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Sediment Quality Sampling Design for Darwin Harbour



Sediment Quality Sampling Design for Darwin Harbour


Brinkman, Richard; Logan, Murray; Northern Territory. Department of Environment and Natural Resources; Australian Institute of Marine Science


E-Publications; E-Books; PublicationNT; Report Number 44/2019




Darwin Harbour


In the context of increasing development and associated pressures, this project aims to inform the development of a first systematic, long-term, sediment monitoring program for Darwin Harbour which takes into consideration the physicochemical nature of Darwin Harbour sediment and the oceanographic processes which will influence the movement of contaminated sediment in the Harbour. The rationale for the program is that seabed and estuarine sediments are both an extensive habitat and the ultimate repository for many contaminants that enter waterways. In addition, monitoring of contaminants in sediment may facilitate the identification of increasing contaminant loads in the Harbour which may not be detected by water monitoring programs due to the high flushing rate within Darwin Harbour and infrequent water sample collection.


Made available by via Publications (Legal Deposit) Act 2004 (NT).

Table of contents

1. Executive summary -- 2. Introduction -- 3. Methods -- 3.1 Overview of methodology -- 3.2 Tidal Hydrodynamics -- 3.3 Wave Dynamics -- 3.4 Sediment Modelling -- 3.5 Sediment sampling design analysis -- 4. Results -- 4.1 Tidal and wave driven hydrodynamic processes -- 4.2 Sediment transport modelling -- 4.3 Sediment characteristics from previous sampling programs -- 4.4 Sediment sampling design analysis -- 4.4.1 Existing chemical sediment data, Outer Harbour sediment monitoring data, and designated sampling sites -- 4.4.2 Hydrodynamic modelling layers -- 4.4.3 Exclusion zone masks -- 4.5 Spatial Model fitting -- 4.5.1 Background on statistical techniques for designing sediment sampling program -- 4.5.2 Results from statistically derived sampling design - East Arm -- 4.5.3 Results from statistically derived sampling design - Outer Harbour -- 4.5.4 Representation of sampling sites mapped with hydrodynamic and sediment modelling parameters -- 4.6 Harbour Sediment Zonation, and conceptual representation: -- 5 Conclusions -- 6 References -- 7 Appendix 1




Sediment quality; Tidal hydrodynamics; sediment sampling; design analysis

Publisher name

Northern Territory Government

Place of publication



Report Number 44/2019


43, 74 pages : colour maps ; 30 cm

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Attribution International 4.0 (CC BY 4.0)

Copyright owner

Northern Territory Government



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-27 mean, then a 2D spatially balanced design is arguably most appropriate as it should represent the general underlying conditions. If on the other hand, the purpose is to be able to model the underlying patterns and understand where any changes in these patterns occur, then arguably a design that has been optimised around the underlying conditions (such as a n-dimensional spatially balanced design or conditioned latin hypercube sampling technique) is arguably more appropriate. 4.1. East Arm For a range of sample sizes (5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 200, 1000), for East Arm, each of the above sampling approaches was repeated five times. For each run, the Error metric was calculated. The results are presented in Figures 22 (mean error) and 23 (max error). As expected, as the sample sizes increase, the error declines. The simple random sampling design performs worst. The regular grid sampling is better than the random sampling. Whilst clusters of samples might be appropriate for representing conditions when the conditions cluster correspondingly, totally random samples are highly unlikely to resemble the correct cluster configuration. The non uniform distribution of cLHS on the other hand was directly due to the clustering patterns in the underlying conditions and thus it was not surprising that this technique had the least error. Interestingly, the reduction in error after a sample size of 50 is relatively mild (notwithstanding that the figure is presented on a log-y scale). For each of sample sizes 50, 60, 70, 80 and 100, the best (based on lowest error) cLHS configuration is presented in Figure. Comma delimited text files are also available with the Latitude and Longitudes of these coordinates. 0.001953125 0.015625000 0.125000000 5 10 20 30 40 50 60 70 80 90100 120 200 1000 Number of samples M ea n E rr or Method cLHS Random Regular 2D Spatially balanced nD Spatially balanced Figure 22: Comparison of the mean Error conditional on sample size and sampling method for the East Arm