modelling pollutant load changes due to improved management … · 2018-11-21 · in this report...

Modelling pollutant load changes due to

improved management practices in the

Great Barrier Reef catchments: updated

methodology and results

Technical Report for Reef Report Card

2015

Prepared by Contact

Gillian McCloskey and David Waters David Waters 07 4529 1395

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To reference this volume

McCloskey, G.L., Waters. D., Baheerathan, R., Darr, S., Dougall, C., Ellis, R., Fentie, B., Hateley, L. 2017.

Modelling pollutant load changes due to improved management practices in the Great Barrier Reef catchments:

updated methodology and results – Technical Report for Reef Report Cards 2015, Queensland Department of

Natural Resources and Mines, Brisbane, Queensland.

Disclaimer

The information contained herein is subject to change without notice. The Queensland Government shall not be

liable for technical or other errors or omissions contained herein. The reader/user accepts all risks and responsibility

for losses, damages, costs and other consequences resulting directly or indirectly from using this information.

Acknowledgments:

This project is part of the Queensland and Australian Government’s Paddock to Reef program. The project is

funded by the Queensland Department of Natural Resources and Mines ‘Queensland Regional Natural Resource

Management Investment Program 2013–2018’ with support from the Department of Science, Information

Technology, Innovation and the Arts (DSITIA).

Technical support was provided by a number of organisations and individuals, including Environmental Monitoring

and Assessment Science (DSITIA), Queensland Hydrology (DSITIA) and the Commonwealth Scientific and

Industrial Research Organisation (CSIRO). We would also like to thank the reviewers for providing feedback on

this report.

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Source Catchments modelling – Updated methodology and results

3

Executive Summary

In this report the updated methodology and latest Great Barrier Reef (GBR) Report Card load reduction

results are presented. Minor methodological changes have occurred since the release of the previous

report card (Report Card 2014 (RC2014)). These include, recalibration of hydrology in a number of

regions to better represent the baseflow portion of total flow, the implementation of instream deposition

and remobilisation in three regions, and minor updates to adjustable parameters.

The recalibration of hydrology to align baseflow proportions with calculated values produced more

typical ranges of baseflow particularly in the larger, drier, grazing catchments (Burdekin, Fitzroy and

Cape York (CY)). In these regions many gauges were recalibrated and commonly the baseflow portion

decreased from previous calibration proportions. The recalibration had negligible impact on average

annual flow across the GBR with a small increase of 1% to 73,500,000 ML/yr.

Instream deposition and remobilisation functionality were implemented in the Wet Tropics (WT), Mackay

Whitsunday (MW) and Burnett Mary (BM) regions for RC2015.

The average annual baseline loads for RC2015 are presented in Table 1. Generally the updated

average annual baseline loads are higher than the RC2014 loads, except for the photosynthesis II toxic

equivalent (PSII TE) pesticides which are approximately 10% lower than RC2014. The minor increase

in loads from the previous Report Card are in part due to the recalibration of hydrology resulting in a

minor increase in flow and therefore constituent loads. The decrease in PSII TE load from RC2014 is

likely a product of a shift to alternative products.

Table 1 Baseline GBR constituent loads

TSS (kt/yr)

TN (t/yr)

DIN (t/yr)

DON (t/yr)

PN (t/yr)

TP (t/yr)

DIP (t/yr)

DOP (t/yr)

PP (t/yr)

PSII TE (kg/yr)

9,925 55,083 12,028 18,303 24,753 13,418 2,143 1,074 10,201 6,758

The progress towards Reef Plan targets is presented in Figure 1. ‘Moderate’ progress has been made

towards the fine sediment load reduction target of 20%. The cumulative reduction across the GBR is

12.3%, with the Burdekin region at 17.2%. ‘Very good’ progress is reported for Particulate Phosphorus

(PP) reduction, which is well over half way towards the target at 14.7%. The WT is the only region to

have reached the target having achieved a 20.7% reduction. For Particulate Nitrogen (PN), the

cumulative reduction is 11.6%, which is classified as ‘moderate’ progress, with the Burdekin region

showing the greatest progress with a reported 15.3% reduction thus far. Progress towards the DIN

target of 50% is classified as ‘very poor’ as it stands at 18.1% for RC2015. Only the Burnett Mary region

is making ‘good’ progress at 31.5%. Reductions in PSII TE load are rated as ‘moderate’ with a reduction

of 33.7% across the GBR. Progress has been most improved in the MW region with a 44% reduction

thus far.


4

In summary, progress is being made for all constituents towards reaching the Reef Plan water quality

targets. There is scope for greater reductions to be achieved managing DIN in the WT where progress

is at 14.7% or ‘very poor’, and for sediment in the Fitzroy and BM regions, with both regions reporting

‘very poor’ progress at 5.5% and 3% respectively. The ratings of performance for load reductions are

available at www.reefplan.qld.gov.au. It should be noted that the scoring system has changed since

RC2014.

Figure 1 Progress towards Reef Plan targets till 2015 for the whole of GBR

http://www.reefplan.qld.gov.au/


5

Contents

Executive Summary ................................................................................................................................ 3

List of Tables ......................................................................................................................................... 11

List of Figures ........................................................................................................................................ 15

Acronyms .............................................................................................................................................. 18

Definitions of sources and sinks ........................................................................................................... 19

Modelling assumptions .......................................................................................................................... 20

Introduction............................................................................................................................................ 22

Model Improvement Cycle ................................................................................................................. 23

Integrating modelling and monitoring ............................................................................................. 24

Application of modelling outputs .................................................................................................... 25

Quality assurance and peer review ............................................................................................... 25

Methods................................................................................................................................................. 27

GBR methodology updates ............................................................................................................... 28

Hydrology ....................................................................................................................................... 28

Hillslope generation model ............................................................................................................ 29

Sugarcane constituent generation ................................................................................................. 30

HowLeaky cropping constituent generation ................................................................................... 30

In-stream models ........................................................................................................................... 30

Predevelopment Model .................................................................................................................. 31

Constituent load validation ................................................................................................................ 31

Performance ratings ....................................................................................................................... 31

Scenario modelling – All A and All B management ........................................................................... 32

Baseline Model .............................................................................................................................. 32

All B Scenario ................................................................................................................................ 33

All A Scenario ................................................................................................................................ 33

Cape York .......................................................................................................................................... 35

Hydrology ....................................................................................................................................... 35

Hillslope sediment, nutrient and herbicide generation ................................................................... 35

Gully sediment and nutrient generation models ............................................................................ 35



6


Other land uses: Event Mean Concentration (EMC)/Dry Weather Concentration (DWC) ............ 35

In-stream models ........................................................................................................................... 35

Predevelopment model .................................................................................................................. 35

Wet Tropics ....................................................................................................................................... 36

Hydrology ....................................................................................................................................... 36






In-stream models ........................................................................................................................... 37


Burdekin............................................................................................................................................. 38

Hydrology ....................................................................................................................................... 38






In-stream models ........................................................................................................................... 38


Mackay Whitsunday .......................................................................................................................... 39

Hydrology ....................................................................................................................................... 39






In-stream models ........................................................................................................................... 39



7

Fitzroy ................................................................................................................................................ 40

Hydrology ....................................................................................................................................... 40






In-stream models ........................................................................................................................... 41


Burnett Mary ...................................................................................................................................... 43

Hydrology ....................................................................................................................................... 43






In-stream models ........................................................................................................................... 43


Results .................................................................................................................................................. 45

Hydrology and regional discharge comparison ................................................................................. 45

Modelled loads .................................................................................................................................. 45

Contribution by land use ................................................................................................................ 49

Progress towards Reef Plan 2013 targets ......................................................................................... 51

Scenario modelling – All A and All B management ........................................................................... 51

Cape York .......................................................................................................................................... 55

Hydrology calibration performance ................................................................................................ 55

Modelled Loads .............................................................................................................................. 55

Constituent load validation ............................................................................................................. 56


Sources and sinks .......................................................................................................................... 59

Progress towards Reef Plan 2013 targets ..................................................................................... 60


8

Wet Tropics ....................................................................................................................................... 61


Modelled Loads .............................................................................................................................. 62





Burdekin............................................................................................................................................. 72


Modelled Loads .............................................................................................................................. 72





Mackay Whitsunday .......................................................................................................................... 81


Modelled Loads .............................................................................................................................. 81

Constituent Load Validation ........................................................................................................... 84




Fitzroy ................................................................................................................................................ 93


Modelled Loads .............................................................................................................................. 94



Sources and sinks ........................................................................................................................ 100

Progress towards Reef Plan 2013 targets ................................................................................... 101

Burnett Mary .................................................................................................................................... 104

Hydrology calibration performance .............................................................................................. 104

Modelled Loads ............................................................................................................................ 104


9

Constituent load validation ........................................................................................................... 106

Contribution by land use .............................................................................................................. 108

Sources and sinks ........................................................................................................................ 109

Progress towards Reef Plan 2013 targets ................................................................................... 110

Discussion ........................................................................................................................................... 113

Particulate nutrients ......................................................................................................................... 113

Progress towards targets ................................................................................................................. 114

Scenario modelling – All A and All B management ......................................................................... 115

Cape York ........................................................................................................................................ 116

Hydrology ..................................................................................................................................... 116

Fine sediment generation, sources and sinks ............................................................................. 116

Particulate nutrients ..................................................................................................................... 116

Progress towards targets ............................................................................................................. 117

Wet Tropics ..................................................................................................................................... 118

Hydrology ..................................................................................................................................... 118

Fine sediment, particulates sources and sinks ............................................................................ 118

Nutrients ....................................................................................................................................... 119

Herbicides .................................................................................................................................... 120


Burdekin........................................................................................................................................... 121

Hydrology ..................................................................................................................................... 121

Fine sediment and particulates generation, sources and sinks ................................................... 121

Sugarcane constituent generation ............................................................................................... 122

Herbicides .................................................................................................................................... 122

Targeting land management for Reef WQ protection .................................................................. 122


Future model improvement .......................................................................................................... 123

Mackay Whitsunday ........................................................................................................................ 124

Hydrology ..................................................................................................................................... 124

Constituent generation and validation ......................................................................................... 125

Constituent sources and sinks ..................................................................................................... 126


10


Fitzroy .............................................................................................................................................. 128

Hydrology ..................................................................................................................................... 128

Fine sediment and particulates generation, sources and sinks ................................................... 128

Event Mean Concentration (EMC)/Dry Weather Concentration (DWC) ...................................... 130

Particulate Nutrients ..................................................................................................................... 131


Burnett Mary .................................................................................................................................... 132

Hydrology ..................................................................................................................................... 132

Fine sediment and particulate nutrient generation, sources and sinks ....................................... 132

Sugarcane dissolved nutrients and PSII loads ............................................................................ 133


Conclusion........................................................................................................................................... 135

References .......................................................................................................................................... 136

Appendix 1 – Hydrology calibration results ......................................................................................... 140

Cape York ........................................................................................................................................ 140

Wet Tropics ..................................................................................................................................... 141

Burdekin........................................................................................................................................... 142

Mackay Whitsunday ........................................................................................................................ 144

Fitzroy .............................................................................................................................................. 148

Burnett Mary .................................................................................................................................... 151

Appendix 2 – Dynamic SedNet global parameters and global requirements ..................................... 153

Grazing constituent generation ....................................................................................................... 153

In-stream models ............................................................................................................................. 153

Sugarcane and cropping constituent generation ............................................................................. 154

Other land uses (EMC/DWC values) ............................................................................................... 154

Appendix 3 – Report Card 2015 modelling results ............................................................................. 159

Appendix 4 – Model validation ............................................................................................................ 177

Wet Tropics model validation .......................................................................................................... 177

Burdekin model validation ............................................................................................................... 181

Mackay Whitsunday model validation ............................................................................................. 183


11

List of Tables

Table 1 Baseline GBR constituent loads ................................................................................................ 3

Table 2 Summary of catchment modelling cycle .................................................................................. 24

Table 3 Model updates undertaken for Report Card 2015 ................................................................... 27

Table 4 Number of gauges that underwent recalibration for baseflow ................................................. 29

Table 5 Performance ratings for recommended statistics for a monthly time-step (from (Moriasi et al.

2007), Moriasi et al. (2015)) .................................................................................................................. 32

Table 6 Summary of changes to sugarcane parameters in the WT ..................................................... 37

Table 7 Summary of changes to sugarcane parameters in the BM ..................................................... 43

Table 8 Total baseline loads for the GBR region .................................................................................. 46

Table 9 PSII pesticide load, PSII Toxic Equivalent (TE) load for the GBR region ................................ 46

Table 10 Regional contribution of each constituent as a percentage of the GBR total baseline load .. 47

Table 11 Baseflow proportions (%) for RC2014, RC2015 and calculated for seven CY gauges ......... 55

Table 12 Contribution from CY basins to the total CY baseline load .................................................... 56

Table 13 Moriasi et al. (2007), Moriasi et al. (2015) annual statistical measures of performance for each

constituent at the Kalpowar Gauge, Cape York .................................................................................... 58

Table 14 Sources and sinks of fine sediment in CY and the Normanby Basin (as percent of supply total)

.............................................................................................................................................................. 59

Table 15 CY grazing management practice proportions and change summary; values are percent of

total area ............................................................................................................................................... 60

Table 16 WT baseflow proportions for the current and previous Report Card ..................................... 61

Table 17 Contribution from WT basins to the total baseline load ......................................................... 62

Table 18 Moriasi et al. (2015) annual performance ratings at the North Johnstone River gauging station

compared to GBRCLMP annual loads .................................................................................................. 66

Table 19 Moriasi et al. (2015) monthly performance rating at the North Johnstone River gauging station

.............................................................................................................................................................. 66

Table 20 Moriasi et al. (2015) overall monthly performance rating for each constituent for the five gauge

sites, Wet Tropics .................................................................................................................................. 67

Table 21 TSS, DIN and PSII sugarcane areal rates by basin for the WT ............................................. 69

Table 22 Wet Tropics sugarcane management practice change summary; values are percent of total

.............................................................................................................................................................. 71

Table 23 Wet Tropics banana management practice change summary; values are percent of total... 71

Table 24 Burdekin baseflow proportions for six gauges the current and previous Report Card .......... 72

Table 25 Contribution from Burdekin basins to the total Burdekin baseline load ................................. 73

Table 26 Moriasi et al. (2015) overall annual performance rating for each constituent at 120001A

Burdekin River at Home Hill (120006B Burdekin River at Clare), n = 8 years for flow and n = 8 years for

WQ) ....................................................................................................................................................... 75


12

Table 27 Burdekin grazing management change; values are percent of total ..................................... 78

Table 28 Burdekin sugarcane management practice change summary; values are percent of total ... 79

Table 29 Burdekin grains management change; values are percent of total ....................................... 80

Table 30 MW baseflow proportions for the current and previous Report Card .................................... 81

Table 31 Contribution from MW basins to the total MW baseline load ................................................. 82

Table 32 Moriasi et al. (2007) annual statistics for GBCLMP versus modelled data for the Pioneer River

at Dumbleton Pump Station (125013A) n = 8 year for flow data, n = 8 years for WQ data .................. 85

Table 33 Moriasi et al. (2007) annual statistics for GBCLMP versus modelled data for Sandy Creek at

Homebush (126001A) n = 5 year for flow data, n = 5 years for WQ data ............................................ 86

Table 34 Modelled Fine Sediment export in the O’Connell River against LiDAR estimates for fine

sediment export between 2010 and 2014 ............................................................................................. 89

Table 35 MW grazing management practice change summary; all values are in percent ................... 91

Table 36 MW sugarcane management practice change summary; all values are in percent .............. 92

Table 37 Modelled flow differences for RC2014 and RC2015 compared to observed flows ............... 93

Table 38 Base flow proportions (%) for RC2014 and RC2015 compared to observed estimates ....... 94

Table 39 Contribution from Fitzroy basins to the total Fitzroy baseline load ........................................ 95

Table 40 RC2015 (1300000 Fitzroy River at Rockhampton, n = 7 years for flow and n = 7 years for WQ)

.............................................................................................................................................................. 98

Table 41 RC2015 (130504B Comet River at Comet Weir, n = 5 years for flow and n = 5 years for WQ)

.............................................................................................................................................................. 99

Table 42 Fitzroy Grazing Management Change ................................................................................. 102

Table 43 Fitzroy Grains Management Change (% change) ............................................................... 103

Table 44 Burnett Mary baseflow proportions for six gauges the current and previous Report Card .. 104

Table 45 Contribution from Burnett Mary basins to the total Burnett Mary baseline load .................. 105

Table 46 Moriasi et al. (2015) overall annual performance rating for RC2015 (136014A Burnett River at

Ben Anderson Barrage (HW), n = 8 years for flow and n = 5 years for WQ) ...................................... 108

Table 47 BM sugarcane management practice change summary; all values are in percent ............. 111

Table 48 BM grazing management Change; all values are percent of total ....................................... 112

Table 49 Cape York hydrology calibration results (1970–2014) ......................................................... 140

Table 50 Wet Tropics hydrology calibration results (1970–2014) ....................................................... 141

Table 51 Burdekin hydrology calibration results (1970–2014) ............................................................ 142

Table 52 Mackay Whitsunday hydrology calibration results (1970–2014) ......................................... 144

Table 53 Fitzroy hydrology calibration results (1970–2014) ............................................................... 148

Table 54 Baseflow proportions (%) for the current and previous Report Card for all gauges in the Fitzroy

Basin ................................................................................................................................................... 149

Table 55 Burnett Mary hydrology calibration results (1970–2014) ..................................................... 151

Table 56 Baseflow proportions (%) for the current and previous Report Card for all gauges in the Burnett

Mary Basin .......................................................................................................................................... 152

Table 57 Hillslope erosion parameters ............................................................................................... 153

Table 58 Gully erosion parameters ..................................................................................................... 153


13

Table 59 Particulate nutrient generation parameter values ................................................................ 153

Table 60 Parameter values for in-stream models in RC2015 baseline models .................................. 153

Table 61 A summary of changes for RC2015 changes for cropping and sugarcane (Changes from 2014

Report Card) ....................................................................................................................................... 154

Table 62 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Cape York ............... 155

Table 63 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Wet Tropics ............. 155

Table 64 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Burdekin .................. 156

Table 65 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Mackay Whitsunday 156

Table 66 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Fitzroy ..................... 157

Table 67 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Burnett Mary ........... 157

Table 68 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for predevelopment model

............................................................................................................................................................ 158

Table 69 Constituent loads for predevelopment, baseline and 2015 Report Card change model runs for

the CY NRM Region ............................................................................................................................ 159

Table 70 Sources and sinks of TSS for each basin in CY (kt/yr) ........................................................ 162

Table 71 Constituent loads for predevelopment, baseline and RC2015 change model runs for the WT

NRM Region ........................................................................................................................................ 162

Table 72 Sources and sinks of each constituent in the Wet Tropics .................................................. 165


the Burdekin NRM Region .................................................................................................................. 165

Table 74 Sources and sinks of each constituent in the Burdekin NRM Region ................................. 168


the Mackay Whitsunday NRM Region ................................................................................................ 168

Table 76 Sources and sinks of TSS for each basin MW (kt/yr) .......................................................... 170

Table 77 Constituent loads for predevelopment, baseline and RC2015 change model runs for the

Fitzroy NRM Region ............................................................................................................................ 171

Table 78 Sources and sinks of TSS for each basin in the Fitzroy NRM Region ................................ 173

Table 79 Constituent loads for predevelopment, baseline and RC2015 change model runs for the

Burnett Mary NRM Region .................................................................................................................. 174

Table 80 Sources and sinks of each constituent in the Burnett Mary NRM Region ........................... 176

Table 81 Fine sediment and particulate nutrient comparison with GBRCLMP monitored data for five WT

sites ..................................................................................................................................................... 177

Table 82 Dissolved nitrogen species comparison with GBRCLMP monitored data for five WT sites 177

Table 83 Dissolved phosphorus comparison with GBRCLMP monitored data for five WT sites ....... 177

Table 84 Barron River model validation compared to GBRCLMP annual loads ................................ 177

Table 85 Barron River model validation compared to Joo monthly loads .......................................... 178

Table 86 North Johnstone River model validation compared to GBRCLMP annual loads ................ 178

Table 87 North Johnstone River model validation compared to Joo monthly loads ........................... 178

Table 88 South Johnstone River model validation compared to GBRCLMP annual loads ................ 179

Table 89 South Johnstone River model validation compared to Joo monthly loads .......................... 179


14

Table 90 Tully River model validation compared to GBRCLMP annual loads ................................... 179

Table 91 Tully River model validation compared to Joo monthly loads .............................................. 180

Table 92 Herbert River model validation compared to GBRCLMP annual loads ............................... 180

Table 93 Herbert River model validation compared to Joo monthly loads ......................................... 180

Table 94 RC2015 (120302B Cape River at Taemas, n = 7 years for flow and n = 7 years for WQ) .. 181

Table 95 RC2015 (119101A Barratta Creek at Northcote, n = 5 years for flow and n = 5 years for WQ)

............................................................................................................................................................ 181

Table 96 RC2015 (120002C Burdekin River at Sellheim, n = 8 years for flow and n = 7 years for WQ)

............................................................................................................................................................ 182

Table 97 RC2015 (120301B Belyando River at Gregory Development Road, n = 7 years for flow and n

= 6 years for WQ) ................................................................................................................................ 182

Table 98 RC2015 (120001A Burdekin River at Home Hill (120006B Burdekin River at Clare), n = 8

years for flow and n = 8 years for WQ) ............................................................................................... 182

Table 99 RC2015 (120310A Suttor River at Bowen Development Road, n = 8 years for flow and n = 8

years for WQ) ...................................................................................................................................... 183


15

List of Figures

Figure 1 Progress towards Reef Plan targets till 2015 for the whole of GBR ......................................... 4

Figure 2 Model build process ................................................................................................................ 26

Figure 3 Catchment area vs. stream width used to determine streambank erosion parameters ......... 41

Figure 4 Catchment area vs. bank height used to determine streambank erosion parameters ........... 42

Figure 5 Average annual modelled discharge for the GBR RC2015 (1986–2014) .............................. 45

Figure 6 Predevelopment and anthropogenic baseline loads for all 35 basins in the GBR for TSS .... 48

Figure 7 Predevelopment and anthropogenic baseline loads for all 35 basins in the GBR for DIN ..... 49

Figure 8 Baseline GBR TSS load contribution by land use .................................................................. 50

Figure 9 Baseline GBR DIN load contribution by land use ................................................................... 50

Figure 10 Progress towards Reef Plan targets till 2015 for the whole of GBR ..................................... 51

Figure 11 Estimated TSS reduction under All A and All B management practice scenarios ............... 52

Figure 12 Estimated DIN reduction under All A and All B management practice scenarios ................ 53

Figure 13 Estimated PSII TE reduction under All A and All B management practice scenarios .......... 53

Figure 14 Estimated PN reduction under All A and All B management practice scenarios ................. 54

Figure 15 Estimated PP reduction under All A and All B management practice scenarios .................. 54

Figure 16 Average annual modelled versus measured load comparison for 2006-2014 period,

Normanby River Gauge at the Kalpowar Crossing (105017A) ............................................................. 57

Figure 17 Contribution to fine sediment, particulate nutrient and DIN export by land use for the CY

region .................................................................................................................................................... 58

Figure 18 Fine sediment source sink budget diagram for the Normanby Basin ................................... 59

Figure 19 TSS (kt/yr) loads for the WT basins, predevelopment and anthropogenic baseline

contributions .......................................................................................................................................... 63

Figure 20 DIN (t/yr) loads for the WT basins, predevelopment and anthropogenic baseline contributions

.............................................................................................................................................................. 63

Figure 21 Fine sediment comparison between GBRCLMP measured loads and Source modelled loads

for five sites in the WT ........................................................................................................................... 64

Figure 22 DIN comparison between GBRCLMP measured loads and Source modelled loads for five

sites in the WT ...................................................................................................................................... 65

Figure 23 PSII comparison between GBRCLMP measured loads and Source modelled loads for five

sites in the WT ...................................................................................................................................... 65

Figure 24 Contribution to fine sediment, particulate nutrient and DIN export by land use for the WT

region .................................................................................................................................................... 67

Figure 25 WT DIN load and areal rate per land use ............................................................................. 68

Figure 26 Fine sediment source sink budget diagram for WT .............................................................. 70

Figure 27 TSS (kt/yr) loads for the BU basins, highlighting the predevelopment and anthropogenic

baseline contributions ........................................................................................................................... 74

Figure 28 DIN (kt/yr) loads for the BU basins, highlighting the predevelopment and anthropogenic



16

Figure 29 Contribution to fine sediment, particulate nutrient and DIN export by land use for the BU

region .................................................................................................................................................... 76

Figure 30 DIN export load and areal rate for each land use in the BU ................................................. 76

Figure 31 Fine sediment source sink budget for the Burdekin ............................................................. 77

Figure 32 TSS (kt/yr) loads for the MW basins, highlighting the predevelopment and anthropogenic


Figure 33 DIN (kt/yr) loads for the MW basins, highlighting the predevelopment and anthropogenic


Figure 34 Mean Annuals Modelled VS. GBRCLMP in the Pioneer River at Dumbleton Pump Station 84

Figure 35 Mean Annuals Modelled VS. GBRCLMP in Sandy Creek at Homebush ............................. 84

Figure 36 Long-term annual TSS Loads Modelled VS. FRCE in the Pioneer River at Dumbleton Pump

Station ................................................................................................................................................... 86

Figure 37 Long-term annual DIN Loads Modelled VS. FRCE in the Pioneer River at Dumbleton Pump

Station ................................................................................................................................................... 87

Figure 38 Mean annual Source Catchments modelled and P2R at Multifarm during low and medium

flow events ............................................................................................................................................ 88

Figure 39 Constituent contribution (%) by land use for four constituents ............................................. 89

Figure 40 DIN export load and areal rate for each land use in the MW ............................................... 90

Figure 41 Fine sediment source sink budget diagram for MW ............................................................. 91

Figure 42 Fine sediment average annual modelled (Source Catchments) versus measured load

comparison for 2006-2014* period, for the four monitoring sites in the Fitzroy region. *Data for Theresa

Creek is only for two years, and for the Dawson River three years. ..................................................... 96

Figure 43 Particulate nitrogen average annual modelled versus measured load comparison for 2006-

2014* period, for the four monitoring sites in the Fitzroy region. *Data for Theresa Creek is only for two

years, and for the Dawson River three years........................................................................................ 96

Figure 44 Particulate phosphorus average annual modelled versus measured load comparison for

2006-2014* period, for the four monitoring sites in the Fitzroy region. *Data for Theresa Creek is only

for two years, and for the Dawson River three years. ........................................................................... 97

Figure 45 Constituent contribution (%) by land use for four constituents ........................................... 100

Figure 46 Fine sediment source sink budget for the Fitzroy ............................................................... 101

Figure 47 TSS (kt/yr) loads for the BM basins, highlighting the predevelopment and anthropogenic

baseline contributions ......................................................................................................................... 106

Figure 48 DIN (t/yr) loads for the BM basins, highlighting the predevelopment and anthropogenic

baseline contributions ......................................................................................................................... 106

Figure 49 Comparison of monitored and modelled mean annual flow and constituent loads at Burnett

EOS site (136014A) ............................................................................................................................ 107

Figure 50 Contribution to fine sediment, particulate nutrient and DIN export by land use for the BM

region .................................................................................................................................................. 108

Figure 51 DIN export load and areal rate for each land use in the BM .............................................. 109

Figure 52 Fine sediment source sink budget for the Burnett Basin .................................................... 110


17

Figure 53 TSS, PN and PP trapping efficiencies (%) of storages....................................................... 110

Figure 54 Time Series, Flow Duration (log scale), and Scatter plot Comparisons of Modelled VS.

Measured Daily Flows in Lethe Brook Creek at Hadlow Road (122012A) ......................................... 144


Measured Daily Flows in the O’Connell River at Staffords Crossing (124001B) ................................ 145


Measured Daily Flows in St.Helens Creek at Calen (124002A) ......................................................... 145

Figure 57 Time Series, Flow Duration (log scale), and Scatter Plot Comparisons of Modelled VS.

Measured Daily Flows in the Andromache River at Jochheims (124003A) ........................................ 145


Measured Daily Flows in the Pioneer River at Pleystowe Recorder (125001B) ................................. 146


Measured Daily Flows in the Pioneer River at Sarichs (125002C) ..................................................... 146


Measured Daily Flows in Cattle Creek at Gargett (125004B) ............................................................. 146


Measured Daily Flows in Sandy Creek at Homebush (126001A)....................................................... 147


Measured Daily Flows in Plane Creek at Sarina (126002A) ............................................................... 147


Measured Daily Flows in Carmila Creek at Carmila (126003A) ......................................................... 147

Figure 64 Long-term Annual PN Loads Modelled against FRCE and GBRCLMP Estimates in the

Pioneer River at Dumbleton Pump Station ......................................................................................... 183

Figure 65 Long-term Annual PP Loads Modelled against FRCE and GBRCLMP Estimates in the


Figure 66 Long-term Annual DIP Loads Modelled against FRCE and GBRCLMP Estimates in the


Figure 67 Long-term Annual DON Loads Modelled against FRCE and GBRCLMP Estimates in the


Figure 68 Long-term Annual DOP Loads Modelled against FRCE and GBRCLMP Estimates in the



18

Acronyms

Acronym Description

BM Burnett Mary

BU Burdekin

CY Cape York

DIN Dissolved inorganic nitrogen

DIP Dissolved inorganic phosphorus

DNRM Department of Natural Resources and Mines

DON Dissolved organic nitrogen

DOP Dissolved organic phosphorus

DR Delivery ratio e.g. HSDR hillslope delivery ratio

DS

Dynamic SedNet—a Source Catchments ‘plug-in’ developed by DNRM/DSITIA, which provides a suite of constituent generation and in-stream processing models that simulate the processes represented in the SedNet and ANNEX catchment scale water quality model at a finer temporal resolution than the original average annual SedNet model

DSITI Department of Science, Information Technology, and Innovation

DWC Dry weather concentration—a fixed constituent concentration to base or slowflow generated from a functional unit to calculate total constituent load

EMC Event mean concentration—a fixed constituent concentration to quickflow generated from a functional unit to calculate total constituent load

EOS End-of-system

ERS Environment Resource Sciences

FRCE Flow Range Concentration Estimator—a modified Beale ratio method used to calculate average annual loads from monitored data

FU Functional Unit

FZ Fitzroy

GBR Great Barrier Reef

GBRCLMP Great Barrier Reef Catchment Event Monitoring Program (supersedes GBRI5)

HowLeaky Water balance and crop growth model based on PERFECT

LH Lyne-Hollick baseflow

MW Mackay Whitsunday

NRM Natural Resource Management

NSE Nash Sutcliffe Coefficient of Efficiency

P2R Paddock to Reef Integrated Monitoring, Modelling and Reporting Program

PSII herbicides Photosystem II herbicides—ametryn, atrazine, diuron, hexazinone and tebuthiuron

PSII TE PSII herbicide Toxic Equivalents—the total load of PSII herbicides weighted for their toxicity

RC2–RC5 Report Card 2–Report Card 5—published outputs of Reef Plan/Paddock to Reef

Reef Rescue

An ongoing and key component of Caring for our Country. Reef Rescue represents a coordinated approach to environmental management in Australia and is the single largest commitment ever made to address the threats of declining water quality and climate change to the Great Barrier Reef World Heritage Area

RUSLE Revised Universal Soil Loss Equation


19

Acronym Description

SedNet Catchment model that constructs sediment and nutrient (phosphorus and nitrogen) budgets for regional scale river networks (3,000–1,000,000 km2) to identify patterns in the material fluxes

TSS Total suspended sediment

TN Total nitrogen

TP Total phosphorus

WT Wet Tropics

Definitions of sources and sinks

Supply Definitions Particulate Hillslope: Source of TSS, PP and PN supplied to the stream network by surface erosion processes (surface and rill erosion). Gully: TSS, PP and PN supplied to the stream network by gully erosion processes). Streambank: TSS, PP and PN supplied to the stream network by streambank erosion processes. Channel remobilisation: TSS, PP and PN supplied by channel remobilisation processes. Dissolved Diffuse dissolved: Source of dissolved constituents supplied to the stream network from all land uses. Seepage: Dissolved constituents supplied to the stream network from sugarcane via subsurface loss pathways. Point source: Dissolved constituents supplied to the stream network from point sources; in this case Sewerage Treatment Plants (STP) Loss Definitions Storage deposition: TSS, PP and PN losses via the process of particle settling and resultant deposition in storages. Floodplain deposition: TSS, PP and PN losses via the process of particle settling and resultant deposition in on floodplains. Storage decay: Dissolved constituents lost via the process of decay processes. Extraction: Dissolved and particulate constituents lost via extraction, (e.g. pumped losses for irrigation, stock or domestic water supplies) from the stream network. Stream deposition: Particulate constituents lost to the stream network via instream and resultant deposition.


20

Modelling assumptions

The key modelling assumptions to note are:

It is important not to lose sight of the main objective of the modelling, to report the water quality

benefits as a result of management changes. Therefore the relativity between scenarios is

important not the absolute load. However the goal of the modelling program is for continual

improvement of modelled load predictions. This approach will continue.

Loads reported for each scenario are the modelled average annual load for the specified model

run period (July 1986 – June 2014).

Land use areas in the model are static over the model run period and were based on the latest

available QLUMP data in each NRM region.

The predevelopment land use scenario includes all water storages, weirs and water extractions

are represented as in the baseline model. There is no change from the baseline scenario

hydrology. This approach was recommended by the Independent Reef Science Panel (ISP), to

isolate changes to water quality which are due solely to a change in land management

practices. Therefore it is important to recognise that the term predevelopment in this report

does not refer to a true representation of a pre-European scenario. The scenario is more

reflective of a pre agricultural development scenario

Paddock model runs used in the catchment models represent ‘typical’ management practices

in each management class and do not reflect the actual array of management practices being

applied on a particular area of land.

This applies to application rates of herbicides and fertilisers used to in the paddock models

which were derived through consultation with relevant local industry groups and technical

experts in each region.

Distribution of baseline land management practices is not able to be captured (in its entirety) in

a spatial format. Regional and sub-regional estimates of this distribution have been made, and

these estimates are used as required in the preparation of the data inputs for the baseline

scenarios used for each Report Card. The distribution will vary between reporting years, to

accommodate the reported land management improvements that get supplied in spatial format.

All land management improvements to be represented in the relevant scenarios are supplied

in a spatial format. This data is used to inform the data preparation for the necessary scenarios.

In situations where the spatial data does not coincide with a representative modelling unit, the

land management improvement is reallocated to the nearest suitable location for grazing and

riparian investment. For cropping and cane management improvements, the area of

improvement that is not coincident with a relevant modelling unit is applied evenly throughout

the relevant region or sub-region for that industry.

Water quality improvements for the horticulture, dairy, and cotton industries are currently not

modelled due to a lack of management practice adoption data and limited experimental data

on which to base load reductions, and also the small area these industries occupy.


21

Modelling of the banana industry and the associated water quality improvements have been

included in the Wet Tropics and Cape York models in Report Card 2014.

Dissolved inorganic nitrogen (DIN) load reductions are modelled for sugarcane systems with

APSIM. DIN load reductions are not modelled in grains system as no DIN export are provided

from these simulations and few if any investments are made.

The number of paddock model simulations available to represent the vast array of possible

management practices within each region has increased markedly from the simple ‘ABCD’

approach used prior to RC2014. The increase in available simulations allows many more

management improvements to be represented, however these are often representing changes

to processes that yield a small benefit in terms of reducing pollutant generation. As a result the

estimated loads reductions associated with this more detailed paddock modelling regime are

significantly smaller than experienced previously. The smaller load reduction estimates are

considered to be more accurate and reliable than estimates made using the old, limited suite

of simulations.

For land uses that require spatially variable data inputs for pollutant generation models (USLE

based estimates of hillslope erosion and SedNet-style gully erosion), data pre-processing

captures the relevant spatially variable characteristics using the specific ‘footprint’ of each land

use within each sub-catchment. These characteristics are then used to provide a single

representation of aggregated pollutant generation per land use in each subcatchment.

Groundwater movement to streams is not explicitly modelled. Groundwater to streams is

represented as a calibrated baseflow and ‘dry weather concentrations’ (DWC) of constituents.

However, these loads are not subject to management effects.

Whilst DIN movement below the root zone is not explicitly modelled, a proportion of the DIN

modelled by APSIM in sugarcane areas, can be delivered to the stream to represent lateral or

seepage flow. The proportion of seepage load delivered to the stream is calibrated for the

baseline and the loads will vary with the given management practice simulated.

It is important to note that this report summarises simulated, not actual, average annual load

reductions of key constituents to the GBR lagoon. The modelled changes in water quality are

a function of the area of improved land management adoption. Estimates of adoption areas are

supplied by regional NRM groups. Results from this modelling project are therefore a useful

indicator of the likely (theoretical) effects of investment in changed land management practices

rather than a measured (empirical) reduction in loads.


22

Introduction

The Great Barrier Reef (GBR) is the world’s largest living organism, and one of the seven natural

wonders of the world. However, the health and resilience of the GBR is under threat from many

pressures including declining water quality and climate change. Over the past 150 years, the GBR

basins have been extensively modified for agricultural production and urban settlement, leading to a

decline in water quality entering the GBR lagoon (Brodie et al. 2013). The largest contribution of poor

water quality comes from agricultural land use activities within the GBR basins. Poor water quality, by

way of high sediment, nutrient and pesticide loads, can affect the GBR ecosystem by reducing light

availability, smothering corals, promoting algal growth and propagating crown of thorns (COTS) starfish

outbreaks by increasing plankton blooms on which the COTS larvae feed (GBRMPA 2016).

The Reef Water Quality Protection Plan (Reef Plan) was established in 2003, updated in 2009 (Reef

Plan 2009 (Cabinet 2009)) and again in 2013 (Reef Plan 2013 (Department of the Premier and Cabinet

2013)) in response to the decline in water quality entering the GBR lagoon. Reef Plan is a collaborative

program that includes a set of water quality and land management practice targets for catchments

discharging to the GBR. The long-term goal is to ensure the quality of water entering the GBR has no

detrimental impact on the health and resilience of the Reef. The plan is a joint commitment of the

Australian and Queensland Governments.

To strengthen the Australian and Queensland government’s commitment to the Reef, in 2014, Reef

2050 Plan (Australia 2015) was launched as the overarching framework for the long term (2015 to 2050)

protection and management of the GBR. The Reef 2050 Plan is a key component of the Australian

Government’s response to the recommendations of the UNESCO World Heritage Committee in 2015

(UNESCO 2015).

Reef Plan is the main mechanism for implementing the Reef 2050 Plan. The Paddock to Reef Integrated

Monitoring, Modelling and Reporting (P2R) Program provides the monitoring and evaluation framework

to measure and report on progress towards both Reef Plan and Reef 2050 Plan water quality targets.

Monitoring and evaluation is undertaken for a range of targets including water quality targets in the

annual Reef Report Card. These targets will be reviewed in 2017 and basin scale water quality targets

will be set. The Report Cards show cumulative progress towards the Reef Plan 2013 water quality

targets resulting from regional investments in improved land management practices each year. Six

previous report cards (Report Cards 2009-2014) have been released (www.reefplan.qld.gov.au)

tracking the good progress being made towards the Reef Plan targets. The Reef Plan 2013 water quality

targets were set for the whole of GBR. Progress was reported for the whole of GBR plus the six

contributing NRM regions: Cape York (CY), Wet Tropics (WT), Burdekin (BU), Mackay Whitsunday

(MW), Fitzroy (FZ) and Burnett Mary (BM).



23

The Reef Plan 2013 water quality targets state that by 2018 there will be:

a minimum 20% reduction in anthropogenic sediment and particulate nutrient loads at the

end of catchment in priority areas

a minimum 50% reduction in anthropogenic dissolved inorganic nitrogen (DIN) loads at

the end of catchment in priority areas

a minimum 60% reduction in pesticide loads at the end of catchment in priority areas

Catchment modelling is used as one of multiple lines of evidence to report on progress towards the

Reef Plan water quality targets. In this technical report the Report Card 2015 modelled load reductions

resulting from the adoption of improved land management practices for the 2015 year for the GBR and

for the six NRM regions are summarised, and model updates are outlined.

This report outlines:

Changes to Source Catchments water quantity and quality model methodology since the

previous report card

Updated predevelopment, baseline and anthropogenic loads for the 1986 – 2014 climate period

Progress towards meeting Reef Plan 2013 water quality targets over the past 12 months

following the adoption of improved management practices.

Model Improvement Cycle

The P2R Program strives for continual improvement in all areas. Therefore, the catchment and paddock

modelling teams aim for continual improvement of the modelling approach to improve predictions of

pollutant loads and the associated improvements in water quality as a result of improved management

practice adoption.

In Table 2 the catchment modelling cycle is presented with the green highlighted cell indicating the

current Report Card (RC2015). Phase 1 relates to the Reef Plan 2 period, which covers the models

produced from 2010–2013. Report Card 2009 provided the first GBR-wide load estimates of

predevelopment, total baseline and total anthropogenic loads using the best available monitoring and

modelling data at the time (Kroon et al. 2010). Subsequent Report Cards used Source Catchment

modelling framework to report on load reductions due to improved practice adoption. Major updates to

the Source Catchment models occur on a five year cycle to align with the Reef Plan funding cycle. Minor

changes occur between the individual Report Cards, where required. In RC2015 only minor model

changes have been made. All model updates will be discussed at length in this Report. Each successive

Report Card is considered the best representation of loads from each GBR NRM region, and the most

recent Report Card should always be used when reporting or referencing Source Catchments GBR

loads. A full summary of model changes for RC2015 are listed in Table 3.


24

Table 2 Summary of catchment modelling cycle

Phase 1 Modelling 2009 - 2013

Management Practice data baseline year; climate period

Report Card 2009 Kroon et al. (2010) model results

2009; 1986-2009

Report Card 2010 Model build; hydrology calibration;

Report Card 2011 Minor model changes



Phase 2 Modelling 2014 - 2018

Management Practice data baseline year; climate period

Report Card 2014

Major model changes; update Source platform; hydrology recalibration; incorporate new management practice framework; climate period for reporting extended

2013; 1986-2014

Report Card 2015 Minor model changes; hydrology calibration modified for better alignment of baseflow in some regions

Report Card 2016 Minor model changes (if required)

Report Card 2017 Minor model changes; research major changes for post RC 2018 rebuild

Report Card 2018 Minor model changes; research major changes for post RC 2018 rebuild

Integrating modelling and monitoring Modelling is a way to extrapolate monitoring data through space and time, and provides an opportunity

to explore the climate and land management interactions and their associated impacts on water quality.

Monitoring data is the most important point of truth for model validation and parameterisation. With the

continuation of the Reef Plan projects, increased monitoring data becomes available, and as such, the

models become more refined and a better representation of reality as they aim to integrate the sum of

all information (measured and modelled data). Given the scale of the P2R program and its objectives,

it would be impossible to measure the impact of land management practice change on water quality,

hence models are applied that can predict changes in water quality when set against a baseline or

benchmark (in this case the model run period of 1986–2014).

Combining the two programs (catchment modelling and monitoring) ensures continual improvement in

the models while at the same time identifying data gaps and priorities for future monitoring. A key

example of this is the recent endorsement by the Independent Science Panel of 16 new monitoring

sites as part of the GBR Catchment Loads Monitoring Program. The selection of these sites followed a

two-step process, including consultation with other Reef Plan programs, particularly the catchment

modellers, as well as correspondence with each regional NRM body. The additional 16 sites means

that 20 of the 35 GBR basins are now monitored, and measure approximately 95% and 89% of the TSS

and DIN loads (respectively) discharged to the GBR lagoon.


25

Application of modelling outputs While the sole objective of the Catchment Modelling program is to report on changes to water quality

discharging to the GBR lagoon as a result of improved land management practices, the modelled load

estimates have been used by a number of other Reef Plan funded programs. Most recently the RC2015

catchment modelling results will be used a major input dataset to the GBR Scientific Consensus

Statement (Waterhouse in prep., specifically Chapter 2). In the Scientific Consensus Statement the

scientific knowledge of water quality issues in the GBR were reviewed and synthesised to reach a

consensus statement on the current understanding of the system (Waterhouse in prep.). Also, the latest

modelling results are incorporated into a spatial prioritisation project to manage sediment, nutrient and

pesticide runoff in the Burdekin NRM region (Waterhouse et al. 2016).

Scenario modelling – All A and All B management

Modelling provides us with the ability to assess alternative management scenarios such as moving from

current practices to some future improved state. One such example is modelling of the water quality

response if all landholders were to adopt all A class management practices (All A scenario run) or an

All B scenario. These scenarios were previously run using an earlier version of the GBR Source

modelling framework (Waters et al. 2013).

Following the release of Report Card 2015, the All A and All B scenarios were rerun at the request of

the Office of the Great Barrier Reef (OGBR) to assess whether the updated targets in Reef Plan 2013

could be achieved taking into account the updated modelling framework and the significant progress

made to date.

Quality assurance and peer review At the program inception, the eWater CRC best practice modelling guidelines (Black et al. 2011) were

used to develop the modelling approach and Quality assurance process described in Waters and Carroll

(2012). A systematic model build process has been developed by the catchment modelling team, which

is presented in Figure 2. Modelled loads generated for annual reef report cards are reviewed in the first

instance internally by scientists from DNRM and DSITI. Once approved, loads and the associated

reductions are reviewed externally, before final approval by the Queensland Government. A

Coordination and Advisory Group (CAG) and an Independent Science Panel (ISP) oversee model

development and proposed updates or changes (see Waters and Carroll (2012) for description of

project governance arrangements). An external review of the catchment and paddock modelling is

required every three years. The first was in 2012 (Jakeman et al. 2012) the second in 2015 (Jakeman

et al. 2015). In addition the Queensland Audit Office undertook a review of the Paddock to Reef Program

in 2015 (Queensland Audit Office 2015). Recommendations from the peer review process and the Audit

form the basis for future model development and improvements.

Figure 2 Model build process

Methods

The Source Catchments water quantity and quality modelling framework is used to simulate how

catchment and climate variables affect runoff and constituent transport, by integrating a range of

component models, data and knowledge. Between Report Card releases there have been several

upgrades to the Source Catchments platform, developed and maintained by eWater Ltd. In conjunction

with the eWater upgrades, there have been some upgrades to the GBR component models maintained

by DNRM. These upgrades, and in particular, the changes to input data sets are summarised in Table

3 and described in detail below. Those methods that have not changed since RC2014 modelling are

not discussed, but rather can be reviewed in the series of Technical Reports completed post–2013

Report Card (see Waters et al. (2014) and the six supplementary regional reports), and following

RC2014 (McCloskey et al. 2017).

Table 3 Model updates undertaken for Report Card 2015

Modification Rationale Impact Report page

number

Recalibration of flow to better represent baseflow proportion – all regions

Baseflow was not well represented across the GBR when compared to the Lyne-Hollick calculated baseflow of monitored data

Baseflow proportion decreased in most instances of recalibration. This increased surface runoff volumes hence saw a slight (<10%) increase in loads exported

28

Use of maximum quickflow concentration for capping hillslope erosion

Previously, a daily implementation of ‘maximum annual erosion load’ was used to scale transported daily loads, however this approach does not allow for daily runoff differences to be reflected in transported sediment masses, nullifying the use of a daily RUSLE model

Typically increase RUSLE TSS load in all regions

29

Instream deposition and channel remobilisation were enabled in three additional regions

Measured data is now available in a limited number of GBR catchments which can assist in constraining this component of the sediment budget. Can be a significant component of the sediment budget.

Increase in TSS supply due to remobilised sediment from instream

Losses in sediment due to deposition.

Generally aim for remobilisation/deposition to be in equilibrium

30

Replacement of subsurface particulate nutrient layers with surface layers

In a number of regions, under-prediction of particulate nutrients. Identified derivation of subsurface nutrient layers have high degree of uncertainty.

Resulted in improved estimates of particulate nutrients. For example CY now within 30% of observed PP/PN loads previously 60% in early RC2015 iterations.

35,36

Adjustment of DIP:DOP ratio in sugarcane

Default in previous models was 80:20 ratio DIP/DOP. DOP was consistently under predicted against observed loads, so this ratio was adjusted regionally so results would better match monitored loads

A decrease in DIP export loads, and an increase in DOP export loads

36,43


28

Modification Rationale Impact Report page

number

HowLeaky cropping Changes to CY HowLeaky models to better represent cropping systems in CY

Additional pesticides now incorporated into CY model

35

Application of A class management to streambanks in the predevelopment scenario

Current baseline streambank erosion was in some instances less than predevelopment scenario because the riparian vegetation percentage was greater in the baseline model thus reducing streambank erosion in a predevelopment scenario.

Predevelopment streambank contribution is now always lower than that of the baseline model

31

The methodology updates described below are split into two sections: updates applied across the whole

of GBR, and regionally specific updates listed under NRM region headings. Only changes to the

methodologies are described in this report.

GBR methodology updates

Hydrology Hydrology underwent a major recalibration prior to RC2014, where the rainfall-runoff model was

changed from SimHyd to Sacramento to improve the calibration of high flows. Following the release of

RC2014 it was noted that the proportion of baseflow runoff was higher than would be expected in many

regions. A recalibration was undertaken to include a baseflow optimisation term to align with an

estimates derived from observed gauged flow.

Sacramento rainfall-runoff model calibration for baseflow

The hydrology model was recalibrated in all regions to better align baseflow to observed flow estimates.

This was important because sediment generation and transport are associated with different flow

components. To improve the modelled baseflow proportion, the existing Objective Function used in

calibration was modified to include a term which enables the minimisation of the difference between the

simulated baseflow and an ”observed” baseflow proportion derived from gauged flow data. The

observed baseflow was estimated using the Lyne-Hollick (1979) filter method which applies a smoothing

filter to daily flow which provides a means of splitting daily flow into baseflow and quickflow. The

baseflow proportion was then calculated for each calibration gauge over the calibration period. If the

difference between the modelled baseflow proportion in the previous calibration and the Lyne-Hollick

baseflow estimate derived from gauged data was greater than 10% then that gauge was selected for

re-calibration (Table 4). It is evident from Table 4 that the wetter regions such as WT and MW with high

baseflow proportions required considerably less recalibration.


29

Table 4 Number of gauges that underwent recalibration for baseflow

NRM Region

Number of gauges calibrated originally

Number of gauges recalibrated to

improve baseflow

CY 15 15

WT 30 9

Burdekin 46 34

MW 10 4

Fitzroy 44 32

BM 29 29

Hillslope generation model

Concentration cap

The daily RUSLE model (as implemented in the Dynamic SedNet plugin) is susceptible, on occasion,

to generating large daily eroded soil masses when there is not an equally significant runoff volume

available to transport this mass (via the rainfall-runoff model pathway). This can occur due to the fact

that daily eroded mass is calculated (from the RUSLE equation) as function of the daily rainfall and not

the rainfall-runoff model generated daily runoff. This can be the result of erroneous topographic data

inputs, or poor rainfall-runoff parameterisation, or both. To avoid transporting disproportionate eroded

soil masses on days of smaller runoff hence concentration, the RUSLE model (as implemented in the

Dynamic SedNet plugin) uses a “maximum daily quickflow concentration parameter” to keep the

transported daily fine sediment load within acceptable tolerances of the runoff magnitude. Previously,

a daily implementation of ‘maximum annual erosion load’ was used to scale transported daily loads,

however this approach does not allow for daily runoff differences to be reflected in transported sediment

masses, nullifying the use of a daily RUSLE model. The values implemented for the maximum daily

quickflow (QF) concentration parameter for each NRM region are regionally specific based on

monitoring data, and are presented in Appendix 2, Table 57.

Subsurface Nitrogen and Phosphorus layers change

In the majority of regions there was a considerable under-prediction of particulate nutrient loads when

compared to the measured load at monitoring locations in RC2014 and early iterations of RC2015. The

existing particulate nutrient generation model utilises a clay percentage layer from the ASRIS database

to estimate the associated particulate nutrient loads, on the assumption that particulates are exclusively

bound to the clay fraction (Packett et al. 2014). Hence the model may have underestimated the

particulate contribution from the non-clay sediments (e.g. fine silt) if these non-clay sediments have the

ability to retain and carry particulate nutrients. This may be indicating further investigations are required

to better estimate the export of particulate nutrient to the GBR lagoon. Wilkinson et al. (2010) describe

the differences between the ASRIS spatial data sets and local point data. The surface and subsurface

ASRIS nutrient datasets are extrapolated from direct measurements of soil nutrient content to provide

a uniform spatial layer as required by the model. In remote areas such as CY there may be very few

samples from which to extrapolate a continuous dataset, and hence there can be significant differences


30

between observed and modelled values. Often point source data of nutrient content is sourced from

areas of intensive agriculture and as such the input datasets may be more reliable in these areas

(Wilkinson et al. 2010). A review of the chemistry data for CY (CSIRO data in Biggs and Philip (1995)),

suggests that there was no significant difference between surface and subsurface TN and TP chemistry.

However, for the ASRIS derived layers surface concentrations of nutrients in CY were typically three

times greater than that of sub-surface concentrations.

The sediment budget estimates suggest the main source of particulate nutrients was from subsurface

derived particulate loads. Therefore, the subsurface nutrient concentration layers were replaced with

surface layer in CY and WT regions. This is in line with the recommendation by Wilkinson et al. (2010)

who suggested that users should choose the appropriate dataset for their region including from locally

derived measurements or the ASRIS dataset.

In a number of the regions the hillslope PN and PP enrichment ratios were increased within acceptable

ranges as reported in the literature (Sharpley 1985, Rose and Dalai 1988, Sharpley et al. 2002) to

further increase the particulate nutrient load in line with measured data.

Sugarcane constituent generation Irrigation and drainage modelling were heavily revised in the BU paddock modelling (Shaw 2016), as

were the management scenarios. This will impact the drainage, runoff and DIN components of the

APSIM runs. Soils data was also reviewed in the Burdekin and WT. A summary of APSIM paddock

model changes are presented in Table 61, Appendix 3. In addition to changes to the APSIM paddock

model runs, there were a number of changes to specific parameters for sugarcane, especially in the

WT and BM regions which are presented below.

HowLeaky cropping constituent generation For RC2014 a uniform slope of 1% was applied across all cropping areas modelled by HowLeaky and

model runs were corrected for slope within Source Catchments. For RC2015 slope was accounted for

in the HowLeaky paddock modelling as opposed to within the Source Catchments model.

In-stream models In-stream processes are an important component of regional sediment budgets. This functionality was

only enabled in the CY model in RC2014. The in-stream model was applied in CY due to the availability

of measured sediment data in the Normanby basin which provided a source of data to constrain the

model parameters. All regions, with the exception of Burdekin and Fitzroy, have in-stream deposition

and remobilisation enabled in RC2014. The in-stream models were enabled in RC2015 to incorporate

this important component of the sediment budget, however the initial aim was to take a conservative

approach and keep deposition and remobilisation in equilibrium until such time as more relevant local

data becomes available. The parameter values used in each of the regional models are listed in

Appendix 2, Table 60.


31

Predevelopment Model In all regional models there instances where the load from the predevelopment scenario is higher than

that for the baseline scenario, particularly for streambank and gully erosion. In order to manage this it

is possible to scale down the predevelopment loads using a ‘management factor’ parameter for both

gullies and streambanks. This is based on the assumption that the predevelopment load should always

be less than the baseline, or current, load. In RC2015 and the streambank management factor has

been set to ‘A’ class (0.6), while gullies remained at 1 (‘C’ class). This change was made on the

assumption that streambanks were in a better condition in the predevelopment scenario, and that the

changes already applied to gullies (e.g. reduction of cross-section by 90%) were sufficient to reflect a

predevelopment scenario. That is, gullies were assumed to be of a lesser volume than current

conditions. For the predevelopment model, the ‘management factor’ for both gullies and streambanks

has been previously set to 1. A full description of how the management factor is applied to each gully

and streambank management class is presented in McCloskey et al. (2014). In short, streambank and

gully erosion rates are scaled relative to a ‘C’ class practice (adapted from Thorburn and Wilkinson

(2012), where ‘C’ class management practice factor equals 1.

Constituent load validation

Two main approaches were used to evaluate the GBR Source Catchments modelling. Firstly, a short-

term comparison was made using load estimates from monitoring results that commenced in 2006 in

ten high priority catchments (Turner et al. 2012, Joo et al. 2012). Secondly, a long-term comparison

was made with catchment load estimates derived from all available measured data for the high priority

catchments for the 23 year modelling period, using a Flow Regression Concentration Estimator (FRCE

model) (Joo et al. 2014). Both of these sources are discussed in detail in the previous Report Card

modelling technical reports (Waters et al. 2014).

As the number of years of load estimates from the short term monitoring program increases (currently

eight years) there is less reliance on the long term load estimates as a source of validation given their

high uncertainty. As a result the short term loads form the basis for validation in all regions with long

term load estimates used in some regions where they are deemed to be of sufficient quality for load

comparison.

Performance ratings Model evaluation performance ratings were established by Moriasi et al. (2007) and Moriasi et al.

(2015), uing four quantitative statistics: Nash-Sutcliffe coefficient of efficiency (NSE), percent bias

(PBIAS), the ratio of the root mean square error to the standard deviation of validation data (RSR), and

the coefficient of determination (R2) (Table 5). In this report modelled and measured loads are assessed

against these ratings where applicable.

The PBIAS performance measure can be applied for all constituents at an annual scale, as can flow for

R2 and NSE. However, sediment and nutrient ratings for the R2, NSE, and RSR measures are


32

recommended to be used at a monthly scale (Moriasi et al. 2015). In the absence of another method

for rating model performance against measured data, the monthly performance rating have been

applied at an annual scale. It was not possible to calculate daily or monthly loads for all sites due to the

highly variable sampling frequency not being appropriate for the load estimation technique applied.

Table 5 Performance ratings for recommended statistics for a monthly time-step (from (Moriasi et al. 2007),

Moriasi et al. (2015))

Performance Measure

Constituent Performance evaluation criteria

Very good Good Satisfactory Unsatisfactory

R2

(Moriasi, 2015)

Flow R2 >0.85 0.75 < R2 ≤ 0.85 0.60 < R2 ≤ 0.75 R2 ≤ 0.60

TSS/P R2 >0.80 0.65 < R2 ≤ 0.80 0.40 < R2 ≤ 0.65 R2 ≤ 0.40

N R2 >0.7 0.60 < R2 ≤ 0.70 0.30 < R2 ≤ 0.60 R2 ≤ 0.30

NSE (Moriasi,

2015)

Flow NSE > 0.80 0.70 < NSE ≤ 0.80 0.50 < NSE ≤ 0.7 NSE ≤ 0.5

TSS NSE > 0.80 0.70 < NSE ≤ 0.80 0.45 < NSE ≤ 0.70 NSE ≤ 0.45

N/P NSE > 0.65 0.50 < NSE ≤ 0.65 0.35 < NSE ≤ 0.50 NSE ≤ 0.35

PBIAS (%) (Moriasi,

2015)

Flow PBIAS < ±5 ±5 ≤ PBIAS < ±10 ±10 ≤ PBIAS < ±15 PBIAS ≥ ±15

TSS PBIAS < ±10 ±10 ≤ PBIAS < ±15 ±15 ≤ PBIAS < ±20 PBIAS ≥ ±20

N/P PBIAS < ±15 ±15 ≤ PBIAS < ±20 ±20 ≤ PBIAS < ±30 PBIAS ≥ ±30

RSR (Moriasi,

2007)

Flow

RSR ≤ 0.5 0.50 < RSR ≤ 0.60 0.60 < RSR ≤ 0.70 RSR > 0.7 TSS

N/P


Modelling the water quality response as a result of all landholders were to adopt ‘All A’ class

management practices or ‘All B’ class practices were run following the release of RC2014. These

scenarios were previously run using an earlier version of the GBR Source modelling framework (Waters

et al. 2013).

There were a number of limitations to the modelling approach reported in Waters et al (2013), including

the inability to account for improved gully or streambank management and the inability to represent

investments in recycle pits on cane farming systems. The GBR Source modelling framework was

updated in 2014 to incorporate the new functionality and any updated input data sets were added. The

following outlines the approach taken and any assumptions used in the running of these scenarios.

Baseline Model The RC2015 loads and land management proportions were used as the baseline data sets from which

the All A and All B land management proportions and associated load reductions were derived.

A practice improvement that was added to the cane model simulations in RC2015 was the installation

of recycle pits (Shaw and Silburn 2016). Projects funded recycle pit installation in the BU and BM

regions.


33

When applying hypothetical management improvements to the whole of the grazing industry within each

regional catchment model, it is necessary to spatially define the baseline management distribution. The

baseline distribution of the hillslope, gully and streambank management are not provided spatially. The

method used previously to convert the non-spatial distributions into a useful spatial layer have relied

upon the relationship between grazing (hillslope) management and ground cover (Scarth et al. 2006,

Ellis and Searle 2014, Waters et al. 2014). This method only allows the hillslope baseline distribution to

be represented spatially, yet the All A and All B scenarios require that gullies and streambanks are also

addressed. For this reason, the spatial representation of baseline hillslope management was also used

to create spatially variable parameter values for the ‘management factors’ for all relevant gully and

streambank models. The gully management factors were calculated in this manner for grazing areas

only, and the management factors for streambank models were calculated in a way that recognised the

proportionality of grazing within a sub-catchment (as a surrogate for determining how much streambank

actually aligned with grazing).

All B Scenario Cropping & Sugarcane:

Areas with allocated management ‘worse’ than B class in the baseline were modelled with B

class management simulations (specific soils x climate simulation allocations were provided by

paddock modellers)

In the Burnett Mary, under All B, recycle pits were implemented on all sugarcane properties.

There was no specific data available to reflect differences in how these pits might be managed

between A and B class management hence were modelled the same in All A and All B scenarios

in this region

In the Burdekin area ‘recycle pits’ were implemented on all sugarcane properties, with their

trapping efficiency reflecting B class management practices

Grazing:

In the hillslope generation model, the C factor (i.e. cover term) was re-calculated with the

assumption that all areas classified as having C or D class management in the baseline

scenario, were improved to B class reflected as improved cover

Gully and streambank management factors were recalculated under the assumption that areas

in the baseline scenario at C or D class management were improved to B class

No specific adjustment was made to the percentage of riparian vegetation under an All B

scenario in the stream bank erosion calculations

All A Scenario Cropping & Sugarcane:

Areas with management ‘worse’ than A class were modelled with A class management (specific

simulation allocations were provided by paddock modellers)

In the Burnett Mary, under All A, recycle pits were implemented on all sugarcane properties.

There was no specific data available to reflect differences in how these pits might be managed


34

between A and B class management hence were modelled the same in All A and All B scenarios

in this region

In the Burdekin area ‘recycle pits’ were implemented on all sugarcane properties, with their

trapping efficiency reflecting A class management practices

Grazing:

Gully and bank management factors were recalculated under the assumption the and B, C and

D class management was improved to A class

The percentage of riparian vegetation parameter, used in the stream bank generation

calculations, was increased to 100% where possible

Hillslope generation model inputs were re-calculated with C Factor data adjusted assuming that

B, C and D class management was improved to A class


35

Cape York

The following section describes methodology changes that are only applicable to the CY region.

Hydrology Recalibration was undertaken to improve baseflow representation, as described on page 28.

Hillslope sediment, nutrient and herbicide generation Maximum quickflow concentration adjusted, as described above on page 29. Particulate nutrient

enrichment ratios were increased to account for under-predicted modelled loads. To further increase

the particulate nutrient load contribution, the surface N and P concentration layers were used as inputs

for both surface and subsurface data as described on page 29. These combined changes have

increased particulate nutrient export loads to better align with measured loads, however both N and P

loads remain under-predicted and this will be investigated in future modelling.

Gully sediment and nutrient generation models Nil changes occurred since RC2014.

Sugarcane constituent generation Not applicable to CY.

HowLeaky cropping constituent generation A CY specific farming system was applied for irrigated and dryland cropping in the HowLeaky paddock

model runs. Previously the Fitzroy farming system had been previously applied due to the lack of better

localised information. The CY system incorporated peanuts and maize into the crop cycle, and removed

crops not grown in CY (e.g. wheat).

Other land uses: Event Mean Concentration (EMC)/Dry Weather Concentration

(DWC) The EMC/DWC values were updated to better align with monitored data. The values used in this Report

Card are in Appendix 2, Table 62.

In-stream models Nil changes occurred since RC2014.

Predevelopment model Streambank management factor set to A-class, as described above. Predevelopment EMC/DWC

values are in Appendix 2, Table 68.


36

Wet Tropics

The following section describes methodology changes that are only applicable to the WT region.

Hydrology Recalibration to improve baseflow representation, as described on page 28.

Hillslope sediment, nutrient and herbicide generation Maximum quickflow concentration adjusted, as described above on page 29. Particulate nutrient

enrichment ratios were increased to account for under-predicted modelled loads. To further increase

the loads of particulate nutrients the surface N and P concentration layers were used to as inputs for

both surface and subsurface data as described on page 29. This has the desired effect of increasing

particulate loads, however both N and P remain under-predicted, and this will be investigated for future

report cards.

Gully sediment and nutrient generation models Gully cross sectional area was increased to 10m2 (from 5m2) to increase the amount of subsurface

erosion in line with empirical evidence. Gully cross section was doubled to increase the amount of

subsurface erosion (gully and streambank erosion) being generated from grazing lands. This has

resulted in a doubling of gully erosion supply. The increase in subsurface erosion aligns with sediment

tracing work in the Herbert (Tims et al. 2010) and this is further outlined in the discussion.

Sugarcane constituent generation Nil changes made since RC2014.

HowLeaky cropping constituent generation The ratio of FRP:DOP in sugarcane is an adjustable parameter in the model and in Report Card 2014

was set at a default of 80:20. Following further investigation, this was changed to 50:50 in Report Card

2015 to correct the under prediction of DOP at model evaluation sites, and is supported by field data.

Instream water quality monitoring data collected in a sugarcane subcatchment in the Tully Basin

showed that the spilt was 50:50 (Bainbridge et al. 2009). Additionally, data from the banana paddock

scale trial at South Johnstone showed that the average of FRP:DOP was 50:50 (n =147) (Masters et

al. 2016, unpublished data). Additional parameter changes for sugarcane include a reduction to the

DWC for fine sediment, and a reduction to the HSDR for TSS and particulate nutrients. This was to

better align with measured load estimates at the five monitoring sites (Table 6). The PP enrichment

ratio was also reduced as it was over predicted at some sites, especially at Tully Euramo for the 2014

Report Card. In the WT, there were soils included in the modelling in RC2014 that we excluded in

RC2015 and reallocated to different APSIM simulations.


37

Table 6 Summary of changes to sugarcane parameters in the WT

NRM Region Parameter RC2014 RC2015

WT

FRP:DOP ratio 80:20 50:50

TSS DWC 72 50

TSS & particulate HSDR 20 15

PP enrichment ratio 5 3

PN enrichment ratio 2 2


(DWC) For land uses where Event Mean Concentration/Dry Weather Concentration (EMC/DWC) were used to

generate loads, a number of changes were made to EMC/DWC concentrations to better align with loads

estimated from GBRCLMP monitoring data (Wallace et al. 2015). Table 63, in Appendix 2 shows the

values for RC2015. Concentrations of DIN, TSS and particulates upstream of the Barron River at Myola

gauge were reduced by 25% and increased by 25% respectively to align better with the monitored loads

at this gauge.

In-stream models For the WT, the instream and floodplain settling velocities were applied based on the stream erosion

and deposition graph in Earle (2015). In-stream deposition and remobilisation was implemented in this

Report Card. The WT instream model parameters are in Appendix 2, Table 60.

Predevelopment model As described on page 31 there are often instances where the predevelopment streambank load is

greater than that of the baseline, or current, load, and the streambank management factor can be used

to address this. The streambank management factor set to A-class, as described above.

Predevelopment EMC/DWC values are in Appendix 2, Table 68.


38

Burdekin

The following section describes methodology changes that are only applicable to the BU region.


Hillslope sediment, nutrient and herbicide generation Maximum quickflow concentration adjusted, as described above on page 29.


Sugarcane constituent generation Nil changes occurred since RC2014.

HowLeaky cropping constituent generation Nil changes occurred since RC2014.


(DWC) The EMC/DWC values were updated to better align with monitored data. The values used in this Report

Card are in Appendix 2, Table 64.

In-stream models Nil changes occurred since RC2014.






39

Mackay Whitsunday

The following section describes methodology changes that are only applicable to the MW region.


Hillslope sediment, nutrient and herbicide generation Maximum quickflow concentration adjusted, as described above on page 29.


Sugarcane constituent generation The DWC for TSS was reduced from 70mg/L to 10mg/L to better match the raw TSS concentration

measurements collated during baseflow conditions as part of various water quality monitoring

programs.

HowLeaky cropping constituent generation Nil changes occurred since RC2014.


(DWC) The DWC for TSS was reduced from 25 to 10 in conservation and from 35 to 10 in forestry land uses

to better represent the dry weather concentration measurements recorded as part of water quality

monitoring programs (Turner et al. 2012, Turner et al. 2013, Garzon-Garcia et al. 2015, Wallace et al.

2015). The EMC/DWC values used in this Report Card are in Appendix 2, Table 65.

In-stream models Instream deposition and remobilisation were implemented in RC2015. As was the case for all models

in which the in-stream models were enabled, deposition and remobilisation were kept in a similar

magnitude where there was a lack of better local information. The MW instream model parameters are

in Appendix 2, Table 60.






40

Fitzroy

The following section describes methodology changes that are only applicable to the FZ region.


Hillslope sediment, nutrient and herbicide generation The soil erodibility layer (k-factor) was altered by applying a rock factor that reduced soil erodibility in

areas where surface rock fragments are present. The rock factor was produced using the approach

described by McCloskey et al. (2014) and was applied across all land uses. Applying a k-factor that

accounts for rock fragments on the soil surface was implemented to account for high hillslope erosion

rates from steep, usually rocky, areas.

Gully sediment and nutrient generation models Two related changes were made to the Fitzroy gully model. The gully full maturity year was lowered

from 2010 to 1980 to reflect the reduction in the growth rate of gullies. Published literature indicates that

gully activity has been declining in the Fitzroy (Hughes et al. 2009) and neighbouring Burdekin

(Wilkinson et al. 2013). The activity factor, which works in combination with the gully maturity year

parameter, was raised to 0.7 and now better reflects gully activity in the Fitzroy region based on visual

assessments of gully erosion in aerial photography.

Sugarcane constituent generation Not applicable in the Fitzroy.

HowLeaky cropping constituent generation Hillslope erosion in cropping areas is modelled within HowLeaky and daily sediment loads are imported

into the regional Source models. For RC2014 a uniform slope of 1% was applied across all cropping

areas modelled by HowLeaky. Once imported into Source Catchments the slope-factor was used to

scale the loads to account for the variable slope across cropping land uses. For RC2015 accounting for

the spatial variability of slope was dealt with within the HowLeaky paddock modelling as opposed to in

the Source Catchments model previously (Owens 2016).

Hillslope erosion has not been modelled in irrigated cropping land uses for this report card. In previous

Report Cards, dryland cropping simulations were used to simulate hillslope erosion in irrigated cropping.

This was not an accurate estimate of loads from irrigated cropping due to tailwater capture generating

minimal runoff to stream except in large events. This approach will be adopted until a better

understanding of the proportion of pollutants transported off irrigated cropping areas is acquired. The

Queensland Department of Agriculture and Fisheries (DAF) are currently investigating irrigation

practices and runoff capture within the Fitzroy region. The results of this research will be used to improve

the modelling of hillslope erosion in irrigated cropping areas within the Fitzroy region.


41


(DWC) Concentrations for DIN, DON and DIP were reduced by a factor of 0.5, 0.8 and 0.8 respectively for all

land uses and tebuthiuron concentrations were decreased by a factor of 0.8 for open grazing to better

align with observed load estimates (Joo et al. 2012, Turner et al. 2012). The EMC/DWC values used in

RC2015 are in Appendix 2, Table 66.

In-stream models Large increases in hillslope erosion were recorded for this report card due to increased loads from

dryland cropping. This resulted in high export loads being generated compared to monitored loads. As

a result the bank full flow recurrence interval was lowered to 4.7 years to increase floodplain deposition

which reduced exported loads and improved model estimates compared to GBRCLMP monitored loads.

To better reflect stream characteristics updated regression relationships between upstream catchment

area and stream width (Figure 3) and bank height and upstream catchment (Figure 4) were derived

from local gauging station data and from imagery interpretation of channel width.

Instream deposition and remobilisation were investigated prior to the release of this report card,

however more work is required to fully understand the interactions between load reductions and

deposition and remobilisation of sediment and particulate nutrients. Further research into these

processes will be conducted with a view to incorporating these processes into the model for the next

report card.

Figure 3 Catchment area vs. stream width used to determine streambank erosion parameters


42

Figure 4 Catchment area vs. bank height used to determine streambank erosion parameters






43

Burnett Mary

The following section describes methodology changes that are only applicable to the BM region.


Hillslope sediment, nutrient and herbicide generation Enrichment ratios for PN and PP have been increased from 4 to 9 and from 5 to 10 respectively, to

improve model agreement with loads estimated from catchment water quality monitoring.

Gully sediment and nutrient generation models Application of a 100% delivery ratio in the previous report cards contributed to the overestimation of

TSS by the model. Because of this and the expectation that not all fine sediment generated from gullies

will be supplied to the streams, the gully delivery ratio has been reduced from 100% in RC2014 to 80%

in RC2015.

Sugarcane constituent generation The conversion factor parameter for DOP and DIP in APSIM (which is simply a calibration parameter

to be able to scale modelled DOP and DIP values up or down) has been increased from the default of

0.8 to 1 and 1.2, respectively. This is to account for the application of mill mud (Moody and Schroeder

2014), which is not otherwise captured in the model.

Table 7 Summary of changes to sugarcane parameters in the BM

NRM region Parameter RC2014 RC2015

BM

FRP:DOP ratio 80:20 100:120*

TSS & Particulate HSDR 20 5

PP enrichment ratio 5 10

PN enrichment ratio 4 9

HowLeaky cropping constituent generation Nil changes made since RC2014.


(DWC) The DWC for USLE FUs (grazing, conservation and forestry land uses) for fine sediment and particulate

nutrients has been set to zero. This was implemented to offset the high hillslope erosion loads being

generated in the model in RC2014. The EMC/DWC values used in this Report Card are in Appendix 2,

Table 67.

In-stream models Instream deposition and remobilisation were implemented in RC2015. As was the case for all models

in which the in-stream models were enabled, deposition and remobilisation were kept in a similar

magnitude where there was a lack of better local information. However, the relative effect of this on


44

modelled sediment and particulate loads needs to be investigated further in line with published

literature.



to address this. In the BM the streambank management factor was set to a constant value (0.71) as

this was the average of the values applied in an A-class scenario model run. For consistency with the

other regions, the universal value of 0.6 will be considered and tested. Predevelopment EMC/DWC

values are in Appendix 2, Table 68.


45

Results

The overall GBR results, and the results section for each of the six NRM regions are separated into

hydrology and modelled loads. In the hydrology component the updated flow and baseflow calibration

results are presented. The modelled loads section includes the updated baseline load and the overall

progress towards the Reef Plan 2013 targets. For each NRM region, the predevelopment loads,

constituent load evaluation, loads by land use, identification of sources and sinks, and management

change loads are also presented.

Hydrology and regional discharge comparison

The modelled average annual flow (1986–2014) for the GBR was 74,000,000 ML/yr, with the Wet

Tropics contributing 32% (23,500,000 ML/yr) of the total flow (Figure 5). The Burnett Mary was the

lowest contributor (7%). Flow increased by 750,000ML/yr, or 1% as a result of the baseflow

recalibration. The greatest changes to average annual flow were in the BM region with an increase of

16% and CY has a decrease of 3% average annual flow.

Figure 5 Average annual modelled discharge for the GBR RC2015 (1986–2014)

Modelled loads

For Report Card 2015, the modelled average annual load of fine sediment exported from the six GBR

NRM regions is approximately 9,925 kt/yr. Of this, approximately 20% is the predevelopment load, and

~7,894 kt/yr anthropogenic load. In Table 8 the total constituent baseline load for all regions is

presented, while in Table 10 this data is presented as a per cent contribution.

The Wet Tropics is the greatest contributor for five of 11 constituents (Table 10), including TN, DIN, and

PN. Almost 50% of the DIN baseline load exported to the GBR comes from the Wet Tropics. The


46

Burdekin contributes the greatest fine sediment load of all regions, while the Fitzroy contributes the

greatest TP, DIP and PP loads. The MW region has the greatest load of PSII herbicides, primarily a

function of land use, specifically large areas of sugarcane, at ~4,000 kg/yr. Typically, CY was the lowest

contributor for most constituents which again is a function of land use and the limited area of intensive

agriculture. However, CY was the highest contributor for DON which is consistent with monitoring data.

The total photosystem II (PSII) and the diuron toxic equivalent (TE) pesticide loads are presented in

Table 9. The third column is the ratio of the TE PSII load to the PSII load. The closer the number is to

1, the higher the proportion of diuron making up the total load. The WT and MW regions have a high

ratio compared to all other regions indicating that diuron is the dominant chemical in use (according to

the modelled simulations). It is important to note that on average whilst MW exports approximately

1,000 kg/yr more PSII load than WT, they export a similar toxic equivalent load and a similar proportion

of PSII/PSII TE load, meaning that WT exports more diuron.

Table 8 Total baseline loads for the GBR region

Basin TSS

(kt/yr) TN

(t/yr) DIN (t/yr)

DON (t/yr)

PN (t/yr)

TP (t/yr)

DIP (t/yr)

DOP (t/yr)

PP (t/yr)

Cape York 526 6,854 423 4,536 1,896 682 76 152 454

Wet Tropics 1,516 16,577 5,496 4,385 6,696 2,301 192 376 1,733

Burdekin 3,781 8,793 2,574 2,562 3,657 2,823 440 144 2,239

Mackay Whitsunday

818 4,806 1,355 1,301 2,150 1,306 249 65 992

Fitzroy 1,824 10,907 1,140 3,406 6,361 4,665 1,047 260 3,357

Burnett Mary 1,459 7,146 1,040 2,113 3,993 1,642 139 77 1,426

Total 9,925 55,083 12,028 18,303 24,753 13,418 2,143 1,074 10,201

Table 9 PSII pesticide load, PSII Toxic Equivalent (TE) load for the GBR region

Basin PSII

(kg/yr)

PSII Toxic

Equivalent (TE)

load (kg/yr)

Proportion of

PSII TE load to

PSII load

Cape York 16 1 0.06

Wet Tropics 3,090 2,415 0.78

Burdekin 2,394 1,284 0.54

Mackay Whitsunday 4,044 2,925 0.72

Fitzroy 2,210 47 0.02

Burnett Mary 346 86 0.25

Total 12,100 6,758 0.56


47

Table 10 Regional contribution of each constituent as a percentage of the GBR total baseline load

Basin TSS

(kt/yr) TN

(t/yr) DIN (t/yr)

DON (t/yr)

PN (t/yr)

TP (t/yr)

DIP (t/yr)

DOP (t/yr)

PP (t/yr)

PSII (kg/yr)

PSII TE (kg/yr)

Cape York 5 12 4 25 8 5 4 14 4 0 0

Wet Tropics 15 30 46 24 27 17 9 35 17 26 36

Burdekin 38 16 22 14 15 21 21 13 22 20 19

Mackay Whitsunday

8 9 11 7 9 10 12 6 10 33 43

Fitzroy 18 20 9 19 26 35 49 24 33 18 1

Burnett Mary

15 13 9 12 16 12 6 7 14 3 1

Total 100 100 100 100 100 100 100 100 100 100 100

Across the GBR, the modelling suggests that current fine sediment loads are approximately five times

the pre-development load (Figure 6). This varies for each region from twice the pre-development load

in CY, to seven times the load in the Fitzroy and Burdekin NRM regions. Total phosphorus loads are

approximately three times the pre-development load across the GBR, ranging from twice the pre-

development load in CY to three times in the Fitzroy and Burdekin. Total nitrogen is more consistent

across the regions with an approximate doubling of the load. The predevelopment load for DIN is also

about half of the anthropogenic load (Figure 7) in those regions where sugarcane is prevalent. In the

other regions (CY and FZ) there is minimal anthropogenic DIN load.


48

Figure 6 Predevelopment and anthropogenic baseline loads for all 35 basins in the GBR for TSS


49

Figure 7 Predevelopment and anthropogenic baseline loads for all 35 basins in the GBR for DIN

Contribution by land use Grazing (43%) and streambank erosion (31%) are the greatest contributors to the baseline TSS load

from the GBR (Figure 8). Nature conservation (13%) and sugarcane (7%) also make notable

contributions to total loads. Sugarcane (43%) is by far the greatest contributor to the GBR DIN load

(Figure 9), followed by grazing (23%) and nature conservation (20%). Unlike their contributions to TSS,

the DIN load from bananas, horticulture and urban/other is no longer negligible despite their small area.

Bananas are modelled only in the WT and CY, and have a greater load per unit area (12.3kg/ha/yr) of

DIN than sugarcane (9.6kg/ha/yr) does across the GBR. Whilst the contribution from land uses such as

bananas, dairy, and horticulture are negligible to DIN on a whole of GBR basis (Figure 8), within a


50

region individual industries may contribute a reasonable amount of the total load exported. For example

in the WT, bananas contributes 5% of the total DIN load (Figure 24).

Figure 8 Baseline GBR TSS load contribution by land use

Figure 9 Baseline GBR DIN load contribution by land use


51

Progress towards Reef Plan 2013 targets

Progress towards the Reef Plan targets for the whole of GBR is presented in Figure 10. Fine sediment

has been reduced by 0.5%, with the greatest reduction occurring in the Fitzroy region. Total nitrogen

has been reduced by 0.5%, with the greatest reductions in the Burdekin (DIN reduction of 4%) and MW

region (DIN reduction of 1%). A 3% reduction has been achieved for the toxic equivalent load of PSIIs,

with the greatest reductions in the WT (3.5%), Burdekin (3.5%) and MW (3%) regions.

Figure 10 Progress towards Reef Plan targets till 2015 for the whole of GBR


Modelled load reductions were calculated from the All A and All B management practice adoption

scenarios for TSS, both particulate and dissolved nutrients and for the five PSII herbicides of interest,

aggregated to a PSII TE. The reductions reported represent the modelled load reductions from 2009 –

2015.

The modelling results suggest that under an All B scenario, the 20% TSS target is close to being met

for the whole of GBR and can be achieved in the MW and Fitzroy regions (Figure 11). Under an All A

scenario, all regions can achieve the target with the exception of CY, achieving just under the 20%

target.

For DIN, the 50% target cannot be met for the whole of GBR under the All A or All B scenario. The

Burdekin region is the only region that achieved the target under the All B scenario (Figure 12). For

pesticides, the PSII TE target can be achieved under an All B scenario (Figure 13). Alternative,


52

innovative management practices should be explored that could assist in reaching the Reef Water

Quality targets.

For particulate nitrogen, under an All B scenario the 20% target can be met for the whole of GBR and

can be achieved in the MW and Fitzroy regions (Figure 14). Under an All A scenario all regions can

achieve the target (Figure 14). For particulate phosphorus, under an All B scenario the 20% target can

be met for the whole of GBR and can be achieved in the majority of regions namely WT, BU, MW, and

FZ (Figure 15). Under an All A scenario all regions can achieve the target with the exception of CY,

achieving just under the 20% target (Figure 15).

In summary, the modelling results suggest that the TSS and particulate nutrient targets are achievable

under an All A scenario with the majority of the regions close to achieving the TSS target under an All

B scenario. The DIN target may be more challenging to achieve even under the All A scenario. The

PSII TE target is achievable even under an All B scenario.

Figure 11 Estimated TSS reduction under All A and All B management practice scenarios


53

Figure 12 Estimated DIN reduction under All A and All B management practice scenarios

Figure 13 Estimated PSII TE reduction under All A and All B management practice scenarios


54

Figure 14 Estimated PN reduction under All A and All B management practice scenarios

Figure 15 Estimated PP reduction under All A and All B management practice scenarios


55

Cape York

The results of the Cape York modelling are presented below, separated into hydrology and modelled

loads. In the hydrology component, the results of the updated calibration process will be presented, as

well as the impact on the total flow for the CY region. The modelled loads section includes the results

of the total baseline, anthropogenic baseline and predevelopment loads. The validation of the CY

Source Catchments modelled data is then presented using load estimates from measured data.

Progress towards targets due to investment is reported against the anthropogenic baseline for RC2015.

A summary of the total baseline load by land use, and land use by basin is also reported, as well as a

mass balance summary of the sources and sinks by constituent. For a full list of the CY region loads

for RC2015 refer Appendix 3, Table 69.

Hydrology calibration performance Recalibrating to improve baseflow did not change the number of gauges meeting the performance

criteria. All gauges continued to meet the R2 and PBIAS criteria, and eight of 15 continued to meet the

daily NSE criteria. The purpose of the baseflow recalibration was to improve the proportion of flow

attributed to quick and baseflow. The Lyne-Hollick filter (Lyne and Hollick 1979) was used to separate

baseflow in the observed data (Table 11); the modelled baseflow percent was also calculated. There is

a very clear improvement in the RC2015 hydrology calibration in which the modelled baseflow

proportion better matches the Lyne-Hollick calculated baseflow.

The recalibration of hydrology in CY to improve the baseflow component has seen average annual flow

reduced by 3% of total annual flow (~500,000 ML/yr) to an average annual flow of 16,415,407 ML/yr.

Table 11 Baseflow proportions (%) for RC2014, RC2015 and calculated for seven CY gauges

Gauge Basin RC2014

baseflow (%)

RC2015 baseflow

(%)

LH calculated baseflow

104001A Stewart 63 43 29

105001B Normanby 75 42 42

105105A Normanby 68 29 29

105107A Normanby 95 40 33

107001B Annan 65 38 38

106001A Jeannie 66 36 35

106002A Jeannie 78 31 29

Modelled Loads Cape York contributes 5% of the total baseline TSS load being exported to the GBR. CY was the lowest

contributor of TSS load, and is one of the lowest contributors for all constituents other than DON. Within

the CY region, the Normanby River Basin was the greatest contributor for all constituents (Table 12).

All pesticide loads modelled in CY come from cropping and banana land uses in the Normanby,

Endeavour or Jeannie River basins.


56

Table 12 Contribution from CY basins to the total CY baseline load

Basin TSS

(kt/yr) TN

(t/yr) DIN (t/yr)

DON (t/yr)

PN (t/yr)

TP (t/yr)

DIP (t/yr)

DOP (t/yr)

PP (t/yr)

PSII (kg/yr)

PSII TE (kg/yr)

Jacky Jacky

52 1,064 67 728 269 77 11 22 44 0 0

Olive Pascoe

72 1,613 98 1,064 451 126 17 34 75 0 0

Lockhart 67 896 49 532 315 118 8 15 95 0 0

Stewart 49 516 30 333 153 60 5 10 45 0 0

Normanby 186 1,428 105 1,070 253 142 21 43 78 11.2 0.40

Jeannie 42 617 35 378 204 59 6 12 41 0.2 0.01

Endeavour 59 721 40 430 251 101 8 16 77 4.4 0.16

Total 526 6,854 423 4,536 1,896 682 76 152 454 16 1

Constituent load validation The extension of the modelling period through to 2014 for RC2015 provides eight years of water quality

monitoring data from the Kalpowar gauging station (Joo et al. 2012, Turner et al. 2012, Turner et al.

2013, Garzon-Garcia et al. 2015, Wallace et al. 2015), collected as part of the GBR Catchment Loads

Monitoring Program (GBRCLMP). Having eight years of water quality data collected by the GBRCLMP,

the data calculated by Kroon et al. (2012) and Joo et al. (2014) used as previous sources of validation

has not been utilised for RC2015. The GBRCLMP data sets are well sampled over the majority of the

events, this is in comparison to previous estimates which use historic ambient data often collected

sporadically and in low flow conditions.

A comparison was made between the mean GBRCLMP flow and loads (averaged over eight years,

2006–2014) and the modelled flow and load for the same location and the same time period (Figure

16). The modelled flow was within ±11% of the observed flow and constituent loads within ±35% at the

Kalpowar Crossing gauging station for the eight year period. Across the monitoring period, the model

is under-predicting eight of nine constituents, with only TSS over-predicted. Particulate nitrogen (PN)

and DON are the two constituents that have the greatest difference compared to monitored data, at -

35% and -29% respectively. The under-prediction of particulate nutrients has been previously discussed

(page 29), with questions around the reliability of the input data (number of samples used in

extrapolation across the region). With respect to DON, the monitoring data will be reviewed further to

improve the modelled load estimates. For example monitoring data indicates that nature conservation

areas are a large contributor of DON to the end of system load.


57

Figure 16 Average annual modelled versus measured load comparison for 2006-2014 period, Normanby River

Gauge at the Kalpowar Crossing (105017A)

The performance of the RC2015 baseline model to predict the range of constituents modelled across

the CY region was also assessed using a number of quantitative statistics (Moriasi et al. 2015). The

statistics and the corresponding model performance ratings are presented in Table 13. An overall

performance rating was established as recommended quantitative statistics revealed

conflicting/unbalanced performance ratings for the majority of the constituents. For ease of

presentation, ‘very good’ and ‘good’ rated sites are coloured green, while ‘satisfactory’ and ‘indicative’

are coloured orange, signalling that further work is required to improve the representation of these

constituents.

The RSR, R2 and NSE yield similar results for all constituents, with a mix of ‘good’ or better rankings

and some ‘indicative’ rankings. Eight of the 10 constituents achieved a ‘satisfactory’ or better rating.

Flow was ranked as ‘very good’ across three measures; TP, DOP and DIP also ranked ‘satisfactory’ or

better, while TSS was only ranked ‘good’ through the PBIAS measure. DIN was better than ‘good’ for

all measures while TN, PN and DON achieved mixed results.


58

Table 13 Moriasi et al. (2007), Moriasi et al. (2015) annual statistical measures of performance for each

constituent at the Kalpowar Gauge, Cape York

Constituent

RSR PBIAS NSE R2

Value Rating

(Moriasi 2007)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Flow 0.3 Very good 11.4 Satisfactory 0.9 Very good 1.0 Very good

TSS 1.3 Indicative -25.2 Indicative -0.8 Indicative 0.1 Indicative

TN 0.4 Very good 21.2 Satisfactory 0.9 Very good 1.0 Very good

PN 0.9 Indicative 36.8 Indicative 0.2 Indicative 0.0 Indicative

DIN 0.5 Good 1.2 Very good 0.8 Very good 0.8 Very good

DON 0.4 Very good 18.1 Good 0.9 Very good 0.8 Very good

TP 0.6 Good 21.8 Satisfactory 0.7 Very good 0.9 Very good

DIP 0.7 Satisfactory 20.0 Good 0.6 Good 0.6 Satisfactory

PP 0.4 Very good 20.4 Satisfactory 0.8 Very good 0.7 Good

DOP 0.6 Good 19.1 Good 0.7 Very good 0.5 Satisfactory

Contribution by land use Nature conservation and grazing (open and closed) account for 61% and 37% of the CY area

respectively and generate 99% of the fine sediment load and over 95% of all other constituent loads

exported (Figure 17). PSII toxic load is only derived from cropping land uses.

Figure 17 Contribution to fine sediment, particulate nutrient and DIN export by land use for the CY region


59

Sources and sinks Of the total fine sediment generated (902 kt/yr) in CY, 51% is exported to the GBR lagoon. Of the

material remaining in the catchment, 92% is deposited on the floodplain, and 8% instream. Over 90%

of TN is exported, while 80% of the TP is exported. DON makes up over half of the TN load, while PP

makes up 74% of the TP load. Thus, less TP is exported to the GBR than TN, because more TP is

deposited on the floodplain or instream via PP. In Appendix 3, Table 70 the sources and sinks for each

constituent are presented. The source sink breakdown for fine sediment is presented as a percent of

total supply in Table 14, for both CY and the Normanby Basin, and diagrammatically in Figure 18 for

the Normanby Basin. For the Normanby Basin, the improved gully density map (updated in RC2014)

has resulted in a more realistic spatial representation of gullies based on field measurements gully

erosion is now the major sediment source in that Basin.

Table 14 Sources and sinks of fine sediment in CY and the Normanby Basin (as percent of supply total)

CY (%) Normanby (%)

SUPPLY

Channel remobilisation 3 4

Gully 45 75

Hillslope surface soil 47 17

Streambank 5 4

LOSS

Floodplain deposition 38 58

In-stream deposition 3 5

Figure 18 Fine sediment source sink budget diagram for the Normanby Basin


60

Progress towards Reef Plan 2013 targets Minimal management changes were reported for CY for RC2015, with two gully improvement projects

(total 93Ha) and riparian fencing (four projects, total 36,391 Ha) reported in the Normanby Basin (Table

15). The gully improvement projects resulted in a 0.01% shift out of C class management practices into

B; while the riparian fencing projects are reflected as streambank management practise and resulted in

a 0.25% shift into C class, and a 0.72% shift into B class. There were no other grazing, cropping or

banana improvements reported. These small changes resulted in no impact on load reduction targets

from CY.

Table 15 CY grazing management practice proportions and change summary; values are percent of total area

Hillslope Baseline RC2014 Change RC2015 Change

A 0 0 0 0 0

B 20 20 0 20 0

C 47 47 0 47 0

D 33 33 0 33 0

Gully Baseline RC2014 Change RC2015 Change

A 0 0 0 0 0

B 34.21 34.21 0 34.22 0.01

C 46.55 46.55 0 46.54 -0.01

D 19 19 0 19 0

Streambank Baseline RC2014 Change RC2015 Change

A 0 0 0 0.00 0

B 27.8 27.8 0 28.47 0.72

C 42.0 42.0 0 42.27 0.25

D 30.2 30.2 0 29.26 -0.97


61

Wet Tropics

The results of the Wet Tropics modelling are presented below, separated into hydrology and modelled

loads. In the hydrology component, the results of the updated calibration process will be presented per

gauge, as well as the impact on the total flow for the WT region. The modelled loads section includes

the results of the total baseline, anthropogenic baseline and predevelopment loads. The validation of

the WT Source Catchments modelled data is then presented using load estimates from measured data.



mass balance summary of the sources and sinks by constituent. For a full list of the WT region loads

for RC2015 refer to Appendix 3, Table 71.

Hydrology calibration performance The modelled hydrology was recalibrated to improve the baseflow proportion contributing to total flow.

There is an improvement in the RC2015 baseflow proportion when compared to the Lyne-Hollick

calculated baseflow (Table 16). All gauges continued to meet the daily Nash-Sutcliffe, R2 and PBIAS

criteria. There was little difference in the range of total flow bias between this Report Card and RC2014.

Overall the annual flow for the WT increased by 1% to ~23,500,000 ML/yr with the biggest changes

occurring the Tully (6% increase) and in the Barron (8% decrease). All other basins had a <1% change

in total flow. The range of baseflow proportions of the 10 gauges before recalibration was 23%–97%

and after calibration was 31%–55%. The biggest changes in baseflow proportions from RC2014 and

RC2015 was from the Herbert gauge 116014A from 82% to 31%, closely matching the Lyne-Hollick

filter value of 29%. The other biggest change was in the Barron gauge 110016A from 97% to 55%,

again aligning closer to the Lyne-Hollick filter value of 47%.

Table 16 WT baseflow proportions for the current and previous Report Card

Gauge Basin RC2014

baseflow (%)

RC2015 baseflow

(%)

LH baseflow filter (%)

108002A Daintree 31 47 47

110016A Barron 97 55 47

110001A-D Barron 23 40 42

110020A Barron 27 40 44

111101A-D Russell-Mulgrave 31 48 52

113006A Tully 46 51 61

114001A Murray 36 48 49

116001A-F Herbert 24 38 39

116014A Herbert 82 31 29

116010A Herbert 62 47 45


62

Modelled Loads The WT contributes the third highest proportion of the total baseline TSS load being exported to the

GBR at 15%, equal to the Burnett Mary region (Table 10). For particulate nutrients, the WT and the

Fitzroy had the highest contributions of PN ~27% each and the third highest contribution of PP at 17%.

For dissolved nutrients, the DIN contribution was 46% of the GBR export load, roughly double that of

the second highest contributor, the Burdekin. The WT contributes the second highest contribution of

PSIIs (26%) after the Mackay Whitsundays (33%). The contribution to total WT load for each basin is

shown in Table 17. Within the WT region, the Herbert and/or Johnstone basins produced the largest

loads for most constituents, with the Mulgrave-Russell Basin also an important contributor. The

Johnstone, Mulgrave-Russell and Tully produced the largest PSII and PSII TE export loads. These

three basins contributed 74% of the PSII TE export load from the WT. Of the total PSII load, 73% was

attributed to Diuron. The proportion of PSII TE load to PSII load is 0.78. The total pesticide load exported

from the WT was 8,953 kg/yr, which includes both PSIIs and non-PSIIs. Compared to the baseline load

for RC2014, the biggest changes in export loads (± 20%) are for phosphorus species. All phosphorus

loads decreased except for DOP load which had a large increase (89%). The TSS load decreased by

9% and the export loads for nitrogen species increased (4–15%). The PSII load decreased by 16% and

the PSII TE load decreased by 14%.

Table 17 Contribution from WT basins to the total baseline load

Basin TSS

(kt/yr) TN

(t/yr) DIN (t/yr)

DON (t/yr)

PN (t/yr)

TP (t/yr)

DIP (t/yr)

DOP (t/yr)

PP (t/yr)

PSII (kg/yr)

PSII TE (kg/yr)

Daintree 103 1,841 478 784 579 146 19 44 83 118 98

Mossman 17 323 160 62 101 28 3 8 17 75 65

Barron 55 568 152 215 201 72 12 15 45 120 37

Mulgrave-Russell

253 2,769 934 609 1,227 392 40 76 276 746 625

Johnstone 379 3,799 1,059 754 1,986 864 35 72 756 922 753

Tully 157 2,155 777 503 875 259 24 54 181 492 414

Murray 74 1,121 414 307 400 121 12 25 84 367 310

Herbert 478 4,001 1,522 1,152 1,327 419 45 82 291 248 113

Total 1,516 16,577 5,496 4,385 6,696 2,301 192 376 1,733 3,090 2,415

Anthropogenic baseline and predevelopment loads

The anthropogenic baseline load was calculated by subtracting the predevelopment load from the total

baseline load. The TSS anthropogenic baseline load was 936 kt/yr or 62% of the total baseline load

with the remaining 38% attributed to the predevelopment load (Figure 19). For all basins except the

Daintree and Mossman the anthropogenic load was over half of the total TSS load (Figure 20) and

(Table 71 (Appendix 3). The anthropogenic baseline DIN load was 2,750 t/yr or 50% of the total baseline

load. The Mossman Basin had the highest proportion of the anthropogenic DIN baseline load to the

total load at 65%. The Herbert, Barron and Murray basins had similar anthropogenic proportions of

~57% each. Predevelopment DIN proportions to the total load were highest in the Daintree (72%),

followed by the Mulgrave-Russell, Johnstone and Tully basins (just over 50% each). Comparing across


63

the constituents, the highest anthropogenic baseline loads were PP at 70% followed by FRP at 65%

and TSS at 62%.

Figure 19 TSS (kt/yr) loads for the WT basins, predevelopment and anthropogenic baseline contributions

Figure 20 DIN (t/yr) loads for the WT basins, predevelopment and anthropogenic baseline contributions


64

Constituent load validation The first source of validation data was the GBRCLMP monitored loads for six sites within the WT NRM

region, for the period 2006–2014. For the modelled baseline loads, there is a general under prediction

in modelled TSS and particulate loads compared to the GBRCLMP loads, except for Tully River at

Euramo (Figure 21). The average percent bias across the five under predicted sites was -33% for TSS,

-29% for PN and -40% for PP. For Tully River at Euramo, the average across TSS and particulates is

within +50%. There was also a general under prediction for DIN modelled loads, except for Barron River

at Myola (Figure 22). All modelled DIN loads are within ±43% of the measured estimates. DON, FRP

and DOP modelled loads are all within ±38% of the measured estimates, mostly under predicted. Two

of the three PSII loads were also under predicting compared to the load derived from measured

estimates (Figure 23). See Table 81 to Table 83, Appendix 4 for the tabular results of the GBRCLMP

validation.

Figure 21 Fine sediment comparison between GBRCLMP measured loads and Source modelled loads for five

sites in the WT


65

Figure 22 DIN comparison between GBRCLMP measured loads and Source modelled loads for five sites in the

WT

Figure 23 PSII comparison between GBRCLMP measured loads and Source modelled loads for five sites in the

WT

The performance of the RC2015 baseline model was also assessed using a number of quantitative

statistics recommended in Moriasi et al. (2015). An overall performance rating was established as

recommended quantitative statistics revealed conflicting/unbalanced performance ratings for most of

the constituents. The overall performance was described conservatively using the least rating of the

recommended statistics. For ease of presentation, ‘very good’ and ‘good’ rated sites are coloured green,

while ‘satisfactory’ and ‘indicative’ are coloured orange, signalling that further work is required to

improve the representation of these constituents.


66

Of the 10 modelled constituents (flow, sediment and nutrients) across the five sites, 60% were ranked

as ‘indicative’, 18% ‘satisfactory’ and 22% ‘good’/’very good’. The North Johnstone site (Table 18 and

Table 19) ranked as the best overall, followed by South Johnstone and Barron. DON had the highest

performance ratings followed by DIP and flow. The lowest performing basin was Tully, ranked as

‘indicative’ for all model parameters. TSS and PP at all sites ranked as ‘indicative’. The data at each of

the five sites is presented in Appendix 4. Statistics could not be calculated at the daily or monthly time-

step due to the load calculation method used in the GRBCLMP data. Load comparisons were not

undertaken for the Tully Gorge at Tully river site nor for any of the PSII herbicides as there were less

than the required five years of data available.

Table 18 Moriasi et al. (2015) annual performance ratings at the North Johnstone River gauging station

compared to GBRCLMP annual loads

Site 112004A RC7 Annual Time period 2006-2014 (excludes 2008-2009)

Modelled parameter

RSR rating PBIAS rating NSE rating R2

Flow Good Satisfactory Good Very good

TSS Indicative Indicative Indicative Satisfactory

TN Good Good Very good Very good

PN Indicative Satisfactory Satisfactory Very good

DIN Very good Very good Very good Very good

DON Very good Very good Very good Very good

TP Satisfactory Satisfactory Good Good

DIP Very good Good Very good Very good

PP Good Indicative Satisfactory Satisfactory

DOP Indicative Satisfactory Satisfactory Very good

Table 19 Moriasi et al. (2015) monthly performance rating at the North Johnstone River gauging station

Site 112004A RC7 monthly Time period 1986–2009

Modelled parameter

RSR rating PBIAS rating NSE rating R2

Flow Very good Very good Very good Very good

TSS Good Indicative Satisfactory Good

TN Good Indicative Very good Very good

PN Indicative Indicative Satisfactory Very good

DIN Very good Very good Very good Very good

DON Very good Very good Very good Very good

TP Satisfactory Indicative Good Very good

DIP Very good Very good Very good Very good

PP Satisfactory Indicative Good Very good

DOP Indicative Indicative Satisfactory Very good

The same quantitative statistics were also calculated on the long-term monitored monthly loads (Joo et

al. 2014) (1986–2009) versus the modelled loads with the overall ranking for each constituent at each

of the five gauging stations (see Table 20). Of the 10 modelled constituents (flow, sediments and


67

nutrients) across the five sites, 50% were ranked as ‘indicative’, 10% ‘satisfactory’ and 40% ‘good’/’very

good’. The South Johnstone site ranked as the best overall, followed by the Herbert site. Flow

performed as the best parameter followed by DON. The lowest performing basin was the Barron.

Table 20 Moriasi et al. (2015) overall monthly performance rating for each constituent for the five gauge sites,

Wet Tropics

Overall rating RC2015 (1986–2009) Monthly

Modelled parameter

Barron North

Johnstone South

Johnstone Tully Herbert

Flow Very good Very good Very good Very good Very good

TSS Indicative Indicative Satisfactory Indicative Indicative

TN Indicative Indicative Good Indicative Very good

PN Indicative Indicative Satisfactory Indicative Satisfactory

DIN Indicative Very good Very good Very good Indicative

DON Good Very good Very good Good Satisfactory

TP Indicative Indicative Good Indicative Good

DIP Very good Very good Indicative Satisfactory Very good

PP Indicative Indicative Good Indicative Indicative

DOP Indicative Indicative Indicative Indicative Indicative

Contribution by land use Sugarcane and nature conservation account for 8% and 45% of the WT area respectively, and

generated 77% of the DIN export load (Figure 24), with sugarcane generating the largest load 2,572

t/yr (Figure 25). By areal rate, bananas generate the highest areal rate 16 kg/ha/yr, followed by

sugarcane 14 kg/ha/yr (Figure 25). The average DIN areal rate for all land uses in the WT is 3 kg/ha/yr.

Figure 24 Contribution to fine sediment, particulate nutrient and DIN export by land use for the WT region


68

Figure 25 WT DIN load and areal rate per land use


69

Sugarcane areal rates of TSS, DIN and PSIIs by basin

The average annual sugarcane areal rate for TSS was 2.4 t/ha/yr, with the Johnstone Basin

approximately 2.5 times the WT average (Table 21). The Daintree and Mossman basins had the highest

DIN areal rates of ~ 21.5 kg/ha/yr each. For PSIIs, the average areal rate was 16 g/ha/yr and the highest

rates were for Johnstone and Mulgrave-Russell basins at ~31 g/ha/yr each.

Table 21 TSS, DIN and PSII sugarcane areal rates by basin for the WT

Basin Sugarcane

TSS (t/ha/yr)

Sugarcane DIN

(kg/ha/yr)

Sugarcane PSIIs

(g/ha/yr)

Daintree 1.6 22 26

Mossman 1.2 21 16

Barron 0.3 9 7

Mulgrave-Russell 4.3 16 30

Johnstone 5.6 15 32

Tully 3.1 16 26

Murray 1.7 14 21

Herbert 0.8 13 3

WT 2.4 14 16

Sources and sinks The sources, sinks and resultant export of TSS is presented in Appendix 3, Table 72, and the source

sink budget is presented diagrammatically in Figure 26. The greatest sources of TSS in the WT were

from hillslopes 66% and streambanks 26%, with contribution from gullies 5%. Instream channel

remobilisation and deposition were enabled in RC2015 and channel remobilisation accounts for the

remaining 3% of supply. The gully supply load doubled from RC2014 due to a doubling of the gully

cross sectional area. Of the TSS generated (1,766 kt/yr) 86% is exported to the GBR lagoon. Of the

250 kt/yr TSS loss, the majority of this was floodplain deposition (48%) and instream deposition (45%).

A large proportion of the dissolved nitrogen supply (DIN and DON) (99%) was from diffuse dissolved

sources such as agriculture and nature conservation areas with the remaining 1% from point sources

(sewage treatment plants). A large proportion (99%) of the dissolved nutrients were exported. The DIN

export load for sugarcane is spilt into a surface runoff load and a seepage load. Of the total DIN supply

load from sugarcane (2,582 t/yr), 90% of the DIN load to the stream was from seepage (2,325 t/yr) and

10% to surface runoff (257 t/yr). The modelled PSII pesticide supplied to the stream was 3,505 kg/yr,

with 88% of supply (3,090 kg/yr) exported to the end of system. For herbicides, the main loss pathway

was from instream decay.


70

Figure 26 Fine sediment source sink budget diagram for WT

Progress towards Reef Plan 2013 targets In RC2015, investments in improved management practices were reported for sugarcane and bananas.

No changes were reported for grazing, streambank or gully management. Soil and nutrient

management changes for sugarcane were typically less than 1%. For example, the greatest change

was a shift in area into A class soil management of 0.8%. Pesticide management for sugarcane reported

larger shifts, with an increase of 2.8% from D and C class management into B class, and an increase

of 1.1% into A class. The management changes for sugarcane are presented in Table 22. There was a

much greater shift out of D and C class practices in RC2014 compared to RC2015.

For bananas there were shifts out of D and C class practices for soils, into B class (3.9%) with a similar

trend occurring for nutrients. No shifts into ‘A’ class management was reported for soil or nutrients for

bananas in the WT. The management practice summary for bananas is presented in Table 23. Similar

to sugarcane, there was a much greater shift out of D and C class practices into B in RC2014 compared

to RC2015.

The impact of RC2015 sugarcane management changes for pesticides is a 3.5% reduction in PSII toxic

load, while reductions for other constituents were in the range of 0.1–0.2% combined for sugarcane and

banana practice changes. The cumulative reductions since RC2010 are 13.6% for TSS, 14.7% for DIN

and 31.9% for PSII TE.


71

Table 22 Wet Tropics sugarcane management practice change summary; values are percent of total

Soil Baseline

2013 RC2014 Change RC2015 Change

A 9.2 13.4 4.2 14.2 0.8

B 31.4 31.9 0.4 32.0 0.1

C 43.4 42.4 -1.0 41.6 -0.8

D 16.0 12.3 -3.7 12.2 -0.1

Nutrients

A 2.0 7.9 5.9 7.9 0.0

B 10.4 11.1 0.7 11.6 0.5

C 84.3 77.7 -6.6 77.2 -0.5

D 3.2 3.2 0.0 3.2 0.0

Pesticides

A 3.9 4.1 0.2 5.3 1.1

B 8.4 15.9 7.5 18.7 2.8

C 80.9 74.9 -6.0 71.2 -3.7

D 6.9 5.2 -1.7 4.9 -0.2

Table 23 Wet Tropics banana management practice change summary; values are percent of total

Soil Baseline


A - - - - -

B 45 53.5 8.75 57.4 3.9

C 38 29.4 -8.46 28.1 -1.3

D 17 17.2 -0.29 14.6 -2.6

Nutrients

A - - - - -

B 42 52.3 10.74 55.6 3.3

C 50 40.0 -10.07 37.3 -2.7

D 8 7.7 -0.67 7.1 -0.6


72

Burdekin

The results of the Burdekin modelling are presented below, separated into hydrology and modelled


gauge, as well as the impact on the total flow for the Burdekin region. The modelled loads section

includes the results of the total baseline, anthropogenic baseline and predevelopment loads. The

validation of the Burdekin Source Catchments modelled data is then presented using load estimates

from measured data. Progress towards targets due to investment is reported against the anthropogenic

baseline for RC2015. A summary of the total baseline load by land use, and land use by basin is also

reported, as well as a mass balance summary of the sources and sinks by constituent. For a full list of

the Burdekin region loads for RC2015 refer Appendix 3.

Hydrology calibration performance Recalibrating to improve baseflow did not change the number of gauges meeting the performance

criteria. All gauges continued to meet the R2 and PBIAS criteria, and 42 of 46 continued to meet the

daily NSE criteria. The purpose of the baseflow recalibration was to improve the proportion of flow

attributed to quick and baseflow. In Table 24 the Lyne-Hollick filter (Lyne and Hollick 1979) was used

to separate baseflow in the observed data; and the modelled baseflow percent is also presented. There

is a very clear improvement in the RC2015 hydrology calibration in which the modelled baseflow

proportion better matches the Lyne-Hollick calculated baseflow.

In terms of impacts on discharge, the recalibration of hydrology in the Burdekin Region to improve the

baseflow component has seen average annual flow increased by 4% (~450,000 ML/yr) from RC2014

to an average annual flow of 12,720,701 ML/yr.

Table 24 Burdekin baseflow proportions for six gauges the current and previous Report Card

Gauge Basin RC2014

baseflow (%) RC2015

baseflow (%) LH baseflow

filter (%)

119101A Haughton 60 16 17

120302B Cape 100 14 16

120304A Belyando Suttor 24 17 11



Modelled Loads The Burdekin region contributes 38% of the total baseline TSS load being exported to the GBR. The

Burdekin Region was the highest contributor of TSS load, and is one of the highest contributors for all

constituents other than DON and DOP. The estimated baseline loads from RC2015 have not changed

significantly from RC2014, with a 2% increase in TSS modelled. Within the Burdekin region, the

Burdekin River Basin was the greatest contributor for all constituents, apart from PSII which is sourced


73

from the Haughton (Table 25). All pesticide loads modelled in the Burdekin Region come from

sugarcane and cropping land uses, with the load dominated by sugarcane from the Burdekin Irrigation

area.

Table 25 Contribution from Burdekin basins to the total Burdekin baseline load

Basin TSS (kt/y)

TN (t/yr)

DIN (t/yr)

DON (t/yr)

PN (t/yr)

TP (t/yr)

DIP (t/yr)

DOP (t/yr)

PP (t/yr)

PSII (kg/y)

PSII TE (kg/yr)

Black 62 385 97 145 143 105 21 7 77 2 2

Ross 62 416 180 138 98 94 33 11 50 2 0

Haughton 183 1,474 1,016 235 223 236 73 21 141 1,708 937

Burdekin 3,260 5,849 1,104 1,857 2,887 2,177 285 94 1,797 503 255

Don 213 669 177 186 305 211 27 10 174 179 90

Total 3,781 8,793 2,574 2,562 3,657 2,823 440 144 2,239 2,394 1,284


The TSS increase factor for the whole of the BU region is ~5.6; the Haughton, Don and Burdekin basins

have the greatest increase of ~6.3 and ~5.9 (both Don and Burdekin) times respectively, while the Ross

Basin has a ~3.8 times increase and the Black Basin has the least increase of ~1.2 times to the

predevelopment load (Figure 27).

In regard to DIN, the anthropogenic increase factor was 1.01 for the BU region. The Haughton Basin

was the main contributor to anthropogenic DIN with an increase factor of nine. All the other basins had

anthropogenic increase factors of less than 2.2 times, with the Burdekin (0.2 times), Black (0.3 times)

and DON (0.6 times) having a negligible anthropogenic DIN load. (Figure 28).


74

Figure 27 TSS (kt/yr) loads for the BU basins, highlighting the predevelopment and anthropogenic baseline

contributions

Figure 28 DIN (kt/yr) loads for the BU basins, highlighting the predevelopment and anthropogenic baseline

contributions


75

Constituent load validation Of the 10 modelled constituents (flow, sediment and nutrients) across the six sites, 70% were ranked

as ‘satisfactory’ or worse, with the remaining 30% being rated as ‘good’ or ‘very good’. The Burdekin

River at Home Hill site (Table 26) ranked as the best overall, followed by the Suttor River and Burdekin

River at Selheim sites (Appendix 4). Flow and DON were the best performing parameters. TN and PP

were the worst performing parameters, being rated as ‘indicative’ at each of the six sites, apart from the

Suttor where PN is rated as ‘good’. The data at each of the five sites is presented in Appendix 4.

Statistics could not be calculated at the daily or monthly time-step due to the load calculation method

used in the GRBCLMP data.

Table 26 Moriasi et al. (2015) overall annual performance rating for each constituent at 120001A Burdekin River

at Home Hill (120006B Burdekin River at Clare), n = 8 years for flow and n = 8 years for WQ)

Parameter

RSR PBIAS NSE R2

Value Rating (Moriasi

2007) Value

Rating (Moriasi

2015) Value

Rating (Moriasi

2015) Value

Rating (Moriasi

2015)

Flow 0.17 Very good 0.29 Very good 0.97 Very good 0.97 Very good

TSS 0.45 Very good 2.46 Very good 0.80 Good 0.80 Very good

TN 0.67 Satisfactory 33.23 Indicative 0.56 Good 0.87 Very good

PN 0.88 Indicative 47.64 Indicative 0.22 Indicative 0.77 Very good

DIN 0.69 Satisfactory -9.19 Very good 0.53 Good 0.85 Very good


TP 0.50 Very good 23.80 Satisfactory 0.75 Very good 0.93 Very good

DIP 0.46 Very good -4.13 Very good 0.79 Very good 0.83 Very good

PP 0.58 Good 28.76 Satisfactory 0.67 Very good 0.93 Very good

DOP 0.89 Indicative 44.41 Indicative 0.21 Indicative 0.50 Indicative

Contribution by land use Grazing (open and closed) and Conservation account for ~90% and ~5% of the Burdekin Region

respectively by area and therefore area generate 99% of the fine sediment load, and over 95% of all

other constituents, apart from DIN and PSII. DIN and PSII toxic loads are predominantly derived from

sugarcane with sugarcane accounting for ~45 % of the modelled DIN export.


76

Figure 29 Contribution to fine sediment, particulate nutrient and DIN export by land use for the BU region

Sugarcane and grazing account for <1% and 90% of the BU area respectively, and between them

generate 87% of the DIN export load (Figure 30). Sugarcane generated the largest load (1,175 t/yr).

Per unit area, sugar also generates the highest average annual load (11 kg/ha/yr), while grazing has

the lowest (<0.1 kg/ha) (Figure 30). The large grazing load is a function of its large area.

Figure 30 DIN export load and areal rate for each land use in the BU

Sources and sinks Of the total modelled fine sediment load (6,527 kt/yr) in the Burdekin region (Appendix 3, Table 74),

58% is exported to the GBR lagoon with the remaining 42% largely lost to reservoir deposition (61%),


77

floodplain deposition (32%), and the remainder lost to extractions (7%). No instream deposition of fine

sediment was modelled due to a lack of supporting data to quantify the deposition component at the

time of model development, though instream deposition and remobilisation could be enabled in future

models if data to support it become available. For TN, 78% of the generated load is exported, while

64% of the generated TP load is exported. The Burdekin fine sediment source sink budget is presented

diagrammatically in Figure 31.

Figure 31 Fine sediment source sink budget for the Burdekin

Progress towards Reef Plan 2013 targets Management practice changes that occurred in grazing lands are presented in Table 74. Small changes

were made in streambank and gully management, with a 2% shift from C to B practices occurring in

hillslope targeted practices. No changes were recorded for sugarcane soil erosion (Table 27), with minor

changes occurring in cropping (grains) land management for soil erosion (Table 28) where a shift from

C to A class was recorded. Collectively these changes result in no load reduction reported for fine

sediment from the Burdekin region, as the changes result in a less than 0.5% reduction in load.

With respect to nutrients, for sugarcane (Table 27) there was a shift from D/C classes to C/B for

nutrients, while no management changes were reported from cropping (grains) (Table 28). The land

management practice improvements in sugarcane resulted in a 4% reduction in DIN from the Burdekin

region. This contributed to the 1% overall GBR reduction.

Improvements were recorded in both sugarcane and cropping (grains) land uses for pesticide use. In

sugarcane there was a big shift (10%) out of C class practices into A and B practices, while for cropping

areas there was a 4.5% shift from B to A. This resulted in a 3.5% reduction in PSII toxic equivalent

loads from the Burdekin region, contributing to the 3% overall GBR reduction.


78

Table 27 Burdekin grazing management change; values are percent of total


A 0.4 0.7 0.3 0.7 0.0

B 28.7 29.2 0.5 31.5 2.3

C 59.1 58.6 -0.5 56.3 -2.3

D 11.8 11.6 -0.2 11.6 0.0


A 5.5 6.0 0.5 6.0 0.0

B 20.7 20.2 -0.5 20.5 0.3

C 55.9 55.9 0 55.5 -0.3

D 18.0 18.0 0 18.0 0.0


A 32.6 32.6 0 32.7 0.1

B 29.0 29.1 0.1 29.2 0.1

C 13.8 13.7 -0.1 13.6 -0.1

D 10.0 9.9 -0.1 9.9 -0.1

NA 0.4 0.7 0.3 14.7 0.0


79

Table 28 Burdekin sugarcane management practice change summary; values are percent of total

Soil Baseline


A 3 3 0 3 0

B-A 2 2 0 2 0

B 11 12 1 12 0

C-B 32 32 0 32 0

C 26 25 -1 25 0

D-C 12 12 0 12 0

D 14 14 0 14 0

Nutrients Baseline


A 3 3 0 3 0.4

B 5 7 2 10 2.9

C-B 12 11 -1 13 1.3

C 54 53 -1 50 -3.3

D-C 16 15 -1 15 -0.1

D 11 10 -1 9 -1.1

Pesticides Baseline


A 7 7 0 8 0.6

B 19 18 -1 29 10.2

C 47 47 0 37 -10.3

Df 28 28 0 27 -0.5

Irrigation Baseline


A 9 9 0 9 0.0

B-C 8 8 0 10 1.2

D 83 83 0 81 -1.2

Recycle Pits Baseline


A 19 20 1 21 1.8

B 16 19 3 26 6.6

C 27 25 -2 22 -2.7

D 38 36 -2 31 -5.7


80

Table 29 Burdekin grains management change; values are percent of total

Soil Baseline 2013 RC2014 Change RC2015 Change

A 4 8 4 9 1.2

B 82 83 1 83 0.0

C 14 9 -5 8 -1.2

D 0 0 0 0 0.0

Nutrient Baseline 2013 RC2014 Change RC2015 Change

A 5 5 0 5 0.0

B 43 43 0 43 0.0

C 52 52 0 52 0.0

D 0 0 0 0 0.0

Pesticide Baseline 2013 RC2014 Change RC2015 Change

A 5 6 1 10 4.5

B 26 26 0 21 -4.5

C 69 69 0 69 0.0

D 0 0 0 0 0.0


81

Mackay Whitsunday

The results of the Mackay Whitsunday modelling are presented below, separated into hydrology and

modelled loads. In the hydrology component, the results of the updated calibration process will be

presented per gauge, as well as the impact on the total flow for the MW region. The modelled loads

section includes the results of the total baseline, anthropogenic baseline and predevelopment loads.

The validation of the MW Source Catchments modelled data is then presented using load estimates

from measured data. Progress towards targets due to investment is reported against the anthropogenic

baseline for RC2015. A summary of the total baseline load by land use, and land use by basin is also

reported, as well as a mass balance summary of the sources and sinks by constituent. For a full list of

the MW region loads for RC2015 refer to Appendix 3, Table 75.

Hydrology calibration performance The Proserpine River Basin produces the highest modelled mean annual flows (~35%) in the region,

followed by the O’Connell River, Plane Creek, and Pioneer River Basins contributing approximately

29%, 20% and 16% for the regional flows respectively. This is consistent with RC2014. Only four

gauges were recalibrated for hydrology (which are highlighted green in Table 30), and in all instances

the baseflow contribution increased. However, the recalibration of hydrology to better represent the

regional baseflow has reduced the average annual flow only by approximately 0.5% compared to

RC2014.

Table 30 MW baseflow proportions for the current and previous Report Card

Gauge Basin RC2014

baseflow (%) RC2015


filter (%)

122012A Proserpine <1 9 9

124001B O’Connell 23 23 20

124002A O’Connell 15 28 28

124003A O’Connell 28 28 29

125001B Pioneer 24 24 30

125002C Pioneer 21 21 22

125004B Pioneer 33 33 38

126001A Plane 6 14 14

126002A Plane 18 18 18

126003A Plane 10 11 18

Modelled Loads In Table 31 the contribution to total MW load of each constituent by basin is presented. The estimated

baseline TSS load exported from MW has increased by 34% from RC2014 (611kt/yr). The O’Connell

River Basin has the highest modelled mean annual TSS exports of 314 kt/yr (~38% of MW load); this

is consistent with RC2014. Plane Creek Basin has the lowest modelled mean annual TSS export of 146

kt/yr, which is ~18% of the regional TSS export; in RC2014 the Proserpine Basin was the lowest

exporter though only 3kt/yr was the difference between Plane Creek and Proserpine Basins. Dominance


82

of the modelled sediment erosion from grazing land use and stream banks in the O’Connell River Basin

are the key reasons for its highest contribution to the regional TSS load.

The Plane Creek Basin has the highest proportion of sugarcane land use (~42%) when compared to

other MW Basins. Consequently, the Plane Creek Basin has the highest modelled mean annual exports

of PSIIs 1,758 kg/yr and DIN 464 t/yr for the MW region. This equates to 43% of the PSII load and 34%

of the DIN load for the MW region. In contrast, the Proserpine Basin, with the least sugarcane land use

(~15% of the MW sugarcane area) had the lowest modelled mean annual loads for total toxic load 331

kg/yr (~8% of MW load). Similarly, the modelled mean annual load for DIN is lowest with 256 kg/yr

(~19% of the MW load) in the Pioneer River Basin, which has the third lowest sugarcane land use

(~22% of the MW sugarcane area), next to the O’Connell River Basin’s contribution of 21% to the MW

sugarcane area. Dominance of the grazing land use in the Proserpine River Basin (~29% of the MW

grazing area), compared to that of the Pioneer River Basin (~14% of the MW grazing area), may have

increased DIN contributions in the Proserpine River Basin, given that the RC2015 model indicated the

grazing land use contributes approximately 22% of the regional DIN load.

Table 31 Contribution from MW basins to the total MW baseline load

Basin TSS (kt/y)

TN (t/yr)

DIN (t/yr)

DON (t/yr)

PN (t/yr)

TP (t/yr)

DIP (t/yr)

DOP (t/yr)

PP (t/yr)

PSII (kg/y)

PSII TE

(kg/yr)

Proserpine 131 1,066 310 370 387 255 73 19 164 331 248

O'Connell 314 1,528 325 358 845 495 62 16 416 971 701

Pioneer 227 933 256 227 450 237 42 11 184 985 707

Plane Creek

146 1,279 464 347 468 319 73 19 228 1,758 1269

Total 818 4,806 1,355 1,301 2,150 1,306 249 65 992 4,044 2,925

Anthropogenic baseline and pre-development loads

The TSS increase factor for the whole of the MW region is ~3.6; the Pioneer and O’Connell basins both

have the greatest increase of ~4.3 times, while the Plane Creek Basin has a ~3.1 times increase and

the Proserpine Basin has the least increase of ~2.3 times to the predevelopment load (Figure 32).

In regard to DIN, the anthropogenic increase factor was 3 for the MW region. All four basins had a

significant anthropogenic contribution to DIN, primarily due to sugarcane production in the area. The

Proserpine had the lowest increase factor of 2, while the Plane Creek Basin was exporting nearly 4.7

times the predevelopment load (Figure 33).


83

Figure 32 TSS (kt/yr) loads for the MW basins, highlighting the predevelopment and anthropogenic baseline

contributions

Figure 33 DIN (kt/yr) loads for the MW basins, highlighting the predevelopment and anthropogenic baseline

contributions


84

Constituent Load Validation

GBRCLMP Comparisons

The modelled mean annual flows and pollutant loads are compared against the GBRCLMP load

estimates in the Pioneer River at Dumbleton Pump Station (Figure 32) and Sandy Creek at Homebush

(Figure 33).

Figure 34 Mean Annuals Modelled VS. GBRCLMP in the Pioneer River at Dumbleton Pump Station

Figure 35 Mean Annuals Modelled VS. GBRCLMP in Sandy Creek at Homebush

Overall the RC2015 baseline model estimated mean annual flows and loads of most pollutants fairly

well at the Pioneer River and Sandy Creek End of System (EoS) sites. However, the model

overestimated the mean annual DIN load at the Sandy Creek EoS site. The model also overestimated

total toxic loads at both EoS sites. It should be noted that sugarcane covers ~50% and ~20% of the

upstream area of the Sandy Creek and Pioneer River EoS sites respectively. It should be noted that


85

the modelled DIN and pesticide (PSII) loads will not match observed loads because typical rates and

timing of fertiliser and pesticide applications are used in the model not actual values. Hence, any

inconsistency between the amount and timing of fertilisers and pesticides applied on field and the values

used in the APSIM model simulations will be reflected in outputs. Therefore the comparison between

modelled and observed loads should only be seen as indicative until local data becomes available.

The performance of the RC2015 baseline model to predict the inter-annual loads across the MW region

was also assessed using a number of quantitative statistics recommended in Moriasi et al. (2015). The

estimated quantitative statistics and the corresponding model performance ratings are presented in

Table 32 and Table 33 for the Pioneer River and Sandy Creek EoS sites. An overall performance rating

was established as recommended quantitative statistics revealed conflicting/unbalanced performance

ratings for most of the constituents. The overall performance was described conservatively using the

least rating of the recommended statistics. For ease of presentation, ‘Very Good’ and ‘Good’ rated sites

are coloured green, while ‘Satisfactory’ and ‘Indicative’ are coloured orange, indicating that further work

is required to improve the representation of these constituents.

Based on the overall performance rating the water quality model at the Pioneer River EoS site achieved

a ‘Very Good’ rating for annual TSS loads. The model achieved a rating of ‘Good’ for annual particulate

phosphorus loads while it was ‘indicative’ only to estimate annual particulate nitrogen loads. The model

performance was ‘very good’ at the Sandy Creek EoS site to estimate annual particulate phosphorus

while it was ‘indicative’ to estimate annual TSS and particulate nitrogen loads. The RC2015 baseline

model was ‘Indicative’ to estimate DIN loads at both EoS sites.

Table 32 Moriasi et al. (2007) annual statistics for GBCLMP versus modelled data for the Pioneer River at

Dumbleton Pump Station (125013A) n = 8 year for flow data, n = 8 years for WQ data

Constituent

RSR PBIAS NSE R2

Value Rating

(Moriasi 2007) Value

Rating (Moriasi 2015)

Value Rating


Rating (Moriasi

2015)


TSS 0.4 Very good -1.2 Very good 0.9 Very good 1.0 Very good

TN 0.6 Satisfactory 26.1 Satisfactory 0.6 Good 0.9 Very good

PN 0.8 Indicative 36.8 Indicative 0.4 Satisfactory 1.0 Very good

DIN 0.9 Indicative -3.4 Very good 0.3 Indicative 0.3 Satisfactory

DON 0.5 Good 21.0 Satisfactory 0.7 Very good 0.9 Very good

TP 0.5 Good 16.5 Good 0.7 Very good 1.0 Very good

DIP 0.5 Very good 3.3 Very good 0.8 Very good 0.8 Very good

PP 0.5 Good 13.9 Very good 0.7 Very good 1.0 Very good

DOP 0.7 Indicative 44.3 Indicative 0.3 Indicative 0.9 Very good


86

Table 33 Moriasi et al. (2007) annual statistics for GBCLMP versus modelled data for Sandy Creek at Homebush

(126001A) n = 5 year for flow data, n = 5 years for WQ data

Constituent

RSR PBIAS NSE R2

Value Rating

(Moriasi 2007)

Value Rating



Value Rating

(Moriasi 2015)


TSS 0.7 Indicative -56.1 Indicative 0.5 Satisfactory 0.8 Very good

TN 0.3 Very good -16.7 Good 0.9 Very good 0.9 Very good

PN 0.2 Very good 2.3 Very good 1.0 Very good 0.9 Very good

DIN 1.6 Indicative -102.9 Indicative -1.5 Indicative 0.2 Indicative

DON 0.2 Very good 2.5 Very good 1.0 Very good 0.9 Very good

TP 0.3 Very good -16.4 Good 0.9 Very good 1.0 Very good

DIP 0.2 Very good 15.8 Good 1.0 Very good 1.0 Very good

PP 0.5 Very good -34.9 Indicative 0.8 Very good 0.9 Very good

DOP 0.3 Very good -3.0 Very good 0.9 Very good 0.9 Very good

FRCE Comparisons

The following plots compare the long-term annual TSS (Figure 36) and DIN (Figure 37) loads estimated

by the model against that estimated by Joo et al. (2014). There is good agreement between the FRCE

and modelled loads for TSS across the model period. Long-term comparisons for DIN also showed

good agreement though a larger range of under/over prediction compared to TSS. For both TSS and

DIN it is evident from the graphs that for the period 2009-2014 the model has improved its predictive

capability and modelled loads are more in line with measured loads. This is given the availability of

more data against which to calibrate, and validate, the models. Plots comparing the annual loads of the

rest of the pollutants estimated by the model and Joo et al. (2014) are presented in Appendix 4.

Figure 36 Long-term annual TSS Loads Modelled VS. FRCE in the Pioneer River at Dumbleton Pump Station


87

Figure 37 Long-term annual DIN Loads Modelled VS. FRCE in the Pioneer River at Dumbleton Pump Station

P2R Comparisons

The performance of the RC2015 baseline model to predict sub-catchment scale water quality was

assessed by comparing the modelled flows and pollutant loads leaving the Multifarm subcatchment

against those estimated as part of the P2R monitoring program (Joo et al. 2012). The multi-farm sub-

catchment is located in the Plane Creek basin, approximately 9 km north-west of the Sandy Creek at

Homebush gauging station. The sub-catchment covers approximately 3000 hectares and contains 48

farms (some whole and others part farms). The Multifarm subcatchment is dominated by sugarcane

(~78% sugarcane, 14% grazing, 6% other and 2% nature conservation and forestry) and thus

comparison of modelled and measured flows and pollutant loads is critical to assess the model

performance to represent sugarcane related water quantity and quality processes. The modelled annual

flows and pollutant loads at the Multifarm subcatchment were modified to compare against those

measured by the P2R program. This was done to rectify the irregularities observed between the

modelled and measured sub-catchment area and discharge characteristics of the subcatchment.

Detailed descriptions of the modifications to the modelled and measured flows and loads are

summarised in Appendix 1.

In Figure 38 the modelled mean annual flows and pollutant loads during the low and medium flow events

are compared against those of the P2R estimates at the Multifarm site. Overall the RC2015 baseline

model estimated mean annual flows and loads of most pollutants well at the Multifarm site during low

and medium flow events.


88

Figure 38 Mean annual Source Catchments modelled and P2R at Multifarm during low and medium flow events

Streambank Erosion Model Validation

A recent stream stability assessment (Alluvium, 2014) estimated sediment export from major erosion

sites due to streambank erosion in the following five reaches of the O’Connell River system:

Reach 1- Extends from Dingo Creek confluence to the sea.

Reach 2 - Extends from a point two hundred metres downstream of the Boundary Creek

confluence to the Dingo Creek confluence.

Reach 3 –Extends from Horse Creek confluence to two hundred metres downstream of the

Boundary Creek confluence.

Reach 4 –Extends from Cathu-O’Connell River Road to Horse Creek confluence.

Reach 5 –Extends from the headwaters to Cath-O’Connell River Road.

The volume of sediment eroded between 2010 and 2014 was estimated using digital elevation models

(DEM) derived from LiDAR data sets acquired in those years. It should be noted here that the sediment

export estimated using temporal analysis of LiDAR derived DEMs between 2010 and 2014 is likely to

include erosion of both fine and coarse sediment fractions. Therefore, sediment export estimated as

part of the stream stability assessment were halved to assess the performance of the RC2015 baseline

model to estimate only the fine sediment export from streambank erosion along the O’Connell River

between 2010 and 2014 (Table 34).

The comparison is encouraging given the modelled streambank erosion being within 11, 20, and 33

percent of the Alluvium estimates for reaches 1, 3, and 4-5 respectively. Overall the RC2015 model has

underestimated streambank erosion only by approximately 16% in the O’Connell River. The model

under-predicts streambank erosion at three of four reaches but a maximum of 33% (Table 34).


89

Table 34 Modelled Fine Sediment export in the O’Connell River against LiDAR estimates for fine sediment export

between 2010 and 2014

River Reaches Corresponding

Catchments in the Model

Stream Stability Assessment

Estimates for Fine Sediment Export

(m³)

Modelled Fine Sediment Export (m³)

Reach 1 SC #42,SC #43,SC #45 85,000 68,146

Reach 2 SC #148 2,500 8,112

Reach 3 SC #46 25,000 16,534

Reach 4 & Reach 5 SC #47 28,500 25,452

Contribution by land use Sugarcane and grazing (closed and open) account for approximately 19% and 44% of the MW area

respectively, and between them generate approximately 86% of the DIN export load (Figure 40).

Grazing and sugarcane contribute approximately 75% of the regional TSS and particulate nitrogen and

phosphorus loads (Figure 39). Sugarcane land use produces most of the PSIIs (and thus total toxic)

loads and the largest DIN load (~67%) in the region.

Figure 39 Constituent contribution (%) by land use for four constituents

Sugarcane generated the largest average annual DIN load (~ 885 t/yr). Per unit area, sugarcane

generate the highest average annual load (~ 5.3 kg/ha/yr), followed by cropping (~ 2.5 kg/ha/yr) (Figure

40). The average DIN per unit area rate for all land uses in the MW is approximately 1.8 kg/ha/yr.


90

Figure 40 DIN export load and areal rate for each land use in the MW

Sources and sinks The fine sediment source (hillslope/gully/streambank) and sink (extractions/deposition) budget for MW

is presented in Figure 41. Hillslope erosion is the dominant erosion supply (~54% of total) generated

from grazing, sugarcane, and cropping land uses. Streambank erosion is the other dominant supply

(~31%). Erosion from all other land use types (e.g. nature conservation, forestry, urban, etc.) constitutes

~14% of the regional TSS supply. Gully erosion contribution for the regional TSS supply is low (~1%).

RC2015 baseline model indicates that approximately 96% of the regional TSS supply is exported, while

only ~4% is lost to reservoir deposition, extraction and floodplain deposition. Reservoirs in the region

trap approximately 50% of the total TSS load that is lost, while extraction and flood plain deposition

account for 32% and 19% of the total TSS loss respectively.


91

Figure 41 Fine sediment source sink budget diagram for MW

Progress towards Reef Plan 2013 targets Overall there has been only marginal progress made towards the reef targets for the regional TSS, DIN

and total toxic loads following the RC2015 management practice investments and associated

improvements (Table 36 and Table 35). RC2015 models estimated < 0.1% reduction in TSS and

particulate nutrient loads due to 252ha of hillslope, 252ha of gully, 252ha of riparian, and approximately

1.7 km of streambank improvement projects around grazing management. Approximate reductions of

1.1% and 2.9% were estimated for DIN and total toxic loads.

Table 35 MW grazing management practice change summary; all values are in percent


A 8.00 8.01 0.01 8.01 0.00

B 30.00 30.75 0.75 31.02 0.27

C 25.00 24.61 -0.39 24.44 -0.17

D 37.00 36.63 -0.37 36.53 -0.10


A 0.02 0.02 0.00 0.03 0.01

B 37.00 37.00 0.00 37.08 0.07

C 38.00 38.00 0.00 37.97 -0.02

D 24.00 24.00 0.00 23.94 -0.06


A 0.96 0.96 0.00 1.02 0.05

B 17.00 17.10 0.10 17.23 0.13

C 46.00 45.94 -0.06 45.78 -0.15

D 35.00 34.96 -0.04 34.93 -0.03


92

Table 36 MW sugarcane management practice change summary; all values are in percent

Soil Baseline


A 1.00 1.00 0.00 1.00 0.00

B-A 3.21 3.24 0.02 3.24 0.02

B 36.79 36.76 -0.02 36.88 0.10

C-B 14.00 14.00 0.00 13.75 -0.25

C 17.00 17.00 0.00 18.97 1.97

D-C 8.96 9.01 0.05 7.18 -1.78

D 19.04 18.99 -0.05 18.99 -0.05

Nutrients Baseline


A 0.00 0.00 0.00 0.00 0.00

B 19.45 19.89 0.44 20.88 1.43

C-B 28.50 28.34 -0.17 30.65 2.14

C 30.29 30.12 -0.17 29.66 -0.63

D-C 19.68 19.57 -0.11 19.68 0.01

D 2.09 2.09 0.00 -0.86 -2.95

Pesticides Baseline


A 4.13 4.13 0.00 4.13 0.00

B 32.48 32.48 0.00 36.50 4.02

C 30.81 30.81 0.00 27.55 -3.26

Df 32.58 32.58 0.00 31.82 -0.76

Irrigation Baseline


A 4.60 4.60 0.00 4.60 0.00

B-C 14.00 14.07 0.07 14.07 0.07

D 81.40 81.33 -0.07 81.33 -0.07

Recycle Pits

Baseline 2013

RC2014 Change RC2015 Change

A 3 3 0 3 0

B 19 19 0 19 0

C 46 46 0 46 0

D 31 31 0 31 0


93

Fitzroy

The results of the Fitzroy modelling are presented below, separated into hydrology and modelled loads.

In the hydrology component, the results of the updated calibration process will be presented per gauge,

as well as the impact on the total flow for the Fitzroy region. The modelled loads section includes the

results of the total baseline, anthropogenic baseline and predevelopment loads. The validation of the

Fitzroy Source Catchments modelled data is then presented using load estimates from measured data.



mass balance summary of the sources and sinks by constituent. For a full list of the Fitzroy region loads


Hydrology calibration performance The hydrology in the Fitzroy region was recalibrated to improve the baseflow proportion. Of the 44

gauges used for the previous calibration, 39 were recalibrated. Gauges in the Calliope and Boyne

basins were not recalibrated as the base flow proportions in these basins were considered satisfactory.

Recalibrating the hydrology in the Fitzroy region to improve the baseflow proportion has resulted in the

total annual flow for the Fitzroy region decreasing by 1% from RC2014, a reduction of approximately

50,000 ML/yr. This resulted in a total average annual flow for this report card of ~9,200,000 ML/yr.

There was a decrease of approximately 90,000 ML/yr for the Fitzroy Basin and a ~20,000 ML/yr

increase in the Styx and Shoalwater basins. There was little change (<1%) for flow in the remainder of

basins. The difference between the estimated model flow and the observed flow at gauges for RC2014

and this RC2015 are shown in Table 37 for a number of key gauges that were recalibrated.

Table 37 Modelled flow differences for RC2014 and RC2015 compared to observed flows

Gauge Gauge Name

RC2014 Flow

Difference (%)

RC2015 Flow Difference

(%)

129001A Waterpark Creek at Byfield 0 0

130005A Fitzroy River at The Gap 1 -7

130206A Theresa Creek at Gregory Highway

9 0

130322A Dawson River at Beckers 0 0

130401A Isaac River at Yatton 0 2

130506A Comet River at The Lake 5 -4

An assessment of the models predicted baseflow compared to the Lyne-Hollick baseflow filter applied

to observed data shows that the average difference between observed and modelled baseflow for all

calibrated gauges improved from a 29% difference for RC2014 to 2% for RC2015. In the previous report

card 15 gauges had a difference between the modelled base flow and the Lyne-Hollick calculated base

flow of greater than 50%, for this report card no gauge had a difference greater than 10%. In Table 38


94

a sample of the base flow percentages for the recalibrated gauges and the improvement in estimated

base flow volumes that occurred is shown.

Table 38 Base flow proportions (%) for RC2014 and RC2015 compared to observed estimates

Gauge Gauge Name RC2014

baseflow (%)

RC2015 baseflow

(%)


129001A Waterpark Creek at Byfield 47 29 27

130005A Fitzroy River at The Gap 67 31 26


34 10 8

130322A Dawson River at Beckers 89 24 19

130401A Isaac River at Yatton 99 23 18

130506A Comet River at The Lake 98 23 20

Modelled Loads The Fitzroy region is the second largest contributor of fine sediment (18%) to the GBR behind the BU

and is the highest contributor of DIP (49%) and PP (33%). It is also second largest contributor for PN

(26%). In terms of total PSII load, the Fitzroy is the fourth highest contributor (18%), but when this is

converted to diuron toxic equivalent load the contribution is less than 1% due to the low amount of

diuron used in the region. There has been a 2% increase in TSS loads from RC2014

The Fitzroy Basin occupies the largest area in the Fitzroy region at 142 000 km2 or 91% of the area

and as a result, it produces the largest load for all constituents. It contributes 83% of the TSS load

(1,507 kt/yr), 58% of the TN load (6,276 t/yr), and 59% (2,750 t/yr) of the TP load (Table 39). Both the

Styx and Shoalwater contribute significant nutrient loads for their size, in part due to their proximity to

the coast. The Styx at 1.9% of the region area contributes 11% (1,210 t/yr) and 12% (541 t/yr) of the

TN and TP loads respectively, while the slightly larger Shoalwater Creek Basin (2.3% of the total area)

contributes 9% for both TN and TP. There has been a significant increase in PN (55%), with decreases

in DIN and DON. PP has decreased by 37%, as has DIP (11%) while DOP increased. PSII TE has

remained about the same.


95

Table 39 Contribution from Fitzroy basins to the total Fitzroy baseline load

Basin TSS

(kt/yr) TN

(t/yr) DIN (t/yr)

DON (t/yr)

PN (t/yr)

TP (t/yr)

DIP (t/yr)

DOP (t/yr)

PP (t/yr)

PSII (kg/yr)

PSII TE (kg/yr)

Styx 104 1,210 91 293 829 541 91 22 428 203 4

Shoalwater Creek

67 984 100 259 646 408 80 20 308 107 2

Water Park Creek

65 1,478 65 161 1,268 579 50 12 517 15 0

Fitzroy 1,507 6,276 799 2,425 3,066 2,750 743 185 1,822 1,749 38

Calliope 57 638 47 152 439 281 47 12 221 97 2

Boyne 24 264 37 116 113 105 36 9 60 39 1

Total 1,824 10,851 1,140 3,406 6,361 4,665 1,047 260 3,357 2,210 47

Constituent load validation Three data sources were used to assess model performance. The key validation dataset used to

validate the model were the GBRCLMP 2006–14 monitoring program established by the Qld State

Government (Turner et al. 2012). Further validation was conducted against the long-term (1986 – 2009)

loads report (Joo et al. 2014). The third source was event sampling data collected throughout the Fitzroy

Basin via a number of short term monitoring programs.

A comparison was made between the mean GBRCLMP flow and loads (averaged over eight years,

2006–2014) and the modelled flow and load for the same location and the same time period. Due to

limited availability of measured data the comparison for Theresa Creek only covers two years and for

the Dawson River only three years. The modelled fine sediment load matches well at the Fitzroy River

at Rockhampton site, with a slight over-prediction (~3%), and also matches well at the Comet River

(10%) (Figure 42). Fine sediment is over-predicted at the Dawson River and Theresa Creek sites. For

PN, the model is under-predicting at three of four sites (Figure 43), only over-predicting at Theresa

Creek. PP is under-predicted at the Fitzroy River at Rockhampton and Comet River at Comet Weir

sites, and over-predicted at the other sites (Figure 46).


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Figure 42 Fine sediment average annual modelled (Source Catchments) versus measured load comparison for

2006-2014* period, for the four monitoring sites in the Fitzroy region. *Data for Theresa Creek is only for two years, and for the Dawson River three years.

Figure 43 Particulate nitrogen average annual modelled versus measured load comparison for 2006-2014*

period, for the four monitoring sites in the Fitzroy region. *Data for Theresa Creek is only for two years, and for the Dawson River three years.


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Figure 44 Particulate phosphorus average annual modelled versus measured load comparison for 2006-2014*

period, for the four monitoring sites in the Fitzroy region. *Data for Theresa Creek is only for two years, and for the Dawson River three years.

A number of quantitative statistics, recommended in Moriasi et al. (2015) were calculated using the

measured (GBRCLMP data (2006-2014)) and modelled (RC2015 baseline) loads across the Fitzroy

region. The estimated quantitative statistics and the corresponding model performance ratings are

presented in Table 40 and Table 41 for the Fitzroy and Comet Rivers, respectively. An overall


conflicting/unbalanced performance ratings for most of the constituents. The overall performance was

described conservatively using the least rating of the recommended statistics. For ease of presentation,

‘very good’ and ‘good’ rated sites are coloured green, while ‘satisfactory’ and ‘indicative’ are coloured

orange, signalling that further work is required to improve the representation of these constituents.

At the Fitzroy River site, all four measures yield similar results for all constituents, typically with ‘very

good’ rankings. This represent an extremely good result overall and indicates that the baseline model

is performing well predicting loads for all constituents at the end of system site. Only PN had two

‘satisfactory’ ratings for the RSR and R2 measures. The PBIAS measure shows the model results were

very good results with a bias within 8% for all constituents but PN. Statistics could not be calculated at

the daily or monthly time-step due to the load calculation method used in the GRBCLMP data. At the

Comet River site, the ratings for RSR, NSE and R2 were generally consistent, with largely very good

ratings for all constituents except for TSS which was rated as indicative. With respect to the PBIAS

measures at the Comet River site, TSS, TN, DIN, DON, PP and DOP are all rated good or better, while

flow, PN, TP and DIP can be considered indicative at best.


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Table 40 RC2015 (1300000 Fitzroy River at Rockhampton, n = 7 years for flow and n = 7 years for WQ)

Parameter

RSR PBIAS NSE R2

Value Rating

(Moriasi 2007)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Flow 0.2 Very good -0.3 Very good 1.0 Very good 1.0 Very good

TSS 0.3 Very good -2.7 Very good 0.9 Very good 0.9 Very good

TN 0.3 Very good 6.1 Very good 0.9 Very good 1.0 Very good

PN 0.7 Satisfactory 13.1 Very good 0.5 Good 0.6 Satisfactory

DIN 0.3 Very good -3.9 Very good 0.9 Very good 0.9 Very good


TP 0.4 Very good 4.0 Very good 0.8 Very good 0.9 Very good

DIP 0.3 Very good 0.3 Very good 0.9 Very good 1.0 Very good

PP 0.6 Good 8.0 Very good 0.7 Very good 0.7 Very good

DOP 0.5 Very good 1.2 Very good 0.8 Very good 0.9 Very good


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Table 41 RC2015 (130504B Comet River at Comet Weir, n = 5 years for flow and n = 5 years for WQ)

Parameter

RSR PBIAS NSE R2

Value Rating

(Moriasi 2007)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Flow 0.2 Very good 14.4 Satisfactory 1.0 Very good 1.0 Very good

TSS 0.8 Indicative -10.3 Good 0.4 Indicative 0.4 Indicative

TN 0.2 Very good 11.9 Very good 1.0 Very good 0.9 Very good

PN 0.3 Very good 21.2 Satisfactory 0.9 Very good 0.9 Very good

DIN 0.2 Very good 14.4 Very good 1.0 Very good 0.9 Very good



DIP 0.5 Good 49.5 Indicative 0.7 Very good 1.0 Very good

PP 0.4 Very good 15.6 Good 0.9 Very good 0.8 Very good

DOP 0.6 Good 0.0 Very good 0.7 Very good 0.7 Good

Contribution by land use The major contributors to the TSS load are open and forested grazing (68%) (Figure 45). Dryland

cropping’s contribution to the export load increased from 3% in RC2014 to 15% in RC2015 due to the

cropping model simulation now accounting for slope, this equates to an increase in the generated areal

rate of 0.2 t/ha/yr for RC2014 to 2.0 t/ha/yr for this report card which is a better estimate of typical

erosion rates in cropping areas of Central Queensland (Thornton et al. 2007, Waters and Packett 2007).

The particulate nutrient contribution is dominated by open and forested grazing, and nature

conservation. The contribution from nature conservation has increased significantly for this report card

and is the second highest contributor at ~28% behind forested grazing (~35%). Together the three land

uses contribute approximately 85% of the export load for both particulate nitrogen and phosphorus.

All dissolved nutrient loads are dominated by contributions from open and forested grazing, with ~75%

of the total export load coming from these two land uses. Conservation and forestry essentially supply

the remaining load for DIN, DON, DIP and DOP however dryland cropping does make a small

contribution to the DIN and DON export loads.


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Figure 45 Constituent contribution (%) by land use for four constituents

Sources and sinks The total fine sediment load generated for the Fitzroy region is 8,650 kt/y. Of this load 52% is generated

by gully erosion, 29% by hillslope erosion and 19% by streambank erosion. As a result of losses

throughout the system only 21% of the sediment generated throughout the region is delivered to the

GBR lagoon. This results in an average annual load of 1,824 kt/y of fine sediment being exported to the

GBR lagoon. Sediment is lost throughout the system to floodplain deposition (61%), reservoir deposition

(19%) and the remainder is lost to extractions. The Fitzroy source sink budget for fine sediment is

presented in Figure 46; while the source sink table for all constituents can be found in Appendix 3,

Table 78.

A higher proportion of generated nutrient load is exported to the GBR (than for sediment) with 44% of

the generated nitrogen load and 37% of the generated phosphorus load being transported to the GBR

lagoon. Hillslope erosion is the largest contributor to both these loads through the generation of

particulate nutrients. The total nitrogen load exported to the GBR is 10,957 t/y. Particulate nitrogen

contributes almost three-quarters of the total generated nitrogen load. A significant portion of the PN is

lost throughout the system through deposition and as a result only contributes 59% to the exported

load. DON makes up 31% of the exported load and DIN 10%. Particulate phosphorus contributes 87%

to the total generated phosphorus load. A significant proportion of PP is lost throughout the system to

floodplain and reservoir deposition, however it still contributes the largest proportion to the total exported

phosphorus load at 72%. Minor amounts of DIP and DOP are lost throughout the system and they

contribute 23% and 6% respectively to the exported phosphorous load. The total phosphorous load

exported to the GBR is 1,689 t/yr.


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Figure 46 Fine sediment source sink budget for the Fitzroy

Progress towards Reef Plan 2013 targets Management changes reported for the Fitzroy region for RC2015 included riparian fencing, gully erosion

and cropping management practices. No changes were reported for hillslope management in grazing

land uses. The modelling results indicate that the management changes resulted in a 15 kt/y reduction

in the exported sediment load or a 1% reduction of the anthropogenic load. A reduction of 0.2% for the

total phosphorous load occurred which was solely the result of a 5 t/y reduction in the particulate

phosphorous load. Total nitrogen also had a 0.2% reduction of the anthropogenic load which was a

result of an 11 t/y reduction of the particulate nitrogen load. The toxic load reduced by 0.3%

predominantly due to a 3.75 kg/y reduction in the atrazine load.

The majority (78%) of the streambank protection investment that was reported for the GBR occurred in

the Fitzroy Basin. A total of 399 km of streambank fencing was constructed in the Fitzroy region. This

represents an improvement in streambank management for 1% of the 40,970 km of streambank

modelled for the Fitzroy region. The streambank management changes resulted in the greatest

reduction of fine sediment exported to the reef from the Fitzroy region and reduced the load by 13 kt/yr.

The streambank fencing resulted primarily in a shift from C and D class management to B and some A

class management. There was also a small shift from B class to A class management.

Sixty-six kilometres of streambank fencing undertaken within the Styx and Shoalwater coastal

catchments was not modelled this year. Due to the flat terrain in these coastal catchments, little or no

streambanks were defined from the DEM. Therefore investment in streambank protection in these areas

cannot be modelled. This issue will be investigated further to ensure that investment that occurs along

coastal waterways can be included next year.


102

Limited gully management change occurred in the Fitzroy region for this Report Card. A total of 533 ha

changed from C management to B class management. This change in management represented only

1% of the total gully management change that occurred for the entire GBR. Within the Fitzroy region,

the modelled gully erosion within the project area that the management change occurred in, represents

only 0.01% of the total Fitzroy modelled gullied area. The management change produced a 19t/yr

reduction in the generated load and negligible change to the export load.

Table 42 Fitzroy Grazing Management Change


A 7.9 7.9 0.01 7.9 0.0

B 13.3 13.9 0.66 13.9 0.0

C 49.2 48.6 -0.59 48.6 0.0

D 29.6 29.6 -0.08 29.6 0.0


A 3.9 4.0 0.06 4.0 0.0

B 16.2 16.3 0.05 16.3 0.0

C 54.6 54.7 0.02 54.6 0.0

D 25.2 25.1 -0.14 25.1 0.0


A 18.0 18.1 0.14 18.6 0.5

B 17.3 17.3 0.00 17.6 0.3

C 16.7 16.9 0.26 16.5 -0.4

D 35.6 35.2 -0.39 34.8 -0.4

NA 12.5 12.5 0.00 12.5 0.0

Within the Fitzroy region, improvements in cropping management practices occurred for nutrient and

soil management. No changes were reported for pesticide specific management change, however as a

result of other management changes and the combination of scenarios modelled in HowLeaky a

reduction in the pesticide load occurred. All cropping nutrient management change for the GBR

occurred in the Fitzroy region with 0.6% (5,540 ha) of dryland cropping transferred from C class to A

class management. Three-quarters of all soil management change occurred in the Fitzroy, with 17,536

ha of cropping that was in D class management transferred to A, B and C class management with

slightly more shifting to B class than the other two classes.

The cropping management changes produced a reduction of 23,723 t/yr in the fine sediment generation

rate which translated to a 2,160 t/yr reduction in the fine sediment export load. The Atrazine load was

reduced by 3.75 kg/yr.


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Table 43 Fitzroy Grains Management Change (% change)

Soil Baseline RC2014 Change RC2015 Change

A 12.74 12.74 0.00 13.30 0.56

B 29.13 29.57 0.44 29.98 0.41

C 56.14 55.79 -0.35 55.45 -0.35

D 1.99 1.90 -0.09 1.27 -0.63

Nutrient Baseline RC2014 Change RC2015 Change

A 1.28 1.28 0.00 1.28 0.00

B 52.26 52.26 0.00 52.86 0.61

C 40.11 40.11 0.00 39.50 -0.61

D 6.35 6.35 0.00 6.35 0.00

Pesticide Baseline RC2014 Change RC2015 Change

A 2.90 2.90 0.0 2.90 0.0

B 67.13 67.36 0.2 67.36 0.0

C 28.52 28.32 -0.2 28.32 0.0

D 1.45 1.42 0.0 1.42 0.0


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Burnett Mary

The results of the Burnett Mary modelling are presented below, separated into hydrology and modelled


gauge, as well as the impact on the total flow for the BM region. The modelled loads section includes

the results of the total baseline, anthropogenic baseline and predevelopment loads. The validation of

the BM Source Catchments modelled data is then presented using load estimates from measured data.



mass balance summary of the sources and sinks by constituent. For a full list of the BM region loads


Hydrology calibration performance All gauges in the BM region were recalibrated to improve the baseflow proportion, with six examples

presented in Table 44. Baseflow was reduced in 76% (22 out of 29 sites) of the gauges as a result of

the recalibration process. In terms of mean annual flow, the impact of recalibration caused small

decreases of less than 5% in average annual flow in the Baffle, Kolan and Burnett basins. There was a

1% increase in average annual flow in the Burnett Basin and a 35% increase in average annual flow in

the Mary River Basin as a result of the recalibration. Overall, there was a regional increase in average

annual flow of 13%.

Table 44 Burnett Mary baseflow proportions for six gauges the current and previous Report Card

Gauge Basin RC2014

baseflow (%) RC2015


filter (%)

136002D Burnett River at Mount Lawless 78 23 21

136094A Burnett River at Jones Weir Tailwater 86 21 18

136209A Barker Creek at Glenmore 96 23 18

136306A Cadarga Creek at Brovinia Station 56 12 8

137003A Elliott River at Dr Mays Crossing 81 24 29

137101A Gregory River at Isis Highway 88 15 12

The hydrology recalibration produced similar agreement with gauged flow data compared to RC2014.

Out of the 29 gauges used in the recalibration, (52%) met the daily Nash-Sutcliffe criterion (NSE >0.75),

compared to 60% in RC2014; 97% met the total volume error criterion (PBIAS of ± 20%) compared to

100% in RC2014. The improved estimate of baseflow estimates due to the recalibration outweighs

these minor compromises in the RC2015 calibration results compared to RC2014. Overall this can be

regarded as an extremely good calibration result given that a global review of hydrology calibrations

rated Nash Sutcliffe E values > 0.75 as “very good” performance (Moriasi et al. 2007).

Modelled Loads Despite being the third largest GBR NRM region (13% of total GBR area), the BM region exports the

second lowest TSS, TP, TN and PSII inhibiting herbicides of the six regions draining to the GBR lagoon.

The relatively low contributions in TP, PP, TN and PN export are attributed to the BM having the lowest


105

annual runoff and a high degree of constituent trapping in the 11 storages included in the model. The

model estimated 21% of TSS and particulate nutrients generated, were trapped in storages. The

contribution from each of the basins in the BM, to the total load exported from the BM region are

presented in Table 45.

Within the BM region, the Mary Basin is the greatest contributor to export of TSS and particulate

nutrients mainly due to the high modelled streambank erosion in this catchment. The Mary also exports

the largest proportions of the other constituents with the exception of PSII inhibiting herbicides for which

the Burnett Basin exports the largest load.

Table 45 Contribution from Burnett Mary basins to the total Burnett Mary baseline load

Basin TSS

(kt/yr) TN

(t/yr) DIN (t/yr)

DON (t/yr)

PN (t/yr)

TP (t/yr)

DIP (t/yr)

DOP (t/yr)

PP (t/yr)

PSII (kg/y)

PSII TE

(kg/yr)

Baffle Creek

75 546 58 220 268 148 14 9 125 8 3

Kolan 40 312 78 133 101 48 7 4 37 70 31

Burnett 548 1,407 246 494 667 317 32 17 268 119 22

Burrum 26 420 199 163 58 44 13 6 25 81 16

Mary 770 4,460 459 1,102 2,899 1,084 72 41 972 68 14

Total 1,459 7,146 1,040 2,113 3,993 1,642 139 77 1,426 346 86


There has been a fivefold increase in TSS and DIN loads and a threefold increase in particulate nutrient

loads from predevelopment scenario (Figure 47). The greatest anthropogenic load is from the Mary

Basin (six-times the predevelopment load), followed by the Burnett Basin (three-times the

predevelopment load). The anthropogenic DIN loads are presented in Figure 48. While the overall BM

anthropogenic contribution is five-times the predevelopment load, in the Burrum Basin it is 14-times the

predevelopment load. The predevelopment and anthropogenic baseline loads for all constituents in

each of the five BM basins are presented in Appendix 3, Table 79. The DIN and PSII anthropogenic

baseline exports are associated with the prevalence of sugarcane land use in each catchment in the

region.


106

Figure 47 TSS (kt/yr) loads for the BM basins, highlighting the predevelopment and anthropogenic baseline

contributions

Figure 48 DIN (t/yr) loads for the BM basins, highlighting the predevelopment and anthropogenic baseline

contributions

Constituent load validation The constituent loads for the BM region were validated against load estimates from catchment

monitoring at four sites in the Burnett Basin and two sites in the Mary Basin. Generally, loads estimated


107

by the RC2015 model more closely match loads estimated from catchment loads monitoring than loads

modelled in the previous report card. Modelled and monitored loads at the Burnett Basin end of system

(EOS) site (Burnett River at Ben Anderson Barrage) are compared in Figure 49. Five of the 10 modelled

constituent loads were within 30% of the monitored load (Figure 28). In contrast, only one constituent

was within 30% of the monitored load in RC2014. Overestimation of flow by the model at this site is

attributed to the fact that monitored flow at this site has been estimated by summing up flows from

upstream gauging stations that the sum of catchment areas of is less than the catchment area of the

Burnett EOS site.

Figure 49 Comparison of monitored and modelled mean annual flow and constituent loads at Burnett EOS site

(136014A)

The performance of the RC2015 baseline model was also assessed using a number of quantitative

statistics recommended in Moriasi et al. (2015). The estimated quantitative statistics and the

corresponding model performance ratings are presented in Table 46 for the Burnett River. An overall


conflicting/unbalanced performance ratings for most of the constituents. The overall performance was

described conservatively using the least rating of the recommended statistics. For ease of presentation,

‘very good’ and ‘good’ rated sites are coloured green, while ‘satisfactory’ and ‘indicative’ are coloured

orange, signalling that further work is required to improve the representation of these constituents.

Over 90% of the rating achieved a ‘good’ or ‘very good’ ranking for RSR, R2 and NSE for all constituents.

For PBIAS, about half of the constituents achieved a ‘good’ or ‘very good’ ranking, while flow, TSS, PN,

DIN, DIP and DOP were indicative or ‘satisfactory’. Statistics could not be calculated at the daily or

monthly time-step due to the fact that Beale Ratio which was one of the methods used to calculate

loads from the GRBCLMP data needed at least three data points, which were not available in most

cases.


108

Table 46 Moriasi et al. (2015) overall annual performance rating for RC2015 (136014A Burnett River at Ben

Anderson Barrage (HW), n = 8 years for flow and n = 5 years for WQ)

Parameter

RSR PBIAS NSE R2

Value Rating

(Moriasi 2007)

Value Rating

(Moriasi 2015)

Value Rating



Flow 0.1 Very good -15.4 Indicative 1.0 Very good 1.0 Very good

TSS 0.9 Indicative -89.1 Indicative 0.1 Indicative 1.0 Very good

TN 0.3 Very good 15.5 Good 0.9 Very good 0.9 Very good

PN 0.5 Very good 26.7 Satisfactory 0.8 Very good 0.8 Very good

DIN 0.5 Very good 23.8 Satisfactory 0.8 Very good 0.8 Very good

DON 0.2 Very good -12.9 Very good 1.0 Very good 1.0 Very good

TP 0.3 Very good 12.1 Very good 0.9 Very good 0.9 Very good

DIP 0.4 Very good 30.9 Indicative 0.8 Very good 1.0 Very good

PP 0.3 Very good 11.7 Very good 0.9 Very good 0.9 Very good

DOP 0.6 Good 25.1 Satisfactory 0.7 Very good 0.7 Very good

Contribution by land use Grazing contributed 21% of the TSS export from the region. With a contribution of 65% (the Burnett and

Mary contributing 28% and 34%, respectively, and the remaining 3% contributed by the other three

basins), streambank erosion is the dominant source of TSS export from the region. Grazing contributes

38% and 39% of the PN and PP, respectively, and is the dominant source of export of particulate

nutrients, most of which is generated from the Mary Basin. Sugarcane contributes 56% of the DIN

export, followed by grazing accounting for 24%. All of the PSII inhibiting herbicides export is from the

sugarcane land use.

Figure 50 Contribution to fine sediment, particulate nutrient and DIN export by land use for the BM region


109

Despite accounting for less than 2% of the BM region by area, sugarcane contributes 58% of the DIN

export from the region. Grazing is the dominant land use accounting for 69% of the region and

contributes 25% of the DIN export from the region (Figure 50). In terms of areal rates, sugarcane at 6.7

kg/ha/y, urban at 0.54 kg/ha/y and horticulture at 0. 48 kg/ha/y are the dominant sources of DIN in the

BM region. The average DIN per unit area rate for all land uses in the BM is 0.19 kg/ha/yr.

Figure 51 DIN export load and areal rate for each land use in the BM

Sources and sinks In RC2015, streambank erosion contributes the greatest load (52%) to total TSS export from the Burnett

Mary region, followed by hillslope erosion (23%) and gullies (15%). The remaining 10% was contributed

by instream fine sediment remobilisation which was not included in previous report cards. The

constituent budget also shows that 21% of the TSS supplied to the stream network is trapped in

storages and 3% is taken out of the stream network through water extractions for different uses with

floodplain deposition, losses and stream deposition each accounting for 4% of the total TSS loss. Given

the significance of the percentage trapped in storages in the constituent budget, it is important that this

component is verified by future monitoring data which has been lacking to date in the region and the

GBR as a whole. The fine sediment source sink diagram for the Burnett Basin is presented in Figure

52. The sources and sinks for each constituent are presented in Appendix 3, Table 80.


110

Figure 52 Fine sediment source sink budget for the Burnett Basin

Storage trapping is a significant loss pathway for fine sediment and particulate nutrients in the BM

region, accounting for 21% of TSS being lost. In Figure 53 the trapping efficiencies of storages in the

BM model are shown. It should be noted that the relatively low TSS trapping efficiency of the Burnett

River Dam (aka Paradise Dam - 33%) is due to the two extreme floods in 2010/11 and 2012/13 the

region experienced, which accounted for over 90% of sediment export from the region in the 28 years

modelling period (1986–2014).

Figure 53 TSS, PN and PP trapping efficiencies (%) of storages

Progress towards Reef Plan 2013 targets There has been very little progress towards meeting the reef targets in RC2015, as was the case in

RC2014. This is due to the very low level of investment in management changes required to achieve

water quality targets for these two report cards. Percentage load reductions of all constituents from the


111

BM region are less than the average GBR-wide percentage reductions, which is attributed to the low

level of investment in management interventions in this region during this report card. The Burnett and

Burrum basins were the only basins where there were some DIN load reductions as management

interventions were limited to these areas for this report card. Management changes for sugarcane

nutrients was limited to a 0.5% partial improvement from a Cf (C-full) practice to Cp (C-partial).

Sugarcane and grazing management practice change summaries are presented in Table 47 and Table

48. For ease of presentation, management classes are aggregated for sugarcane investments (partial

steps are lumped to whole).

Table 47 BM sugarcane management practice change summary; all values are in percent

Soil Baseline


A 7.75 7.79 0.04 7.79 0

B 30.5 30.83 0.33 30.83 0

C 47.75 47.38 -0.37 47.38 0

D 14 14 0 14 0

Nutrients Baseline


A 1 1 0 1 0

B 12 12.14 0.14 12.14 0

C 59.6 59.52 -0.08 59.52 0

D 27.4 27.35 -0.05 27.35 0

Pesticides Baseline


A 18 18.53 0.53 18.53 0

B 29 28.95 -0.05 29.13 0.17

C 48 47.51 -0.49 47.34 -0.17

Df 5 5.00 0 5.00 0

Irrigation Baseline


A 6 6 0.00 6 0

B-C 28.5 28.54 0.04 28.54 0

D 65.5 65.46 -0.04 65.46 0

Recycle Pits

Baseline 2013

RC2014 Change RC2015 Change

A 14 14.04 0.04 14.13 0.09

B 11 11.05 0.05 11.08 0.04

C 44 43.92 -0.08 43.79 -0.13

D 29 29 0 29 0


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Table 48 BM grazing management Change; all values are percent of total


A 1.66 1.68 0.02 1.68 0

B 44.04 44.14 0.10 44.16 0.03

C 54.27 54.16 -0.11 54.14 -0.02

D 0.02 0.02 0.00 0.02 0


A 8.76 8.76 0.00 8.77 0.01

B 20.98 20.99 0.01 21.04 0.04

C 54.12 54.12 0.01 54.10 -0.02

D 16.14 16.12 -0.02 16.09 -0.03


A 28.98 28.98 0.01 29.01 0.03

B 14.61 14.65 0.04 14.69 0.04

C 18.40 18.39 -0.02 18.34 -0.04

D 27.54 27.51 -0.02 27.49 -0.02

NA 10.47 10.47 0.00 10.47 0

There have been very little reductions in export loads in RC2015 across all constituents of concern,

reflecting the level of investment in management changes during these two investment years. This is

generally similar to other GBR regions with less than 0.1% TSS export reduction from the BM compared

to 0.3% from the GBR as a whole. There has also been a very low DIN export reduction of about 0.03%

from the BM compared to 1.1% from the GBR as a whole. The story is similar for PSII herbicides with

0.2% reduction from the BM region compared to 2.5% from the GBR as a whole. These small reductions

from the BM region indicate the relatively low level of investment in the BM region for RC2015.


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Discussion

Report Card 2015 is the sixth iteration of the Source Catchments modelling of load reductions for

reporting on progress towards Reef Plan Water Quality targets. The second external review of the

modelling was undertaken in November 2015. Recommendations from the external review and the

Queensland Audit Office, have formed the basis of model improvements. In contrast to RC2014,

RC2015 saw a number of minor updates to the modelling approach, with only three more significant

improvements to note.

Hydrology calibration was updated to ensure modelled estimates of baseflow better aligned with

baseflow estimates at gauging stations. The recalibration resulted in small changes in average annual

runoff across the six regions with an average of +/- 5% from RC2014 with improved baseflow alignment

observed at all gauges (refer Results on page 45).

Instream deposition and channel remobilisation functionalities were enabled in four of the six regions

to represent this important component of the sediment budget. In general, the deposition and

remobilisation were maintained at a similar magnitude. Further work will be undertaken to refine this

component of the sediment budget.

It is important not to lose sight of the main objective of the modelling, to report the water quality benefits

as a result of management changes. Therefore the relativity between scenarios is important not the

absolute load. However the goal of the modelling program is for continual improvement of modelled

load predictions. This approach will continue.

Particulate nutrients

There was an under-prediction of particulate nutrient loads in CY, WT and BM. Particulate nutrient

under-prediction was also an issue in the remaining regions however it was not as pronounced as in

CY and WT.

The gully and streambank models use the subsurface Total Nitrogen (TN) and Total Phosphorus (TP)

layers from ASRIS (Henderson et al. 2001) to estimate the particulate nutrient contribution. Wilkinson

et al. (2010) describe the differences between the ASRIS spatial data sets and local point data. The

surface and subsurface ASRIS nutrient datasets are extrapolated from direct measurements of soil

nutrient content to provide a uniform spatial layer as required by the model. In remote areas such as

CY there may be very few samples from which to extrapolate a continuous dataset, and hence there

can be significant differences between observed and modelled values. Often point source data of

nutrient content is sourced from areas of intensive agriculture and as such the input datasets may be

more reliable in these areas (Wilkinson et al. 2010). A review of the chemistry data for CY (CSIRO data

in Biggs and Philip (1995)), suggests that there was no significant difference between surface and

subsurface TN and TP chemistry. However, for the ASRIS derived layers surface concentrations of

nutrients in CY were typically three times greater than that of sub-surface concentrations.


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Therefore, the subsurface nutrient concentration layers were replaced with surface layer in CY and WT

regions. This is in line with the recommendation by Wilkinson et al. (2010) who suggested that users

should choose the appropriate dataset for their region including from locally derived measurements or

the ASRIS dataset. The updated layers resulted in PN and PP estimates improving from under-

predicting loads by 60% to now being within 30% of the observed loads for the Kalpowar gauge in CY.

Progress towards targets

Overall the percentage load reductions were low (<1%) ranging from 0.1 – 4.3% across the GBR. These

reductions are of a similar order to RC2014 which was the first year under the new water quality

management framework.

The cumulative total GBR load reduction for TSS for RC2015 is 12.3% - an increase of 0.3% from

RC2014. Four regions contributed to the reduction: WT (0.1%), BU (0.2%), MW (0.1%) and FZ (1%).

The Fitzroy reduction was due to significant riparian fencing and investment in improved hillslope

management in grains and grazing. The reductions in other regions were due to a mix of improved

practices in grazing, sugarcane and banana industries

The cumulative total GBR load reduction for DIN for RC2015 is 18.1% - an increase of 1.1% on RC2014.

The majority of the investment occurred in the WT, BU and MW sugarcane areas, with a small amount

of investment in improved practices banana growing areas in the WT also contributing.

The cumulative total GBR toxic equivalent load reduction for PSII TE for RC2015 is 33.7% - an increase

of 3.2% on RC2014. Small reductions (< 0.3%) were modelled in the Fitzroy and Burnett Mary Regions

whilst the Wet Tropics, Burdekin and Mackay Whitsundays all had modelled reductions over 3% (3.4%,

3.6% and 3% respectively). The majority of the reductions were attributed to sugarcane industry

investments.

The trend of significant progress in the early GBR Report (RC2010 – RC2103) has slowed in the past

two Report Cards (RC2014 and RC2015). This is mainly due to the updated management practice

water quality framework resulting in more conservative reductions but likely a more realistic

representation of investment and water quality response. A significant feature of the P2R program is

that the data produced from both monitoring and modelling can be used to target funding to maximise

water quality investment. For example, an outcome of the Reef Water Quality Taskforce investigation

was that investment in innovative management practices and monitoring should be undertaken in two

key regions (WT and BU) to achieve an improved water quality response for DIN and TSS. The Major

Integrated Projects (MIPs) in these regions are well into the planning phase, and the results of the

projects will be used to further inform model development, and well as, ideally, have a measurable

benefit to GBR water quality.


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The modelling results suggest that under an All B scenario the 20% TSS Reef Plan water quality target

could be met in the MW and Fitzroy regions. Under an All A scenario, the majority of regions can achieve

the target, the exception being CY, which was just under the 20% target. For DIN, the 50% Reef Plan

water quality target cannot be met under the All B scenario. The Burdekin region is the only region that

achieved the target under the All B scenario. For pesticides, the PSII TE target can be achieved under

an All B scenario. In summary, the modelling results suggest that the TSS and particulate nutrient

targets are achievable under an All A scenario with the majority of the regions close to achieving the

TSS target under an All B scenario. The DIN target may be more challenging to achieve even under

the All A scenario and new, innovative management practices need to be explored. The PSII TE target

is achievable even under an All B scenario. It is important to note that variations in load reductions

between regions are related to differences in the dominant sources of pollutants within a region.

Therefore reductions in constituent loads from hillslope/gully and streambank sources may not occur

proportionally.

The catchment models were set up to assess relative reductions in exported loads due to investment

within a region (that might be implemented over an annual funding cycle). The hypothetical

representation of the All A and All B scenarios reflect large changes in constituent loads (that would not

be expected to be implemented in a short timeframe).

The results of the modelling scenarios should be seen as an approximation of reality. The results do

however offer an estimate of the relative magnitude of change that may be required to move closer to

achieving the Reef Plan targets. However the many other sources of information available to guide

investment should be utilised. Modelling should not be seen as the only driver for decision making.

The following sections provide an overview discussion of results for all six NRM regions.


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Cape York

The results from RC2015 are considered to be the most refined set of model runs for the Cape York

region thus far. The availability of additional datasets against which to compare the model results, in

particular, has enabled specific components of the model to be more comprehensively reviewed and

refined. This is especially applicable to the representation of the major sediment sources within the

region. Furthermore, minor inconsistencies in model parameters have been addressed and updated for

this Report Card.

Hydrology The recalibration of hydrology in CY to improve the baseflow component has seen average annual flow

reduced by ~3% compared to RC2014, with almost half the reduction in the Normanby Basin.

Recalibration has seen a significant improvement in the Normanby Basin baseflow, previously

comprising 95% of the total flow, which now contributes around 35% of total flow.

Fine sediment generation, sources and sinks The hillslope fine sediment load is higher in RC2015 than for RC2014 (increased by 42%), resulting in

a higher end of system TSS load for CY (RC2014 baseline TSS was 371 kt/yr, compared to 526 kt/yr

in RC2015). The modelled load is now within 25% of the Kalpowar gauge monitored load compared to

45% for RC2014. The capping of daily instream sediment concentrations and the reduction in hillslope

delivery ratio (HSDR) from 10% to 5% has been the major reason for the improved estimates. Applying

the 5% HSDR was in agreement with Rustomji et al. (2010) whilst Brooks et al. (2013) reported much

lower generation rates from hillslope plots and higher HSDR.

The updated model has resulted in changes to the fine sediment source sink budget. In the current

Report Card, the source sink breakdown in the Normanby Basin remains approximately the same as

the previous Report Card, however for the whole of CY there has been an increase of 11% to the

hillslope supply load. The gully and hillslope load for CY are now similar (45% and 47% respectively),

whereas in RC2014 gullies were the major contributor at 57%. The streambank load remained constant

between the two Report Cards. Further investigation of the impact of setting a maximum concentration

parameter will be carried out, and application of a spatially variable value will be considered. Due to the

lack of monitoring data in CY catchments, data from other regions (e.g. rainforest areas in the WT for

application in CY steep, wet catchments) will be considered when setting this value variably.

Particulate nutrients The modelling of particulate nutrients were another component of significant change in this Report Card.

Initial iterations of the model showed particulate nutrient loads were well under predicted (up to 60%)

for RC2015 compared to loads estimated from measured data. The under-prediction of particulate

nutrients was not observed for CY in RC2014 due to minor oversights. In RC2014, a dry weather

concentration (DWC) was not applied to the hillslope TSS component in an attempt to better match

monitored instream data from the Kalpowar gauge. However, this was not carried over to particulate

nutrients supplied from the hillslope. That is, given that particulate nutrients are attached to the fine


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sediment load, parameters applied to fine sediment should similarly be applied to particulate nutrients.

Also in RC2014, the TSS hillslope delivery ratio (HSDR) for particulate nutrients was higher than for

TSS. This has been corrected in RC2015 with a consistent HSDR of 5% now applied for particulate

nutrients and TSS. To compensate, the resultant decrease in particulate nutrients (by significantly

reducing the HSDR from 20% to 5%), combined with reduced baseflow runoff volume (and hence the

loss of particulate nutrient generation by implementing a zero DWC) has meant that the enrichment

ratio for PN and PP has been increased. This parameter is only applicable to the hillslope erosion

derived particulate nutrients, not the gully or streambank derived load. Considering that in some areas

up to 80% of the load in CY is derived from gullies, the parameters used to derive gully and streambank

particulate nutrients were reviewed. As described, the surface nutrient ASRIS data layers were applied

to represent both the surface and sub-surface nutrient concentrations to increase the particulate nutrient

load from gully and streambank erosion.

Progress towards targets There were only minor management changes reported in CY for RC2015, with some gully

improvements (two projects, total 93Ha) and riparian fencing (four projects, total 36,391 Ha) reported

in the Normanby Basin. There were no other grazing, cropping or banana management practices

reported. These small changes resulted in no impact on load reduction targets from CY, for any

constituents. At the completion of RC2015, the cumulative reduction of fine sediment from CY is 8%,

with no contribution from RC2014 or RC2015.


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Wet Tropics

The major model enhancements for this Report Card include: recalibration of hydrology for selected

gauges, in-stream deposition and remobilisation functionality being enabled and adjustments of

dissolved phosphorus model in APSIM for sugarcane. Following is a discussion of each of these factors

and how they have influenced the modelling results, producing in an improved estimate of constituent

loads from WT.

Hydrology The recalibration of hydrology in WT to improve the baseflow component has seen average annual flow

increase by 1% compared to RC2014. However it did change total flow in the Barron and Tully basins.

Of the 10 gauges that were recalibrated, the baseflow proportion increased for eight of them.

Fine sediment, particulates sources and sinks The total TSS supply increased by 4%, with increases in subsurface erosion (stream and gully) and

instream remobilisation compared to the RC2014 baseline model. The small increase in discharge due

to the recalibration accounts for the increase in streambank erosion with no other changes made to the

streambank model since the last Report Card. Gully cross section was doubled to increase the amount

of subsurface erosion (gully and streambank erosion) being generated from grazing lands. This has

resulted in a doubling of gully erosion supply. The increase in subsurface erosion aligns with sediment

tracing work in the Herbert (Tims et al. 2010). Tims et al. (2010) found that the subsurface to surface

ratio was approximately 91% to 9% from two tributaries draining mostly grazing land. Using a

comparable subcatchment in the Herbert (Rudd Creek) to that of the Tims et al. (2010) work, with

available monitored data to compare to, showed that increasing the gully cross sectional area has

resulted in a modelled spilt of 59% subsurface to 41% surface erosion in a predominantly grazed

catchment. The modelled value instream at the Rudd Creek gauge was 198 mg/L and the modelled

average annual load was 14,265 t/yr. While the monitored instream concentration was 90 mg/L and the

monitored load was 8,000 t/yr. It is likely that the hillslope contribution from this area is too high, while

the subsurface contribution is still under-predicted despite changes to gully parameters increasing the

load from this source. The gully mapping initiative that is currently being undertaken by DNRM across

many reef catchments will provide an improved gully density map for the Herbert Basin. It is anticipated

that this may result in an increased load of sediment from gully sources.

The implementation of instream deposition and remobilisation in RC2015 resulted in a large proportion

of TSS lost to instream deposition and a small proportion of TSS supplied from instream remobilisation.

Significant changes in the amount of TSS ‘lost’ occurred compared to RC2014. The large increases in

losses of instream and floodplain deposition were due to increasing the sediment settling velocities

required for deposition. The sediment settling velocities were updated based on the stream erosion and

deposition graph in Earle (2015). This change increased floodplain deposition x20. It is acknowledged

that the floodplain deposition may be too high therefore further investigation of the bank full recurrence

interval term will be conducted and implemented spatially prior to the next report card. Reducing the

amount of floodplain deposition will help to improve the under prediction of TSS at five sites.


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Particulate nutrients were mostly under-predicted (-57% to -11%) compared to loads from measured

estimates across the five model evaluation sites, except at Tully at Euramo where both PN and PP are

over-predicted. To increase the particulate nutrient loads, the sub-surface nutrient concentration layers

were replaced with the surface concentration layers used in the calculation of subsurface erosion

contribution (refer to overall discussion). Further, due to the general under prediction of fine sediment

and particulate nutrient, storage deposition was disabled. Initial iterations of the model for the Tully at

Euramo site, showed both PN and PP to be over-predicted. To counter this, the PP enrichment ratio

was reduced from five to three in this Report Card to reduce the PP load to better match the monitored

loads as it was significantly over-predicted in RC2014 (129%). The Tully at Euramo site is a nested

gauge, where the Tully Gorge site (50 km upstream) drains predominantly Nature Conservation, and

all of TSS, PN and PP are under-predicted. However, the modelled loads of TSS, PN and PP are over-

predicted at the Euramo gauge indicating a significant contribution of these constituents from the major

land uses between these gauging stations: sugarcane, grazing and bananas, is occurring. Given the

over-prediction at Tully at Euramo, and the under-prediction at all remaining sites, a spatial approach

to parameter application should be considered for future Report Cards.

For model evaluation, there was no change in the overall TSS ranking at each site for the short term

annual modelled loads or the long term monthly modelled loads compared to RC2014. The modelled

loads of TSS and particulates had overall better performance ratings at the long-term monthly scale

compared to the shorter annual scale. The South Johnstone River performed overall the best compared

to the other sites for TSS and particulates at the monthly scale.

Nutrients For dissolved nutrients, there were large changes in the export load of DOP and DIP, and small

increases of DIN and DON export loads compared to RC2014. The largest change occurred for DOP

where the export load increased by 89% compared to RC2014. This was due to changing the ratio of

DOP to DIP from 0.2 to 0.5 of total dissolved P for sugarcane and increasing the DOP EMC and DWC

values where applicable. The average percent difference between the modelled loads and the

GBRCLMP observed loads at the six sites was -61% in RC2014 and decreased to -27% in this report

card. The DIN loads at all gauging stations were under predicting compared to the load derived from

measured data (-33% to -9%) except for Barron River at Myola (43%), but were all within ±50%. Of the

five sites that were under predicting DIN, the average was -25% for RC2014 and decreased to -22% in

this Report Card. The DIN seepage delivery ratios remained the same as RC2014 and were 100% for

all basins except for upstream Barron at Myola (15% DR) and upstream Herbert at Ingham (50% DR).

The DWC was set to zero. For the next Report Card, an appropriate DWC will be used and delivery

ratios will be further adjusted to align with monitored loads. Similar to sediments and particulates,

dissolved nutrients performed better at the long-term monthly scale compared to the shorter annual

time scale. The North Johnstone site overall had the best performance rankings for dissolved nutrients

at the monthly scale. The incorporation of DIN in deep drainage from banana land use is planned for

future Report Cards.


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Herbicides The PSII export load for the baseline scenario decreased by 16%, compared to RC2014, predominantly

in the Herbert Basin. The differences are due to the approaches used in the allocation of management

practice proportions across the APSIM simulations. RC2014 used a database approach to allocate

management across available simulations while in RC2015 the assignment was done using expert

opinion.

Progress towards targets The WT progress towards the targets for RC2015 were small and less than the changes in RC2014. A

number of factors have contributed to the smaller reductions compared to previous Report Cards, which

have been discussed in (McCloskey et al. 2017).

For sugarcane, 4% of the sugarcane area had a soil management change, 1% of the area for nutrient

management (only partial step changes) and 5% of the area for pesticide management (all full step

changes). However, the modelling currently represents four levels of management for pesticide

applications (Af, Bf, Cf and Df) and there is no improvement modelled unless a full step change has

been achieved. Due to the full step changes that occurred in this Report Card, all of the sugarcane area

that underwent a herbicide management change contributed to a reduction in the herbicide load.

For soil management 4% of the banana area underwent a change and for nutrients 3% of the area.

Overall, sugarcane and banana represent 8% and 0.7% of the WT area. The overall percent reductions

in the export loads are sensible given that the area of management change is very small.


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Burdekin

Hydrology The RC2015 model utilised a new hydrology model parameter set, which was produced from a

recalibration focused on improving the representation of quickflow (runoff) and slowflow (baseflow)

components. The recalibration of hydrology in the Burdekin Region to improve the baseflow component

has seen average annual flow increased by 4% (~450,000 ML/yr)from RC2014 to an average annual

flow of 12,720,701 ML/yr. In terms of hydrology changes, volume and performance statistics are similar

to that of RC2014. However, the prediction of quickflow and slow flow has improved significantly due to

the more robust and detailed calibration objective functions. For example, a gauge in the upper Burdekin

for RC2014 had a base flow proportion of 67% of the total flow. After recalibration with a baseflow

optimiser, the baseflow proportion was reduced to 34%. This aligned with a baseflow estimate of 30%

for observed flow estimated using the Lyne-Hollick baseflow separation method (Lyne and Hollick

1979). The improved calibration has led to better process simulation, therefore improving the model

comparison dataset for all constituents.

Fine sediment and particulates generation, sources and sinks The RC2015 baseline or current load scenario has generally seen a small increase in sediment (~2%),

particulate nutrient (~3%) and dissolved nutrient loads compared to RC2014 (~3-5%). These changes

are well within the uncertainty boundaries of load estimates. Individually the model modifications have

produced varied results, the changes and the effect they had on model loads are discussed below. The

RC2015 baseline sediment sources are dominated by gully and streambank erosion, which aligns with

sediment tracing data (~80% subsurface, mainly gully scald, and 20% hillslope) (Hancock et al. 2013,

Wilkinson et al. 2013), and also the RC2014 results.

Hillslope

In RC2014 spatial patterns in sediment supply at a sub-catchment and sub-basin scale were improved,

through the use of improved gully mapping, and seasonal cover combined with more realistic erosion

based cover values and the inclusion of a generation ceiling for steep hillslopes. For RC2015, the

methodology for calculating hillslope erosion was changed, with the introduction of a new daily sediment

ceiling parameter. The RC2014 model used a maximum allowable daily load (t/ha/day), while the

RC2015 model parameter uses a maximum allowable daily concentration (mg/L/day). The driver behind

this was observation of unrealistically high sediment concentrations being generated on days of low

flows in the MW region. The change has improved baseflow loads (TSS, PN, PP) in other GBR

catchments and importantly ameliorated technical issues with reporting RUSLE management change.

Particulate nutrients, especially PN, are under-predicted against measured estimates in RC2015. This

was also an issue in the RC2013 and RC2014 models despite increases in particulate delivery ratios.

It is still unknown which components of the particulate nutrient simulation are the cause of the low

estimates (that is, the streambank, hillslope or gully PN or PP loads). The ASRIS nutrient soil


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concentration layers used in the calculation of subsurface stream and gully erosion are another potential

source of error, due to a lack of soil core data or poor spatial extrapolation of boundaries. Further

investigation of the ASRIS layers will be undertaken by the modelling team.

Cropping

The change in methodology that altered the way slope was applied to dryland cropping resulted in the

generated hillslope load for dryland cropping increasing from 28 kt/yr to 168 kt/yr. This increase may

be a better estimate of the loads being generated from dryland cropping, however there is no water

quality data at an appropriate scale in the Suttor region to test model performance. Average hillslope

generation rates in dryland cropping are now 1.2 t/ha/yr whereas the average was ~0.2 t/ha/yr for

previous report cards. The increase in the dryland cropping load resulted in minimal changes in the

contribution of cropping to end of valley loads due to the high trapping efficiency of the Burdekin Falls

Dam and the relatively small loads.

Sugarcane constituent generation The Burdekin region was the second highest contributor of DIN (22%) to the total GBR DIN export load,

which is consistent with previous Report Cards. At the regional scale the Burdekin Basin is the largest

contributor of DIN, followed by the Haughton and this ranking is the same as previous report cards.

Conceptual improvements were made to the sugarcane paddocking modelling scenario set by the

paddock modelling team. These included changes to how soil water balance is modelled across soil

type. Previously the minor soil types had sub optimal representation of the runoff and drainage split.

This facilitated better modelling of irrigation practices in these minor soils. An extra irrigation

management level has been added and there has been some slight modifications to the annual irrigation

amounts applied in the Burdekin River and Delta irrigation areas (BRIA). Overall these changes have

had little impact on the modelled loads. For example The Burdekin region DIN export load decreased

by 5% compared to RC2013, which is well within the uncertainty boundaries of the monitoring data,

sugarcane contributing 45% of this load and this is comparable to RC6 (44%) . In addition the majority

of the FU scale sugarcane DIN loss is still from seepage (~90%) and the remaining (~10%) from surface

runoff. Seepage DRs were set at 100% to better match the limited monitoring’s data at Barratta Creek.

Herbicides The BU contributes approximately a fifth of the PSII herbicide load to the GBR. There was a minor

decrease in the baseline RC2015 PSII load compared to the RC2014 PSII load of ~100 kg/yr.

Targeting land management for Reef WQ protection There has been relatively small changes in RC2015 loads compared to RC2014. Therefore readers are

referred to the work of Waterhouse et al. (2016). Here the RC2014 catchment modelling results along

with other lines of evidence are synthesised in detail providing information on appropriate prioritisation.


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Progress towards targets Collectively, management practice changes for grazing and cropping for sediment resulted in zero

reported load reduction for fine sediment from the Burdekin region, as the load reduction was less than

0.5%. It is important to acknowledge the considerable difficulties in reporting small changes in grazing

land management across the large Burdekin region. In general the scale of investment in terms of area

are logical in terms of expectations from investments, with considerable variation in area to reduction

ratios to be expected. However as the modelling further improves confidence at these smaller scales

should increase. At the completion of RC2015, the modelled cumulative fine sediment reduction for the

Burdekin is 17%. The land management practice improvements in sugarcane for nutrients resulted in a

4% reduction in DIN from the Burdekin region, with the cumulative reduction for DIN from the Burdekin

being 20%. Improvements were recorded in both sugarcane and cropping (grains) land uses for

pesticide use. In sugarcane there was a big shift (10%) out of C class practices into A and B practices,

while for cropping areas there was a 4.5% shift from B to A. This resulted in a 3.5% reduction in PSII

toxic equivalent loads from the Burdekin region and this is as a result of investment in sugarcane

pesticide management.

Future model improvement There is much potential for improvement in the modelling of all constituents. In terms of data inputs

large model improvements may be possible through the inclusion, and construction, of LIDAR derived

data sets for coastal catchments (it is envisaged that a broader complete of high resolution DEM for the

GBR will exist within the next few years). It would be pertinent to start looking at trialling approaches at

smaller scales. This would allow for an improved parameterisation of streambanks, gullies and slope

factors for the USLE. In addition, approaches could be trialled for a variable hillslope delivery ratio, plus

better delineation of scaled “low cover” areas, using higher resolution remote sensing. The sourcing of

improved rainfall data, or calibration of the actual rainfall data itself, could further improve hydrology

prediction in the Burdekin. Also perhaps some improvement may arise from the inclusion of a rainfall

intensity factor, but this idea is more speculative in nature.


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Mackay Whitsunday

The results from RC2015 are considered to be the best representation of landscape processes for the

Mackay Whitsunday region thus far due to the model improvements undertook as part of this report

card. The stream stability assessment of the O’Connell River (Alluvium 2014) has provided a new

source of data for validating the streambank erosion component of the model.

Hydrology The performance of the RC 2015 baseline model to predict the hydrology across the MW region was

assessed using a number of recommended quantitative statistics (Moriasi et al. 2007), namely Nash-

Sutcliffe efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R2). Estimated NSE

and PBIAS values for the ten gauges (see Table 52, Appendix 1 – Hydrology calibration results) used

in the calibration of the regional hydrologic model were compared against a general performance ratings

developed in Moriasi et al. (2007). The quantitative performance of the hydrologic model for the

O’Connell and Pioneer River Basins were rated as ‘Very Good’. The quantitative performance for Plane

Creek Basin was rated as ‘Very Good’ at Sandy Creek at Homebush (126001A) and ‘Good’ at Plane

Creek at Sarina (126002A) and Carmila Creek at Carmila (126003A). The Proserpine River Basin was

rated ‘Satisfactory’ due to the low NSE and high PBIAS statistics at Lethe Brook at Hadlow Road

(122012A). Gauge 122012A may be not suitable for calibration because it is situated at the downstream

of a highly regulated catchment for water extraction for irrigation and inflow from Peter Faust Dam

(Packett et al, 2014). This may be indicating further investigations are required to assess the suitability

of this gauge (or any other gauge) for better calibrating the hydrologic properties of the Proserpine River

Basin.

Both quantitative and qualitative assessments of catchment model hydrologic calibrations are critical to

ensure that the hydrologic performance was suitable for not only predicting long term data such as

mean annual flows and loads, but also to ensure that the characteristics of the key flow events were

also simulated correctly. Hence, the qualitative model performance was assessed using time-series

plot, flow duration curve (log scale) and one to one scatter plot (Appendix A).

The RC2015 baseline hydrologic model predicts the baseflow contributions well at six of 10 gauges

across the MW region. The recent hydrology calibration has improved estimates of baseflow

contributions at three gauges in the Proserpine River, O’Connell River and Plane Creek basins. The

model still underestimates the baseflow contributions at three gauges in the Pioneer River and Plane

Creek 1-7% lower than the Lyne-Hollick filter estimates.

The RC2015 baseline model estimated mean annual discharges very well against that of GBRCLMP

estimates in the Pioneer River at Dumbleton and in Sandy Creek at Homebush sites and P2R estimates

at Multifarm site. The RC2015 model also well matched the long-term annual flows estimated by Joo et

al. (2014) in the Pioneer River at Dumbleton.


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Constituent generation and validation Flows and pollutant loads estimated as part of the Great Barrier Reef Catchments Load Monitoring

Program (GBRCLMP) and P2R programs were compared against the RC2015 baseline model

estimates to assess the model performance at catchment and sub-catchment scales respectively.

Flows and pollutant loads estimated by Joo et al. (2014) in the Pioneer River at Dumbleton Pump Station

between 1987 and 2009 were also utilised to assess the RC2015 baseline model performance to

capture the hydrologic and water quality responses to the long-term (23 years) seasonal climatic

variability.

The RC2015 baseline model showed good agreement with the mean annual flows and pollutant loads

estimated at two GBRCLMP EoS sites. The model also showed good agreement with the mean annual

flows and pollutant loads estimated at the P2R monitoring site during low and medium flow events. The

model showed good correlation to the long-term estimates derived by Joo et al. (2014). It should also

be noted that there is a degree of uncertainty around the annual loads estimated by the reef water

quality monitoring programs and Joo et al. (2014). Estimation of uncertainty bounds around the

observed loads will be beneficial to better assess the model performance more fully.

Several improvements were made to enhance the performance of the RC2015 baseline model. One of

the main improvements to the RC2015 baseline model was the recalibration of hydrology to better

represent the baseflow indices1 estimated from long-term gauged flow measurements using the Lyne

and Hollick filter (Lyne & Hollick, 1979), a widely used approach for baseflow separation. The

recalibration of hydrology for the MW region has seen average annual flow reduced by approximately

0.5% compared to RC2014.

The GBR Source catchments model platform utilises the Revised Universal Soil Loss Equation

(RUSLE) to estimate daily hillslope soil loss from the grazing land use in the Mackay-Whitsunday region.

In the RC2015 models, a ‘maximum concentration’ cap was applied as opposed to previous ‘maximum

load’ cap. The modification has improved modelled predictions against raw concentration

measurements available for grazing dominant catchments. Moreover, the RC2014 baseline model

underestimated fine sediment (<20 µm) contribution from the grazing land use. Given that the grazing

land use dominates the Mackay-Whitsunday region, the relative contribution of fine sediment from the

grazing land use was increased in the RC2015 baseline model.

The RC2014 baseline model under-predicted fine sediment and associated particulate nutrient loads

estimated at the Pioneer River at Dumbleton Pump Station. It was noted that the RC2014 model was

set to trap significant amount of fine sediment and associated particulate nutrient loads (~50% of the

exported loads) at weirs located along the Pioneer River system (i.e. Mirani, Marian, and Dumbleton).

Trapping at these weirs was turned off for RC2015 model as minimal trapping of fine sediment is

1 Baseflow index is the ratio of long-term baseflow to total stream flow and it represents the contribution of seepage of water from the ground to river flow.


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expected at those weirs. The trapping model can be calibrated/validated should water quality data be

made available for both up and down stream of the weirs.

Constituent sources and sinks Hillslope erosion from grazing, sugarcane, and cropping land uses is the dominant (~54%) source of

TSS supply in the MW region (see Table 3) followed by streambank erosion (~31%). Erosion from the

remaining land uses constitutes ~14% of the regional TSS supply. Gully erosion contribution for the

regional TSS supply is low (~1%). RC 2015 baseline model indicates that approximately 96% of the

regional TSS supply is exported, while only ~ 4% is lost due to reservoir deposition, extraction and

floodplain deposition. Reservoirs in the region trap approximately 50% of the total TSS load that is lost,

while extraction and flood plain deposition claims approximately 32% and 19% of the total TSS loss.

Hillslope erosion dominates the particulate nitrogen and phosphorus supply by respectively generating

approximately ~63% and ~73% of the regional loads. Although streambank erosion is the second

highest source for the regional TSS supply, it only contributes approximately 8% and 6% to the regional

particulate supply. This indicates that the regional particulate nutrient levels are higher in the surface

soil than sub-surface soil.

Approximately 46% (~635 t/yr) of the regional dissolved inorganic nitrogen supply is through deep-

drainage in sugarcane, while approximately 20% (~273 t/yr) of the regional DIN supply is from

sugarcane surface runoff. The RC2015 baseline model indicated that only 20% of the total DIN leached

into the deep-drainages reaches the surface water, while a significant portion of DIN (~2,500 t/yr) is

permanently lost to the groundwater system. This needs further investigation as DIN contribution by

fallow legumes in the region may be overestimated in regional paddock models. The remaining land

uses contribute ~33% of the regional DIN supply. Point source (e.g. Sewage treatment plants) DIN

contribution is low (~2%). Extractions dominate the loss of DIN in the region, with approximately 3% of

the total DIN supply is lost via extractions. PSIIs, and thus the toxic load, supply is dominated by

sugarcane, while only approximately 2.6% of the PSII supply is lost through extractions.

The comparison between the modelled fine sediment export due to streambank erosion along the

O’Connell River against that estimated as part the stream stability assessment conducted by Alluvium

Consultancy is encouraging given the modelled streambank erosion is underestimated only by

approximately 16% for the O’Connell River.

Progress towards targets RC2015 models estimated marginal reductions in fine sediment and particulate nutrient loads (< 0.1

%), while estimating 1.1% and 2.9% reductions in dissolved inorganic nitrogen (DIN) and total toxic

loads respectively. All-A and All-B scenario modelling outcomes indicated that Reef Plan water quality

targets can be achieved for all pollutants, except DIN, should all land use practices improve to meet ‘B’

management standards. The modelling indicated that the WQ target for DIN cannot be achieved even

if all land use practices are improved to meet ‘A’ management standards. This may suggest further


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investigations are required to assess the efficacy of the proposed best practice to manage DIN

reductions for water quality improvement as well as the accuracy of methods used to represent DIN

management practices in paddock and (or) catchment models.


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Fitzroy

The updates to the model for RC2015 have resulted in modelled loads being better aligned to observed

loads. The inclusion of the rock cover layer provides an improved representation of hillslope erosion

rates in nature conservation and forestry areas; while improvements to paddock scale modelling of

dryland cropping have significantly improved the representation of these FUs in the model. Furthermore,

minor inconsistencies in model parameters have been addressed and updated for this Report Card.

Hydrology The modelled baseflow and quick flow ratios have been improved by the recalibration of the hydrology.

There has been a significant improvement in the modelled baseflow when compared to the Lyne-Hollick

baseflow filter estimate for the observed baseflows. The average difference between observed and

modelled baseflow at gauges improved from 29% for the previous report card to 2% for this report card.

Compared to the previous report card, the changes have significantly increased the proportion of

surface runoff (quick flow) for many catchments and as a result have increased erosion in these

catchments. The effect of this change on the various erosion processes is discussed in the relevant

sections below. The improvement in the models baseflow and quick flow ratios has resulted in small

changes to the total predicted flow. Previously the model over predicted the combined flow at all gauges

used for calibration by 1%; however for this report card, the model under predicts the flow by 2%. The

reduction in flow was observed at most gauges and on average each gauge had a reduction in flow of

1%.

Fine sediment and particulates generation, sources and sinks The changes to the RC2015 input data, parameters and methodology has produced a small increase

(2%) in the total fine sediment load exported to the GBR lagoon from the Fitzroy region. Individually the

modifications have produced varied results, the changes and the effect they had on model loads are

discussed below.

The model results indicate that the Fitzroy Basin contributes 83% of the TSS load for the region and

greater than 70% of the exported load for all other constituents except PP (~48%) and PN (~54%). Of

the minor contributors, Water Park Creek, the smallest basin in area (~1%), produces the second

highest contribution to the export load for PN at ~20% and PP at ~15%. This is due to the high proportion

of the export load that is derived from hillslope erosion as a result of the high rainfall and steep slopes

within this basin and its proximity to the GBR lagoon. The Boyne has the lowest contribution to export

loads for all constituents.

Hillslope

The rock factor layer was introduced into the model to reduce soil erodibility factor in conservation and

forestry areas with steep slopes that were producing very high erosion rates which in reality have low

erosion rates due to the surface rock cover. The rock factor layer was applied to the entire Fitzroy region

and produced reductions in soil erodibility in approximately half of the Fitzroy region. In ten per cent of


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the region, soil erodibility was reduced by over half of the original values and the overall average

reduction was a 27% decrease in the soil erodibility factor.

The largest reductions in soil erodibility occurred in the Isaac, Styx and Fitzroy catchments along the

Connors, Broadsound and Boomer ranges as well as in an area between Marlborough and Yeppoon.

These reductions occurred in forestry, grazing and conservation areas. Similar reductions occurred in

the Boyne and Calliope catchments. For the majority of the Fitzroy Basin there were little or no

reductions in soil erodibility; however significant reductions occurred in Callide Creek area in an area

northwest of Biloela and some smaller reductions occurred in an area south of Emerald. These

reductions occurred in open and forested grazing, forestry and conservation areas.

The reduction in soil erodibility enabled the hillslope delivery ratio to be altered in conservation and

forestry areas where a very low delivery ratio of 2.5% had previously been used to reduce very high

erosion rates. The delivery ratio was made consistent with the surrounding areas and was changed to

5% for the Fitzroy Basin and 10% in the wetter and steeper coastal catchments.

The combined effect of the reduction in soil erodibility factor and increased delivery ratio resulted in a

45% reduction in generated hillslope loads in grazing and forestry areas and a 50% increase in the

conservation generated hillslope load compared to the previous report card.

Cropping

The incorporation of spatially variable slope as opposed to an average slope, into the HowLeaky

cropping model resulted in the generated hillslope load for dryland cropping increasing more than

tenfold from 147 kt/yr to 1,591 kt/yr. This large increase is considered to be a better estimate of the

loads being generated from dryland cropping. Average hillslope generation rates in dryland cropping

are now 2 t/ha/yr whereas the average was ~0.2 t/ha/yr for previous report cards. The increase in the

dryland cropping load resulted in the total generated hillslope load increasing by 60% and the exported

hillslope load increasing by 20%.

In previous Report Cards, the dryland cropping HowLeaky simulations were used to approximate the

irrigated cropping land use in the Fitzroy NRM region. A decision was made not to use this approach

for RC2015, because there is no evidence to support the assumption that irrigated cropping produces

similar erosion rates as dryland cropping. Typically, within irrigated cropping areas of the Fitzroy region

all irrigation tail water is captured and stored on farm. The decision was made not to model hillslope

erosion within these areas until a better understanding of the proportion of sediment that is transported

off irrigated cropping areas is acquired. The Queensland Department of Agriculture and Fisheries (DAF)

is currently investigating irrigation practices and runoff capture within the Fitzroy region. The results of

this research will be used to improve the modelling of hillslope erosion in irrigated cropping areas within

the Fitzroy region. By choosing not to use dryland cropping HowLeaky runs, there is now a zero fine

sediment contribution for hillslope erosion for irrigated cropping areas. This change had a minor impact,


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as irrigation is <1% of the catchment, thus only reducing the exported sediment load by 10kt/yr

compared to the previous Report Card.

Gully

Changes to the gully full maturity year and gully activity parameters implemented to reduce gully erosion

contribution, resulted in a slight increase in the total fine sediment load generated by gully erosion. The

change to the gully full maturity year resulted in a reduced rate of gully erosion activity being applied to

the entire model period rather than just the last few years as used in the previous report card.

Streambank

The combined effect of the changes described above prompted a reassessment of end of system loads,

sources of sediments and losses. An assessment of bank full flow recurrence interval and floodplain

deposition indicated that there was potential to increase the deposition by reducing the recurrence

interval from 6 years to 4.7. This change increased floodplain deposition by 8% and improved the

model’s end of system estimation. Losses to floodplain deposition now accounts for 48% of the

sediment generated by the model which fits within the findings of Trimble (1983), Phillips (1991) and

Hughes et al. (2009) who reported that half of river sediment loads can be deposited on floodplains.

The application of a variable bank full flow recurrence interval across the Fitzroy region will be explored

for future report cards to improve the spatial distribution of streambank erosion and floodplain

deposition.

The reduction in the recurrence interval produces a small reduction in the sediment load generated by

streambank erosion and the changes to the regression relationship for channel height also reduced

streambank erosion. The combined result of these changes produced a 5% reduction in the generated

streambank load.

As a result of all the changes the average annual modelled fine sediment load at Rockhampton is 1,424

kt/yr which is 7% below the combined GBRCLMP monitored load (2006–2014) and the FRCE estimate

(1986–2006). The modelled average annual sediment concentration for fine sediment is 247 mg/L which

provides a good estimate of the combined GBRCLMP monitored load (2006–2014) and the FRCE

estimate (1986–2006) average concentration of 274 mg/L.

Event Mean Concentration (EMC)/Dry Weather Concentration (DWC) The adjustment of Event Mean Concentration/Dry Weather Concentration (EMC/DWC) values

improved prediction of nutrient loads, as expected. Constituents modelled solely with the EMC/DWC

approach (DIN, DON, DIP and DOP) are within ±2% of monitored loads (GBRCLMP) with most within

±1%. Concentrations similarly are within ±1% of monitored concentrations. DIN and DON are generally

over predicted during high flow events whereas DIP and DOP are under predicted during these events.


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Particulate Nutrients The model under predicts particulate nutrient loads by 9 % for nitrogen and 5% for phosphorus.

Concentrations of particulate nutrients were also under predicted by similar proportions. With both

particulate nitrogen and phosphorous being under predicted by the model future investigation will be

focused on the relationship between reservoir deposition of sediment and particulate nutrients within

the model.

Progress towards targets Management changes within the Fitzroy region for RC2015 were again relatively small. Whilst they

were larger than the previous report card the changes were generally smaller than previously reported

changes. The changes in management practices resulted in load reductions for fine sediment,

particulate nutrient loads and toxic load. The largest reduction in the fine sediment load across the GBR

occurred within the Fitzroy region.

Improvements occurred in gully, streambank and cropping management practices. A 1% reduction in

the anthropogenic fine sediment load was modelled for the Fitzroy region for RC2015 and resulted in

the cumulative reduction within the Fitzroy region increasing to 5.5%. Nitrogen and phosphorus loads

were both reduced by 0.2% producing a cumulative load reduction of 3.2% and 6.7% respectively. The

toxic load reduced by 0.3% which improved the cumulative reduction to 4.3%.

Significant management changes are required to be implemented in the coming years to achieve the

Reef Plan targets within the Fitzroy region. The size of the required changes exceeds all changes

reported thus far.


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Burnett Mary

The results from RC2015 are an improvement on previous model runs for the Burnett Mary region thus

far. The main reason for the improved representation of constituent loads in this Report Card is the

recalibration of hydrology for baseflow, and the resultant increase in average annual flow from the BM

region to better align with gauged flows. Furthermore, minor inconsistencies in model parameters have

been addressed and updated for this Report Card.

Hydrology The modelled hydrology has been improved by recalibrating the model to better predict the proportion

of total flow represented as baseflow. Overall this increased the average annual flow of the BM region

by 16% compared to the baseline model for RC2014. Baseflow was reduced in 76% of the calibration

gauges. In three basins (Baffle, Kolan and Burrum) the average annual flow was reduced by less than

5%. In the Burnett Basin average annual flow increased by 1%, while in the Mary, the wettest basin, it

increased by almost 35%. In the Mary Basin there were six gauges used for hydrology calibration. In

RC2014 the average baseflow for these gauges was 33% (ranging from 1% to 60%), while the

recalibration in RC2015 resulted in an average baseflow for the same six gauges of 24% (ranging from

18% to 29%). This decrease in baseflow leads to an increase in quickflow; which is the major cause of

the 35% increase in average annual modelled flow for the Mary Basin. In summary, the reason for the

discrepancy between modelled flows in the two report cards is the adjustment of baseflow proportions

as the result of the hydrology re-calibration in RC2015. Since modelled baseflow proportions in RC2015

better align with baseflow proportions from monitored data, the hydrology model results in RC2015 are

an improvement over those in RC2014. It should also be noted that increase in flow in the Mary will

lead to resultant increases in constituent generation in this basin.

Fine sediment and particulate nutrient generation, sources and sinks In RC2015, the BM Source Catchments modelling estimates the TSS contribution exported from the

region to the coast is 23% from hillslope erosion, 15% gully, with the largest contribution from

streambank erosion (52%). The remaining 10% is contributed by instream fine sediment remobilisation

which was not included in previous report cards. This is in contrast to results from RC2014 where

contributions to TSS export were 38%, 20% and 42% from hillslope, gully and streambank erosion,

respectively. Since streambank erosion is a function of flow, the fact that flow modelling in RC2015 is

an improvement from that in RC2014 implies that the percentage contribution from streambank erosion

in RC2015 is also an improvement on that in RC2014.

The contribution from hillslope erosion to the fine sediment load has decreased in the BM from RC2014

by 15%, and there are a number of parameter changes which have caused this. In the first instance,

the improved quickflow/baseflow representation has meant an overall increase in average annual flow,

which will result in increased constituent generation for the quickflow component. To offset this, the fine

sediment (and particulate nutrient) hillslope delivery ratio has been decreased from 20% to 5%, in order

to better match monitored loads. Further, the DWC for USLE parameterisation has been reduced from

100 mg/L to 0 mg/L. In RC2014 baseflow was much greater than for RC2015, hence more TSS load


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will have been generated by the DWC component of the model. By setting this value to zero in RC2015

there is now no load generated during baseflow (or slow flow) conditions.

Streambank

Streambank erosion is the dominant source of fine sediment in the Mary Basin, accounting for 65% of

the sediment export from the basin itself and 34% of the regional TSS export. In both RC2015 and

RC2014 the Mary catchment had the greatest contribution of streambank erosion from the BM region.

The dominance of streambank erosion as a source of TSS export is a consequence of relatively high

channel capacity and stream power, as a result of large historical channel incision and widening coupled

with localised clearing of riparian vegetation. In both RC2014 and RC2015 the Mary Basin had the

greatest contribution of streambank erosion from the region, and this was the case from earlier

modelling undertaken by DeRose et al. (2002) and the National Land and Water Audit (NLWRA,2001).

Whilst there is a general lack of measured data to evaluate the extent and severity of streambank

erosion in the GBR as whole (Bartley et al. 2015), there is evidence that streambank erosion is a very

important issue in the region (Simon, 2014 and Department of Natural Resources and Mines, 2015).

Storage trapping

The constituent budget for the Burnett Mary region shows that 21% (498 kt/y) of the TSS supplied to

the stream network is trapped in storages and a further 7% (174 kt/y) is taken out of the stream network

through water extractions and other losses (e.g. sediment still in route through the system), floodplain,

and stream deposition, respectively, account for 4% (91 kt/y) and 5% (101 kt/y) of the fine sediment

budget. The Burnett catchment generated almost 48% (1,119 kt/y) of the regional TSS load to the

stream network, with 38% (548 kt/y) exported from the basin. On the other hand, the Mary catchment

supplied 44% (1,017 kt/y) of the total TSS to the stream network and exported 53% (770 kt/y). The

relatively small TSS per cent contribution to export from the Burnett catchment in RC2015 compared to

the Mary is a result of the high sediment trapping efficiency of six of the 11 sediment water storages in

the lower reaches of the catchment and losses associated with water extractions during the modelling

period. In terms of the overall constituent budget, it is worth noting that sediment deposition in storages

is the major sink of TSS within the region accounting for about 21% of the TSS supply. The presence

of fewer and smaller water storages in the Mary catchment (three out of 11 can partly explain the large

contribution of TSS export from this basin). Given the significance of storage trapping on the constituent

budget, it is important that this component is verified by future monitoring data which has been lacking

to date in the region and in the GBR as whole.

Sugarcane dissolved nutrients and PSII loads The load conversion factor in the sugarcane parameteriser is an adjustable parameter in the model and

in RC2014 (and all previous Report Cards) was set at a default of 0.8 for DIP and 0.2 for DOP. As a

result of the under-prediction of DIP and DOP loads at model evaluation sites, these were changed to

1.0 for DIP and 1.2 for DOP in RC2015. This change can be justified by the fact that mill mud which is

commonly applied to supplement nutrients in sugarcane has not been accounted for by the model and


134

the existence of evidence there bioavailable phosphorous increases with mill mud application (Moody

and Schroeder 2014).

DIN and PSII herbicide exports are predominantly associated with the sugarcane and cropping land

uses in each catchment in the region. In the Burnett Mary region as a whole, DIN export increased by

20% in this report card compared to the previous report card due to the increase in flow in the Mary and

Burrum basins. PSII loads reduced by 38% compared to the previous report card. These differences

are attributed baseline management distributions were improved between RC2014 and RC2015.

Most of the DIN export is associated with fertiliser losses in sugarcane growing areas. Adoption of the

6 easy steps program for nutrient management is expected to result in major contribution to reduction

of DIN export. All of the PSII inhibiting herbicides exported are due to the application of these chemicals

to control weeds in sugarcane farms. The sugarcane industry in the Burnett, Burrum, Mary and Kolan

basins contributes 82% of the PSII export.

Progress towards targets There has been very little progress towards meeting the reef targets in RC2015, as was the case in

RC2014. This is due to the very low level of investment in management changes required to achieve

water quality targets for these two report cards. Moreover, the impact of management changes on loads

in both report cards has been moderate compared to previous report cards. Percentage load reductions

of all constituents from the BM region are less than the average GBR-wide percentage reductions,

which is attributed to the low level investment in management interventions in this region during this

report card. The Burnett and Burrum basins were the only basins where there were some DIN load

reductions as management interventions were limited to these areas for this report card.


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Conclusion

The catchment scale water quality modelling as described in this report is one of multiple lines of

evidence used to report on progress towards Reef Plan 2013 targets. Investments in improved land

management practices between 2008 and 2015 have resulted in a fine sediment load reduction of

12.3%. Particulate nitrogen and phosphorus have declined by 11.6% and 14.7% respectively. Progress

towards meeting the 50% DIN target by 2018 has been ‘very poor’, with an 18.1% reduction reported

to date. PSII TE load, however, is over halfway to its 60% reduction target, with a 33.7% reduction

reported.

The baseline loads presented in this report are typically higher than for previous Source Catchments

sediment and nutrient loads. The reasons for the higher loads are methodological changes such as

hydrology model recalibration to better represent baseflow proportion in line with modelled data.

Generally this had the effect of reducing the baseflow portion and increasing quickflow which in turn

increases constituent generation. Also, instream deposition and remobilisation were enabled in four

regions, and while the relative contribution of both is similar, this has also increased end of system

loads slightly.

Overall, the catchment scale water quality modelling has been successful, and the aim of reporting

towards Reef Plan 2013 targets had been achieved. The results show that while progress is being made

towards meeting the sediment, nutrient and pesticide targets, further work still needs to occur to ensure

we reach the targets by 2018.


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140

Appendix 1 – Hydrology calibration results

Below is the objective function used to recalibrate the hydrology to improve the baseflow component:

SDEB= {[α∑ (𝑛𝑖=1 Oi

0.5 – Si0.5)2 + (1- α) ∑ (𝑛

𝑘=1 Ek0.5 – Sk

0.5)2] × [1 + ∣(∑Oi – ∑Si∣

∑Oi ]}×{ 1 +

∣(Obf – Sbf)∣

Obf }

Where n is the number of calibration days, Oi is the observed flow on day i (Ml/d), Si is the simulated

flow on day i (Ml/d), Ek and Sk are the kth values of the observed and simulated flow series of the

exceedance curve, α is a weighting factor. The term ‘1 + ∣(Obf – Sbf∣)

Obf ’ calculates the simulation bias of

baseflow component. The designated PEST’s parameters optimisers search a broad range of

parameter combinations to minimise the difference between the simulated daily flow, total flow volume

and exceedance time series and also the difference between the simulated baseflow rate and the value

calculated by Lyne-Hollick filter.

Cape York

Table 49 Cape York hydrology calibration results (1970–2014)

Gauge Gauge name Catchment area (km2)

Years of record^

R2 NSE Total flow

volume bias

High flow volume

bias

102102A Pascoe R. at

Garraway Creek 1,313 44 0.806 0.634 -5.0 -6.9

104001A Stewart R. at

Telegraph Road 470 44 0.752 0.504 1.2 0.5

105001B Hann R. at Sandy

Creek 984 44 0.839 0.678 0.3 0.4

105101A Normanby R. at

Battle Camp 2,302 44 0.818 0.63 0.6 1.0

105102A Laura R. at

Coalseam Creek 1,316 44 0.723 0.468 1.3 0.4

105103A Kennedy R. at

Fairlight 1,083 18 0.731 0.489 -0.2 -1.2

105104A Deighton R. at

Deighton 590 18 0.794 0.596 -1.7 -1.8

105105A East Normanby R.

at Mulligan Highway

297 44 0.774 0.537 2.2 1.3

105106A West Normanby R.

at Sellheim 839 19 0.746 0.463 0.9 0.9

105107A Normanby R. at

Kalpowar Crossing 12,934 9 0.876 0.734 0.1 0.3

106001A McIvor R. at

Elderslie 175 18 0.644 0.299 3.0 3.0

106002A Jeannie R. at

Wakooka Road 323 18 0.687 0.369 3.7 4.3

106003A Starcke R. at

Causeway 192 18 0.69 0.383 4.3 3.5

107001B Endeavour R. at

Flaggy 337 44 0.705 0.378 1.1 1.2

107002A Annan R. at Mt

Simon 373 21 0.787 0.561 0.5 1.1

NSE (Nash Sutcliffe coefficient of Efficiency) ^ Years of record = number of years of flow data that was within the hydrology calibration period (1972–2014). (EOS) end-of-system. It refers to the furthest downstream gauge on a stream or river


141

Wet Tropics

Table 50 Wet Tropics hydrology calibration results (1970–2014)


Years of record^

R2 NSE Total flow

volume bias

High flow volume

bias

108002A Daintree River at Bairds (EOS)

911 42 0.85 0.71 -0.1 -1.2

108003A# Bloomfield River at China camp

264 42 0.86 0.73 0 -0.2

109001A# Mossman River at Mossman (EOS)

106 41 0.85 0.62 -2 7.4

109002B-C# South Mossman River at Loco Bridge

54 20 0.9 0.8 -7.3 -4.6

110001A-D Barron River at Myola (EOS)

1,945 42 0.95 0.91 0 -0.7

110003A Barron River at Picnic Crossing

228 42 0.92 0.85 -0.1 -0.1

110011B Flaggy Creek at Recorder

150 42 0.88 0.77 -3 -3.9

110016A Barron River @ Koah

1,434 13 0.94 0.86 1.8 7.4

110020A Barron River at Bilwon

1,258 22 0.88 0.77 0 -0.4

111007A Mulgrave River at the Fisheries (EOS)

520 42 0.91 0.83 0 -0.8

111101A-D# Russell River at Buckland’s (EOS)

315 34 0.95 0.9 -1.5 -3.6

111102A-B# Babinda Creek at Babinda

82 16 0.9 0.81 -6.2 -4.5

112001A* North Johnstone River at Tung Oil (EOS)

936 42 0.75 0.52 0.5 1.1

112003A North Johnstone River at Glen Allyn

165 42 0.96 0.92 -1 -1.7

112101A-B South Johnstone River at Upstream Central Mill (EOS)

401 42 0.94 0.87 -0.1 -0.9

112102A Liverpool Creek at Upper Japoonvale

78 42 0.9 0.81 0.7 1.2

113004A# Cochable Creek at Powerline

95 42 0.88 0.76 0 0.4

113006A Tully River at Euramo (EOS)

1,450 42 0.93 0.86 1 -0.6

114001A Murray River at Upper Murray (EOS)

156 42 0.87 0.75 -3.7 -5.6

116001A-D Herbert River at Ingham (EOS)

8,581 42 0.92 0.84 -4.6 -5.7

116004A-C Herbert River at Glen Eagle

5,236 42 0.92 0.85 3.1 -0.8

116006A Herbert River at Abergowrie College

7,440 42 0.93 0.87 -0.3 -2.5

116008B# Gowrie Creek at Abergowrie

124 42 0.78 0.61 -4.8 -6.9

116010A Blencoe Creek at Blencoe Falls

226 42 0.88 0.77 -2.1 -3.3

116012A Cameron Creek at 8.7 km

360 42 0.77 0.59 -7.7 -10.3


142


Years of record^

R2 NSE Total flow

volume bias

High flow volume

bias

116013A Milstream at Archer Creek

308 42 0.92 0.84 0 0

116014A Wild River at Silver Valley

591 42 0.92 0.83 0.4 0.1

116015A Blunder Creek at Wooroora

127 42 0.85 0.7 -0.3 -0.1

116016A Rudd Creek at Gunnawarra

1,450 42 0.88 0.75 -0.1 0.3

116017A Stone Creek at Running Creek

157 42 0.89 0.79 0.1 0.4

NSE (Nash Sutcliffe coefficient of Efficiency) * The flow from 112004A was added onto flow from 112001A. ^ Years of record = number of years of flow data that was within the hydrology calibration period (1972–2014). (EOS) end-of-system. It refers to the furthest downstream gauge on a stream or river #rainfall data for the sub-catchment draining to the gauge were scaled

Burdekin

Table 51 Burdekin hydrology calibration results (1970–2014)


Years of record

R2 NSE Total flow

volume bias

High flow

volume bias

117002A Black River at Bruce Highway

256 41 0.85 0.7 0.0 -0.3

117003A Bluewater Creek at Bluewater

86 41 0.85 0.7 -3.3 -3.9

118001B Bohle River at Mount Bohle

183 29 0.87 0.7 31.7 28.1

118003A Bohle River at Hervey Range Road

143 29 0.86 0.7 -4.0 -4.3

118004A Little Bohle River at Middle Bohle River junction.

54 24 0.87 0.7 10.8 10.5

119003A Haughton River at Powerline

1,773 42 0.87 0.7 8.9 12.0

119005A Haughton River at Mount Piccaninny

1,133 42 0.90 0.8 0.0 -0.1

119006A Major Creek at Damsite

468 36 0.79 0.6 -2.8 -3.3

119101A Barratta Creek at Northcote

753 40 0.83 0.7 0.0 0.0

120002C Burdekin River at Sellheim

36,260 42 0.96 0.9 0.0 0.0

120004A Burdekin River at Burdekin Falls D/S

114,654 11 0.96 0.9 -3.7 -4.9

120005B Bogie River at Strathbogie

1,031 23 0.88 0.8 -0.1 -0.6

120006B Burdekin River at Clare

129,876 24 0.97 0.9 -1.1 -2.7

120102A Keelbottom Creek at Keelbottom

193 42 0.78 0.6 -4.9 -5.2

120106B Basalt River at Bluff Downs

1,301 42 0.88 0.8 0.0 0.3

120107B Burdekin River at Blue Range

10,528 42 0.89 0.8 0.1 -1.5

120108C Fletcher Creek at Fletchervale

1,034 17 0.92 0.8 0.0 1.7

120110A Burdekin River at Mount Fullstop

17,299 42 0.95 0.9 0.0 -0.6


143


Years of record

R2 NSE Total flow

volume bias

High flow

volume bias

120111A Burdekin River at Lucky Downs

6,277 17 0.79 0.6 0.1 -0.7

120112A Star River at Laroona 1,212 42 0.82 0.7 -2.8 -3.2

120113A Clarke River at Wandovale

1,802 22 0.84 0.7 0.4 -0.7

120114A Douglas Creek at Kangaroo Hills

663 17 0.91 0.8 0.4 1.1

120118A Burdekin River at Lake Lucy

1,087 17 0.82 0.6 -0.4 -0.1

120119A Fanning River at Fanning River

498 19 0.59 0.3 -3.6 -4.1

120120A Running River at Mt. Bradley

490 39 0.85 0.7 -0.6 -0.8

120121A Burdekin River at Lake Lucy Dam Site

2,216 38 0.86 0.7 -3.0 -4.8

120122A Burdekin River at Gainsford

26,316 10 0.94 0.9 0.9 -1.9

120205A Bowen River at Myuna

7,104 42 0.90 0.8 0.0 -0.9

120206A Pelican Creek at Mt Jimmy

545 34 0.81 0.6 -0.1 -0.4

120207A Broken River at Urannah

1,103 42 0.83 0.6 5.4 7.2

120209A Bowen River at Jacks Creek

4,305 42 0.88 0.7 3.2 5.7

120212A Emu Creek at The Saddle

431 17 0.69 0.4 -0.1 0.6

120213A Grant Creek at Grass Humpy

325 17 0.72 0.5 0.0 0.8

120214A Broken River at Mt. Sugarloaf

2,269 22 0.89 0.7 1.0 0.9

120219A Bowen River at Red Hill Creek

8,280 14 0.90 0.8 -0.2 -3.2

120301B Belyando River at Gregory Development Rd.

35,411 38 0.87 0.7 -1.1 -0.2

120302B Cape River at Taemas

16,074 42 0.90 0.8 0.0 0.4

120303A Suttor River at St Anns

50,291 42 0.91 0.8 -0.2 -0.7

120304A Suttor River at Eaglefield

1,915 42 0.70 0.4 0.0 -0.1

120305A Native Companion Creek at Violet Grove

4,065 42 0.89 0.8 -0.5 -0.5

120306A Mistake Creek at Charlton

2,583 22 0.78 0.6 0.0 -0.5

120310A Suttor River at Bowen Developmental Road

10,758 8 0.84 0.7 0.0 0.0

121001A Don River at Ida Creek

604 42 0.80 0.6 0.0 0.4

121002A Elliot River at Guthalungra

273 41 0.86 0.7 -4.8 -4.8

121003A Don River at Reeves 1,016 30 0.87 0.7 0.1 0.6

121004A Euri Creek at Koonandah

429 16 0.82 0.7 -5.7 -6.3



144

Mackay Whitsunday

Table 52 Mackay Whitsunday hydrology calibration results (1970–2014)


Years of record^

R Squared

NSE Total flow

volume bias

High flow volume

bias

122012A C Lethe Brook at Hadlow Road

89 13 0.8 0.63 -25.1 -26.5

124001B O’Connell River at Staffords Crossing

342 44 0.9 0.8 -1.9 -2.1

124002A St.Helens Creek at Calen

118 41 0.92 0.85 -2.2 -3.2

124003A Andromache River at Jochheims

230 38 0.91 0.81 0 0

125001B C Pioneer River at Pleystowe Recorder

1434 12 0.94 0.88 -0.1 -0.4

125002C Pioneer River at Sarichs

757 44 0.91 0.82 -1.5 -1.9

125004B Cattle Creek at Gargett

326 44 0.94 0.89 -0.7 -0.9

126001A Sandy Creek at Homebush (EOS)

326 44 0.88 0.77 0 0.3

126002A C Plane Creek at Sarina

92 16 0.85 0.68 1 1.2

126003A Carmila Creek at Carmila

84 41 0.79 0.62 -4.4 -4.7

NSE (Nash Sutcliffe coefficient of Efficiency). C closed/historic gauging station. ^ Years of record = approximate number of years of flow data that was within the hydrology calibration period (1970–2014). (EOS) end-of-system. It refers to the furthest downstream gauge on a stream or river.

Figure 54 Time Series, Flow Duration (log scale), and Scatter plot Comparisons of Modelled VS. Measured Daily

Flows in Lethe Brook Creek at Hadlow Road (122012A)


145


Flows in the O’Connell River at Staffords Crossing (124001B)


Flows in St.Helens Creek at Calen (124002A)

Figure 57 Time Series, Flow Duration (log scale), and Scatter Plot Comparisons of Modelled VS. Measured Daily

Flows in the Andromache River at Jochheims (124003A)


146


Flows in the Pioneer River at Pleystowe Recorder (125001B)


Flows in the Pioneer River at Sarichs (125002C)


Flows in Cattle Creek at Gargett (125004B)


147


Flows in Sandy Creek at Homebush (126001A)


Flows in Plane Creek at Sarina (126002A)


Flows in Carmila Creek at Carmila (126003A)


148

Fitzroy

Table 53 Fitzroy hydrology calibration results (1970–2014)


Years of record^

R Squared

NSE Total flow

volume bias

High flow volume

bias

129001A Waterpark Creek at Byfield

213 44 0.88 0.78 0.0 -0.1

130003B Fitzroy River at Riverslea

131,413 44 0.94 0.89 0.3 -0.7

130004A Raglan Creek at Old Station

389 44 0.80 0.58 0.0 0.3

130005A Fitzroy River at The Gap

135,788 44 0.93 0.85 0.9 -2.4

130008A Neerkol Creek at Neerkol

505 25 0.91 0.83 -1.3 -1.7

130009A Marlborough Creek at Slopeaway

679 16 0.95 0.90 -2.0 -2.9

130103A Mackenzie River at Carnangarra

45,367 29 0.90 0.72 9.7 12.2

130105A Mackenzie River at Coolmaringa

76,659 43 0.94 0.88 -4.3 -5.5

130106A Mackenzie River at Bingegang

50,851 43 0.88 0.77 0.0 -0.1

130108B Blackwater Creek at Curragh

776 37 0.82 0.64 -0.1 0.3

130109A Mackenzie River at Tartrus Weir Headwater

75,182 26 0.94 0.82 11.9 12.1


8,485 44 0.90 0.81 9.2 9.2

130208A Theresa Creek at Ellendale

758 35 0.83 0.66 0.1 0.3

130209A Nogoa River at Craigmore

13,871 42 0.92 0.84 0.0 -2.4

130210A Theresa Creek at Valeria

4,421 43 0.81 0.60 0.0 -0.8

130219A Nogoa River at Duck Ponds

27,146 21 0.92 0.84 2.2 1.9

130302A Dawson River at Taroom

15,847 44 0.84 0.70 -0.1 -0.8

130305A Dawson River at Theodore

27,332 32 0.89 0.74 0.0 2.7

130306B Don River at Rannes Recorder

6,806 44 0.93 0.87 -1.1 -1.1

130313A Palm Tree Creek at La Palma

2,660 44 0.77 0.55 4.3 4.8

130315C Callide Creek at Stepanoffs

547 32 0.82 0.67 -0.1 0.5

130316A Mimosa Creek at Redcliffe

2,487 44 0.76 0.52 -2.8 -0.8

130317B Dawson River at Woodleigh

28,503 44 0.93 0.86 6.3 6.9

130322A Dawson River at Beckers

40,501 44 0.91 0.80 0.2 0.4

130324A Dawson River at Utopia Downs

6,039 44 0.88 0.77 -0.2 -0.6

130327A Callide Creek at Goovigen

4,468 43 0.90 0.74 1.6 1.6

130344A Juandah Creek at Windamere

1,679 40 0.82 0.65 0.0 0.1

130349A Don River at Kingsborough

594 38 0.85 0.69 0.0 1.2


149


Years of record^

R Squared

NSE Total flow

volume bias

High flow volume

bias

130401A Isaac River at Yatton

19,736 44 0.92 0.84 0.0 -0.2

130403A Connors River at Mount Bridget

1,292 44 0.86 0.74 -6.6 -7.3

130404A Connors River at Pink Lagoon

8,741 44 0.92 0.85 0.1 -1.6

130406A Funnel Creek at Main Road

1,065 44 0.95 0.91 -0.1 -0.3

130407A Nebo Creek at Nebo

257 44 0.89 0.79 -0.5 0.3

130410A Isaac River at Deverill

4,091 44 0.82 0.65 -0.7 -1.0

130413A Denison Creek at Braeside

757 43 0.88 0.77 -0.3 -0.3

130414A Isaac River at Goonyella

1,213 31 0.72 0.45 0.0 -0.3

130504B Comet River at Comet Weir

16,423 43 0.87 0.72 0.6 0.6

130506A Comet River at The Lake

10,188 42 0.89 0.79 4.6 5.5

130509A Carnarvon Creek at Rewan

351 29 0.82 0.64 0.0 0.6

132001A Calliope River at Castlehope

1,288 44 0.91 0.83 0.0 0.1

132002A Calliope River at Mount Alma

165 18 0.90 0.79 2.1 2.7

133001B Boyne River at Riverbend

2,258 18 0.77 0.59 -1.7 -3.9

133003A Diglum Creek at Marlua

203 42 0.88 0.76 -0.1 0.3

133004A Boyne River at Milton

1,170 21 0.93 0.86 -0.8 -1.2


Table 54 Baseflow proportions (%) for the current and previous Report Card for all gauges in the Fitzroy Basin

Gauge Basin RC 2015 baseflow

(%)

RC 2014 baseflow

(%)


129001A Waterpark Creek 29 47 27

130003B Fitzroy 29 82 25

130004A Fitzroy 17 10 19

130005A Fitzroy 39 67 26

130008A Fitzroy 10 2 12

130009A Fitzroy 10 10 8

130103A Fitzroy 18 6 21

130105A Fitzroy 28 80 24

130106A Fitzroy 18 97 19

130108B Fitzroy 15 15 5

130109A Fitzroy 23 16 24

130206A Fitzroy 10 34 8

130208A Fitzroy 13 13 7

130209A Fitzroy 13 90 11

130210A Fitzroy 10 99 8


150

Gauge Basin RC 2015 baseflow

(%)

RC 2014 baseflow

(%)


130219A Fitzroy 23 41 22

130302A Fitzroy 15 25 13

130305A Fitzroy 17 71 14

130306B Fitzroy 13 23 11

130313A Fitzroy 17 92 12

130315C Fitzroy 15 27 14

130316A Fitzroy 21 74 19

130317B Fitzroy 20 74 18

130322A Fitzroy 24 89 19

130324A Fitzroy 20 88 15

130327A Fitzroy 12 69 9

130344A Fitzroy 4 4 4

130349A Fitzroy 12 12 10

130401A Fitzroy 23 99 18

130403A Fitzroy 18 26 14

130404A Fitzroy 21 98 17

130406A Fitzroy 15 8 20

130407A Fitzroy 22 49 20

130410A Fitzroy 11 13 9

130413A Fitzroy 15 15 17

130414A Fitzroy 17 41 12

130504B Fitzroy 20 41 20

130506A Fitzroy 23 98 20

130509A Fitzroy 19 19 18

132001A Calliope 13 13 14

132002A Fitzroy 30 30 27

133001B Boyne 25 25 20

133003A Boyne 29 29 23

133004A Boyne 22 22 24


151

Burnett Mary

Table 55 Burnett Mary hydrology calibration results (1970–2014)


Years of

record^

R Squared

NSE

Total flow

volume bias

High flow

volume bias

134001B Baffle Creek at Mimdale 1,401 46 0.854 0.725 -0.4 -0.2

135002A Kolan River at Springfield 548 49 0.915 0.81 9.1 8.9

135004A Gin Gin Creek at Brushy Creek 537 49 0.836 0.695 -0.2 -0.6

136002D Burnett River at Mount Lawless 2,130 39 0.945 0.892 2.5 3.6

136007A Burnett River at Figtree Creek 1,318 18 0.824 0.675 1.9 1.9

136011A Degilbo Creek at Coringa 686 28 0.929 0.838 0.2 0.3

136019A Perry River at Mt Rawdon 155 19 0.931 0.833 -2.4 -2.0

136094A Burnett River at Jones Weir Tailwater 2,817 33 0.907 0.822 0.9 2.1

136101C Three Moon Creek at Abercorn 1,537 50 0.896 0.787 -0.3 -1.8

136103B Burnett River at Ceratodus 2,349 54 0.964 0.925 -0.1 -0.1

136106A Burnett River at Eidsvold 3,208 54 0.948 0.858 -20.3 -15.1

136207A Barambah Creek at Ban Ban 4,154 48 0.809 0.652 -0.3 -0.8

136209A Barker Creek at Glenmore 1,366 27 0.914 0.827 -0.1 0.1

136304A Stuart River at Proston Rifle Range 1,555 49 0.911 0.811 -0.3 0.7

136305A Auburn River at Dykehead 5,287 49 0.878 0.748 0.1 0.2

136306A Cadarga Creek at Brovinia Station 1,288 49 0.639 0.29 1.0 1.2

136315A Boyne River at Carters 1,619 35 0.92 0.841 1.8 1.6

136319A Boyne River at Cooranga 2,103 20 0.846 0.705 -5.3 -7.1

137001B Elliott River at Elliott 232 53 0.829 0.668 -0.1 -0.2

137003A Elliott River at Dr Mays Crossing 25 41 0.752 0.408 4.2 15.8

137101A Gregory River at Isis Highway 441 49 0.801 0.619 -1.2 -1.1

137103A Gregory River at Leesons 161 8 0.807 0.618 3.5 4.5

137201A Isis River at Bruce Highway 457 49 0.819 0.647 0.0 -0.1

138001A Mary River at Miva 1,713 105 0.833 0.674 1.9 0.1

138004B Munna Creek at Marodian 1,190 40 0.941 0.883 -0.4 -0.6

138007A Mary River at Fishermans Pocket 2,246 46 0.872 0.76 -0.2 1.7

138014A Mary River at Home Park 897 32 0.903 0.811 -9.6 -8.3

138111A Mary River at Moy Pocket 813 51 0.854 0.712 0.6 1.3

138903A Tinana Creek at Bauple East 756 33 0.941 0.885 -1.3 -1.6

NSE (Nash Sutcliffe coefficient of Efficiency) ^ Years of record = number of years of flow data that was within the hydrology calibration period (1972–2014).


152

Table 56 Baseflow proportions (%) for the current and previous Report Card for all gauges in the Burnett Mary

Basin

Gauge Gauge name RC 2014 baseflow

(%)

RC 2015 baseflow

(%)

Lyne- Hollick

baseflow filter (%)

134001B Baffle Creek at Mimdale 24 21 18

135002A Kolan River at Springfield 23 18 15

135004A Gin Gin Creek at Brushy Creek 22 19 17

136002D Burnett River at Mount Lawless 78 23 21

136007A Burnett River at Figtree Creek 23 19 18

136011A Degilbo Creek at Coringa 52 17 14

136019A Perry River at Mt Rawdon 23 17 13

136094A Burnett River at Jones Weir Tailwater 86 21 18

136101C Three Moon Creek at Abercorn 21 16 15

136103B Burnett River at Ceratodus 9 12 13

136106A Burnett River at Eidsvold 9 19 21

136207A Barambah Creek at Ban Ban 59 21 19

136209A Barker Creek at Glenmore 96 23 18

136304A Stuart River at Proston Rifle Range 12 15 13

136305A Auburn River at Dykehead 48 15 10

136306A Cadarga Creek at Brovinia Station 56 12 8

136315A Boyne River at Carters 61 15 11

136319A Boyne River at Cooranga 0 14 16

137001B Elliott River at Elliott 51 20 18

137003A Elliott River at Dr Mays Crossing 81 24 29

137101A Gregory River at Isis Highway 88 15 12

137103A Gregory River at Leesons 33 21 18

137201A Isis River at Bruce Highway 1 12 10

138001A Mary River at Miva 60 27 25

138004B Munna Creek at Marodian 31 18 16

138007A Mary River at Fishermans Pocket 50 29 27

138014A Mary River at Home Park 1 22 23

138111A Mary River at Moy Pocket 18 27 29

138903A Tinana Creek at Bauple East 38 23 22


153

Appendix 2 – Dynamic SedNet global parameters and global

requirements

Grazing constituent generation

Table 57 Hillslope erosion parameters

Parameter CY WT Burdekin MW Fitzroy BM

TSS delivery ratio 5 15/20/30 5/10 15/100 5/10 5

Max. QF concentration

2,000 1,000 10,000/5,000/2,500 1,000 10,000 10,000

DWC (mg/L) 5 20 0 10 0 0

Table 58 Gully erosion parameters


Daily runoff power factor 1.4 1.4 1.4 1.4 1.4 1.4

Gully model type DERM DERM DERM DERM DERM DERM

TSS DR % 100 100 100/25 100 100 80

Gully cross-sectional area (m2) 7.5 10 16/8 5 5 10

Average gully activity factor 1 1 1 1 0.7 1

Management practice factor Variable Variable Variable Variable Variable Variable

Default gully start year 1870 1870 1870 1861 1870 1900

Gully full maturity year 2010 2014 2014 2010 1980 2007

Density raster year 2001 2001 2003 2001 2013 2003

Table 59 Particulate nutrient generation parameter values


PP/PN enrichment ratio 8/8 3/2 2/1.2 4/2.4 10/6 10/9

Hillslope DR % 5 20 10/20 20 20 5

Gully DR % 100 100 100/200 100 100 80

In-stream models

Table 60 Parameter values for in-stream models in RC2015 baseline models


Bank height proportion for fine sediment deposition (%)

0.05 0.05 0 0.2 0 0.05

Initial proportion of fine bed store (%)

0.01 0.01 0 0 and 0.25 0 0.01

Floodplain sediment settling velocity

0.002 0.0003 0.000001 0.000001 0.0001 0.000001

Sediment settling velocity for remobilisation

0.5 0.1 0.1 0.1 0.1 0.1

Sediment settling velocity for instream deposition

0.002 0.0003 0.000001 0.000001 0.0001 0.00005

Sugarcane and cropping constituent generation

Table 61 A summary of changes for RC2015 changes for cropping and sugarcane (Changes from 2014 Report Card)

Change CY WT Burd MW Fitz BM Possible effect

Updated rainfall To 2015 To 2014 To 2015 To 2014 To 2015 To 2014

Dryland grains cropping - HowLeaky

Area 20 km2 (<1%) 142 (0.7%) 1336 (0.9%) 3 km2 (<1) 7934 km2 (5%) 820 km2 (1.6%)

Management scenarios

Cape York specific farming system modelled including “maize and peanuts”

No change No change No change No change No change Additional pesticides in Cape York due to new farming systems

Irrigated grains cropping - HowLeaky

Area 31 km2 (<1%) 8 km2 (<0.05%) 70 km2 (<0.0%) <1 km2 (<1%) 1213 km2 (1%)

466 km2 (0.9%)

Management scenario

No change No change No change No change “set to zero” plug in for the Fitzroy (due to water recycling).

No change No discharge from irrigated land uses for the Fitzroy

Bananas - HowLeaky

Model set up Row/Inter-row and Fallow modelled separately

Row/Inter-row and Fallow modelled separately

--- --- --- --- More consistent inter-annual erosion estimates – removes “fallow” peak.

Sugarcane - APSIM

Management scenarios

--- Run of ‘Df’ soils management to include additional tillage operations

Heavily revised irrigation management scenarios

No change --- Run of ‘Bp’ nutrient management to change fertiliser applied

Soils --- Revised allocation of

simulations to mapped soils.

Reduced number of soils modelled.

No change ---

Irrigation modelling --- No change Heavily revised No change --- No change Change in runoff, drainage, DIN

Drainage modelling --- No change Heavily revised No change --- No change Change in runoff, drainage, DIN

Other land uses (EMC/DWC values)

Although not all FUs have an EMC/DWC model applied, there are some constituents that use this approach, as well as those FUs that have an EMC/DWC

model applied for all constituents (commonly, urban, other, and horticulture with some exceptions). During the development of the appropriate EMC/DWC


155

values all land uses were considered and a concentration value determined. This was important for the predevelopment model as nature conservation values

were applied to all FUs, excluding those where the USLE approach was taken in the baseline model, and maintained in the predevelopment model (commonly,

open and closed grazing, forestry, and nature conservation areas with some exceptions).

Table 62 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Cape York

FU TSS EMC TSS DWC

PN EMC PN

DWC DIN EMC

DIN DWC

DON EMC DON DWC

PP EMC PP

DWC DOP EMC

DOP DWC

DIP EMC DIP

DWC

Bananas HowLeaky 30 HowLeaky 0.1125 HowLeaky 0.03 HowLeaky 0.225 HowLeaky 0.02625 HowLeaky 0.015 HowLeaky 0.0075

Dryland cropping HowLeaky 30 HowLeaky 0.1125 HowLeaky 0.03 0.45 0.225 HowLeaky 0.02625 HowLeaky 0.015 HowLeaky 0.0075

Forestry USLE 20 USLE 0.075 USLE 0.02 0.3 0.15 USLE 0.0175 0.02 0.01 0.01 0.005

Horticulture 60 30 0.225 0.1125 0.06 0.03 0.45 0.225 0.0525 0.02625 0.03 0.015 0.015 0.0075

Irrigated cropping HowLeaky 30 HowLeaky 0.1125 HowLeaky 0.03 0.45 0.225 HowLeaky 0.02625 HowLeaky 0.015 HowLeaky 0.0075

Nature conservation

USLE 10 USLE 0.04 USLE 0.016 0.2 0.1 USLE 0.005 0.01 0.005 0.005 0.0025

Other 40 20 0.15 0.075 0.04 0.02 0.3 0.15 0.0525 0.0175 0.02 0.01 0.01 0.005

Urban 40 20 0.15 0.075 0.04 0.02 0.3 0.15 0.0525 0.0175 0.02 0.01 0.01 0.005

Water 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Grazing closed USLE 0 USLE 0.06 USLE 0.016 0.24 0.15 USLE 0.014 0.016 0.008 0.008 0.004

Grazing open USLE 0 USLE 0.075 USLE 0.025 0.3 0.15 USLE 0.0175 0.02 0.01 0.01 0.005

Table 63 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Wet Tropics

FU TSS EMC TSS DWC

PN EMC PN

DWC DIN EMC

DIN DWC

DON EMC DON DWC

PP EMC

PP DWC

DOP EMC DOP DWC

DIP EMC DIP

DWC

Nature conservation 30 15 0.25 0.13 0.03 0.02 0.16 0.08 0.12 0.06 0.02 0.01 0.00 0.00

Forestry 40 20 0.25 0.13 0.03 0.02 0.20 0.10 0.72 0.36 0.02 0.01 0.01 0.01

Closed grazing USLE 20 USLE 0.48 USLE 0.02 0.16 0.08 0.30 0.15 0.03 0.01 0.02 0.01

Open grazing USLE 20 USLE 0.48 USLE 0.02 0.16 0.08 0.30 0.15 0.03 0.01 0.02 0.01

Dairy 150 75 0.48 0.24 0.04 0.02 0.19 0.09 0.12 0.06 0.03 0.01 0.02 0.01

Other 105 35 0.48 0.24 0.04 0.02 0.76 0.38 0.27 0.14 0.03 0.01 0.04 0.02

Urban 105 35 0.48 0.24 0.04 0.02 0.76 0.38 0.27 0.14 0.03 0.01 0.04 0.02


156

FU TSS EMC TSS DWC

PN EMC PN

DWC DIN EMC

DIN DWC

DON EMC DON DWC

PP EMC

PP DWC

DOP EMC DOP DWC

DIP EMC DIP

DWC

Horticulture 120 60 0.50 0.25 0.10 0.05 0.41 0.21 0.21 0.11 0.03 0.02 0.05 0.02

Dryland cropping HowLeaky 60 HowLeaky 0.30 HowLeaky 0.10 0.75 0.38 0.50 0.25 HowLeaky 0.03 HowLeaky 0.01

Irrigated cropping HowLeaky 60 HowLeaky 0.30 HowLeaky 0.10 0.75 0.38 0.50 0.25 HowLeaky 0.03 HowLeaky 0.01

Banana HowLeaky 60 HowLeaky 0.55 HowLeaky 0.06 HowLeaky 1.50 0.21 0.11 HowLeaky 0.03 HowLeaky 0.02

Sugarcane APSIM 50 APSIM 0.30 APSIM 0.02 APSIM 0.00 0.50 0.30 APSIM N/A APSIM N/A

Water 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table 64 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Burdekin

FU TSS EMC TSS DWC

PN EMC PN

DWC DIN EMC

DIN DWC

DON EMC DON DWC

PP EMC

PP DWC

DOP EMC DOP DWC

DIP EMC DIP

DWC

Conservation USLE 0 USLE 0 USLE 0 0.1 0.1 0.2 0.2 0.01 0.01 0.03 0.03

Forestry USLE 0 USLE 0 USLE 0 0.1 0.1 0.2 0.2 0.01 0.01 0.03 0.03

Grazing Forested USLE 0 USLE 0 USLE 0 0.1 0.1 0.2 0.2 0.01 0.01 0.03 0.03

Grazing Open USLE 0 USLE 0 USLE 0 0.1 0.1 0.2 0.2 0.01 0.01 0.03 0.03

Horticulture 80 0 USLE 0 USLE 0 0.74 0.74 0.25 0.25 0.02 0.02 0.01 0.01

Dryland Cropping HowLeaky 0 HowLeaky 0 HowLeaky 0 0.5 0.5 0.37 0.37 HowLeaky 0 HowLeaky 0

Irrigated Cropping HowLeaky 0 HowLeaky 0 HowLeaky 0 0.5 0.5 0.37 0.37 HowLeaky 0 HowLeaky 0

Other 80 0 USLE 0 USLE 0 0.1 0.1 0.2 0.2 0.01 0.01 0.03 0.03

Sugarcane APSIM 0 APSIM 0 APSIM 0 APSIM 0 0.25 0.25 APSIM N/A APSIM N/A

Urban 80 0 USLE 0 USLE 0 0.16 0.16 0.26 0.26 0.02 0.02 0.01 0.01

Water 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table 65 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Mackay Whitsunday

FU TSS EMC TSS DWC

PN EMC PN

DWC DIN EMC

DIN DWC

DON EMC DON DWC

PP EMC

PP DWC

DOP EMC DOP DWC

DIP EMC DIP

DWC

Nature conservation 50 10 0.15 0.075 0.07 0.035 0.058 0.029 0.092 0.046 0.003 0.002 0.011 0.005

Forestry 70 10 0.4 0.2 0.1 0.05 0.077 0.039 0.122 0.0061 0.004 0.002 0.015 0.007

Closed grazing USLE 10 USLE 0 USLE 0 0.097 0.048 0.153 0.076 0.005 0.003 0.018 0.009

Open grazing USLE 10 USLE 0 USLE 0 0.193 0.097 0.306 0.153 0.01 0.005 0.036 0.018


157

FU TSS EMC TSS DWC

PN EMC PN

DWC DIN EMC

DIN DWC

DON EMC DON DWC

PP EMC

PP DWC

DOP EMC DOP DWC

DIP EMC DIP

DWC

Other 109 55 0.448 0.224 0.127 0.064 0.193 0.097 0.306 0.153 0.01 0.005 0.036 0.018

Urban 218 109 0.895 0.448 0.255 0.127 0.386 0.193 0.611 0.306 0.021 0.01 0.073 0.036

Horticulture 109 55 0.448 0.224 0.127 0.064 0.193 0.097 0.306 0.153 0.01 0.005 0.036 0.018

Dryland cropping HowLeaky 0 HowLeaky 0 HowLeaky 0 0.58 0.29 0.917 0.458 Howleaky 0 Howleaky 0

Irrigated cropping HowLeaky 0 HowLeaky 0 HowLeaky 0 0.483 0.242 0.764 0.382 Howleaky 0 Howleaky 0

Sugarcane APSIM 10 APSIM 0.56 APSIM 0.159 APSIM 0 0.65 0.33 APSIM N/A APSIM N/A

Water 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table 66 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Fitzroy

FU TSS EMC TSS DWC

PN EMC PN

DWC DIN EMC

DIN DWC

DON EMC DON DWC

PP EMC

PP DWC

DOP EMC DOP DWC

DIP EMC DIP

DWC

Nature conservation USLE 0 USLE 0 USLE 0 0.12 0.06 0.246 0.123 0.019 0.01 0.077 0.038


Closed grazing USLE 0 USLE 0 USLE 0 0.12 0.06 0.394 0.197 0.03 0.015 0.123 0.061

Open grazing USLE 0 USLE 0 USLE 0 0.15 0.075 0.492 0.246 0.038 0.019 0.154 0.077

Other 80 40 USLE 0 USLE 0 0.15 0.075 0.492 0.246 0.038 0.019 0.154 0.077

Urban 80 40 USLE 0 USLE 0 0.15 0.075 0.492 0.246 0.038 0.019 0.154 0.077

Horticulture 120 60 USLE 0 USLE 0 0.225 0.112 0.738 0.369 0.057 0.029 0.23 0.115

Dryland cropping HowLeaky 0 HowLeaky 0 HowLeaky 0 0.225 0.112 0.738 0.369 HowLeaky N/A HowLeaky N/A

Irrigated cropping 0 0 0 0 0 0 0 0 0 0 0 N/A 0 N/A

Sugarcane 140 70 USLE 0 USLE 0 0.225 0.112 0.738 0.369 0.057 0.029 0.23 0.115

Water 0 0 0 0 0 0 0.01 0.005 0.025 0.012 0.001 0.001 0.003 0.002

Table 67 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for Burnett Mary

FU TSS EMC TSS DWC

PN EMC PN

DWC DIN EMC

DIN DWC

DON EMC DON DWC

PP EMC

PP DWC

DOP EMC DOP DWC

DIP EMC DIP

DWC

Conservation USLE 0 USLE 0 USLE 0 0.038 0.019 0.166 0.083 0.007 0.003 0.011 0.005


Grazing Forested USLE 0 USLE 0 USLE 0 0.064 0.073 0.270 0.227 0.011 0.005 0.018 0.004


158

FU TSS EMC TSS DWC

PN EMC PN

DWC DIN EMC

DIN DWC

DON EMC DON DWC

PP EMC

PP DWC

DOP EMC DOP DWC

DIP EMC DIP

DWC

Grazing Open USLE 0 USLE 0 USLE 0 0.127 0.064 0.554 0.277 0.022 0.011 0.036 0.018

Other 110 55 0.521 0.260 0.156 0.078 0.127 0.064 0.554 0.277 0.022 0.011 0.036 0.018

Urban 220 110 1.041 0.521 0.312 0.156 0.254 0.127 1.108 0.554 0.044 0.022 0.072 0.036

Horticulture 275 137.5 1.302 0.651 0.390 0.195 0.318 0.159 1.385 0.693 0.055 0.028 0.090 0.045

Dryland Cropping HowLeaky 165 HowLeaky 0.781 HowLeaky 0.234 0.381 0.191 1.662 0.831 HowLeaky 0.033 HowLeaky 0.054

Irrigated Cropping HowLeaky 137.5 HowLeaky 0.651 HowLeaky 0.195 0.318 0.159 1.385 0.693 HowLeaky 0.028 HowLeaky 0.045

Sugarcane APSIM 0 APSIM 0 APSIM 0 APSIM 0 2 0 APSIM N/A APSIM N/A

Water 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table 68 Event Mean Concentrations/Dry Weather Concentrations (mg/L) for predevelopment model

Basin TSS EMC

TSS DWC PN EMC PN

DWC PP EMC PP DWC DIN EMC DIN DWC DON EMC DON DWC DOP EMC DOP DWC DIP EMC DIP DWC

Cape York USLE/20 USLE/10 USLE/0.08 0.04 USLE/0.032 0.016 0.2 0.1 0.01 0.005 0.01 0.005 0.005 0.0025

Wet Tropics

30 15 0.25 0.13 0.03 0.015 0.160 0.080 0.119 0.060 0.020 0.010 0.003 0.003

Burdekin USLE 0 USLE 0 USLE 0 0.100 0.100 0.200 0.200 0.010 0.010 0.030 0.030

Mackay Whitsunday

50 10 0.15 0.075 0.07 0.035 0.058 0.029 0.092 0.046 0.003 0.002 0.011 0.005

Fitzroy USLE/40 20 USLE 0 USLE 0 0.120 0.060 0.246 0.123 0.190 0.010 0.077 0.038

Burnett Mary USLE 35 USLE 0 USLE 0 0.038 0.019 0.166 0.083 0.007 0.003 0.011 0.005

Appendix 3 – Report Card 2015 modelling results

Table 69 Constituent loads for predevelopment, baseline and 2015 Report Card change model runs for the CY

NRM Region

Total fine sediment (kt/yr)

Predevelopment Baseline (12/13)

Anthropogenic Baseline

Increase Factor

Total (14/15)

Total Change (%)

Jacky Jacky 9 52 43 4.78 52 0.00

Olive Pascoe

18 72 54 3.00 72 0.00

Lockhart 13 67 54 4.15 67 0.00

Stewart 8 49 41 5.13 49 0.00

Normanby 35 186 151 4.31 186 0.01

Jeannie 11 42 31 2.82 42 0.00

Endeavour 31 59 27 0.87 59 0.00

TOTAL 126 526 400 3.17 526 0.00

Total phosphorus (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Jacky Jacky 38 77 38 1.00 77 0.00

Olive Pascoe

61 126 65 1.07 126 0.00

Lockhart 38 118 80 2.11 118 0.00

Stewart 21 60 39 1.86 60 0.00

Normanby 69 142 73 1.06 142 0.00

Jeannie 27 59 31 1.15 59 0.00

Endeavour 65 101 36 0.55 101 0.00

TOTAL 320 682 361 1.13 682 0.00

Particulate phosphorus (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Jacky Jacky 7 44 37 5.29 44 0.00

Olive Pascoe

16 75 59 3.69 75 0.00

Lockhart 15 95 80 5.33 95 0.00

Stewart 7 45 38 5.43 45 0.00

Normanby 24 78 53 2.21 78 0.00

Jeannie 11 41 30 2.73 41 0.00

Endeavour 47 77 30 0.64 77 0.00

TOTAL 127 454 327 2.57 454 0.00

Dissolved inorganic phosphorus (DIP) (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Jacky Jacky 10 11 1 0.10 11 0.00


160

Olive Pascoe

15 17 2 0.13 17 0.00

Lockhart 8 8 0 0.00 8 0.00

Stewart 5 5 0 0.00 5 0.00

Normanby 15 21 7 0.47 21 0.00

Jeannie 5 6 1 0.20 6 0.00

Endeavour 6 8 2 0.33 8 0.00

TOTAL 64 76 11 0.17 76 0.00

Dissolved organic phosphorus (DOP) (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Jacky Jacky 21 22 1 0.05 22 0.00

Olive Pascoe

30 34 4 0.13 34 0.00

Lockhart 15 15 0 0.00 15 0.00

Stewart 10 10 0 0.00 10 0.00

Normanby 30 43 13 0.43 43 0.00

Jeannie 11 12 1 0.09 12 0.00

Endeavour 12 16 3 0.25 16 0.00

TOTAL 129 152 23 0.18 152 0.00

Total nitrogen (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Jacky Jacky 847 1,064 217 0.26 1,064 0.00

Olive Pascoe

1,267 1,613 346 0.27 1,613 0.00

Lockhart 638 896 258 0.40 896 0.00

Stewart 396 516 120 0.30 516 0.00

Normanby 1,240 1,428 188 0.15 1,428 0.00

Jeannie 469 617 148 0.32 617 0.00

Endeavour 604 721 117 0.19 721 0.00

TOTAL 5,462 6,854 1,393 0.26 6,854 0.00

Particulate nitrogen (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Jacky Jacky 52 269 217 4.17 269 0.00

Olive Pascoe

108 451 343 3.18 451 0.00

Lockhart 57 315 258 4.53 315 0.00

Stewart 33 153 120 3.64 153 0.00

Normanby 100 253 153 1.53 253 0.01

Jeannie 58 204 147 2.53 204 0.00

Endeavour 139 251 111 0.80 251 0.00

TOTAL 547 1,896 1,349 2.47 1,896 0.00

Dissolved inorganic nitrogen (DIN) (t/yr)


161



Increase Factor

Total (14/15)

Total Change (%)

Jacky Jacky 67 67 0 0.00 67 0.00

Olive Pascoe

97 98 1 0.01 98 0.00

Lockhart 49 49 0 0.00 49 0.00

Stewart 30 30 0 0.00 30 0.00

Normanby 96 105 9 0.09 105 0.00

Jeannie 34 35 0 0.00 35 0.00

Endeavour 39 40 1 0.03 40 0.00

TOTAL 412 423 11 0.03 423 0.00

Dissolved organic nitrogen (DON) (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Jacky Jacky 728 728 0.2 0.00 728 0.00

Olive Pascoe

1,062 1,064 1.7 0.00 1,064 0.00

Lockhart 532 532 0.1 0.00 532 0.00

Stewart 333 333 0.2 0.00 333 0.00

Normanby 1,045 1,070 25.3 0.02 1,070 0.00

Jeannie 377 378 0.8 0.00 378 0.00

Endeavour 426 430 4.1 0.01 430 0.00

TOTAL 4,503 4,536 32 0.01 4,536 0.00

PSII Load (kg/yr)



Increase Factor

Total (14/15)

Total Change (%)

Jacky Jacky 0 0 0.0 NA 0.0 0.00

Olive Pascoe

0 0 0.0 NA 0.0 0.00

Lockhart 0 0 0.0 NA 0.0 0.00

Stewart 0 0 0.0 NA 0.0 0.00

Normanby 0 11 11.2 NA 11.2 0.00

Jeannie 0 0 0.2 NA 0.2 0.00

Endeavour 0 4 4.4 NA 4.4 0.00

TOTAL 0 16 16 NA 16 0.00

PSII toxic equivalent load (kg/yr)



Increase Factor

Total (14/15)

Total Change (%)

Jacky Jacky 0 0 0.00 NA 0 0.00

Olive Pascoe

0 0 0.00 NA 0 0.00

Lockhart 0 0 0.00 NA 0 0.00

Stewart 0 0 0.00 NA 0 0.00

Normanby 0 0 0.40 NA 0 0.00

Jeannie 0 0 0.01 NA 0 0.00

Endeavour 0 0 0.16 NA 0 0.00


162

TOTAL 0 1 1 NA 1 0.00

Table 70 Sources and sinks of TSS for each basin in CY (kt/yr)

Jacky Jacky

Olive-Pascoe

Lockhart Stewart Normanby Jeannie Endeavour

Supply 54 81 77 59 512 47 71

Channel remobilisation

1 2 1 1 22 1 1

Gully 3 6 0 5 386 3 1

Hillslope 50 66 73 47 86 42 62

Streambank 1 7 3 6 18 1 6

Loss 2 10 10 10 326 6 12

Floodplain deposition

2 9 8 10 299 6 11

Stream deposition 0 0 1 0 26 0 2

Export 52 72 67 49 186 42 59

Table 71 Constituent loads for predevelopment, baseline and RC2015 change model runs for the WT NRM Region




Increase Factor

Total (14/15)

Total Change (%)

Daintree 75 103 28 0.37 103 0.00

Mossman 10 17 6 0.60 17 0.00

Barron 23 55 32 1.39 55 0.01

Mulgrave-Russell

97 253 156 1.61 253 0.18

Johnstone 119 379 260 2.18 379 0.14

Tully 74 157 83 1.12 157 0.07

Murray 34 74 39 1.15 74 0.24

Herbert 147 478 331 2.25 478 0.02

TOTAL 581 1,516 936 1.61 1,516 0.09




Increase Factor

Total (14/15)

Total Change (%)

Daintree 118 146 28 0.24 146 0.00

Mossman 18 28 10 0.56 28 0.00

Barron 35 72 38 1.09 72 0.01

Mulgrave-Russell

165 392 227 1.38 392 0.15

Johnstone 224 864 640 2.86 862 0.28

Tully 124 259 136 1.10 259 0.04

Murray 56 121 65 1.16 121 0.32

Herbert 194 419 224 1.15 419 0.03

TOTAL 934 2,301 1,367 1.46 2,298 0.18




Increase Factor

Total (14/15)

Total Change (%)


163

Daintree 66 83 17 0.26 83 0.01

Mossman 10 17 7 0.70 17 0.00

Barron 19 45 27 1.42 45 0.01

Mulgrave-Russell

89 276 187 2.10 276 0.18

Johnstone 141 756 616 4.37 755 0.29

Tully 64 181 117 1.83 181 0.04

Murray 28 84 55 1.96 83 0.37

Herbert 101 291 191 1.89 291 0.03

TOTAL 518 1,733 1,215 2.35 1,731 0.20

Dissolved inorganic phosphorus (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Daintree 8 19 11 1.38 19 0.00

Mossman 1 3 2 2.00 3 0.00

Barron 3 12 9 3.00 12 0.00

Mulgrave-Russell

12 40 28 2.33 40 0.00

Johnstone 14 35 21 1.50 35 0.00

Tully 10 24 14 1.40 24 0.00

Murray 4 12 8 2.00 12 0.00

Herbert 14 45 31 2.21 45 0.00

TOTAL 68 192 124 1.82 192 0.00

Dissolved organic phosphorus (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Daintree 43 44 1 0.02 44 0.00

Mossman 7 8 1 0.14 8 0.00

Barron 14 15 2 0.14 15 0.00

Mulgrave-Russell

64 76 12 0.19 76 0.00

Johnstone 70 72 2 0.03 72 0.00

Tully 49 54 5 0.10 54 0.00

Murray 23 25 2 0.09 25 0.00

Herbert 79 82 2 0.03 82 0.00

TOTAL 349 376 27 0.08 376 0.00




Increase Factor

Total (14/15)

Total Change (%)

Daintree 1,120 1,841 722 0.64 1,841 0.01

Mossman 176 323 147 0.84 323 0.03

Barron 258 568 309 1.20 567 0.02

Mulgrave-Russell

1,594 2,769 1,176 0.74 2,768 0.07

Johnstone 1,746 3,799 2,053 1.18 3,796 0.13


164

Tully 1,222 2,155 934 0.76 2,155 0.01

Murray 558 1,121 563 1.01 1,120 0.29

Herbert 1,739 4,001 2,262 1.30 3,998 0.14

TOTAL 8,413 16,577 8,165 0.97 16,569 0.10




Increase Factor

Total (14/15)

Total Change (%)

Daintree 520 579 59 0.11 579 0.00

Mossman 79 101 22 0.28 101 0.00

Barron 112 201 88 0.79 201 0.00

Mulgrave-Russell

702 1,227 525 0.75 1,226 0.17

Johnstone 769 1,986 1,217 1.58 1,984 0.16

Tully 535 875 340 0.64 875 -0.01

Murray 240 400 160 0.67 400 0.19

Herbert 629 1,327 697 1.11 1,327 0.03

TOTAL 3,587 6,696 3,109 0.87 6,693 0.10

Dissolved inorganic nitrogen (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Daintree 344 478 135 0.39 478 0.03

Mossman 55 160 104 1.89 159 0.05

Barron 65 152 87 1.34 152 0.05

Mulgrave-Russell

511 934 423 0.83 934 0.00

Johnstone 560 1,059 499 0.89 1,058 0.15

Tully 393 777 384 0.98 777 0.04

Murray 182 414 232 1.27 413 0.58

Herbert 636 1,522 886 1.39 1,519 0.33

TOTAL 2,746 5,496 2,750 1.00 5,491 0.19

Dissolved organic nitrogen (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Daintree 256 784 528 2.06 784 0.00

Mossman 41 62 20 0.49 62 0.00

Barron 81 215 134 1.65 215 0.00

Mulgrave-Russell

381 609 228 0.60 609 0.00

Johnstone 417 754 337 0.81 754 0.00

Tully 293 503 210 0.72 503 0.00

Murray 136 307 171 1.26 307 0.00

Herbert 474 1,152 679 1.43 1,152 0.00

TOTAL 2,080 4,385 2,305 1.11 4,385 0.00

PSII Load (kg/yr)



Increase Factor

Total (14/15)

Total Change (%)


165

Daintree 0 118 118 NA 118 0.00

Mossman 0 75 75 NA 75 0.00

Barron 0 120 120 NA 119 1.13

Mulgrave-Russell

0 746 746 NA 738 1.09

Johnstone 0 922 922 NA 866 6.11

Tully 0 492 492 NA 485 1.51

Murray 0 367 367 NA 347 5.44

Herbert 0 248 248 NA 248 0.03

TOTAL 0 3,090 3,090 NA 2,997 3.02




Increase Factor

Total (14/15)

Total Change (%)

Daintree 0 98 98 NA 98 0.00

Mossman 0 65 65 NA 65 0.00

Barron 0 37 37 NA 36 3.22

Mulgrave-Russell

0 625 625 NA 618 1.14

Johnstone 0 753 753 NA 703 6.57

Tully 0 414 414 NA 407 1.61

Murray 0 310 310 NA 293 5.66

Herbert 0 113 113 NA 113 0.01

TOTAL 0 2,415 2415 NA 2,333 3.40

Table 72 Sources and sinks of each constituent in the Wet Tropics

Daintree Mossman Barron Mulgrave-

Russell Johnstone Tully Murray Herbert

Supply 109 18 108 267 410 163 76 612

Channel remobilisation 3 0 3 2 3 2 1 27

Gully 0 0 8 1 5 1 0 60

Hillslope 91 18 79 224 335 142 62 236

Streambank 15 1 18 41 67 19 12 289

Loss 6 1 53 14 31 7 2 134

Extractions 0 10 1 2 1 0 1

Floodplain deposition 5 1 14 12 11 3 1 73

Stream deposition 1 29 2 18 3 1 59

Residual 0 0 0 0 0 0 0 0

Export 103 17 55 253 379 157 74 478

Table 73 Constituent loads for predevelopment, baseline and 2015 Report Card change model runs for the

Burdekin NRM Region




Increase Factor

Total (14/15)

Total Change (%)

Black 28 62 34 1.21 62 0.00


166

Ross 13 62 49 3.77 62 0.00

Haughton 25 183 157 6.28 183 0.10

Burdekin 475 3,260 2,786 5.87 3,256 0.18

Don 31 213 183 5.90 212 0.73

TOTAL 571 3,781 3,209 5.62 3,774 0.20




Increase Factor

Total (14/15)

Total Change (%)

Black 82 105 23 0.28 105 0.00

Ross 27 94 67 2.48 94 0.01

Haughton 59 236 177 3.00 236 0.16

Burdekin 685 2,177 1,492 2.18 2,173 0.25

Don 67 211 144 2.15 209 1.13

TOTAL 919 2,823 1,903 2.07 2,817 0.30




Increase Factor

Total (14/15)

Total Change (%)

Black 53 77 23 0.43 77 0.00

Ross 7 50 42 6.00 50 0.01

Haughton 23 141 118 5.13 141 0.12

Burdekin 319 1,797 1,479 4.64 1,794 0.24

Don 32 174 142 4.44 172 1.14

TOTAL 434 2,239 1,805 4.16 2,234 0.30




Increase Factor

Total (14/15)

Total Change (%)

Black 21 21 0 0.00 21 0.14

Ross 14 33 19 1.36 33 0.00

Haughton 27 73 47 1.74 73 0.25

Burdekin 275 285 11 0.04 285 1.33

Don 26 27 1 0.04 27 -0.49

TOTAL 362 440 78 0.22 440 0.32




Increase Factor

Total (14/15)

Total Change (%)

Black 7 7 0 0.00 7 0.14

Ross 6 11 5 0.83 11 0.00

Haughton 9 21 12 1.33 21 0.24

Burdekin 92 94 3 0.03 94 1.33

Don 9 10 0 0.00 10 -0.49

TOTAL 123 144 21 0.17 143 0.31




Increase Factor

Total (14/15)

Total Change (%)


167

Black 309 385 76 0.25 385 0.00

Ross 177 416 239 1.35 416 0.00

Haughton 322 1,474 1,152 3.58 1,440 3.01

Burdekin 3,256 5,849 2,593 0.80 5,835 0.53

Don 347 669 322 0.93 657 3.80

TOTAL 4,411 8,793 4,382 0.99 8,732 1.39




Increase Factor

Total (14/15)

Total Change (%)

Black 89 143 54 0.61 143 0.00

Ross 15 98 83 5.53 98 0.00

Haughton 36 223 187 5.19 223 0.12

Burdekin 480 2,887 2,407 5.01 2,882 0.21

Don 55 305 250 4.55 302 1.14

TOTAL 675 3,657 2,982 4.42 3,649 0.27




Increase Factor

Total (14/15)

Total Change (%)

Black 76 97 21 0.28 97 0.00

Ross 57 180 123 2.16 180 0.00

Haughton 102 1,016 914 8.96 982 3.75

Burdekin 933 1,104 171 0.18 1,095 5.06

Don 109 177 68 0.62 168 13.81

TOTAL 1,278 2,574 1,297 1.01 2,522 4.03




Increase Factor

Total (14/15)

Total Change (%)

Black 143 145 2 0.01 145 0.00

Ross 105 138 33 0.31 138 0.00

Haughton 184 235 52 0.28 235 0.39

Burdekin 1,844 1,857 14 0.01 1,857 1.07

Don 183 186 4 0.02 186 -0.47

TOTAL 2,458 2,562 104 0.04 2,561 0.32

PSII load (kg/yr)



Increase Factor

Total (14/15)

Total Change (%)

Black 0 2 2 NA 2 -0.01

Ross 0 2 2 NA 2 0.04

Haughton 0 1,708 1,708 NA 1,645 3.66

Burdekin 0 503 503 NA 481 4.37

Don 0 179 179 NA 179 0.27

TOTAL 0 2,394 2,394 NA 2,309 3.55




Increase Factor

Total (14/15)

Total Change (%)


168

Black 0 2 2 NA 2 0.00

Ross 0 0 0 NA 0 0.04

Haughton 0 937 937 NA 902 3.68

Burdekin 0 255 255 NA 244 4.34

Don 0 90 90 NA 89 0.32

TOTAL 0 1,284 1,284 NA 1,238 3.57

Table 74 Sources and sinks of each constituent in the Burdekin NRM Region

Black Burdekin Don Haughton Ross

Supply 64 5,943 221 194 104

Channel remobilisation

Gully 19 3,919 109 92 43

Hillslope 42 1,205 87 81 57

Streambank 4 819 25 20 4

Loss 2 2,683 8 11 42

Floodplain deposition 2 861 8 11 6

Stream deposition

Reservoir Deposition 1,649 36

Extraction 167

Residual 0 6 0 0 0

Export 62 3,260 213 183 62

Table 75 Constituent loads for predevelopment, baseline and 2015 Report Card change model runs for the

Mackay Whitsunday NRM Region




Increase Factor

Total (14/15)

Total Change (%)

Proserpine 56 131 75 1.34 131 0.35

O'Connell 73 314 241 3.30 314 0.01

Pioneer 54 227 173 3.20 227 0.03

Plane 47 146 99 2.11 146 0.07

TOTAL 230 818 589 2.56 818 0.07




Increase Factor

Total (14/15)

Total Change (%)

Proserpine 112 255 143 1.28 255 0.26

O'Connell 145 495 350 2.41 495 0.00

Pioneer 83 237 154 1.86 237 0.03

Plane 107 319 212 1.98 319 0.05

TOTAL 447 1,306 859 1.92 1,306 0.06




Increase Factor

Total (14/15)

Total Change (%)


169

Proserpine 76 164 88 1.16 164 0.43

O'Connell 112 416 304 2.71 416 0.00

Pioneer 68 184 116 1.71 184 0.04

Plane 83 228 144 1.73 228 0.08

TOTAL 339 992 653 1.93 991 0.08




Increase Factor

Total (14/15)

Total Change (%)

Proserpine 29 73 44 1.52 73 0.00

O'Connell 26 62 37 1.42 62 0.00

Pioneer 12 42 30 2.50 42 0.00

Plane 18 73 54 3.00 73 0.00

TOTAL 85 249 165 1.94 249 0.00




Increase Factor

Total (14/15)

Total Change (%)

Proserpine 8 19 11 1.38 19 0.00

O'Connell 7 16 9 1.29 16 0.00

Pioneer 3 11 7 2.33 11 0.00

Plane 5 19 14 2.80 19 0.00

TOTAL 24 65 41 1.71 65 0.00




Increase Factor

Total (14/15)

Total Change (%)

Proserpine 556 1,066 510 0.92 1,067 -0.20

O'Connell 579 1,528 950 1.64 1,528 0.01

Pioneer 308 933 625 2.03 930 0.50

Plane 417 1,279 862 2.07 1,270 1.06

TOTAL 1,860 4,806 2,946 1.58 4,795 0.38




Increase Factor

Total (14/15)

Total Change (%)

Proserpine 161 387 226 1.40 386 0.36

O'Connell 221 845 624 2.82 845 0.00

Pioneer 145 450 305 2.10 450 0.03

Plane 163 468 305 1.87 468 0.07

TOTAL 690 2,150 1,460 2.12 2,149 0.08




Increase Factor

Total (14/15)

Total Change (%)

Proserpine 153 310 157 1.03 312 -1.16

O'Connell 139 325 186 1.34 325 0.03

Pioneer 63 256 193 3.06 253 1.56

Plane 98 464 366 3.73 455 2.43


170

TOTAL 453 1,355 902 1.99 1,344 1.12




Increase Factor

Total (14/15)

Total Change (%)

Proserpine 242 370 127 0.52 370 0.00

O'Connell 219 358 139 0.63 358 0.00

Pioneer 100 227 127 1.27 227 0.00

Plane 156 347 191 1.22 347 0.00

TOTAL 717 1,301 584 0.81 1,301 0.00

PSII load (kg/yr)



Increase Factor

Total (14/15)

Total Change (%)

Proserpine 0 331 331 NA 329 0.74

O'Connell 0 971 971 NA 960 1.12

Pioneer 0 985 985 NA 983 0.23

Plane 0 1758 1758 NA 1651 6.06

TOTAL 0 4,044 4044 NA 3,922 3.02




Increase Factor

Total (14/15)

Total Change (%)

Proserpine 0 248 248 NA 246 0.71

O'Connell 0 701 701 NA 693 1.10

Pioneer 0 707 707 NA 705 0.24

Plane 0 1,269 1,269 NA 1,194 5.93

TOTAL 0 2,925 2,925 NA 2,838 2.96

Table 76 Sources and sinks of TSS for each basin MW (kt/yr)

Proserpine O’Connell Pioneer Plane

Supply 147 316 242 148

Channel remobilisation 0 0 0 0

Gully 5 4 2 2

Hillslope 117 241 100 119

Streambank 25 71 140 27

Loss 16 2 11 2

Floodplain deposition 1 1 2 2

Stream deposition 0 0 0 0

Reservoir deposition 15 0 2 0

Extraction 0 0 6 0

Export 131 314 231 146


171

Table 77 Constituent loads for predevelopment, baseline and RC2015 change model runs for the Fitzroy NRM

Region




Increase Factor

Total (14/15)

Total Change (%)

Styx 9 104 96 9.40 104 0.59

Shoalwater 7 67 60 7.38 67 0.09

Water Park 8 65 57 7.13 65 0.00

Fitzroy 181 1,507 1,326 6.01 1,493 1.07

Calliope 7 57 50 7.14 57 0.04

Boyne 8 24 16 2.00 24 1.09

TOTAL 219 1,824 1,605 6.13 1,809 0.94




Increase Factor

Total (14/15)

Total Change (%)

Styx 128 541 413 3.22 540 0.07

Shoalwater 122 408 287 2.34 408 0.01

Water Park 109 579 471 4.32 579 0.00

Fitzroy 1,054 2,750 1,696 1.57 2,745 0.32

Calliope 74 281 206 2.75 281 0.00

Boyne 76 105 29 0.38 105 0.18

TOTAL 1,563 4,665 3,102 1.96 4,659 0.19




Increase Factor

Total (14/15)

Total Change (%)

Styx 64 428 364 5.69 427 0.08

Shoalwater 46 308 262 5.57 308 0.01

Water Park 60 517 457 7.62 517 -

Fitzroy 558 1,822 1,265 2.19 1,817 0.43

Calliope 41 221 180 4.39 221 0.00

Boyne 48 60 12 0.25 60 0.44

TOTAL 817 3,357 2,540 3.03 3,351 0.23

Dissolved inorganic phosphorus (DIP) (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Styx 52 91 39 0.75 91 0.00

Shoalwater 61 80 20 0.33 80 0.00

Water Park 39 50 11 0.28 50 0.00

Fitzroy 397 743 345 0.87 743 0.00

Calliope 27 47 21 0.78 47 0.00

Boyne 22 36 14 0.64 36 0.00

TOTAL 597 1,047 450 0.75 1,047 0.00

Dissolved organic phosphorus (DOP) (t/yr)


172



Increase Factor

Total (14/15)

Total Change (%)

Styx 13 22 10 0.77 22 0.00

Shoalwater 15 20 5 0.33 20 0.00

Water Park 10 12 3 0.30 12 0.00

Fitzroy 98 185 86 0.88 185 0.00

Calliope 7 12 5 0.71 12 0.00

Boyne 5 9 3 0.60 9 0.00

TOTAL 148 260 112 0.76 260 0.00




Increase Factor

Total (14/15)

Total Change (%)

Styx 372 1,212 840 2.25 1,212 0.04

Shoalwater 388 1,005 617 1.58 1,005 0.01

Water Park 333 1,494 1,161 3.49 1,494 0.00

Fitzroy 2,875 6,290 3,416 1.17 6,280 0.30

Calliope 208 639 430 2.06 639 0.01

Boyne 195 266 71 0.36 266 0.10

TOTAL 4,371 10,907 6,535 1.48 10,896 0.16




Increase Factor

Total (14/15)

Total Change (%)

Styx 124 829 705 5.68 828 0.05

Shoalwater 98 646 548 5.59 646 0.01

Water Park 146 1,268 1,122 7.68 1,268 -

Fitzroy 918 3,066 2,148 2.28 3,056 0.48

Calliope 81 439 358 4.42 439 0.01

Boyne 90 113 23 0.26 113 0.31

TOTAL 1,456 6,361 4,904 3.31 6,350 0.22

Dissolved inorganic nitrogen (DIN) (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Styx 81 91 10 0.12 91 0.00

Shoalwater 95 100 5 0.05 100 0.00

Water Park 61 65 4 0.07 65 0.00

Fitzroy 641 799 159 0.25 799 0.00

Calliope 42 47 6 0.14 47 0.00

Boyne 35 37 3 0.09 37 0.00

TOTAL 954 1,140 186 0.19 1,140 0.00

Dissolved organic nitrogen (DON) (t/yr)



Increase Factor

Total (14/15)

Total Change (%)

Styx 167 293 126 0.75 293 0.00


173

Shoalwater 196 259 64 0.33 259 0.00

Water Park 126 161 35 0.28 161 0.00

Fitzroy 1,316 2,425 1,109 0.84 2,425 0.00

Calliope 86 152 67 0.78 152 0.00

Boyne 71 116 45 0.63 116 0.00

TOTAL 1,961 3,406 1,445 0.74 3,406 0.00

PSII Load (kg/yr)



Increase Factor

Total (14/15)

Total Change (%)

Styx 0 203 203 NA 203 0.00

Shoalwater 0 107 107 NA 107 0.00

Water Park 0 15 15 NA 15 0.00

Fitzroy 0 1,749 1,749 NA 1,745 0.21

Calliope 0 97 97 NA 97 0.00

Boyne 0 39 39 NA 39 0.00

TOTAL 0 2,210 2,210 NA 2,206 0.17




Increase Factor

Total (14/15)

Total Change (%)

Styx 0 4 4 NA 4 0.00

Shoalwater 0 2 2 NA 2 0.00

Water Park 0 0 0 NA 0 0.00

Fitzroy 0 38 38 NA 38 0.35

Calliope 0 2 2 NA 2 0.00

Boyne 0 1 1 NA 1 0.00

TOTAL 0 47 47 NA 47 0.29

Table 78 Sources and sinks of TSS for each basin in the Fitzroy NRM Region

Styx River

Shoalwater Creek

Water Park Creek

Fitzroy River

Calliope River

Boyne River

Supply 120 75 67 8,153 80 146

Channel remobilisation 0 0 0 0 0 0

Gully 13 11 2 4,435 13 6

Hillslope 80 49 64 2,139 53 105

Streambank 27 15 1 1,579 14 35

Loss 16 8 2 6,646 23 122

Floodplain deposition 16 8 2 4,061 23 45

Stream deposition 0 0 0 0 0 0

Reservoir deposition 0 0 0 1,207 0 70

Extraction 0 0 0 408 0 5

Node loss 0 0 0 952 0 0

Residual 0 0 0 18 0 2

Export 104 67 65 1,507 57 24


174

Table 79 Constituent loads for predevelopment, baseline and RC2015 change model runs for the Burnett Mary

NRM Region




Increase Factor

Total (14/15)

Total Change (%)

Baffle 23 75 53 2.30 75 0.11

Kolan 10 40 30 3.00 40 0.02

Burnett 122 548 426 3.49 548 0.07

Burrum 9 26 17 1.89 26 0.01

Mary 104 770 666 6.40 770 0.02

TOTAL 267 1,459 1,192 4.46 1,459 0.04




Increase Factor

Total (14/15)

Total Change (%)

Baffle 60 148 88 1.47 148 0.15

Kolan 17 48 31 1.82 48 0.00

Burnett 140 317 177 1.26 317 0.03

Burrum 14 44 30 2.14 44 0.01

Mary 228 1084 856 3.75 1084 0.01

TOTAL 459 1642 1183 2.58 1641 0.02




Increase Factor

Total (14/15)

Total Change (%)

Baffle 48 125 77 1.60 125 0.17

Kolan 11 37 25 2.27 37 0.00

Burnett 122 268 146 1.20 268 0.04

Burrum 8 25 17 2.13 25 0.01

Mary 182 972 789 4.34 972 0.01

TOTAL 372 1,426 1,054 2.83 1,426 0.03




Increase Factor

Total (14/15)

Total Change (%)

Baffle 7 14 7 1.00 14 0.00

Kolan 3 7 4 1.33 7 0.00

Burnett 11 32 21 1.91 32 0.00

Burrum 4 13 10 2.50 13 0.00

Mary 28 72 44 1.57 72 0.00

TOTAL 53 139 85 1.60 139 0.00




Increase Factor

Total (14/15)

Total Change (%)

Baffle 5 9 4 0.80 9 0.00

Kolan 2 4 2 1.00 4 0.00

Burnett 7 17 10 1.43 17 0.00


175

Burrum 2 6 3 1.50 6 0.00

Mary 18 41 23 1.28 41 0.00

TOTAL 34 77 43 1.26 77 0.00




Increase Factor

Total (14/15)

Total Change (%)

Baffle 244 546 302 1.24 546 0.10

Kolan 91 312 221 2.43 312 0.00

Burnett 534 1,407 873 1.63 1,407 0.05

Burrum 90 420 330 3.67 420 0.02

Mary 1,075 4,460 3,385 3.15 4,460 0.01

TOTAL 2,034 7,146 5,112 2.51 7145 0.02




Increase Factor

Total (14/15)

Total Change (%)

Baffle 105 268 164 1.56 268 0.19

Kolan 33 101 68 2.06 101 0.00

Burnett 326 667 341 1.05 667 0.06

Burrum 20 58 38 1.90 58 0.00

Mary 552 2,899 2,347 4.25 2,899 0.01

TOTAL 1,036 3,993 2,957 2.85 3,993 0.02




Increase Factor

Total (14/15)

Total Change (%)

Baffle 26 58 32 1.23 58 0.00

Kolan 11 78 68 6.18 78 0.00

Burnett 39 246 207 5.31 246 0.12

Burrum 13 199 186 14.31 199 0.03

Mary 97 459 361 3.72 459 0.00

TOTAL 186 1,040 854 4.59 1,039 0.03




Increase Factor

Total (14/15)

Total Change (%)

Baffle 114 220 107 0.94 220 0.00

Kolan 47 133 86 1.83 133 0.00

Burnett 169 494 325 1.92 494 0.00

Burrum 57 163 106 1.86 163 0.00

Mary 426 1,102 676 1.59 1,102 0.00

TOTAL 812 2,113 1,301 1.60 2,113 0.00

PSII load (kg/yr)



Increase Factor

Total (14/15)

Total Change (%)

Baffle 0 8 8 NA 8 0.00

Kolan 0 70 70 NA 70 0.00

Burnett 0 119 119 NA 119 0.15


176

Burrum 0 81 81 NA 81 0.00

Mary 0 68 68 NA 68 0.92

TOTAL 0 346 346 NA 345 0.24




Increase Factor

Total (14/15)

Total Change (%)

Baffle 0 3 3 NA 3 0.00

Kolan 0 31 31 NA 31 0.00

Burnett 0 22 22 NA 22 0.06

Burrum 0 16 16 NA 16 0.00

Mary 0 14 14 NA 14 0.35

TOTAL 0 86 86 NA 86 0.07

Table 80 Sources and sinks of each constituent in the Burnett Mary NRM Region

Basin Baffle Kolan Burnett Burrum Mary

Supply 79 77 1,119 31 1,017

Channel Remobilisation 9 11 174 7 22

Gully 13 15 288 3 32

Hillslope surface soil 48 24 104 16 339

Streambank 8 27 553 5 623

Loss 3 37 571 5 247

Extraction 1 3 17 1 58

Flood Plain Deposition 3 2 78 1 7

Node Loss 1 32 56

Reservoir Deposition 26 346 3 123

Residual Node Storage 2 5

Stream Deposition 4 93 3

Export 75 40 548 26 770


177

Appendix 4 – Model validation

Wet Tropics model validation

Table 81 Fine sediment and particulate nutrient comparison with GBRCLMP monitored data for five WT sites

Site

TSS (t/yr) PN (t/yr) PP (t/yr

Measured Modelled %

diff Measured Modelled

% diff

Measured Modelled %

diff

Barron River at Myola 208,770 86,025 -59 656 284 -57 143 74 -48

North Johnstone River at Tung Oil

166,495 123,650 -26 924 697 -25 294 210 -28

South Johnstone River at Upstream Central mill

77,850 45,556 -41 367 287 -22 140 96 -31

Tully River at Tully Gorge

25,522 19,990 -22 179 158 -11 39 19 -51

Tully River at Euramo 110,901 147,170 33 475 805 69 129 185 44

Herbert River at Ingham

566,999 459,453 -19 1,764 1,203 -32 438 245 -44

Table 82 Dissolved nitrogen species comparison with GBRCLMP monitored data for five WT sites

Site DIN (t/yr) DON (t/yr)

Measured Modelled % diff Measured Modelled % diff

Barron River at Myola 57 81 43 294 250 -15

North Johnstone River at Tung Oil 311 282 -9 229 251 10


160 122 -24 93 95 2

Tully River at Tully Gorge 148 99 -33 119 87 -27

Tully River at Euramo 778 657 -16 459 419 -9

Herbert River at Ingham 1,055 748 -29 1,190 926 -22

Table 83 Dissolved phosphorus comparison with GBRCLMP monitored data for five WT sites

Site FRP (t/yr) DOP (t/yr)

Measured Modelled % diff Measured Modelled % diff

Barron River at Myola 10 10 -4 17 16 -3

North Johnstone River at Tung Oil 15 13 -13 49 31 -37


10 7 -29 18 13 -28

Tully River at Tully Gorge 3 3 0 20 12 -38

Tully River at Euramo 22 22 1 63 49 -22

Herbert River at Ingham 51 38 -25 111 77 -31

Table 84 Barron River model validation compared to GBRCLMP annual loads

Site 110001D RC7 Annual Time period 2006-2014 (excludes 2008-2009)

Modelled parameter

RSR rating PBIAS rating NSE rating R2 Overall rating

Flow Very good Satisfactory Very good Very good Satisfactory

TSS Indicative Indicative Indicative Very good Indicative

TN Indicative Indicative Satisfactory Very good Indicative

PN Indicative Indicative Indicative Very good Indicative


178


Modelled parameter


DIN Indicative Indicative Indicative Very good Indicative

DON Very good Good Very good Very good Good

TP Indicative Indicative Satisfactory Very good Indicative

DIP Very good Very good Very good Very good Very good

PP Indicative Indicative Indicative Very good Indicative

DOP Satisfactory Very good Good Good Satisfactory

Table 85 Barron River model validation compared to Joo monthly loads

Site 110001D RC7 monthly Time period 1986–2009

Modelled parameter



TSS Indicative Indicative Satisfactory Very good Indicative

TN Very good Indicative Very good Very good Very good

PN Indicative Indicative Satisfactory Very good Indicative

DIN Indicative Satisfactory Good Very good Indicative

DON Very good Good Very good Very good Very good

TP Good Indicative Very good Very good Good


PP Satisfactory Indicative Good Very good Satisfactory

DOP Good Indicative Very good Very good Good

Table 86 North Johnstone River model validation compared to GBRCLMP annual loads


Modelled parameter


Flow Good Satisfactory Good Very good Satisfactory

TSS Indicative Indicative Indicative Satisfactory Indicative

TN Good Good Very good Very good Good

PN Indicative Satisfactory Satisfactory Very good Indicative

DIN Very good Very good Very good Very good Very good

DON Very good Very good Very good Very good Very good

TP Satisfactory Satisfactory Good Good Satisfactory

DIP Very good Good Very good Very good Good

PP Good Indicative Satisfactory Satisfactory Indicative

DOP Indicative Satisfactory Satisfactory Very good Indicative

Table 87 North Johnstone River model validation compared to Joo monthly loads


Modelled parameter



TSS Good Indicative Satisfactory Good Good

TN Good Indicative Very good Very good Good

PN Indicative Indicative Satisfactory Very good Indicative


179


Modelled parameter




TP Satisfactory Indicative Good Very good Satisfactory



DOP Indicative Indicative Satisfactory Very good Indicative

Table 88 South Johnstone River model validation compared to GBRCLMP annual loads

Site 112101B RC7 Annual Time period 2006-2014 (excludes 2008-2009)

Modelled parameter


Flow Very good Good Very good Very good Good


TN Good Good Very good Very good Good

PN Satisfactory Satisfactory Good Very good Satisfactory

DIN Indicative Satisfactory Indicative Satisfactory Indicative


TP Indicative Satisfactory Good Very good Indicative

DIP Indicative Indicative Indicative Very good Indicative

PP Indicative Indicative Satisfactory Very good Indicative

DOP Indicative Satisfactory Indicative Very good Indicative

Table 89 South Johnstone River model validation compared to Joo monthly loads

Site 112101B RC7 monthly Time period 1986–2009

Modelled parameter



TSS Satisfactory Very good Satisfactory Good Satisfactory

TN Very good Good Very good Very good Very good

PN Satisfactory Satisfactory Good Good Satisfactory



TP Good Very good Very good Very good Good

DIP Indicative Indicative Indicative Very good Indicative

PP Good Very good Good Good Good

DOP Indicative Indicative Indicative Very good Indicative

Table 90 Tully River model validation compared to GBRCLMP annual loads


Modelled parameter


Flow Indicative Indicative Indicative Very good Indicative


TN Indicative Very good Satisfactory Very good Indicative



180


Modelled parameter


DIN Indicative Good Indicative Satisfactory Indicative

DON Indicative Very good Indicative Satisfactory Indicative

TP Indicative Indicative Indicative Very good Indicative

DIP Indicative Very good Indicative Indicative Indicative


DOP Indicative Satisfactory Indicative Satisfactory Indicative

Table 91 Tully River model validation compared to Joo monthly loads


Modelled parameter



TSS Very good Indicative Good Very good Very good

TN Satisfactory Indicative Good Very good Satisfactory



DON Very good Good Very good Very good Very good

TP Indicative Indicative Indicative Very good Indicative

DIP Very good Satisfactory Very good Very good Very good


DOP Indicative Indicative Indicative Very good Indicative

Table 92 Herbert River model validation compared to GBRCLMP annual loads


Modelled parameter


Flow Very good Satisfactory Very good Very good Satisfactory

TSS Very good Indicative Very good Very good Indicative

TN Very good Satisfactory Very good Very good Satisfactory

PN Good Satisfactory Very good Very good Satisfactory

DIN Indicative Good Satisfactory Indicative Indicative

DON Very good Good Very good Very good Good

TP Good Indicative Very good Very good Indicative

DIP Very good Satisfactory Very good Very good Satisfactory

PP Satisfactory Indicative Good Very good Indicative

DOP Good Satisfactory Very good Very good Satisfactory

Table 93 Herbert River model validation compared to Joo monthly loads

Site 116001F RC7 monthly Time period 1986–2009

Modelled parameter



TSS Good Indicative Satisfactory Very good Good

TN Very good Very good Very good Very good Very good


181

Site 116001F RC7 monthly Time period 1986–2009

Modelled parameter


PN Satisfactory Satisfactory Good Very good Satisfactory

DIN Satisfactory Indicative Good Very good Satisfactory

DON Very good Satisfactory Very good Very good Very good

TP Good Very good Very good Very good Good



DOP Good Indicative Very good Very good Good

Burdekin model validation

Table 94 RC2015 (120302B Cape River at Taemas, n = 7 years for flow and n = 7 years for WQ)

Parameter

RSR PBIAS NSE R2

Value Rating

(Moriasi 2007)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Flow 0.22 Very good -5.06 Good 0.95 Very good 0.96 Very good

TSS 0.88 Indicative 51.20 Indicative 0.23 Indicative 0.78 Good

TN 0.88 Indicative 51.70 Indicative 0.22 Indicative 0.83 Very good

PN 1.28 Indicative 80.72 Indicative -0.63 Indicative 0.56 Satisfactory

DIN 2.86 Indicative -207.49 Indicative -7.18 Indicative 0.02 Indicative

DON 0.64 Satisfactory 37.50 Indicative 0.59 Good 0.93 Very good

TP 0.68 Satisfactory 15.12 Good 0.54 Good 0.45 Satisfactory

DIP 5.17 Indicative -299.15 Indicative -25.70 Indicative 0.82 Very good

PP 0.88 Indicative 35.77 Indicative 0.22 Indicative 0.25 Indicative


Table 95 RC2015 (119101A Barratta Creek at Northcote, n = 5 years for flow and n = 5 years for WQ)

Parameter

RSR PBIAS NSE R2

Value Rating


Rating (Moriasi

2015) Value

Rating (Moriasi

2015) Value

Rating (Moriasi

2015)

Flow 0.40 Very good 32.58 Indicative 0.84 Very good 0.94 Very good

TSS 0.76 Indicative 39.16 Indicative 0.42 Indicative 0.74 Good


PN 0.94 Indicative 76.52 Indicative 0.11 Indicative 0.64 Good

DIN 0.92 Indicative -39.29 Indicative 0.16 Indicative 0.17 Indicative

DON 0.86 Indicative 72.09 Indicative 0.26 Indicative 0.88 Very good

TP 0.71 Indicative 52.41 Indicative 0.50 Satisfactory 0.82 Very good

DIP 0.43 Very good 28.86 Satisfactory 0.81 Very good 0.69 Good

PP 0.72 Indicative 51.60 Indicative 0.48 Satisfactory 0.82 Very good



182

Table 96 RC2015 (120002C Burdekin River at Sellheim, n = 8 years for flow and n = 7 years for WQ)

Parameter

RSR PBIAS NSE R2

Value Rating

(Moriasi 2007)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)


TSS 0.44 Very good 26.47 Indicative 0.81 Very good 0.90 Very good

TN 0.74 Indicative 49.17 Indicative 0.45 Satisfactory 0.94 Very good


DIN 4.08 Indicative -131.73 Indicative -15.67 Indicative 0.64 Good


TP 0.63 Satisfactory 37.17 Indicative 0.60 Good 0.86 Very good

DIP 0.28 Very good -15.25 Good 0.92 Very good 0.98 Very good

PP 0.69 Satisfactory 40.47 Indicative 0.53 Good 0.83 Very good

DOP 0.74 Indicative 44.05 Indicative 0.45 Satisfactory 0.72 Very good

Table 97 RC2015 (120301B Belyando River at Gregory Development Road, n = 7 years for flow and n = 6 years

for WQ)

Parameter

RSR PBIAS NSE R2

Value Rating

(Moriasi 2007)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)


TSS 1.84 Indicative -72.95 Indicative -2.40 Indicative 0.13 Indicative

TN 0.49 Very good 34.02 Indicative 0.76 Very good 0.88 Very good

PN 0.56 Good 36.82 Indicative 0.69 Very good 0.78 Very good

DIN 6.16 Indicative -401.03 Indicative -36.99 Indicative 0.88 Very good


TP 0.60 Good 8.73 Very good 0.64 Good 0.54 Satisfactory

DIP 0.69 Satisfactory 41.74 Indicative 0.53 Good 0.57 Satisfactory

PP 0.65 Satisfactory -6.70 Very good 0.58 Good 0.54 Satisfactory

DOP 0.73 Indicative 50.73 Indicative 0.47 Satisfactory 0.69 Good

Table 98 RC2015 (120001A Burdekin River at Home Hill (120006B Burdekin River at Clare), n = 8 years for flow

and n = 8 years for WQ)

Parameter

RSR PBIAS NSE R2


2007) Value

Rating (Moriasi

2015) Value

Rating (Moriasi

2015) Value

Rating (Moriasi

2015)


TSS 0.45 Very good 2.46 Very good 0.80 Good 0.80 Very good



DIN 0.69 Satisfactory -9.19 Very good 0.53 Good 0.85 Very good




183

Parameter

RSR PBIAS NSE R2


2007) Value

Rating (Moriasi

2015) Value

Rating (Moriasi

2015) Value

Rating (Moriasi

2015)

DIP 0.46 Very good -4.13 Very good 0.79 Very good 0.83 Very good

PP 0.58 Good 28.76 Satisfactory 0.67 Very good 0.93 Very good

DOP 0.89 Indicative 44.41 Indicative 0.21 Indicative 0.50 Indicative

Table 99 RC2015 (120310A Suttor River at Bowen Development Road, n = 8 years for flow and n = 8 years for

WQ)

Parameter

RSR PBIAS NSE R2

Value Rating

(Moriasi 2007)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Value Rating

(Moriasi 2015)

Flow 0.43 Very good -3.99 Very good 0.82 Very good 0.77 Good

TSS 1.28 Indicative -99.47 Indicative -0.63 Indicative 0.64 Satisfactory

TN 0.42 Very good 31.25 Indicative 0.82 Very good 0.80 Very good

PN 0.37 Very good 19.97 Good 0.86 Very good 0.77 Very good

DIN 5.37 Indicative -391.86 Indicative -27.79 Indicative 0.83 Very good


TP 0.39 Very good -10.74 Very good 0.85 Very good 0.72 Very good

DIP 0.55 Good 34.64 Indicative 0.70 Very good 0.57 Satisfactory

PP 0.73 Indicative -48.95 Indicative 0.46 Satisfactory 0.74 Very good

DOP 0.92 Indicative 76.05 Indicative 0.15 Indicative 0.48 Satisfactory

Mackay Whitsunday model validation

Figure 64 Long-term Annual PN Loads Modelled against FRCE and GBRCLMP Estimates in the Pioneer River

at Dumbleton Pump Station


184

Figure 65 Long-term Annual PP Loads Modelled against FRCE and GBRCLMP Estimates in the Pioneer River at

Dumbleton Pump Station

Figure 66 Long-term Annual DIP Loads Modelled against FRCE and GBRCLMP Estimates in the Pioneer River



185

Figure 67 Long-term Annual DON Loads Modelled against FRCE and GBRCLMP Estimates in the Pioneer River


Figure 68 Long-term Annual DOP Loads Modelled against FRCE and GBRCLMP Estimates in the Pioneer River


modelling pollutant load changes due to improved management … · 2018-11-21 · in this report...

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