DEVELOPMENT AND APPLICATION OF A EUTROPHICATION WATER QUALITY MODEL FOR RIVER NETWORKS

The Preissmann implicit scheme was used to discretize the one-dimensional Saint-Venant equations, the river-junction-fiver method was applied to resolve the hydrodynamic and water quality model for river networks, and the key issues on the model were expatiated particularly in this article. This water quality module was designed to compute time dependent concentrations of a series of constituents, which are primarily governed by the processes of advection, dispersion and chemical reactions. Based on the theory of Water Quality Analysis Simulation Program （WASP） water quality model, emphasis was given to the simulation of the biogeochemical transformations that determine the fate of nutrients, in particular, the simulation of the aquatic cycles of nitrogen and phosphorus compounds. This model also includes procedures for the determination of growth and death of phytoplankton. This hydrodynamic and water quality model was applied to calculate two river networks. As illustrated by the numerical examples, the calculated water level and discharge agree with the measured data and the simulated trends and magnitudes of water quality constituents are generally in good agreement with field observations. It is concluded that the presented model is useful in the pollutant control and in the determination of pollutant-related problems for river networks.

Artificial neural network modeling of water quality of the Yangtze River system:a case study in reaches crossing the city of Chongqing

An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network(ANN) models,a feed-forward back-propagation(BP) model and a radial basis function(RBF) model,to simulate the water quality of the Yangtze and Jialing Rivers in reaches crossing the city of Chongqing,P. R. China. Our models used the historical monitoring data of biological oxygen demand,dissolved oxygen,ammonia,oil and volatile phenolic compounds. Comparison with the one-dimensional traditional water quality model suggest that both BP and RBF models are superior; their higher accuracy and better goodness-of-fit indicate that the ANN calculation of water quality agrees better with measurement. It is demonstrated that ANN modeling can be a tool for estimating the water quality of the Yangtze River. Of the two ANN models,the RBF model calculates with a smaller mean error,but a larger root mean square error. More effort to identify out the causes of these differences would help optimize the structures of neural network water-quality models.

Water Quality Model Establishment for Middle and Lower Reaches of Hanshui River,China

With the development of industry and agriculture,nitrogen,phosphorus and other nutrients in the Hanshui River greatly increase and eutrophication has become an important threat to the water quality of the Hanshui River,especially in the middle and lower reaches.The primary objective of this study was to establish the water quality model for the middle and lower reaches of the Hanshui River based on the model of MIKE 11.The main pollutants migration and transformation process could be simulated using the water quality model.The rainfall-runoff model,hy-drodynamic model and water quality model were established using MIKE 11.The pollutants,such as chemical oxygen demand(COD),biochemical oxygen demand(BOD),ammonia nitrogen,nitrate nitrogen,phosphorus,dissolved oxy-gen(DO),were simulated and predicted using the above three models.A set of methods computing non-point source pollution load of the Hanshui River Basin was proposed in this study.The simulated and observed values of COD,BOD5,ammonia,nitrate,DO,and total phosphorus were compared after the parameter calibration of the water quality model.The simulated and observed results match better,thus the model can be used to predict water quality in the fu-ture for the Hanshui River.The pollution trend could be predicted using the water quality model according pollution load generation.It is helpful for government to take effective measures to prevent the water bloom and protect water quality in the river.

Three-Dimensional Water Quality Model Based on FVCOM for Total Load Control Management in Guan River Estuary,Northern Jiangsu Province

Guan River Estuary and adjacent coastal area(GREC) suffer from serious pollution and eutrophicational problems over the recent years.Thus,reducing the land-based load through the national pollutant total load control program and developing hydrodynamic and water quality models that can simulate the complex circulation and water quality kinetics within the system,including longitudinal and lateral variations in nutrient and COD concentrations,is a matter of urgency.In this study,a three-dimensional,hydrodynamic,water quality model was developed in GREC,Northern Jiangsu Province.The complex three-dimensional hydrodynamics of GREC were modeled using the unstructured-grid,finite-volume,free-surface,primitive equation coastal ocean circulation model(FVCOM).The water quality model was adapted from the mesocosm nutrients dynamic model in the south Yellow Sea and considers eight compartments:dissolved inorganic nitrogen,soluble reactive phosphorus(SRP),phytoplankton,zooplankton,detritus,dissolved organic nitrogen(DON),dissolved organic phosphorus(DOP),and chemical oxygen demand.The hydrodynamic and water quality models were calibrated and confirmed for 2012 and 2013.A comparison of the model simulations with extensive dataset shows that the models accurately simulate the longitudinal distribution of the hydrodynamics and water quality.The model can be used for total load control management to improve water quality in this area.

Application of QUAL2K for Water Quality Modeling of River Ghataprabha （India）

River Ghataprabha, during its course through Belgaum district in Karnataka state （India）, receives untreated domestic waste from Gokak town and other neighboring villages situated on the bank of the river. The present study involves the application of water quality model QUAL2K to predict the water quality of this polluted segment of the river. The model was calibrated and validated for biochemical oxygen demand （BOD）, dissolved oxygen （DO） and total nitrogen （TN） in pre-monsoon season. Data for calibration and validation were obtained after the field and laboratory measurements. The performance of the model was evaluated using statistics based on standard errors （SE） and mean multiplicative errors （MME）. The model represented the field data quite well with some exceptions. In spite of some differences between the measured and simulated data sets at some points, the calibration and validation results are acceptable especially for the developing countries where the financial resources are often limited for frequent monitoring campaigns and higher accuracy data analysis.

Water level updating model for flow calculation of river networks

Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.

River Water Quality Model Based on Remote Sensing Information Methods——A Case Study of Lijing River in Guilin City

River water quality models based on remote sensing information models are superior to pure water quality models because they combine the inevitability and risk of geographical phenomena and can take complex geographical characteristics into account. A water quality model for forecasting COD has been established with remote sensing in- formation modeling methods by monitoring and analyzing water quantity and water quality of the Lijing River reach which flows through a complicated Karst mountain area. This model provides a good tool to predict water quality of complex rivers. It is validated by simulating contaminant concentrations of the study area. The results show that remote sensing information models are suitable for complex geography. It is not only a combined model of inevitability and risk of the geographical phenomena, but also a semi-theoretical and semi-empirical formula, providing a good tool to study organic contaminants in complicated rivers. The coefficients and indices obtained have limited value and the model is not suitable for all situations. Some improvements are required.

Assessment of Point and Nonpoint Sources Pollution in Songhua River Basin,Northeast China by Using Revised Water Quality Model

Individual participation of pollutants in the pollution load should be estimated even if roughly for the appropriate environmental management of a river basin.It is difficult to identify the sources and to quantify the load, especially in modeling nonpoint source.In this study a revised model was established by integrating point and nonpoint sources into one-dimensional Streeter-Phelps(S-P) model on the basis of real-time hydrologic data and surface water quality monitoring data in the Jilin Reach of the Songhua River Basin.Chemical oxygen demand(COD) and ammonia nitrogen(NH 3-N) loads were estimated.Results showed that COD loads of point source and nonpoint source were 134 958 t/yr and 86 209 t/yr, accounting for 61.02% and 38.98% of total loads, respectively.NH 3-N loads of point source and nonpoint source were 16 739 t/yr and 14 272 t/yr, accounting for 53.98% and 46.02%, respectively.Point source pollution was stronger than nonpoint source pollution in the study area at present.The water quality of upstream was better than that of downstream of the rivers and cities.It is indispensable to treat industrial wastewater and municipal sewage out of point sources, to adopt the best management practices to control diffuse pollutants from agricultural land and urban surface runoff in improving water quality of the Songhua River Basin.The revised S-P model can be successfully used to identify pollution source and quantify point source and nonpoint source loads by calibrating and validating.

Joint optimization scheduling for water conservancy projects incomplex river networks

In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.

Water Quality Evaluation in Tidal River Reaches of Liaohe River Estuary, China Using a Revised QUAL2K Model

Rivers in the Liaohe River Estuary area have been seriously polluted by discharges of wastewater containing petroleum pol- lutants and nutrients. In this paper, The Enhanced Stream Water Quality Model （QUAL2K） and its revised model as well as One-dimensional Tide Mean Model （1D model） were applied to predict and assess the water quality of the tidal fiver reach of the Liaohe River Estuary. Dissolved oxygen （DO）, biochemical oxygen demand （BODs）, ammonia nitrogen （NH3-N） and total phosphorus （TP） were chosen as water quality indices in the two model simulations. The modelled results show that the major reasons for degraded rivers remain petroleum and non-point source pollution. Tidal water also has a critical effect on the variation of water quality. The sensitivity analysis identifies that flow rate, point load and diffuse load are the most sensitive parameters for the four water quality indices in the revised QUAL2K simulation. Uncertainty analysis based on a Monte Carlo simulation gives the probability distribution of the four wa- ter quality indices at two locations （6.50 km and 44.84 km from the river mouth）. The statistical outcomes indicate that the observed data fall within the 90% confidence intervals at all sites measured, and show that the revised QUAL2K gives better results in simulating the water quality of a tidal fiver.

Three-Dimensional Water-Quality Simulation for River Based on VOF Method

In the present study, considering the transport and transformation processes of variables, a threedimensional water quality model for the river system was established, which coupled the volume of fluid(VOF) method with the k-ε turbulence mathematical model. Then, the water hydrodynamic characteristics and transport processes for BOD_5, NH_(3^-)N and TP were analyzed. The results showed that the water surface of convex bank was a little lower than that of concave bank due to the centrifugal force near the bend, and most concentrations were inferior to the type Ⅴ standard indexes of surface water environmental quality. The model validation indicated that the errors between the simulated and monitored values were comparatively small, satisfying the application demands and providing scientific basis and decision support for the restoration and protection of water quality.

Application of Water Quality Model QUAL2K to Model the Dispersion of Pollutants in River Ndarugu, Kenya

Ndarugu River, Kenya, during its course through the different agricultural and industrial areas of Gatundu, Gachororo and Juja farms, receives untreated industrial, domestic and agricultural waste of point source discharges from coffee and tea factories. During wet season the water is also polluted by non-point (diffuse) sources created by runoff carrying soil, fertilizer and pesticide residues from the catchment area. This study involved the calibration of water quality model QUAL2K to predict the water quality of this segment of the river. The model was calibrated and validated for flow discharge (Q), temperature (T&deg), flow velocity (V), biochemical oxygen demand (BOD5), dissolved oxygen (DO) and nitrate (NO3-N), using data collected and analyzed during field and laboratory measurements done in July and November-December 2013. The model was then used in simulation and its performance was evaluated using statistical criteria based on correlation coefficient (R2) and standard errors (SE) between the observed and simulated data. The model reflected the field data quite well with minor exceptions. In spite of these minor differences between the measured and simulated data set at some points, the calibration and validation results are acceptable especially for developing countries where the financial resources for frequent monitoring works and higher accuracy data analysis are very limited. The water is being polluted by the human activities in the catchment. There is need for proper control of wastewater by various techniques, and preliminary treatment of waste discharges prior to effluent disposal. Management of the watershed is necessary so as to protect the river from the adverse impacts of agricultural activities and save it from further deterioration.

Application of River Network Hydrodynamic Model in Determining Water Distribution Scale of Haishu Plain

The water distribution network is an important part of the plain water environment improvement system. To make efficient use of the regional water diversion source, scientifically distribute the water diversion flow and improve the water environment carrying capacity of Haishu Plain, the river network hydrodynamic model is used in this paper to simulate the water intake location, reasonable water quantity and influence range of water transfer in Haishu Plain. The simulation results have high accuracy, which can provide a scientific basis for the scale, water transfer mechanism and project layout of water transfer construction in Haishu Plain and show a strong reference value for the study of water diversion and distribution scheme of coastal plain river network.

Risk-based water quality decision-making under small data using Bayesian network

A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small data.Among all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed.The final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.

Modelling of water quality of Minjiang estuary

In order to establish economic development region at Mawei districtwhich is nearby downstream of Minjiang river and to answer the question of impact of economic development on water quality of Minjiang estuary, the analyses of hydrologic and hydraulic characteristics of Mawei reach of Minjiang tidal river, a two-dimensional mathematical model has been established and simulation of water quality was studied. The results show that the flushing time of a conservative pollutant during dry and raining period are 12 and 7 days respectively from Mawei to Minjiang mouth, the decay rate constants of BOD and NH3-N are 0.1 to 0.4 and 0.18 to 0.45 d-1 respectively. The capacity of dilution and assimilation for pollutants is larger.

The Changes of Water Quality in Space and Time in the Mekong River, Bassac River and Adjacent Waterways

Catfish （Pangasianodon hypophthalmus） farming along Mekong river and Bassac river depends heavily on water quality of the two rivers, whereas water quality of these rivers are affected by the waste of aquaculture activities, agricultural production, industrial and municipal waste. This report analyzes the monitoring data on Mekong river, Bassac river and adjacent waterways in the period of 2011-2012, focusing on parameters of organic pollution to assess the current quality of these two rivers. Based on the results, the water quality in the river-head was generally better than in the middle and at the end of the river, and the quality of water of the Mekong river was better than Bassac river. In terms of time, water quality in July was considered the best in all the basins. At adjacent natural rivers and canals, ammonia levels increased and exceeded the Vietnamese standard in April, and BOD values were also much higher compared to two major rivers. The results of the model also showed that the levels of pollution index of the Mekong and Bassac river were very low （1.33 and 1.47）, and the values （Y） in the canals were higher （1.63-1.67） but still in permitted level. Therefore, the water quality of the Mekong and Bassac river in the period 2011-2012 was generally still quite good.

The Research and Application of BP Neural Networks in River-basin Water and Sediment

Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serving as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute the runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Comparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.

RESEARCH ON HYDRODYNAMIC AND WATER QUALITY MODEL FOR TIDAL RIVER NETWORKS

Hydrodynamic and water quality model for tidal river network is set up withMIKE11 modeling system, according to the features of tidal river networks in plain area. The modelwas calibrated using the hydrological and water quality data of 1999, and the results show that thesimulated values agree with the measured data very well. This model is used to numerically analyzethe effects of low flow augmentation on hydrodynamic and water quality conditions of Suzhou Creek.The simulation results show that the flow augmentation can increase net discharge of Suzhou Creekand improve its ability of re-aeration; and its concentration of dissolved oxygen in the rivernetworks can also increase correspondingly.

Source apportionment of river water pollution in a typical agricultural city of Anhui Province, eastern China using multivariate statistical techniques with APCS– MLR

The deterioration of the surface water environment has become a serious challenge for water resources management due to increasing anthropogenic disturbance. Water resources protection requires control of potential pollution sources. In this study, 99 water samples were collected from a river in a typical agricultural city of Anhui Province in eastern China, and these samples were analyzed in terms of pH, electrical conductivity, and the concentrations of F-, Cl-, SO42-, Na+, K+, Mg2+, Ca2+, As, Cr, Cu, Zn, and Pb. Cluster analysis, co-occurrence network analysis, and principal component analysis/factor analysis were conducted to qualitatively identify the potential sources of river water pollution in the study area. An absolute principal component score-multiple linear regression receptor model was used to quantitatively evaluate the contribution of each source to water quality parameters. The results showed that all observed water quality indices met the quality criteria specified in the Chinese drinking water standards, except for pH, ρ(F-), ρ(SO42-), and ρ(As). The heat map showed that the frequent recharge of pollutants from the tributaries during the wet season was the main reason for the deterioration of water quality. Five sources of river water pollution were identified, and their contribution ratios in a descending order were as follows: the geogenic process (24%) > agricultural activities (21%) > poultry farming sources (17%) > domestic pollution (9%) > transportation pollution (5%). Therefore, controlling pollution from agricultural activities, strengthening the regulation of livestock farming, and improving the sewage network are the recommended strategies for improving the quality of surface water resources in this area.