The Hawkesbury–Nepean River System (HNRS) is one of the most important inland river systems in Australia, which supplies over 90% of Sydney's potable water. In this paper, 25 water quality parameters from nine sampling stations in the HNRS covering a period of 12 years are used to examine the trends in the water quality data in the HNRS. It has been found that there is an overall increasing trend of turbidity, chlorophyll-a, alkalinity, total iron, total aluminium, total manganese and reactive silicate, indicating an overall water quality deterioration in the HNRS during the last decade. The parameters such as phosphorus, suspended solids and ammonical nitrogen do not show any marked change over the period of study. Although an improvement in water quality can be seen at some stations downstream of the undisturbed parts of the catchment, there is a clear trend of increased chemical and physical water quality deterioration at many locations in the HNRS. Better land use planning is recommended to achieve an overall improvement in the water quality of the HNRS in future.

INTRODUCTION

River water quality depends on various geologic, climatic, catchment and land use characteristics. Among these, climate and land use are the key drivers of water quality in a river system, but determining the relative influence of these factors on water quality remains a significant challenge for aquatic science and management (Interlandi & Crockett 2003). Various pollutant sources related to industries, urbanisation, agriculture and mining can have a strong impact on a river system (Tian & Fernandez 2000; Kendall et al. 2007). In recent years, an increasing awareness has been noticed in different countries about the impacts of anthropogenic activities on river water quantity and quality (Dawson & Macklin 1998; Ma et al. 2009; Whitehead et al. 2009; Futter et al. 2009; Erturk et al. 2010). Climate change and urbanisation are key factors that affect the water quality and quantity in urbanised rivers (Astaraie-Imani et al. 2012). Pollutant build up and wash off in connection with urban catchments have become a focus of current research in different countries (Rahman et al. 2002; Egodawatta et al. 2009; Van der Sterren et al. 2013; Haddad et al. 2013). Watershed management and catchment scale studies have become increasingly more important in determining the impact of human development on water quality (Sliva & Williams 2001). Several studies have been undertaken worldwide to assess the relationship between land use and water quality. For example, Wang (2001) exhibited the importance of integrating water quality management and land use planning. Sliva & Williams (2001) used buffer zone versus whole catchment approaches to study the land use impact on river water quality. Lenat & Crawford (1994), Johnes (1996), Mattikalli & Richards (1996), Johnes & Heathwaite (1997), Tong & Chen (2002) and Lee et al. (2010) adopted modelling approaches to study the relationship between land use and surface water quality.

To assess the health of freshwater for biotic species and humans, various guidelines have been developed internationally, e.g., the International Union for Conservation of Nature (IUCN) Global Freshwater Initiative, the Healthy Watershed Initiative in the USA (Young & Sanzone 2002), the Pressure, State, Response model in Australia (Commonwealth of Australia 1996), the EU Water Framework Directive in Europe (Kaika 2003), and the Australian and New Zealand Guidelines for Fresh and Marine Water Quality (ANZECC 2000).

One of the long-term goals of the monitoring programme of stream water quality is to detect changes or trends in pollution levels over time and to identify and explain the major factors affecting such trends and to devise a strategy to improve the overall water quality of a river system (Yu et al. 1993). The identification of trends in water quality can also be used to either confirm the effectiveness of certain adopted management actions or to establish a need for possible new management intervention. There have been limited studies on the trend analysis of water quality data, mainly due to the absence of longer periods of data in comparison to streamflow and rainfall data (e.g., Vasiljevic et al. 2012; Chen et al. 2013; Yilmaz & Perera 2013; Yilmaz et al. 2014; Laz et al. 2014).

Many water quality monitoring networks have been established in Australia with the primary objective of detecting temporal trends in water quality to meet ANZECC guidelines (ANZECC 2000). Statistical tests for trend analysis provide evidence, if a trend is detected, but not the reason and hence the reason for the change/trend should be investigated (WQA 2013). There are many previous research studies on spatial and temporal changes in water quality in river systems, such as the Han River in South Korea (Chang 2008), the Struma River in Bulgaria (Astel et al. 2007), the Lake Tahoe basin in the USA (Stubblefield et al. 2007), the Amu Darya River in Central Asia (Crosa et al. 2006), the water bodies of the New Seine River in France (Meybeck 2002) and the Frome River in the UK (Hanrahan et al. 2003). It is important to understand the land–water relationship in a watershed to minimise pollution from happening and to plan for a sustainable future (Wang 2001).

Many peri-urban rivers draining from extensive urban and agricultural areas in Australia have become highly degraded over the past few decades and remain a sensitive issue in the agenda of river management authorities (Pinto & Maheshwari 2011). With the expansion of cities into peri-urban areas, there has been a rapid increase in the number of sewage treatment plants (STPs) that discharge effluent into peri-urban waterways, which may affect the river water quality. Similarly, land use patterns can alter the quality and quantity of nutrients and sediment-rich stormwater runoff during high runoff events (Pinto et al. 2012). Algal blooms in Australian freshwaters cost the community between AUD180 and AUD240 million every year (Atech 2000). Rivers that are severely impacted due to anthropogenic influence are said to be suffering from ‘urban stream syndrome’ (Walsh et al. 2005). Hence, prediction of water quality for river health management and issuing short- and long-term advisories on the suitability of water quality to a wide range of river users are important factors in water quality management (Pinto et al. 2012).

Sydney is the most populous city in Australia, with a population of over 4.5 million. The surroundings of Sydney are highly urbanized compared with the rest of Australia due to continued high residential developments in the region over the past several decades. Populations have moved away from city centres to the peri-urban surrounding areas at higher rates, resulting in exponential increases in commercial and residential developments.

The Hawkesbury–Nepean River System (HNRS) is one of the most important river systems in Sydney as it supplies over 90% of Sydney's potable water. It is vital to preserve and enhance the water quality of the HNRS to safeguard Sydney's potable water supply and to support various recreational activities, such as swimming and boating, that regularly take place in this river system. Hence, it is important to examine the trends of water quality parameters and assess the river water quality against water quality standards/guidelines (Markich & Brown 1998).

To date, there have been limited studies on the trend analysis of the water quality parameters for the HNRS. Therefore, the objectives of this paper are threefold: (i) to assess the trends of various water quality parameters at different monitoring stations along the HNRS using the latest data; (ii) to examine whether the observed water quality parameters meet the ANZECC (2000) trigger values; and (iii) to identify possible links between the observed water quality at different monitoring stations and the surrounding land uses. These results are then used to make an overall assessment of the water quality of the HNRS. It is expected that the outcomes of this study would assist in setting up appropriate management strategies to improve the overall health of the HNRS in the near future.

METHODOLOGY

In this study, the median values of various water quality parameters were compared against the ANZECC (2000) guidelines for fresh water to assess the water quality of the HNRS. Trend analysis was undertaken to assess whether a water quality parameter had improved over time.

The rank-based non-parametric Mann–Kendall (MK) statistical test was used to assess the trends in water quality parameters (Mann 1945; Kendall 1975). The MK test was performed at a significance level of 0.05. The main reason for using non-parametric statistical tests is that these tests are more suitable for non-normally distributed and censored data, which are frequently encountered in hydro-meteorological time series (Laz et al. 2014). For the MK test, data are not needed to conform to any particular distribution, and moreover, it has less sensitivity to data gaps. In this study, Sen's slope estimator was also used to estimate the magnitude of the trend (Sen 1968).

The MK test is based on the test statistics (S) defined as follows (Equation (1)): 
formula
1
where sgn(Θ) is as Equation (2): 
formula
2
where xi and xj are the sequential data values, n is the length of the data set, and E(S) and V(S) are as follows (Equations (3) and (4)): 
formula
3
 
formula
4
where ti is the number of ties of extent i. The standard test statistics Z is computed by Equation (5): 
formula
5
A positive value of Z indicates an increasing trend while a negative value indicates a decreasing trend. When testing either increasing or decreasing monotonic trends at an α significance level, the null hypothesis is rejected for absolute values of Z greater than Z(1−α/2), obtained from the standard normal cumulative distribution table.
Sen's method uses a linear model to estimate whether the slope of the trend and variance of the residuals remain constant over time (Drápela & Drápelová 2011). If a linear trend is present in a time series, the true slope (change per unit time) can be estimated by using a simple non-parametric procedure (Drápela & Drápelová 2011). This linear model F(t) can be described as follows (Equation (6)): 
formula
6
where Q is the slope and B is a constant.
Slopes of all data pairs are calculated and the median value is taken as the Sen's slope (Equation (7)): 
formula
7
where xk is data at sequence k.

STUDY AREA AND DATA

This study focuses on the HNRS located in NSW, Australia. The HNRS is one of the longest coastal rivers in eastern Australia. It supplies over 90% of Sydney's potable water, as well as being used for a variety of agricultural, industrial, recreational and tourist activities. The HNRS is a combination of two major rivers, the Nepean River (155 km) and the Hawkesbury River (145 km) (Markich & Brown 1998). This river system has been subjected to multiple disturbances since European settlement, including extensive clearing of over 37% of the catchment for agriculture, urban and industrial land use, nutrient enrichment associated with sewage, urban runoff and wastewater disposal, extractive industries, regulation and diversion of river flows and mining sites with minimal anthropogenic disturbance (Gehrke & Harris 1996; Markich & Brown 1998; Gehrke et al. 1999). Approximately 66% of the HNR catchment is forested (of which one-half consists of national parks), agricultural land comprises 28%, and 6% has been developed for urban and industrial use (Markich & Brown 1998). Many of the tributary sub-catchments to the north and north-west are undeveloped, rugged and forested, whereas the flatter terrain to the west and south-west of Sydney, originally pastoral and agricultural land, is becoming increasingly urbanised. Urbanisation within the Hawkesbury–Nepean catchment has invariably led to the input of pollutants into the river system from non-point sources such as urban runoff, and point sources such as licensed discharges of effluents from sewage treatment plants and light industry (Markich & Brown 1998). With the population growth, land cover is changing day by day. Agricultural runoff is also an important source of pollutants in the catchment.

The size of the Hawkesbury–Nepean catchment is 22,000 km2, with the portion of the catchment downstream of Warragamba Dam being relatively small. The estuarine section is approximately 5,000 ha in area, or 0.2% of the total catchment area, but the estuary receives much of the upstream runoff in the form of sediments and pollutants, and is dependent on flooding and tidal flushes to remove these inputs (Williams & Thiebaud 2007).

In this study, a total of 25 physical, chemical and biological water quality parameters have been considered (Table 1) from nine water quality monitoring stations in the HNRS covering the period from 2002 to 2013. These water quality parameters were measured by the Sydney Catchment Authority fortnightly following the standard testing methods (e.g., Australian Standards for Microbiology, the APHA Handbook (Franson 2005) and USEPA methods) (SCA 2012). The locations of the selected water quality monitoring stations are presented in Table 2 and Figure 1. A schematic diagram of the HNRS with the land use details is presented in Figure 2 (Sydney Water 2012).
Table 1

Water quality parameters considered in this study

Water quality parameterAbbreviationUnits
pH PH  
Temperature TEMP Deg C 
Dissolved oxygen DO mg/L 
Conductivity field EC mS/cm 
Suspended solids SS mg/L 
Turbidity TUR NTU 
True colour TCOL  
Nitrogen total TN mg/L 
Nitrogen oxidised NO mg/L 
Nitrogen ammonical NH-N mg/L 
Nitrogen TKN TKN mg/L 
Phosphorus total TP mg/L 
Phosphorus filterable FP mg/L 
Chlorophyll-a CHLA ug/L 
Alkalinity ALK mgCaCO3/L 
Dissolved organic carbon DOC mg/L 
Iron total TI mg/L 
Iron filtered FI mg/L 
Aluminium total TA mg/L 
Aluminium filtered FA mg/L 
Manganese total TM mg/L 
Manganese filtered FM mg/L 
Reactive silicate RS  
Escherichia coli ECOL orgs/100 mL 
Enterococci ECOC cfu/100 mL 
Water quality parameterAbbreviationUnits
pH PH  
Temperature TEMP Deg C 
Dissolved oxygen DO mg/L 
Conductivity field EC mS/cm 
Suspended solids SS mg/L 
Turbidity TUR NTU 
True colour TCOL  
Nitrogen total TN mg/L 
Nitrogen oxidised NO mg/L 
Nitrogen ammonical NH-N mg/L 
Nitrogen TKN TKN mg/L 
Phosphorus total TP mg/L 
Phosphorus filterable FP mg/L 
Chlorophyll-a CHLA ug/L 
Alkalinity ALK mgCaCO3/L 
Dissolved organic carbon DOC mg/L 
Iron total TI mg/L 
Iron filtered FI mg/L 
Aluminium total TA mg/L 
Aluminium filtered FA mg/L 
Manganese total TM mg/L 
Manganese filtered FM mg/L 
Reactive silicate RS  
Escherichia coli ECOL orgs/100 mL 
Enterococci ECOC cfu/100 mL 
Table 2

Water quality monitoring stations used in this study

Site codeSiteLongitudesLatitudes
N92 Nepean River at Maldon Weir upstream of Stone Quarry Creek and Picton Sewage Treatment Plant 150.62 −34.2 
N75 Nepean River at Sharpes Weir downstream of Matahil Creek and Camden Sewage Treatment Plant 150.67 −34.03 
N67 Nepean River at Wallacia Bridge upstream of Warragamba River 150.63 −33.86 
N57 Nepean River at Penrith Weir upstream of Boundary Creek and Penrith Sewage Treatment Plant 150.68 −33.74 
N44 Nepean River at Yarramundi Bridge upstream of Grose River 150.69 −33.61 
N42 Hawkesbury River at North Richmond upstream of North Richmond Water Treatment Works 150.71 −33.59 
N35 Hawkesbury River at Wilberforce upstream of Cattai Creek 150.83 −33.58 
N21 Hawkesbury River at Lower Portland upstream of Colo River 150.88 −33.43 
N14 Hawkesbury River at Wisemans Ferry downstream of Car Ferry 150.98 −33.38 
Site codeSiteLongitudesLatitudes
N92 Nepean River at Maldon Weir upstream of Stone Quarry Creek and Picton Sewage Treatment Plant 150.62 −34.2 
N75 Nepean River at Sharpes Weir downstream of Matahil Creek and Camden Sewage Treatment Plant 150.67 −34.03 
N67 Nepean River at Wallacia Bridge upstream of Warragamba River 150.63 −33.86 
N57 Nepean River at Penrith Weir upstream of Boundary Creek and Penrith Sewage Treatment Plant 150.68 −33.74 
N44 Nepean River at Yarramundi Bridge upstream of Grose River 150.69 −33.61 
N42 Hawkesbury River at North Richmond upstream of North Richmond Water Treatment Works 150.71 −33.59 
N35 Hawkesbury River at Wilberforce upstream of Cattai Creek 150.83 −33.58 
N21 Hawkesbury River at Lower Portland upstream of Colo River 150.88 −33.43 
N14 Hawkesbury River at Wisemans Ferry downstream of Car Ferry 150.98 −33.38 
Figure 1

Locations of the nine sampling stations adopted in this study (BOM 2013).

Figure 1

Locations of the nine sampling stations adopted in this study (BOM 2013).

Figure 2

Schematic diagram of the HNRS with land use details.

Figure 2

Schematic diagram of the HNRS with land use details.

RESULTS

The median water quality parameters and the corresponding ANZECC (2000) trigger values are presented in Table 3, where the medians above the trigger values are marked in bold. The trend test results for the water quality parameters are summarised in Table 4, where an upward and downward arrow indicate an upward and downward trend respectively, and a dash (–) indicates no detected trend at 5% level of significance.

Table 3

Median values of water quality parameters and ANZECC (2000) guidelines

N14N21N35N42N44N57N67N75N92ANZECC trigger value
PH 7.47 7.60 7.50 7.69 7.70 7.77 7.79 7.88 8.21 6–8 
TEMP 20.90 21.10 20.90 20.50 20.90 20.70 20.50 20.70 19.05  
DO 7.85 8.70 8.00 9.00 8.60 9.15 8.40 9.00 9.47 Minimum 5 
EC 6.28 0.34 0.40 0.25 0.34 0.28 0.57 0.54 0.36 0.35 
SS 12.00 8.00 12.00 2.00 2.00 2.00 4.00 3.00 2.00 20 
TUR 11.00 8.31 13.60 2.42 2.70 1.76 5.13 3.80 1.40 20 
TCOL 8.00 10.50 12.00 10.00 11.00 9.00 10.00 11.00 10.00 15 
TN 0.40 0.42 0.82 0.50 0.70 0.35 0.61 1.20 0.43 0.350 
NO 0.12 0.05 0.44 0.26 0.40 0.07 0.26 0.77 0.20 0.250 
NH-N 0.01 0.01 0.02 0.01 0.02 0.01 0.01 0.02 0.01 1.000 
TKN 0.27 0.30 0.40 0.24 0.34 0.25 0.36 0.44 0.25  
TP 0.02 0.02 0.04 0.01 0.02 0.01 0.02 0.02 0.01 0.050 
FP 0.01 0.01 0.01 0.01 0.01 0.00 0.01 0.01 0.01 20 
CHLA 8.55 18.90 18.20 5.10 6.20 3.80 5.80 8.50 3.00 
ALK 44.75 32.75 46.00 32.00 49.00 40.00 81.50 83.50 122.00 20 
DOC 4.00 4.30 5.00 4.00 4.90 4.40 5.10 5.20 4.00  
TI 0.43 0.34 0.53 0.28 0.18 0.19 0.24 0.19 0.15 0.300 
FI 0.05 0.05 0.05 0.12 0.07 0.08 0.05 0.05 0.08  
TA 0.24 0.14 0.25 0.04 0.04 0.02 0.10 0.06 0.03 0.200 
FA 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.020 
TM 0.04 0.04 0.06 0.03 0.04 0.03 0.07 0.03 0.02 0.100 
FM 0.01 0.00 0.01 0.01 0.02 0.01 0.02 0.00 0.00  
RS 1.21 0.80 1.40 2.23 1.40 1.82 1.50 1.93 1.59  
ECOL 12.00 5.00 23.00 11.00 46.00 55.00 22.00 23.00 9.00  
ECOC 6.00 6.00 26.00 20.00 53.00 50.00 40.00 20.00 11.00  
N14N21N35N42N44N57N67N75N92ANZECC trigger value
PH 7.47 7.60 7.50 7.69 7.70 7.77 7.79 7.88 8.21 6–8 
TEMP 20.90 21.10 20.90 20.50 20.90 20.70 20.50 20.70 19.05  
DO 7.85 8.70 8.00 9.00 8.60 9.15 8.40 9.00 9.47 Minimum 5 
EC 6.28 0.34 0.40 0.25 0.34 0.28 0.57 0.54 0.36 0.35 
SS 12.00 8.00 12.00 2.00 2.00 2.00 4.00 3.00 2.00 20 
TUR 11.00 8.31 13.60 2.42 2.70 1.76 5.13 3.80 1.40 20 
TCOL 8.00 10.50 12.00 10.00 11.00 9.00 10.00 11.00 10.00 15 
TN 0.40 0.42 0.82 0.50 0.70 0.35 0.61 1.20 0.43 0.350 
NO 0.12 0.05 0.44 0.26 0.40 0.07 0.26 0.77 0.20 0.250 
NH-N 0.01 0.01 0.02 0.01 0.02 0.01 0.01 0.02 0.01 1.000 
TKN 0.27 0.30 0.40 0.24 0.34 0.25 0.36 0.44 0.25  
TP 0.02 0.02 0.04 0.01 0.02 0.01 0.02 0.02 0.01 0.050 
FP 0.01 0.01 0.01 0.01 0.01 0.00 0.01 0.01 0.01 20 
CHLA 8.55 18.90 18.20 5.10 6.20 3.80 5.80 8.50 3.00 
ALK 44.75 32.75 46.00 32.00 49.00 40.00 81.50 83.50 122.00 20 
DOC 4.00 4.30 5.00 4.00 4.90 4.40 5.10 5.20 4.00  
TI 0.43 0.34 0.53 0.28 0.18 0.19 0.24 0.19 0.15 0.300 
FI 0.05 0.05 0.05 0.12 0.07 0.08 0.05 0.05 0.08  
TA 0.24 0.14 0.25 0.04 0.04 0.02 0.10 0.06 0.03 0.200 
FA 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.020 
TM 0.04 0.04 0.06 0.03 0.04 0.03 0.07 0.03 0.02 0.100 
FM 0.01 0.00 0.01 0.01 0.02 0.01 0.02 0.00 0.00  
RS 1.21 0.80 1.40 2.23 1.40 1.82 1.50 1.93 1.59  
ECOL 12.00 5.00 23.00 11.00 46.00 55.00 22.00 23.00 9.00  
ECOC 6.00 6.00 26.00 20.00 53.00 50.00 40.00 20.00 11.00  
Table 4

Mann-Kendal test results and yearly Sen's slope

pHTEMPDOECSSTURTCOLTNNONH-NTKNTPFPCHLAALKDOCTIFITAFATMFMRSECOLECOC
N14 ↓ ↑ ↓ ↓ ↓ ↑ ↑ ↑ ↑ – – – – ↑ ↑ ↑ ↑ ↑ – – – – ↓ ↓ ↑ 
 0.065 0.307 0.109 3.502 0.317 1.040 1.391 0.003 0.003     1.625 6.742 0.442 0.039 0.005     0.029 1.131 0.723 
N21 ↓ – ↓ ↓ – ↑ ↑ – – – ↓ – – ↑ ↑ ↑ ↑ ↑ ↑ – ↓ – ↑ ↑ ↑ 
 0.073  0.148 0.060  0.372 1.084    0.013   0.026 2.569 0.143 0.036 0.018 0.003  0.003  0.008 0.406 0.499 
N35 ↓ ↑ ↓ ↓ ↑ ↑ ↑ ↓ ↓ – ↓ – – ↓ ↑ ↑ ↑ ↑ ↑ – ↑ ↑ ↑ ↑ ↑ 
 0.078 0.130 0.317 0.023 0.801 1.438 0.998 0.109 0.081  0.018   0.751 2.954 0.325 0.062 0.016 0.013  0.008 0.008 0.239 3.000 1.009 
N42 ↓ ↑ ↓ ↓ – ↑ ↑ ↓ ↓ – ↓ – – ↑ ↑ ↑ ↑ ↑ ↑ – ↑ ↑ ↑ ↑ ↑ 
 0.047 0.143 0.060 0.023  0.484 1.084 0.049 0.047  0.008   0.497 2.785 0.122 0.042 0.018 0.013  0.003 0.003 0.122 1.856 3.206 
N44 ↑ ↑ ↑ ↓ – ↑ ↑ ↓ ↓ ↓ ↓ – – ↑ ↑ ↑ ↑ ↑ ↑ – ↑ ↑ ↑ ↓ ↓ 
 0.013 0.281 0.185 0.031  0.380 1.040 0.096 0.081 0.003 0.010   1.022 4.228 0.096 0.036 0.016 0.008  0.003 0.003 0.096 1.999 2.600 
N57 ↓ – ↑ ↓ – ↑ ↑ ↑ ↑ – − – – ↑ ↑ ↑ ↑ ↑ ↑ – ↑ – ↑ ↑ ↓ 
 0.073  0.083 0.010  0.364 0.634 0.013 0.003     0.736 2.642 0.224 0.031 0.013 0.005  0.003  0.101 12.667 0.702 
N67 ↓ – – ↓ – ↑ ↑ ↓ ↑ ↓ ↓ – – ↑ ↑ – ↑ ↑ ↑ – ↑ ↑ ↑ ↑ ↑ 
 0.065   0.070  0.983 0.650 0.039 0.003 0.003 0.029   0.679 10.517  0.055 0.016 0.010  0.005 0.003 0.096 3.588 4.703 
N75 ↓ ↑ ↓ ↓ – ↑ ↑ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↑ – ↑ – ↑ ↑ ↑ 
 0.125 0.354 0.231 0.081  0.676 0.541 0.530 0.421 0.005 0.104 0.003 0.003 0.198 12.467 0.075 0.055 0.021 0.013  0.005  0.026 6.323 3.182 
N92 ↓ ↑ ↑ ↓ – ↓ ↑ ↓ ↓ ↓ ↓ – – ↓ ↑ ↑ ↑ ↑ ↑ – ↑ – ↑ ↑ ↑ 
 0.114 0.195 0.109 0.068  0.052 1.019 0.075 0.039 0.003 0.023   0.406 25.732 0.216 0.029 0.234 0.003  0.003  0.213 1.547 2.062 
pHTEMPDOECSSTURTCOLTNNONH-NTKNTPFPCHLAALKDOCTIFITAFATMFMRSECOLECOC
N14 ↓ ↑ ↓ ↓ ↓ ↑ ↑ ↑ ↑ – – – – ↑ ↑ ↑ ↑ ↑ – – – – ↓ ↓ ↑ 
 0.065 0.307 0.109 3.502 0.317 1.040 1.391 0.003 0.003     1.625 6.742 0.442 0.039 0.005     0.029 1.131 0.723 
N21 ↓ – ↓ ↓ – ↑ ↑ – – – ↓ – – ↑ ↑ ↑ ↑ ↑ ↑ – ↓ – ↑ ↑ ↑ 
 0.073  0.148 0.060  0.372 1.084    0.013   0.026 2.569 0.143 0.036 0.018 0.003  0.003  0.008 0.406 0.499 
N35 ↓ ↑ ↓ ↓ ↑ ↑ ↑ ↓ ↓ – ↓ – – ↓ ↑ ↑ ↑ ↑ ↑ – ↑ ↑ ↑ ↑ ↑ 
 0.078 0.130 0.317 0.023 0.801 1.438 0.998 0.109 0.081  0.018   0.751 2.954 0.325 0.062 0.016 0.013  0.008 0.008 0.239 3.000 1.009 
N42 ↓ ↑ ↓ ↓ – ↑ ↑ ↓ ↓ – ↓ – – ↑ ↑ ↑ ↑ ↑ ↑ – ↑ ↑ ↑ ↑ ↑ 
 0.047 0.143 0.060 0.023  0.484 1.084 0.049 0.047  0.008   0.497 2.785 0.122 0.042 0.018 0.013  0.003 0.003 0.122 1.856 3.206 
N44 ↑ ↑ ↑ ↓ – ↑ ↑ ↓ ↓ ↓ ↓ – – ↑ ↑ ↑ ↑ ↑ ↑ – ↑ ↑ ↑ ↓ ↓ 
 0.013 0.281 0.185 0.031  0.380 1.040 0.096 0.081 0.003 0.010   1.022 4.228 0.096 0.036 0.016 0.008  0.003 0.003 0.096 1.999 2.600 
N57 ↓ – ↑ ↓ – ↑ ↑ ↑ ↑ – − – – ↑ ↑ ↑ ↑ ↑ ↑ – ↑ – ↑ ↑ ↓ 
 0.073  0.083 0.010  0.364 0.634 0.013 0.003     0.736 2.642 0.224 0.031 0.013 0.005  0.003  0.101 12.667 0.702 
N67 ↓ – – ↓ – ↑ ↑ ↓ ↑ ↓ ↓ – – ↑ ↑ – ↑ ↑ ↑ – ↑ ↑ ↑ ↑ ↑ 
 0.065   0.070  0.983 0.650 0.039 0.003 0.003 0.029   0.679 10.517  0.055 0.016 0.010  0.005 0.003 0.096 3.588 4.703 
N75 ↓ ↑ ↓ ↓ – ↑ ↑ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↑ – ↑ – ↑ ↑ ↑ 
 0.125 0.354 0.231 0.081  0.676 0.541 0.530 0.421 0.005 0.104 0.003 0.003 0.198 12.467 0.075 0.055 0.021 0.013  0.005  0.026 6.323 3.182 
N92 ↓ ↑ ↑ ↓ – ↓ ↑ ↓ ↓ ↓ ↓ – – ↓ ↑ ↑ ↑ ↑ ↑ – ↑ – ↑ ↑ ↑ 
 0.114 0.195 0.109 0.068  0.052 1.019 0.075 0.039 0.003 0.023   0.406 25.732 0.216 0.029 0.234 0.003  0.003  0.213 1.547 2.062 

The median values of pH at all the stations except N92 are within the trigger values (i.e., between 6 and 8). At station N92, the pH is 2.6% above the upper limit of the trigger value. The change in the median value of pH along the river is presented in Figure 3, which shows that pH reduces from upstream to downstream of the HNRS, i.e., an increasing acidification from upstream to downstream. The decreasing pH level from upstream to downstream of the HNRS is related to the higher level of urbanisation and agricultural activity towards the downstream of the river. The upstream of the HNRS is mainly pristine; however, the downstream is located within the peri-urban and urban regions of Sydney where the degree of urbanisation and industrialisation is notably high. The pH shows a decreasing trend for all the stations except for station N44. The maximum level of decrease in trend is seen for station N75 (0.125 per year). The overall decreasing trend of pH indicates an increasing acidification of water in the HNRS over the last decade.
Figure 3

Median values of pH along the HNRS.

Figure 3

Median values of pH along the HNRS.

The median values of dissolved oxygen (DO) at all the nine stations are above the ANZECC (2000) trigger value. The DO has a decreasing trend for five stations. Its maximum decreasing trend of 0.317 mg/L per year is seen for station N35. The upstream of N35 is affected by quality and magnitude of runoff coming from the South Creek and the discharge from North Richmond STP. The dominant land uses in this part of the catchment include rural, grazing, commercial gardening, intensive agriculture and urban and industrial activities. These land uses can be attributed to the decreasing trend of DO at N35. The increasing trends of DO for N92 and N57 demonstrate the influence of natural undeveloped catchment upstream of these two stations.

The median values for electrical conductivity are higher than the trigger values for stations N14, N35, N67, N75 and N92. In particular, the median value for station N14 is 10 times higher than the ANZECC (2000) trigger value, which is much higher than any other station. Electrical conductivity has a decreasing trend for all the stations, with a maximum decreasing trend of 3.5 mS/cm per year at station N14. Its overall decreasing trend for all the nine stations demonstrates an overall improvement of the HNRS water quality in terms of the total solids dissolved in water.

The median suspended solids (SS) levels are within the ANZECC (2000) trigger value for all the nine stations. For most of the stations SS does not show any trend; however, it has a decreasing trend at station N14 (0.317 mg/L per year) and an increasing trend at N35 (0.801 mg/L per year). The low SS levels in the river indicate that the river water is not notably polluted with particulate matter, which is a positive aspect of the water quality of the HNRS.

The median values for turbidity are well within the ANZECC (2000) trigger value for all the nine stations. However, it has an increasing trend at all the stations except N92. It should be noted that station N92 is located at the most upstream part of the river of all nine stations (see Figure 2). This part of the river has the lowest level of anthropogenic activity as it has the smallest degree of urbanisation and industrialisation, and thus has the lowest turbidity level (1.40 NTU) with an overall decreasing trend. The increasing trend of turbidity for eight of the nine stations demonstrates the influence of increasing urbanisation and industrialisation within the downstream parts of the catchment that has intensified over recent time. The construction activity in urban areas of Sydney has been increasing notably with time, which is likely to result in a greater volume of sediments being transported to the HNRS, leading to a higher turbidity level.

The median values for total nitrogen (TN) are above the ANZECC (2000) trigger value for eight out of the nine stations. In the boxplot (Figure 4), it can be seen that for N35, N42, N44, N67 and N75, the TN values are above the ANZECC (2000) trigger value for most of the samples. Also, the TN shows an increasing trend at N14 and N57. At stations N35, N42, N44, N67 and N74, the median values for oxidised nitrogen are above the ANZECC (2000) trigger value by 76.0%, 3.6%, 60.0%, 4.0% and 208.0%, respectively. It has an increasing trend for N14, N57 and N67. Ammonical nitrogen shows a decreasing trend or no trend for all the stations. All the median values are within the ANZECC (2000) guidelines. Nitrogen TKN (total Kjeldahl nitrogen) has decreasing trends for all the stations except for stations N14 and N57; the maximum slope of 0.104 mg/L can be seen for station N75. Considering the median values, it may be stated that NOx is the main contribution to the high value of TN. The median value of TN for N75 is 242% higher than the ANZECC (2000) trigger value, which appears to be associated with the intensive agricultural activities in the catchment part located upstream of station N75. The reduction of TN from N75 to N67 by 49% and from N67 to N57 by 43% can be attributed to the natural pristine undeveloped condition of the HNRS in between N75 to N67 and N57. Furthermore, the agricultural activities upstream of N44 have possibly increased the TN value at N44. The overall TN levels in the HNRS are notably higher than the ANZECC (2000) trigger value, which is likely to make the river prone to eutrophication.
Figure 4

Distribution of TN levels at nine sampling points.

Figure 4

Distribution of TN levels at nine sampling points.

The median total phosphorus and filterable phosphorus levels are within the ANZECC (2000) trigger values for all the stations; however, for station N35, the total phosphorus level is very close to the trigger value (0.04 versus 0.05). No station shows a significant trend for total phosphorus except N75, which shows a decreasing trend.

The determination of photosynthetic chlorophyll pigments and their degradation products are frequently performed analyses in aquatic ecology (Gitelson 1992). The median values for chlorophyll-a are above the ANZECC (2000) trigger value for seven of the nine stations. Figure 5 shows the box plot of sample values, which shows that for N14, N21, N35, N44 and N75 the observed values are mostly above the ANZECC (2000) trigger value. Stations N92 and N57, which flow through natural undeveloped parts of the catchments, have median values within the ANZECC (2000) trigger value. The median value of chlorophyll-a for station N75 is 70% higher than the trigger value. It has been reduced by 68% while flowing through the natural undeveloped parts of the catchment between N75 and N67. It is expected that water quality would be improved at N67 due to nutrient assimilation and loss processes while travelling this section of the catchment without further input of nutrients. The median value for chlorophyll-a has been further improved at station N57, demonstrating further assimilation of nutrients while flowing through a pristine catchment area which is largely undeveloped. The Warragamba River joins the Nepean River in this section, carrying discharge from the Wallacia STP as well as environmental flow releases from the Warragamba dam. Nutrients that entered via Matahil Creek from the West Camden plant and via the Warragamba River from Wallacia STP experience long residence time and distance from assimilation, as well as dilution by low nutrient water from Warragamba dam. When considering the median values for chlorophyll-a at stations N35 and N21 (which are 374 and 378% higher than the ANZECC (2000) trigger value), it can be seen that industrialisation, urban developments and agricultural activities in the catchment have contributed to degrade the water quality. The land use at the upstream part of the catchment of N35 predominantly includes rural, grazing and market gardening, intensive agriculture, such as poultry farming, and both urban and industrial activities. Also it receives water from the South Creek tertiary treated wastewater discharges from three STPs. The high nutrient level, tidal influence, high residence time and low flows make it ideal for excessive algal growth and hence there is a very high chlorophyll-a level at N35 and N21. Figure 6 presents how the median values of chlorophyll-a have changed along the river, which shows a remarkably high peak at stations N35 and N21. A good sign though is that station N35 shows a downward trend for chlorophyll-a.
Figure 5

Distributions of chlorophyll-a levels at nine sampling points.

Figure 5

Distributions of chlorophyll-a levels at nine sampling points.

Figure 6

Median values of chlorophyll-a along the HNRS.

Figure 6

Median values of chlorophyll-a along the HNRS.

Station N14 is located just before the confluence with the Macdonald River. The water quality of this site is influenced by flow from the Colo River and downstream of the Hawkesbury River. The Colo River catchment is the best in terms of nutrient enrichments among all the other sub-catchments of the HNRS because it consists primarily of pristine and undisturbed areas. About 80% of these catchments are national parks of the Blue Mountains world heritage area. There are also limited upstream areas that support agricultural activities. Water quality at station N14 has been improved, as expected, because of dilution by high quality inflows from the Colo River and the undisturbed upstream catchment. Algae growth, and thus chlorophyll-a level, has directly been affected by the amount of nutrients in the river (e.g., Station 35 has a very high chlorophyll-a level and it has the highest total phosphorus level and the second highest TN level of the nine stations). Low level of chlorophyll-a suggests good river health; however, a high level may not be necessarily bad. It is the long-term persistence of high levels that is a problem (NLWRA 2008). It should also be noted that six out of the nine stations show an increasing trend for chlorophyll-a, indicating an overall deterioration of water quality over the last decade. This is likely to be related to the increasing trend of urbanisation and industrialisation of the HNRS, in particular along the lower parts of the catchment.

The median values of alkalinity are above the guidelines for all the nine stations: N14 (123.7%), N21 (63.7%), N35 (130%), N42 (60%), N44 (145%), N57 (100%), N67 (307.5%), N75 (317.5%) and N92 (510%). It has an increasing trend for eight of the nine stations, with the maximum trend being seen for station N92 (25.7 mg/L per year), which has a median value of 510% above the trigger value. It should be noted that station N92 is located the most upstream among all the nine stations and the highest level of alkalinity at this station is somewhat unexpected, which needs further investigation.

Dissolved organic carbon shows an increasing trend for eight of the nine stations. Organic carbon occurs as the result of decomposition of plant or animal material. Total aluminium has an increasing trend for all the stations except for N14. The median values are within ANZECC (2000) guidelines for all the stations except N14 and N35, which are 20% and 25% above the ANZECC (2000) trigger value, respectively. Filtered aluminium does not show any trend for most of the stations. It was found in a study of the water quality of Roanoke River, Virginia that the STPs were the most significant anthropogenic contributor of aluminium to the river (Butcher 1988). Total manganese shows an increasing trend at all the stations except for N21 and N14. Its median values are within the ANZECC (2000) trigger value for all the stations. Filtered manganese shows an increasing trend for most of the stations. Reactive silicate shows an increasing trend for all the stations except for N14. It has the maximum increasing trend at N35. The ratios between silicate and phosphorus, and silicate and nitrogen, largely determine which algae is dominantly present in the water. Water moving over and through natural deposits is expected to dissolve a small amount of various silicate minerals. The overall increasing trends of aluminium, manganese and reactive silicate demonstrate the influence of intensified land use in recent years that has occurred along the HNRS.

CONCLUSIONS

With the aim of finding a link between land use and surface water quality, this study examines the water quality of the HNRS, located in New South Wales, Australia, using data for 25 water quality parameters from nine sampling stations covering a period of 12 years. It is found that the trend of a given water quality parameter varies from station to station along the river system, demonstrating the differences in land use that dominate different parts of the catchment. A general pattern of downward trends of pH, nitrogen TKN, alkalinity, DO and electrical conductivity is detected. Total iron, filterable iron, true colour, total aluminium, reactive silicate and dissolved organic carbon demonstrate an increasing trend at most of the selected stations, and total phosphorus, SS, filterable aluminium, ammonical nitrogen and filterable phosphorus do not show any trend at most of the stations. The median values for chlorophyll-a, TN and alkalinity are above ANZECC (2000) trigger values for most of the stations. The increasing trend of turbidity, chlorophyll-a, alkalinity, dissolved organic carbon, total iron, total aluminium, total manganese and reactive silicate, and the exceedance of the ANZECC (2000) trigger values for chlorophyll-a, TN and alkalinity, indicate an overall water quality deterioration in the HNRS during the last decade. The parameters such as phosphorus, SS and ammonical nitrogen do not show any marked change over the period of study. Although an improvement in water quality can be seen at some stations downstream of the undisturbed parts of the catchment, there is an overall water quality deterioration in the HNRS during the last decade. Better land use planning is recommended to achieve an overall improvement in the water quality of the HNRS in the future.

It has been found that the observed trends in the water quality data of the HNRS are mainly related to the land use changes. To examine the impacts of climate change on the water quality parameters, a relatively longer data period is needed, which however is not currently available for the HNRS. It is thus recommended that future studies should use a longer database when such data will be available.

ACKNOWLEDGEMENTS

The authors would like to thank Sydney Catchment Authority, New South Wales, Australia for providing water quality data for this study and the anonymous reviewer for making constructive comments, which have helped to improve the paper significantly. Opinions or comments presented in this paper are only of the authors and do not reflect, in any way, that of any of the organisations mentioned in this paper.

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