A Catchment Sediment and Nutrient Budget for the Daly River, Northern Territory Paul Rustomji and Gary Caitcheon

A catchment sediment and nutrient budget for the Daly River, Northern Territory Paul Rustomji and Gary Caitcheon

December 2010

Report to Tropical Rivers and Coastal Knowledge Research Program

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Citation: Rustomji, P.and G. Caitcheon (2010) A catchment sediment and nutrient budget for the Daly River, Northern Territory. A report to the Tropical Rivers and Coastal Knowledge (TRaCK) Research Program. CSIRO Water for a Healthy Country National Research Flagship. 68 pp. Cover Photograph: Daly River under dry season conditions (Gary Caitcheon). For further information about this publication: Paul Rustomji, CSIRO Land and Water [email protected]

Tropical Rivers and Coastal Knowledge (TRaCK): TRaCK brings together leading tropical river researchers and managers from Charles Darwin University, Grifﬁth University, University of Western Australia, CSIRO, James Cook University, Australian National University, Geoscience Australia, Environmental Research Institute of the Supervising Scientist, Australian Institute of Marine Science, North Australia Indigenous Land and Sea Management Alliance, and the Governments of Queensland, Northern Territory and Western Australia. TRaCK receives major funding for its research through the Australian Government’s Common- wealth Environment Research Facilities initiative; the Australian Government’s Raising National Water Standards Program; Land and Water Australia; the Fisheries Research and Development Corporation and the Queensland Government’s Smart State Innovation Fund. To ﬁnd out more about TRaCK: Visit: http://www.track.gov.au email: [email protected] phone: 08 8946 7444 Authorship Attribution:

• Paul Rustomji conducted the SedNet modelling and wrote the report

• Gary Caitcheon provided the geochemical and fallout radionuclide tracer data for the catchment and was the project leader. Contents

Acknowledgements viii

Executive Summary ix

1 Introduction 1

2 Catchment Description 3

3 Existing Sediment and Nutrient Budget Data 5 3.1 Sediment Tracing ...... 5 3.2 Catchment sediment yields ...... 5

4 Hydrologic Parameterisation 9 4.1 Mean Annual Flow ...... 11 4.2 RDSQ ...... 13 4.3 Bank-full Discharge ...... 14 4.4 Median overbank flow ...... 15 4.5 Median daily flow ...... 16 4.6 Scale correction factor for runoff coefficient ...... 17

5 Channel morphology 18 5.1 Channel width ...... 18 5.2 Channel depth ...... 18 5.3 River network deﬁnition ...... 21

6 Sediment Budget Terms 22 6.1 Hillslope erosion ...... 22 6.2 Gully erosion ...... 22 6.3 Riverbank erosion ...... 22 6.4 Deposition on ﬂoodplains ...... 26 6.5 Nutrient Budget Modelling ...... 27

7 Results 30 7.1 SedNet Calibration and Indicative Suspended Sediment Budget ...... 30 7.2 Iteration One: Default model parameterisation (DALY7) ...... 30 7.3 Iteration Two: Capping Hillslope Erosion Rates (DALY8) ...... 32 7.4 Iteration Three: Reducing the Hillslope Sediment Delivery Ratio (DALY9) . . . . 34 7.5 Iteration Four: Adding a “Gully Erosion” sub-soil Sediment Source (DALY10) . . 35 7.6 Iteration Five: Increasing Sediment Supply from Bank Erosion (DALY11) . . . . 37 7.7 Iteration Six: Increasing Sediment Settling Velocity (DALY12) ...... 38 7.8 Model Sensitivity to Sediment Supply Variations ...... 43 7.9 Catchment Nutrient Budget Results ...... 44

8 Discussion 47

9 Conclusions 51

iv List of Figures

1 Land use map of the Daly River catchment from 2001-2002...... 4 2 Area specific catchment sediment yields for Australia, as compiled by Wasson (1994), along with two sediment yield estimates from previous research, and a yield estimate from the Mitchell River at Koolatah, from Rustomji et al. (in preparation)...... 8 3 Map of the Daly River catchment showing major tributaries, location of gauging stations and rainfall isohyets. The inset figure shows the location of the catchment within Australia...... 9 4 Left: Fitted runoff coefficient model. The dashed line indicates the predicted values according to the fitted model, whilst the solid line shows the capping of the runoff coefficient at 0.3. Right: Observed versus predicted plot of mean annual flow...... 12 5 Discrepancy ratio for three mean annual flow regression models. The discrepancy ratio indicates the factor by which the observed and predicted MAF values agree within. Perfect agreement is indicated by 1...... 12 6 Distribution of RDSQ with respect to catchment area for the Daly River catchment. The two fitted curves represent inclusion and exclusion of station 8140011. 13 7 Left: Relationship between mean annual flow and bankfull discharge (as represented by Q4) for the Daly River catchment. Right: Discrepancy ratios for the fitted points...... 14 8 Left: Relationship between catchment area and median overbank flow for the Daly River catchment. Right: Discrepancy ratios for the fitted points...... 15 9 Left: Relationship between catchment area and median daily discharge for the Daly River catchment. Right: Discrepancy ratios for the fitted points...... 16 10 Channel width and depth measurement sites in the Daly River catchment. . . . 19

11 Distribution of channel width with respect to Q4 for the Daly River catchment. The ﬁtted regression curve is shown as a solid line...... 19

12 Distribution of channel depth with respect to Q4 for the Daly River catchment. The fitted regression curve is shown as a solid line...... 20 13 Shaded relief digital elevation model of the Daly River catchment with the links and nodes of the SedNet river network plus the catchment gauging stations utilised in the hydrologic regionalisations...... 21 14 Revised Universal Soil Loss Equation (RUSLE) hillslope erosion model predictions for the Daly River catchment...... 23 15 Sednet stream network (black line) shown against 25 metre landcover data classified into woody vegetation, water and other landcover classes. The discrepancy in position between the SedNet river links and the main stem of the Daly River means that this landcover data cannot be used to define reliable riparian vegetation cover percentages...... 24 16 Sednet stream network (red line) shown against the predicted inundated area under the one in five year flood (shown in blue) and the catchment’s shaded relief digital elevation model...... 26 17 Nitrogen sources for the Daly River catchment...... 28 18 Phosphorus sources for the Daly River catchment...... 29 19 Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area specific loads) for first model iteration (DALY7). 31 20 Distribution of RUSLE hillslope erosion rate values for the Daly River catchment 32

v 21 Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area specific loads) for second model iteration (DALY8)...... 33 22 Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area specific loads) for third model iteration (DALY9). 34 23 Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area specific loads) for fourth model iteration (DALY10) ...... 36 24 Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area specific loads) for fifth model iteration (DALY11). 37 25 Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area specific loads) ...... 39 26 Maps of sediment input to the river network from hillslope erosion, “diffuse” sub- soil input (of which there is only a single value) and river bank erosion...... 40 27 Map of suspended sediment contribution to end-of-catchment export...... 41 28 Map of suspended sediment contribution to end-of-catchment export, showing sediment source via a red (bank erosion), green (“gully erosion”) and blue (hillslope erosion) color scheme. Line widths are scaled proportional to the contribution a river link makes to the end of catchment export...... 42 29 Variations in suspended sediment load transport at four locations as a function of variations in sediment input from hillslope, river bank and gully erosion. . . . 43 30 Spatial patterns of total nitrogen and phosphorus contributions to the catchment outlet...... 45 31 Observed versus predicted total nitrogen loads (left panel shows absolute loads and right panel shows area specific loads)...... 46 32 Observed versus predicted total phosphorus loads (left panel shows absolute loads and right panel shows area specific loads)...... 46 33 Observed versus predicted total nitrogen loads (left panel shows absolute loads and right panel shows area specific loads) with gully and bank derived sub-soil nutrient concentrations increased to 2 and 3 g/kg respectively, from their default value of 1 g/kg each...... 46 34 Photograph of an eroding river bank along the Daly River, upstream of the Nancar gauging station...... 48 35 Predicted rates of floodplain deposition in the Daly River catchment...... 49

vi List of Tables

1 2007/8 constituent budgets based on in-stream water quality sampling from Rob- son et al. (in preparation)...... 6 2 Gauging station details...... 10 3 Mean dissolved nutrient concentrations in the Daly River catchment...... 27 4 Summary suspended sediment budget for the Daly River (first iteration using “default” model paramters)...... 31 5 Comparison of geochemical and fallout radionuclide tracer data with SedNet model results for first model iteration. Note the columns labelled “Contribution” refer to the load contribution...... 31 6 Summary suspended sediment budget for the Daly River (second iteration DALY8). 32 7 Summary suspended sediment budget for the Daly River (third iteration DALY9). 34 8 Comparison of geochemical and fallout radionuclide tracer data with SedNet model results for third model iteration...... 34 9 Summary suspended sediment budget for the Daly River (fourth iteration, DALY10). 36 10 Comparison of geochemical and fallout radionuclide tracer data with SedNet model results for fourth model iteration...... 36 11 Summary suspended sediment budget for the Daly River (fifth iteration, DALY11). 37 12 Comparison of geochemical and fallout radionuclide tracer data with SedNet model results for fifth model iteration...... 37 13 Summary suspended sediment budget for the Daly River (sixth iteration). All values are mean annual rates (ie. rates per year)...... 39 14 Comparison of geochemical and fallout radionuclide tracer data with SedNet model results for sixth model iteration...... 39 15 Summary nutrient budget for the Daly River outlet...... 44 16 Summary nutrient budget for the Daly River outlet, with modified (increased) sub- soil nutrient concentrations...... 44 17 Summary of changes to model parameters in model calibration process. . . . . 47 18 Measured sediment accumulation rates from the Daly River catchment. ∗ denotes data from Wasson et al. (2010)...... 50

vii Acknowledgements

This research was funded as part of the Tropical Rivers and Coastal Knowledge (TRaCK) Re- search Program. TRaCK is funded jointly by:

• the Australian Government Department of the Environment, Water, Heritage and the Arts

• the National Water Commission’s Raising National Water Standards Programme

• Land & Water Australia’s Tropical Rivers Programme

• the Queensland Government’s Smart State Strategy

• the Fisheries Research and Development Corporation

• and CSIRO’s Water for a Healthy Country Flagship.

The Northern Territory Government collected and provided the hydrologic data. Danny Hunt, Jim Brophy, Chris Leslie and Colin McLachlan are thanked for providing ﬁeld and laboratory technical support pertaining to the collection and analysis of the geochemical tracer data. Chris Leslie and Gary Hancock are thanked for reviewing a draft version of this manuscript. Farah Sattar (Charles Darwin University) is thanked for sharing her knowledge of gully erosion within the Daly River catchment.

viii Executive Summary

This report presents a catchment sediment and nutrient budget for the Northern Territory’s Daly River catchment. A catchment sediment or nutrient budget accounts for the major sources, transport pathways and sinks of sediments and nutrients within a catchment. A catchment sediment or nutrient budget is not something that can be directly measured, consequently a modelling framework is required to bring the individual components of the budget (which themselves are sub-models) together in a coherent manner. The SedNet and ANNEX models have been used in this case; these models have been widely applied in tropical Queensland settings and elsewhere in Australia. Each sediment budget term comprises its own sub-model and here, a combination of national-scale models (e.g. terrain hillslope erosion, surface soil nutrient concentrations), and locally-derived models (bank erosion, floodplain extent) have been used for input data. For some aspects of the model, such as sub-soil sediment generation from “gully erosion” processes, no catchment-wide local data was available and values have been inferred from comparisons between model predictions and observational data. The model predicts budgets for fine sediment readily transported in suspension, and dissolved and particulate nutrients. The model has been calibrated to (a) station-based load estimates at three locations, (b) geochemical tracer data indicating relative tributary contributions at river confluences, and (c) fallout radionuclide tracer data indicating the proportion of surface soil in-transit at a variety of locations. Calibration to these observational data sets are critical to obtaining realistic model results. The calibration process involved capping predicted hillslope erosion rates, reducing the hillslope sediment delivery ratio, modifying the bank erosion and overbank sediment settling velocity terms, applying a spatially uniform rate of sub-soil sediment generation from gully erosion and modifying sub-soil nitrogen concentrations. The model was consequently able to predict erosion intensity moderately well for two of the three load estimate stations, was able to correctly predict the dominant tributary contributions, and predicted surface-soil to be a minor component of the transported load, consistent with the tracer data. Predicted floodplain deposition rates were consistent with eight floodplain aggradation rate measurements from the catchment. The calibrated model predicts 503 kt per year of fine sediment export from the catchment. Bank erosion is predicted to be the main sediment input term, exceeding the combined estimates of hillslope and gully erosion. Bank erosion also contributes strongly to the sediment load exported from the catchment. Sediment deposition upon floodplains accounts for approximately 81% of the catchment sediment supply. Areas strongly contributing to sediment export from the catchment are predominantly located in the north-west of the catchment (including the Douglas River) and along the stems of the major channels. The upper Dry River and Katherine Rivers are predicted to contribute relatively modestly to the catchment export. Sensitivity modelling of load variations to spatially uniform variations in sediment supply indicated a percentage change in hillslope and gully erosion applied across the catchment yielded ∼0.16 percentage variation in sediment load at the catchment outlet, whereas for bank erosion, this coefficient was ∼0.65. This reflects the high rate of sediment input from bank erosion and the strong contribution of this input to the catchment export. The high rate of bank erosion is likely a product of a shift to wetter conditions since 1996, with runoff estimated to be 66% higher than the mean for previous decades, resulting in systematic channel widening along at least the main stem of the Daly River, however this remains to be confirmed by more detailed field investigations. The modelled nutrient budgets indicated 855–1369 and 178 t/yr of nitrogen and phosphorus export respectively. In the case of phosphorus this is approximately evenly split between dissolved and particulate phosphorus, though nitrogen may comprise a particulate-dominated load, depending on model parameterisation. The spatial pattern of nutrient contribution to catchment export is again dominated by input from the north-west of the catchment.

ix 1 Introduction

This document describes the calibration and application of the SedNet model (Prosser, Rus- tomji, Young, Moran and Hughes 2001; Wilkinson et al. 2006) to the task of calculating sediment and nutrient budgets for the Daly River catchment in the Northern Territory. This research has been undertaken as a part of the Tropical Rivers and Coastal Knowledge (TRaCK) research program. TRaCK’s objectives are to:

• Increase our understanding of the social, cultural, economic and environmental beneﬁts that our tropical rivers and estuaries provide.

• Develop methods and tools for assessing the implications of current use and potential developments.

• Identify opportunities to develop sustainable enterprises.

• Build the capacity and knowledge of local communities to manage Australia’s tropical rivers and estuaries.

SedNet is a catchment-scale sediment and nutrient budget model that can be used to predict the major source areas of these potential pollutants in a catchment (Prosser, Rutherfurd, Olley, Young, Wallbrink and Moran 2001). A catchment sediment (or nutrient) budget, as modelled here, provides a consistent framework to account for the major sediment (and associated pollutants) sources and sinks within the catchment, in both spatial and aspatial senses. It is worthwhile at the outset considering some of the reasons for applying a model such as SedNet to the task of calculating a catchment sediment budget; for this we must consider some of the conceptual uses of models in general as outlined by Silberstein (2006). The first use is as a framework to assemble our process understanding and to explore the the implied system behaviours that come from that understanding. A model such as SedNet has many inbuilt con- ceptualisations of how sediment is generated, redistributed and stored in a catchment. When such a model is applied to a catchment there will inevitably be error and poor predictions. The critical task is to understand why errors and mis-predictions are arising and to ask what does this tell us about either our conceptualisation of the system and its processes, or potentially about aspects of the data and parameters that have been used to run the model. In this sense, a poorly performing model is as or more useful than a well performing model as it highlights knowledge and data gaps and deficiencies, thus leading us to an improved understanding of the system in question. Another use of a model (alluded to above) is as a mechanism to test data and check for data inconsistencies and errors. Certainly as will be seen below, the modelling process followed here has identified issues with existing data sets that are used to construct elements of the catchment sediment budget. The third use of models is to explore scenario options. No detailed land use scenarios have been considered here, though a sensitivity analysis of sediment load responses to spatially uniform variations in sediment generation provides some guidance as to how varying sediment generation rates translates to variations in end-of-catchment loads. A related use to this also as a tool for hypothesis generation for guiding future research. In this sense a model can be used to elucidate system behaviour and facilitate targeted field based sampling that can allow for testing of key system behaviours, assuming these characteristics can be measured in a practical way. This report comprises a number of sections:

1. A description of the catchment.

2. A description of the parameterisation of the hydrologic and channel morphology sub- models.

3. A description of the sediment and nutrient sources currently represented in the model.

1 4. An evaluation of the model’s predictions arising from the application mainly of default model parameters, followed by a step wise calibration process involving target changes to selected parameters.

5. A discussion section evaluating a number of key knowledge gaps, uncertainties and sen- sitivities identiﬁed in the modelling process.

6. Conclusions drawn from this research.

2 2 Catchment Description

The Daly River catchment is located in the north of the Northern Territory, Australia and flows to the Timor Sea via Anson Bay. The catchment is generally of low relief with a maximum elevation of less than 500 m above sea level. Rocky plateaus occur in the headwaters of the Katherine River (as part of the Arnhem Land plateau) and along the catchment’s western margin. The central part of the catchment comprises generally flat to undulating terrain. The Daly River passes through the prominent Nancar Range (on the upstream side of which gauging station 8140040 is located) and a short distance downstream of this, the river is tidal under dry season flow. Chappell and Bardsley (1985) have analysed the hydrology of this reach of the river. Extensive alluvial and estuarine floodplains flank the river downstream of the Nancar Range. Floodplain geometry is variable upstream. The estuarine reaches of the Daly River are macroti- dal, experiencing a maximum spring tide range of 6–8 m (Chappell and Ward 1985). Faulks (1998) describes Eucalypt woodland with grass understorey as the dominant catchment vegetation and estimates that roughly 6% of the catchment’s original vegetation has been cleared for intensive agriculture. Much of the catchment presently experiences low intensity cattle grazing with more intensive agricultural landuse occurring in the vicinity of Katherine and along the Douglas River (see below). The catchment experiences a tropical savanna climate as defined by Stern et al. (2000) that is characterised by two distinct seasons: a dry season from May to September and a wet season from November to March. Approximately 90% of the catchment’s rainfall falls during the wet season, usually in high-intensity falls (Faulks 1998) and maximum observed daily rainfalls are between 100 and 200 mm. A comprehensive description of the catchment can be found in Faulks (1998). A catchment land use map is shown in Figure 1. This map is based on the “Land use of Australia (version 3)” data set prepared by the Australian Bureau of Rural Sciences (Walker and Mallawaarachchi 1998; Stewart et al. 2001; Anonymous 2003). Most of the catchment comprises “Production from relatively natural environments”, which essentially covers the extensive cattle grazing landuse. Conservation areas also represent a major land use and a minor area of “Intensive use” is located along the Katherine River and covers the residential and rural-residential areas of the Katherine township. Unfortunately this land use data set does not discriminate between some of the known areas of more “intensive” agricultural activity involving cropping where practices such as tilling of fields for example, which occur around the Douglas-Daly River confluence and in the vicinity of Katherine, and the less intense landuse of broadscale cattle grazing.

3 Anson Bay

River Douglas

River

Daly River

Fergusson

River

Katherine

River King River

Flora

Land Use

CONSERVATION AND NATURAL ENVIRONMENTS INTENSIVE USES NO DATA PRODUCTION FROM DRYLAND AGRICULTURE AND PLANTATIONS PRODUCTION FROM IRRIGATED AGRICULTURE AND PLANTATIONS PRODUCTION FROM RELATIVELY NATURAL ENVIRONMENTS WATER

Figure 1: Land use map of the Daly River catchment from 2001-2002.

4 3 Existing Sediment and Nutrient Budget Data

3.1 Sediment Tracing

Two sediment tracing studies have been completed in the Daly River. The first was by Wasson et al. (2010), who conducted a geochemical tracing study of sediments deposited along the Daly River between Stray Creek and Daly River Crossing. On the basis of rare earth geochemistry and lithogenic and fallout radionuclide concentrations, it was concluded that sub-soil sediments represented ∼95% of the fine sediment load transported by the Daly River and had done so for much of the last 50 years. Wasson et al. (2010) attributed the high rate of sub-soil sediment contribution primarily to erosion of river banks (channel widening), though they noted the presence of gully erosion in the vicinity of the Ferguson and Daly River confluence. Field observations indicate that bank erosion along the main stem of the Daly River upstream of Daly River Crossing (the only reach examined by boat) is extensive (note Daly River Crossing is approximately 5 km downstream of gauge 8140040 in Figure 3). Furthermore, the riparian vegetation cover is largely undisturbed and bank erosion can readily be seen in areas where riparian vegetation is both present and absent (though absences of riparian vegetation can actually indicate areas of bank erosion and channel widening). Along the main stem of the Daly River, bank heights exceed 15 metres and periods of high duration flow typically last 10–20 days at a time. This can result in saturated river banks and anecdotal reports indicate periods of rapid fall in the water level of the main channel are associated with extensive bank slumping. The degree to which riparian vegetation can stabilise river banks of this size is an open question. The Daly River is also characterised by a large dry season baseflow component and groundwater discharge points into the channel do in some cases appear to be related to cases of bank erosion. As part of this TRaCK research, further geochemical and fallout radionuclide tracing was conducted by Caitcheon et al. (in preparation), covering some of the river reaches examined by Wasson et al. (2007), but also including a number of other locations in the upper catchment. Caitcheon et al. (in preparation) obtained similar topsoil proportions to Wasson et al. (2010) for the lower Daly River catchment (typically 5–10% surface soil), though higher values in the range 23–35% surface soil were obtained for the Katherine, King and upper Daly Rivers, and possibly the Douglas River (17±12%). This suggests bank erosion becomes a more important sediment source as catchment area gets larger. This result is intuitively sensible, with higher river banks, less exposed bedrock in channel margins and wetter conditions in the lower catchment all potentially contributing to potentially greater bank erosion. Caitcheon et al. (in preparation) also conducted geochemical tracing of sediment contributions from tributaries, the results of which are examined below. However, one difference is apparent between these two studies: Wasson et al. (2010) could not identify any substantial sediment contribution from the Douglas River to the sediment load of the Daly River, despite sufficiently distinct geochemical signatures of the two sources. In contrast, the data of Caitcheon et al. (in preparation) suggest that the Douglas River contributes 29±2% of the fine sediment load of the Daly River. Such a contribution is disproportionately large given the catchment area of the Douglas River relative to the Daly at its confluence. This result in isolation could suggest the Douglas River is a very significant sediment source on a per unit area basis. However, given the difference between the two studies, it could be highlighting the influence of localised input of sediment from the Douglas River in the analysed sediment due to localised runoff from this catchment, at the end of the wet season for example. Caitcheon et al. (in preparation) drew upon samples collected at the end of two dry seasons from several locations along sampled reaches.

3.2 Catchment sediment yields

An estimate of the long term sediment load of the Daly River can be estimated from the stratigraphic study of Chappell (1985), from the estuarine reach of the Daly River, combined with a

5 regional erosion rate parameter derived from the nearby West Alligator River. Chappell (1985) indicates the area of the estuarine plains is 1700 km2 and these plains represent 6 m of sediment accumulated over the last 6500 years; therefore 1700 × 0.006 km3 = 10.2 km3 of sediment has been stored within the estuarine embayment over the last 6500 years. Again using a South Alligator River derived sediment bulk density estimate of 1.72 ± 0.08 t/m3, this equates to 1.7 ×1010t. Sediments in the estuarine plains of the South Alligator River have a carbon-free dry weight of 49% of the fresh core weight. Therefore the sediment stored in the plains represents 0.49 ×1.7 × 1010t = 8.6 × 109t of minerogenic sediment. If this value is divided by the 6500 years of sediment accumulation, an annual storage mass of 1.3 ×106t per year is obtained. The one in two year recurrence interval flood at the Mt Nancar gauging station on the Daly River is equivalent to approximately 3000 m3s−1, or 2.8 ×108 cubic meters per day. Chappell (1985), based on accoustic surveys indicates the tidal volume of the estuary is 4 ×108 cubic meters. Therefore under conditions equivalent to the one in two year flood, less than two days of flow would be required for riverine water to completely displace any marine or estuarine water within the estuary. Thus sediment export to the ocean is likely to be a significant component of the sediment budget of the lower Daly River. Chappell (1985) calculates that 82% of the sediment load delivered to the lower Daly River is lost to the ocean. This means that the 1.3 ×106t per year of stored sediment represents only about 18% of the long term mean annual load and therefore that the latter is approximately 7.2 ×106t per year. Chappell notes that these figures are “somewhat sensitive” to an erosion parameter used in the model, and if this erosion parameter is a factor of two too high, then the long term yield is reduced by half to 3.6 ×106t per year. Thus from a stratigraphic basis, a long term catchment sediment yield somewhere in the range of 5 ± 3Mt/yr would seem justified for the Daly River catchment. In more recent years as part of the TRaCK research program, in stream water quality sampling has been undertaken at four locations in the Daly River catchment during the 2007/8 wet season and 2008 dry season, with the results described by Robson et al. (in preparation). These locations are:

1. the Daly River in the vicinity of Daly River crossing (close to the Mt Nancar gauging station, ∼ 47000km2))

2. the Katherine River at the Katherine River Bridge (near gauge G814001, 8640 km2)

3. Douglas River at Douglas River bridge (832 km2)

4. an unnamed tributary of Hayes Creek (sometimes referred to as Fenton Creek) with a catchment area of 20 km2

Total suspended sediment load estimates for the 2007/8 wet season and following dry season were derived using a variety of methods (Robson et al. in preparation) and the results are summarised below, as well as for total nitrogen and phosphorus, which were also measured:

Area TSS TN TP Site km2 (kt/yr) (t/yr) (t/yr) Hayes (Fenton) Creek tributary 20 1.2 4.4 0.31 Douglas River 830 22 170 18 Katherine River 8640 84 610 58 Lower Daly River 47000 420 3350 410

Table 1: 2007/8 constituent budgets based on in-stream water quality sampling from Robson et al. (in preparation).

92 to 95% of the total load of each of these constituents is estimated to be transported during the wet season (along with 94% of the water). The 2007/8 hydrologic year recorded 15000 Gl of ﬂow at the Nancar gauge, placing it in the top 10% of annual ﬂow totals. Modelling of suspended sediment load estimates for nine stations in the Mitchell River (Queensland) catchment for multi- decadal periods by Rustomji et al. (in preparation) indicated that the mean annual load was in all cases less than the third quartile (top 25%) of the distribution of load estimates, with mean

6 annual loads typically 65-80% of the third quartile values. On this basis it could be argued that a load estimate associated with a river ﬂow in the top 10th percentile would itself be substantially above a notional ‘mean annual load’ if one were to be calculated for the Daly River. This lower Daly River load estimated by Robson et al. (in preparation) equates to a sediment yield of 9 t km−2 yr−1, which is a value in the middle of an admittedly wide spread of catchment sediment yield data collated by Wasson (1994), as shown in Figure 2. This load estimate is approximately an an order of magnitude less than the long term, stratigraphically derived load estimate (using a value of 5Mt/yr) of Chappell (1985), which plots at the upper edge of the Wasson (1994) data. It is notable that Chappell’s estimate plots above the 2 Mt/yr (45 t km−2 yr−1) yield estimate for the Mitchell River at the Koolatah gauge, as calculated by Rustomji et al. (in preparation) from in-stream water quality sampling. The load at the Koolatah gauge includes very high rates of sediment input from upstream alluvial gully erosion (Brooks et al. 2009; Shellberg et al. 2010) which arguably have no equivalent in the Daly River catchment. This potentially suggests that the yield estimated by Chappell (1985) was too high, possibly based on an over-estimate of regional erosion rates and/or an excess estimate of loss of sediment to the ocean. Chappell’s estimate equates to an area speciﬁc yield of 106 t km−2 yr−1, which is an exceptionally high yield for a catchment of this size. These load estimates can be used, in conjunction with the geochemical and radionuclide tracer data, to help calibrate the SedNet model. Given the preceding discussion, it is argued that a suspended sediment load estimate of approximately 0.5 Mt/yr would seem a reasonably value against which to compare the model’s performance at the Nancar gauge.

7 105

104 ) 1 − 3 yr

10 2 − km

t 2 ( 10 Chappell (1985)

Mitchell @ Koolatah load 1 10 Robson et al. specific 100 Area 10−1

10−2 100 101 102 103 104 105 106 107 Area (km2)

Figure 2: Area speciﬁc catchment sediment yields for Australia, as compiled by Wasson (1994), along with two sediment yield estimates from previous research, and a yield estimate from the Mitchell River at Koolatah, from Rustomji et al. (in preparation).

8 4 Hydrologic Parameterisation

In order to apply the SedNet model to the Daly River catchment, a number of hydrologic variables related to the generation, transport and deposition of sediments and nutrients are required to be derived for each link of the river network. This requires that empirical models be developed to predict the value of these variables based on independently available data. The calibration of SedNet’s hydrologic sub-models to the Daly River catchment are based on analysis of river and stream gauging data collected and provided by the Northern Territory Department of Natural Resources, Environment and the Arts. A listing of the stations selected for analysis plus, details such as period of record and start and end date etc. is listed in Table 2 whilst Figure 3 shows the location of the gauging stations. Eleven gauging stations were found to have sufﬁcient data to be used for the hydrologic regionalisations and these have catchment areas ranging from 102 km2 to 49,000 km2.

Anson Bay 1200 mm

1400 mm 8140161 River Douglas 8140040 8140042 8140063 River

Daly River 8140008

Fergusson 8140159 8140067 8140158 River 1200 mm 8140001

Katherine 1000 mm 8140044 River

8140011

Flora

Dry River 120°E 130°E 140°E 150°E 10°S

800 mm Daly 20°S River Elevation High : 462.767 30°S

Low : 0.144629 40°S 0 50 100 km

Figure 3: Map of the Daly River catchment showing major tributaries, location of gauging stations and rainfall isohyets. The inset ﬁgure shows the location of the catchment within Australia.

9 Abbreviated Start End N Area Rainfall n Qmean MAF Qmed Q4 Qmo Station Name Name Date Date (days) (km2) (mm/yr) PET/R AMS Ml/d Ml Ml ROC RDSQ RDSQ-1 Ml/d Ml/d

8140161 Green Ant Creek at Tipperary GrnAntCrkatT 25/08/1966 19/08/2007 14211 402 1209 1.69 34 217 79429 21.34 0.156 2.376 1.376 8278 1218 8140001 Katherine River at Railway Bridge KthrnRvratRB 17/12/1961 31/05/2007 16087 8590 1059 1.92 38 6427 2347411 475.46 0.243 2.205 1.205 141505 37970 8140008 Fergusson River at Old Railway Bridge FrgssnRvaORB 18/06/1957 05/01/2007 16675 1464 1124 1.85 37 1354 494463 7.86 0.281 2.616 1.616 49995 10005 8140011 Dry River at Manbulloo Boundary DryRvratMnbB 21/11/1967 03/02/2007 13134 9470 687 2.42 28 490 179120 0 .024 3.843 2.843 20934 6221 8140040 Daly River at Mount Nancar DlyRvratMntN 12/07/1969 07/02/2007 13299 49362 943 2.16 30 21181 7736396 3010.09 0.154 2.026 1.026 287403 71258 10 8140042 Daly River 2 km downstream of Beeboom Crossing DlyRvr2kdoBC 28/11/1981 21/08/2007 9125 45751 929 2.19 19 17601 6428753 2815.69 0.140 1.901 0.901 229829 36685 8140044 Flora River upstream of Kathleen Falls FlrRvrupsoKF 01/03/1966 19/08/2007 14141 6288 881 2.4 30 2790 1019098 299.03 0.174 2.821 1.821 139846 28663 8140063 Douglas River downstream of Old Douglas Homestead DglsRvrdoODH 09/06/1957 08/01/2007 17714 806 1160 1.78 40 497 181656 91.07 0.178 2.532 1.532 17049 4772 8140067 Daly River upstream of Dorisvale Crossing DlyRvrupsoDC 01/01/1965 23/07/2007 14687 38478 897 2.24 28 14614 5337750 1132.44 0.143 2.201 1.201 344787 53179 8140158 McAdden Creek at Dam Site McAddnCrkaDS 15/11/1962 22/08/2007 14000 115 991 2.14 27 101 37060 1.04 0.300 3.193 2.193 6732 1001 8140159 Seventeen Mile Creek at Waterfall View SvntnMlCraWV 16/11/1962 21/11/2006 15011 651 1052 1.96 31 358 130870 56.07 0.179 2.306 1.306 11873 3876

Table 2: Gauging station details. 4.1 Mean Annual Flow

Young et al. (2001) suggest that a function with the form of the following equation be used to predict mean annual ﬂow across river basins:

× a × b MAF = k Ac P (1)

2 where Ac is catchment area (km ) and P is upstream mean annual rainfall (mm) and k, a and b are fitted parameters. An attempt at fitting such a model to the Daly River data was unsuccessful due to the inability to adequately define the three model parameters. This suggests Equation 1, with three parameters, may be over parameterised given that there are only 11 data points. An alternative model, based on Wilkinson et al. (2006) which explicitly accounts for spatial variation in a catchment’s runoff coefficient driven by a catchment’s water balance has instead been adopted. Wilkinson et al. (2006) show that the runoff coefficient, Rc, can be calculated as a function of annual precipitation and evapotranspiration (E0) in the upstream catchment:

[ ( ) ] 1 w E w E R = 1 + 0 − 0 (2) c P P

where w is a fitted parameter. Satisfactory predictions of the runoff coefficient could not be obtained with this relationship, partly due to the limited number of data points. The runoff coefficient was significantly related to the PET/Rainfall ratio (Pearson product moment correlation coefficient = -0.626, p= 0.0395), suggesting that inclusion of this term within Equation 2 may lead to improved predictions by capturing the variation in mean annual flow resulting from different runoff coefficients. A statistically significant linear model could be fitted to the data, however at the upper extend of the E0/P values for the Daly River catchment (1.31 to 2.94), negative runoff coefficients were predicted. Consequently a power function with a positive lower asymptote has been fitted to the E0/P data with fitted parameters k1 and k2: ( ) E k2 MAF = k × 0 × Rainfall × Area (3) 1 P

The fitted values for k1 and k2 are shown below and both parameters are statistically significant. The fit to the runoff coefficient and resulting mean annual flow predictions are shown in Figure 4. The adjusted R2 value for this model is 0.979.

Parameters: Estimate Std. Error t value Pr(>|t|) k1 1.2564 0.4916 2.556 0.03089 * k2 -2.8262 0.5194 -5.442 0.00041 *** Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 Residual standard error: 242600 on 9 degrees of freedom

One drawback of using the adjusted R2 values to evaluate model performance is that the large range of values (spanning two orders of magnitude) may hide serious over or under prediction. To gain a clearer, scale independent picture of model performance we use the distribution of a discrepancy ratio statistic, DR:

| log(Observed)−log(Predicted)| DR = 10 (4)

DR = 1 indicates perfect agreement whilst DR = 2 indicates a factor of two difference between observed and predicted. Figure 5 shows that for most stations the predicted values agree to within a factor of ∼ 1.5 of the observed values. The two larger discrepancy ratio points are for station 8140161 and 8140011, which in any case have relatively low mean annual ﬂow values.

11 0.7 8

0.6 )

ML 6 0.5 6 10 ( 0.4 4 coefficient

MAF 0.3

Runoff 0.2 2 Predicted 0.1

R = 1.26 * (E P)−2.83 0 C 0 0 1.5 2 2.5 3 0 2 4 6 8 ( 6 ) E0 P Observed MAF 10 ML

Figure 4: Left: Fitted runoff coefficient model. The dashed line indicates the predicted values according to the fitted model, whilst the solid line shows the capping of the runoff coefficient at 0.3. Right: Observed versus predicted plot of mean annual flow.

1.0 8140011 8140161 0.8

0.6

0.4

Cumulative Proportion 0.2

0.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Discrepancy Ratio (MAF)

Figure 5: Discrepancy ratio for three mean annual ﬂow regression models. The discrepancy ratio indicates the factor by which the observed and predicted MAF values agree within. Perfect agreement is indicated by 1.

12 4.2 RDSQ

The SedNet variable RDSQ reﬂects the time-integrated effect of ﬂow on the sediment transport capacity of each river link and is used in the bedload transport model. RDSQ is calculated as: ( ) ∑n 1.4 1 Q RDSQ = i (5) n Q i=1

where Qi is discharge of the ith of n days (ML) and Q is mean daily ﬂow(ML). Whilst this parameter needs to be predicted for each link in the river network, there is little theoretical basis for selection of the predictive variables. For the Daly River data, a reasonable negative correlation was evident between RDSQ and catchment area, though gauging station 8140011 appears as a distinctive point. As RDSQ > 1 it is desirable to adopt a function that has an asymptote ≥ 1 at least for the predictive range required for the catchment. Consequently the function:

× k4 RDSQ = k3 Ac (6)

2 has been adopted. The distribution of RDSQ with respect to Ac (km ) is shown in Figure 6. The ﬁtted forms of Equation 6 are shown, one including station 8140011 and the other without. Inclusion of this station degrades the quality of the prediction of RDSQ at large catchment area, which is undesirable, though the two curves are virtually identical at small catchment areas (to a minimum of 20 km2). Consequently, the solid curve omitting station 8140011, with parameters listed below has been adopted:

Parameters: Estimate Std. Error t value Pr(>|t|) k3 3.73168 0.52374 7.125 9.95e-05 *** k4 -0.05429 0.01773 -3.063 0.0155 * Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 Residual standard error: 0.2772 on 8 degrees of freedom

Both regression parameters are statistically signiﬁcant. For prediction of RDSQ in the Dry River sub-catchment, a uniform value of RDSQ = 3.84 has been adopted.

4.0 8140011

3.5

3.0 RDSQ

2.5 8140011 included

8140011 ommitted 2.0

0 1 2 3 4 5 6 Area (10000 km2)

Figure 6: Distribution of RDSQ with respect to catchment area for the Daly River catchment. The two ﬁtted curves represent inclusion and exclusion of station 8140011.

13 4.3 Bank-full Discharge

Bank-full discharge denoted QBF , is another hydrologic parameter for which prediction is required for each link in the SedNet network. Bank-full discharge corresponds to the maximum discharge (Ml/day) that is contained within the channel, before flow starts spreading onto the floodplain. There were eight gauging stations in the Daly River for which bankfull discharge values could be calculated (based on surveys of channel cross section and the shape of the stage-discharge rating curve). The recurrence interval of bankfull discharge for these eight stations (based on a flood frequency analysis) ranged from 2 to 8 years. Consequently, we have adopted the 1 in 4 year event (referred to as Q4) as indicative of bankfull discharge as this was approximately in the middle of the observed values. Empirical relation- ships against a range of different catchment variables were examined for prediction of bankfull discharge (and also bankfull discharge normalised by catchment area). However, overall, the best model was found to be a simple power function of mean annual flow:

k6 QBF = k5 × MAF (7)

Whilst mean annual flow is a predicted variable, the results presented previously indicated that this variable can be predicted with considerable accuaracy. The fitted curve and the discrepancy ratios for QBF are shown in Figure 7. The diagnostics for the model fit are listed below and indicate that the exponent is a statistically significant parameter, though there is more uncertainty about the k5 parameter. For the calibration data, the maximum discrepancy ratio between the observed and predicted bankfull discharge values was 2.8 (against a 50-fold variation in QBF across the catchment).

Parameters: Estimate Std. Error t value Pr(>|t|) k5 34.6183 59.1797 0.585 0.572940 k6 0.5741 0.1105 5.195 0.000568 *** Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1

0.57 3.5 QBF = 34.6 * MAF 1.0

3.0

) 0.8 1 − 2.5 day

0.6

ML 2.0

1.5 0.4 100000 ( 1.0 BF Cumulative Proportion Q 0.2 0.5

0.0 0.0 0 20 40 60 80 1.0 1.5 2.0 2.5 3.0 MAF (100000 ML) Discrepancy Ratio (Qbf)

Figure 7: Left: Relationship between mean annual ﬂow and bankfull discharge (as represented by Q4) for the Daly River catchment. Right: Discrepancy ratios for the ﬁtted points.

14 4.4 Median overbank ﬂow

Median overbank flow, QMO, is used in the modelling of sediment deposition on floodplains. A power function again based on mean annual flow was found to be the best model for predicting this variable:

k8 QMO = k7 × MAF (8)

The regression diagnostics are listed below and the ﬁtted curve along with the discrepancy ratio for this variable are shown in Figure 8. At most stations the agreement between observed and predicted values of QMO is within a factor of 2.

Parameters: Estimate Std. Error t value Pr(>|t|) k7 215.8415 319.7841 0.675 0.51666 k8 0.5161 0.1422 3.630 0.00549 ** Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1

0.52 8 QMO = 215.8 * Area 1.0

) 0.8 1

− 6 day 0.6 ML 4 0.4 10000 (

MO 2 Cumulative Proportion Q 0.2

0 0.0 0 10 20 30 40 50 60 1.0 2.0 3.0 4.0 5.0 6.0 Area (1000 km2) Discrepancy Ratio (Qbf)

Figure 8: Left: Relationship between catchment area and median overbank ﬂow for the Daly River catchment. Right: Discrepancy ratios for the ﬁtted points.

15 4.5 Median daily ﬂow

Median daily ﬂow (QMED) is used in the annual nutrient export sub-model of SedNet (known as ANNEX). A linear relationship between QMED and catchment area with zero intercept (zero ﬂow would be expected at zero area) has been adopted.

QMED = k9 × Area (9)

The k9 parameter was highly signiﬁcant. Figure 9 shows the relationship between these two variables plus the discrepancy ratio curve (note that station 8140011 has a median ﬂow of 0 hence it has been omitted from this graph). At eight out of ten stations, the discrepancy ratio is ≤ 2.2 and is less than 1.2 for at least half the stations.

Coefficients: Estimate Std. Error t value Pr(>|t|) k9 0.05261 0.00473 11.12 5.94e-07 *** Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1

Residual standard error: 372.9 on 10 degrees of freedom Multiple R-Squared: 0.9252, Adjusted R-squared: 0.9178 F-statistic: 123.7 on 1 and 10 DF, p-value: 5.94e-07

3500 QMED = 5.261e−02 * Area 1.0

3000 0.8

) 2500 1 − 0.6

day 2000

ML ( 1500 0.4 MED

Q 1000

Cumulative Proportion 0.2 500

0 0.0 0 10 20 30 40 50 60 1 2 3 4 5 6 7 8 9 10 2 Area (1000 km ) Discrepancy ratio QMED

Figure 9: Left: Relationship between catchment area and median daily discharge for the Daly River catchment. Right: Discrepancy ratios for the ﬁtted points.

16 4.6 Scale correction factor for runoff coefﬁcient

Internal catchment runoff for each SedNet subcatchment is predicted on a cell by cell basis using sur- faces of rainfall and then aggregated up for each SedNet sub-catchment. As Wilkinson et al. (2006) describe, a number of scale issues arise when modelling runoff at the grid-cell scale using parame- terisations derived from gauging station sub-catchments, necessitating a runoff correction factor to be applied. This correction factor is calculated by estimating mean annual ﬂow on a grid cell basis and then iteratively estimating a correction factor to be applied to these estimates such that there no systematic bias exists between observed and predicted values. In the case of the Daly River catchment a runoff correction factor of 0.8835 was found to be adequate.

17 5 Channel morphology

5.1 Channel width

Channel width was measured at 110 locations in the Daly River catchment using remotely sensed imagery accessible via the Google Earth mapping facility. Measurement sites were predominantly restricted to areas of the catchment for which the higher resolution imagery was available. Figure 10 shows the location of the 110 channel width measurement sites. Channel width has been modelled as a power function of Q4 (ie. the modelled bankfull discharge):

× k11 width = k10 Q4 (10) The regression diagnostics are listed below and Figure 11 shows the regression data and the ﬁtted curve. Both parameters in the regression model are statistically signiﬁcant.

Parameters: Estimate Std. Error t value Pr(>|t|) k10 0.30456 0.10753 2.832 0.00513 ** k11 0.46903 0.02908 16.131 < 2e-16 *** Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 Residual standard error: 24.34 on 186 degrees of freedom

5.2 Channel depth

Seventy seven channel depth measurements were obtained from Faulks (1998) and Figure 10 shows the location of these measurement sites. Channel depth has also been modelled as a power function of Q4 (the event taken as representative of bankfull discharge):

× k13 depth = k12 Q4 (11) The regression diagnostics are listed below and Figure 12 shows the regression data and the fitted curve. The estimated value of k13 is highly significant though a lower level of significance is associated with k12.

Parameters: Estimate Std. Error t value Pr(>|t|) k12 0.03793 0.01917 1.978 0.0516 . k13 0.46808 0.04175 11.211 <2e-16 *** Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 Residual standard error: 2.69 on 75 degrees of freedom

18 Channel width measurement sites

Channel depth measurement sites

025 50 100 km

Figure 10: Channel width and depth measurement sites in the Daly River catchment.

0.469 200 width = 0.305 * Q4

150 ) m (

width

100

Channel 50

0 0 50000 100000 150000 200000 250000 300000 350000

Q4 (Ml Day)

Figure 11: Distribution of channel width with respect to Q4 for the Daly River catchment. The ﬁtted regression curve is shown as a solid line.

19 0.468 20 depth = 0.038 * Q4

) 15 m (

depth 10

Channel 5

0 0 50000 100000 150000 200000 250000 300000 350000

Q4 (Ml Day)

Figure 12: Distribution of channel depth with respect to Q4 for the Daly River catchment. The ﬁtted regression curve is shown as a solid line.

20 5.3 River network deﬁnition

The national nine second resolution digital elevation model (DEM) described by Hutchinson et al. (2008) has been used to deﬁne the stream network for the Daly River, using a threshold catchment area of 20 km2. This stream network is shown in Figure 13 and comprises 1286 river links. The mean internal catchment area for the SedNet sub-catchments is 43 km2 (ie. each sub-catchment covers on average an area seven by six kilometres) and the mean length of each river link is 6.34 km.

Anson Bay

8140161

8140040 8140042 8140063

8140008

8140159 8140067 8140158

8140001

8140011

0 50 100 km

Figure 13: Shaded relief digital elevation model of the Daly River catchment with the links and nodes of the SedNet river network plus the catchment gauging stations utilised in the hydrologic regionalisations.

21 6 Sediment Budget Terms

6.1 Hillslope erosion

Gross hillslope erosion is represented using a national grid of mean annual erosion rate predictions prepared for the National Land and Water Resources Audit (Lu et al. 2003) prepared using the Revised Universal Soil Loss Equation (RUSLE). The term gross hillslope erosion is used here to denote total sediment movement on hillslopes. Figure 14 shows a map of modelled gross hillslope erosion for the Daly River catchment. Note this map is not a map of sediment delivery to the river network because gross hillslope erosion is larger than the actual amount of hillslope-derived sediment delivered to the river network (due to storage of eroded sediment prior to reaching a drainage line). Predicted values extend up to approximately 180 tonnes per hectare per year, with areas of high predicted hillslope erosion in the upper Katherine River, some areas of the Ferguson River catchment and most notably along the Nancar Range (running north-south downstream of the Douglas-Daly River confluence. Only a proportion of the gross hillslope erosion within a sub-catchment is considered to be delivered to the river network due to deposition of eroded hillslope sediment prior to reaching the stream network. The ratio of gross hillslope erosion to the amount of sediment reaching the river network is referred to as the hillslope sediment delivery ratio (HSDR) and this requires specification in the model. In many previous applications of SedNet, a spatially uniform HSDR value has been used due to difficulties in parameterising a spatially variable value. However Lu et al. (2006) modelled the hillslope sediment delivery ratio across the Murray Darling basin and predicted HSDR values ranged from 0 to 0.7, though obtained a basin wide mean value of 0.052. Spatially uniform HSDR values in the range 0.05–0.10% have been successfully used in a number of SedNet modelling studies. In two south eastern Australia applications of the SedNet model, where fallout radionuclide tracer data have been available to assess the ratio of surface versus sub-soil erosion (Hancock et al. 2007; Wilkinson et al. 2009; Rustomji et al. 2008) approximately similar values have been adopted though both of these studies have suggested higher ratios may be appropriate in certain settings, such as steeper catchments. Conversely, in a companion study, Rustomji et al. (in preparation) found that a mean hillslope sediment delivery ratio of 1% (though spatially varied) produced good results in the Mitchell River catchment of Queensland. The fallout radionuclide tracer data provide a useful method of assessing whether a hillslope sediment delivery ratio is producing reliable model predictions, though initially a 5% hillslope sediment delivery ratio is adopted.

6.2 Gully erosion

Gully erosion represents a potential source of sub-soil sediment. No catchment wide mapping or modelling of gully erosion in the Daly River catchment exists though research into sediment generation from gullies is currently underway for an area within the Ferguson River catchment (F. Sattar, PhD research, Charles Darwin University, see Sattar et al., 2010). Observations in the ﬁeld do not clearly indicate whether gully erosion is a widespread erosion process and hence its importance in the catchment sediment budget. Initial model runs without gully erosion included as a sediment source are conducted.

6.3 Riverbank erosion

River bank erosion is another source of sub-soil sediment to the river network. River bank erosion is modelled as a function of stream power, with bank erosion rates normally reduced with increasing proportion of a river bank’s length covered with riparian vegetation. Whilst vegetation mapping from 1995 exists and could potentially be used for quantification of riparian vegetation extent, in practice this is difficult because the vegetation mapping has occurred on a 25 metre pixel basis which is a different spatial resolution to the ∼ 278m pixel size from which the DEM derived flow paths are calculated. However, there are digital elevation models available at this finer spatial resolution. The SedNet model and its river definition algorithm has been applied to the national nine second resolution digital elevation model which has a grid cell size of 278 metres. The flow paths from the nine second DEM and hence the position of the SedNet river links differ in many cases from the true location of the stream channel, as shown in Figure 15. Standard algorithms for calculating riparian vegetation cover require a much closer spatial congruence between the SedNet river network and the vegetation mapping such that the two can be sensibly analysed. However, one of the characteristics evident in Figure 15 is that nearly 100% of the riparian zone is shown to be covered in woody vegetation. This situation is a fairly common characteristic of the Daly River and its tributaries. Consequently, it has been assumed that riparian cover is universally 100%.

22 Anson Bay

River Douglas

River

Daly River Fergusson

River

Katherine

River

Flora

Dry River

Hillslope Erosion (RUSLE) -1 -1 (t.ha .yr ) 0 - 5 6 - 15 16 - 35 36 - 50 51 - 180

Figure 14: Revised Universal Soil Loss Equation (RUSLE) hillslope erosion model predictions for the Daly River catchment.

23 Legend

SedNet [daly5] not classified other water native and exotic woody

0 2.5 5 km

Figure 15: Sednet stream network (black line) shown against 25 metre landcover data classiﬁed into woody vegetation, water and other landcover classes. The discrepancy in position between the SedNet river links and the main stem of the Daly River means that this landcover data cannot be used to deﬁne reliable riparian vegetation cover percentages.

24 For each link, a potential river bank erosion retreat rate (R, metres of bank retreat per year) is modelled as a function of stream power:

R = kρw g × QBF × S (12)

−5 3 where k is a coefﬁcient (initially set to 2 × 10 ), ρw is the density of water (1000 kg/m , g is gravitational acceleration, QBF is bank full discharge and S is slope. The term “active bank”, Bact is deﬁned as the proportion of river bank along each link that is potentially erodible: [ ( ( ))] Bact = 1 − ripveg − 0.95/100) × bank_erodible (13)

where ripveg is the proportional cover or riparian vegetation along the river bank (taken here to be fixed at 1) and bank_erodible is the proportion of the river bank that has potentially erodible soils. The bankerodible term is calculated by identifying the proportion of cells within 600 m of each link in the stream network that have a MrVBF value ≥ 1.5. MrVBF is an index of valley bottom flatness, described by Gallant and Dowling (2003). The rationale for this is that flat areas situated low in the landscape (ie. with high MrVBF values) are likely to be deposited sediments (which are hence relatively highly erodible) as opposed to steeper and high regions which are likely to be bedrock dominated and hence not particularly erodible (at least within the timescales over which SedNet operates). Under this scenario, predicted bank erosion rates are 5% of the values that would have been predicted for non-vegetated river banks.

Total sediment input from bank erosion for each link, BT , is then calculated as:

BT = Bact LHRρsed (14)

where L is link length, H is bank height, R is the bank retreat rate as deﬁned in equation 12 and ρsed is the bulk density of sediment. The suspended (BS) and bedload (BB)input terms from bank erosion are simply:

BS =BT ∗ (1 − c) (15) BB =BT ∗ c

where c is the percentage of eroded sediment that is coarse (taken to be 0.5 here).

25 6.4 Deposition on ﬂoodplains

Loss of sediment from the river to deposition on floodplains is represented using an algorithm based on the residence time of sediment laden water upon floodplains (Prosser, Rustomji, Young, Moran and Hughes 2001). This requires that floodplain areas be defined for each link in the river network. The backwater inundation model of Pickup and Marks (2001) has been used to map areas of floodplain inundation. This model defines valley bottom cross sections from the nine second digital elevation model. For each cross section, empirically defined equations derived from Rustomji (2009) are used to predict the magnitude of a specified flood quantile as a function of catchment area (area) and mean upstream catchment rainfall (rain): √ Q5 = 0.018 ∗ area × rain (16)

In this case the one in five year recurrence interval event (Q5) has been chosen to delineate floodplain areas. Hydraulic modelling is then used to “fill” each cross section to the appropriate level and the inundated width of all the cross sections are combined to produce a map of inundated areas. Figure 16 shows the predicted floodplain area used in the SedNet modelling based on a one in five year recurrence interval event. This map is used to calculate a nominal floodplain width for each river link. The mean and median floodplain widths are 1.4 and 1.0 km respectively and 11 links have widths greater than 10 km.

Anson Bay

0 50 100 km

Figure 16: Sednet stream network (red line) shown against the predicted inundated area under the one in ﬁve year ﬂood (shown in blue) and the catchment’s shaded relief digital elevation model.

26 6.5 Nutrient Budget Modelling

The Annual Network Nutrient Export model (ANNEX) of (Young et al. 2001), which it self is a companion model of SedNet, has been used to conduct a preliminary nutrient budget for the Daly River. Like SedNet, ANNEX predicts the average annual loads of phosphorus and nitrogen in each link in the river network under given catchment conditions. The model accounts for both dissolved and particulate nutrients and is thus highly dependent upon the catchment suspended sediment budget for both nutrient input and losses, though it also includes denitriﬁcation processes (loss of nitrogen gas to the atmosphere).

In each link of the river network, the mean annual yield of nitrogen or phosphorus (Yi , tonnes per year) is:

Yi = Ti + Hi + Gi + Bi + Di + Pi − Li (17)

where Ti is tributary particulate and dissolved input, Hi is particulate input from hillslope erosion, Gi is particulate input from gully erosion, Bi is particulate input from river bank erosion, Di is diffuse dissolved input, Pi is point source dissolved input and Li is the net loss of particulate and dissolved forms during transport through the river link. Mean annual particulate inputs were calculated as the product of mean annual fine sediment erosion rate multiplied by soil nutrient concentration. For hillslope erosion, national scale grids of surface sediment phosphorus and nitrogen concentrations produced as part of the Australian Soil Resources Information System (ASRIS) project (Henderson et al. 2001; Johnston et al. 2003) were used. Nutrient loads from riverbank and gully erosion were estimated, given the lack of local concentration measurements, using “default” values of 0.25 g/kg of phosphorus and 1 g/kg of nitrogen. These values as described in McKergow et al. (2005) as being derived from a personal communication from Jon Olley. A search of the National Pollutant Inventory database (http://www.npi.gov.au/) indicated the only registered point source of nitrogen and phosphorus to be the Katherine Wastewater Stablisation Ponds, with annual exports of 490 and 110 kg of total nitrogen and phosphorus respectively.. Estimation of diffuse dissolved input typically involves specification of a nominal dissolved nutrient concentration that is then applied to the runoff volume generated within each sub-catchment to generate a load. Commonly, concentration values are assigned to specific land use classes (e.g. DeRose et al., 2002 or Bartley et al., 2004). As a first pass analysis for the Daly River, a spatially uniform dissolved nutrient concentrations is applied based on water quality samples collected over the 2007/8 wet season at the Fenton Creek Tributary, Lower Daly River and Katherine River monitoring sites described by Robson et al. (in preparation). Total dissolved nitrogen concentration was calculated as the sum of the means of the NO2, NO3 and NH3 measurements and total dissolved phosphorus was calculated as the mean of the filterable reactive phosphorus measurements. Table 3 lists the mean values for each sampling location, and the mean values which have been adopted here to model dissolved nutrient generation.

Dissolved N Dissolved P Site µg/l µg/l Fenton Ck Tributary 25 5 Douglas River 45 7 Katherine River 42 6 Lower Daly River 56 8 mean 42 7

Table 3: Mean dissolved nutrient concentrations in the Daly River catchment.

By way of comparison, these values are less than the equivalent concentrations of 200 and 20 µg/l for dissolved nitrogen and phosphorus respectively that were applied in the Mitchell River catchment (Rustomji et al. in preparation) as part of the companion catchment sediment budget study to this one. Figures 17 and 18 show the spatial patterns of nitrogen and phosphorus input to the river network. For both nutrients, the main areas of hillslope-derived particulate nutrients (which was in essence the only nutrient term for which spatially variable concentration data were used) are the headwaters of the Katherine and Ferguson Rivers and also in some left-bank tributaries of the Daly River in the lower catchment. The bank and gully erosion inputs are simply reﬂections of the patterns of the sediment generation by these processes as no spatially variable concentration data were used. Similarly, the dissolved input is a reﬂection of catchment runoff generation, with high rates of input in the north and west of the catchment where rainfall is highest. Total input appears spatially dominated by the input from hillslope erosion with a secondary contribution from bank erosion input.

27 Hillslope Erosion (kg/ha) 0.00 - 0.25 0.26 - 0.50 0.51 - 1.00 1.01 - 2.00 2.01 - 6.75

Bank Erosion (kg/m) 0.0 - 0.2 0.3 - 0.4 0.5 - 0.8 Gully Erosion 0.9 - 3.0 > 3 (kg/ha) 0.19

Dissolved Input 2 (t/km ) 0.002 - 0.003 0.004 - 0.003 0.004 - 0.009 0.010 - 0.012 0.013 - 0.018 Total Input -1 (kg.ha ) 0.2 - 0.5 0.6 - 1.0 1.1 - 1.5 1.6 - 2.5 >2.5

Figure 17: Nitrogen sources for the Daly River catchment.

28 Hillslope Erosion (kg/ha) 0.000 - 0.050 0.051 - 0.100 0.101 - 0.200 0.201 - 0.500 0.501 - 1.105

Bank Erosion (kg/m) 0.0000 - 0.0100 0.0101 - 0.0500 0.0501 - 0.1000 0.1001 - 0.5000 Gully Erosion >0.5 (kg/ha) 0.047

Dissolved Input -2 (t/km ) 0.0000 - 0.0006 0.0007 - 0.0012 0.0013 - 0.0018 0.0019 - 0.0024 0.0025 - 0.0029 Total Input -1 (kg.ha ) 0.0501 - 0.1000 0.1001 - 0.1500 0.1501 - 0.3000 0.3001 - 0.6000 0.6001 - 100.7793

Figure 18: Phosphorus sources for the Daly River catchment.

29 7 Results

7.1 SedNet Calibration and Indicative Suspended Sediment Budget

The approach adopted here is to apply the SedNet model initially with essentially “default” parameter values as a first pass analysis. Then, after evaluation against both geochemical and fallout radionuclide tracer and station based load data (as described above) apply targeted modifications to relevant model parameters to try and improve the model’s predictions. The geochemical and fallout radionuclide data pertain to fine sediment particles and include the relative contribution of surface derived sediment (as determined using fallout radionuclides) as well as data used to determine relative sediment contributions from selected tributaries at river junctions (which employ both fallout radionuclides and major and minor element concentrations). Further details of these tracer data can be obtained in Caitcheon et al. (in preparation) and other examples of the application of geochemical tracer data to tropical river catchment sediment budget research Hancock and Pietsch (2008) can be found in Wasson et al. (2010) and Tims et al. (2010). In all five model iterations are presented below. The reason they are presented is that an understanding of why the model predictions were sub-optimal and the effects of changing model parameters to try and improve predictions is highly informative from both a modelling perspective and also about understanding key aspects of the catchment’s sediment budget.

7.2 Iteration One: Default model parameterisation (DALY7)

An initial suspended sediment budget for the Daly River, derived mainly from “default” model parameters, is presented in Table 4. This budget shows hillslope erosion to be the dominant source of suspended sediment to the Daly River, supplying 2.2 million tonnes per year. As noted above, gully erosion is not currently represented as a sediment source due to the lack of data. River bank erosion is predicted to account for 0.1 million tonnes of sediment input. Approximately 0.6 million tonnes of sediment are predicted to be deposited across the 10,466 km2 of ﬂoodplain within the catchment. This leaves a predicted rate of sediment export to Anson Bay of 1.7 million tonnes per year. At the catchment outlet 95% of the sediment load is predicted to be sourced from hillslope erosion (Table 5). Several conclusions, based on data presented in Tables 4 and 5 and Figure 19, can be drawn at this point:

1. The proportion of the modelled sediment load originating from hillslope erosion is an extreme overprediction, based on the comparison between the model and fallout radionuclide data. 2. The observed sediment yield at the Douglas River is about right, yet strong over predictions occur for the Katherine and Nancar gauging stations. 3. The conﬂuence predictions approximately match the tracer data, though modelled conﬂuence proportions have proven to be relatively insensitive to model parameterisation in previous research.

30 Area Hillslope input Gully input Riverbank input Floodplain deposition Sediment yield Tributary km2 (kt/yr) (kt/yr) (kt/yr) (kt/yr) (kt/yr) Katherine 10152 546 0 25 92 479 King 14098 341 0 11 118 234 Flora 6673 286 0 8 56 238 Ferguson 4492 213 0 9 30 192 Douglas 1900 51 0 4 7 49 Mt Nancar 49362 1863 0 85 499 1450 Daly Outlet 55792 2191 0 100 625 1666

Table 4: Summary suspended sediment budget for the Daly River (ﬁrst iteration using “default” model paramters).

1500 60 ) 1 − 50 8140001 yr

2 − km

t ) 1000 ( 40

kt (

load

load 8140063 30 8140040 specific

Modelled 500 20 area

10 Modelled

0 0 0 500 1000 1500 0 10 20 30 40 50 60 − − Observed load (kt) Observed area specific load (t km 2 yr 1)

Figure 19: Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area speciﬁc loads) for ﬁrst model iteration (DALY7).

Suspended load Contribution Contribution Hillslope contribution Hillslope contribution Tributary (model, kt/yr) (model %) (tracer %) (model %) (tracer %) Katherine upstream of King 479 67 62 2 95 23 9 King upstream of Katherine 234 33 38 2 96 35 12 Katherine downstream of King 712 100 96 15 7

Katherine upstream of Flora 762 76 59 1 96 15 7 Flora upstream of Katherine 246 24 41 1 97 8 6 Katherine downstream of Flora 1007 100 96 24 9

Daly upstream of Ferguson 1022 84 67 2 96 24 9 Ferguson upstream of Daly 192 16 33 2 96 17 5 Daly downstream of Ferguson 1211 100 96 5 5

Daly upstream of Douglas 1313 97 71 2 95 5 5 Douglas upstream of Daly 49 4 29 2 92 17 12 Daly downstream of Douglas 1360 100 95 18 5

Lower Daly 1605 95 9 7

Table 5: Comparison of geochemical and fallout radionuclide tracer data with SedNet model results for ﬁrst model iteration. Note the columns labelled “Contribution” refer to the load contribution.

31 7.3 Iteration Two: Capping Hillslope Erosion Rates (DALY8)

To address some of the deﬁciencies identifed in the initial model’s performance, this iteration involves capping hillslope erosion rates at 35 t/ha/yr, with the aim of reducing sediment input from hillslope erosion. Such a change was found to improve model performance in the Mitchell River catchment (Rustomji et al. in preparation) where it was questionable as to whether areas of predicted high hillslope erosion rates actually had sufﬁcient erodible soil to sustain, over the longer term, such high predicted rates. In- deed, this question can be raised in the case of the Daly River catchment, where areas of high predicted hillslope erosion rates, such as the Nancar Range and upper Katherine River (see Figure 14), have extensive areas of poor soil cover. In the case of the Daly River catchment, this capping only affects the hillslope erosion rate predictions for 4% of the catchment, as shown by Figure 20.

103

101 102

35 ) 1

0 −

) 10 1 1 − 10 yr mm.yr 1 ( −

t ( rate

10−1 100 RUSLE Denudation

10−2 10−1

10−3 10−2

0.0 0.2 0.4 0.6 0.8 1.00.98 Proportion less than or equal to

Figure 20: Distribution of RUSLE hillslope erosion rate values for the Daly River catchment

The results of this change are summarised in Table 6. Total sediment input from hillslope erosion has been reduced from 2.19 Mt/yr to 2.06 Mt/yr, so in actual fact this was quite a minimal change and loads at the Katherine and Nancar sites are still strongly over predicted, as shown in Figure 21.

Area Hillslope input Gully input Riverbank input Floodplain deposition Sediment yield Tributary km2 (kt/yr) (kt/yr) (kt/yr) (kt/yr) (kt/yr) Katherine 10152 528 0 25 89 464 King 14098 341 0 11 118 234 Flora 6673 280 0 8 55 233 Ferguson 4492 209 0 9 30 189 Douglas 1900 51 0 4 7 48 Mt Nancar 49362 1829 0 85 491 1424 Daly Outlet 55792 2058 0 100 602 1556

Table 6: Summary suspended sediment budget for the Daly River (second iteration DALY8).

32 1500 50 8140001 ) 1 − yr 40 2 − km

t ) 1000 (

kt (

8140063 30 8140040 load

load

specific

Modelled 500 area

10 Modelled

0 0 0 500 1000 1500 0 10 20 30 40 50 − − Observed load (kt) Observed area specific load (t km 2 yr 1)

Figure 21: Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area speciﬁc loads) for second model iteration (DALY8).

33 7.4 Iteration Three: Reducing the Hillslope Sediment Delivery Ratio (DALY9)

The previous analysis indicated that further reductions in sediment supply from hillslope erosion will improve the surface to sub-soil ratio predictions and reduce sediment loads, both of which are required. This scenario incorporates a reduction in the hillslope sediment delivery ratio to 1.5%. Hillslope sediment input has decreased to 618 kt/yr (Table 7) and the catchment loads are within the right order of magnitude (Figure 22). However, the model still predicts a strong hillslope dominance to the predicted sediment input which is in direct contrast to the fallout radionuclide tracer data. Thus, there is a need to increase sub-soil sediment input (relative to hillslope erosion) yet simultaneously ensuring the overall load does not increase substantially.

Area Hillslope input Gully input Riverbank input Floodplain deposition Sediment yield Tributary km2 (kt/yr) (kt/yr) (kt/yr) (kt/yr) (kt/yr) Katherine 10152 159 0 25 29 155 King 14098 102 0 11 37 76 Flora 6673 84 0 8 17 75 Ferguson 4492 63 0 9 9 62 Douglas 1900 15 0 4 2 17 Mt Nancar 49362 549 0 85 158 476 Daly Outlet 55792 618 0 100 195 522

Table 7: Summary suspended sediment budget for the Daly River (third iteration DALY9).

500 30 ) 1 − yr 400 25 2 − km

t ) (

kt (

300 20 load

load

8140001 specific

200 15 Modelled area

100 10 8140040 8140063 Modelled

0 5 0 100 200 300 400 500 5 10 15 20 25 30 − − Observed load (kt) Observed area specific load (t km 2 yr 1)

Figure 22: Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area speciﬁc loads) for third model iteration (DALY9).

Suspended load Contribution Contribution Hillslope contribution Hillslope contribution Tributary (model, kt/yr) (model %) (tracer %) (model %) (tracer %) Katherine upstream of King 155 67 62 2 86 23 9 King upstream of Katherine 76 33 38 2 88 35 12 Katherine downstream of King 231 100 87 15 7

Katherine upstream of Flora 247 76 59 1 87 15 7 Flora upstream of Katherine 77 24 41 1 91 8 6 Katherine downstream of Flora 324 100 88 24 9

Daly upstream of Ferguson 329 84 67 2 88 24 9 Ferguson upstream of Daly 62 16 33 2 87 17 5 Daly downstream of Ferguson 391 100 88 5 5

Daly upstream of Douglas 428 96 71 2 86 5 5 Douglas upstream of Daly 17 4 29 2 76 17 12 Daly downstream of Douglas 446 100 86 18 5

Lower Daly 510 85 9 7

Table 8: Comparison of geochemical and fallout radionuclide tracer data with SedNet model results for third model iteration.

34 7.5 Iteration Four: Adding a “Gully Erosion” sub-soil Sediment Source (DALY10)

To increase the amount of sub-soil sediment contributed to the model, a spatially uniform “gully density” value of 0.05 km/km2 has, in the absence of other data, been adopted. There is no direct evidence to support this value, rather it is used as an approximate placeholder in the absence of better data. In fact, given the assumed cross sectional area of 23 m2 and gully erosion period of 100 years, this term should more correctly be considered as a spatially uniform diffuse sub-soil input rate of 19 t/km2/yr from essentially unknown erosion processes (or 9.5 t/km2/yr of fine sediment, given the assumed 50:50 split between coarse and fine sediment). These may include traditional gully erosion processes or other processes responsible for contributing sub-soil sediment to the drainage network, such as erosion of scalds or channel bank erosion from minor tributaries. As can be seen from Table 9, this addition has increased the “gully erosion” input to 529 kt/yr and an approximate 50:50 surface:sub-soil sediment split is predicted by the model across the network (Table 10). However, the additional 529 kt/yr of sediment input has meant that for two of the three gauging stations, loads now exceed the “observed” values. It should be noted that this diffuse sub-soil input rate, which is essentially an inferred rate of sub-soil input, is broadly consistent with our current understanding of sediment generation within the catchment. Sattar et al. (2010) have presented some initial rates of sediment generation from an approximately 1500 km2 area within the Ferguson River catchment, which covers an area thought to have the most extensive gully erosion in the Daly River catchment. Based on mapping gully development between 1948 and 2008, an estimated 4.6 ×106m3 of sediment was estimated to have been eroded. Assuming a bulk density of 1.65 t/m3 this equates to 7.42 Mt of sediment eroded in 60 years. Dividing this by the effective catchment area of 1.5 ×103km2 yields an area specific sediment yield of 82 t/km2/yr. If 50% of this sediment is assumed to be fine sediment then this equates to a fine sediment generation rate of 41 t/km2/yr, which is approximately 4 times greater than the catchment wide rate of 9.5 t/km2/yr adopted above. As mentioned above, the region Sattar et al. (2010) have studied is thought to be an area of relatively high gully erosion in the Daly River catchment, so the fact that their estimates are four times greater than what could be considered a catchment wide mean rate, suggests the latter is a plausible value. More correctly, it would appear that input of approximately 0.5 Mt per year of “gully-derived” or sub-soil sediment is a value not entirely inconsistent with what is known about the catchment.

35 Area Hillslope input Gully input Riverbank input Floodplain deposition Sediment yield Tributary km2 (kt/yr) (kt/yr) (kt/yr) (kt/yr) (kt/yr) Katherine 10152 159 96 25 45 235 King 14098 102 134 11 88 159 Flora 6673 84 63 8 30 125 Ferguson 4492 63 43 9 15 99 Douglas 1900 15 18 4 4 33 Mt Nancar 49362 549 468 85 290 812 Daly Outlet 55792 618 529 100 354 893

Table 9: Summary suspended sediment budget for the Daly River (fourth iteration, DALY10).

1000 30 ) 1 − yr 800 25 2 8140001 − km

t ) (

kt (

600 20 load

8140063 load

8140040 specific

400 15 Modelled area

200 10 Modelled

0 5 0 200 400 600 800 1000 5 10 15 20 25 30 − − Observed load (kt) Observed area specific load (t km 2 yr 1)

Figure 23: Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area speciﬁc loads) for fourth model iteration (DALY10)

Suspended load Contribution Contribution Hillslope contribution Hillslope contribution Tributary (model, kt/yr) (model %) (tracer %) (model %) (tracer %) Katherine upstream of King 235 60 62 2 56 23 9 King upstream of Katherine 159 40 38 2 42 35 12 Katherine downstream of King 394 100 51 15 7

Katherine upstream of Flora 421 77 59 1 51 15 7 Flora upstream of Katherine 130 24 41 1 54 8 6 Katherine downstream of Flora 550 100 52 24 9

Daly upstream of Ferguson 559 85 67 2 52 24 9 Ferguson upstream of Daly 99 15 33 2 55 17 5 Daly downstream of Ferguson 657 100 52 5 5

Daly upstream of Douglas 723 96 71 2 51 5 5 Douglas upstream of Daly 33 4 29 2 40 17 12 Daly downstream of Douglas 756 100 51 18 5

Lower Daly 857 51 9 7

Table 10: Comparison of geochemical and fallout radionuclide tracer data with SedNet model results for fourth model iteration.

36 7.6 Iteration Five: Increasing Sediment Supply from Bank Erosion (DALY11)

This model iteration has seen the bank erosion coefﬁcient increased from 2 × 10−5 to 3 × 10−4 (ie. a 15 fold increase) to increase the amount of non-hillslope derived sediment generated. This was done with the aim of further reducing the predicted hillslope erosion dominance. Under this parameterisation, the model predicts sub-soil sediments to be the main sediment source (Table 12), however the additional ∼ 1400kt of sediment generated by bank erosion now means that the estimated loads are strongly in excess of the “observed” load values.

Area Hillslope input Gully input Riverbank input Floodplain deposition Sediment yield Tributary km2 (kt/yr) (kt/yr) (kt/yr) (kt/yr) (kt/yr) Katherine 10152 159 96 376 89 541 King 14098 102 134 163 114 285 Flora 6673 84 63 119 45 221 Ferguson 4492 63 43 131 28 208 Douglas 1900 15 18 67 9 91 Mt Nancar 49362 549 468 1279 508 1788 Daly Outlet 55792 618 529 1494 646 1995

Table 11: Summary suspended sediment budget for the Daly River (ﬁfth iteration, DALY11).

2000 60 8140001 ) 1 − 50 yr

2 8140063 1500 − km

t ) ( 40

kt (

8140040 load

load 1000 30 specific

Modelled 20 area

500 10 Modelled

0 0 0 500 1000 1500 2000 0 10 20 30 40 50 60 − − Observed load (kt) Observed area specific load (t km 2 yr 1)

Figure 24: Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area speciﬁc loads) for ﬁfth model iteration (DALY11).

Suspended load Contribution Contribution Hillslope contribution Hillslope contribution Tributary (model, kt/yr) (model %) (tracer %) (model %) (tracer %) Katherine upstream of King 541 65 62 2 25 23 9 King upstream of Katherine 285 34 38 2 24 35 12 Katherine downstream of King 827 100 24 15 7

Katherine upstream of Flora 874 79 59 1 25 15 7 Flora upstream of Katherine 232 21 41 1 30 8 6 Katherine downstream of Flora 1106 100 26 24 9

Daly upstream of Ferguson 1126 84 67 2 26 24 9 Ferguson upstream of Daly 208 16 33 2 26 17 5 Daly downstream of Ferguson 1341 99 26 5 5

Daly upstream of Douglas 1553 94 71 2 24 5 5 Douglas upstream of Daly 91 6 29 2 15 17 12 Daly downstream of Douglas 1646 100 23 18 5

Lower Daly 1924 23 9 7

Table 12: Comparison of geochemical and fallout radionuclide tracer data with SedNet model results for ﬁfth model iteration.

37 7.7 Iteration Six: Increasing Sediment Settling Velocity (DALY12)

In order to compensate for the load over-predictions in the previous model iteration, the floodplain sediment settling velocity parameter has been increased from 1 × 10−6 to 1 × 10−5 (ie. a ten fold increase). Whilst this is a large relative increase in settling velocity it actually corresponds to a change in nominal particle size from ∼ 1µm to ∼ 6µm, which is well within the range of plausible values for floodplain sediments. The results of this model iteration indicates a sediment yield to Anson Bay of 503 kt per year (Table 13). This comprises 618 kt per year of hillslope input, 529 kt of diffuse “sub-soil” input and 1494 kt of river bank input and 2138 kt per year of floodplain deposition. Thus bank erosion is predicted to be the dominant sediment input term. 2138 kt per year are predicted to be deposited on floodplains, which is 81% of the total sediment input. The area specific (and hence total) loads at the Nancar and Douglas gauging sites are well predicted, though the model is strongly over predicting (by a factor of three perhaps) the area specific load at the Katherine gauging station (8140001). Table 13 indicates the sediment budget for the Katherine River as a whole (10152 km2 versus 8640 km2 at the gauging station) is dominated by river bank input, but also receives a substantial amount of hillslope-derived sediment. It should be noted that a large part of the Katherine River above Katherine drains the Arnhem Land plateau, which comprises extensive areas of exposed bedrock as the ground surface. Soil erosion rates are presumably much reduced in these areas and this aspect is not accounted for the in the RUSLE model. Indeed moderately high hillslope erosion rates (commonly 10–20 t/ha/yr) are predicted for the upper Katherine River; these rates are quite likely too high. There are also moderate to high rates of bank erosion predicted which are likely to be incorrect given the bedrock dominated landscape. The other major characteristic of this budget to examine are the model’s predictions relative to the fallout radionuclide tracer data. The model is, under this parameterisation, doing a fair job of predicting the appropriate proportions of hillslope and sub-soil sediment. Overall the model is correctly predicting the dominance of sub-soil sediments as a sediment source. Of the eleven independent sampling locations listed in Table 14, agreement between the tracer data and the model is attained in eight cases. Poorer agreement occurs for the King River, where the proportion of hillslope-derived sediment should be higher (though possibly not by much given the ±12% error on the tracer data), the Flora River, where too much hillslope-derived sediment is predicted and potentially the Daly River downstream of the Fer- guson River, where the model predicts a 20% hillslope contribution compared to a 5±5% contribution derived from the tracer data. This tracer derived contribution is rather low, though not inconsistent with previous work by Wasson et al. (2010). The confluence tracing proportions are also moderately well reproduced. In all cases the dominant tributary is correctly identified though there are discrepancies of up to 20 percentage points in cases. One notable discrepancy concerns the confluence tracing for the Douglas-Daly River confluence. The model is predicting 10% of the downstream load to be sourced from the Douglas River, yet the geochemical tracer data indicates approximately a 30% contribution from the Douglas River. Two aspects are noteworthy here. The first concerns the catchment areas and the implications for area specific erosion rates The Douglas River has a 1900 km2 catchment area at its confluence with the Daly River, which itself has a 42,000 km2 catchment area at the confluence. Erosion rates would need to be exceptionally high in the Douglas River for it to contribute ∼ 30% of the sediment to the downstream reach given it only contributes one twenty-fifth (4%) of the catchment area. There is no evidence to support this and the existing load estimates given in Table 1, indicate the Douglas River contributes 63 kt/yr of the 539 kt/yr load at the Nancar gauge a short distance downstream. This contribution is approximately in proportion to the relative catchment areas of these two tributaries. Wasson et al. (2010), in their tracing study, could not identify a detectable contribution of sediment from the Douglas River to the Daly River, despite sufficiently distinct geochemical signatures. Again, this is consistent with the Douglas River contributing a very small proportion of the Daly River’s downstream load. If the contribution were around 4% (as it would be if erosion intensity was approximately similar between the Douglas and Daly Rivers), this is potentially within the error of this technique and may therefore not have been detectable in the analysis method adopted by Wasson et al. (2010). Thus, the 29 ± 2 % contribution estimated by Caitcheon et al. (in preparation) for the Douglas River seems, on balance, unrealistically high. Given that this model scenario appears to acceptably fit a number of key criteria, a number of summary figures are presented below. Figure 26 shows the spatial pattern of sediment input to the river network from the three modelled erosion sources. Areas of highest hillslope erosion input cover the Nancar Range in the lower reaches of the catchment, and also the upper Katherine River. This latter area in particular was noted as quite likely having unrealistically high predicted hillslope erosion rates on account of extensive areas of exposed bedrock, rather than soil. Low rates of hillslope erosion are predicted for the King and Dry River regions in the south of the catchment and also for the Douglas River catchment. Diffuse subsoil input is represented as a spatially uniform value though the rate of 0.095

38 Area Hillslope input Gully input Riverbank input Floodplain deposition Sediment yield Tributary km2 (kt/yr) (kt/yr) (kt/yr) (kt/yr) (kt/yr) Katherine 10152 159 96 376 395 236 King 14098 102 134 163 287 112 Flora 6673 84 63 119 173 94 Ferguson 4492 63 43 131 123 113 Douglas 1900 15 18 67 37 63 Mt Nancar 49362 549 468 1279 1757 539 Daly Outlet 55792 618 529 1494 2138 503

Table 13: Summary suspended sediment budget for the Daly River (sixth iteration). All values are mean annual rates (ie. rates per year).

600 35 8140063 ) 1 500 − 30

yr 8140001

2 − km

t ) 400 ( 25

kt (

load

load 300 20 specific

Modelled 200 15 area

8140040 100 10 Modelled

0 5 0 100 200 300 400 500 600 5 10 15 20 25 30 35 − − Observed load (kt) Observed area specific load (t km 2 yr 1)

Figure 25: Observed versus predicted suspended sediment loads (left panel shows absolute loads and right panel shows area speciﬁc loads)

Suspended load Contribution Contribution Hillslope contribution Hillslope contribution Tributary (model, kt/yr) (model %) (tracer %) (model %) (tracer %) Katherine upstream of King 236 70 62 2 22 23 9 King upstream of Katherine 112 33 38 2 19 35 12 Katherine downstream of King 338 103 21 15 7

Katherine upstream of Flora 330 78 59 1 22 15 7 Flora upstream of Katherine 101 24 41 1 26 8 6 Katherine downstream of Flora 425 101 23 24 9

Daly upstream of Ferguson 413 79 67 2 23 24 9 Ferguson upstream of Daly 113 22 33 2 24 17 5 Daly downstream of Ferguson 522 101 22 5 5

Daly upstream of Douglas 524 90 71 2 19 5 5 Douglas upstream of Daly 63 11 29 2 12 17 12 Daly downstream of Douglas 585 100 18 18 5

Lower Daly 624 18 9 7

Table 14: Comparison of geochemical and fallout radionuclide tracer data with SedNet model results for sixth model iteration.

39 t/ha/yr is relatively low when compared to equivalent inputs from hillslope erosion. Sediment input from river bank erosion (tonnes of input normalised by metres of channel length) is focussed upon the main river channels.

Hillslope Input -1 -1 (t.ha .yr ) 0.005 - 0.050 0.051 - 0.100 0.101 - 0.200 0.201 - 0.300 0.301 - 0.600

Diffuse Sub-Soil Input -1 -1 (t.ha .yr ) 0.095

Bank Input -1 -1 (t.m .yr ) 0.00 - 0.02 0.03 - 0.10 0.11 - 0.20 0.21 - 0.50 0.51 - 33.70

Figure 26: Maps of sediment input to the river network from hillslope erosion, “diffuse” sub-soil input (of which there is only a single value) and river bank erosion.

Figure 27 shows the contribution of each sub-catchment to the total sediment export to the ocean (as distinct from sediment input to the river network). The southern, driest region of the catchment is predicted to contribute relatively little to catchment export. This region has both low erosion rates and ample opportunity for sediment deposition en-route to the catchment outlet and hence connectivity with the outlet is relatively low. The main stems of the Katherine, King, Ferguson, Flora and Daly Rivers are predicted as important sediment sources with respect to catchment export on account of their predicted high bank erosion inputs and relatively strong connectivity to the catchment outlet. Likewise, the Douglas River, and tributaries to the Daly River downstream of the Douglas-Daly River conﬂuence are also predicted to be important source areas. Again, this is related to their higher connectivity to the catchment outlet. Figure 28 is another presentation of the predicted sources of sediment contributing to catchment

40 Anson Bay

Sediment Contribution to Catchment Export -1 -1 (t.ha .yr ) 0.00 - 0.01 0.02 - 0.05 0.06 - 0.10 0.11 - 0.50

> 0.5 0 50 100 km

Figure 27: Map of suspended sediment contribution to end-of-catchment export.

41 export. The sediment sources are shown in the inset ﬁgure using a ternary diagram with circle size representing the relative contribution to catchment export and the position and color (on a red-green-blue color scale indicating river bank, gully erosion and hillslope erosion respectively) indicating the sediment source. The same principles are applied to the map of the catchment. The Dry River for example has overall a low rate of contribution to catchment export, which is dominated by the inferred diffuse subsoil input term. The predominance of red along the main channels indicates a bank erosion dominance and the generally thicker lines indicate stronger contributions to catchment export. A hillslope sediment source is predicted to dominate many ﬁrst order sub-catchments.

Fine sediment contribution (t/ha/yr) Min. 0 1st Qu. 0.015 Median 0.045 0 1 3rd Qu. 0.11

Mean 0.18

Max. 55

bank hillslope

1 0 0 gully 1

Figure 28: Map of suspended sediment contribution to end-of-catchment export, showing sediment source via a red (bank erosion), green (“gully erosion”) and blue (hillslope erosion) color scheme. Line widths are scaled proportional to the contribution a river link makes to the end of catchment export.

42 7.8 Model Sensitivity to Sediment Supply Variations

Here the sensitivity of the suspended sediment load predictions to spatially uniform, ﬁxed proportion variations in sediment supply from hillslope, river bank and gully erosion are examined. This analysis serves two purposes:

1. it provides a sensitivity analysis of model predictions to variations or uncertainties in sediment input terms. 2. it provides some guidance concerning likely changes in sediment loads from management actions that either increase or decrease sediment generation.

Figure 29 shows the variation in suspended sediment load estimates at six points in the river network to a sequence of proportional, spatially uniform variations in sediment generation extending to ± 15% of modern conditions (for example increasing hillslope erosion rates everywhere by 10%). The response curves are linear within this range, thus allowing for a series of “response coefficients” (equal to the slope of the lines) to be calculate that express the proportional change in load from a proportional change in input (essentially the slope of the lines in Figure 29). These response coefficients were quite similar for all sub-catchments for each sediment source and were approximately ∼ 0.16 for hillslope and gully erosion and ∼ 0.65 for river bank erosion. These coefficient values indicate for a unit change in sediment generation from hillslope erosion for example, a 0.16 unit change in end of catchment load would be expected at downstream locations. It is not surprising that hillslope and gully erosion have low coefficient values: both are predicted to be minor sediment input terms. The higher coefficient for river bank erosion reflects the strong connectivity of this sediment source to the catchment outlet and other locations examined. It should be noted that these sensitivity coefficients are linked to the spatial pattern of sediment sources and sinks within the catchment and assume spatially uniform proportional changes in sediment generation.

15 outlet 15 8140040 (Nancar)

10 10

5 5

0 0

−5 −5

Change in load (%) −10 Change in load (%) −10

−15 −15 −15 −10 −5 0 5 10 15 −15 −10 −5 0 5 10 15 change in input (%) change in input (%)

15 Douglas River 15 Ferguson River

10 10

5 5

0 0

−5 −5

Change in load (%) −10 Change in load (%) −10

−15 −15 −15 −10 −5 0 5 10 15 −15 −10 −5 0 5 10 15 change in input (%) change in input (%)

15 Katherine River 15 King River

10 10

5 5

0 0

−5 −5

hill

Change in load (%) −10 Change in load (%) −10 bank gully −15 −15 −15 −10 −5 0 5 10 15 −15 −10 −5 0 5 10 15 change in input (%) change in input (%)

Figure 29: Variations in suspended sediment load transport at four locations as a function of variations in sediment input from hillslope, river bank and gully erosion.

43 7.9 Catchment Nutrient Budget Results

The modelled catchment nutrient budget is given in Table 15. Particulate nitrogen and phosphorus are predicted to be the dominant nutrient sources, with a predominance of hillslope and bank erosion in terms of total mass of input. Deposition of particulate nutrients on ﬂoodplains accounts for approximately 90% of the total nutrient supply. Dissolved nutrient export is approximately 5% of the total load. Figure 30 shows the spatial pattern of contribution to catchment export. For total nitrogen contribution, the contribution intensity increases in general towards the catchment outlet, though the main stems of the Katherine, Ferguson and Daly Rivers appear as important sources on account of the relatively high rates of sediment (and hence particulate nitrogen) input from bank erosion. The large predicted losses of particulate nitrogen to ﬂoodplain deposition, means that the catchment headwaters and particularly the King River have poor connectivity with the catchment outlet and hence a low contribution rate. For total phosphorus contribution, the predicted main contribution areas are again those in closer proximity to the outlet, though again the King River is notable for the low rate of contribution..

Budget Total Nitrogen Total Phosphorus item (t/yr) (t/yr) Hillslope input 3454 663 Gully input 1058 264 Bank input 2988 747 Point input 0.49 0.11 Dissolved input 449 75 Total supply 7949 1749

Floodplain deposition 7088 1571 Denitriﬁcation 7

Particulate export 413 103 Dissolved export 442 75 Total export 855 178

Table 15: Summary nutrient budget for the Daly River outlet.

These nutrient load estimates can be compared with those calculated by Robson et al. (in preparation) based on in-stream water quality sampling, as summarised in Table 1, with the comparison shown in Figures 31 and 32. The total nitrogen load for station 814001 (Katherine River) is well predicted, though the loads for the other two stations (Mt Nancar and Douglas River) are substantially underpredicted. Total phosphorus loads are moderately well predicted on average. Some experimentation indicated that increasing the sub-soil nitrogen concentrations for gully and river bank derived sediments to 2 and 3 g/kg respectively produced somewhat better results, as shown in Figure 33. Again, we do not have data by which to independently determine whether these are appropriate values but they do highlight that obtaining future measurements of sub-soil nitrogen concentrations would assist in ﬁlling this knowledge gap. However, under this scenario, gully and river bank derived inputs are two and three times higher, particulate input is approximately double the dissolved input and total export increases to 1369 tonnes per year. This does not however change the spatial pattern of nutrient contribution to catchment export from that shown in Figure 30.

Budget Total Nitrogen item (t/yr) Hillslope input 3454 Gully input 2117 Bank input 8965 Point input 0.49 Dissolved input 449 Total supply 14985

Floodplain deposition 13609 Denitriﬁcation 7

Particulate export 926 Dissolved export 442 Total export 1369

Table 16: Summary nutrient budget for the Daly River outlet, with modiﬁed (increased) sub-soil nutrient concentrations.

44 Total Nitrogen Contribution to Catchment Export -1 -1 (kg.ha .yr ) 0.000 - 0.010 0.011 - 0.020 0.021 - 0.050 0.051 - 0.100 > 0.101

Total Phosphorus Contribution to Catchment Export -1 -1 (kg.ha .yr ) 0.0000 - 0.0010 0.0011 - 0.0020 0.0021 - 0.0050

0.0051 - 0.1000 0 50 100 km > 0.10

Figure 30: Spatial patterns of total nitrogen and phosphorus contributions to the catchment outlet.

45 3500 0.25 ) 1

3000 − yr 0.20 2 −

2500 km

t ) (

kt (

0.15

2000 load

load

1500 specific

0.10 8140063 Modelled area 1000 8140001 0.05 500 Modelled 8140040 0 0.00 0 500 1000 1500 2000 2500 3000 3500 0.00 0.05 0.10 0.15 0.20 0.25 − − Observed load (kt) Observed area specific load (t km 2 yr 1)

Figure 31: Observed versus predicted total nitrogen loads (left panel shows absolute loads and right panel shows area speciﬁc loads).

500 0.025 ) 1 − yr 400 0.020 8140063 2 − km

t ) (

kt (

300 0.015 load

load

8140001 specific

200 0.010 Modelled area

100 0.005 8140040 Modelled

0 0.000 0 100 200 300 400 500 0.000 0.005 0.010 0.015 0.020 0.025 − − Observed load (kt) Observed area specific load (t km 2 yr 1)

Figure 32: Observed versus predicted total phosphorus loads (left panel shows absolute loads and right panel shows area speciﬁc loads).

3500 0.25 ) 1

3000 − yr 0.20 2 −

2500 km 8140063 t ) (

kt (

0.15

2000 load

load

1500 8140001 specific

0.10 Modelled area 1000 0.05 500

Modelled 8140040

0 0.00 0 500 1000 1500 2000 2500 3000 3500 0.00 0.05 0.10 0.15 0.20 0.25 − − Observed load (kt) Observed area specific load (t km 2 yr 1)

Figure 33: Observed versus predicted total nitrogen loads (left panel shows absolute loads and right panel shows area speciﬁc loads) with gully and bank derived sub-soil nutrient concentrations increased to 2 and 3 g/kg respectively, from their default value of 1 g/kg each.

46 8 Discussion

This report documents the process of setting up, calibrating and running a catchment sediment and nutrient budget model, in this case SedNet. Ultimately the model has been configured to provide an acceptable match to the independent data sources available for calibration purposes. However, a number of key parameter and data gaps were identified in this process and to address these data and knowledge gaps, assumptions have had to be made about certain parameter values. Table 17 lists the major changes made to the model from what could be considered a “default” parameterisation and each of these changes highlights important aspects of the catchment sediment budget that have been identified as important, and which may be worthy of future research.

Original Revised Item Value Problem value Outcome Hillslope erosion uncapped esti- over prediction of yields capped at 35 t/ha/yr Lower yields, lower pro- mates extending and proportion of surface portion of surface derived to 178 t/ha/yr derived sediment sediment

Hillslope sediment mean = 0.05 as above mean = 0.015 as above delivery ratio

Sub-soil input from nil under prediction of sub-soil 9.5 t/km2 (spatially more sub-surface soil “gully” erosion sediment uniform) added as a sediment source

Bank erosion coef- 2 × 10−5 under prediction of sub-soil 3 × 10−4 more sub-surface soil ﬁcient sediment added as a sediment source

Settling velocity for 1 × 10−6m/s over prediction of yields in 1 × 10−5 more accurate yields in ﬂoodplain deposi- lower catchment lower catchment tion

Nitrogen concen- 1 g/kg under prediction of nitrogen 3 g/kg higher nitrogen loads tration in river loads bank sediment

Nitrogen concen- 1 g/kg under prediction of nitrogen 2 g/kg higher nitrogen loads tration in gully sed- loads iment

Table 17: Summary of changes to model parameters in model calibration process.

Both in the Daly River and the Mitchell River in Queensland (for which a similar study has been undertaken as part of this TRaCK research program), initial model runs vastly over-predicted the contribution of surface soil to the catchment sediment budget. This has been addressed by both capping what appear to be unfeasibly high hillslope erosion rates in certain areas, and reducing the hillslope sediment delivery ratio to, in the case of the Daly River, 1.5% of gross hillslope erosion (a value of 1% was used in the Mitchell River). The issue of very high hillslope erosion rate predictions was common to both catchments and the areas inferred to be over-predicted typically occurred in the steeper regions of the catchments with lower vegetation cover (such as the Nancar Range and upper Katherine River). It is worth noting that the hillslope erosion sub-model is based on the revised universal soil loss equation (RUSLE, Renard et al., 1997), which essentially assumes that there is an unlimited supply of soil on all hillslopes, regardless of slope, with supply mediated by a vegetation cover factor and rainfall erosivity. Hence, the model always predicts that gross hillslope erosion rates increase with slope. In some environments, steeper slopes may have been stripped of their soil mantle by the intense monsoonal rains that occur every year on slopes that have often been burnt in the late dry season, leaving very little vegetative cover. Consequently gross hillslope erosion may be systematically over-predicted by the model in parts of this landscape, a point acknowledged as a possibility by Lu et al. (2003), who state: “erosion rate(s) could be overestimated in some of the steeper arid and tropical mountain ranges, which are predicted to have some of the highest erosion rates in the country. The vegetation cover is sparse in those areas and the land is steep but the erosion rate is limited by shallow soils with frequent wind erosion, rock outcrops, and high gravel content. These conditions are not represented in the USLE”. If the predicted rates were applied over a timescale of a century, almost complete stripping of the soil mantle would occur, which is often not observed. Thus there appears to be a systematic issue with regards to the RUSLE predictions in steeper bedrock-dominated landscape settings. There is no direct basis for asserting what the hillslope sediment delivery ratio should be other than reductions to values

47 in the range 1–1.5% result in substantial improvements between the model’s predictions and the fallout radionuclide tracer data, lending support to the approach. However, this conclusion has been reached now in two studies. The modelling also suggests that approximately 0.5 Mt/yr of sub-soil sediment may be being contributed to the river from diffuse catchment erosion processes, the exact nature of which remains unknown. Certainly a form of “gully erosion” is recognised in a portion of the granitic Ferguson River catchment, but how this source manifests elsewhere is not clearly known, nor are clearer details of its spatial distribution. It should be borne in mind that this sub-soil contribution of 0.5 Mt/yr could alterna- tively be generated by an increase in input from bank erosion, rather than the “gully erosion” processes currently modelled. This is an area worthy of future research. The model’s prediction that bank erosion is the single largest sediment source is not inconsistent with field observations, anecdotal evidence and recent hydrologic analysis. Wasson and Bayliss (2010), on the basis of sediment deposition upon the nearby Magela Plain suggest that the last 250 years have been a relatively wet phase in the context of the last 1000 years of river flow. Moreover, recent decades have also been particularly wet. The Daly River region has a recent (1996 to 2007) climate record that is statistically significantly wetter than the historical (1930 to 2007) record. Recent rainfall was 25 percent higher and runoff was 66 percent higher (CSIRO 2009). Higher river flow rates are generally associated with channel enlargement and bank erosion and certainly evidence of eroding river banks is visible in field, with lengths of exposed vertical river bank and numerous bank slumps evident in multiple locations upstream of the Nancar Range, as shown in Figure 34.

Figure 34: Photograph of an eroding river bank along the Daly River, upstream of the Nancar gauging station.

However, the modelling thus far also predicts moderate bank erosion along the Katherine River; there is less field observation to support elevated bank erosion rates along this section of the catchment. Overall though, the high sub-soil sediment contribution observed in transported sediments by the fallout radionuclide tracer data, and upwards adjustment in the bank erosion coefficient (and hence bank erosion rates) necessary to match this tracer data are conceptually consistent. However, given the predicted and inferred importance of bank erosion as a sediment source, it is clearly an area worthy of future research. Indeed, detailed hydrodynamic models are in the process of being configured for the Daly River catchment at Charles Darwin University, Darwin, which could potentially provide the capabil- ity to simulate in more detail the effects of varying flow regimes on channel widening and contraction, however at the time of writing such simulations had not been conducted. Another aspect of the model’s parameterisation that was changed was an increase in the settling velocity of floodplain sediments, from 1 × 10−6 m/s to 1 × 10−5 m/s. Given the spatial aggregation

48 involved with a model such as sediment, it is difficult to conducted detailed evaluations of floodplain deposition predictions. However, this can be partially attempted. Figure 35 shows a map of floodplain deposition rates predicted by SedNet. The highest vertical accumulation rates, typically 0.6 to 2.5 mm/yr, are found along the main rivers, which is consistent with the formation of levees along almost all the major rivers. Deposition rates are low (< 0.2 mm/yr) in most of the rest of the catchment.

Anson Bay

Legend Deposition (mm/yr) 0.00 - 0.20 0.21 - 0.40 0.41 - 0.60 0.61 - 1.20 1.21 - 2.50 floodplain extent

Figure 35: Predicted rates of ﬂoodplain deposition in the Daly River catchment.

Vertical sediment accumulation rates have been measured at a number of locations within the catchment by Wasson et al. (2010) using fallout radionuclide and optically stimulated luminescence dating methods, as well as in the course of this study. The observed rates are listed below in Table 18, divided into “recent” (last few decades) and “long-term” rates (extending back up to 2000 years before present). It should be noted that almost all of these samples come from either in-channel benches or locations upon the river banks themselves, rather than from more distal (and in terms of area, spatially dominant) locations. These sampling locations were chosen typically because of what were assumed to be high deposition rates where the stratigraphic detail could be most clearly resolved. Given sediment deposition rates commonly fall with distance from the channel they should be interpreted as near-maximal deposition rates. By contrast, sediment deposition within SedNet is effectively smeared uniformly across the entire floodplain area within a given link, and thus would be expected to be less than the near channel rates for a given mass of deposited sediment on a given floodplain area. Recent observed sediment accumulation rates are centered around 13–18 mm/yr, which is higher than the longer term rates which are typically 1–5 mm/yr. The SedNet predictions (for essentially “modern” conditions from along the main channel typically fall within the range 0.6 to 2.5 mm/yr. These rates are below the observed “modern” rates but this is actually a reasonable result given that these rates conceptually apply to a larger spatial extent than the near-maximal rates listed in Table 18. It would have been undesirable for the SedNet rates to have been markedly greater than the observed rates as this would have likely indicated an unrealistic over prediction of total floodplain deposition. Thus the predicted floodplain deposition rates are within the range of plausible values given what is known about existing floodplain deposition patterns and rates. With regards to the nutrient budget, it should be noted that the budget is somewhat speculative and rather sensitive to model parameterisation. There was some limited observational data with regards to dissolved nutrient generation rates from the catchment but certainly not enough to apply distinct values to individual land use classes. In any case dissolved input is predicted to be a relatively minor nutrient 49 Recent Rate Long Term Rate Site (mm/yr) (mm/yr) King River (Tony Green Crossing 12.3 1.0 Douglas River above Daly 5.8 0.8 Katherine River at Carbeen 5.3 1.0 Katherine River at Olympic 32.2 0.7

Daly River at Nancar Hideout∗ 5.7 3.6 Daly River D/S Nancar Range∗ 22.0 1.5 Daly River at Maneroo∗ 47.0 23.0 Daly River at Furlonger∗ 13.0 5.3 median 12.7 1.3 mean 17.9 4.6

∗ Table 18: Measured sediment accumulation rates from the Daly River catchment. denotes data from Wasson et al. (2010). source. Given the predicted dominance of sediment attached nutrients from river bank erosion in particular, the use of “default” soil nutrient concentrations, as was done here, is undesirable. Indeed modelling indicated soil nitrogen concentrations 2–3 times the default values produced nitrogen loads in closer agreement with the loads estimated on the basis of in-stream sampling, yet this remains essentially a hypothesis awaiting testing by empirical data. One of the important data sets by which a model such as SedNet can be evaluated is the load estimates derived from in-stream water quality sampling. The existing load estimates are essentially derived from a single, wetter than average wet-season. Thus it remains uncertain how much of a legitimate comparison it is to compare the “mean annual loads” predicted by SedNet with the values generated by a single wet-season’s sampling. However at present this is all the data that is available. It would thus be highly desirable for the in-stream water quality monitoring program to be extended into future years to allow better quantiﬁcation of inter-annual variability in sediment and nutrient loads. This will also be beneﬁcial in its own right in terms of examining temporal trends in water quality attributes within the catchment.

50 9 Conclusions

This study has constructed catchment sediment and nutrient budgets for the Daly River in the Northern Territory, Australia. The budgets were constructed using the SedNet and ANNEX models, which in turn rely on a series of sub-models to represent a number of important sediment and nutrient sources. In the process of configuring and parameterising the model a number of key knowledge gaps were identified, specifically pertaining to sub-soil generation from “gully erosion” type sources, as well as dissolved nutrient concentrations in runoff and sediment nutrient concentrations, for example. The model was able to be used to infer some likely values for a number of these unknown characteristics; however these model predictions should be viewed essentially as hypothetical values awaiting more detailed testing. A model calibration process was undertaken that involved targeted modifications to a select number of parameters. The process of model calibration is actually informative with regards to the nature of the catchment sediment budget (for example) and how the various components of the budget combine to produce a statistic such as an end of catchment sediment yield. The data against which the model was calibrated included station-based load estimates derived from in-stream water quality sampling (but only for one wet season), fallout radionuclide sediment tracers that indicate the balance between surface and sub-soil derived sediment at a number of locations across the catchment, and geochemical confluence tracer data, indicating the relative contributions from river junction tributaries. The calibrated model was able to correctly predict erosion intensity (tonnes of sediment per kilometre square) for two of the three gauging station locations considered (Douglas River and Nancar Gauge), though over-predicted the sediment yield from the Katherine River at Katherine. The predicted surface versus sub-soil sediment ratio was in the range 12–26%. This range is higher overall than the fallout radionuclide tracer data and the study of Wasson et al. (2010) indicate, though hillslope-derived sediment is a minor component of the overall catchment sediment budget. The relative tributary contributions were also correctly ordered. Floodplain deposition rates predicted by the model were within the range of plausible values, at least for the major channels, given existing measurements of vertical sediment accumulation within the catchment. The model predictions indicated 618 kt/yr input from hillslope erosion, 529 kt/yr input from “gully erosion” (or equivalent sub-soil eroding processes) and 1494 kt/yr input from river bank erosion and 2138 kt/yr sediment loss to floodplain deposition and a mean annual catchment export rate of 503 kt/yr. Thus river bank erosion is predicted to be the main input term. The spatial pattern of sediment contribution to catchment export is focussed along the main river channels (due to bank erosion input) and the north east of the catchment (including the Douglas River) where connectivity to the catchment outlet is highest. The King River and southern Flora Rivers contribute relatively little sediment to the catchment outlet on account of low erosion rates for the King River and larger opportunities for floodplain deposition en-route to the outlet. Predictions of nutrient input to the river network are dominated by bank and hillslope erosion. Dis- solved nutrient input is relatively low, comprising 5% of total nutrient supply. Particulate nutrient deposition on floodplains accounts for approximately 90% of total nutrient supply. Predicted end-of-catchment export rates for total nitrogen and phosphorus are 855–1369 and 178 tonnes per year, respectively, with the areas of highest contribution to catchment export located in the northwest of the catchment, where connectivity to the catchment outlet is greatest.

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