River Water Quality Model Research Articles

Simulating Agricultural Drainage Water Reuse Using QUAL2K Model: Case Study of the Ismailia Canal Catchment Area, Egypt

AbstractThis paper proposes and evaluates a number of drainage water mixing projects in canals applying QUAL2K, which is a river and stream water quality model recommended by the United States Environmental Protection Agency (U.S. EPA). Mitigation measures and precautions were suggested fur use as guidelines for environmentally safe application. The results showed that QUAL2K proved to be an effective tool in the comparative evaluation of potential water quality improvement programs, particularly on the salinity and water quality parameters that address agriculture practices. Simulation results showed that most selected water quality parameters for most of the considered projects complied with the standards of Egyptian law. Moreover, electrical conductivity (EC) predictions were found within the permissible limit with the recommended fit crops. The reduction in drain discharge reduces pollutant loads entering Temsah Lake and might help save the flora and fauna to support recreation activities.

Removal of selected pharmaceuticals, personal care products and artificial sweetener in an aerated sewage lagoon

A sewage lagoon serving the small municipality of Lakefield in Ontario, Canada was monitored in the summer, fall and winter to determine removals of carbamazepine, trimethoprim, sulfamethoxazole, ibuprofen, gemfibrozil, triclosan, sucralose, HHCB and AHTN. Concentrations of these compounds in untreated and treated wastewater were estimated by deploying POCIS and SPMD passive samplers in the sewage lagoon. Passive samplers were also deployed at several points upstream and downstream of the point of discharge from the lagoon into the Otonabee River. LC–MS/MS and GC–MS were utilized to determine the concentrations of pharmaceuticals and personal care products (PPCPs) and sucralose, an artificial sweetener. Among PPCPs sampled by POCIS, the highest estimated concentration in untreated wastewater was ibuprofen sampled during the fall, at an estimated concentration of 60.3ng/L. The estimated average concentration of sucralose was 13.6ng/L in the untreated wastewaters. Triclosan, HHCB and AHTN in SPMDs were highest during fall season, at 30, 1677 and 109ng/L, respectively. For all compounds except gemfibrozil, carbamazepine and sucralose, removals were highest in the summer (83.0 to 98.8%) relative to removals in the fall (48.4 to 91.4%) and winter (14.0 to 78.3%). Finally, the estimated concentrations of carbamazepine, sulfamethoxazole, triclosan and HHCB were compared with predicted values obtained through application of the WEST® modeling tool, with a new model based on the River Water Quality Model No. 1 and extended with dynamic mass balances describing the fate of chemicals of emerging concern subject to a variety of removal pathways. The model was able to adequately predict the fate of these four compounds in the lagoon in summer and winter, but the model overestimated removals of three of the four test compounds in the fall sampling period. This lagoon was as effective at removing PPCPs as many conventional WWTPs, but removals were better during the summer.

Parameter identification of river water quality models using a genetic algorithm

For solving the multi-parameter identification problem of a river water quality model, analytical methods for solving a river water quality model and traditional optimization algorithms are very difficult to implement. A new parameter identification model based on a genetic algorithm (GA) coupled with finite difference method (FDM) was constructed for the determination of hydraulic and water quality parameters such as the longitudinal dispersion coefficient, the pollutant degradation coefficient, velocity, etc. In this model, GA is improved to promote convergence speed by adding the elite replacement operator after the mutation operator, and FDM is applied for unsteady flows. Moreover the influence of observation noise on identified parameters was discussed for the given model. The method was validated by two numerical cases (in steady and unsteady flows respectively) and one practical application. The computational results indicated that the model could give good identification precision results and showed good anti-noise abilities for water quality models when the noise level ≤10%.

Applicability Analysis on the Improved One-Dimension Steady-State River Water Quality Model

Guanzhong Segment of Wei River was studied in this article. Based on water quality observational data (ammonia-nitrogen) and hydrology observational data (flow of the sections) of Guanzhong Segment of Wei River in the rainy, normal and wet season in 2006, improved one-dimensional steady-state river water quality model was calibrated. River water quality was predicted in the normal season which were compared with the observational data in 2005. The results show that the water quality computed by improved one-dimensional steady-state river water quality model is grossly consistent with the observational water quality data and the model may provide the technical supports for the response relationships of water quality and water quantity of Guanzhong Segment of Wei River.

Based on Index Difference of River Water Quality Model and Application in Dianchi Lake Basin

Based on the river advection and diffusion process, the application of mass balance principle, a suitable diffusion model in Dianchi Lake Basin rivers burst water pollution emergency treatment. River water quality model based on the exponential difference solving simulated the spatial and temporal distribution of pollutants in the sudden water pollution accidents. This paper quantitatively simulated time and concentration values at the different locations of the river downstream of the pollutants reach Finally, GIS technology to achieve the simulation results of the real-time dynamic visualization.

Physico-chemical factors alone cannot simulate phytoplankton behaviour in a lowland river

Summary There are still a number of gaps in our understanding regarding phytoplankton behaviour in rivers. Given predicted future changes in climate, which appear superficially at least, to favour larger phytoplankton blooms, this study was initiated to assess how well we can currently simulate this behaviour with a river water quality model. The river quality model (QUESTOR) was run for a 45 km stretch of the upper Thames for 2009–2011 (UK). To identify the most suitable model representation, phytoplankton was simulated and compared to actual observed data under three alternative assumptions. The first of these was of a Mixed Phytoplankton population and the other two being that there was domination by either of two groups (Green Algae, or cool water diatoms such as Stephanodiscus hantzschii ) known to be abundant in the river. The factors for controlling the phytoplankton populations were found to be flow, temperature and radiation. Of these controlling factors, river flow has the larger effect on depletion or build-up of phytoplankton, based on residence time. The nutrient concentrations (phosphate and nitrate) seem to be in excess and not limiting or controlling of the phytoplankton behaviour. The data highlighted two main blooms in late spring and summer, which were successfully modelled with a Mixed Phytoplankton population (which explained 16–35% of the weekly variability throughout 2009–10). On a year-to-year time frame there is clear evidence of between-year differences in grazing loss rates. This can be accounted for by a combination of benthic filter feeders and zooplankton, both having been observed in sufficient numbers in the Thames.

Using a systematic approach to develop water quality management strategies in the Nankan River, Taiwan

The Nankan River, located in northern Taiwan, is one of the most contaminated rivers in Taiwan. The pollution of this river causes odor problems and affects use of the water resources. In this study, a systematic river basin water quality management strategy was developed to simulate water quality, evaluate wastewater management alternatives, and cost-effectiveness strategy plans for the Nankan River restoration and its water quality improvement. The main tasks consist of river water sampling and analysis, water quality modeling, total maximum daily load (TMDL) calculation, river pollution index (RPI) evaluation, and cost-effectiveness analysis (CEA). The QUAL2K model, developed by US Environmental Protection Agency, was adopted as the river water quality modeling framework in this study. The modeling effort was supported including four water quality data-sets of the river. Results of the water quality modeling show that the calculated TMDL for biochemical oxygen demand (BOD) and ammonia loading were 1,334 and 889 kg/day, respectively. Approximately, 1,334 kg/day of BOD and 889 kg/day of ammonia needed to be reduced to improve the RPI from “serious pollution” level to “moderate pollution” level. Results also reveal that the odor problem caused by dimethyl sulfide and dimethyl trisulfide could be removed after the water quality improvement. Results from the CEA show that an annual cost of US$ 8 million is required to reach the acceptable RPI level (moderate pollution). The developed strategies can be used as decision-making tools for water pollution control and river basin water quality management for the Nankan River and other similar rivers.

Neural Network Modelling of TDS Concentrations in Cauvery River Water, Tamilnadu, India

Monitoring and Modeling of river water quality is one of the key elements in the global environmental monitoring policy and management. Artificial Neural Networks (ANNs) have been used for modeling hydrological elements that are highly non-linear in both spatial and temporal scales. With the help of the available data on the physico-chemical characteristics, an attempt has been made to model the concentrations of Total Dissolved Solids (TDS) in the Cauvery River of approximately 40km stretch from Bhavani to Noyyal. The validation of the neural network model showed good agreement for predictions of the TDS concentrations between observed and predicted values. The coefficient of correlation during the validation process was found to be 0.951 and the mean squared error was 0.015. In conclusion, the work supports the concept that the neural network approach is a successful method of modelling such complex and nonlinear behaviour of TDS in the river with different environmental conditions.

Non Negative Solutions Conditions of Nonlinear Urban River Model

In this paper, the properties of urban river water quality model’s solutions have been discussed by comparison system, and the result is that the model has nonnegative solutions when all the parameters of model are positive.

Assessment of the Klang River Quality Using the Water Quality Indices

The Klang river basin is located within the state of Selangor and Kuala Lumpur, Malaysia. The Klang River drains an area of 1,288 km2 from the steep mountain rain forests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, covering a distance of 120 km. It originates from the northern part of Selangor, drains the Klang Valley, and finally discharges itself into the Straits of Malacca. The pollution discharges for various locations along the river basin was obtained from the Water Quality and GIS group. The pollutants can come from point sources (PS) such as industrial wastewater, municipal sewers, wet market, sand mining and landfill. Pollutants can also come from non-point sources (NPS) such as agricultural or urban runoff, and commercial activity such as forestry, and construction due to rainfall event. Mathematical–computational modeling of river water quality is possible but requires an extensive validation. Besides it requires previous knowledge of hydraulics and hydrodynamics. To overcome these difficulties, a water quality index (WQI) was developed. The water quality index (WQI) is a mathematical instrument used to transform large quantities of water quality data into a single number. The purpose of this research is to classify the upstream and downstream of the Klang main river based on WQI value.

Modification, calibration and verification of the IWA River Water Quality Model to simulate a pilot-scale high rate algal pond

We implemented the IWA River Water Quality Model No. 1 (Reichert et al., 2001. River Water Quality Model No. 1, IWA Scientific & Technical Report No. 12) to simulate water-quality characteristics in two pilot-scale High Rate Algal Ponds. Simulation results were compared with two years' of data from the ponds. The first year's data from one pond were used for model calibration; the remaining data were used for validation. As originally formulated and parameterized, the model consistently yielded summer-time algal biomass concentrations which were too low – with consequent failures in its reproduction of dissolved oxygen, pH and nutrient dynamics. We experimented with various structural/parametric changes to improve the model's performance. The most effective strategy was to greatly increase the respiratory losses suffered by the heterotrophic osmotrophs (thereby giving the algae access to a larger fraction of the incoming dissolved organic carbon and nitrogen). This suggests that CO2-bubbling alone cannot entirely preclude resource-limitation of algal production. We doubt that our parameterization of heterotrophic osmotrophs is correct and infer that the algae derive a large fraction of their nutrition by direct osmotrophic uptake of dissolved organic matter. This inference is supported by the literature concerning the physiology of the dominant algal species in our ponds.

Modeling the impacts of climate change on nitrogen retention in a 4th order stream

Climate induced changes of temperature, discharge and nitrogen concentration may change natural denitrification processes in river systems. Until now seasonal variation of N-retention by denitrification under different climate scenarios and the impact of river morphology on denitrification have not been thoroughly investigated. In this study climate scenarios (dry, medium and wet) have been used to characterize changing climatic and flow conditions for the period 2050–2054 in the 4th order stream Weise Elster, Germany. Present and future periods of nitrogen turnover were simulated with the WASP5 river water quality model. Results revealed that, for a dry climate scenario, the mean denitrification rate was 71% higher in summer (low flow period between 2050 and 2054) and 51% higher in winter (high flow period) compared to the reference period. For the medium and wet climate scenarios, denitrification was slightly higher in summer (3% and 4%) and lower in winter (9% and 3% for medium and wet scenarios, respectively). Additionally, the variability of denitrification rates was higher in summer compared to winter conditions. For a natural river section, denitrification was a factor of 1.22 higher than for a canalized river reach. Besides, weirs along the river decrease the denitrification rate by 16% in July for dry scenario conditions. In the 42 km study reach, N-retention through denitrification amounted to 5.1% of the upper boundary N load during summer low flow conditions in the reference period. For the future dry climate scenario this value increased up to 10.2% and for the medium climate scenario up to 5.4%. In our case study the investigated climate scenarios showed that future discharge changes may have a larger impact on denitrification rates than future temperature changes. Overall results of the study revealed the significance of climate change in regulating the magnitude, seasonal pattern and variability of nitrogen retention. The results provide guidance for managing nitrogen related environmental problems for present and future climate conditions.

Mathematical model to identify nitrogen variability in large rivers

AbstractThe river Swale in Yorkshire, northern England has been the subject of many studies concerning water quality. This paper builds on existing data resources and previous 1D river water quality modelling applications at daily resolution (using QUESTOR) to provide a different perspective on understanding pollution, through simulation of the short‐term dynamics of nutrient transport along the river. The two main objectives are (1) building, calibration and evaluation of a detailed mathematical model (Advection‐Dispersion Model: ADModel), for nutrient transport under unsteady flow conditions and (2) the development of methods for estimating key parameters characterizing pollutant transport (velocity, dispersion coefficient and transformation rates) as functions of hydrological parameters and/or seasonality.The study of ammonium and nitrate has highlighted temporal variability in processes, with maximum nitrification and denitrification rates during autumn. Results show that ADModel is able to predict the main trend of measured concentration with reasonable accuracy and accounts for temporal changes in water flow and pollutant load along the river. Prediction accuracy could be improved through more detailed modelling of transformation processes by taking into account the variability of factors for which existing data were insufficient to allow representation. For example, modelling indicates that interactions with bed sediment may provide an additional source of nutrients during high spring flows. Copyright © 2010 John Wiley & Sons, Ltd.

Evaluation of non-point source pollution and river water quality using a multimedia two-model system

Summary In this study, an integrated two-model system composed of a multimedia watershed model and a river water quality model was developed to effectively simulate the impacts of non-point source (NPS) pollution on river water quality. NPS pollution loadings from Kaoping River Basin were calculated using the Integrated Watershed Management Model (IWMM). Results from the IWMM modeling were used as the input data for the Kaoping River water quality evaluation using the Water Quality Analysis Simulation Program (WASP) modeling. The land use patterns classified using SPOT images and Digital Elevation Model techniques with the aid of Erdas Imagine® process and ArcView® geographical information system were applied to assist the NPS pollution simulation. Results indicate that land use patterns of orchard farms and farmland areas were the major causes of the NPS pollution, and they should be effectively controlled. Results show that higher flow rates (>200 m 3 /s) were observed in the wet seasons, which would cause the increase in NPS pollution loadings. Results demonstrate that the integral approach could develop a direct linkage between upstream land use changes and downstream water quality. Using water quality modeling alone would underestimate the impact of NPS pollution on river water quality. The introduction of the integrated two-model system shows a significant advance in estimating the water quality.

Water Quality Management Strategies for Various River Flows with QUAL2K Model

This study utilizes the river and stream water quality (QUAL2K) model as a tool to simulate and forecast the water quality and then to develop the management strategies for various flow rates above the Kaoping weir of Kaoping River in Taiwan. Model results based on the river assimilative capacity of four various flow rates including Q95, Q75, Q60, and Q25 were conducted to determine the corresponding amounts of pollution reduction. The average pollution reduction ratios of biochemical oxygen demand (BOD5) and ammonia nitrogen (NH3-N) should at least reach to 42.0% and 28.3% respectively to meet the restoration goals of Class A water quality standards regulated by Taiwan. The modeling results demonstrate that an increase of water extraction rates in the upstream reaches will have significant adverse effects on the water quality of the Kaoping River. Higher water extraction rates will lower the assimilative capacity that reduces the buffering capacity of river. It also induces that the water quality is susceptible to the impacts of the influent pollutant loads from the tributaries or drainages.

Extension of the River Water Quality Model No. 1 with the fate of pesticides

The existing River Water Quality Model No. 1 (RWQM1) was extended with processes determining the fate of non-volatile pesticides in the water phase and sediments. The exchange of pesticides between the water column and the sediment is described by three transport processes: diffusion, sedimentation and resuspension. Burial of sediments is also included. The modified model was used to simulate the concentrations of diuron and chloridazon in the river Nil. A good agreement was found between the simulated pesticide concentrations and measured values resulting from a four-month intensive monitoring campaign. The simulation results indicate that pesticide concentrations in the bulk water are not sensitive to the selected biochemical model parameters. It seems that these concentrations are mainly determined by the imposed upstream concentrations, run-off and direct losses. The high concentrations in the bulk water were not observed in the sediment pore water due to a limited exchange between the water column and the sediment. According to a sensitivity analysis, the observed pesticide concentrations are highly sensitive to the diffusion and sorption coefficients. Therefore, model users should determine these parameters with accuracy in order to reduce the degree of uncertainty in their results.

CALHIDRA 3.0 – New software application for river water quality prediction based on RWQM1

This paper presents CalHidra 3.0, a new software package developed for dynamic simulation of water quality in rivers. CalHidra 3.0 combines a 1-D hydrodynamic model based on Saint Venant equations, a transport sub-model that incorporates the advection–dispersion terms, and a simplified version of the River Water Quality Model 1 (RWQM1) for the biochemical transformations. This advanced biochemical sub-model allows the dynamic simulation of the bacterial populations in rivers, making possible the simulation of the river acclimatisation to changes of pollutant load or environmental conditions. The software also includes new tools for a Monte Carlo based Bayesian calibration of the unknown model parameters. CalHidra 3.0 is implemented based on the Component Object Model (COM) programming paradigm and uses the Windows graphical environment. Three case studies illustrate the possibilities of the CalHidra 3.0 software.

On the effect of scaling conceptual model complexity on stochastic response for water quality modeling

This paper extends previous work comparing the response of water quality models under uncertainty. A new model, the River Water Quality Model no. 1 (RWQM1), is compared to the previous work of two commonly used water quality models. Additionally, the effect of conceptual model scaling within a single modelling framework, as allowed by RWQM1, is explored under uncertainty. Model predictions are examined using against real-world data for the Potomac River with a Generalized Likelihood Uncertainty Estimation used to assess model response surfaces to uncertainty. Generally, it was found that there are tangible model characteristics that are closely tied to model complexity and thresholds for these characteristics were discussed. The novel work has yielded an illustrative example but also a conceptually scaleable water quality modelling tool, alongside defined metrics to assess when scaling is required under uncertainty. The resulting framework holds substantial, unique, promise for a new generation of modelling tools that are capable of addressing classically intractable problems.

Receiving water quality assessment: comparison between simplified and detailed integrated urban modelling approaches

Urban water quality management often requires use of numerical models allowing the evaluation of the cause-effect relationship between the input(s) (i.e. rainfall, pollutant concentrations on catchment surface and in sewer system) and the resulting water quality response. The conventional approach to the system (i.e. sewer system, wastewater treatment plant and receiving water body), considering each component separately, does not enable optimisation of the whole system. However, recent gains in understanding and modelling make it possible to represent the system as a whole and optimise its overall performance. Indeed, integrated urban drainage modelling is of growing interest for tools to cope with Water Framework Directive requirements. Two different approaches can be employed for modelling the whole urban drainage system: detailed and simplified. Each has its advantages and disadvantages. Specifically, detailed approaches can offer a higher level of reliability in the model results, but can be very time consuming from the computational point of view. Simplified approaches are faster but may lead to greater model uncertainty due to an over-simplification. To gain insight into the above problem, two different modelling approaches have been compared with respect to their uncertainty. The first urban drainage integrated model approach uses the Saint-Venant equations and the 1D advection-dispersion equations, for the quantity and for the quality aspects, respectively. The second model approach consists of the simplified reservoir model. The analysis used a parsimonious bespoke model developed in previous studies. For the uncertainty analysis, the Generalised Likelihood Uncertainty Estimation (GLUE) procedure was used. Model reliability was evaluated on the basis of capacity of globally limiting the uncertainty. Both models have a good capability to fit the experimental data, suggesting that all adopted approaches are equivalent both for quantity and quality. The detailed model approach is more robust and presents less uncertainty in terms of uncertainty bands. On the other hand, the simplified river water quality model approach shows higher uncertainty and may be unsuitable for receiving water body quality assessment.

Modeling non-point source pollution from rice farming in the Thachin River Basin

With the worldwide intensification of agriculture, non-point source pollution of surface waters has become a pressing issue. Conventional river water quality models consider non-point sources as accumulated entries into the rivers and do not investigate into the processes generating the pollution at its source, thus preventing the determination of effective mitigation measures. The models require extensive data inputs, which is a deficiency in many developing and emerging countries with limited data availability. The current study applies a Material Flow Model as a complementary approach to quantify non-point source pollution from agricultural areas. Rice farming in the Thachin River Basin is presented as a case study, with a focus on nutrients. The total nitrogen and phosphorus flows from rice farming to the river system are quantified, the key parameters influencing these flows are determined and potential mitigation measures are discussed. The results show that rice contributes considerable nutrient loads to the Thachin River Basin. Scenario simulations demonstrate that a significant nutrient load reduction could be achieved by following the official recommendations for fertilizer application, thus confirming the local efforts to introduce best management practice. Our results underline the importance of non-point source pollution control in intensive agricultural areas, particularly of tropical lowland delta areas such as the Central Plains of Thailand. The specific benefit of applying a Material Flow Model in this context is that with limited data availability, one can reach an understanding of the system and gain a first overview over its key pollution problems. This can serve as supportive basis for determining consecutive in-depth research requirements.