INVESTIGATING RESIDUAL CHLORINE IN WATER STREAMS AT LOW FLOW CONDITIONS USING 2D WATER QUALITY MODEL

Downstream of Capim Branco I hydroelectric dam (Minas Gerais state, Brazil), there is the need of keeping a minimum flow of 7 m3/s. This low flow reach (LFR) has a length of 9 km. In order to raise the water level in the low flow reach, the construction of intermediate dikes along the river bed was decided. The LFR has a tributary that receives the discharge of treated wastewater. As part of this study, water quality of the low-flow reach was modelled, in order to gain insight into its possible behaviour under different scenarios (without and with intermediate dikes). QUAL2E equations were implemented in FORTRAN code. The model takes into account point-source pollution and diffuse pollution. Uncertainty analysis was performed, presenting probabilistic results and allowing identification of the more important coefficients in the LFR water-quality model. The simulated results indicate, in general, very good conditions for most of the water quality parameters The variables of more influence found in the sensitivity analysis were the conversion coefficients (without and with dikes), the initial conditions in the reach (without dikes), the non-point incremental contributions (without dikes) and the hydraulic characteristics of the reach (with dikes).

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The influence of artificial cutoff on a monitoring system and the water quality of the Keelung River

Water Science & Technology ◽

10.2166/wst.2002.0743 ◽

2002 ◽

Vol 46 (11-12) ◽

pp. 231-236 ◽

Cited By ~ 2

Author(s):

S.L. Lo ◽

J.T. Kuo ◽

S.M. Wang

Keyword(s):

Water Quality ◽

Monitoring Network ◽

Water Quality Monitoring ◽

Water Quality Model ◽

Quality Monitoring ◽

Low Flow ◽

Quality Model ◽

Significance Level ◽

Water Quality Monitoring Network ◽

State Water

The purpose of this study was to design a water quality monitoring network for the Keelung River in order to evaluate the effects of artificial cutoff across two bend channels. A steady-state water quality model was used to simulate the BOD and DO curves. The Kriging theory was applied to select the optimal locations for a water quality monitoring network. The sampling frequency was determined by the coefficients of variation of water quality and by considering the significance level and confidence interval. After calibration and verification of the water quality model, the model was applied and the simulation results indicated that the values of DO in the new channel would be higher than those of the old channel reaches. The critical point of the oxygen sag curve would shift to the mouth of river under Q75 low-flow conditions, and the BOD values in the new channel would also slightly increase. The results further indicated that more monitoring stations would be needed in the downstream reaches.

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Importance of demand modelling in network water quality models: a review

Drinking Water Engineering and Science Discussions ◽

10.5194/dwesd-1-1-2008 ◽

2008 ◽

Vol 1 (1) ◽

pp. 1-20 ◽

Cited By ~ 7

Author(s):

E. J. M. Blokker ◽

J. H. G. Vreeburg ◽

S. G. Buchberger ◽

J. C. van Dijk

Keyword(s):

Water Quality ◽

Time Scale ◽

Water Quality Model ◽

Transport Systems ◽

Residual Chlorine ◽

Short Time Scale ◽

Quality Model ◽

Network Water ◽

Demand Models ◽

Demand Modelling

Abstract. Today, there is a growing interest in network water quality modelling. The water quality issues of interest relate to both dissolved and particulate substances, with the main interest in residual chlorine and (microbiological) contaminant propagation, respectively in sediment leading to discolouration. There is a strong influence of flows and velocities on transport, mixing, production and decay of these substances in the network. This imposes a different approach to demand modelling which is reviewed in this article. For transport systems the current hydraulic models suffice; for the more detailed distribution system a network water quality model is needed that is based on short time scale demands that considers the effect of dispersion and transients. Demand models that provide stochastic residential demands per individual home and on a one-second time scale are available. A stochastic demands based network water quality model needs to be developed and validated with field measurements. Such a model will be probabilistic in nature and will offer a new perspective for assessing water quality in the DWDS.

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Extension of the QUASAR River-Water Quality Model to Unsteady Flow Conditions

Water and Environment Journal ◽

10.1111/j.1747-6593.2002.tb00361.x ◽

2002 ◽

Vol 16 (1) ◽

pp. 12-17 ◽

Cited By ~ 4

Author(s):

A. M. Sincock ◽

M. J. Lees

Keyword(s):

Water Quality ◽

Unsteady Flow ◽

River Water ◽

Water Quality Model ◽

River Water Quality ◽

Flow Conditions ◽

Quality Model

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Calibration and sensitivity analysis of a river water quality model under unsteady flow conditions

Journal of Hydrology ◽

10.1016/s0022-1694(03)00127-6 ◽

2003 ◽

Vol 277 (3-4) ◽

pp. 214-229 ◽

Cited By ~ 34

Author(s):

Andrew M. Sincock ◽

Howard S. Wheater ◽

Paul G. Whitehead

Keyword(s):

Water Quality ◽

Sensitivity Analysis ◽

Unsteady Flow ◽

River Water ◽

Water Quality Model ◽

River Water Quality ◽

Flow Conditions ◽

Quality Model

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Optimization of Suzhou Creek Rehabilitation Project Stage I

Water Science & Technology ◽

10.2166/wst.2005.0290 ◽

2005 ◽

Vol 52 (9) ◽

pp. 69-75 ◽

Cited By ~ 2

Author(s):

Z. Xu ◽

Z. Liao

Keyword(s):

Water Quality ◽

Water Quality Model ◽

Ecological System ◽

Tidal River ◽

Stage I ◽

Low Flow ◽

Quality Model ◽

The Third ◽

Suzhou Creek ◽

Using Data

Suzhou Creek is a seriously polluted tidal river in Shanghai, China. With the development of Shanghai, there is more and more concern over the “blackness and stink” phenomenon of Suzhou Creek. Suzhou Creek Rehabilitation Project was launched in 1998, and the planning investment of the Project Stage I was 8.65 billion yuan (about 1.05 billion US dollars). It is important to predict the effectiveness of the project and to optimize it. In this study, the USEPA's WASP model is employed to establish a water quality model of Suzhou Creek by using data from the Third Trial Low Flow Augmentation on Suzhou Creek in 1999 and other monitoring results. Based on this, Suzhou Creek Rehabilitation Project Stage I is optimized and adjusted, so the actual investment of the Project Stage I is reduced to 6.99 billion yuan. By implementing the Project Stage I, the targets of Stage I have been met, the “blackness and stink” phenomenon of the mainstream was eliminated by the end of 2000, and the ecological system was improved step by step.

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