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Journal articles on the topic 'Water quality modelling'

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Published: 4 June 2021
Last updated: 31 January 2023

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1

Wood, R. G. "Water quality modelling." Marine Environmental Research 37, no. 3 (January 1994): 329–30. http://dx.doi.org/10.1016/0141-1136(94)90058-2.

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2

Kompare, Boris. "Water quality modelling." Ecological Modelling 72, no. 1-2 (March 1994): 145–49. http://dx.doi.org/10.1016/0304-3800(94)90149-x.

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3

van Griensven, A., and W. Bauwens. "Integral water quality modelling of catchments." Water Science and Technology 43, no. 7 (April 1, 2001): 321–28. http://dx.doi.org/10.2166/wst.2001.0441.

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ESWAT - Extended Soil and Water Assessment Tool - was developed to allow for an integral modelling of the water quantity and quality processes in river basins. The diffuse pollution sources are assessed by considering crop and soil processes and - together with the point sources - further transformed by an in-stream water quality module. An autocalibration procedure allows for the optimisation of the process parameters. The optimisation uses a global optimisation criterion, whereby several objective functions can be considered or combined and whereby several output variables can be taken into account simultaneously. The model and the calibration procedure are applied to the river Dender in Belgium.
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4

Seyoum, Alemtsehay G., and Tiku T. Tanyimboh. "Pressure-dependent network water quality modelling." Proceedings of the Institution of Civil Engineers - Water Management 167, no. 6 (June 2014): 342–55. http://dx.doi.org/10.1680/wama.12.00118.

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5

Høybye, Jan A. "Uncertainty Analysis in Water Quality Modelling." Hydrology Research 27, no. 3 (June 1, 1996): 203–14. http://dx.doi.org/10.2166/nh.1996.0005.

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An important part of regional planning of water resources and quality is efficient design of monitoring systems and proper use of hydrologic models (Beven 1993). In the design of monitoring systems as well as validation of numerical models, based on, for example, the equation of continuity such as hydrologic routing models and mass balance nutrient models, it is essential to estimate the uncertainties of the model-predictions. This paper presents an implementation of a first-order analysis for estimating the error-propagation when introducing mass balance models as to predict nutrient-concentrations. The uncertainty assessment, developed from a first order theory, is implemented in the analysis and modelling of Hjarbaek fjord in Denmark. The project includes hydrological modelling of input of water and nutrients to the fjord from tributaries, and a hydrodynamic estimation of water levels and velocities in the fjord. A two-system water quality box-model is used for estimation of concentrations in water and sediment phases. The system uncertainties are analysed, starting with input data uncertainties and the error propagation to the final concentration estimates, in order to optimise the future monitoring programme, and to control the model results.
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6

Margeta, J., and I. Fistanic. "Water quality modelling of Jadro spring." Water Science and Technology 50, no. 11 (December 1, 2004): 59–66. http://dx.doi.org/10.2166/wst.2004.0671.

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Management of water quality in karst is a specific problem. Water generally moves very fast by infiltration processes but far more by concentrated flows through fissures and openings in karst. This enables the entire surface pollution to be transferred fast and without filtration into groundwater springs. A typical example is the Jadro spring. Changes in water quality at the spring are sudden, but short. Turbidity as a major water quality problem for the karst springs regularly exceeds allowable standards. Former practice in problem solving has been reduced to intensive water disinfection in periods of great turbidity without analyses of disinfection by-products risks for water users. The main prerequisite for water quality control and an optimization of water disinfection is the knowledge of raw water quality and nature of occurrence. The analysis of monitoring data and their functional relationship with hydrological parameters enables establishment of a stochastic model that will help obtain better information on turbidity in different periods of the year. Using the model a great number of average monthly and extreme daily values are generated. By statistical analyses of these data possibility of occurrence of high turbidity in certain months is obtained. This information can be used for designing expert system for water quality management of karst springs. Thus, the time series model becomes a valuable tool in management of drinking water quality of the Jadro spring.
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7

Câmara, A. S., M. J. Seixas, M. D. Pinheiro, and M. P. Antunes. "“GLOBAL” Modelling for Water Quality Planning." IFAC Proceedings Volumes 18, no. 14 (October 1985): 87–91. http://dx.doi.org/10.1016/s1474-6670(17)60039-9.

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8

Jaffé, Peter R. "An introduction to water quality modelling." Advances in Water Resources 9, no. 2 (June 1986): 108. http://dx.doi.org/10.1016/0309-1708(86)90019-9.

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9

Cebe, Kağan, and Lale Balas. "Water quality modelling in kaş bay." Applied Mathematical Modelling 40, no. 3 (February 2016): 1887–913. http://dx.doi.org/10.1016/j.apm.2015.09.037.

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10

Kumar, Amit, Santosh Subhash Palmate, and Rituraj Shukla. "Water Quality Modelling, Monitoring, and Mitigation." Applied Sciences 12, no. 22 (November 10, 2022): 11403. http://dx.doi.org/10.3390/app122211403.

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In the modern era, water quality indices and models have received attention from environmentalists, policymakers, governments, stakeholders, water resource planners, and managers for their ability to evaluate the water quality of freshwater bodies. Due to their wide applicability, models are generally developed based on site-specific guidelines and are not generic; therefore, predicted/calculated values are reported to be highly uncertain. Thus, model and/or index formulation are still challenging and represent a current research hotspot in the scientific community. The inspiration for this Special Issue came from our desire to provide a platform for sharing results and informing young minds around the world to develop suitable models to understand water quality so that mitigation measures can be taken in advance to make water fit for drinking and for life-supporting activities.
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11

Piriou, P., S. Dukan, and L. Kiene. "Modelling bacteriological water quality in drinking water distribution systems." Water Science and Technology 38, no. 8-9 (October 1, 1998): 299–307. http://dx.doi.org/10.2166/wst.1998.0819.

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Because on-site experimentation raises numerous problems, the study and the modelling of bacterial regrowth phenomena in drinking water distribution systems has been performed using a pipe loop pilot under various operating conditions. As a result, experiments have shown that inlet bacterial counts have little influence on the biofilm behavior which is mainly driven by the amount of available nutrients (BDOC). Biofilm detachment has a significant influence on the increase of suspended bacterial counts with time in relation to the net growth in the bulk water. All these results have been used to develop and validate a deterministic type of model, called PICCOBIO. Some guidelines to achieve water bacteriological stability have been proposed using model simulations.
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12

Sekhar, M. Chandra, and P. Anand Raj. "Landuse – water quality modelling: a case study." Water Science and Technology 31, no. 8 (April 1, 1995): 383–86. http://dx.doi.org/10.2166/wst.1995.0335.

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There can be no doubt that landuse profoundly affects the quality of water in streams, rivers, lakes and shallow aquifers. However, the task of finding specific cause - effect relationships between different landuses and Water Quality (WQ) is one of the most important ecological challenges of out times. At the present time, few tested procedures are available to study the landuse and Non-Point Source (NPS) pollution impacts on WQ. One methodology which offers considerable promise is the use of statistical analysis of landuse and WQ data from selected regions. Facilitating the systematic application of statistical procedures, in the present investigation, regression equations have been developed between landuse and WQ parameters. The results of the study indicated that landuse can account for up to 45% of the observed variation in mean nitrates, 39% of the observed variation in mean phosphates, 58% of the observed variation in mean fluoride concentration, 46% of the observed variation in mean COD concentrations and 72% of the observed variation in mean potassium concentrations.
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13

Ahyerre, M., G. Chebbo, B. Tassin, and E. Gaume. "Storm water quality modelling, an ambitious objective?" Water Science and Technology 37, no. 1 (January 1, 1998): 205–13. http://dx.doi.org/10.2166/wst.1998.0050.

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As a consequence of the awareness of the pollution impact of storm sewer overflows, managers need tools to evaluate and control stormwaters according to water quality criteria. After an experience of 25 years in storm water quality modelling, very few models are widely and regularly used. According to managers this is due to their cost and their low level of accuracy. The generation and the transport of the pollution in urban systems during a storm event are very complex because they concern many media and many space and time scales. Nevertheless, a typology of the existing models shows that this complexity has been inscribed into the models. This tendency towards complexity makes sewer quality models difficult to put into operation and three main difficulties can be underlined: doubtful mathematical formulation of processes, uncertainties on input and calibration data, difficulties and cost of calibration. Further research is needed to improve the modelling approach and basic knowledge, and we think that a clear distinction should be made between management tools and research models.
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14

Susilowati, Y., Y. Kumoro, and W. H. Nur. "Integrated water quality modelling for spatial planning." IOP Conference Series: Earth and Environmental Science 483 (June 13, 2020): 012041. http://dx.doi.org/10.1088/1755-1315/483/1/012041.

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15

Rajar, Rudi, and Matjaz Cetina. "Hydrodynamic and water quality modelling: An experience." Ecological Modelling 101, no. 2-3 (August 1997): 195–207. http://dx.doi.org/10.1016/s0304-3800(97)00047-1.

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16

Rajar, Rudi, Matjaz Cetina, and Andrej Sirca. "Hydrodynamic and water quality modelling: case studies." Ecological Modelling 101, no. 2-3 (August 1997): 209–28. http://dx.doi.org/10.1016/s0304-3800(97)00052-5.

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17

EATHERALL, A., D. BOORMAN, R. WILLIAMS, and R. KOWE. "Modelling in-stream water quality in LOIS." Science of The Total Environment 210-211 (March 24, 1998): 499–517. http://dx.doi.org/10.1016/s0048-9697(98)00034-5.

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18

Waspodo, R. S. B., and M. I. Sahana. "River water quality modelling in Barito watershed." IOP Conference Series: Earth and Environmental Science 399 (December 31, 2019): 012018. http://dx.doi.org/10.1088/1755-1315/399/1/012018.

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19

MARSILILIBELLI, S., and E. GIUSTI. "Water quality modelling for small river basins." Environmental Modelling & Software 23, no. 4 (April 2008): 451–63. http://dx.doi.org/10.1016/j.envsoft.2007.06.008.

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20

Vieira, Judite, André Fonseca, Vítor J. P. Vilar, Rui A. R. Boaventura, and Cidália M. S. Botelho. "Water quality modelling of Lis River, Portugal." Environmental Science and Pollution Research 20, no. 1 (September 23, 2012): 508–24. http://dx.doi.org/10.1007/s11356-012-1124-5.

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21

Chen, Lon-Gyi, and Huynh Ngoc Phien. "WAter Quality Modelling Using Csmp And Dynamo." International Journal of Modelling and Simulation 5, no. 1 (January 1985): 5–8. http://dx.doi.org/10.1080/02286203.1985.11759898.

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22

Molykutty, M. V., R. Srinivasaraghavan, and S. Thayumanavan. "Ground Water Quality Modelling of Upper Palarbasin." International Journal of Modelling and Simulation 27, no. 3 (January 2007): 252–57. http://dx.doi.org/10.1080/02286203.2007.11442424.

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23

Gassmann, Matthias, Jens Lange, and Tobias Schuetz. "Erosion modelling designed for water quality simulation." Ecohydrology 5, no. 3 (March 22, 2011): 269–78. http://dx.doi.org/10.1002/eco.207.

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24

Rode, Michael, George Arhonditsis, Daniela Balin, Tesfaye Kebede, Valentina Krysanova, Ann van Griensven, and Sjoerd E. A. T. M. van der Zee. "New challenges in integrated water quality modelling." Hydrological Processes 24, no. 24 (November 4, 2010): 3447–61. http://dx.doi.org/10.1002/hyp.7766.

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25

Booty, W. G., D. C. L. Lam, A. G. Bobba, I. Wong, D. Kay, J. P. Kerby, and G. S. Bowen. "An expert system for water quality modelling." Environmental Monitoring and Assessment 23, no. 1-3 (December 1992): 1–18. http://dx.doi.org/10.1007/bf00406949.

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26

Priya, R., and Dr R. Mallika. "Ground Water Quality Modelling Using Data Mining Techniques and Artificial Neural Network Based Approach." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 1001–7. http://dx.doi.org/10.5373/jardcs/v11sp10/20192897.

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27

Juntunen, Petri, Mika Liukkonen, Marja Pelo, Markku J. Lehtola, and Yrjö Hiltunen. "Modelling of Water Quality: An Application to a Water Treatment Process." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/846321.

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The modelling of water treatment processes is challenging because of its complexity, nonlinearity, and numerous contributory variables, but it is of particular importance since water of low quality causes health-related and economic problems which have a considerable impact on people’s daily lives. Linear and nonlinear modelling methods are used here to model residual aluminium and turbidity in treated water, using both laboratory and process data as input variables. The approach includes variable selection to find the most important factors affecting the quality parameters. Correlations of∼0.7–0.9 between the modelled and real values for the target parameters were ultimately achieved. This data analysis procedure seems to provide an efficient means of modelling the water treatment process and defining its most essential variables.
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28

Heip, Lennart, Johan Van Assel, and Patrick Swartenbroekx. "Sewer flow quality modelling." Water Science and Technology 36, no. 5 (September 1, 1997): 177–84. http://dx.doi.org/10.2166/wst.1997.0192.

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Within the framework of an EC-funded SPRINT-project, a sewer flow quality model of a typical rural Flemish catchment was set up. The applicability of such a model is demonstrated. Furthermore a methodology for model building, data collection and model calibration and verification is proposed. To this end an intensive 9 month measuring campaign was undertaken. The hydraulic behaviour of the sewer network was continuously monitored during those 9 months. During both dry weather flow (DWF) and wet weather flow (WWF) a number of sewage samples were taken and analysed for BOD, COD, TKN, TP and TSS. This resulted in 286 WWF and 269 DWF samples. The model was calibrated and verified with these data. Finally a software independent methodology for interpretation of the model results is proposed.
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29

Furness, H. D., and W. N. Richards. "WATER QUALITY MODELLING PERCEPTIONS OF A WATER RESOURCE MANAGEMENT ORGANIZATION." Journal of the Limnological Society of Southern Africa 13, no. 2 (January 1987): 119–22. http://dx.doi.org/10.1080/03779688.1987.9633128.

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30

Furness, H. D., and W. N. Richards. "WATER QUALITY MODELLING PERCEPTIONS OF A WATER RESOURCE MANAGEMENT ORGANIZATION." Southern African Journal of Child and Adolescent Mental Health 13, no. 2 (January 2001): 119–22. http://dx.doi.org/10.1080/16826108.2001.9632412.

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31

Falconer, Roger A. "Flow and water quality modelling in coastal and inland water." Journal of Hydraulic Research 30, no. 4 (July 1992): 437–52. http://dx.doi.org/10.1080/00221689209498893.

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32

LINDLEY, E., and S. E. DAVIES. "Cost-Effective Water-Quality Modelling of Potable Water Distribution Systems." Water and Environment Journal 9, no. 5 (October 1995): 470–76. http://dx.doi.org/10.1111/j.1747-6593.1995.tb01485.x.

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33

Willems, P. "Random number generator or sewer water quality model?" Water Science and Technology 54, no. 6-7 (September 1, 2006): 387–94. http://dx.doi.org/10.2166/wst.2006.581.

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Integrated urban drainage modelling and environmental impact assessment require sewer emission models to be linked with submodels for treatment infrastructure and receiving rivers. The uncertainty in current water quality modelling is, however, huge, and environmental impact assessment looses more and more credibility. Based on an integrated modelling case for a combined sewer – WWTP – river system, it is shown in the paper that the integrated model does not produce more accurate results in comparison with the random simulation of emission concentrations from a frequency distribution. This should, however, not pose a serious problem as in most applications of impact assessment, model results are not needed in real time but in statistical terms. Further investigation makes clear that detail/sophistication in water quality modelling is not so important, but that more focus has to be given to long-term simulations, the use of parsimonious models and model validation based on concentration frequencies.
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34

Tillman, Pei, Jonathan Dixon, Yue-Cong Wang, and Merran Griffith. "HYDRODYNAMIC AND WATER QUALITY MODELLING IN SYDNEY HARBOUR." Coastal Engineering Proceedings, no. 36v (December 31, 2020): 58. http://dx.doi.org/10.9753/icce.v36v.papers.58.

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The Sydney Harbour waterway modelling suite examines the changes in water quality in the harbour estuary and its tributaries associated with stormwater runoff and wet weather sewage overflows from the upstream catchments, in Sydney Australia. This paper discusses the development and performance of the numerical models. The models have been used to investigate the spatial variability of catchment pollutant loads and the impacts of sewer overflows on the water quality in the Sydney Harbour estuary. The scenario modelling results demonstrate that sewer overflows have a minimal impact on the Sydney Harbour estuary water quality, with stormwater dominating most changes in water quality.
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35

Koh, Hock-Lye, Poh-Eng Lim, and Hooi-Ling Lee. "Water Quality Modelling for an Estuary in Johore." Water Quality Research Journal 30, no. 1 (February 1, 1995): 45–52. http://dx.doi.org/10.2166/wqrj.1995.008.

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Abstract A tidal tributary of the Johore River with a palm oil mill located upstream was selected for this study. Hydrographic and hydrological data were collected to study the hydraulic regimes in the tributary due to freshwater flows and tidal forces subject to various meteorological conditions. A simple tidal model was built to represent the hydraulic flows. Water quality parameters including BOD5, DO, pH, ammonia nitrogen, suspended solids and temperature were determined during three tidal phases, namely high water, intermediate water and low water, to assess the existing water quality of the river. The impacts of the discharge of palm oil mill effluent and other likely discharges on the water quality of the river were assessed by means of a computer model, modified from the WASP4 model developed by the U.S. EPA, for contaminant fate and transport in surface water.
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36

McIntyre, Neil R., Thorsten Wagener, Howard S. Wheater, and Zeng Si Yu. "Uncertainty and risk in water quality modelling and management." Journal of Hydroinformatics 5, no. 4 (October 1, 2003): 259–74. http://dx.doi.org/10.2166/hydro.2003.0022.

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The case is presented for increasing attention to the evaluation of uncertainty in water quality modelling practice, and for this evaluation to be extended to risk management applications. A framework for risk-based modelling of water quality is outlined and presented as a potentially valuable component of a broader risk assessment methodology. Technical considerations for the successful implementation of the modelling framework are discussed. The primary arguments presented are as follows. (1) For a large number of practical applications, deterministic use of complex water quality models is not supported by the available data and/or human resources, and is not warranted by the limited information contained in the results. Modelling tools should be flexible enough to be employed at levels of complexities which suit the modelling task, data and available resources. (2) Monte Carlo simulation has largely untapped potential for the evaluation of model performance, estimation of model uncertainty and identification of factors (including pollution sources, environmental influences and ill-defined objectives) contributing to the risk of failing water quality objectives. (3) For practical application of Monte Carlo methods, attention needs to be given to numerical efficiency, and for successful communication of results, effective interfaces are required. A risk-based modelling tool developed by the authors is introduced.
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37

Moradi, Sina, Christopher W. K. Chow, David Cook, Mary Drikas, Patrick Hayde, and Rose Amal. "A NEW APPROACH FOR WATER QUALITY NETWORK MODELLING." Water e-Journal 3, no. 2 (2018): 1–7. http://dx.doi.org/10.21139/wej.2018.021.

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38

Rudra, Ramesh Pal, Satish C. Negi, and Neelam Gupta. "Modelling Approaches for Subsurface Drainage Water Quality Management." Water Quality Research Journal 40, no. 1 (February 1, 2005): 71–81. http://dx.doi.org/10.2166/wqrj.2005.006.

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Abstract Contamination of surface waters by agricultural activities is a serious problem. Two different modelling approaches to simulate nutrient and pesticide transport in subsurface drained soils were investigated in this study. First, artificial neural network (ANN) models, a trainable fast back-propagation (FBP) network and a self-organizing radial basis function (RBF) network, were developed for simulation of NO3--N concentration in tile effluent. Second, a hydrologic model, DRAINMOD, was linked with a chemical transport model, GLEAMS, to simulate chemical transport of atrazine through the soil into subsurface drain outflow. The ANN models and linked DRAINMOD-GLEAMS model were calibrated and validated against experimental data collected at the Greenbelt Research Farm of Agriculture Canada during the years 1988, 1989 and from 1991 to 1994. Several statistical parameters were calculated to evaluate model performance. A comparison of results indicated that the RBF neural network model was superior to the FBP model in predicting drain outflow and NO3--N concentration. Results obtained from the linked DRAINMOD-GLEAMS model demonstrate that atrazine simulations were underpredicted in subsurface drain outflows for spring and fall seasons. Both modelling approaches provide a useful tool for management of fertilizer/manure and pesticides transport through soil and crop root zones into surface water.
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39

Pirozzi, F., D. Pianese, and G. d'Antonio. "Water quality decay modelling in hydraulic pressure systems." Water Supply 2, no. 4 (September 1, 2002): 111–18. http://dx.doi.org/10.2166/ws.2002.0128.

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A mathematical model able to evaluate water quality variations in complex hydraulic pressure systems has been developed. The model consists of a set of mass balance equations for tanks, pipes and nodes. It has been applied to predict chlorine concentration inside a case-study water network described in literature by using different expressions of chlorine decay coefficients, obtained in laboratory tests and expressed as a function of the residence time of water in pipes, the initial chlorine concentration, the history and temperature of water. The results showed little variations of the chlorine concentrations and the need to calibrate the parameters of chlorine decay coefficients expressions on full scale hydraulic systems.
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40

Azmi, Mohammad, and Nima Heidarzadeh. "Dynamic modelling of integrated water resources quality management." Proceedings of the Institution of Civil Engineers - Water Management 166, no. 7 (July 2013): 357–66. http://dx.doi.org/10.1680/wama.11.00117.

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41

Džal, Daniela, Ivana Nižetić Kosović, Toni Mastelić, Damir Ivanković, Tatjana Puljak, and Slaven Jozić. "Modelling Bathing Water Quality Using Official Monitoring Data." Water 13, no. 21 (October 26, 2021): 3005. http://dx.doi.org/10.3390/w13213005.

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Predictive models of bathing water quality are a useful support to traditional monitoring and provide timely and adequate information for the protection of public health. When developing models, it is critical to select an appropriate model type and appropriate metrics to reduce errors so that the predicted outcome is reliable. It is usually necessary to conduct intensive sampling to collect a sufficient amount of data. This paper presents the process of developing a predictive model in Kaštela Bay (Adriatic Sea) using only data from regular (official) bathing water quality monitoring collected during five bathing seasons. The predictive modelling process, which included data preprocessing, model training, and model tuning, showed no silver bullet model and selected two model types that met the specified requirements: a neural network (ANN) for Escherichia coli and a random forest (RF) for intestinal enterococci. The different model types are probably the result of the different persistence of two indicator bacteria to the effects of marine environmental factors and consequently the different die-off rates. By combining these two models, the bathing water samples were classified with acceptable performances, an informedness of 71.7%, an F-score of 47.1%, and an overall accuracy of 80.6%.
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42

BOOTY, W. G., and I. W. S. WONG. "WATER QUALITY MODELLING WITHIN THE RAISON EXPERT SYSTEM." Journal of Biological Systems 02, no. 04 (December 1994): 453–66. http://dx.doi.org/10.1142/s0218339094000283.

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The RAISON (Regional Analysis by Intelligent Systems ON a microcomputer) system is a multimedia environmental data analysis tool-kit that contains a fully integrated database management system, spreadsheet, G.I.S., graphics, statistics, modelling and expert system modules as well as a programming language that allows the user to create specialized applications. This paper presents case studies of modelling applications which illustrate the utility of the system in assisting the users of water quality models to make the models more user friendly. This is accomplished through the use of added visualization of inputs and auxiliary information as well as on-line knowledge added. This system also enables the user to represent model results in numerous graphical forms as well as animated results presented on maps. In addition, it has the ability to interface models with expert systems to aid in the selection and use of models and in the interpretation of results.
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43

Anthony, Steve G., Martyn Silgram, Adrian L. Collins, and Laura E. Fawcett. "Modelling nitrate river water quality for policy support." International Journal of River Basin Management 7, no. 3 (September 2009): 259–75. http://dx.doi.org/10.1080/15715124.2009.9635388.

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44

Bassin, J. K., M. R. Sharma, and A. B. Gupta. "WATER QUALITY MODELLING OF RIVERS IN HILLY REGION." ISH Journal of Hydraulic Engineering 15, no. 3 (January 2009): 1–10. http://dx.doi.org/10.1080/09715010.2009.10514955.

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45

Drolc, Andreja, and Jana Zagorc Končan. "Water quality modelling of the river Sava, Slovenia." Water Research 30, no. 11 (November 1996): 2587–92. http://dx.doi.org/10.1016/s0043-1354(96)00154-6.

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46

Mau, Russell E., Paul F. Boulos, and Robert W. Bowcock. "Modelling distribution storage water quality: An analytical approach." Applied Mathematical Modelling 20, no. 4 (April 1996): 329–38. http://dx.doi.org/10.1016/0307-904x(95)00129-8.

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Freni, Gabriele, Giorgio Mannina, and Gaspare Viviani. "Identifiability analysis for receiving water body quality modelling." Environmental Modelling & Software 24, no. 1 (January 2009): 54–62. http://dx.doi.org/10.1016/j.envsoft.2008.04.013.

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Ellis, J. "Modelling water quality for urban flood storage reservoirs." Environment International 21, no. 2 (1995): 177–86. http://dx.doi.org/10.1016/0160-4120(95)00007-0.

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Song, Jung-Hun, Jeong Hoon Ryu, Jihoon Park, Sang Min Jun, Inhong Song, Jeongryeol Jang, Sang Min Kim, and Moon Seong Kang. "Paddy Field Modelling System For Water Quality Management." Irrigation and Drainage 65 (June 1, 2016): 131–42. http://dx.doi.org/10.1002/ird.2034.

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Kopmann, R., and M. Markofsky. "Three-dimensional water quality modelling with TELEMAC-3D." Hydrological Processes 14, no. 13 (2000): 2279–92. http://dx.doi.org/10.1002/1099-1085(200009)14:13<2279::aid-hyp28>3.0.co;2-7.

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