Estimates of sediment and nutrient loads in 10 major catchments draining to the Great Barrier Reef during 2006–2009

Marine Pollution Bulletin 65 (2012) 150–166

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Estimates of sediment and nutrient loads in 10 major catchments draining to the Great Barrier Reef during 2006–2009 Marianna Joo ⇑, Myriam A.A. Raymond, Vivienne H. McNeil, Raethea Huggins, Ryan D.R. Turner, Satish Choy Department of Environment and Resource Management, Ecosciences Precinct, Level 1, Block C, 41 Boggo Road, Dutton Park, Queensland 4102, Australia

a r t i c l e

i n f o

Keywords: Great Barrier Reef Flood event Calculated load Monitoring data Nutrient Suspended solids

a b s t r a c t The Great Barrier Reef (GBR) catchment area has been monitored simultaneously for sediment and nutrient exports from 10 priority catchments discharging into the GBR lagoon between 2006 and 2009. This allows GBR catchment-wide exports to be estimated and spatially compared within a discrete timeframe. Elevated levels of sediment and nutrient exports were recorded in all monitored catchments as compared to pre-European estimates, but vary around previous estimates of mean annual loads. During the period of monitoring, the Burdekin and Fitzroy catchments contributed the highest sediment and nutrient exports, however when loads were normalised for area, these catchments produced the lowest unit yields. In contrast, the highest yields were produced in the wetter and proportionately more intensively cultivated Johnstone, O’Connell, and Pioneer catchments particularly for dissolved nitrogens. This assessment offers the necessary scientific foundation for future monitoring, assessment, and management of sediment and nutrient loads entering the GBR. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Coral ecosystems throughout the world, already vulnerable because of broad scale pressures such as coral bleaching, are coming under increasing threat from the impacts of contaminants discharged from adjoining catchments (for example Pandolfi et al., 2003; Berkelmans et al., 2004; Baird and Marshall, 2002; Devlin and Brodie, 2005; Richmond, 1993). There are significant efforts on the global scale to reduce sediment and other contaminant exports discharging into the marine environment. For instance, in the European Union, the implementation of the Water Framework Directive (European Parliament, 2000) drives the reduction of pollutant exports affecting beneficial uses in rivers. In the USA, section 303(d) of the Clean Water Act (1972) requires the determination of total maximum daily loadings on a catchment basis to protect all forms of aquatic biota. As the largest coral reef ecosystem in the world and a World Heritage listed area (Furnas, 2003; Rayment, 2003), the Great Barrier Reef (GBR) which is situated off the north-east coast of Australia is a major responsibility for the Queensland and Australian governments. There is now compelling evidence of elevated nutrient and sediment exports from rivers to the Reef since European settlement (Moss et al., 1992; Brodie et al., 2003, 2009; Furnas, 2003; McCulloch et al., 2003a; Hunter and Walton, 2008; Brodie ⇑ Corresponding author. E-mail addresses: [email protected], m.joo@griffith.edu.au (M. Joo). 0025-326X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpolbul.2012.01.002

et al., 2012; Kroon, 2012). For instance, Moss et al. (1992) considered that total nutrient input to the GBR had risen by approximately 30% since European settlement, with most of the increase occurring post 1950. McCulloch et al. (2003a) observed increased sediment flux to the inner GBR recorded in the chemistry of coral and Wooldridge et al. (2006) modelled the extent of historical plumes and demonstrated that under pre-European conditions, the nutrient enrichment from river runoff was likely to have been largely constrained within 1–2 km of the coast, whereas existing conditions suggest impact on reefs up to 30 km off the coast. Many studies have shown that these increased contaminant exports, delivered mainly during terrestrial runoff events from the nearby catchments have negatively affected coral reef ecosystems and resulted in reduced reef calcification and biodiversity and altered coral community structure (Brodie, 1997; Furnas, 2003; McCulloch et al., 2003a; Fabricius and De’ath, 2004; Fabricius et al., 2005; DeVantier et al., 2006). The increase in contaminant exports has often been associated with land clearing for development, agriculture, and other uses (Bramley and Roth, 2002; Brodie and Mitchell, 2005; McKergow et al., 2005; Rohde et al., 2008; Bainbridge et al., 2009; Mitchell et al., 2009; Packett et al., 2009; Waterhouse et al., 2012). For instance, it is postulated by Bramley and Roth (2002), McKergow et al. (2005), Packett et al. (2009), and Rohde et al. (2008) that generation of suspended material is dominated by grazing and cropping. Studies supporting the significance of cropping by Brodie et al. (2003), Bainbridge et al. (2009), Rohde et al. (2008), and Pulsford (1993) demonstrated that intensive

M. Joo et al. / Marine Pollution Bulletin 65 (2012) 150–166

sugarcane cultivation, widespread on coastal floodplains, contributes relatively high levels of dissolved nutrients. In view of these concerns, the Queensland and Australian governments launched the Reef Water Quality Protection Plan in 2003 with an update in 2009 (Reef Plan, 2003, 2009) to protect the GBR. Its primary goal is to halt and reverse the decline in water quality from anthropogenic loads entering the GBR lagoon within 10 years (State of Queensland and Commonwealth of Australia, 2003). To achieve this, a reasonable assessment of present conditions was required with their probable relationship to pre-European conditions. The pre-European estimates have been modelled by Brodie et al. (2009) using SedNet models and Kroon et al. (2010) recently established the current mean annual loads from each catchment using monitoring and modelled data which provides average conditions from the period immediately preceding Reef Plan (2009). However, although modelling is widely used globally to estimate mean annual contaminant yields, it is recognised that field data is required to validate the results of spatially distributed modelling, particularly at larger scales (Jetten et al., 2003; de Vente et al., 2008; Huhges and Croke, 2011). In addition, long term averages do not provide the temporal variations with which loads are delivered. It is crucial to evaluate natural variability to distinguish land use signals from climate signals and to define the impact on the ecology of the frequency and extent of extreme events, such as flood plumes (Restrepo et al., 2006; Brodie et al., 2010b; Collins et al., 2011). Accordingly, an approach was established by the Reef Plan that combines monitoring and modelling to assess the current catchment conditions and to provide evidence of links between land management activities, water quality and reef health (Carroll et al., 2012). As part of this, the Great Barrier Reef Catchment Event Monitoring Program (GBRI5) was implemented to monitor concentrations of suspended solids and nutrients and produce best load estimates at end of system sites (EOS) and to assess the range of annual variability in 10 priority catchments. This initiative has resulted in a large proportion of the GBR catchment area being monitored simultaneously for the first time. This paper reports on the results of the monitoring from July 2006 to June 2009. It identifies the temporal and spatial distribution of sediment and nutrient exports in the region over that period and compares present condition to the pre-European (Brodie et al., 2009) and mean annual estimates (Kroon et al., 2010). Because of the complexity of the hydrographs and the varying quality of datasets that have been obtained to date, a new methodology was developed that optimises method selection when analysing parameter behaviour. This paper makes a substantial contribution to the protection of the GBR by providing direct estimates of the present magnitudes of the sediment and nutrient yields over a wide area and variety of catchment types. These estimates also supply baseline exports against which targets for ecosystem protection can be assessed and water quality models can be validated. In the longer term, as sufficient data becomes available, the developed methodology will allow long-term trends and processes to be identified.

2. Study area overview All of the catchments that feed into the GBR lagoon cover an area of approximately 430,000 km2 and extend over 2000 km from the tropics to the subtropics. Ten catchments had previously been identified by the Reef Plan (2003) as high risk to the Reef in terms of contaminant exports and therefore they were recommended for priority monitoring. The selected catchments constitute 82% of the total GBR catchment area and include the Normanby in the Cape York region, the Barron, Johnstone, Tully, and Herbert in the Wet

151

Tropics, the Pioneer and O’Connell in the Whitsundays, the Burdekin and the Fitzroy in the Dry Tropics, and the Burnett in the Subtropics. The data collected from these catchments over the period 2006–2009 is the subject of this study. Because of their wide geographical area, the selected catchments cover considerable variations in climate, topography and landuse, but the rainfall is universally characterised by a distinct summer wet season (December–April). In the Wet Tropics, the annual rainfall can exceed 2000 mm, but declines southward and inland to an annual average of approximately 500 mm over the majority of the area (Gordon, 2007) (Fig. 1). As a result, rivers in the dry inland areas are highly ephemeral, whereas those of the Wet Tropics have permanent flows (Gordon, 2007). This precipitation pattern accounts for large differences in the proportion of rainfall which becomes runoff, varying from 7% in the large inland Burdekin and Fitzroy catchments to 74% in the steep, Wet Tropical Tully catchment (Pulsford, 1993). The results of strong spatial and temporal bias in discharge is that the smaller Wet Tropical and Whitsundays catchments, with 3% of the total GBR catchment area, contribute 32% of the discharge into the Reef lagoon in an average year. By contrast, a single major flood in any one of the large Dry Tropical catchments, which occurs periodically because of the unusually high variability in Queensland weather (Nicholls, 1988), may dwarf the combined total flow of the smaller catchments in that year (Furnas et al., 1995; Brodie, 1997). Further complexity is added to discharge patterns by extensive stream regulation through large storages such as Tinaroo Dam in the upper Barron catchment, the Burdekin Falls Dam in the Burdekin, several reservoirs in the Pioneer, Fairbairn Dam in the Fitzroy and by a series of weirs, particularly in the Fitzroy and Burnett catchments. In line with the irregularity in discharge, high variability is observed in the sediment and nutrient loads between catchments. This variability in loads is accentuated by differences in catchment size and land use (Furnas et al., 1995; Devlin and Brodie, 2005; Brodie et al., 2007).

2.1. Land use Land use information required to conceptually interpret the results was mapped following the method of Cogle et al. (2006) using the 1999 state-wide land use data set obtained from the Queensland Land Use Mapping Project (Witte et al., 2006) (Fig. 2; Table 1). Although grazing is the predominant land use throughout the GBR catchments (over 90%, Gordon, 2007) particularly in the drier inland areas, a variety of other, more intensive activities, such as large scale irrigated agriculture and mining are locally significant. The most northerly monitored catchment, the Normanby in Cape York Peninsula, is dominated by grazing but with a significant proportion of rainforest/conservation areas (Fig. 2; Table 1). The Barron, Johnstone, Tully and Herbert catchments in the Wet Tropics region are small to moderate in size (<10,000 km2) and have significant areas of rainforest as well as a variety of intensive land uses including improved pastures, grazing and sugar cane cultivation with dairy farming in the Barron and North Johnstone. By contrast, the comparatively large Burdekin and Fitzroy catchments (>100,000 km2) occupy drier inland plains which are mostly grazing country but also contain considerable areas of state forest, grain production and, in some cases inland irrigation areas. The coastal O’Connell and Pioneer catchments include grazing country and forestry, but with relatively large areas of sugarcane on the floodplains. In the south, the Burnett is the smallest and most intensively cultivated of the large inland catchments, with a variety of irrigated crops supported by a system of dams, weirs, and groundwater.

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Fig. 1. Spatial patterns of the mean annual rainfall (mm) in Queensland from July 1900 to June 1999.

3. Methods 3.1. Selection of monitoring sites Ten catchments were referred to as areas of high impact by the Reef Water Quality Protection Plan in terms of their potential pollutant loads to the GBR lagoon (Reef Plan 2003, 2009). Monitoring was carried out at eleven end-of-system (EOS) sites (Table 1), since the Johnstone catchment, which counts as one priority catchment in the Reef Plan, contains two EOS sites representing the north and the south branches. The Johnstone will be assessed as two separate catchments for the purpose of this study. In general, the EOS sites capture freshwater flows from 40% to 99% of total basin areas and do not include tidal areas and small coastal streams.

during high flow conditions to a number of days when flow ceases in ephemeral streams during dry periods. At sites where flow data was not directly measured, flow was factored from the closest available upstream measuring points as indicated for individual cases in Table 1. Automatic and manual ‘grab’ water quality samples were collected from the EOS sites according to the methods outlined in the Event Monitoring Methods Manual (Polaschek et al., 2007) in accordance with the Australian and New Zealand standard (AS/NZS 5667.1, 1998). Sample collection targeted high flow events. Water quality samples were analysed for total suspended solids (TSS), total nitrogen (TN), oxidised nitrogen (NOx-N), ammonium nitrogen (NH4-N), total phosphorus (TP) and filterable reactive phosphorus (FRP) by the ERS Chemistry Centre (Indooroopilly, QLD) using NATA accredited methods (APHA-AWWA-WPCF 2005a,b,c,d).

3.2. Flow and water quality sampling 3.3. Rainfall data Flow is automatically recorded at gauging stations at each EOS by the Department of Environment and Resource Management (DERM). The recording frequency varies from between 10 min

Rainfall during the monitoring period is presented in comparison to long term means as a method of determining relative

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Fig. 2. Land use within the major river basins draining into the Great Barrier Reef lagoon.

normality of the prevailing climatic conditions. This is required to enable interpretation of results in terms of the long term averages and pre-European conditions as defined by Kroon et al. (2010) and Brodie et al. (2009). Total annual rainfall data sets and long term averages were obtained from the SILO Australian climate archive (data courtesy of DERM http://www.longpaddock.qld.gov.au/silo/ and the Australian Bureau of Meteorology), which stores daily time step rainfall interpolations in 5 km grids (Jeffrey et al., 2001) based on point rainfall data provided by the Bureau of Meteorology. 3.4. Sediment and nutrient load estimation The monitoring year, for the purposes of this study, was defined between July and June in order to capture the wet season and to evaluate results within a timeframe that would allow preparation for the following wet season sampling. For accurate total annual load determination it is necessary to calculate a discrete load for each high flow event and each low flow period. This required separating significant high flow events from baseflow conditions and was carried out by graphical means based

on flow records. The start and end of significant high flow events were identified by a sudden increase/decrease in the slope of the flow hydrograph. It is acknowledged that defining high flow events based on a constant flow value (e.g. percentiles) is desirable; however the highly fluctuating nature of Queensland rivers restricts this option. Calculation of contaminant loads involves integrating the product of concentration and flow over the period of interest. Contaminant loads for an individual high flow event or low flow period were based on the flow record and contaminant concentration using hourly time steps which were found to be appropriate for the rapidly evolving event characteristic of these catchments (Eq. (1)):

Lðev entÞk or LðlowflowÞl ¼

m X

Q i  C i  ti

ð1Þ

i¼i

where m is the number of time steps (t) within the relevant period, ti is the ith time period (hour), Qi is the ith flow rate, and Ci is the ith contaminant concentration.

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Table 1 Catchment area, mean annual flow and major land use for the monitored catchments. Main landuses Gauging station

Region

105107A Cape York 110001D Wet Tropics 112004A Wet Tropics

River

Total river Monitored area basin area* above gauging station (km2) (km2)

Normanby 24,550 Barron 2250 North 2240 Johnstoneb 112101B Wet Tropics South Johnstoneb 113006A Wet Tropics Tully 1740 116001E Wet Tropics Herbert 9800 1240062 Whitsundays O’Connell 2080 125013A Whitsundays Pioneer 1680 120001A Dry Tropics Burdekin 130,050 1300000 Dry Tropics Fitzroy 142,750 136014A Dry Tropics Burnett 33,740

*

12,950 1950 950

Monitored area of river basin (%)

Mean annual discharge (GL)

(%)

(%)

(%)

(%)

(%)

(%)

53 86 60

1234a 782 1739

16 19 37

1 20 <1

82 39 34

1 2 2

<1 10 4

0 8 23

738

79

0

12

4

3

2

2995 3429 342c 700d 8930e 4311 1319f

68 23 1 8 2 4 3

2 4 35 23 <1 7 13

14 68 56 45 96 82 78

11 3 7 22 <1 0 <1

3 <1 <1 0 1 6 4

1 1 <1 1 <1 1 2

400 1450 8600 825 1450 129,950 139,300 32,850

83 88 40 80 99.9 98 97

Conservation Forestry Grazing Sugarcane Other Dairy cropping

From Brodie et al. (2009). Estimated from Battle Camp flow data GS 105101A with area correction factor of 1.82. b Johnstone is considered as one catchment by the Reef Plan (2003, 2009). c Estimated as sum of flows from GS 124001B and GS 124003A and factored by their contributing area of 1.24 and 1.74, respectively. d Estimated from flow data for Mirani Weir GS 125007A (QDP = QMW * 1.226; TimeDP = TimeMW + 1.5 h). e Estimated from flow data for Clare GS 120006B. f Estimated from flow data for Walla GS 136001B. a

This calculation requires that flow and concentration records be collected with a frequency that permits hourly timesteps to be available. This was true for all sites in terms of flow; however the sampling of contaminant concentrations was much more variable, so that interpolation required appropriate procedures. Because of the complexity of the hydrographs and the varying temporal coverage of sampling that have been obtained to date, a new methodology was developed that optimises method selection when analysing parameter behaviour. Adequate sampling must capture concentration fluctuations representing all in-stream transport processes occurring during an event. During high flow events contaminant concentrations frequently peak at a different time to the flow peak. This can be attributed to rapid delivery, remobilization and subsequent exhaustion of sediments, variation of rainfall intensity, bank collapse, contaminants arriving from distant sources, etc. (e.g. Gregory and Walling, 1973; Walling and Webb, 1980, 1982; Klein, 1984; Slattery et al., 2002; Rose, 2003; Lefrançois et al., 2007; Smith and Dragovich, 2009). This lag between peak concentration and peak flow generates a loop in the concentration-flow relationship called hysteresis (Olive and Rieger, 1985; Hudson, 2003; Smith and Dragovich, 2009). If sampling is not adequate, the presence of a hysteresis loop complicates the interpolation of hourly concentration time steps. A variety of algorithms were trialled for the estimation of hourly concentration values. The algorithms are widely used international methods for a variety of data types, which include linear interpolation, regression, Beale ratio (e.g. Preston et al., 1989; Richards, 1989). These methods were found to vary in power and significance due to their data requirements and underlying assumptions. Because sites and events varied in both complexity and adequacy of sampling over the hydrograph, the type of event (its magnitude, complexity and order of occurrence in the wet season), and also the behaviour of the contaminant concentrations during the event, employing one algorithm consistently was not found to be appropriate (Preston et al., 1989; Richards, 1989; Tennakoon et al., 2007). A new methodology was therefore developed that optimises method selection when analysing parameter behaviour. Fig. 3 shows typical sampling distributions for which the methods trialled and discussed further below are appropriate.

Linear interpolation is the most accurate and reliable method, provided the event is adequately sampled, or at least with reasonably representative sampling including the peak concentration, and if the event is discrete and well defined so that linearity in concentrations between sampling can be assumed as shown in Fig. 3a. In these cases, load estimation was made using the algorithm:

Ci ¼

C2  C1 ðti  t1 Þ þ C 1 t2  t1

ð2Þ

where Ci is the ith intermediate concentration, ti is the ith time for which Ci needs to be estimated, t1 and t2 are the times of sampling for C1 and C2, which are the neighbouring measured concentrations. Alternatively, when the event was inadequately sampled, or highly complex, event regression or the Beale ratio was used. The regression method is generally preferred for those events where high flows have been sampled and where the event has exhibited minimal hysteresis or complexity, resulting in a meaningful and representative correlation between event concentrations and flow (Fig. 3b). Linear or non-linear regression methods may be applied, and the algorithm developed for this investigation takes the form of power curve:

C ¼ aC f Q b

ð3Þ

where C is the contaminant concentration, Q is flow, where a and b are regression parameters using log-transformed flow and concentration data, and Cf is a correction factor to remove an inherent bias introduced as a result of log-transformation (Ferguson 1986). The correction factor Cf is calculated in form of a smearing estimate after Duan (1983):

Cf ¼

n X 1 ei 10 n i¼1

ð4Þ

where n is the sample size ei is the residual. Otherwise, for poorly sampled and/or complex events, as shown in Fig. 3c, where interpolated concentrations would be too unreliable, the Beale ratio is recommended for estimating total event loads. The Beale ratio is the ratio between the mean of the instantaneous loads (product of observed concentrations and sampled

155

350

120

300

100

250

80

200

60

150

40

100

20

50

0 24/2/07 25/2/07 26/2/07 27/2/07 28/2/07

1/3/07

300

0 2/3/07

1,000

150

100

100 50

26/2/07

27/2/07

10

1,400

600

1,200

500

1,000

400

800

300

600

200

400

100

200

7/1/08

8/1/08

100

1,000

Discharge (m3/s) 1,000

3

700

0 6/1/08

Error in load estimates: Regression: 3 % Beale Ratio: 17 %

28/2/07

TSS conc (mg/L)

25/2/07

100

10

50

24/2/07

TSS conc (mg/L)

200

3

250

150

Discharge (m /s)

300 200

Discharge (m /s)

TSS conc. (mg/L)

Sampling time

350

250

TSS conc. (mg/L)

Discharge

400

(b)

(c)

TSS conc.

3

400

140

TSS conc (mg/L)

(a) 160

Discharge (m /s)

M. Joo et al. / Marine Pollution Bulletin 65 (2012) 150–166

100

Error in load estimates: Regression: 46 % Beale Ratio: 8 %

10

1

9/1/08 10/1/08 11/1/08 12/1/08

1

10

100

1,000

10,000

Discharge (m3/s) Fig. 3. Examples of high flow events with different sampling adequacy: (1) adequate sampling fully capturing all processes recommended for linear interpolation; (2) event with no hysteresis recommended for regression; (3) event exhibiting hysteresis recommended for Beale ratio. (Errors were calculated as percentage difference between the load by the given method and the total event load.)

flow rate) and mean of the sampled flows. The algorithm for the Beale ratio used in this study was:

! l 1 þ 1n covðl;qÞ lq  L¼Q  1 þ 1 varðqÞ q 2 n q

ð5Þ

where l is the observed load, q is the discharge measured at the time of the observed load. The main weakness of the Beale method is that the calculated event load is highly sensitive to the distribution of sampling points over the hydrograph. Consequently, sampling biased towards lower or higher flow rates during an event, as in Fig. 4, can cause considerable errors in the load estimated by the Beale. Since errors in the load estimates are more substantial when only low flow rates are sampled, the Beale ratio was used only for those events where high flow rates were included. For those events where concentration data is very scarce or missing, regression or Beale ratio methods based on historical data may be used to provide concentration or load estimates but with a very low level of certainty, depending on catchment size and variability.

In those periods where flow and concentrations were stationary, such as during low flow conditions, the average concentrations were employed to calculate loads. These periods contributed an insignificant proportion of exports to the GBR except in the case of NOx-N, where Beale and regression methods were applied. The algorithm used to calculate the average concentration was:

¼1 C n

n X

Ci:

ð6Þ

i¼1

The calculated event and low flow loads were integrated to produce the annual loads for each EOS site in a monitoring year (July to June). The algorithm used to calculate the annual catchment load was:

TL ¼

y X k¼1

Lðev entÞk þ

x X

Lðlow flowÞl

ð7Þ

l¼1

where TL is the total annual load, L(event)k is the load for the kth event, y is the number of events, L(low flow)l is the load for the lth low flow period, and x is the number of low flow periods.

500

4,000

450

3,500

350

3,000

300

2,500

250

2,000

200

1,500

150 100 50 -

Error in load estimates: when flow > 2000m3/s sampled only: 13% when flow < 2000m3/s sampled only: 50%

3

400

Discharge (m /s)

M. Joo et al. / Marine Pollution Bulletin 65 (2012) 150–166

TSS conc. (mg/L)

156

1,000

TSS conc. Discharge

500

1/2/07 2/2/07 3/2/07 4/2/07 5/2/07 6/2/07

Fig. 4. Example of performance of Beale ratio when different flow ranges sampled. (Errors were calculated as percentage difference between the load by the scenario and the total event load.)

Mean annual concentrations (MAC) accounts for the effect of flow in the annual load and was calculated by dividing the annual load by the flow volume:

TL MAC ¼ Q

ð8Þ

where TL is the total annual load and Q is the annual flow volume. Annual yields (loads per unit area) were used to compare exports of catchments of different areas. The yields were computed by dividing the annual load by the catchment area:

TL Yield ¼ A

ð9Þ

where A is the catchment area. Annual yields, rather than loads or concentrations, were also compared to the mean annual pre-European (Brodie et al., 2009) and current mean annual yields (MAY) (Kroon et al., 2010). This was necessary, since the pre-European and the mean annual yields were based on basin wide estimates, whereas the present study considers exports from the end-of system sites only. Catchments areas were used accordingly, as defined in Table 1.

Table 2 Reliability scores of load estimates. Data type used in models for a given period

Reliability score

High flow events Event specific data that fully represents event Event specific data that partially represents event Year specific data (no reliable event data) Historical data (no reliable annual or event data) Occasional historical measurements only

8–10 6–7 4–5 2–3 1

Low flow period Period specific data fully representative period Year specific data (partially represents period) Historical data (no reliable annual or event data) Occasional historical measurements only

8–10 5–7 2–4 1

Table 3 Final reliability codes for total annual loads.

3.5. Reliability Due to the different methods used to establish parameter – flow relationships, the reliability of load estimates varied for the individual periods, and this had to be taken into account when rating the certainty of the annual load estimates. A rating procedure was therefore developed, which categorised periods by assigning to them a reliability score. This was based on the sampling frequency, a subjective evaluation of the load estimates, and the level of confidence in the method required to infill missing values (Table 2). Since the uncertainty associated with the larger events weighted more in the overall calculations, these reliability scores were also weighted by the proportion of the annual load contributed by the period. The weighted reliability scores were then summed to obtain annual reliability scores for the annual loads. To facilitate interpretation, the annual final scores were categorised into five groups of codes (Table 3).

Reliability score

Reliability category

10–8 8–6 6–4 <4

High Moderate Low Indicative only

07, particularly in southern areas, while above-average rainfalls and flows occurred during 08/09, especially in the northern half of the study area (Figs. 5 and 6). The largest temporal distribution in rainfall was noted in the inland catchments of the Burdekin and Fitzroy. In 06/07 the southern part of the inland region was particularly dry, whereas very high rainfall occurred in 07/08 in the Fitzroy and in 07/08 and 08/09 in the Burdekin producing above average flows in both catchments. Flow in the Fitzroy was consistent with rainfall; however flows in the lower Burdekin catchment were disrupted by releases from the Burdekin Falls Dam. In the heavily regulated Burnett catchment, the relatively dry conditions throughout the monitoring period did not result in appreciable annual runoffs to the GBR.

4. Results 4.2. Sampling 4.1. Climatic conditions during study period A range of rainfall and flow conditions were observed during the 3 years of monitoring. This is a very short timeframe in a region where the rainfall is known to be highly variable, and is therefore not likely to represent average conditions in most cases. For example, rainfalls were below-average during the monitoring year 06/

During the 3 years of monitoring 1248 samples were collected at the EOS sites (Table 4). Of these, 1036 were manually collected (‘‘grab’’ sampling) and 212 were collected using ISCO automatic pump samplers in the Normanby and Barron Rivers. Of the total samples, 14% were taken during baseflow periods, whilst 86% represented high flow events.

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Discharge (million ML)

Fig. 5. Spatial patterns annual rainfall relative to average annual in (a) 2006/07; (b) 2007/08; (c) 2008/09 monitoring years.

35 mean annual 06/07 07/08 08/09

30 25 20 15 10 5

Burnett

Fitzroy

Pioneer

O'Connell

Burdekin

Herbert

Tully

South Johnstone

North Johnstone

Barron

Normanby

0

Fig. 6. Annual discharge during the monitoring period (2006–09) and long term mean annual discharge in the major catchments of the GBR. (Note that in O’Connell the 2006/ 07 flow is from Staffords Crossing only.)

Table 4 Number of samples taken at the monitored EOS sites. Gauging station

River

Number of grab samples

Number of automatic samples

Total number of samples

105107A 110001D 112004A 112101B 113006A 116001E 1240062 125013A 120001A 1300000 136014A

Normanby Barron North Johnstone South Johnstone Tully Herbert O’Connell Pioneer Burdekin Fitzroy Burnett

116 60 67 226 206 18 31 93 116 67 36

13 199 – – – – – – – – –

129 259 67 226 206 18 31 93 116 67 36

4.3. Annual total loads and mean annual concentrations (MAC) The results of this monitoring indicated that over the 3 years a total of 134 million ML of water was discharged into the GBR lagoon from the 10 major catchments, along with 39 million tonnes of TSS, 114,000 tonnes of TN, 30,000 tonnes of TP, 13,000 tonnes of NOx-N, 2000 tonnes of NH4-N, and 3600 tonnes of FRP. In terms of

percentage variation, water discharge and TSS load varied by 150%, TN by 200%, TP and NH4-N by 250%, NOx-N by 50% and FRP by 350% (Table 5). Large variability was also noted in the MAC ranging between 40% by TSS and TN, 60% by TP, 80% by NH4-N and 100% by NOx-N and FRP (Table 6). Comparative distributions of total annual loads and MAC of TSS, TN, NOx-N, NH4-N, TP and FRP for each EOS site, with their

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Table 5 Combined annual total and mean sediment and nutrient loads at the monitored EOS sites and for the total GBR catchment area. Monitoring year

Discharge (million ML)

TSS (t)

TN (t)

TP (t)

N0x-N (t)

NH4-N (t)

FRP (t)

2006/07 2007/08 2008/09 Total monitored Mean monitored

25 57 53 134 45

7,677,000 18,909,000 12,689,000 39,275,000 13,092,000

18,519 58,236 37,257 114,012 38,004

4743 16,369 9222 30,334 10,111

3940 5678 3824 13,442 4481

316 635 1054 2005 668

429 1992 1140 3561 1187

Table 6 Combined flow standardised TSS and nutrient loads (mean annual concentrations) at the monitored EOS sites. Monitoring year

Discharge (million ML)

TSS (mg/L)

TN (lg/L)

TP (lg/L)

NOx-N (lg/L)

NH4-N (lg/L)

FRP (lg/L)

2006/07 2007/08 2008/09 Mean

25 57 53 45

308 334 242 295

744 1029 709 827

190 289 176 218

158 100 73 110

13 11 20 15

17 35 22 25

Fig. 7. Annual TSS, TN, and TP loads and flow standardised loads (mean annual concentration) for the GBR catchments 06/07–08/09. (Reliability of the load estimates are given as superscripts of the circles.)

reliabilities, are presented in Figs. 7 and 8 and the actual load figures in Table 7. The reliabilities are indicated by superscripts. Overall, the load estimates were highly reliable at the Normanby, Burdekin, Pioneer EOS sites (in 06/07 and 07/08) and at the Fitzroy River EOS site. Moderate reliability was assigned to loads estimated for the Barron, South Johnstone Tully and Burnett EOS sites. Low reliabilities were assigned to the North Johnstone and

O’Connell EOS sites because only partial events could be sampled. Loads estimated for the Herbert River EOS site were considered as indicative only because events could not be specifically targeted. Contaminant loads were exported in decreasing orders of magnitude from the Burdekin, Fitzroy, Herbert, Pioneer, Barron, Normanby, North Johnstone, Tully, South Johnstone and O’Connell Rivers. Except for NOx-N, the highest MAC were produced for all

M. Joo et al. / Marine Pollution Bulletin 65 (2012) 150–166

159

Fig. 8. Annual NOx-N, NH4-N, and FRP loads and flow standardised loads (mean annual concentration) for the GBR catchments during 06/07–08/09. (Reliability of the load estimates are given as superscripts of the circles.)

parameters in the Burdekin, O’Connell, Pioneer, and Fitzroy Rivers, with relatively high MAC also generated in the Barron, Johnstone and Herbert in some years. NOx-N MAC was consistently the highest in the Johnstone, Tully, Pioneer, and Fitzroy catchments in all years and in the Barron, Herbert, Burdekin and O’Connell in some years. 4.4. Unit yields (area weighted loads) The pre-European yields and the MAY were defined for wholeof-basin areas (Table 8). However, the field measurements were taken at the EOS sites, which excluded variable percentages of the basin area (Table 1). Therefore the measured loads could only be directly compared with the pre-European and the MAY as contaminant yields per unit area. For dissolved nitrogens, dissolved inorganic nitrogen (DIN) was calculated as the sum of NOx-N and NH4-N since the pre-European and MAY yields were only available in this form. Results below pre-European levels were treated as representing no significant change, as suggested by Brodie et al. (2009). Although more than half of the sediment and nutrient loads from the monitored catchment area were discharged from the Burdekin and Fitzroy, these rivers, together with the Normanby and Herbert, produced the lowest yield per unit area during the 3 years of monitoring (Table 8). The highest TSS and nutrient yield per unit area were produced in the Wet Tropics and in the Whitsundays catchments. The Burnett in the southern end of the

monitored area did not receive any significant flows during the monitoring period, and therefore its potential yields cannot be commented on. The DIN was generally below or comparable to MAY throughout the catchments over the time period and appears to have been independent of flow. However the other parameters appear to have been strongly flow related. Prevailing hydrology therefore seemed to have a strong influence on the distribution of loads through the time period. In the Normanby catchment, which represented Cape York, the yields were below the MAY, apart from the dissolved inorganic phosphorus (DIP = FRP). DIN was similar to pre-European estimates, despite above average flow conditions. However, above average flow in the Wet Tropics region (Barron, Johnstone, Tully, and Herbert) produced yields that were above the MAY (although DIN was below-MAY). An exception in the Wet Tropics was the Barron catchment in 06/07, where below-average flow conditions resulted in below-MAY yields and in DIN yields that were below/ similar the pre-European estimate. In the Whitsundays (O’Connell and Pioneer), flow conditions were higher than average. Despite this, the yields in the O’Connell were below-MAY (except for DIP) although the sampling point could not account for the highly developed southern coastal areas of the catchment. By contrast, yields were above-MAY in the Pioneer catchment (although DIN was below-MAY and apparently unaffected by flow). In the large inland catchments of the Burdekin and Fitzroy, flow was exceptionally variable throughout the period, commencing

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Table 7 Discharge, annual contaminant loads and mean annual concentrations for the monitored period. Annual loads River

Monitoring year

Discharge (GL)

TSS (kt)

TN (t)

TP (t)

N0x-N (t)

NH4-N (t)

FRP (t)

TSS (mg/L)

TN (lg/L)

TP (lg/L)

N0x-N (lg/L)

NH4-N (lg/L)

FRP (lg/L)

Normanby

2006/07 2007/08 2008/09 2006/07 2007/08 2008/09 2006/07 2007/08 2008/09 2006/07 2007/08 2008/09 2006/07 2007/08 2008/09 2006/07 2007/08 2008/09 2006/07 2007/08 2008/09 2006/07 2007/08 2008/09 2006/07 2007/08 2008/09 2006/07 2007/08 2008/09 2006/07 2007/08 2008/09

1762 3646 2350 472 1581 780 2218 1870 1989 955 810 1042 4225 3228 3762 4319 3406 9609 166 597 368 866 1340 911 8997 27,970 29,490 885 12,051 2193 35 87 30

59 211 104 30 397 163 81 115 113 51 46 128 116 88 113 337 220 1888 24 121 50 156 255 111 6503 12,700 9614 320 4751 404 0 5 1

711 1814 1098 239 1744 660 828 1042 848 504 427 632 1872 1367 1531 2348 1726 5966 156 618 343 927 1378 849 9724 32,816 23,283 1178 15,197 2016 32 107 31

84 168 98 26 252 89 153 248 150 97 86 161 175 94 135 369 202 1025 13 133 50 181 326 134 3240 9179 6721 403 5671 657 2 10 2

33 43 57 28 86 38 303 267 328 196 123 134 940 666 623 367 366 683 26 44 29 146 107 106 1704 2424 1478 197 1542 347 0 10 1

21 50 29 4 16 6 14 12 17 5 7 8 39 20 29 23 27 70 4 10 9 48 35 19 111 222 846 47 231 20 0 5 1

20 30 16 5 24 6 17 13 16 12 10 12 33 18 23 53 26 86 8 24 13 34 58 36 211 768 711 36 1019 221 0 2 0

33 58 44 64 251 209 37 61 57 53 57 123 27 27 30 78 65 197 143 203 137 181 191 122 723 454 326 361 394 184 9 58 17

404 498 467 506 1103 846 373 557 426 528 527 607 443 423 407 544 507 621 940 1035 932 1070 1028 932 1081 1173 790 1331 1261 919 914 1230 1033

48 46 42 55 159 114 69 133 75 102 106 155 41 29 36 85 59 107 80 223 136 209 243 147 360 328 228 455 471 300 56 115 67

19 12 24 60 54 49 137 143 165 205 152 129 222 206 166 85 107 71 154 74 79 169 80 116 189 87 50 223 128 158 3 115 33

12 14 12 9 10 8 6 6 9 6 8 8 9 6 8 5 8 7 24 17 24 56 26 21 12 8 29 54 19 9 6 60 33

11 8 7 10 15 8 8 7 8 13 12 12 8 6 6 12 8 9 48 40 35 39 43 40 23 27 24 40 85 101 7 23 0

Barron

North Johnstone South Johnstone Tully

Herbert

O’Connell

Pioneer

Burdekin

Fitzroy

Burnetta

a

MAC

No high flow event was recorded during the monitoring period.

with similar to or below average flows in 06/07 and followed by exceptional floods in 07/08 in both catchments and in 08/09 in the Burdekin. Overall the Fitzroy produced lower yields per unit area as compared to the Burdekin. During 2006/07 flow and dissolved constituents were about average in the Burdekin, however particulates were above MAY. In the following 2 years exceptional floods produced correspondingly excessive loads in comparison to MAY. All parameters remain above pre-European levels throughout the monitoring period. During the early below average flow period in the Fitzroy, yields were consistently below MAY and pre-European levels. When major floods occurred in the second year all yields were above MAY. However, the slightly larger flood in the third year of monitoring produced yields that were lower per unit area although still above MAY.

4.5. NOx-N distribution The NOx-N concentrations and loads were distributed differently to other parameters and were notably less sensitive to flow (Table 9). In comparison to TSS loads, which were transported mostly during high flows, substantial amounts of NOx-N were discharged during low flow. The percentage of NOx-N load during low flow conditions were 20% to 65% in the Wet Tropical catchments (mean 30–40%), 5–45% in the Whitsundays catchments (mean 20–30%), and 1–25% in the Normanby, Burdekin and Fitzroy catchments (mean 2–15%). In the Wet Tropics the percentage of the TN load represented by NOx-N load was substantial at 20–50%, except from the Barron, where this was below 10%. However in the

Whitsundays and Dry Tropics the NOx-N contribution was only 5–20% and was below 5% in the Normanby River in Cape York. 5. Discussion This study has defined the distribution of regional sediment and nutrient loads to the GBR lagoon over a 3 year period between July 2006 and June 2009. Because this paper is reporting on EOS sites, the sources of loads within the catchments are not described in detail. The main observations during the 3 years is that catchments in the Wet Tropics and the Whitsundays region, with a comparatively moist and consistent climate generally exhibited fast responses to rainfall events, but with baseflow a consistent contributor to flow throughout the year. By comparison, the larger catchments, such as the Normanby, Burdekin and Fitzroy Rivers are more ephemeral with relatively slow-rising water levels and long residence times. Although the large inland catchments generated the highest loads, the moister catchments exported higher yields per unit area. Major floods were observed in the two largest catchments (Burdekin and Fitzroy) which would have raised their loads beyond their usual range. 5.1. Spatial and temporal variability Contrasts in sediment and nutrient exports between the regions are due to the spatial and temporal distribution of rainfall and to the different physical characteristics of the major catchments (Figs. 7 and 8; Table 7). Regional contrast is especially evident between the large, dry, grazed inland catchments and the smaller,

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M. Joo et al. / Marine Pollution Bulletin 65 (2012) 150–166 Table 8 Pre-European (Brodie et al., 2009), mean annual (Kroon et al., 2010), and measured discharge and contaminant yields for the monitored catchments. Annual yields Discharge (GL)

Normanby

Barron

Johnstone

Tully

Herbert

O’Connell

Pioneer

Burdekin

Fitzroy

Burnetta

a

Pre-European Mean annual 2006/07 2007/08 2008/09 Pre-European Mean annual 2006/07 2007/08 2008/09 Pre-European Mean annual 2006/07 2007/08 2008/09 Pre-European Mean annual 2006/07 2007/08 2008/09 Pre-European Mean annual 2006/07 2007/08 2008/09 Pre-European Mean annual 2006/07 2007/08 2008/09 Pre-European Mean annual 2006/07 2007/08 2008/09 Pre-European Mean annual 2006/07 2007/08 2008/09 Pre-European Mean annual 2006/07 2007/08 2008/09 Pre-European Mean annual 2006/07 2007/08 2008/09

1234 1762 3646 2350 782 472 1581 780 2477 3173 2680 3031 2995 4225 3228 3762 3429 4319 3406 9609 342 166 597 368 700 866 1340 911 8934 8997 27,970 29,490 4311 885 12,051 2193 1319 35 87 30

TSS (t km2)

TN (kg km2)

TP (kg km2)

DIN (kg km2)

DIP (FRP) (kg km2)

7 45 5 16 8 11 45 15 204 84 18 142 213 236 439 14 53 80 61 78 11 39 39 26 220 48 300 29 146 61 30 31 108 176 77 4 31 50 98 74 8 24 2 34 3 3 42 0 0 0

47 272 55 140 85 99 312 123 894 338 488 1711 2132 2163 2473 407 815 1291 943 1056 76 263 273 201 694 142 1106 189 748 415 122 434 639 950 586 17 66 75 253 179 9 91 8 109 14 12 153 1 3 1

5 27 6 13 8 12 34 13 129 46 54 223 404 476 561 45 61 121 65 93 9 25 43 23 119 24 295 16 161 61 20 60 125 225 92 2 14 25 71 52 1 25 3 41 5 1 44 0 0 0

21 39 5 7 6 26 22 16 52 22 108 942 837 619 718 99 481 675 473 450 26 136 46 46 87 60 237 36 65 46 50 163 134 98 86 8 14 13 20 18 4 6 1 13 2 5 10 0 0 0

0.4 0.5 2 2 1 1.3 4 3 12 3 12 21 48 39 47 10 12 23 12 16 1 3 6 3 10 2 11 10 29 16 1 18 23 40 25 0.1 2 2 6 5 0.1 1 0 7 2 0.1 1 0 0 0

No high flow event was recorded during the monitoring period, therefore yields are not representative.

Table 9 Percentage of TN loads as NOx-N loads over the monitoring period and during low flows. Gauging station

105107A 110001D 112004A 112101B 113006A 116001E 1240062 125013A 120001A 1300000 136014A

River

Normanby Barron North Johnstone South Johnstone Tully Herbert O’Connell Pioneer Burdekin Fitzroy Burnett

Percentage of NOx-N load during low flow periods (%)

Percentage of TN load as NOx-N load (%)

2006/07

2007/08

2008/09

Mean

2006/07

2007/08

2008/09

Mean

2 56 38 32 35 64 26 6 26 18 na

1 19 31 33 25 8 28 16 9 1 1

2 44 44 51 30 36 38 42 9 11 0

2 40 38 39 30 36 31 21 15 10 0

5 12 37 39 50 16 17 16 18 17 0

2 5 26 29 49 21 7 8 7 10 9

5 6 39 21 40 11 8 12 6 17 3

4 8 34 30 46 16 11 12 10 15 4

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wetter coastal catchments, which are under more intensive land use (Figs. 1 and 2; Table 1). The highest discharges and the greatest loads for most measured water quality parameters occurred during the 07/08 year (Tables 5 and 6). The exception was for the loads of NH4-N which is a highly volatile parameter and therefore less certainty can be attributed to it. The year 08/09 produced the highest load of NH4-N. This was mostly due to floods in the upper and coastal areas of the Burdekin where comparatively high concentrations were further raised in comparison to the previous 2 years (Fig. 8). As the Burdekin delivered a very large proportion of total flow to the GBR in 08/09, it significantly impacted on the total NH4-N load. The large Dry Tropical catchments (Burdekin and Fitzroy), despite having the lowest contaminant yields (Table 8) as would be expected from predominantly grazing country (Bainbridge et al., 2007), were the highest contributors in terms of both MAC and loads for all parameters in all years (Figs. 7 and 8). In terms of percentages, they represent 80% of the area monitored, and contributed 85% of the TSS load (although only 52% of the NOx-N load). This is likely due to the effect that large catchment areas have on particular loads, as described by authors such as Behrendt and Opitz (2000) and Nedwell et al. (2002). The annual variability for both loads and MAC in the Burdekin and Fitzroy were substantial, with moderate to high flow conditions generating localised major floods following prolonged periods of below-average rainfall. This variability could be attributed to differing contributions of subcatchments with contrasting hydrology, land use and geology. For example, Joo et al. (2005) reported differences in mean annual flow volumes, TSS loads and MAC from individual subcatchments within the Fitzroy, and Packett et al. (2009) showed higher event mean concentrations when rain is predominantly falling on cropping areas. In the Burdekin, Bainbridge et al., 2007 showed a higher TSS generation from the Bowen subcatchment as opposed to the rest of the catchment. In the highly regulated Subtropical catchment of the Burnett River no high flow events occurred (Figs. 5 and 6) and, as a result, typical event-based exports from this catchment could not be estimated. Nevertheless, MACs were comparable to that of the Dry Tropical catchments during the minor events in 07/08 (Figs. 7 and 8). Fentie et al. (2006) modelled mean annual sediment and nutrient loads and found that the Burnett was potentially the third highest reef catchment in producing loads after the Burdekin and Fitzroy. This indicates that the Burnett may have the capacity to generate considerable loads under high flow conditions. As distinct from the Dry Tropics region, the Wet Tropics are made up of small to medium-sized catchments. During the monitoring period rainfall was average to above-average for the region, except in the upland areas of the smallest and wettest catchments (Tully and Johnstone) during 06/07 and 07/08 (Fig. 5). The EOS sites in the Wet Tropics cover only 4% of the total monitored area, but were responsible for 7% of TSS and were a major contributor of nitrogen with 10% of TN and 15% of NOx-N. In the Wet Tropics TSS and total nutrient load and yield distributions were both spatially and temporally variable (Figs. 7 and 8; Table 7 and 8). Exports, however, were mainly in the low to medium ranges, except for TP and NOx-N. The anomalies in load distribution over the Wet Tropics can be accounted for by high rainfall over certain areas associated with very variable topography and land use including large proportions of rainforest, accompanied by urbanisation, live stock, and intensively irrigated sugarcane with associated fertiliser use (Pulsford, 1993). In particular, relatively low TSS and TP concentrations were noted in the Tully, which is in accordance with the findings of Bainbridge et al. (2009) and Wallace et al. (2009). They attributed low exports to high groundcover and agricultural activities being confined to the flat coastal plains. On the other hand, relatively high TP concentrations were noted in

the more intensely cultivated Barron and South Johnstone catchments during this study (Fig. 7). These elevated TP levels could be related to agriculture, as Furnas (2003) noted that phosphorus becomes attached to soils when applied as fertiliser. Relatively high TP has also been attributed to tertiary basalts which are present in the upper part of the catchments (McCulloch et al., 2003b), and to rainforest areas (Hunter and Walton, 2008). It has been noted by Congdon and Herbohn (1993) that present exports within current rainforest areas may be affected by lagged impacts from past land uses, for instance nutrient losses as a result of selective logging may still be apparent 25 years after the initial disturbance. The Wet Tropics also contributed the highest loads of dissolved NOx-N, but in contrast to TN and TP, MAC were also high (Figs. 7 and 8; Table 9). Baseflow contributed 30–40% of the annual NOxN load (Table 9). Unlike most other areas of Queensland, baseflow is a significant proportion of the total stream flow in all the Wet Tropical catchments because of the high and reliable rainfall. The association between NOx-N and land use and baseflow has been observed by other authors. For instance high NOx-N concentrations were associated with sugarcane production in the Tully by Bainbridge et al. (2009) and in the Johnstone by Hunter and Walton (2008). Rasiah et al. (2004) and Hunter and Walton (2008) also noted baseflow contributing a major proportion of the NOx-N load in association with significant levels of NOx-N in nearby groundwater. Further support is provided by Hubbard et al. (2010) who discuss nitrogen cycling within the hyporheic zone, in-stream biochemistry and aquatic biota. The Pioneer and O’Connell Rivers in the Whitsundays are relatively small catchments with permanently flowing streams. They occupy 1% of the monitored area and delivered 2% of the TSS but 4% NOx-N. The NOx-N contribution by baseflow in these catchments were similar to that of the Wet Tropics. The annual contaminant loads in this region were also similar to the Wet Tropics, however MACs were higher (Figs. 7 and 8; Table 9). This is in accordance with rainfall in the region being average to above-average during the monitoring period, and the fact that the Whitsunday catchments contain very large areas of sugarcane which were considered by Rohde et al. (2008) to be the major source of nutrients and sediments in streams. These relatively small coastal catchments can still have a significant episodic impact on the GBR lagoon. For instance, discharges from the Whitsundays region during the intense January 2005 wet season produced a plume and algal bloom which, according to Brodie et al. (2010a), spread 150 km offshore, surrounding inner-shelf reefs. This was attributed to high concentrations of both particulate and dissolved nutrients from a landscape dominated by sugarcane cultivation, beef grazing and urban uses as shown by Rohde et al. (2008). The Cape York region is represented by the Normanby catchment which has relatively low intensity agricultural land use and experienced only average rainfall which produced comparatively low to moderate exports during the monitoring period. 5.2. Comparison of methods with those of previous yield estimates The results of this study were compared with modelled estimates of pre-European exports (Brodie et al. 2009) and with mean annual yields (MAY) estimated by Kroon et al. (2010) (Table 8). It is acknowledged that these studies were based on different estimation methods and time periods, which need to be considered when the presented information is compared. For instance, SedNet modelling (Wilkinson et al., 2004), on which the pre-European estimates are based, uses the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). SedNet predicts the long term average annual rates of erosion based on rainfall, soils, topography, crops and their management practices (Lane et al., 1988). The model was developed in the western USA

M. Joo et al. / Marine Pollution Bulletin 65 (2012) 150–166

with data from gently sloping croplands, and was adapted for Australian conditions (Prosser et al., 2001). When tested by Dougall et al. (2005) under current conditions for the Fitzroy catchment, it was found that sediment yields compared favourably with recent studies at a basin scale (in order of 100,000 km2), although discrepancies were present at a sub-basin scale (in order of 30,000 km2). However, the output of the pre-European yield supply calculated by SedNet is reliant on underlying assumptions that cannot be verified for pre-European conditions. These assumptions are that the catchment has natural vegetation cover, no gullies and no agriculture or urban development (Brodie et al. 2003). The issue of gullying is particularly significant, as shown by Fentie et al. (2005) who carried out a sensitivity analysis indicating that gully age, hillslope delivery ratio, and bankfull discharge recurrence interval are the most sensitive parameters for SedNet and that these are variable and do not necessarily correspond to the default values as defined by Prosser et al. (2001). Brodie et al. (2009) emphasized that the assumptions made for estimating the preEuropean exports may not be valid and could have introduced large enough error margins to consider their revision. The current MAY estimates were calculated by Kroon et al. (2010) to define baseline (anthropogenic) loads with which subsequent annual exports can be compared. The MAY estimates were based on data collated from DERM, the Australian Institute of Marine Science (AIMS), and the Australian Centre for Tropical Freshwater Research (ACTFR) and was supplemented by estimates from other studies. Datasets compiled for individual EOS sites were assessed for representativeness with respect to flow, using a scoring system based on two criteria developed by DERM (unpublished manuscript). These criteria were: 1) the number of samples in the upper two percentiles of flow, and 2) the highest recorded flow rate sampled. Sites were only included in the field-data based estimates if their representativeness score was sufficient. For these sites (Barron, Johnstone, Tully, Herbert, Burdekin and Pioneer), the process of calculating the mean annual loads involved first calculating loads for individual water years using the Load Regression Estimator methodology as outlined in Wang et al. (2009). Then these water year loads were divided by the total water year flows to give water year concentrations, which were then averaged giving the mean average concentration for each site. The mean annual load for each site was obtained by multiplying the mean average concentration by the mean annual flow of the sampled years and was subsequently multiplied by an area correction factor. The area correction factor was obtained from a SedNet model of current conditions developed for the Short Term Modelling Project by Cogle et al. (2006) which accounted for diffused sources and point sources downstream of the EOS sites. For those EOS sites where field data was not sufficiently representative (Normanby, O’Connell, Fitzroy and Burnett) mean annual yields were estimated using results of other studies including SedNet modelling by Cogle et al. (2006) and McKergow et al. (2005) among others. Both the pre-European and the MAY studies calculated mean annual loads without reference to the variability with which the load is delivered. For example, Joo et al. (2005) showed that in the Fitzroy River the mean annual flow can vary by at least 50% according to the period included, and that this variation can result in more than 300% of variability in the sediment load estimates. These studies also used flow records extending over different time periods, which can contribute to variations in these estimates. The pre-European SedNet estimates are based on 30–100 years of flow records; whereas Kroon’s mean annual loads are based on flow records only for the sampled years of 16–35 years, which may not be sufficient to capture the representative range of natural flows in all catchments. In addition to capturing the range of natural flows and the impacts on transport dynamics on the ecosystem, defining temporal variability is important for indentifying trends and

163

defining the relationship with climate and land use, and to calibrate water quality models (Restrepo et al., 2006; Prosser et al., 2001; McNeil and Cox, 2005, 2007; Wallace et al., 2009; Richards, 1989). The assessment carried out in this study takes advantage of intensive monitoring data to measure the actual annual loads concurrently over the catchments during a defined period of 3 years (between 2006 and 2009) and to examine the natural variability over this period. 5.3. Results compared to pre-European yields and MAY The results of this study varied around the MAY in correspondence with flow variability for those catchments where the MAY was calculated from field data (Barron, Johnstone, Tully, Herbert, Burdekin and Pioneer) and also for the Fitzroy River (Table 8). This indicates that in these catchments the MAY can be regarded as an acceptably reliable mean annual yield estimate. Where the MAY was based on other sources such as modelling (Normanby, O’Connell and Burnett), the measured yields were mainly below the MAY for particulates and for DIN, and above the MAY for DIP regardless of flow variation. As particulates are generally related to flow, the observed below-MAY particulate yields under wetter than average conditions imply that MAY estimates which are not based on field measurements may be over-estimates. This means that, for 15% of the monitored area, a good comparison between the measured annual yields and MAY is still uncertain. This may also suggest that estimates obtained from field measurements cannot presently be compensated for by other indirect methods. 5.4. Uncertainty of measured estimates As is frequent in large-scale natural resource investigations, there are many sources of uncertainty which cannot be avoided. In terms of data collection, sampling was hampered at times by the logistics of reaching sites before the rising limb of a hydrograph had passed. This was particularly so in the fast-rising rivers of the Wet Tropics and Whitsundays. The major information gap was associated with the Burnett catchment because of dry conditions throughout the monitoring period and the Herbert because of lack of samples. However, subsequent monitoring may provide further information for these catchments. The most reliable estimates could be made in the Normanby, Fitzroy, Burdekin and Pioneer as representative sampling was able to be achieved in those areas during the monitoring period. Further uncertainty in the load estimates is acknowledged for the other catchments due to limited data and to uncertainties of interpolation, as described in the Methods section. The representativeness of sampling also has limitations, for instance, grab samples may not be fully representative of the entire cross-section of a stream (Martin et al., 1992; Daas et al., 2007). In addition, errors in flow measurement are acknowledged but not quantified (e.g. Harmel et al., 2006) particularly during large events where overbank flow causes changes in the stream cross-section and the measurements of stream velocity might not be fully representative. In the few cases where EOS sites were ungauged, flow factoring was carried out as explained individually in Table 1, but usually based on the closest upstream gauging station. It is recognised that this contributed further uncertainty to the load calculations at these sites, which is not included in the reliability codes for the load estimates in Figs. 7 and 8. In terms of exports to the GBR, it is acknowledged that the EOS sites only represent loads from upstream of the tidal limit and were not able to capture loads generated downstream. As the pre-European and the MAY were based on basin wide estimates, they were compared to the EOS sites of the present study in terms of annual yields, rather than simply loads or concentrations,

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however, this still relies on the assumption that landuse changes downstream will not be sufficient to impact significantly on the EOS yield estimates. Despite these inconsistencies, comparison with the MAY and the pre-European estimates are still valuable, since the measured exports provide information on the timing and variability of contaminant export which may be significant to the ecological health of the receiving waters. In summary, although the monitoring only covered 3 years, a range of climatic conditions were observed in several catchments, including major floods in the Burdekin and Fitzroy. While flood plumes of these infrequent major events can impact large reef areas, chronic stresses from more frequently occurring smaller floods with continually elevated exports, may reduce a reef’s capacity to recover from major disruptive events (Hillman, 1996; Brodie, 1997). 6. Conclusion It has been recognised that field data is required to validate modelling, particularly at larger scales, and to evaluate natural variability of contaminant exports and its effect on the downstream ecology. The collected data have enabled, for the first time, suspended solids and nutrient exports to be evaluated simultaneously over a large proportion of the total catchment area draining to the GBR. These results allow temporal and spatial comparisons to be made within a discrete seasonal time-frame and will support predictive water quality modelling of land use impacts. The data indicates that, despite high sediment and nutrient loads from large, predominantly grazing catchments such as the Fitzroy, Burdekin and Normanby, these catchments generated the lowest yields. Contrastingly, the yields per area of catchment were the highest throughout the Wet Tropics and the Whitsundays region, particularly under sugarcane and other intensive development. Although discharges from major episodic floods in the large catchments contributed the highest contaminant loads, they occurred as spasmodic pulses. However, chronic stresses, resulting from areas of more intense land uses in the smaller, wetter, more developed catchments may also have a significant impact on the GBR. Despite uncertainty and high spatial variability in sediment and nutrient exports within the monitored catchments, results from this study were able to validate the MAY at most EOS sites. Several more years of data collection will be required to establish meaningful trends and to determine whether implemented land management actions are being effective in reducing contaminant exports over the longer term. Acknowledgements The authors wish to thank a number of internal and external reviewers for their valued input during the development of this paper. Without the contribution of its partners the GBR loads monitoring program would not be possible. Partners include: Cape York – EPA; Lakefield National Park rangers; Cook Shire Council; Wet Tropics; Terrain (formerly FNQ NRM); Douglas Shire Council, and ACTFR (JCU). References APHA-AWWA-WPCF, 2005a. Standard methods for the examination of water and wastewater, 21st ed., Clesceri, L.S., Greenberg, A.E., Trussell, R.R., (Eds.), Am. Public Health Assoc., Washington, USA, 4500-NO3-I, 4500-P G and 4500-NH3 H. APHA-AWWA-WPCF, 2005b. Standard methods for the examination of water and wastewater, 21st ed., 4500-N org D. Clesceri, L.S., Greenberg, A.E., Trussell, R.R., (Eds.), Am. Public Health Assoc., Washington, USA.

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