Water Quality Modeling and Evaluation of Nutrient Control Strategies Using QUAL2K in the Small Rivers (original) (raw)
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Application of Qual2Kw model as a tool for water quality management: Cértima River as a case study
Environmental Monitoring and Assessment, 2011
Modelling can be a useful management tool because models allow the understanding of water body response to different pollution pressure scenarios which may help on the decision-making process and in prosecuting the Water Framework Directive objectives. This study aims to evaluate the usage of simple water quality models (Qual2Kw) applied to small river basins in order to better understand the response of a river to different loads of nitrogen and phosphorus. Qual2Kw model was applied to Cértima River (Portugal), a small river that ends in a shallow lake called Pateira Fermentelos and represents a very important ecosystem to the local community. Along its pathway, Cértima River has a significant enrichment in nutrients due to agriculture, livestock, domestic sewage and industrial effluents discharged into the river. In case of nitrogen, the highest loads are from domestic (44%) and diffuse (35%) sources. The main sources of phosphorous are domestic (46%), livestock (24%) and diffuse sources (20%). Cértima River is strongly enriched with nutrients, and neither nitrogen nor phosphorous is limiting the algal growth. According to the criterion of Dodds et al. (Water Res, 32(5):1455-1462, 1998), the river is classified as eutrophic. By comparing in stream measurements with Qual2Kw simulations, it can be concluded that it would be necessary to decrease the actual pollutants loads of nitrogen and phosphorous 5 and 10 times, respectively, in order to change Cértima River classification from eutrophic to mesotrophic.
Application of QUAL2Kw for water quality modeling and dissolved oxygen control in the river Bagmati
Environmental Monitoring and Assessment, 2007
A stream water quality model, QUAL2Kw, was calibrated and validated for the river Bagmati of Nepal. The model represented the field data quite well with some exceptions. The influences of various water quality management strategies have on DO concentrations were examined considering: (i) pollution loads modification; (ii) flow augmentation; (iii) local oxygenation. The study showed the local oxygenation is effective in raising DO levels. The combination of wastewater modification, flow augmentation and local oxygenation is necessary to ensure minimum DO concentrations. This reasonable modeling guarantees the use of QUAL2Kw for future river water quality policy options.
Water Quality Model for Streams: A Review
Journal of Environmental Protection, 2019
Six main public domain water quality models which are presently available for Rivers and streams are being captured in this article. These main models could produce important results if they are used in the correct manner, because they are different in terms of assumptions, strength and weaknesses, processes they represent, modeling capability and data input requirements. The Model review discussed includes, water quality analysis simulation program (WASP7), simulation catchment (SIMCAT), quality simulation along Rivers (QUASAR), and the temporal overall model for catchment (TOMCAT), QUAL2KW, QUAL2EU. The models are described individually according to a consistent set of criteria-conceptualization, model capability, model strengths, limitations, input data and how it utilized. The outcome showed that TOMCAT and SIMCAT are important in ASSESSING effect of point sources in a very simple way. The QUAL2KW, unlike the QUAL2EU where macrophytes play a major interaction, it can convert algal death to carbonaceous Biochemical Oxygen Demand (CBOD), thereby making it more suitable. In addition to the extensive requirement of data, it is expensive and time consuming to set up these complex models such as QUASAR and WASP7. Therefore, one model cannot be used for all the required functional-ities. Choosing a model would depend on a specific application, financial cost and time availability. This article may be of help in choosing a suitable model for a specific water quality problem.
Model-based analysis of nutrient retention and management for a lowland river
Hydrology and Earth System Sciences Discussions, 2005
In the context of the European Water Framework Directive options for improving the water quality of the lowland river Havel (Germany) were assessed. The lower section of this river is actually a polytrophic river-lake system suffering from high external nutrient loading and exhibiting significant in-river turnover. In order to gain a better understand-5 ing of present conditions and to allow integrated scenarios of nutrient management to be evaluated the catchment models SWIM and ArcEGMO-Urban were coupled with a simple, newly developed nutrient TRAnsport Model (TraM). Using the TraM model, the retention of nitrogen and phosphorus in a 55 km reach of the Lower Havel River was quantified and its temporal variation was analyzed. It was examined that about 30% 10 20 25 the representation of individual water bodies in catchment models is often poor. This 2550 HESSD EGU is especially true for regulated rivers and river-lake systems, which exhibit a unique behavior with respect to nutrient retention. Since the European Water Framework Directive (WFD) focuses on the ecological status of individual river sections and lakes, there is an increasing need for linking catchment models to water quality models of adequate space-time resolution and complexity (Van Griensven and Bauwens, 2003).
Modelling of Nutrient Emissions in River Systems – MONERIS – Methods and Background
MONERIS is a semi-empirical, conceptual model, which has gained international acceptance as a robust meso-to macro scale model for nutrient emissions. MONERIS is used to calculate nitrogen (N) and phosphorus (P) emissions into surface waters, in-stream retention, and resulting loads, on a river catchment scale. This paper provides the first (i) comprehensive overview of the model structure (both the original elements and the new additions), (ii) depiction of the algorithms used for all pathways, and for retention in surface waters, and (iii) illustration of the monthly disaggregation of emissions and the implementation of measures. The model can be used for different climatic conditions, long term historical studies, and for future development scenarios. The minimum validated spatial resolution is 50 km 2 , with a temporal resolution of yearly or monthly time steps. The model considers seven emission pathways (atmospheric deposition on surface waters, overland flow, erosion, tile drainage, groundwater, emissions from sealed urban areas, and point sources), and six emission sources (natural background, fertilizer application, nitrogen atmospheric deposition on arable land and other areas, urban sources, and point sources); and these are calculated separately for different land-uses. The pathway and source-related approach is a prerequisite for the implementation of measures to reduce non-point and point-source emissions. Therefore, we have modified MONERIS by the addition of a "management alternative" tool which can identify the potential effectiveness of nutrient reduction measures. MON-ERIS is an appropriate tool for addressing the scientific and political aspects of river basin management in support of a good surface water quality.
Application of Automated QUAL2Kw for Water Quality Modeling in the Kabini River, Mysuru
International Journal of Scientific Research in Science and Technology, 2021
River Kabini, during its course through Sutturu village, Mysuru district in Karnataka state (India), receives agricultural runoff and untreated domestic waste on the bank of the river. The present study involves the application of water quality model QUAL2Kw to predict the water quality of the selected river stretch. The model was calibrated and validated for Dissolved Oxygen, Organic Nitrogen, Ammonium Nitrogen, Nitrate Nitrogen, Organic Phosphorus, and Inorganic Phosphorus 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 Root mean square errors (RMSE). The RMSE for Dissolved Oxygen, Organic Nitrogen, Ammonium Nitrogen, Nitrate Nitrogen, Organic Phosphorus, and Inorganic Phosphorus during calibration are 2.86, 11.42, 14.11, 12.68, 3.25 and 12.70. Corresponding values for the validation are 1.04, 1.16, 0.05, 0.04, 0.29 and 0.68. In spite of some differences between the measured and simulated data sets at some points, the calibration and validation results are acceptable.
Iraqi Journal of Civil Engineering, 2017
The current study includes application of QUAL2K model to predict the dissolved oxygen (DO) and Biochemical Oxygen Demand (BOD5) of lower reach of the Diyala River in a stretch of 16.90km using hydraulic and water quality data collected from Ministry of Water Resources for the period (January-April 2014). Google Earth and Arc-GIS technique were used in this study as supported tools to provide some QUAL2K input hydro-geometric data. The model parameters were calibrated for the dry flow period by trial and error until the simulated results agreed well with the observed data. The model performance was measured using different statistical criteria such as mean absolute error (MAE), root mean square error (RMSE) and relative error (RE). The results showed that the simulated values were in good agreement with the observed values. Model output for calibration showed that DO and CBOD concentration were not within the allowable limits for preserving the ecological health of the river with range values (2.51-4.80 mg/L) and (18.75-25.10 mg/L) respectively. Moreover, QUAL2K was used to simulate different scenarios (pollution loads modification, flow augmentation and local oxygenation) in order to manage the water quality during critical period (low flow), and to preserve the minimum requirement of DO concentration in the river. The scenarios results showed the pollution loads modification and local oxygenation are effective in raising DO levels. While flow augmentation does not give significant results in which the level of DO decrease even with reduction in the BOD5 for point sources. The combination of wastewater modification and local oxygenation (BOD5 of the discharged effluent from point sources should not exceed 15 mg/L and weir construction at critical positions 6.67km from the beginning of the study region with 1m height) is necessary to ensure minimum DO concentrations.
WATER QUALITY MODELLING AND ASSESSMENT OF THE BURIGANGA RIVER USING QUAL2K
Global Mainstream Journal
The Buriganga River runs through Dhaka's south and west sides. Due to the anthropogenic involvement of essential pollutants such as industrial effluents, urban sewage, and solid wastes in this area, the water quality of this river has been a source of worry. The Dissolved Oxygen of the river water showed a variation from 0.50 mg/L to 3.5 mg/l. The BOD of the river water was shown in a variation of 27.50 mg/L to 129.7 mg/L, where the mean value was 78.6 mg/L. Although Biochemical Oxygen Demand (BOD) is not a water quality parameter, it is the most widely utilized indicator of a surface water body's overall health. The COD of water from the river Buriganga was 65.
River water quality modelling: II. Problems of the art
Water Science and Technology, 1998
The U.S. EPA QUAL2 model is currently the standard for river water quality modelling. While QUAL2 is adequate for the regulatory situation for which it was developed (the U.S. wasteload allocation process), there is a need for a more comprehensive framework for research and teaching. Moreover, QUAL2 and similar models do not address a number of practical problems such as stormwater flow events, nonpoint source pollution, and transient streamflow. Limitations in model formulation affect the ability to close mass balances, to represent sessile bacteria and other benthic processes, and to achieve robust model calibration. Mass balance problems aris e from failure to account for mass in the sediment as well as in the water column and due to the fundamental imprecision of BOD as a state variable.