Overview of water quality modeling

Ejigu, Mamuye Tebebal

doi:10.1080/23311916.2021.1891711

Cited by 66 publications

(24 citation statements)

References 48 publications

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“…Numerous classifications of water quality models have been devised and extensively reviewed (Rode et al, 2010;Fu et al, 2020;Yuan et al, 2020). Models have been classified into lumped, semiand fully-distributed (Fu et al, 2018); simple to complex (Yuan et al, 2020); steady state or dynamic; deterministic and stochastic; physically-based, conceptual and empirical (Ejigu, 2021); catchment to global (Mayorga et al, 2010;Vilmin et al, 2020), each associated with certain assumptions and limitations. Here, we have developed a steady state probabilistic conceptual catchment model, informed by expert knowledge and empirical data.…”

Section: Developing and Testing A Probabilistic Systems-based Decisio...mentioning

confidence: 99%

A systems approach to modelling phosphorus pollution risk in Scottish rivers using a spatial Bayesian Belief Network helps targeting effective mitigation measures

Glendell

Gagkas

Stutter

et al. 2022

Front. Environ. Sci.

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Water quality remains a main reason for the failure of waterbodies to reach Good Ecological Status (GES) under the European Union Water Framework Directive (WFD), with phosphorus (P) pollution being a major cause of water quality failures. Reducing P pollution risk in agricultural catchments is challenging due to the complexity of biophysical drivers along the source-mobilisation-delivery-impact continuum. While there is a need for place-specific interventions, the evidence supporting the likely effectiveness of mitigation measures and their spatial targeting is uncertain. We developed a decision-support tool using a Bayesian Belief Network that facilitates system-level thinking about P pollution and brings together academic and stakeholder communities to co-construct a model appropriate to the region of interest. The expert-based causal model simulates the probability of soluble reactive phosphorus (SRP) concentration falling into the WFD high/good or moderate/poor status classifications along with the effectiveness of three mitigation measures including buffer strips, fertiliser input reduction and septic tank management. In addition, critical source areas of pollution are simulated on 100 × 100 m raster grids for seven catchments (12–134 km2) representative of the hydroclimatic and land use intensity gradients in Scotland. Sensitivity analysis revealed the importance of fertiliser inputs, soil Morgan P, eroded SRP delivery rate, presence/absence of artificial drainage and soil erosion for SRP losses from diffuse sources, while the presence/absence of septic tanks, farmyards and the design size of sewage treatment works were influential variables related to point sources. Model validation confirmed plausible model performance as a “fit for purpose” decision support tool. When compared to observed water quality data, the expert-based causal model simulated a plausible probability of GES, with some differences between study catchments. Reducing fertiliser inputs below optimal agronomic levels increased the probability of GES by 5%, while management of septic tanks increased the probability of GES by 8%. Conversely, implementation of riparian buffers did not have an observable effect on the probability of GES at the catchment outlet. The main benefit of the approach was the ability to integrate diverse, and often sparse, information; account for uncertainty and easily integrate new data and knowledge.

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Section: Developing and Testing A Probabilistic Systems-based Decisio...mentioning

confidence: 99%

A systems approach to modelling phosphorus pollution risk in Scottish rivers using a spatial Bayesian Belief Network helps targeting effective mitigation measures

Glendell

Gagkas

Stutter

et al. 2022

Front. Environ. Sci.

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“…Model selection is made with consideration of various conditions including research purposes, data collection, and the required level of model performance [23]. Models (including water quality models) can be generally classified as process-based and data-driven models [24,25]. The process-based model is based on scientific theories or knowledge, while the data-driven model uses data analytics or statistical techniques.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Modelling for Nitrate Nitrogen Control Using HEC-RAS: Case Study of Nakdong River in South Korea

Kim

Jonoski²,

Solomatine³

et al. 2023

Water

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The World Health Organization (WHO) and the U.S. Environmental Protection Agency (EPA) provide guidelines on the maximum levels of nitrate nitrogen (NO3-N) contained in drinking water since excess nitrate ingestion may harm human health. Thus, monitoring and controlling the NO3-N concentration is of paramount importance, especially in sources of drinking water such as the Nakdong River in South Korea. This study addresses NO3-N pollution in the Nakdong River in South Korea, where such pollution mostly comes from diffuse sources in the catchment due to the agricultural use of fertilizers. The objective of this study is to suggest guidelines for designing strategies to control NO3-N in this river using a process-based model developed with HEC-RAS. The model was built based on water quality parameters (water temperature, dissolved oxygen, ammonia nitrogen, etc.) related to NO3-N dynamics incorporating hydraulic and meteorological data. This model simulated NO3-N dynamics downstream under 55 scenarios while focusing on a section near locations of drinking water intakes. The scenarios were constructed based on variations in water quantity and quality upstream. The simulation results showed that the peak concentration of NO3-N downstream could be directly controlled by limiting the NO3-N concentration upstream. Additionally, control of the flow rate upstream could also lead to a reduction in the overall average concentration of NO3-N downstream, but this predominantly occurred when the NO3-N concentration was decreasing. In conclusion, the design and implementation of strategies for the control of NO3-N downstream should be carried out after performing a quantitative analysis of the impact of different control measures for different downstream conditions using a water quality model.

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“…Choosing any of these models depends on several criteria, such as data accessibility, simulation abilities for water quality, the intricacy of the model, waterbody type, the availability of a good model certification, and simple access to the source code of the software. In addition to the extent of the popular use of the model and its ease of application, taking into account the cost factor [18]. The QUAL2K model will be used to mimic the goodness of the River Euphrates water within Fallujah city in this research.…”

Section: Introductionmentioning

confidence: 99%

Application of QUAL2K for Water Quality Modeling and Management for the Euphrates River in Fallujah City as a Case Study

Hasham¹,

Rama²

2022

IJSDP

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The main objective of this research is an examination of the applicability of the QUAL2K mathematical model on large rivers like the Euphrates, and it is the first study of its kind conducted on this important section within Fallujah Euphrates Reach (FER), using this model for the purpose of evaluating the quality of water and creating a database to predict the quality of river. The tests were conducted during January and April of 2022, where they were calibrated and verified for DO and CBOD5 at a distance of 12.86 km. Google Earth pro and Arc-GIS were utilized to provide certain data as input into the QUAL2K model. The model was calibrated in January using the trial-and-error technique, and the verification was completed in April using the model calibration parameters. For the purposes of evaluating the model's performance, statistical measures such as mean absolute error (MAE), root mean square error (RMSE), and relative error (RE) were used. In addition, three different scenarios were applied (pollution load modification, flow augmentation for point sources, and flow augmentation for headwater) to improve the water quality of Euphrates river. The results show that the difference between observed and anticipated values had a relatively small error percentage, which means that this mathematical model can be applied to this important section of the river. While the concentration of DO was within the permissible limits to perpetuate aquatic life and maintain the ecological health of the river, the concentration of CBOD5 was also within the limits in some locations. In addition, the scenario represented by modifying the pollution loads represented by BOD5 for sewage water were effective in raising the concentration of DO in the river, while increasing point sources discharge and headwater flow does not yield meaningful results.

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Overview of water quality modeling

Cited by 66 publications

References 48 publications

A systems approach to modelling phosphorus pollution risk in Scottish rivers using a spatial Bayesian Belief Network helps targeting effective mitigation measures

A systems approach to modelling phosphorus pollution risk in Scottish rivers using a spatial Bayesian Belief Network helps targeting effective mitigation measures

Water Quality Modelling for Nitrate Nitrogen Control Using HEC-RAS: Case Study of Nakdong River in South Korea

Application of QUAL2K for Water Quality Modeling and Management for the Euphrates River in Fallujah City as a Case Study

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