plains terraces colluvial 1646000, 52%

Figure 19 Breakdown of bank erosion by geomorphic unit for the 90km reach of the upper Brisbane River represented by the repeat LiDAR data

7.4

Erosion Analysis in the Burrows and Weildon’s Reach

The section of the river that is the focus of this study, adjacent to the Weildon’s and Burrow’s properties is encompassed by a sub-set of five of the 91 sub-reaches that are the focus of the broader reach analysis (sub-reaches 75-79 ). For the purposes of this exercise sub-reaches 75-77 are designated as the Weildons, while sub-reaches 78-79, the Burrows section. Analysis at this scale highlights significant spatial variability in the sensitivity of different geomorphic units to erosion. It also highlights the distribution of deposition in this zone, located as it is, at the upper limit of Lake Wivenhoe, under100% domestic water storage capacity. The repeat LiDAR analysis (Figure 17, Figure 20) shows the precise location and degree of erosion within the study area, but also enables the volume of erosion to be placed in the context of the broader reach. As can be seen in Figure 20 significant erosion has occurred on the Burrow’s bend in sub-reach 78, with a significant amount of prime alluvial flats having been eroded from the inset floodplain and benches around the outside of the bend. The other significant area of floodplain erosion occurred on the right bank of sub-reach 79 downstream of the bend. Another focal area is on the left bank in middle of sub-reach 77. While the volumes of sediment being derived from this site are clearly significant, when viewed in the context of some of the sub-reaches upstream, they are clearly not the highest priority across the whole of the upper Brisbane River (Figure 17, Figure 18). As Table 3 demonstrates, however, these sub-reaches are still significant, with the five sub-reaches accounting for around 6.7% of all erosion in the 91 sub-reaches, and around 10% of the bed material deposition.

120000

Sub-reach erosion breakdown for the Burrows/Weildons study reach 2001-2011 bed_material+water zone colluvial

erosion (m3)

100000

terraces

80000

Inset flood plains

60000 40000 20000 0 75

76

77

78

79

sub-reach number Figure 20 Breakdown of erosion by geomorphic unit for the 5 sub-reaches encompassing the Weildon’s/Burrows study reach

The plot of bed material change in Figure 22 shows that deposition (which is assumed to be bed material sands and gravels), is largely focused on the point bar opposite the eroding bank at Burrows. As will be discussed below, the fact that this point bar is in effect the last riverine point bar before the dam backwater, there is evidence that sustained deposition on this particular bar is partially responsible for the erosion of the outer bank. Hence, a legitimate case can be made that some sand and gravel extraction should be allowed on this site as part of the overall management strategy.

In summary, a strong case can be made that these sub-reaches should be a focus for rehabilitation effort. The economic case presented below, certainly indicates that a case can be made for proceeding with a rehabilitation strategy in this sub-reach as a matter of priority.

Figure 21 Map showing deposition within the study reach. Note that there is significant deposition on the point bar adjacent to the Burrow’s property.

bed material deposition by Sub-reach 2001-2011 60000 50000 40000 30000 20000 10000 0 -10000

75

76

77

78

79

-20000 -30000 deposition

net bed material change

Figure 22 Plot of absolute and net (deposition – erosion) bed material change for the 5 study area sub-reaches. Note the sub-reach 78 is one of the few sub-reaches showing significant net deposition, which from Figure 21can be seen to be focused on the large point bar opposite where most of the bank erosion is occurring.

Table 3 Summary table showing the net bed and bank erosion for the upper Brisbane River LiDAR reach between 2001 – 2011 as well as the corresponding changes in the study reach

Sub-reach 1-91 bed material only Sub-reach 1 – 91 all other erosion

sub-reach 75-79 % change rchs 75-79 cf 90 km rch

erosion (m3)

erosion (t)

deposition (m3)

deposition (t)

net change (t)

-1,202,250

-1,923,599

905,243

1,448,389

-475,210

-3,142,064 -4,344,314

-5,027,303 -6,950,902

249,837 1,155,081

399,740 1,848,129

-4,627,563 -5,102,773

-290,598

-464,957

115474

184,758

-212,386

6.69%

6.69%

10.00%

10.00%

4.16%

total

7.5 Detailed Analysis of the recent changes in the Burrows-Weildons Reach The following sequences of images show in greater detail four key areas of major erosion along the study reach. The first is along the boundary between sub-reach 75 and 76 (Figure 23, Figure 24, Figure 25), where a large bench feature was eroded by fluvial scour down to bed level, exposing an unstable length of bank that abuts the margin of the inset floodplain. Two detailed field sites were located on this stretch of bank (WEBP01 and 02, Appendix D), and geotechnical modelling was undertaken as these locations as well (Appendix E). The aerial time series suggests that little additional erosion occurred along this bank during the 2013 flood, but that some bank stabilization is probably warranted here and would have a high likelihood of success. The sequence of images from sub-reach 77 (Figure 26, Figure 27, Figure 28), shows an area of the left bank in the middle of the sub-reach that experienced significant mass failure during the 2011 flood, causing significant erosion into the inset floodplain at that location. The 2013 image shows that the site appears to have been battered after the 2011 flood, but whether that was before or after the 2013 flood is unclear. If the battering occurred before the 2013 event, there is little evidence for ongoing erosion at the site, but this needs to be confirmed with Seqwater. The sequence of images for sub-reach 78 (Figure 29, Figure 30, Figure 31), highlights the extensive erosion that has occurred over an extended length of the outer bench and inset floodplain. The 2013 image indicates that this is an area of active ongoing erosion that does require treatment. Sub-reach 79, on the other hand (Figure 32, Figure 33, Figure 34), also has a a significant amount of erosion into the inset floodplain on the right bank, but the 2013 imagery would tend to suggest that the erosion is no longer particularly active.

Figure 23- Sub-reach 75 in 2009 with 2011 erosion raster superimposed

Figure 24 Sub-reach 75/76 in 2011 with 2011 erosion raster superimposed

Figure 25 Sub-reach 75/76 in 2013 with 2011 erosion raster superimposed. Note there is little evidence of further erosion at this site after the 2013 flood but some is evident on the right.

Figure 26 Sub-reach 77 in 2009 with 2011 erosion raster superimposed

Figure 27 Sub-reach 77 in 2011 with 2011 erosion raster superimposed. Note the mass failures along the left bank (top of picture)

Figure 28 Sub-reach 77 in 2013 with 2011 erosion raster superimposed. Note that at the location of the mass failures the bank seems to have been battered

Figure 29 Sub-reach 78 in 2009 with the 2011 erosion raster superimposed

Figure 30 Sub-reach 78 in 2011 with the 2011 erosion raster superimposed

Figure 31 Sub-reach 78 in 2013 with the 2011 erosion raster superimposed. Note that there has been considerable additional erosion at the bend apex associated with the 2013 flood.

Figure 32 Sub-reach 79 in 2009 with the 2011 erosion raster superimposed

Figure 33 Sub-reach 79 in 2011 with 2011 erosion raster superimposed

Figure 34 Sub-reach 79 with 2011 erosion raster superimposed. There is little evidence of additional erosion this site following the 2013 flood.

7.6

Additional Channel Erosion between 2011 and 2013 at selected sites in the study reach

As outlined in the previous section, while there is clear evidence from the LiDAR change detection analysis that there was significant channel erosion between 2001 and 2011, most of which occurred as a result of the 2011 flood, it is also apparent that in some locations there has been considerable erosion in the subsequent two years, following a moderate sized flood in 2013 (1:15 ARI). The following section demonstrates the extent of erosion since 2011in the blow up box areas A and B.

A

The detailed analysis shown in area A (Figure 36) indicates significant additional erosion as a result of the 2013 flood, equivalent to about half as much erosion again, as occurred between 2001-11 (i.e. most of which occurred in 2011). The volume of erosion that occurred in the 2011 event was around 20,600 m3 (33,000 t) while a further 9800 m3 (15,700 t) occurred in 2013

B

Table 4 Volume of erosion that occurred at the Burrows bend upstream of the tributary (Area A) between 2001-11 and 2011-13.

area eroded (m2)

Volume eroded (m3)

2001-2011

5819

20618

2011-2013

2769

9811

Figure 35 Map showing the location of areas of detailed change analysis between 2011 and 2013

Given the extent of erodible alluvium at this site, all indications are that significant erosion will continue at this site. The tendency of this bank to continue eroding is exacerbated by the fact that sustained bed material accumulation has occurred on the adjacent point bar.

Figure 36 Detail area A from Figure 35 showing the additional erosion between 2011 and 2013 at the Burrows upstream of the tributary confluence.

Downstream of the tributary confluence at the Burrows Bend (Figure 37) further erosion has continued since the 2011 flood which induced extensive erosion along the toe of the bank at

this site and extending for around 560m downstream. The erosion post 2011, however, has largely been confined to the bench immediately downstream of the tributary confluence. Making some assumptions about the bank profile and the depth of erosion we can estimate how much additional erosion occurred between 2011-13 (Table 5)

Figure 37 Bank erosion along the bank downstream of tributary junction between 2001 and 2011 and additional erosion between 2011 and 2013 (area B Figure 35).

Table 5 Erosion below the tributary junction at the Burrows bend within the area defined by blowout box C (Figure 35)

Period

Area m2

Volume m3

2001-2011

1300

2470

2011-2013

694

1318

7.7

Costs of Erosion to Seqwater & Riparian Landholders

The following analysis quantifies the costs associated with the 2011 flood, to Seqwater and the riparian landholders along the Brisbane River as a way of providing a rational basis for comparing the costs of undertaking various rehabilitation activities. In order to quantify the costs of channel erosion associated with the 2011 flood, several factors have been taken into account as outlined in Table 6. Table 6 Costings used to calculate losses associated with the 2011 flood

1 2 3 4 5

opportunity and/or deferred costs dredging costs /m3 Sludge treatment/m3 bulk water price BCC 2014-15 /Ml (lost water storage) loss of alluvial flats (capital cost)/ha loss of production from alluvial flats/ha/yr

unit cost $30 $216 $2,300 $15,000 $10,000

1) Costs associated with dredging sediment deposited with Lake Wivenoe. A cost of $30/ m3 has been estimated by Seqwater 2) Costs associated with sludge disposal at the Mt Crosby water treatment plant (Prof. Jon Olley pers comm.). The volume of sediment actually going through the treatment plant has been conservatively estimated to represent only 1% of the suspended sediment load going into the reservoir (i.e. assuming a 90% dam trap efficiency and that only 10% of the sediment delivered from the dam is captured in the water treatment plant) 3) The opportunity costs associated with the lost water storage capacity, based on the bulk water price to Brisbane City Council for 2014-15 4) The area of high value alluvial land lost along the Brisbane River between Cooyar Ck and Lake Wivenhoe. This was determined directly from the LiDAR data, and includes land lost from benches, inset floodplains and terraces. As outlined in Table 7 just over 100 ha of prime alluvial flats were lost in the 2011 flood and this land has been estimated to have a capital value of $15,000 per ha, based on market prices of farming properties along the Upper Brisbane River. 5) In addition to the capital loss, there is an ongoing loss associated with the net productive capacity of that land as well. This has been conservatively estimated to have a value of $10,000 per ha per year based on a typical lucerne farming property. In the study reach (Table 8) the total loss of alluvial flats amounted to 7.4 ha. Productive losses have only been projected for 5 years. Based on these figures the total cost of the 2011 flood from the 85km reach encompassed by the LiDAR dataset is $107,700,000. For the Weildons/Burrows reach the net cost was $9,700,000. Under a business as usual riparian management strategy, costs equivalent to this are likely to be incurred each time there is a flood of an equivalent magnitude to the 2011 flood (4% AEP). While it is the case that not all of these costs are borne immediately by Seqwater (i.e. some are deferred costs), these figures provide some context for comparing the costs associated with implementing a comprehensive rehabilitation strategy. It is worth highlighting that the estimates of lost water storage capacity associated with deposition in the Lake from the 2011 flood (3189 Ml or 1276 Olympic swimming pools), represent an absolute minimum, as they only account for the sediment derived from an 85km section of the upper Brisbane mainstem channel. Inputs from all tributaries are not accounted for, and as such the total load delivered to Lake Wivenhoe could be significantly greater than has been estimated from this study.

10.0 9.0

7.0

Weildons Burrows

area of alluvial flats lost (Ha)

8.0 Terrace

6.0

Inset Flood Plain Bench

5.0 4.0 3.0 2.0 1.0 0.0

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 sub-reach # Figure 38 Upper Brisbane River - Observed area of alluvial land lost between 2001-2011 by geomorphic unit breakdown (total lost = 100.7 Ha)

Table 7 Calculated losses associated with 2011 flood for the 85km reach of the upper Brisbane river analysed above Lake Wivenhoe

deposited SSed throughput lost water storage

total erosion to Lake Wivenhoe from 2011 flood Bank derived bedload bed material (m3) (m3) 297,006 1,446,113

susp load (m3) 1,301,502

treated load (t)

144,611

11,569

$2,498,884

3,189

$7,335,236

total volume of lost storage (ML) =

land loss

at $15,000 / ha for prime alluvial flats: total area lost = 100.8 Ha

land loss net prod'ctn

at $10,000 / yr over 5 yrs for prime alluvial flats: total area lost = 100.8 Ha

cost $91,338,653

$1,511,942 $5,039,805 $107,724,520 $33.78

total unit cost/m3

Table 8 Calculated losses associated with 2011 flood for the 5-sub-reaches comprising the Burrows and Weildons study reaches

Total contribution from sub-reaches 75-79 to Lake Wivenhoe from 2011 flood

deposited throughput lost water storage land loss land loss net prod'ctn

bed material (m3) 80,785

Bank derived bedload (m3) 104,907

at $15,000 / ha for prime alluvial flats: total area lost = 7.4 Ha at $10,000 / yr over 5 yrs for prime alluvial flats: total area lost = 7.4 Ha

susp load (m3) 94,416 10,491

treated load (t) 839

cost $8,403,228 $181,279 $668,376 $110,693 $368,975 $9,732,550

total

$33.49

unit cost/m3

8 Sub-reach Rehab Options Based on the empirical observations of change in each sub-reach (7.4 and 7.5) the following set of rehabilitation options have been derived for each sub-reach, according to the geomorphic unit template for each sub-reach. The range of rehabilitation strategies canvased are outlined in Table 9, and options have only been provided in some circumstances where this appropriate. When the channel is considered as a whole, but sub-divided by geomorphic unit, it is readily apparent what the strategies should be in most cases, and it is unnecessarily complicating to provide multiple options everywhere, just for the sake of going through the motions of modelling multiple combinations of strategies. Preliminary data looking at the relationship between woody vegetation cover and erosion appears to indicate that a minimum extent of 20% projected foliage cover of woody vegetation (i.e. >5m high) is required within the macro-channel to significantly reduce erosion in a flood the size of the 2011 event (McMahon et al., in prep). Ideally the goal should be to achieve a minimum of woody vegetation 40% cover. Consequently, strategies that aim to maximize woody vegetation cover within the channel underpin the whole strategy. Where possible the optimal, and most cost effective way, to achieve this goal is to utilize “assisted natural regeneration” (Thexton, 1996). This method facilitates natural regeneration within the channel by the suppression of grazing and other disturbances, as well as competition from weeds. Supplementary planting can also be undertaken at strategic locations and at strategic times during the regeneration process. If the system has the capacity to regenerate naturally, with some assistance (i.e. there is a sufficient remnant seed bank), this will always be cheaper and more effective than new planting. It is our view that the Upper Brisbane River does have a sufficient native seed bank and seed supply from upstream, to enable this strategy to work effectively, particularly within the bed zone. It should be stressed that while stabilizing the bed may not be considered to be directly addressing the bank erosion problem, there is a close feedback between the erosion of the

bed zone and erosion of the bank toe. Hence increasing the hydraulic roughness within the bed zone and the resistance of this zone to sediment transport, will have long term benefits to the magnitude and frequency of bank erosion. Options for undertaking major engineering works are only canvassed at highly strategic locations, and the only options worth considering here are major rock rip rap, engineered log jams (or possibly a combination of both) and/or bank battering. Revegetation is always assumed to compliment any engineering strategy, and engineering works should always be accompanied by concerted effort to maximize in-channel woody vegetation (i.e. it is futile to direct all efforts into an engineering solution in isolation from a revegetation strategy). As a general rule, it is not recommended that battering is ever undertaken without some sort of toe protection as well, however in sub-reach 75 battering without toe protection has been costed, because it may be a viable strategy there, given that there is a low probability of channel expansion to that which occurred at this site in 2011 being repeated for some time (i.e. the erosion has already occurred - and would appear unlikely to continue in this straight reach of the river). It must be stressed that the strategies outlined here are only broad brush concept plans and before moving to the implementation of a rehabilitation strategy, detailed designs would be required. Table 9 The various rehabilitation strategies canvassed for implementation in the study reach and associated costings for each strategy. The codes of each strategy and associated colour are used in each of the tables

Costs associated with various Rehabilitation options treatment a Engineered Log Jams/m x 5m b rock rip rap/linear m x 1m c bank battering/linear m x 6m high bank d reveg/Ha e primary weed control/ha f weed maintenance (assisted natural regen) /ha g new fence construction/m h fence maintenance i sand/gravel extraction

* i.e. weed maintenance to be undertaken twice yearly.

unit cost $800 $85 $500 $45,000 $5,000 $2,000 $15

Recurrent/yr

$15,000 $4,000* $5

-$100

8.1 Sub-reach 75-76 The strategy for sub-reaches 75 and 76 is primarily focused around assisted natural regeneration within bed zone, benches and vegetated mid-channel bars and islands. All area assessments are based on the 2001 arrangement of geomorphic units, which in some cases may have changed post 2011. Site B1 denotes the 400m stretch of the left bank that at present are near vertical and difficult to revegetate. Options with and without toe protection have been costed (see Table 11).

Figure 39 Overview of sub-reaches 75 and 76 showing the geomorphic unit layout and the location of the section of bank for which battering has been costed. Table 10 Costings for rehabilitation scenarios by geo-unit for sub-reach 75 and 76

Sub-reach Number Geo-Unit Water Bed Zone Vegetated midchannel bar Bench Inset Flood Plain (option 1) Inset Flood Plain (option 2) Terrace reach wide Total Sub-Reach Area m2

75 & 76 combined Area (Ha) 2.74 8.68 3.28 2.46 4.18 4.18 2.06

23.39

denotes the number of units over a 5 year period Cost over 5 a b c d e f g h yrs -

-

-

-

-

5

-

-

$173,526

-

-

-

-

1 5 1 5

-

-

$82,010 $61,485

400 1 1 5

-

-

$458,374

400 1 1 5 - 1 5

400

2000

$618,374 $51,403 $16,000

total option 1 total option 2

$842,798 $1,002,798

4 0 0 -

-

Table 11 Costings used for calculating the engineering treatments at the five sites within the study area.

Battering option for Sub-reach 75/76 bank length to be stabilised (m)

av bank ht (m)

400

7

7

14

1

400

7

5

10

2

1 (77)

340

8

5

10

2 (78)

290

10

7

3 (78)

280

7

4 (78)

280

6

site/rch B1 (75-6) option 1 no toe B1 (75-6) option 2

residual bank ht battered post toe slope protection length

site #

log toe protection

additional battering revege

total

$200,000

$ 18,240

$218,240

$160,000

$200,000

$ 18,240

$378,240

1

$272,000

$170,000

$ 15,504

$457,504

14

2

$232,000

$145,000

$ 13,224

$390,224

4

8

3

$224,000

$140,000

$ 12,768

$376,768

3

6

4

$224,000

$140,000

$ 12,768

$376,768

$952,000

$595,000

$ 54,264

$1,601,264

ELJ option for Sub-reach 77, 78 bank toe stabilisation

1190 Rock Rip Rap option for Sub-reach 77, 78 bank toe stabilisation 1 (77)

340

8

5

10

1

$289,000

$170,000

$15,504

$474,504

2 (78)

290

10

7

14

2

$246,500

$145,000

$13,224

$404,724

3 (78)

280

7

4

8

3

$238,000

$140,000

$12,768

$390,768

4 (78)

280

6

3

6

4

$238,000

$140,000

$12,768

$390,768

$1,011,500

$595,000

54,264

$1,660,764

1190

8.2

Sub-reach 77

As with the previous sub-reach, a major focus of the rehabilitation strategy in this sub-reach is for assisted natural regeneration of the bed zone and benches. Part of this section has already been battered, but apparently without toe protection. The strategy here would be to augment the works that have already been done with some toe protection, coupled with some additional primary revegetation and maintenance.

Figure 40 Overview of sub-reach 77 showing the geomorphic unit layout and the location of the 340m section of bank for which bank stabilization work has been costed

Table 12 Costings for rehabilitation scenarios by geo-unit for sub-reach 77

Sub-reach Number Geo-Unit Water Bed Zone Vegetated midchannel bar Bench Inset Flood Plain Terrace reach wide Total Sub-Reach Area m2

77 Area (Ha) 2.18 6.57

a

b

c

d

e

f

g

h

-

-

-

-

-

5

-

-

0.00 2.20 3.16 1.62

340 -

-

-

1

-

-

1 -

5 5

15.74

1

500 2000

Cost over 5 yrs $131,480

$55,095 $659,907 $40,540 $17,500 $904,522

8.3 Sub-reach 78 Of the five sub-reaches that are the focus of this study, sub-reach 78 is the highest priority for rehabilitation given that it poses the greatest risk for ongoing erosion into some high banks, that will deliver significant volumes of sediment to Lake Wivenhoe, and result in ongoing loss of alluvial flats. In addition to the standard assisted natural regeneration strategy here (weed management and fencing), major bank stabilization works are warranted as indicated (with caveats associated with the risk of failure for events the magnitude of the 2011 event and larger). The bank stabilization works have been divided into three sections, as a function of bank height. However, the reach should ideally be considered as a single section of around 900m. Given the evidence for sustained sand and gravel accumulation on the point bar at this site, the other option that is available at this site is for some strategic sand and gravel extraction (with volumes and location closely managed). Around 40,000 m3 of sand and gravel could be extracted from this site, proceeds of which could potentially be used to offset the costs of works. Depending on the arrangements that could be put in place by Seqwater to make this happen, this could potentially net Seqwater several $M – which would substantially fund the works required at this site. It should be stressed however, that such a strategy is site specific at locations such as this, that are influenced by the dam backwater, and where it can be demonstrated that there has been sustained aggradation over a period of time. Options for both rock rip rap and ELJs have been costed, and a combination of both might be desirable.

Figure 41 Overview of sub-reach 78 showing the geomorphic unit layout and the location of the three sections of bank for which bank stabilization work has been costed. Also shown are some areas where sand and gravel could be extracted

Sub-reach Number Area of Geo_unit m2 Water Bed Zone Vegetated midchannel bar Bench Inset Flood Plain Terrace reach wide Total Sub-Reach Area m2 Sub-reach Number Area of Geo_unit m2 Water Bed Zone Vegetated midchannel bar Bench Inset Flood Plain Terrace reach wide Total Sub-Reach Area m2

8.4

78

Rehab Options - Scenario 1 - bank stabilisation using ELJs

(Ha) 1.88 9.96

a

b

c

d e

f

g

h

Cost over 5 yrs

-

-

-

-

-

5

-

-

$199,172

0.69 2.57

280

-

-

- 1 1 1

5 5

-

-

$17,183 $552,342

14.28 0.42

570 -

1 - 1

5

-

-

-

-

500 2000 29.79 78

$1,469,538 $10,443 $17,500 $2,266,177

Rehab Options - Scenario 2 - bank stabilisation using Rock RipRap

(Ha) 1.88 9.96

a

b

c

d e

f

g

h

Cost over 5 yrs

-

-

-

-

-

5

-

-

$199,172

0.69

-

-

-

-

1

5

-

-

$17,183

2.57

-

280

1 1

5

-

-

-

570 -

1 - 1

5

-

-

14.28 0.42

-

500 2000 29.79

$566,341 $1,498,038 $10,443 $17,500 $2,308,677

Sub-Reach 79

While there has been major bank erosion along the right bank within sub-reach 79, it would appear that little additional erosion occurred at this site in 2013, and that being a mass failure it is unlikely to fail in a similar way in the near future at the same site. That being the case it is most likely that with some revegetation the site should remain stable in events up to the size of the 2013 flood, and probably floods of a similar size to the 2011 event (with the standard caveat for revegetation programs that there is a sufficient interval between floods and droughts to enable the trees/shrubs to establish). Consequently the stategy for this subreach is focused around revegetation and assisted natural regeneration of the bars and benches.

Figure 42 Overview of sub-reach 79 showing the geomorphic unit layout and the location of the three sections of bank for which bank stabilization work has been costed

Table 13 Costings for rehabilitation scenarios but geo-unit for sub-reach 79

Sub-reach Number Area of Geo_unit m2 Water Bed Zone Vegetated midchannel bar Bench Inset Flood Plain Terrace

79 (Ha) 12.23 8.13

-

-

-

-

-

5

-

-

$162,580

1.82 11.48

-

-

-

-

1 1

5 5

-

-

$45,455 $60,000

1

5

-

-

$592,035 $93,638

11.82 3.75

reach wide Total Sub-Reach Area m2

Rehab Options - Scenario 1 - bank stabilisation using ELJs Cost over 5 a b c d e f g h yrs

1 -

-

-

500 49.23

2000 $971,208

9

Summary and Cost –Risk Analysis

The following summarises the costs to fully implement a comprehensive rehabilitation strategy across the ~5km study reach over a 5 year period (Table 14). The total costs is based on the preferred option of implementing engineered log jams(ELJs) (sensu Brooks., 2006; Abbe and Brooks, 2003, 2011; Daley and Brooks (2014), but the cost differential between rock rip rap and ELJs is neligible. Depending on the availability of appropriate logs, it may be necessary to implement a combination of both approaches. The total cost over 5 years is around $1M per year or $200K/km, however, there is an opportunity at this site to undertake bed material extraction and selling the material to significantly offset the rehabilitation costs. Based on a fairly conservative estimate of $50/m3 (given that river gravel retails for around $200/m3) a volume of 40,000m3 of sand and gravel could be extracted which could potentially net $2M from the site. More detailed analysis of the volume of sediment stored here as a result of the 2013 flood might indicate it is appropriate to extract significantly more than this. However, even based on a conservative estimate of 40000 m3 of extraction, this could reduce the total rehabilitation cost to around $3M, or $600K per year ($120K/km). This would make the implementation of the full rehabilitation strategy a much more viable proposition. Table 14 Summary of rehabilitation costs over 5 years broken down by geo-unit and sub-reach

Subreach Area (Ha) 23.4 23.4 15.7 29.8 29.8 49.2

2

3

4

5

6

Bed zone

Vegetated midchannel bar

Bench

Inset Flood Plain

Terrace

Sub-reach Number 75/76 option 1 $173,526 $82,010 $61,485 $458,374 $51,403 75/76 option 2* $173,526 $82,010 $61,485 $618,374 $51,403 77 $131,480 $55,095 $659,907 $40,540 78 option 1* $199,172 $17,183 $552,342 $1,469,538 $10,443 78 option 2 $199,172 $17,183 $566,342 $1,498,038 $10,443 79 $162,580 $45,455 $60,000 $592,035 $93,638 total preferred option 118.15 $666,758 $144,648 $728,922 $3,339,854 $196,023 * denotes preferred option Potential Cost Offset Sale of Sand and Gravel from sub-reach 78: 40,000 m3 @ net $50/m3

Misc fencing

total

$16,000 $16,000 $17,500 $17,500 $17,500 $17,500

$842,798 $1,002,798 $904,522 $2,266,177 $2,308,677 $971,208

$68,500

$5,144,703

$2,000,000

9.1

Rehabilitation Prioritisation

To further aid the decision making process for prioritising those parts of the channel that should be targeted first in any rehabilitation strategy, the following risk analysis aims to contextualise the different reaches. The approach taken was to first provide the context for how much erosion occurred as a result of the 2011 flood (Table 15) and then to place this in the context of the cost of implementing the preferred rehabilitation option as outlined in Table 14. From this the ratio of the rehabilitation cost to the associated cost of the volume of erosion produced in the 2011 flood, was derived, to produce what might be described as a “bang for your buck” index. It must be stressed however, that this is a guide only because it is not possible to assess each geo-unit independent from one another. The hydraulic, geomorphic and geotechnical processes that occur in any one geo-unit are linked to adjacent units and to the collective condition of the channel, both within the immediate reach and the reaches upstream. Hence, this is a relative index only. While Table 16 provides an index of relative value for implementing rehabilitation measures in the light of past changes that have occurred at that location, not all sites have the same risk of continuing to erode at equivalent levels to those observed in 2011. Hence what is needed in addition to this information is some measure of the likelihood that the site will continue to erode at an equivalent rate to that observed in the 2011 flood, in an equivalent magnitude flood if nothing is done to manage the site (i.e. the do nothing option). One approach to doing this sort of analysis is to undertake a geotechnical modelling approach using a model such as BSTEM to assess this. However, for a variety of reasons, we have concluded that there is so much uncertainty around taking such an approach that the method is completely unreliable and cannot provide a quantitative assessment of the potential for ongoing erosion. In part this is because this is not a mature modelling framework, and many of the processes are not adequately represented in the model. Furthermore, the model is too simplistic to deal with the complex sedimentology that is typically found in this river. So the approach adopted here was to take a more empirical approach and look at a range of variables to estimate the likelihood of erosion continuing in the next 5 years should a 2011 scale flood occur within this time period. Factors considered include; the extent of available alluvium, the position of the geo-unit within the channel (i.e. is it on a bend or a straight reach), the extent of additional erosion in 2013, the change in channel capacity between 2001 and 2011. At some sites there is a high likelihood of erosion continuing because there are no constraints on erosion (e.g. the outside of a migrating bend where there is still plenty of alluvial land to erode). Whereas in other sites, it is likely that the erosion that was going to occur has occurred, and it will not continue at the same rate. For example the benches along the toe of the left bank in sub-reach 79 abut a bedrock hillslope. So now that those benches have been eroded there is little further erosion that can occur. Similarly, the large bench sequence that eroded on the left bank between sub-reaches 75 and 76 has been eroded and the channel capacity through that section of channel has increased to such an extent that the same boundary shear stresses that caused the erosion in 2011 would not be replicated in a similar magnitude event. The channel has changed to such an extent that the risk of further erosion is now significantly reduced. It should also be pointed out that due to the very high index of flood variability (i.e. the ratio of large floods to the mean annual flood; section 4) in this river, it is not possible to design a cost-effective rehabilitation strategy that will work in events larger than the 2011 flood. Consequently, the impacts of larger floods have not been considered. The probability of equivalent change to 2011 in the event that nothing is done, is shown in Table 17. The probability for continued erosion is then combined with the volume of past

erosion to develop a measure of the relative risk between geo-units for continued erosion, if nothing further is done to actively manage the riparian zone (Table 18). The final table (Table 19) provides a relative measure of the cost effectiveness of carrying out rehabilitation works as a function of the risk associated with doing nothing. By this measure, the lower the index, the greater the value in terms of risk reduction for the given treatment. It must be stressed however, that no single index should be relied upon in isolation; rather the various indices should be considered together, in the context of the overall reach. It must also be reiterated that interactions between the individual geomorphic units are not quantified in the analysis, although they are partially considered in the estimated probability of ongoing erosion parameterized in Table 17. Table 15 Summary table showing the volume of erosion sourced from each geo-unit in each subreach

Volume of erosion from 2011 flood (m3) bed_materi Sub-reach al+ water VegebarsIsla Number zone nds 75 76 77 78 79

2,043 33,483 21,431 9,842 13,986 80,785

2,967 3,585 5,079 8,814 20,445

Inset flood benches plains 17,864 13,452 5,666 11,532 29,715 78,229

13,579 46,867 45,849 106,295

terraces 351 2,246 34 2,213 4,844

Total bank erosion 20,831 17,388 21,492 63,512 86,591 209,813

Table 16 ratio of rehab cost (preferred option) /1:25 yr flood cost (the lower the number the better the value per m3 of sediment that is likely to be retained.

Sub-reach Number bed_materi al+water VegebarsIsla 75/76 opt'n 2 zone nds 77 0.2 78 option 1 0.6 0.1 79 0.3 0.2 0.2 0.2

benches 0.06 0.3 1.4 0.1

Inset flood plains 1.4 0.9 0.4

terraces 0.5 9.2 1.3

Total bank erosion 0.8 1.5 1.1 0.3

Table 17 Estimated probability of the geo-unit experiencing on-going erosion under a “do nothing” management scenario. (1.0 = similar to 2011 event; 0 =highly unlikely)

Sub-reach Number 75/76 opt'n 2 77 78 option 1 79

bed_materi al+water zone 0.75 1.0 1.0 1.0

VegebarsIsla nds 0.5 0.5 0.5

Inset flood benches plains 0.10 0.9 1.0 0.9

0.5 1.0 0.4

terraces 0.1 0.1 0.1

Table 18 Risk analysis by geo-unit and sub-reach (i.e. probability x consequences of ongoing erosion) should nothing further be done. This is a relative index that is specific to the study reach)

Sub-reach Number 75/76 opt'n 2 77 78 option 1 79

bed_materi al+water zone 0.09 0.07 0.03 0.05

VegebarsIsla nds

Inset flood benches plains

terraces

0.01

0.01 0.02 0.04 0.09

0.00 0.00 0.00

0.01 0.02

0.02 0.16 0.06

Table 19 Table showing the cost/risk ratio by sub-reach and geo-unit for the study reach (a low number = good value). Cost is based on the cost/unit volume of potential erosion mitigation

Sub-reach Number 75/76 opt'n 2 77 78 option 1 79

bed_materia l+water VegebarsIsla zone nds 1.58 2.46 17.68 7.14

32.85 11.45 10.06

benches 5.39 16.39 35.71 0.65

Inset flood plains

terraces

61.53 5.75 6.92

691 799,443 1,643

10 Summary From the analysis outlined above, it is clear that the most cost-effective rehabilitation strategy is to undertake assisted natural regeneration within the bed and bars of the macro-channel in order to maximize the extent of native woody vegetation within the channel boundary. For this strategy to be effective it requires that grazing is completely excluded from within the channel zone, and that all other direct disturbances that can potentially upset the succession of natural regeneration are removed from the channel zone (e.g. sand and gravel extraction). In the short term, however, a case can be made for undertaking some limited and highly controlled sand and gravel extraction on the point bar in sub-reach 78, in an attempt to increase the cross sectional area of the channel at the bend, reducing shear stress on the outer bank. This must however, be a one-off operation with the proceeds from the sale of the sand and gravel used to fund the broader rehabilitation strategy. It is critical that ongoing disturbance to the bar is permanently excluded after the initial extraction operation to allow for the natural regeneration of vegetation on the point bar. The extraction operation should conclude within a period of 12 months from initiation. Evidence from the analysis at the broader reach scale indicates that channel erosion is minimized per unit stream power when in-channel woody vegetation (>5m high) has a projected foliage cover of > 20% or ideally >30% (McMahon et al., in prep., Figure 43). Hence, the maximisation of in-channel vegetation should be the overriding goal of any rehabilitation strategy, whether it is on the bed zone, bars, benches or inset floodplains. However, the costs associated with revegetation on benches and inset floodplains is

significantly higher than on the bed and bars, given that intensive planting and weed management are required in these highly disturbed pasture grass and herbaceous weed dominant units. The risk of revegetation failure is also much greater in these areas, due to the fact that moisture stress and weed competition are a much greater issue.

bed and inchannel bars

300

benches

terraces

inset floodplains

200

100

0

0.1-0.2 vegetation cover

300

Erosion (m3)

200

100

0

0.2-0.3 vegetation cover

300

200

100

0

>0.3 vegetation cover

300

200

100

0 0

1000 2000 3000 4000 5000 6000 0

1000 2000 3000 4000 5000 6000 0

1000 2000 3000 4000 5000 6000

0

1000 2000 3000 4000 5000 6000

Unit Stream power Figure 43 Relationship between erosion per unit streampower (thresholded at 10 Wm-2) and proportion of large woody vegetation (higher than 5 metres) canopy cover proportion within the macro-channel. The analysis shows that once woody vegetation cover exceeds (From McMahon et al., in prep).

The cost-risk analysis also demonstrates that the benches and inset-floodplain units represent the largest sources of sediment to the river system, and hence the dam, however they are also more expensive to treat. Generally speaking, revegetation alone will not be sufficient to reduce the threat of ongoing erosion at banks that are actively eroding and present a real risk of continuing to erode – such as the bank on the outer bank in sub-reach 78. Nevertheless, given the potential for large volumes of sediment to be eroded from such sites in future floods, they clearly must be regarded as high priorities. Hence an engineering approach must be undertaken at these sites, with an integrated revegetation strategy as part of the postconstruction strategy. The analysis shows that the bench and inset floodplain sites in subreaches 78 and 79 are high priority sites for an integrated engineering/revegtation strategy. The analysis also shows that if the overriding objective of the rehabilitation program is to reduce sediment supply to the stream network, and ultimately to the dam, that the terrace sites are generally a low priority. This is not to say that there may not be other grounds (e.g. terrestrial habitat) for pursuing a revegetation strategy on these sites. Indeed, given that there is a lower risk of damage to the vegetation on the terraces by floods, particularly in the early stages of establishment, there may be a good case to be made for undertaking revegetation on these sites as part of a broader environmental strategy. However, it is clear that it should not be a priority as part of channel stabilization strategy.

11 References Abbe, T.B., A. P. Brooks, and D.R. Montgomery (2003). ‘Wood in river rehabilitation and management. In S. V. Gregory, K L. Boyer, and A M. Gurnell (editors) The Ecology and Management of Wood in World Rivers. American Fisheries Society. Bethesda 444 pp. Abbe, T.B., Brooks, A.P.(2011). Wood in river restoration. Chapter in “Stream Restoration in Dynamic Fluvial Systems: Scientific Approaches, Analyses, and Tools” Simon, A, Bennet, S., Castro, J, Thorne, C. (eds). American Geophysical Union. Babister, M. & Retallick, M. 2011, Brisbane River 2011 Flood Event - Flood Frequency Analysis, Queensland Flood Commission of Inquiry, WMA Water, Brisbane. Baker V.R. 1977. Stream-channel response to floods, with examples from central Texas. Geological Society of America Bulletin 88: 1057-1071. BoM. 2012, Annual Australian Climate Statement 2010/2011 [Online]: Bureau of Meteorology, Available: http://www.bom.gov.au/announcements/media_releases/climate/change/20120104.shtml [Accessed 3rd April 2013]. Breiman, L., 1996. Bragging predictors. Machine Learning 24, 123–140. Brennan, S. and Gardiner, E. 2004. Geomorphic assessment of rivers series draft: Brisbane River: DRAFT. Queensland Government, Natural Resources and Mines, Water Monitoring and Information, Brisbane, Qld, Australia. Brizga, S.O. and Finlayson, B.L. 1996. Geomorphological study of the upper Brisbane River. Prepared by S. Brizga and Associates for the Queensland Department of Natural Resources, Brisbane, Qld, Australia. Brooks, A.P (2006). Guideline for the reintroduction of wood into Australian rivers. Land and Water Australia, Canberra 85 pp. (http://lwa.gov.au/products/px061171). Brooks, A.P., Olley, J., Iwashita, F., Spencer, J., Curwen, G., McMahon, J., Saxton, N. (in prep). Towards the development of an improved approach for understanding and predicting channel bank erosion in Queensland Rivers. Final Report for the Qld Smart Futures Collaborative Investment Fund , Griffith University, Nathan Qld. Caitcheon, G., Read, A., Douglas, G., and Palmer, M. 2005a. Targeting sediment and nutrient source areas in the Bremer, Lockyer and Wivenhoe catchments: progress report: assessment and development of the SedNet model using spatial sediment tracing. CSIRO Land and Water Technical Report. Caitcheon, G., Rustomji, P., Read, A., Douglas, G., and Palmer, M. 2005b. Targeting sediment and nutrient source areas in the Bremer, Lockyer and Wivenhoe catchments: report to the Moreton Bay Waterways and Catchments Partnership. CSIRO Land and Water Technical Report. Daley, J. and Brooks, A.P., (2014). A performance evaluation of Engineered Log Jams in the Hunter Valley., Report to the Hunter and Central Rivers Catchment Management Authority, Griffith University., pp. 53. Dickson, B.L., Giblin, A.M., 2007. An evaluation of methods for imputation of missing trace element data in groundwaters. Geochemistry: Exploration, Environment, Analysis 7, 173–178. Dury G.H. 1965. Theoretical implications of underfit streams. U.S. Geological Survey Professional Paper 452-C. Ehsani, A.H., Quiel, F., 2008. Geomorphometric feature analysis using morphometric parametrization and artificial neural networks. Geomorphology 99, 1–12.

Erskine, W. 1990. Environmental impacts of sand and gravel extraction on river systems . In The Brisbane river: a source-book for the future. Edited by P. Davie, E. Stock, and D.L. Choy. Australian Littoral Society in association with the Queensland Museum, pp. 295-302. Gallant, J. C. and T. I. Dowling (2003). "A multiresolution index of valley bottom flatness for mapping depositional areas." Water Resources Research 39(12): 1347 -1361. Hentati, A., Kawamura, A., Amaguchi, H., Iseri, Y., 2010. Evaluation of sedimentation vulnerability at small hillslide reservoir in the semi-arid region of Tunisia using self-organizing map. Geomorphology 122, 56–64. Hornberger, G.M., Raffensperger, J.P., Wiberg, P.L., Eshleman, K.N., 1998. Element of Physical Hydrology. The John Hopkins University Press, Baltimore. Hoyle, J., Brooks, A.P., Brierley, G.J., Fryirs, K. and Lander, J. (2008). Spatial variability in the timing, nature and extent of channel response to typical human disturbance along the Upper Hunter River, New South Wales, Australia. Earth Surface Processes and Landforms 33(6) pp. 868 – 889. Hoyle, J., Brooks, A.P., Spencer, J. (2012). Modelling reach-scale variability in sediment remobilisation potential: an approach for within-reach prioritisation of river rehabilitation works Iwashita, F., Friedel, M.J., Ribeiro, G. F., Fraser, S.J., 2012. Intelligent estimation of spatially distributed soil physical properties. Geoderma 170, 1-10. Iwashita, F., Friedel, M.J., Souza Filho, C.R., Fraser, S.J., 2011. Hillslope chemical weathering across Paraná state, Brazil: a data mining-GIS hybrid approach. Geomorphology 132, 167–175. Johnson, D.P. 2004. State of the rivers: upper Brisbane and Stanley Rivers and major tributaries: an ecological and physical assessment of the condition of streams in the upper Brisbane and Stanley Rivers catchment: DRAFT. Queensland Department of Natural Resources and Mines, Natural Resource Sciences, Brisbane, Qld, Australia. Joo, M. 2007. Analysis of available sediment transport data collected at the Brisbane River at Gregors Creek stream gauge (143009A). Queensland Department of Natural Resources, Brisbane, Qld, Australia. Kohavi, R., 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 1137–1143. Kohonen, T., 1983. Self-organizing Maps, third edition. Springer-Verlag, Berlin. Löhr, S.C., Grigorescu, M., Hodgkinson, J.H., Cox, M.E., Fraser, S.J., 2010. Iron occurrence in soils and sediments of coastal catchment: a multivariate approach using self-organizing maps. Geoderma 156, 253–266. McCloskey, G. L. (2012). Riparian erosion morphology, processes and causes along the Victoria River, Northern Territory, Australia. McMahon, J., Olley, J, Brooks, A., Curwen, G. (n Prep). The role of in-channel vegetation in reducing channel erosion during large floods. Target submission ESPL. Micevski, T., Franks, S. W., & Kuczera, G. (2006). Multidecadal variability in coastal eastern Australian flood data. Journal of Hydrology, 327(1), 219-225. Nanson, G.C. and Croke, J.C. 1992. A genetic classification of floodplains. Geomorphology 4: 459486. Nanson, G.C. and Erskine, W.D. 1988. Episodic changes of channels and floodplains on coastal rivers in New South Wales. In Fluvial Geomorphology of Australia. Edited by R.F. Warner. Academic Press, Sydney, Australia pp. 201-221.

Nott, J. (2010). Politicians and geomorphological blunders. Some examples relating to rivers and coasts from Queensland, Australia. Singapore Journal of Tropical Geography, 31(3), 283-298. Olley, J., Burton, J., Smolders, K., Pantus, F. & Pietsch, T. 2013, 'The Application of Fallout Radionuclides to Determine the Dominant Erosion Process in Water Supply Catchments of Subtropical South-East Queensland, Australia', Hydrological Processes, 27 pp.885-895. Parker, C., A. Simon and C. R. Thorne (2008). "The effects of variability in bank material properties on riverbank stability: Goodwin Creek, Mississippi." Geomorphology 101(4): 533-543. Patterson, D., Tilbury, B., and McPhee, D. 2002. Moreton Bay sand extraction study: phase 1 . Prepared by WBM Oceanics Australia for the Moreton Bay Sand Extraction Study Steering Committee, Including Appendix C: Description of land based sand resources in south east Queensland, Spring Hill, QLD, Australia. Penn, B.S., 2005. Using self-organizing maps to visualize high-dimensional data. Computers & Geosciences 31, 531–544. Prosser, I.P., Wilkinson, L.J., Hughes, A.O., and Caitcheon, G. 2003. Patterns of erosion and sediment and nutrient transport in the West Brisbane River Catchment, Queensland. CSIRO Land and Water Technical Report. Queensland Department of Natural Resources and Mines (QDNRM) 2004. Upper Brisbane River riverine quarry material management plan: version 2.0. Queensland Department of Natural Resources and Mines, Brisbane, Qld, Australia Queensland Department of Primary Industries (QDPI) 1995. Sand and gravel resources of the upper Brisbane and Lockyer valleys: a technical information paper: Parts I and 11. Queensland Department of Primary Industries, Water Resources, Brisbane, Qld, Australia. Queensland Planning and Environment Court (QPEC) (2004) Cornerstone Properties Ltd v Caloundra City Council & Anor [2004] QPEC 044. QPEC, Brisbane. Brisbane. Available at http://archive.sclqld.org.au/qjudgment/2004/QPEC04-044.pdf (last accessed September 2010). Queensland Planning and Environment Court (QPEC) (2007) Karreman Quarries Pty Ltd v ChiefExecutive under the Water Act 2000 [2007] QPEC 111. QPEC, Brisbane. Available at http://archive.sclqld.org.au/qjudgment/2007/QPEC07-111.pdf (last accessed September 2010). Rustomji, P. & Pietsch, T. 2007, 'Alluvial Sedimentation Rates from Southeastern Australia Indicate Post-European Settlement Landscape Recovery', Geomorphology, 90 pp.73-90. Rustomji, P., Bennett, N., & Chiew, F. (2009). Flood variability east of Australia’s great dividing range. Journal of Hydrology, 374(3), 196-208. Schumm, S. A., Harvey, M. D., & Watson, C. C. (1984). Incised channels: morphology, dynamics, and control. Shellberg , J. & Brooks A.P. (2007) A Fluvial Audit of the Upper Brisbane River: A Basis for Assessing Catchment Disturbance, Sediment Production, and Rehabilitation Potential. Report to SEQ Catchments. Australian Rivers Institute, 113 pp. Simon, A. and C. R. Hupp (1987). Geomorphic and Vegetative Recovery Processes along Modified Tennessee Streams: An Interdisciplinary Approach to Disturbed Fluvial Systems. Forest Hydrology and Watershed Management. I. A. o. H. Sciences. International Association of Hydrological Sciences, Washington, DC. IAHS Publication No. 167.: 251-262. Simon, A., Langendoen, E., Bingner, R., Wells, R.R., Heins, A., Jokay, N., and Jaramillo, I., 2003, Lake Tahoe framework implementation study: Sediment loadings and channel erosion. National Sedimentation Laboratory Research Report No. 39, 375 p. (Peer-Reviewed Agency Report)

Simon, A., N. Pollen-Bankhead and R. E. Thomas (2011). "Development and application of a deterministic bank stability and toe erosion model for stream restoration." Geophysical Monograph Series 194: 453-474. van Niekerk, A.W., Heritage, G.L., Broadhurst, L.W., and Moon, B.P. 1999. Bedrock anastomosing channel systems: morphology and dynamics in the Sabie River, Mpumalanga Province, South Africa. In Varieties Fluvial Form. Edited by A.J. Miller and A. Gupta. John Wiley & Sons, Chichester, U.K. pp. 33-51. Waye, K.J. 1997. An inventory of riverine quarry material in the upper Brisbane River: Monsildale Creek to Lake Wivenhoe headwaters. Queensland Department of Natural Resources, Geotechnical Services Group, Brisbane, Qld, Australia. Witte, C., Norman, P., Denham, R., Turton, D., Jonas, D. & Tickle, P. 2000, Airborne Laser Scanning A Tool for Monitoring and Assessing the Forests and Woodlands of Australia: Laser Altimetry Report 1, DNRQ00098, Department of Natural Resources, Brisbane Wolman, M. G., & Leopold, L. B. (1957). River flood plains: some observations on their formation (pp. 87-107). US Government Printing Office.

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12 Appendices

Disturbances within the Brisbane River likely to Exacerbate Bank Erosion: The role of Sand and Gravel Extraction (excerpts primarily from Shellberg and Brooks, 2007, with some updates) Appendix A

A.1

Riverine Sand and Gravel Extraction

Riverine sand and gravel extraction occurs within many of the active alluvial channel, bench and ledge, floodplain, and terrace deposits of the along the Brisbane River and major tributaries. Both commercial and local-use mining occur. These activities, and in particular extraction within the active river channel, have been cited as major disturbances to natural fluvial processes within the upper Brisbane River (Erskine 1990; QDPI 1995; Johnson 1996; Brizga and Finlayson 1996). Patterson et al. (2002) cited that DNRW policy has a goal to eliminate riverine sand and extraction by 2005 along the upper Brisbane River, however riverine mining is still very active in the upper Brisbane River as of 2011. The “Upper Brisbane River Riverine Quarry Material Management Plan” (QDNRM 2004) indicated that riverine sand and gravel mining will be partially curtailed above Gregors Creek and eliminated below Gregors Creek until further review in 2009. However, evidence from the field and the ortho-photo series collected after the 2011 flood, would suggest this draft policy was never implemented on the ground The regulation of riverine sand and gravel mining, and subsequent jurisdictions and permit systems, have changed over time. Before the year 2000, sand and gravel mining within the “high banks” of water courses was regulated by the Queensland Department of Primary Industries (QDPI) under the Water Resources Act, regardless of land ownership (QDPI 1995). “Controlled Quarry Permits” (CQP’s) were issued by QDPI for mining alluvium on state or publicly owned land within the “bed and banks” of boundary watercourses, where sand and gravel are owned by the state. If the land and alluvium was publicly owned, then royalties were charged for extraction. The entire upper Brisbane River mainstem and major tributaries are boundary watercourses within the “high banks” (Waye 1997). “Quarry Material” (QM) permits were issued for non-state owned land, such as non-boundary watercourses and for the zone between the “high banks” and the “bed and banks”. The zone of “bed and banks” was defined as the “land over which the water of that watercourse …normally flows….permanently or intermittently….but not including….land adjacent to the bed or banks that is…..covered by flood water” (QDPI 1995). The definitions of “normal” and “banks” and “high banks” were all ambiguous, but in practice on the ground, the bed and banks were usually defined minimally as the flat low bed of the watercourse and the first low bank of the watercourse demarcated at its outer edge by the first break in slope, respectively. Most importantly, given that the sand and gravel extractors were targeting the bed material load of the river, the geomorphic definition of the bed would incorporate the part of the channel base that is comprised of bed material deposits. Post 2000, the Queensland Department of Natural Resources and Water (QDNRW) regulated riverine sand and gravel extraction under the Water Act 2000 (QDNRM 2004). The QDNRW attempts to balance the often conflicting goals of permitting sand and gravel extraction while maintaining the physical and biological integrity and stability of streams and other watercourses. “Riverine Protection Permits” are needed to extract “riverine quarry material” from non-tidal watercourses, which include the bed and banks of features that contain or confine water. Apparently it does not include active “floodplain” deposits outside the “banks”

of a watercourse, despite the intrinsic geomorphic connections of channels and their active floodplains, benches and ledges (as opposed to terraces). However, the regulatory definitions of “bank” and “floodplain” remain ambiguous (Figure 44). It would appear that in many instances a very liberal definition of the “bank” is adopted (i.e., one that minimizes the area that falls within the regulatory authority of the state government agencies). As such, much of the ongoing extraction falls outside of the state government regulatory authority but within the continuity of sediment of macro-channel of the Brisbane River For example, Figure 44 and Figure 45 show sand and gravel extraction sites (red dots) on the inset benches and low active floodplains of the river, which were possibly outside the regulatory “banks” of the river.

Figure 44 LiDAR hillshade of the Brisbane River macro-channel (flowing left to right) and Wallaby Creek (entering from bottom) near the town of Moore.

Figure 45 LiDAR hillshade image of the Brisbane River macro-channel (flowing left to right) upstream and downstream of the Gregors Creek confluence and bridge.

In addition to the QDNRW, the Queensland Environmental Protection Agency (QEPA) and Queensland Department of Primary Industries and Fisheries (QDPIF) also have jurisdictional powers over riverine sand and gravel extraction, to protect against pollution and fisheries degradation respectively (QDNRM 2004). Sand and gravel extraction on freehold land outside the “bed and banks” are subject to zoning laws enforced by local governments (e.g., Esk Council). The ambiguous nature of the area defined as being part of the active channel was exploited by the gravel extractors as part of a legal case in 2007 (Karreman Quarries vs Chief Executive under the Water Act 2000, QPEC 111, 2007) heard before the Qld Planning and Environment Court (QPEC), which followed on from another case (Cornerstone Properties vs Caloundra City Council, QPEC 044, 2004). The upshot of these two cases was that the channel was subsequently deemed to only represent that part of the active channel zone

inundated by the 3 month recurrence interval flow, leaving the majority of the active channel (i.e. that part of the channel that which encompasses the majority of the bed material deposits transported by the contemporary channel) outside of the area defined as the channel. Such a definition was clearly inappropriate in the context of the upper Brisbane River, given the extreme variability of flows in this river and the fact that the 3 month recurrence interval flow is incapable of transporting the bed material that is being sought be the extractors. Nevertheless, sand and gravel extraction undertaken on bars and benches within the high banks of the river after this judgement in 2007 was no longer regulated by the state (see Nott, 2010). This anomalous definition was subsequently corrected in 2010, when the definition of the channel under the Water Act 2000, was clarified by amendment to define the channel in more geomorpholigically meaningful terms, to include all of the active channel between the high banks. This definition of the river channel still stands, although the regulatory authority of the state government was recently confused again (June 2014) when a further amendment was made to the Water Act, 2000, specifically relating to in-stream quarrying activities in rivers. Hence it is now unclear as to whether the State has any power to regulate in-stream quarrying following these amendments. Most sand and gravel extraction along the upper Brisbane River could be classified as either “bar scalping” or “dry-pit channel mining” (Kondolf et al. 2001). Bar scalping entails the removal of sand and gravel from the surface of bars located above the low flow water level. Typically material is only extracted to modest depths, but over much of the surface area of the bar (Figure 45). Dry-pit channel mining entails the excavation of a pit in the channel or bar, usually to deeper depths than bar scalping (Figure 44). Often more than one pit is created or sequential pits are excavated in an upstream progressing direction, resulting in similar but deeper results than bar scalping. Excavated pits may or may not be smoothed out before the next flood event. Quantification of the exact volume of sand and gravel removed annually from the upper Brisbane River channel has proved difficult. For over a century, small-scale local sand and gravel mining has occurred along the upper Brisbane River primarily for local use, such as for local road and rail networks (Waye 1997). More recently, significant large-scale commercial extraction from the Brisbane River has taken place over the last 50 years, as evident in historic air photos, for markets in and around the City of Brisbane. However, not all of these large-scale mining efforts have occurred under an official permit system or have been documented. Production prior to 1986 is unknown (Waye 1997), except for an estimate of 170,000 tonnes in 1974 (QDPI 1995). Sand and gravel extraction sites on benches, ledges, and low floodplains of within the Brisbane River macro-channel also may not be included in official state government databases such as the Water Entitlements Register Database (WERD) (Queensland Department of Natural Resources, Mines and Energy). Therefore, complete quantification is impossible, and any estimates should be seen as minimum amounts extracted. QDPI (1995) provided estimates of instream sand and gravel extraction from the upper Brisbane River watercourse between 1986 and 1994, under the Controlled Quarry Permit system. Between 1986 and 1994 over 200,000 m3 (330,000 tonnes) was extracted from the upper Brisbane River mainstem (average 22,222 m3/yr; 36,666 tonnes/yr). This assumes a density of a mixture of dry sand w/gravel of 1650 kg/m3, which is equivalent a sediment particle density of 2.65 g/cm3 and a porosity of 0.377. Minimum actual annual extraction rates

increased gradually from 1987 (10,000 m3/yr), to 1990 and 1991 (13,500 m3/yr), to 1994 (over 110,000 m3/yr) (QDPI 1995). Most of this material was taken from two main areas of the upper Brisbane River: 1. The entrenched meander below the Brisbane River at Gregors stream gauge (AMTD 245 to 252; Reaches 1 and 2 below), and 2. The low-gradient meander immediately above the anabranch above the D’Aguilar Highway Bridge (AMTD 262-264; Reach 7 below). However, additional extraction has occurred that is not included in these figures (e.g., 40,000 m3) where material was extracted from flood channels that are not deemed to be “in-stream” but are part of the continuous active fluvial network (QDPI 1995). As of 1995, future extraction work was planned for many areas within the study area (Colinton, Harlin, upstream D’Aguilar anabranch, Gregors/Mt. Wilson meander bend, Barneys Rocks)(QDPI 1995), some of which have been completed as of 2007. Waye (1997) estimated that approximately 500,000 m3 (825,000 tonnes) of sand and gravel was extracted from the upper Brisbane River between 1986 and 1996 (average 45,454 m3/yr; 75,000 tonnes/yr). More recent estimates of total sand and gravel production upstream of Wivenhoe Dam are between 35,000 and 50,000 tonnes/year (21,212 to 30,303 m3/year)(Patterson et al. 2002). If this volume of material (30,303 m3) were spread over a gravel bar 25 metres wide and mined to a depth of 2 metres, an equivalent bar length per year would be 606 metres long. A search of the Water Entitlements Register Database (WERD) (QDNR) indicates that 261 different permits were issued for sand and gravel extraction between 1971 and 2007. Records of the volume of potential extraction permitted on paper are not readily available before 1997. Between 1997 and 2007, permits were written to extract 1,118,515 m3 or 1,845,550 tonnes total from the upper Brisbane River. This equates to average legally permitted extraction volumes of 111,852 m3/year or 184,555 tonnes/year. However, according to minimum estimates of reported or actual volumes extracted using these permits, an average of 50,000 tonnes/year (30,303 m3/year) was extracted from the upper Brisbane River and major tributaries between 1984 and 2007 (Figure 46). This equates to a 27% extraction rate compared to permitted extraction volumes, if all extracted material was reported. Permitted locations of sand and gravel extraction from the WERD database for the period 1971 to 2007 are displayed below in Figure 47a-b. Distinct clusters of extraction have occurred near Linville, near the town of Moore upstream of Kangaroo Creek, above the head of the anabranch near Spring Creek, near the Gregors stream gauge, near the Scrub Creek Bridge, and near the Fulham Bridge above Cressbrook Creek. These extraction areas are focused on distinct river segments with residual bed elevations higher than average bed elevations along the entire river length. These deposits likely represent legacy alluvium stored upstream of bedrock constrictions at depth.

Sand and Gravel Extraction (Tonnes / Year) _ 3 (Assume 1650 kg/m )

1,000,000 Brisbane River Mainstem Ivory Creek Maronghi Creek Average 100,000

10,000

1,000

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

100

Figure 46 Estimates of minimum annual total sand and gravel extraction from the Brisbane River and Ivory and Maronghi Creeks (1984-2007). Based on or contains data provided by the Queensland Department of Natural

Resources, Mines and Energy, Queensland [2007] which gives no warranty in relation to the data (including accuracy, reliability, completeness or suitability) and accepts no liability (including without limitation, liability in negligence) for any loss, damage or costs (including consequential damage) relating to any use of the data. 600,000 Brisbane River Mainstem Above Deltaic Zone Ivory Creek Sand and Gravel Extraction (Tonnes / Year)_ (Assume 1650 kg/m 3)

Maronghi Creek 500,000

2007-2008 Estimated Commercial Above Deltaic Zone 2007 Western Corridor Pipeline Within Deltaic Zone QDNRM Extraction Cap: Upper Brisbane River Average Annual Bed Material Load Transported By River (Joo 2007)

400,000

Pre-2007 Average Annual Extraction

? 300,000

200,000

100,000

Extrac tion Cap:

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

0 1984

Average Annual Bed Material

+

A)

B)

Figure 47 Locations of permits given to mine sand and gravel within the upper Brisbane River watercourse between 1971 to 2007 from the Water Entitlements Register Database (WERD).

Both the upstream (circle) and downstream (triangle) points of the extent of extraction for individual permits are shown. Data provided by the Queensland Department of Natural Resources, Mines and Energy, Queensland [2007] which gives no warranty in relation to the data (including accuracy, reliability, completeness or suitability) and accepts no liability (including without limitation, liability in negligence) for any loss, damage or costs (including consequential damage) relating to any use of the data.

Waye (1997) estimated the total volume of available sand and gravel reserves within the upper Brisbane River mainstem between baseflow water level and the “high banks”, which largely consist of sand and gravel bars, instream benches and ledges, and low floodplains. Instream sand and gravel deposits were volumetrically estimated from low-level air photographs and photogrammetrically derived cross-sections. Only deposits greater than 50,000 m3 and at least 1 m thick were delineated. For the entire upper Brisbane River mainstem above Lake Wivenhoe, Waye (1997) estimated that 4,790,000 m3 (7,903,500 tonnes) of sand and gravel are economically extractable, with 2,580,000 m3 (4,257,000 tonnes) located above the stream gauge at Gregors, and 1,910,000 m3 (3,151,500 tonnes) located within the study area of this report (Segment 2 below) (Table 20). AMTD Reach

Monsildale to

(Waye 1997)

Segment #

Reach #

(this report)

(this report)

Volume (m3)

Tonnes (1650 kg/m3)

285-278

3

---

860,000

1,419,000

278-271

3

---

730,000

1,204,500

271-260

2

7, 8, 9, 10

690,000

1,138,500

D’Aguilar Highway to Gregors Creek Bridge

260-252

2

3, 4, 5, 6

300,000

495,000

Gregors Creek Bridge to Moorabool

252-246

2

1,2

920,000

1,518,000

Moorabool to

246-237

1

---

600,000

990,000

Barney Rocks Bridge to Watts Bridge

237-231

1

---

500,000

825,000

Watts Bridge to O’Sheas Bridge

231-222

1

---

190,000

313,500

4,790,000

7,903,500

Linville Linville to Moore Moore to D’Aguilar Highway

Barney Rocks Bridge

Total

Table 20 Estimated volumes of extractable sand and gravel within the “high banks” of the upper Brisbane River above Lake Wivenhoe (Waye 1997).

Concerns over the environmental impact of riverine sand and gravel mining in the upper Brisbane River include impacts on the stability of banks and stream bed streams, erosion, sedimentation, water quality, water supply impacts, flooding impacts, aquatic habitat impacts (instream habitat, water quality, riparian vegetation), social concerns (noise, dust, traffic, aesthetics and recreation), and economic impacts to agricultural land (QDPI 1995; Johnson 1996; Patterson et al. 2002). These concerns have lead to the policy goal of curtailing or eliminating the practice of riverine alluvium extraction within the upper Brisbane River (Patterson et al. 2002; QDNRM 2004). are82

GIS Processing of Erosion, Deposition and Geomorphic Units in the Upper Brisbane River Appendix B

B.1

Introduction

The following outlines the GIS methods used to determine 3 areas of analysis in the Upper Brisbane River. 1. Erosion in the Upper Brisbane River between 2001 and 2011 using repeat LiDAR analysis. 2. Deposition in the Upper Brisbane River between 2001 and 2011 using repeat LiDAR analysis. 3. Delineation of geomorphic units of the macro channel. GIS analysis was done using ESRI ArcMap 10.1 software.

B.1.1

Section 1: Processing to find real erosion

The following steps were undertaken to filter noise and identify what was considered to be “real erosion”. Considerabvle care and manual editing was required to filter these data to a point that we were confident has eliminated false positive erosion cells. Consequently, the analysis is extremely conservative as we have endeavored to only include erosion that we had a high degree of certainty was “real”. 1. Identification of the threshold for removal of noise from the erosion layer was done by incrementally masking cell values in 10cm bands from zero upwards to 1.0m and visually checking the effect of removing ‘erosion’ from surfaces where no erosion would be expected to occur, such as roads, flat paddocks, terraces and rocky hillsides. 2. Plotted number of cells above threshold to see if sudden change occurred. 3. Masking cells with values below 0.5m cleared noise from ‘non-erosive’ surfaces. 4. Cells with values >=0.5 were selected for further analysis to identify real erosion. 5. Isolated clumps of 2 or 3 cells were considered more than likely to represent noise. These clumps were identified using the focal statistics tool, and erased from the layer. 6. Cells with focal sum 4 or greater were converted from raster to polygons for hand editing. 7. Polygons with an area 20m2 or smaller were deleted as ‘real erosion’ was deemed to occur in areas larger than 20m2. The remaining 6036 polygons represented ‘erosion’ >= 0.5m in a patch greater than 20m2. 8. ‘Erosion’ cells lying under the 6036 polygons were picked for further analysis. Cells outside the 6036 polygons were set to null. 9. Areas of water in 2001 were used to erase erosion polygons, as it is not possible to detect erosion in locations where water was present in the initial LiDAR. 10. The noise-filtered, patch-size-selected and water-masked ‘erosion’ raster was classified into increments of 0.1m and converted to polygons. This allowed selective masking of narrow bands of erosion values at an erosion site to better understand what was real and what was false. 11. A vector layer of erosion with values >= 0.5m was then produced, with a continuous patch size greater than 20m2 and the water surface in 2001 masked. 12. Manual editing was then undertaken for all 6036 polygons, with decisions made to accept a collection of polygons as real erosion involving the following: a. Switching between 2001 and 2011 LiDAR are83

Number of cells above threshold

b. Switching between 2009 mosaic image and 2011 orthophoto c. Flicking 2011 CHM layer on and off d. In some cases transects would be taken across a patch of erosion, values exported to excel and plotted to visualise if a real change in bank angle, or retreat, had occurred. 13. A considerable number of false positives were removed due to the differential penetration of the LiDAR data between the 2001 and 2011 datsets (i.e. much of the dense vegetation had not been filtered as “non-ground” points from the 2001 data, whereas the 2011 liDAR had penetrated the dense vegetation. Comparison between the two DEMs would detect this as erosion . 14. Evaluation of the information above would confirm polygons as real erosion, or artefacts of the LiDAR process to be deleted. Polygons representing a patch of erosion were merged and assigned an identifying code to allow selecting real erosion from false erosion. 15. Considering that there were over 1.3 million polygons at the start of this process, it was easier to identify those cells worth keeping, rather than trying to delete all the others. 16. The BIG ADVANTAGE of this method was that lots of small polygons could be merged, rather than cutting larger polygons into smaller ones – with associated issues of slivers being left behind and decisions about where to make the cut, and endless digitising. 17. CRITERIA FOR PASSING AS REAL EROSION a. Was the bank obviously eroded, visible by flicking between hills shade LiDAR for 2001 and 2011? b. Was the polygon connected to the channel bed? c. Was the erosion a classic lens shape or a classic mass failure? d. Had the bank top retreated? e. Was there visual evidence of erosion in the 2011 orthophoto? f. Was there other evidence for erosion in the near vicinity? g. Negative evidence for erosion i. Was the polygon on top of vegetation in the 2009 mosaic image? ii. Were the fine scale erosion polygons speckled with high, low and no values? iii. Was the polygon some way up the bank, disconnected from the channel bed? iv. Was the polygon small and irregular shaped? Filtering of noise in UB 2001-2010 erosion layer

22000000 17000000 12000000 7000000 2000000 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Threshold cutoff m

Figure 48 The systematic filtering of ‘erosion' values from the difference layer in increments of 10cm did not show a step change the indicates a threshold value.

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Table 21 Increments of masking for noise and values above and below threshold

Noise reduction for UB erosion threshold m 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Number of cells below threshold 16593100 21049335 23821080 25500406 26635318 27496001 28180099 28732898 29185793 29556915

Number of cells above threshold 16601154 12144919 9373174 7693848 6558936 5698253 5014155 4461356 4008461 3637339

B.2

Examples of Erosion Detection Issues

B.2.2

Difficulty of detecting real erosion where water level fluctuates

• • •

Lower reaches of Upper Brisbane have ponded water from Lake Wivenhoe Backwater height in 2001 LiDAR was 66.8m, in 2011 LiDAR 65.2m; a difference of 1.6m Water level in 2011 was lower, but location of water was sometimes different to 2001 due to channel widening, channel migration, bank failure, scouring.

Figure 49 Comparison of 2001 and 2011 LiDAR showing an obvious slump and different water surface

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Figure 50 Left picture has no raster values masked, right picture has raster values from 0 to 0.5 masked. Noise has been removed from the inset flood plain and the terrace.

Figure 51 Left picture has values from 0 to 1.6 masked, some noise remains over the water. Right picture has values masked from 0 to 1.8, noise cleared from over water, bank erosion better identified.

Erosion visualisation, left bank 78

74 72

2001

76

2011

74

elevation m

76 elevation m

Erosion visualisation, right bank

70 68 66

72 70

2001

68

2011

66

64 0

20

40 distance m

60

80

64 95

105

115 distance m

125

135

Figure 52 Profile of left and right bank in 2001 and 2011 showing bank retreat and differences in water height. Profile is shown by blue line across the channel in figures 1, 2 and 3

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B.2.3

Erosion of an island

Erosion of Island

Elevation m

70

2001 2011

69 68 67 66 65

0

10

20 30 Distance m

40

50

60

Figure 53 Erosion of an island in the backwater of Lake Wivenhoe, showing differences in water level and island profile between 2001 and 2011

B.2.4

Erosion of bank toe Erosion of bank toe

76 Elevation m

74

2001

72

2011

70 68 66 64 0

10

20

30 Distance m

Figure 54 Erosion of the bank toe at the downstream end of the LiDAR capture

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40

50

60

B.2.5

Mass Failure

Elevation m

Mass failure 78 76 74 72 70 68 66 64

2001 2011

0

20

40

Distance m

60

Figure 55 Mass failure

B.2.6

Example of scour erosion

Erosion visualisation, from left to right

110

2001 2011

Elevation m

108 106 104 102 100 98 0

50

100 Distance m

150

200

Figure 56 Orthophoto from 2011 shows erosion and deposition patches scoured in banks and bar, and locations where vegetation removal artefacts showed up as 'erosion' also

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80

B.2.7

Example of false erosion

Figure 57 Red polygon in the hill shade LiDAR on the left appears to be area of erosion in the 2011 image.

Figure 58 Red polygon in picture on left sits above vegetation, which is still present in 2011, seen in the right picture. This polygon of 'erosion' was an artefact of the vegetation removal process.

B.3

Defining Deposition

The process to identify real deposition was the same as that used to identify real erosion, but using values from the difference layer that represented a gain of material between 2011 and 2011. The same threshold to remove noise was used.

B.4

Method for Defining Geomorphic Units in the Upper Brisbane River

The most successful method to delineate geomorphic units along the Upper Brisbane macro channel was to hand digitise. Using any automated process to define the edge of a continuously changing surface resulted in more work to correct edges than to digitise in the first place. Human discretion and interpretation of changing shades in a hillshade raster, plus interpreting vegetation patterns from a satellite image, gave the best results determining surface boundaries. One exception to hand digitising was that the water surface was defined using the MrVBF (Gallant and Dowling, 2003) procedure from the SAGA GIS software. The advantage of hand digitising is that human discernment is used when placing boundaries around river side features that may have complex patterns of slope, elevation and connectivity. A disadvantage is that hand digitising is a very time consuming process. are89

A useful guide to visualise the extent of alluvial surfaces was the slope raster with a threshold of 10 degrees. Reasonable definition of sharp edges was found, such as the drop-off from a terrace to a bank face. Additional data used to differentiate units were the classified bank height raster (i.e. where all pixels were assigned an elevation above the closest thalweg point. This provides a means for normalizing all bank heights throughout the river reach with respect to low point in the immediately adjacent channel. The vegetation percent foliage cover raster was also used to determine the extent of vegetation cover on individual surfaces. All geomorphic units were defined for the 2001 LiDAR DEM, given that we were interested in determining which geomorphic units eroded during the 2011 event (based on the assumption the extent and distribution of geomorphic units was little change between 2001 and just prior to the 2011 flood – given the complete lack of bed mobilizing flows during this period.

B.4.1

Data sets used to define geomorphic units

• • • • • • • • •

High resolution DEM from 2001 LiDAR Hillshade rendering of 2001 LiDAR DEM Slope raster derived from 2001 LiDAR DEM A thalweg from 2001 LiDAR A relative bank height raster derived from the 2001 DEM and thalweg Hillshade rendering of 2011 LiDAR DEM Whole_SEQ_Project_Mosaic_MGA56_2009. Cell size 0.5m Orthophoto from 2011 LiDAR collection, cell size 0.15m Google Earth historical imagery

B.4.2

Processing Issues for 2001 LiDAR into Geomorphological Units

• • • •

•

•

• •

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No air photography for 2001 for a visual reference. Height and slope classification does not give information on vegetation on a surface. The 2011 orthophoto was referred to for general guidance, but there were obvious massive erosion and deposition differences in places. Evidence of flood ravaged vegetation on mid-channel bars in the 2011 orthophoto guided classification of surface towards Mid-Channel Bar – vegetated, though between 2001 and 2011 there may well have been time for new trees such as Melaleucas to establish from scratch. Google Earth historical imagery was used where resolution was suitable, to check the appearance of the Upper Brisbane prior to the massive flood in 2011. The earliest usable images were from 2007. Patterns of vegetation were a useful guide to changes of sediment composition, hydrology, elevation above the watertable, and thus to the extent of a surface. It was assumed that farmer’s paddocks were pushed right out to the edge of terraces, and that the adjacent trees were growing on steeper slopes that were unsuitable to plow and crop. Benches were hand digitised where Hill Shade LiDAR gave evidence that a bench existed. The boundary constraint for defining geo-units was the edges of the high resolution 2001 LiDAR.

B.4.3

Geomorphic Unit Definition

Table 22 Definitions of Geomorphic Units defined within the Macro-channel of the Upper Brisbane River

1.

Water and Wet Area (low flow channel)

Ponded water seen in LiDAR imagery and the lowest 0.4m of surfaces derived from the Bank Height Raster

2.

High Flow Chute Channel

Obvious high level distributary channel that would run as the main channel fills. An island would be formed between the chute channel and the main channel as the chute channel is activated

3.

Gravel bars and Open Riverbed

Adjacent to water, surface is cobbles, not vegetated, generally smooth surfaces, height threshold 0.4 to 1.5m above the adjacent thalweg

4.

Point Bars and extensive sculpted/partially vegetated lateral bars

Wide surface with obvious deposition zone on the inside of bends, with wave form deposits. OR a long, broad surface with tremendous sculpting adjacent to main channel.

5.

Mid-Channel Bar-Bench

Raised, level surface isolated from main channel banks, mostly vegetated. Seem as an “island” of perched material in the channel bed.

6.

Mid-Channel Bar-High Surface

As for Mid-Channel Bar-Bench, but with elevation greater than 5m. Possibly an isolated fragment of flood plain.

7.

Bench

A long, relatively narrow feature running parallel to the river that is relatively level or only gently inclined and is bounded by distinctly steeper slopes above and below (i.e. it forms an intermediate alluvial feature between the elevation of the bed and the floodplain (or inset floodplain).

8.

Inset Floodplain

An extensive flatish surface formed at an intermediate level between the river bed/bars and the high terrace where accommodation space is available(and above the elevation of any benches). Inset floodplains would be inundated regularly (probably every 2-5 years on average)

9.

High Terrace

The upper, flattish surfaces seen in LiDAR. Usually higher than 12m. Often cultivated. Limited extent of LiDAR does not allow seeing if there is a higher surface beyond.

10. Colluvial Slope

Bedrock hillslopes that are impinging directly on the channel margin (i.e. any unconsolidated material that is found on these slopes is a function of accumulation as a result of downslope process rather than alluvial depositions associated with the river.

These 10 categories have been further simplified into just seven units for ease of analysis, most importantly in terms of differentiating the dominant processes of sediment transport and erosion operating in each zone. In this simplified schema, units 2 – 4 are considered to consist primarily of bed material load and as such erosion and deposition within these three units represents the contemporary bed material load of the river. The remaining units will contribute the bulk of the fine sediment (< fine sands) with varying degrees of bed material depending on the site specific sedimentology. are91

B.4.4

Simplified Geomorphic Unit Delineation

Table 23 Simplified geomorphic units that have been used to undertake the erosion analysis. All change that occurs in Units 1 and 2 is considered to represent bed material erosion and deposition, whereas erosion of the remaining units is considered to represent “bank erosion” in its more traditional sense, which delivers a combination of suspended and bed-material load in varying proportions depending on the unit.

GeoUnit 1 2 (units 2-4) 3 (units 5-6) 4 5 6 7

B.4.5

Examples of Geomorphic Units

Chute Channel

Figure 59 Chute Channel

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Simplified Geomorphic Unit Description Water Bed material zone Vegetated mid-channel bars Bench Inset Flood Plain Terrace Colluvial Slope

Bars and open river bed

Figure 60 Cobble and Open Riverbed

Figure 61 Point Bar

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Partially Vegetated Bars

Figure 62 Partially Vegetated Bars

Benches

Figure 63 Bench

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Vegetated Mid-channel Bar/Island

Figure 64 Mid-Channel Bar – Vegetated

Figure 65 Mid-Channel Bar - High Surface

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Inset Floodplain

Figure 66 Inset Flood Plain

Terrace

Figure 67 High Terrace

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Colluvial/Bedrock Hillslope

Figure 68 Colluvial Slope

B.4.6

Example of the process of defining Geomorphic units at Gregors Crossing

Figure 69 Orthophoto and hillshade LiDAR from 2011 at Gregor's Crossing

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Figure 70 Surfaces with slope less than 5 degrees in green shows historical bench surfaces. Cross section shows elevatin of benches above water surface.

Figure 71 Vegetation highlighted with rendering of Canopy Height Raster

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Appendix C

Upper Brisbane C.1

Uncertainty estimation for erosion and deposition in the

Method used for determining water extent

The location of water was defined using a segmentation algorithm within Definiens to segment the DEM based on slope and MrVBF. This created a series of smaller polygons which in most cases corresponded well to the water extent. These then had to be merged into one layer and edited. Table 24 Possible combination of water and land from two time slices and a changing hydrology, with implications listed

2011 land

water River level higher

land

Area was land in both time slices (erosion and/or deposition can be detected)

Channel shifted Bank eroded Bed eroded

2001 River level lower water

Lake level lower Channel shifted Deposition occurred

C.2

Lake level higher

Water was present in both time slices (extent of erosion and deposition not known – estimate included in uncertainty analysis)

Summary of known channel characteristics 1. 2. 3. 4. 5. 6. 7. 8. 9.

C.3

Area of macro channel Area of bed material Extent of water in 2001 Extent of water in 2011 Where water was present in both years Where water was present in one year, but not the other Erosion location and volume Deposition volume and location Height of water surface in 2001 and 2011

Summary of unknown channel characteristics 1. How much erosion occurred under the water surface in 2011 LiDAR 2. How much deposition occurred under the water surface in 2011 LiDAR 3. Where banks have been eroded, and there was a water surface in the 2011 LiDAR, we do not know how deep the water is, i.e. how much material has been eroded under the water

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4. Where deposition filled in water in 2001 and left a mound of bedload, we do not know how deep the water was that was filled in, i.e. how much deposition occurred under the water All of these factors contribute to the uncertainty in the measurements of known erosion and deposition, and as such we have attempted to account for this error through the inclusion of an uncertainty parameter (i.e. error bar on our plots of observed erosion. The following explains how these estimates were made.

C.4

Estimating volumes of erosion and deposition under the water

Where erosion was determined to have occurred between 2001 and 2011, and the surface was water in the 2011 LiDAR, the volume of unknown erosion can be calculated by: (Area common to erosion AND water surface in 2011) × (0.5m higher 2011 water height) × (Estimated water depth)

C.4.1

Water height difference between 2001 and 2011

Some uncertainty ion the observed erosion/deposition was due to the fact that the low flow water surface elevation was different between the two time slices (the LiDAR does not penetrate the water - so the water surface is represented instead of the channel bed wherever there is standing water. To determine the extent of water surface elevation difference between the two time slices, the average elevation of a series of polygons located on the common water surface in both time slices were analysed. To evaluate the effect of different water heights on uncertainty in erosion volumes, it is necessary to calculate the difference in water height of the closest common water body to the erosion location. The area of water common to both LiDAR time slices was split into the 91 reaches and the resulting 201 polygons used to sample the height of water surfaces in 2001 and 2011 LiDAR. Water level was higher in 2011 than 2001 in 171 of the 201 polygons representing common water area. Of the 30 polygons where water level was higher in 2001, 16 of these were less than 35cm different. 14 polygons in the dam backwater area had the water level in 2001 as approximately 1.5m higher than in 2011. Average water height in the 190 polygons above the backwater section was 0.45m higher in 2011 than 2001. The implication is that the river flow was greater at the time of LiDAR capture in 2011.

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Difference between water height in 2001 and 2011 in areas where waterbodies occured at both time steps 2 1.5

0.5 0 -0.5

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151 157 163 169 175 181 187 193 199

Height m

1

-1 -1.5 -2 Figure 72 Water surface elevation difference between 2001 – 2011. Negative values show water was higher in 2011 than 2001. Each of the 91 reaches may have more than one waterbody that was present in both 2001 and 2011 (hence the 201 polygons for the 91 sub-reaches – which are arranged from upstream (L)-downstream (R). Positive values around 1.5m show higher level of backwater in Lake Wivenhoe at the time of LiDAR capture in 2011. Selected pools with water higher in 2001 are investigated in next section.

A separate water height check using 173 small polygons strategically places in major pools had the following results Mean 2001 water height - mean 2011 water height 2 1.5 1 0.5

-0.5

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131 136 141 146 151 156 161 166 171

0

-1 -1.5 -2 Figure 73 Difference in water height between 2001 and 2011 for 173 locations in distinct pools along 85km of Upper Brisbane River. Water level in 2011 was subtracted from 2001.

Excluding the dam backwater effect in the last 5 sub-reaches, the mean water height was approximately 0.5m higher in 2011.

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C.4.2

A check on selected locations where water height was higher in 2001.

Figure 74 Lowering of bed in a riffle sequence is the reason for 0.5m lower water height in 2011. Erosion patch seen in 2011 Hillshade LiDAR covers part of the polygon which was placed on area of water common to both 2001 and 2011. This should not have been possible, as the area of water in 2001 was used to mask out erosion values from the difference layer.

Figure 75 Orthophoto from 2011 shows a large tree over the water, which is visible in the 2001 hillshade LiDAR as a raised lump. Faulty vegetation removal is responsible in this instance for higher "0.5m water height" in 2001.

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Figure 76 Polygon with blue highlight has water 0.3m higher in 2001, though the polygon to the left (upstream) has water 0.6m lower in 2001 and the polygon to the right has water 0.05m higher in 2001, a negligible amount. Hillshade LiDAR from 2001 shows a surface indicating LiDAR artefacts at that location.

Figure 77 This sample location at the upper most end of the LiDAR capture had water height as 0.3m higher in 2001. However, faulty vegetation removal was responsible for the result. Riparian vegetation can be seen in the 2009 Mosaic image.

At other locations where water height was higher in 2001 the value was less than 10cm, and was not investigated. Conclusion – the 4 locations checked in figures 1-4 could all be deleted from the excel dataset.

C.4.3

Uncertainty due to increased water surface area in 2001

Given that the area of water in 2011 was 35% greater 2001, it was important to check where the major areas of expansion occurred and to what extent this was masking observed erosion or deposition. are103

To perform this analysis, the area of water in 2001 was exctracted from the finished geo-units shapefile. The area of water determined in 2011 LiDAR was then checked tosee whether the cell edges aligned with edges of the DEM by converting polygons to raster with edges aligned to 2011 DEM, and converting back to polygon. This was a critical step to ensure accurate results are determined. The relative bank height raster made from the 2001 DEM has the water surface in 2001 as its lowest level. The ArcMap add-in ‘Query Analyst’ was used to model the area of bed covered by an increase in water height of 0.5m. A base height of 0.1m gave similar coverage as the 2001 water extent determined by the process outlined in section C1 The area of channel covered by water between 0.1 and 0.6m water height is shown below (Table 25). Table 25 Area covered by increase of water height by 0.5m, modelled from 2001 relative bank height raster

Modelled water height m 0.1 0.6 Area covered with 0.5m rise Difference as % of 2001 area

Area m2 3176830 4507229 1330399 41.9

The area covered with water was less than the area calculated by analysis of repeat LiDAR, but within 10% of the value.

C.4.4

Increase in area of water between 2001 and 2011 from analysis of repeated LiDAR

The expanded water area in 2011 was found by subtracting the area of water in 2001 from the area of water in 2011. The 55 individual polygons representing the difference in area of water had a total area of 1487912m2. The expansion in water area was intersected with reach polygons to get a picture of where in the Upper Brisbane the water surface had increased between 2001 and 2011 (Figure 78).

Increased area of water m2

Increase in area of water in each reach between 2001 and 2011 100000 80000 60000 40000 20000 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 Reach number

Figure 78 Increase in area of water in each of the 91 reaches in the Upper Brisbane.

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Increase in area of water and volume of erosion between 2001 and 2011 Increase in water area m2

350000

y = 3.0661x - 2383.1 R² = 0.4991

300000 250000 200000 150000 100000 50000 0 -50000 0

10000

20000

30000

40000 50000 60000 Volume of erosion m3

70000

80000

90000 100000

Figure 79 Correlation between volume of erosion in each reach and the increase in area of water.

Given that the water surface was on average 0.5m higher in 2011, this raises the questions as to what would be the volume of erosion not counted between the two water levels? Analysis of area of water in 2001 and 2011 from repeated LiDAR The area of water in 2001 was 16% of the macro channel area. The area of water in 2011 was 21% of the macro channel area. There was an increase in water surface area of 35% between 2001 and 2011. This equated to an additional 5% of the area of macro channel being covered in water between 2001 and 2011. Water surface area common to 2001 and 2011 was 13% of the macro channel area. Table 26 Area of water in 2001 and 2011 relative to macro channel area and bed load area

Area of macro channel m2 Area of bedload material in 2001 m2 Area of water in 2001 m2 Area of water in 2011 m2 Area Water in 2001 as % of macro channel Area Water in 2001 as % of water + bedload area Area Water in 2011 as % of macro channel Area Water in 2011 as % of Water + Bedload area Water in 2011 as % of water in 2001 Area water present only in 2001 m2 Area water present only in 2011 m2 Area water present in 2001 and 2011 m2 are105

19509984 4984157 3024352 4074037 15.5 37.8 20.9 50.9 134.7 438227 1487912 2586125

C.4.5 2011

UNCERTAINTY CALCULATION FOR EROSION DUE TO HIGHER WATER LEVEL IN

Each patch of erosion that occurred where water was standing in 2011 was linked to the closest patch where water was present in both LiDAR time slices. A search radius of 20m was used to prevent spurious results. The area of the erosion patch was multiplied by the difference between the water height in 2001 and 2011 to estimate the volume of ‘unseen erosion due to the overall higher water level in 2011. Assumptions • •

That if the bank was eroded above the 2011 waterline, the erosion process would continue below the water line If the water was higher in 2001 than 2011, the loss of water height would not show up as ‘erosion’ as the vigorous grooming/editing process would have weeded this out. Calculated volume of erosion in area of uncertainty due to higher water in 2011 25000

Volume m3

20000 15000 10000 5000 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 Reach number Figure 80 Volume of erosion per reach estimated to be ‘unseen’ due to higher water level in 2011

Figure 81 Distribution of reaches where water height was higher in 2001 than 2011

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C.4.6

Example of erosion/deposition uncertainty from the Burrows site

Figure 82 Burrows site with detail of water area in 2001, 2011 and area where land was replaced with water at time of 2011 LiDAR capture.

Figure 83 Cross section at Burrows site with detail of erosion and changes in water height. Area of uncertainty has been highlighted. While it is not known if scouring of the channel bed occurred, it could be assumed that bank retreat occurred at least down to the water level of 2001.

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- Burrows and Weildons Bank Sedimentology

Appendix D

elev above thalweg (m)

unit thickness

sed description

2.31

0.3

Very Coarse Silty Fine Sand

2.01

0.8

Very Coarse Silty Medium Sand

1.21

1.1

Very Fine Gravel

0.11

0.11

poorly sorted gravel

0

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elev above thalweg (m)

unit thickness

sed description

8.4

0.4

Very Coarse Silty Medium Sand

8

1

Very Coarse Silty Medium Sand

7

2

Poorly Sorted Medium Sand

5

3.5

Very Coarse Silty Medium Sand

1.5

0.5

Sandy Very Fine Gravel

1

1

poorly sorted gravel

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elev above thalweg (m)

unit thickness

sed description

10.2

0.8

Very Coarse Silty Fine Sand

9.4

1.4

Poorly Sorted Medium Sand

8

1.2

Very Coarse Silty Medium Sand

6.8

1.6

Very Coarse Silty Fine Sand

5.2

5.2

poorly sorted gravel

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elev above thalweg (m)

unit thickness

sed description

4.1

0.4

Very Coarse Silty Medium Sand

3.7

0.5

Very Coarse Silty Medium Sand

3.2

0.7

Very Coarse Silty Coarse Sand

2.5

2.5

silty sand

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elev above thalweg (m)

unit thickness

sed description

5.31

0.5

Very Coarse Silty Medium Sand

4.81

1

Very Coarse Silty Very Fine Sand

3.81

1.1

Very Coarse Silty Fine Sand

2.71

2.71

clayey silt

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elev above thalweg (m)

unit thickness

sed description

8.03

0.5

Very Coarse Silty Medium Sand

7.53

0.7

Slightly Gravelly Muddy Sand

6.83

0.8

Muddy Sandy Gravel

6.03

2.03

poorly sorted gravel

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elev above thalweg (m)

unit thickness

sed description

8.49

0.3

Poorly Sorted Coarse Sand

8.19

1.2

Poorly Sorted Medium Sand

6.99

0.8

Very Coarse Silty Medium Sand

6.19

6.19

poorly sorted gravel

0

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elev above thalweg (m)

unit thickness

sed description

8.22

0.5

Poorly Sorted Coarse Sand

7.72

0.6

Very Coarse Silty Fine Sand

7.12

0.4

Very Coarse Silty Medium Sand

6.72

0.8

Very Fine Gravelly Coarse Sand

5.92

0.5

Slightly Very Fine Gravelly Coarse Sand

5.42

3.11

poorly sorted gravel

0

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Statistical Modelling of Bank Geotechnical Parameters from Sediment Particle size data: The application of Self-Organizing Maps Appendix E

Assessing bank stability using a geotechnical modelling approach (e.g. Simon et al., 2011) traditionally involves the laborious collection of data on the bank and floodplain stratigraphy as well as in-situ geotechnical data for each sedimentary unit within a river bank, if the model is to have any predictive power at all (Parker et al., 2008). The application of geotechnical bank stability models are indeed limited to those sites where extensive field data has been collected, which means that their ability to provide predictions of bank erosion at the reach scale are extremely limited without a very extensive and expensive field data collection program. Attempts have been made to extrapolate site specific data across a reach (e,g. Simon et al., 2003), but in cases where this approach has been attempted in Australia, and where independent empirical data are available to test the validity of these extrapolation approaches (this study), they have extremely poor predictive ability (Figure 84). The predictive power in this example is such that it provides no useful data on the distribution and extent of bank erosion at the reach scale within the O’Connell River, and this is only a function of one facet of the procedure to up-scale the approach from the site to the reach scale. Extrapolating the sedimentology and geotechnical characteristics of the complex banks, renders the approach meaningless. In large part the inability of an approach such as the Bank Stability and Toe Erosion Model (BSTEM) to be extrapolated beyond the site scale is a function of the logistical limitations of collecting sufficient in-situ geotechnical data to adequately parameterise a model to the extent that it can produce meaningful results. Indeed, this is the case in relatively homogeneous rivers, so in highly complex rivers it is prohibitive. The sedimentology of many coastal rivers in eastern Australia, including the upper Brisbane River, are extremely complex, making it impractical to collect sufficient data to adequately parameterise a model based on in-situ data. It should be pointed out that it typically takes a full day in the field for a team of two to three people to collect the geotechnical data for one section of relatively homogeneous bank. Given these practical limitations, any approach that can allow the accurate interpolation of geotechnical data based on some other proxy measure will greatly enhance the ability of this type of approach to be expanded beyond its current limited application a single sites. To this end, a method is outlined herein, that uses an extensive database of geotechnical data collected at over 200 sites throughout Queensland to develop a statistical modelling approach that enables geotechnical parameters to be derived from sediment particle size data. Sediment particle size data can be collected much more readily and quickly than geotechnical data, with the analysis completed in the lab. Providing a robust relationship can be determined that relates all of the key geotechnical parameters to the particle size distribution, this then frees up the field data collection team to focus on the more fundamental tasks of understanding the local stream geomorphology and most importantly characterising the sedimentology. The collection of the sediment samples for analysis in the laboratory then becomes a fairly trivial part of the whole process.

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predicted length %rf method (km)

2.5

y = 0.26x + 0.6811 R² = 0.203

2 1.5 1 0.5 0 0.0

1.0

2.0

3.0

4.0

observed length of eroding bank/2km reach (km) Figure 84 Comparison between Observed bank erosion length on the O'Connell R and % reach fail analysis (as per the method applied in Simon et al. 2003, 2011).

Self-Organising Maps The application of the Self-Organising Maps (SOM) approach is well-suited to the analysis of noisy, sparse, nonlinear, multidimensional, and scale-dependent data. It is a type of unsupervised artificial neural network with hybrid competitive-cooperative learning (Kohonen, 1983). The SOM approach has been used in related studies to explore relations among rock geochemistry and hyper-spectral images (Penn, 2005), classify geomorphometric aspect based on digital elevation models (Ehsani and Quiel, 2008), characterizing hillslope landslide vulnerability (Hentati et al., 2010), analysing soil chemical weathering on hillslopes (Iwashita et al., 2011), predicting soil hydraulic properties with scarce datasets (Iwashita et. al., 2012), and to identify processes controlling the distribution of iron in soil and sediment (Löhr et al., 2010) The SOM learning method used here is based on the stochastic gradient described by Kohonen (1983). It consists of a two-step process that is performed each time an input pattern is presented to the map: 1) competition to determine the best matching unit (BMU), and 2), cooperative learning which spreads information contained in the current input vector across the map. At the beginning of the unsupervised training phase, the weight vectors are initialized to small random numbers. The input data vectors are presented to the map grid in a random fashion to generate data clusters without introducing bias for a specific class. In the first step the BMU with map coordinates (q, u) is determined as the grid neuron, whose weight vector is the closest to the input. In the second step, a weight update is determined which is a function of the distance to the current BMU, as expressed through a chosen neighbourhood function (usually Gaussian), leading to gradual adjustment of the weights. There is also a factor of collaboration in the learning process. The winner neuron has influence on its neighbours, which allows the synaptic weights of the neighbouring neurons to a winning neuron to be updated. It is interesting to note that usually the area of influence of the winner neuron should gradually decrease over the learning process following the chosen function, i.e., the weight association effect takes place at the neighbouring nodes but to a lesser degree because of the Gaussian shape. This adaption procedure stretches the weight vectors of the BMU and its topological neighbours toward the input vector. Presenting similar input vectors to the map provides further activations in the same neighbourhood and thereby tends to produce clustering of data in the feature space. Association between neurons decreases during the learning process (the width of the neighbourhood function is forced to decrease with n preserving large clusters of data while enabling the separation of clusters that are closely spaced). Each time that an input vector is presented to the map grid, the quantization error is calculated and number of times each neuron becomes a BMU is recorded. are117

An introductory video of SOM with a 2D animated example can be found at: https://www.youtube.com/watch?v=H9H6s-x-0YE Methods In this exercise, we collected sedimentology data alone at a further nine bank sections within the study area (Burrows and Weildons sites) and developed an approach for inferring the geotechnical parameters from our broader dataset to these sites. The SOM estimates data values based on distances among the available vectors. The traditional estimation process is by replacement (called imputation), where the values are taken directly from the prototype vectors of the Best matching units (BMUs). The vector framework was constructed based on a subset of the Queensland Bank Erosion Project (Brooks et al., in prep) composed of geotechnical field tests and laboratory analysis for particle size distribution (PSD). A subset of 88 samples of the broader dataset was used with (PSD) values and parameters (effective cohesion and friction angle) derived from a borehole shear test (BST). The second set had 75 samples with PSD values and parameters (erodibility and critical stress) derived from a mini jet test (JET). To predict the geotechnical variables at the nine bank profiles at the Burrow-Weildons (BW) dataset, comprised of 29 samples with PSD values, the vector framework constructed using BST and JET set was applied to the BW set to estimate the missing values of effective cohesion, friction angle, erodibility and critical stress. Table 27 The average of predicted values through SOM for effective cohesion, friction angle, erodibility and critical stress.

ID 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72

Average predicted values (30runs) Effective Friciton Bulk Stress Erodibility Stress JO Erodibility cohesion Angle Density Blaisdell Blaisdell Scour JO Scour 4.633 24.1 1.39 0.315 30.292 1.542 76.978 4.424 24.8 1.37 0.291 33.650 1.525 84.255 2.285 23.4 1.46 0.343 44.234 2.393 38.398 4.655 23.8 1.39 0.307 30.532 1.547 73.129 2.109 6.0 1.45 0.969 13.356 4.598 22.822 5.353 25.3 1.35 0.270 55.031 1.579 66.591 3.844 19.6 1.49 0.395 19.471 2.009 44.273 4.148 20.9 1.44 0.367 24.838 1.706 55.502 11.756 37.3 1.47 0.650 16.194 3.253 24.761 3.518 10.9 1.49 0.706 29.321 3.306 24.836 4.014 18.0 1.48 0.419 20.434 1.972 41.513 5.575 25.3 1.34 0.274 54.525 1.574 66.007 9.411 32.8 1.42 0.486 32.842 2.983 26.839 4.587 24.7 1.38 0.293 34.869 1.527 83.319 4.210 21.4 1.41 0.274 55.002 1.585 66.555 3.885 19.4 1.49 0.365 22.374 1.906 43.699 5.128 32.6 1.45 0.330 32.366 2.073 44.819 4.480 24.7 1.38 0.291 33.583 1.526 84.281 4.365 30.0 1.38 0.322 50.496 1.783 49.050 6.330 33.7 1.56 0.686 16.054 3.482 25.819 6.099 29.6 1.36 0.310 62.481 1.778 53.600 5.586 25.3 1.35 0.272 54.053 1.574 64.813 4.555 23.8 1.39 0.315 30.586 1.549 71.556 5.186 29.7 1.38 0.322 56.897 1.825 47.330 2.184 4.8 1.43 0.920 10.545 5.458 24.173 2.894 7.4 1.47 0.958 16.011 3.926 23.420 10.254 33.3 1.41 0.499 39.244 2.621 31.218 5.565 24.2 1.39 0.303 45.754 1.807 56.281 4.042 18.5 1.48 0.392 20.409 2.179 40.048

The limited data availability and high spatial variability promote increasing amounts of uncertainty in model predictions (Hornberger et al., 1998). Scarce data sets can result in biased predictions (Dickson and Giblin, 2007) requiring a modified scheme based on bootstrapping (Breiman, 1996). are118

The SOM algorithm is objective, but there is subjectivity when choosing the set of data variables as potential predictors, and the samples are spatially limited with varying levels of uncertainty in their measurements and observations. For these reasons, the reliability of the SOM as a model to predict soil geotechnical parameters is evaluated using cross-validation. The model framework was evaluated using two methods, splitting the dataset into training and validation set, and through a Bootstrap approach (Kohavi, 1995). The basis of Bootstrap crossvalidation is a leave-one-out strategy. This requires leaving one data value out of the training set while creating a new SOM to estimate that value based on the remaining data. Because a new SOM is created up to 30 times for each value under scrutiny, it forms the basis for the stochastic framework from which residuals are used to evaluate error statistics and model bias. The first method was carried out according to the following steps: (a) splitting the dataset into training (BST n = 44; JET n = 40) and validation set (BST n = 44; JET n = 35). The validation dataset had its dependent variables (effective cohesion, friction angle, erodibility and critical stress) values extracted in order to be predicted by the model and then compared with the original set. (b) The SOM vector framework was calculated using the training dataset. (c) The validation set was entered and replacement values determined. (d) Finally, the predicted values of the validation set were compared to the observed values.

Figure 85– Model evaluation using the first method. Consists of a single prediction comparing with the measured values of the validation set of effective cohesion, friction angle and critical stress and erodibility (JO scour).

The bootstrap was carried out according to the following steps: (a) The SOM vector framework was calculated using the entire dataset. (b) The first sample of the chosen dependent variable to be evaluated is extracted from the dataset. (c) The SOM framework is calculated again and the missing value is estimated. (d) The previous step is repeated for each sample of the set (88 times for effective cohesion and friction angle as BST n = 88; 75 times for critical stress and erodibility as JET n = 75). (e) Steps c and d are repeated 30 times for each variables, i.e. the model has to run approximately 9800 times. (f) The residue of predicted values are analysed and the average of predictions of 30 runs is compared to the observed values (Figure 85). The SOM demonstrates unbiased behaviour indicated by the one-to-one correspondence and constant variance for critical stress, erodibility, effective cohesion and friction angle . are119

The proposed method is suitable to soil geotechnical properties, revealing and quantifying relationships between geotechnical variables and particle distribution size, not properly observed by linear multivariate statistical approaches, additionally, does not have any statistical assumptions for the collected dataset.

Blaisdell

JO Scour

Figure 86 Cross validation of soil geotechnical properties. The SOM produced unbiased estimation with excellent predicting power

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Appendix F Upper Brisbane Burrows/Weildon Bank Erosion Modelling The modelling exercise undertaken for the reaches of Upper Brisbane at Burrows and Weildon consisted of using the Bank Stability and Toe Erosion Model (BSTEM). This model requires as inputs; geotechnical parameters, bank profiles, and flow data. Geo-technical measurements were taken throughout the Upper Brisbane River catchment as part of an on-going bank erosion research project. These measured values were used to create a model to predict geo-tech values from the results of particle size analysis (PSA) of sediment samples taken from distinct deposition units observed in bank profiles. Using a predictive model generates a range of geo-technical values from which the maximum, average, and minimum values for each geo-technical variable was selected and used to determine a range of possible bank retreat rates. The bank profile sample sites are shown in figure 1. Depositional units are observed as layers with relatively homogeneous sediment characteristics in exposed bank profiles. The sample sites have between 4 and 6 layers. Each layer was treated separately as an idealised bank profile and run through BSTEM numerous times to generate bank retreat values for all combinations maximum, average, and minimum geo-technical values (approximately 20,000 runs of BSTEM). The combination of geo-technical values that produce the maximum, median, and minimum total eroded volume for each layer was chosen as the test data set. The following analyses were carried to perform a sensitivity analysis on geotechnical stability at the sampled bank profiles at the Weildons and Burrows sites. • • • • •

Running each sedimentary layer as to test all combinations Used Jan 2011 flow discretised Used the geotech combinations as the inputs for each layer. Ran BSTEM using LiDAR 2001 bank profile for the maximum, median, and minimum retreat rates. Only five layer inputs possible -- so had to choose which observed layers were included

Comments: • Banks were more complicated than model allowed • Poor match to observed • Conservative values

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Figure 1: Site map

Start Date and Time

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Level (m)

Duration (hrs)

1

06/01/2011 13:30

2.5

07:00

2

06/01/2011 21:30

4.5

08:00

3

07/01/2011 05:30

7

08:00

4

07/01/2011 21:30

5.5

16:00

5

08/01/2011 10:00

8.3

12:30

6

09/01/2011 15:30

5

05:30

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7

10/01/2011 08:30

14.5

17:00

8

11/01/2011 06:30

9.5

22:00

9

11/01/2011 15:45

14

09:15

10

12/01/2011 05:30

10

13:45

11

13/01/2011 11:30

6

06:00

Lateral Retreat (m) Site

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Modelled

Observed

Maximum Median Minimum

2001 to 2011

BURBP02

48.4

47.0

44.9

21

BURBP03

47.9

47.1

45.0

9

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BURBP04

62.4

53.6

49.7

4

BURBP05

85.0

81.2

72.9

19

WEIBP01

66.6

63.9

57.9

37

WEIBP02

125.9

104.1

84.6

35

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Summary of constraints on the use of the BSTEM approach to predict ongoing channel erosion. Appendix G

•

• •

BSTEM is highly sensitive to the sedimentology of a particular bank - and therefore it cannot be extrapolated from one site to another without extensive field data collection to characterize the bank stratigraphy at all sites where the model is to be applied. Attempts to extrapolate without such stratigraphic detail, introduces unacceptable uncertainty into the model. Hence BSTEM can only be used at the sites scale where the bank sedimentology has been characterized in considerable detail. Even in extremely uniform sites, the model is highly sensitive to stratigraphic variability (Parker et. al., 2008) Methods that have been used to extrapolate predicted erosion at a single site across a whole reach (i.e. the percent reach failing methodology) are unreliable and introduce unacceptable error into the analysis.

Figure 87 Analysis of bank erosion according to the “percent reach fail” methodology (Simon et al 2003) in the O’Connell River

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predicted length %rf method (km)

2.5 2 1.5 y = 0.26x + 0.6811 R² = 0.203

1 0.5 0 0

0.5

1

1.5

2

2.5

3

3.5

4

observed length of eroding bank/2km reach (km) Figure 88 Comparison between Observed bank erosion length on the O'Connell R using LiDAR differencing (2010-13) and % reach fail analysis for 2km reach segments as per the method proposed by Simon et al., 2003, 2011

•

•

BSTEM is designed to be used in channels with relatively simple stratigraphy and geomorphology – given that it can only accept 5 distinct horizontally bedded sedimentary layers and very simply bank profile geometry. Such conditions are rarely found in the upper Brisbane (or indeed across Queensland), which is dominated by complex macro-channels which have a sequence of inset geomorphic units (e.g. benches, inset floodplains, terraces) which are not genetically related to one another, and have distinctly different (non-horizontal) sedimentary units. Hence the model is too limited to be of practical use in these circumstances. The model typically only works for incised rivers with extremely uniform floodplain stratigraphy. Given that most channels in the upper Brisbane River, and most Queensland Rivers, have a complex 3 dimensional sedimentary architecture (e.g. Figure 6), the over simplification of the BSTEM model stratigraphy renders it extremely unreliable in this environment.

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Upper Brisbane Hec-RAS modelling and correlation analysis between erosion and key hydraulic and geomorphic drivers Appendix H

Key figures from McMahon, J., Olley, J, Brooks, A., Curwen, G. (n Prep). The role of in-channel vegetation in reducing channel erosion during large floods. Target submission ESPL.

12.1.1.1 Flood Extent A digitised flood extent was created by Somerset Regional Council by field survey and GPS mapping of flood debris immediately after the 2011 flood. This map of observed peak flood extent has been used for calibrating a 1D Hec-Ras model of the Upper Brisbane River, the output of which provides a key input for an analysis of the primary drivers of channel erosion in the upper Brisbane River.

Figure 89 Map showing the maximum extent of the 2011 flood (shown in red) in the vicinity of the study area (yellow markers).

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Figure 90 Geomorphic zones are defined by spatial variation in 2011 flood width and proportion of vegetation higher than 5 metres canopy cover

bed and inchannel bars

300

benches

terraces

inset floodplains

200

100

0

0.1-0.2 vegetation cover

300

Erosion (m3)

200

100

0

0.2-0.3 vegetation cover

300

200

100

0

>0.3 vegetation cover

300

200

100

0 0

1000 2000 3000 4000 5000 6000 0

1000 2000 3000 4000 5000 6000 0

1000 2000 3000 4000 5000 6000

0

1000 2000 3000 4000 5000 6000

Unit Stream power Figure 91 Relationship between erosion per unit streampower (thresholded at 10 Wm-2) and proportion of large woody vegetation (higher than 5 metres) canopy cover proportion

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Appendix I Historical photos from Leslie Burrows

10/6/1995

1999

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17/2/2001

17/2/2001

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Dec 2010

2011 flood

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April 2011

April 2011

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