continuous simulations of nutrients and bod through a stream section
TRANSCRIPT
144 Int. J. Water, Vol. 9, No. 2, 2015
Copyright © 2015 Inderscience Enterprises Ltd.
Continuous simulations of nutrients and BOD through a stream section
Monzur A. Imteaz* and Iqbal Hossain Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, Melbourne, VIC 3122, Australia Email: [email protected] Email: [email protected] *Corresponding author
A.H.M. Faisal Anwar Department of Civil Engineering, Curtin University, GPO Box U1987, PerthWA 6845, Australia Email: [email protected]
Abstract: This paper presents development of a one-dimensional stream water quality model for the continuous simulations of suspended sediments, biochemical oxygen demand (BOD), total phosphorus (TP) and total nitrogen (TN) along a particular stream section. The model first integrates stream hydraulics and suspended sediments transport and deposition processes. Then computations of degradation of water quality parameters (BOD, TP and TN) are incorporated with the hydraulic-sediment transport model. The stream hydraulic model was developed using Muskingum-Cunge method of channel routing. The well known suspended sediment transport/deposition processes have been modified and integrated with the developed hydraulic model. The developed water quality model was applied and simulated for the Saltwater Creek, Gold Coast, Australia. Sensitivity analysis of the model showed that all the particles characteristics within incoming stream flows are important for the better prediction of suspended sediment loads.
Keywords: continuous simulation; suspended sediments; biochemical oxygen demand; BOD; total phosphorus; TP; total nitrogen; TN.
Reference to this paper should be made as follows: Imteaz, M.A., Hossain, I. and Faisal Anwar, A.H.M. (2015) ‘Continuous simulations of nutrients and BOD through a stream section’, Int. J. Water, Vol. 9, No. 2, pp.144–156.
Biographical notes: Monzur A. Imteaz is working as a Senior Lecturer in the Civil Engineering group of Swinburne University of Technology at Melbourne, Australia. He completed his PhD in 1997 on Lake Water Quality Modelling from Saitama University, Japan. After his PhD, he was working with the Institute of Water Modelling, Bangladesh. He was working with several Australian Universities and Government Departments. At Swinburne, he is teaching the subjects ‘urban water resources’ and ‘integrated water design’. He has also been actively involved with various researches on sustainability, water recycling and modelling, developing decision support tools and rainfall forecasting using artificial neural networks.
Continuous simulations of nutrients and BOD through a stream section 145
Iqbal Hossain has received his BSc in Civil Engineering from Bangladesh University of Engineering and Technology in 2005. After that, he was working with the Bangladesh Water Development Board (BWDB). In 2008, he secured a Swinburne University scholarship for doctoral study and completed his PhD in 2012 on Hydrology, Hydraulics and Water Quality Modelling. After the completion of his PhD, he has been working with KF Engineering Pty Ltd. and with the College of Engineering and Science at Victoria University, Melbourne. He has extensive experience in assessing water sensitive road design (WSRD) measures for achieving water quality objectives. He is also teaching the tutorials of infrastructure management project, urban water resources and integrated water design subjects at Swinburne University. He has been actively involved with various researches on water sensitive urban design (WSUD), climate change and water quality modelling.
A.H.M. Faisal Anwar is currently working as a Senior Lecturer in the Department of Civil Engineering at Curtin University in Western Australia. Prior to Curtin, he served as a Lecturer, Assistant Professor and Associate Professor in the Department of Water Resources Engineering at Bangladesh University of Engineering and Technology. He was also a Postdoctoral Research Fellow at Nagoya University, Japan. He is the recipient of a number of Awards including Pro-Vice Chancellor’s Teaching Excellence Award 2011 and Vice-Chancellor’s Teaching Excellence Award 2012 at Curtin University Western Australia and an OLT National Citation Award 2013 from Australian Government. His research interests include groundwater hydrology, climate change effect on water resources, greywater/stormwater management and their quality, contaminant removal from stormwater/wastewater, aquifer remediation and soil pollution and ecological engineering.
1 Introduction
As the recognition of the concept ‘sustainable development’ is increasing throughout the world, assessing adverse impacts of urban development on water quality is highly important for the protection of aquatic environments. Water quality of the seas, lakes and streams depends on the concentration of pollutants (sediments, organic matters and nutrients) brought from the contributed catchment area with surface runoff. During the period of intensive rainfall events, pollutants are transferred to the nearby stream with surface runoff generated from the upstream catchments. Among these transported pollutants, fine sediment particles are settled on the stream bed and in the floodplain, especially during the low flow condition and causes significant impact on the surrounding morphologic environment. Deposition of sediment can raise the stream-bed, reduce the discharge capacity of the stream, affect the navigation facilities and cause floods during high rainfall events. Also sediment transport and/or deposition has significant impacts on water quality as large amounts of organic matter, nutrients and heavy metals are bonded with suspended sediments (Deletic, 2001). Zhou et al. (2003) found that suspended sediment (SS) is closely linked to many environmental problems due to the role of sediment particles in adsorbing and transporting contaminants. Therefore, accurate prediction of sediment and other pollutants and their interactions occurring within the stream is of great importance for the protection and improvement of aquatic ecosystem of the stream environment. However, the severity of the deterioration of an aquatic
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environment depends on the amount of pollutants transported from upstream catchments and the characteristics of receiving environments. Hence, the measurement of water quality parameters is required to protect and improve aquatic environments from the impact of pollution (Fletcher and Deletic, 2007; Vaze and Chew, 2002).
An accurate estimation of runoff and pollutant loads will help watershed management authorities adopt proper mitigation strategies. Inaccurate determination of pollutant loads can lead to the design of undersized and ineffective measures, or oversized measures with excessive capital costs and maintenance burdens. Proper estimation of pollutant loads will help authorities implement appropriate management options and control the transportation of pollutant loads into receiving water bodies (Vaze and Chew, 2004). However, the processes of water quality parameters are interrelated to each other and increase the complexity of the prediction. In addition, the allocated resources for management strategies are small in relation to what is required for the remediation. Therefore, the intensive monitoring, analysis and direct estimation of these pollutants on a wide scale are labour intensive, time consuming and expensive (Davis and Birch, 2009). Consequently, clever management of aquatic ecosystems is essential with the allocated budgetary constraints. Water quality models are frequently used for the prediction of waterborne pollutants in waterways and receiving water bodies.
Water quality models for catchments and lakes were achieved with sufficient accuracy (Hossain et al., 2011, 2012; Imteaz et al., 2009, 2014; Imteaz and Asaeda, 2000). However, generally available river/stream water quality models, MIKE11 (DHI, 2013), XP-SWMM (XP-Software, 2013), QUAL2E (USEPA, 1995), SWAT (Glavan et al., 2011), WASP (Wool et al., 2001), TOMCAT (Cox, 2003) are complex and require huge input data, which often discourage water managers to use these models for their decision making.
This paper presents the development of a one-dimensional (1-D) stream water quality model for the continuous simulation of major water quality parameters along a particular stream reach. The model has two main components: stream flow model and pollutants model. The flow model estimates the rate of stream discharges and the quality model estimates the concentration of waterborne pollutants in stream reach. The advantage of this model is that it can be applied to all streams; it is simple and requires minimal data comparing with other available models. Application of the model was demonstrated for the Saltwater Creek, Gold Coast, Australia. Calibration of the model parameters was performed to determine the parameters of the model for that creek. A sensitivity analysis of model parameters is presented to identify the influential parameters of the model.
2 Governing equations
Stream water quality models typically incorporate both hydraulics and pollutants (and/or sediments) transport components. Modelling of pollutants (and/or sediments) transport processes require describing both pollutants (and/or sediments) and ambient water motions, along with their interactions. Hossain et al. (2013) presented detailed integration of stream hydraulics and sediment transport processes. Following the similar integration processes, current 1-D model was developed to integrating two main processes:
1 hydraulics of stream flow
2 pollutants transport processes.
Continuous simulations of nutrients and BOD through a stream section 147
The hydraulic model estimates the flow of the stream and the pollutants processes estimate the transport, degradation and deposition of the pollutants along a particular stream section.
As a preliminary step, three basic water quality parameters were included for the development of the model, i.e., biochemical oxygen demand (BOD), total nitrogen (TN) and total phosphorus (TP). These pollutants are often used as the indicators of the quality of water as well as their impact on receiving water. Some researchers consider only the dissolve component of the pollutants; however Russel et al. (1998) noted that the consideration of only the dissolved portion of pollutants will underestimate the total nutrients flux. Therefore, TN and TP were considered in developing a meaningful water quality prediction tool. Because of data availability, in the current research only these water quality parameters were selected. However, later other pollutants can be incorporated with the model.
2.1 Stream hydraulics
Stream flow is the major contributor for the transportation and interaction of water quality parameters along a particular stream reach (Ali et al., 2010). Therefore, the stream water quality model starts with the development of the stream hydraulic model. The stream hydraulic model was developed considering the well-known Muskingum-Cunge method of channel routing. This Muskingum method was first developed in the 1930s for the Muskingum River, Ohio (Barry and Bajracharya, 1995). The method is based on the continuity of mass equation within a channel reach. Since the method is simple, efficient and relatively accurate, it has been widely accepted and used in practice (Barry and Bajracharya, 1995; Ponce et al., 1996). The method was theoretically derived by Cunge (1969) by assuming and strictly applying one-to-one stage-discharge relationship. The Muskingum-Cunge model is an improvement of the classical Muskingum model (Chow, 1959).
2.2 Stream pollutants processes
Pollutants processes of the stream water quality model were developed by considering physical, chemical and biological changes of stream water quality parameters. The model starts with development of the BOD model. It is the most important parameter controlling the water pollution. Usually, the processes of BOD decay are described assuming first order kinematics. Although these processes are widely used as an organic quality of water, they are empirical methods. In this research, the main equation employed for the simulation of the BOD decay processes is as follows:
( ){ }_ 1
_
1 BOD sBOD susBOD udk k k tin t t
tout t
Q BOD eBOD
Q
− + − Δ−
⎡ ⎤−⎢ ⎥⎣ ⎦= (1)
where BODt is the concentration of BOD at time ‘t’, BODt–1 is the concentration of BOD at time t–1, kBOD is the rate of oxidation of BOD (i.e., BOD decay rate) (1/day), ksBOD is the rate of BOD loss due to settling (1/day), ksusBOD is the re-suspension rate of BOD (1/day), Δtud is the time taken for a pollutant to reach from the upstream to the
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downstream of a stream reach section (day), Qin t is the inflow discharge at time ‘t’ (m3/s) and Qout_t is the outflow discharge at time ‘t’ (m3/s).
Amongst the factors which influence the BOD decay rate, the temperature is most important (Bowie et al., 1985). The BOD decay occurs at a rate that increases with the increasing temperature up to the point where protein denaturation begins. The following mathematical expression was used in the current model to simulate the temperature effects of BOD.
20( ) (20)
TeBOD Te BOD BOD
BOD
DOk kDO
θδ
−=+
(2)
where kBOD(Te) is the rate of oxidation of BOD at (Te) °C temperature, kBOD(20) is the rate of oxidation of BOD at temperature 20°C, θBOD is the temperature correction factor for organic decay and δBOD is the half saturation constant for BOD.
Later, nutrients model was formulated for the continuous simulation of TN and TP along a particular stream reach. Bowie et al. (1985) noted that in a stream system, the nitrogen dynamics are modelled in complex manner because of their substantial biogeochemical role, important oxidation-reduction reactions and other important water quality variables. In the natural water, the denitrification process reduces the amount of TN. On the other hand, the mineralisation of BOD by the bacteria releases TN in the water. Again, the settling of SS reduces the amount of adsorbed TN in the stream processes. Rusjan et al. (2008) found that the concentration of nitrogen in rainfall is less; and hence the atmospheric input of nitrogen to rainwater is not considered as an important source. Therefore, it is not included in the current modelling. In this study, three major processes were employed in the development of the TN model, i.e., denitrification, mineralisation from BOD and sedimentation. All the processes of TN that occur in a stream reach are described assuming the first order kinematics. The governing equation for the transformation of TN employed in the model is described as follows:
( ){ }_ 1 1
_
1 DEN ud BOD udk t k tin t t TN t sTN SET
tout t
Q TN e k BOD e k SSTN
Q
− Δ − Δ− −− + −
= (3)
where, TNt is the concentration of TN (mg/l) at time ‘t’, TNt–1 is the concentration of TN (mg/l) at time t-1, kDEN is the denitrification coefficient of TN (1/day), kTN is the coefficient for the mineralisation of TN from BOD, ksTN is the release rate of TN from sediment and SSSET is the SS which settles on the bottom.
The temperature influences the rate parameters of all nitrogen transformation processes. Therefore, all the rate coefficients are temperature dependent. A reference temperature of 20°C is usually assumed when specifying each rate coefficient (Bowie et al., 1985). The governing equations for the temperature correction factors are described as follow:
( ) ( )20
20TeDENDEN Te DENk k θ −= (4)
( ) ( )20
20TeTNTN Te TNk k θ −= (5)
Continuous simulations of nutrients and BOD through a stream section 149
( ) ( )20
20TesTNsTN Te sTNk k θ −= (6)
where kDEN(Te) is the denitrification coefficient of TN at (Te) °C temperature (1/day), kDEN(20) is the denitrification coefficient of TN at 20°C temperature (1/day), θDEN is the temperature multiplier for the denitrification coefficient, kTN(Te) is the coefficient for the mineralisation of TN from BOD at (Te) °C temperature (1/day), kTN(20) is the coefficient for the mineralisation of TN from BOD at 20°C temperature (1/day), θTN is the temperature multiplier for the mineralisation of TN from BOD, ksTN(Te) is the release rate of TN from sediment at (Te) °C, ksTN(20) is the release rate of TN from sediment at 20°C and θsTN is the temperature multiplier for sediment TN release.
In the stream, conservation of TP operates similar to TN in many aspects. However, there is no reduction of TP in streamwater, except the settling with SS. In this research, two major processes were considered for the simulation of TP. Mathematical processes of TP that occur in a stream reach were described assuming the first order kinematics. The governing transformation of TP along the stream is as follows:
( ){ }_ 1 1
_
BOD udk tin t t TP t sTP SET TP
tout t
Q TP k BOD e k SSTP
Q
δ− Δ− −+ − +
= (7)
where TPt is the concentration of TP at time ‘t’, TPt–1 is the concentration of TP at time t–1, kTP is the coefficient for the mineralisation of TP from BOD, ksTP is the release rate of TP from sediment and δTP is the adjustment factor for sediment TP release.
Since the rate coefficients are temperature dependent, they require correction for the stream temperature. Similar to temperature effects on TN processes, the governing equations for the temperature correction factors of TP processes are described as follows:
( ) ( )20
20TeTPTP Te TPk k θ −= (8)
( ) ( )20
20TesTPsTP Te sTPk k θ −= (9)
where kTP(Te) is the coefficient for the mineralisation of TP from BOD at (Te) °C, kTP(20) is the coefficient for the mineralisation of TP from BOD at 20°C, θTP is the non-dimensional temperature multiplier for the mineralisation of TP from BOD, ksTP(Te) is the release rate of TP from sediment at (Te) °C, ksTP(20) is the release rate of TP from sediment at 20°C and θsTP is the non-dimensional temperature multiplier for sediment TP release.
3 Model results and discussions
3.1 Hydrologic model
Hydraulic model requires input from hydrologic model in regards to runoff volume and runoff water quality. Current model was calibrated for the Saltwater Creek in Gold Coast, Australia. Hossain et al. (2012) presented detailed calibration results for catchment runoff and water quality parameters at the Saltwater Creek catchment outlet. Simulated results
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from the calibrated catchment water quality model were transferred to the developed stream water quality model, i.e., simulated outputs from the catchment water quality model are used as input for the stream water quality model.
3.2 Hydraulic model calibration
As no discharge measurement was conducted during the water quality data collection period for the creek, the simulation results of the developed hydraulic model were compared with the MUSIC model’s discharge routing along the stream. Model for urban stormwater improvement conceptualisation (MUSIC) is widely used in Australia and was developed by Wong et al. (2002). MUSIC provides the ability to simulate both quantity and quality of stormwater runoff from urban/agricultural/forest/user-defined catchments. Since both the developed model and MUSIC used the Muskingum-Cunge method for stream routing, the results were compared with MUSIC. Figure 1 shows the comparison of the developed hydraulic model discharges with the MUSIC computed discharges for the same stream at a section 1.65 km downstream from the catchment outlet. It can be seen from the figure that the developed model simulated results are very close to MUSIC computed results in regards to runoff. To further ascertain the computational accuracy, statistical comparisons were performed using two error statistics; PBIAS (percent bias) and Nash Sutcliffe efficiency (NSE). The computed value of PBIAS was –0.073; whereas the optimal value is zero. On the other hand NSE value for the model was 0.963; whereas the optimal value is 1.0. Hence, it can be concluded that the developed model can estimate stream flow with sufficient accuracy.
It should be noted that in the stream flow model there are two different options for the calculation of ‘channel reach storage parameter (K)’: fixed and variable. The fixed ‘K’ is given by the user as an input parameter while the variable ‘K’ is determined by the model automatically from the reach length and depth of flow. However, in the MUSIC there is only one option i.e., fixed ‘K’ as an input parameter. In this paper, all the simulation results have been performed by considering the ‘K’ as variable with the depth of flow.
Figure 1 Comparison of stream flow computations (see online version for colours)
Continuous simulations of nutrients and BOD through a stream section 151
3.3 Pollutants model calibration
The parameters of BOD, TN and TP models were estimated for the Saltwater Creek. Estimation of the model parameters were performed with only those water quality data which were collected during selected rainfall events at 1.65 km downstream from the catchment outlet. The initial values of these parameters were selected from available literature. Table 1 summarises the calibrated values of the rate coefficients for the reactive pollutants, i.e., BOD, TN and TP. The calibration results showing comparisons of simulated and measured data are shown in Figures 2 and 3 for TN and TP respectively. Calibrated values of the model parameters are shown in Table 1. Parameter values which are not shown in the table were not considered for the current model. Table 1 The estimated parameters of the reactive pollutants
Parameters Symbol Values Unit
Rate of oxidation of BOD kBOD 0.23 (1/day)
Denitrification coefficient of TN kDEN 0.35 (1/day)
Release rate of TN from BOD kTN 0.75 (1/day)
Release rate of TN from sediment ksTN 0.65 (1/day)
Release rate of TP from BOD kTP 0.40 (1/day)
Release rate of TP from sediment ksTP 0.35 (1/day)
Adjustment factor for sediment TP release δTP 0.31 -
From Figures 2 and 3, it is clear that at the beginning and the end of the simulation, there were more pollutant transportation through the creek. However, during these times, the creek discharge was less. As a result, pollutants per unit discharge increased; and hence the transported pollutant concentrations were higher. Moreover, the input parameters of the stream water quality model were the output of the catchment water quality model. During the beginning and the end of the simulation periods, increased pollutants concentrations were discharged from the catchment to the creek; and hence similar pattern of pollutants concentrations transported towards downstream. There were also sharp changes in the simulations of pollutants at the very beginning and the end of the simulations as shown in the Figures 2 and 3 for TN and TP respectively. This phenomenon also happened due to the incoming pollutants concentrations of the creek. The contributing catchment areas of the creek were mixed land-uses, i.e. impervious and pervious. During the early stages of the rainfall event, only impervious area contributed to the surface runoff as well as pollutants loads, which were comparatively lower. However, as soon both the impervious and pervious areas started contributing to pollutants loads, stream pollutants concentrations increased sharply.
In general the model simulations are reasonably good. Only a slight variation between simulated and measured data is observed for the case of TN. This deviation can be attributed mainly owing to the higher complexity of the phenomena involved in the determination of TN and TP. Indeed the concentration of the variables was the results of the several chemical/physical/biological processes (BOD mineralisation, nitrification, de-nitrification, photosynthesis, atmospheric re-aeration, settling, re-suspension from sediment etc.). A slight miscalculation of these processes may contribute to the higher
152 M.A. Imteaz et al.
disagreement between the observed and simulated values. In addition, the model does not consider the contribution of TN and TP from photosynthesis of benthic plants. This can be considered as reasonable because for small stream like Saltwater Creek, pollutants contribution from algal activity is negligible (Mannina and Viviani, 2010). On the other hand, the model considered saturated level of dissolved oxygen (DO) in the creek as the simulation was performed for storm events only.
Figure 2 Calibration results for TN (see online version for colours)
Figure 3 Calibration results for TP (see online version for colours)
Overall, the calibrated model responses were in agreement with the observed water quality data. It was seen that the model is quite able to predict the dynamics of TN and
Continuous simulations of nutrients and BOD through a stream section 153
TP. Therefore, the model is consistent and can be used to generate scenarios as a part of general strategy to conserve or improve the quality of water. It is to be noted that owing to the limitation of budget only one measurement was conducted and used for calibration. To achieve further accuracy of simulations, it is necessary to measure water quality data for several time steps/periods.
3.4 Sensitivity analysis of TN and TP parameters
Although the modelling processes of TN and TP are widely used in water quality modelling, there is the lack of systematic testing of sensitivities of the rate parameters. In this research, the sensitivity of the developed model parameters was investigated to improve understanding of the behaviour of the developed model. The factor screening sensitivity measure was used to assess the impact of the perturbations of the model parameters. The sensitivity of the rate parameters of the reactive pollutants (TN and TP) for the stream water quality model was identified in terms of the PBIAS values. The summary results for sensitivity analysis of the rate parameters are shown in Table 2 with corresponding PBIAS values. It should be noted that the perturbation of the model parameters was done based on the estimated parameters for the Saltwater Creek Catchment.
From Table 2, it is clear that in terms of the PBIAS values, the output of the TN processes were most sensitive to the perturbation of the coefficient for mineralisation of TN from BOD (kTN), and less sensitive to the denitrification coefficient (kDEN) and release rate of TN from sediment (ksTN). The table reveals that there was no change in the PBIAS values due to the perturbation of kDEN. The initial value of kDEN was assumed based on published literature, which considered only a fragment of nitrogen instead of TN. Similar to kDEN, there was no change in the PBIAS values due to the perturbation of ksTN. This was reasonable because the considered stream was shallow in depth and there was no deposited sediment to release TN during the simulation period. Table 2 also reveals that the predicted values of the TP processes were most sensitive to the perturbation of the adjustment factor for sediment TP release (δTP) and to the coefficient for mineralisation of TP from BOD (kTP); and less sensitive to the release rate of TP from sediment (ksTP) in terms of the PBIAS values. However, kTP and δTP are interrelated because both of these parameters are associated with sediment TP release. Hence, all of the parameters should be estimated with great care from the observed field data. Table 2 Sensitivity of PBIAS values of the rate parameters for TN and TP
Parameters Perturbation –10.00%
Perturbation–5.00%
Perturbation0.00%
Perturbation+5.00%
Perturbation +10.00%
kBOD 0.001 0.0002 0.000 0.0003 0.001 kDEN 0.000 0.000 0.000 0.000 0.000 kTN 0.092 0.0432 0.000 0.041 0.029 ksTN 0.000 0.000 0.000 0.000 0.000 kTP 0.233 0.104 0.000 –0.086 –0.159 ksTP 0.000 0.000 0.000 0.000 0.000 δTP –0.845 –0.043 0.000 0.050 0.102
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4 Conclusions and recommendations
The primary objective of this paper was the development of a stream water quality model for the continuous simulation of major water quality parameters along a particular stream reach. The developed model is 1-D and concurrently simulates two main processes, i.e., stream flow and stream water quality. The main objective was to develop a tool that has the capability to simulate the water quantity and quality of a particular stream system. The model incorporates two main stream processes: stream hydraulics and pollutants transport, degradation and deposition processes. The hydraulics of stream channel was analysed using Muskingum-Cunge method. Owing to the lack of observed stream flow data, simulated results of the hydraulic model were compared with the MUSIC (which is widely used in Australia) simulated results. The two error indices, PBIAS and NSE were computed to assess the accuracy of the developed model simulations. The statistical indices are very much closer to the optimal value, which indicates the model compatibility with the extensively used commercial model. The model is simple, requires minimal data and can be applied to all streams. The model is capable to simulate the concentration of nutrients and other water quality parameters along a particular stream reach.
Application of the developed hydraulic model was performed for the Saltwater Creek, Gold Coast, Australia. Although the current model is capable to simulate three major water quality parameters (BOD, TN and TP), owing to lack of data availability calibration was performed for TN and TP only. In general, performance of calibration is good. The model provided excellent representation of the field data demonstrating the simplicity yet effectiveness of the developed model. The estimated parameters can be used for the analysis of the long term pollutants behaviour and the impact of the land-use changes. They are expected to be useful for the future use of the model and to the development of BMPs in this region. The calibrated model can be used to assess the effectiveness of management decisions.
Sensitivity of the model was performed to investigate the influence of various parameters on transport and deposition rates. The analysis was undertaken to assess sensitivity of individual parameter and to find out the most sensitive parameter of the developed model. From the sensitivity of the TN processes, it was found that the mineralisation coefficient of TN (kTN) from BOD was the most sensitive, and the denitrification coefficient (kDEN) was the least sensitive parameter. For the TP processes, the most sensitive parameter was the adjustment factor (δTP) for sediment TP release. However, the accuracy of the model output responses was based on limited data. For the future sensitivity work of the developed model and similar variants, the investigation of sensitivities across a range of spatial scales and sites is required. The detailed field measurements are essential to specify the appropriate boundary conditions, especially when an accurate morphological prediction is required.
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References Ali, A.M., Yusoff, C.F.C.M. and Musa, S. (2010) ‘Determination of sediment transport at river
system’, 3rd International Conference on Southeast Asian Natural Resources and Environmental Management (SANREM), 3–5 August, Sabah, Malaysia.
Barry, D.A. and Bajracharya, K. (1995) ‘On the Muskingum-Cunge flood routing method’, Environment International, Vol. 21, No. 5, pp.485–490.
Bowie, G.L., Mills, W.B., Porcella, D.B., Campbell, C.L., Pagenkopf, J.R., Rupp, G.L., Johnson, K.M., Chan, P.W.H. and Gherini, S.A. (1985) Rates, Constants, and Kinetics Formulations in Surface Water Quality Modelling, 2nd ed., Report EPA/600/3-85/040, U.S. EPA, Athens, Georgia 30613.
Chow, V.T. (1959) Open Channel Hydraulics, McGraw-Hill Book Company, Inc., New York. Cox, B.A. (2003) ‘A review of currently available in-stream water quality models and their
applicability for simulating dissolved oxygen in lowland rivers’, Science of the Total Environment, Vols. 314–316, pp.335–377, doi:10.1016/S0048-9697(03)00063-9.
Cunge, J.A. (1969) ‘On the subject of flood propagation computation method (Muskingum method)’, Journal of Hydraulic Research, Vol. 7, No. 2, pp.205–230.
Davis, B.S. and Birch, G.F. (2009) ‘Catchment-wide assessment of the cost-effectiveness of stormwater remediation measures in urban areas’, Environmental Science and Policy, Vol. 12, No. 1, pp.84–91.
Deletic, A. (2001) ‘Modelling of water and sediment transport over grassed areas’, Journal of Hydrology, Vol. 248, Nos. 1–4, pp.168–182.
DHI (2013) DHI Water and Environment [online] http://www.dhisoftware.com/ (accessed on 6 August 2013).
Fletcher, T.D. and Deletic, A. (2007) ‘Statistical evaluation and optimisation of stormwater quality monitoring programmes’, Water Science and Technology, Vol. 56, No. 12, pp.1–9.
Glavan, M., White, S. and Holman, I.P. (2011) ‘Evaluation of river water quality simulations at a daily time-step-experience with SWAT in the Axe catchment, UK’, Clean- Soil, Air, Water, Vol. 19, No. 1, pp.43–54.
Hossain, I., Imteaz, M.A. and Hossain, M.I. (2011) ‘Application of build-up and wash-off models for an East-Australian catchment’, International Journal of Civil & Environmental Engineering, World Academy of Science, Engineering and Technology, Vol. 75, No. 162, pp.942–947.
Hossain, I., Imteaz, M.A. and Hossain, M.I. (2012) ‘Application of catchment water quality model for an East-Australian catchment’, International Journal of Global Environmental Issues, Vol. 12, Nos. 2–4, pp.242–255.
Hossain, I., Imteaz, M.A., Arulrajah, A. and Hossain, M.I. (2013) ‘Continuous simulation of suspended sediment through a stream section’, International Journal of Water, Vol. 7, No. 3, pp.206–222.
Imteaz, M.A. and Asaeda, T. (2000) ‘Artificial mixing of lake water by bubble plume and effects of bubbling operations on algal bloom’, Water Research, Vol. 34, No. 6, pp.1919–1929.
Imteaz, M.A., Hossain, I. and Hossain, M.I. (2014) ‘Estimation of build-up and wash-off models parameters for an East-Australian catchment’, International Journal of Water, Vol. 8, Nos. 1, pp.48–61.
Imteaz, M.A., Shanableh, A. and Asaeda, T. (2009) ‘Modelling multi-species algal bloom in a lake and inter-algal competitions’, Water Science & Technology, Vol. 60, No. 10, pp.2599–2611.
Mannina, G. and Viviani, G. (2010) ‘A parsimonious dynamic model for river water quality assessment’, Water Science and Technology, Vol. 61, No. 3, pp.607–618.
Ponce, V.M., Lohani, A.K. and Scheyhing, C. (1996) ‘Analytical verification of Muskingm-Cunge routing’, Journal of Hydrology, Vol. 174, Nos. 3–4, pp.235–241.
156 M.A. Imteaz et al.
Rusjan, S., Brilly, M. and Mikos, M. (2008) ‘Flushing of nitrate from a forested watershed: an insight into hydrological nitrate mobilization mechanisms through seasonal high-frequency stream nitrate dynamics’, Journal of Hydrology, Vol. 354, No. 1–4, pp.187–202.
Russel, M.A., Walling, D.E., Webb, B.W. and Bearne, R. (1998) ‘The composition of nutrient fluxes from contrasting UK river basins’, Hydrological Processes, Vol. 12, No. 9, pp.1461–1482.
USEPA (1995) QUAL2E Windows Interface User’s Guide, US Environmental Protection Agency, EPA/823/B/95/003 p.66.
Vaze, J. and Chew, F.H.S. (2002) ‘Experimental study of pollutant accumulation on an urban road surface’, Urban Water, Vol. 4, No. 4, pp.379–389.
Vaze, J. and Chew, F.H.S. (2004) ‘Nutrient loads associated with different sediment sizes in urban stormwater and surface pollutants’, Journal of Environmental Engineering, Vol. 130, No. 4, pp.391–396.
Wong, T.H.F., Fletcher, T.D., Duncan, H.P., Coleman, J.R. and Jenkins, G.A. (2002) ‘A model for urban stormwater improvement conceptualization, in integrated assessment and decision support’, A. Rizzoli and Jakeman, A. (Eds.): Proceedings of the 1st Biennial Meeting of the International Environmental Modelling and Software Society, pp.48–53, Lugano, Switzerland, 24–27 June.
Wool, T.A., Ambrose, R.B., Martin, J.L. and Comer, E.A. (2001) Water Quality Analysis Simulation Program (WASP) Version 6.0: User’s manual, US Environmental Protection Agency.
XP-Software (2013) [online] http://www.xpsolutions.com/software/ (accessed 6 August 2013). Zhou, J., Lin, B. and Lin, B. (2003) ‘Rational basis for suspended sediment modelling’,
International Journal of Sediment Research, Vol. 18, No. 3, pp.pp.117–195.