Water quality assessment for a coastal zone through numerical modeling

Menéndez, Ángel N.; Badano, Nicolás D.; Lopolito, M. F.; Re, Mariano

doi:10.1080/23249676.2013.827892

Cited by 15 publications

(10 citation statements)

References 21 publications

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“…Ji et al, 2008;Luo et al, 2012). These models have been successfully coupled to biogeochemical models to study nutrient cycling and plankton blooms (Khangaonkar et al, 2012;Ji et al, 2008), and water quality (Menendez et al, 2013). In addition to multiscale resolution which alters resolution spatially, it is also possible to alter the resolution temporally -mesh adaptivity, which aims to alter resolution only when and where it is required (e.g.…”

Section: Introductionmentioning

confidence: 99%

Adapting to life: ocean biogeochemical modelling and adaptive remeshing

et al. 2014

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Abstract. An outstanding problem in biogeochemical modelling of the ocean is that many of the key processes occur intermittently at small scales, such as the sub-mesoscale, that are not well represented in global ocean models. This is partly due to their failure to resolve sub-mesoscale phenomena, which play a significant role in vertical nutrient supply. Simply increasing the resolution of the models may be an inefficient computational solution to this problem. An approach based on recent advances in adaptive mesh computational techniques may offer an alternative. Here the first steps in such an approach are described, using the example of a simple vertical column (quasi-1-D) ocean biogeochemical model.We present a novel method of simulating ocean biogeochemical behaviour on a vertically adaptive computational mesh, where the mesh changes in response to the biogeochemical and physical state of the system throughout the simulation. We show that the model reproduces the general physical and biological behaviour at three ocean stations (India, Papa and Bermuda) as compared to a high-resolution fixed mesh simulation and to observations. The use of an adaptive mesh does not increase the computational error, but reduces the number of mesh elements by a factor of 2-3. Unlike previous work the adaptivity metric used is flexible and we show that capturing the physical behaviour of the model is paramount to achieving a reasonable solution. Adding biological quantities to the adaptivity metric further refines the solution. We then show the potential of this method in two case studies where we change the adaptivity metric used to determine the varying mesh sizes in order to capture the dynamics of chlorophyll at Bermuda and sinking detritus at Papa. We therefore demonstrate that adaptive meshes may provide a suitable numerical technique for simulating seasonal or transient biogeochemical behaviour at high vertical resolution whilst minimising the number of elements in the mesh. More work is required to move this to fully 3-D simulations.

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Section: Introductionmentioning

confidence: 99%

Adapting to life: ocean biogeochemical modelling and adaptive remeshing

et al. 2014

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“…To study pollutant dispersion in the Dumai estuary as impact of domestic sewage from Dumai city, this research simulated a numerical model which was developed using MIKE 21 as in many similar studies [3], [4], [5], [6]. MIKE 21 is a model frequently used for coastal simulations, developed by DHI (Danish Hydraulic Institute Environment).…”

Section: Introductionmentioning

confidence: 99%

Numerical model for pollutant dispersion in the Dumai estuary

Sutikno

Merian

2017

MATEC Web Conf.

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Abstract. The development of industry and the urban population in Dumai city potentially increases the amount of domestic sewage into the Dumai River, which is gradually deposited in the Dumai estuary. This condition has an impact on the environment and public health because of the higher concentration of pollutants. Knowing the dispersion of pollutants is very important in order to manage and monitor the water quality in the Dumai estuary. This research simulated a numerical model that was developed using MIKE 21 to study pollutant dispersion in the Dumai estuary as an impact of domestic sewage from Dumai city. Three important parameters of pollutants were discussed in this study, such as ammonia, nitrates and nitrites. The data used for modeling included winds, tides, bathymetry, and the pollutant concentration. The result of the numerical simulations shows that the concentration of all pollutant parameters tends to increase in the Dumai estuary. The concentration of ammonia, nitrates, and nitrites increase to 0.4 mg/L, 0.15 mg/L, and 0.14 mg/L respectively. Those concentrations exceed the standard maximum value that has been appointed by Indonesian regulations. It means the domestic sewage from Dumai city should be treated before it is discharged into the river.

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“…To know the current status of water quality modelling, a brief literature review on the existing process-based water quality models is summarized in Table 1.2. This table shows that, although recently few studies have been conducted in the catchments in developing countries (Vijay et al, 2016, Nguyen et al, 2016, Menendez et al, 2013, most studies exist only for catchments in developed countries (mainly United States [12] and Europe). This thesis reduces this apparent knowledge gap by focusing on a developing country.…”

Section: Application Of Process-based Modelling and Scenario Analysismentioning

confidence: 99%

“…Sunlight and temperature have the greatest impact on faecal coliform and E. coli (Gao et al, 2015, Liu et al, 2015b, Ouattara et al, 2013. However, due to lack of input data, the decay processes of FIB have been modelled using a constant decay rate in most studies (Bedri et al, 2014, Vijay et al, 2016, Menendez et al, 2013. In this thesis the decay rates of FIB were estimated based on all the three environmental parameters (temperature, salinity and sunlight intensity) measured at the study sites.…”

Section: Modelling the Fate And Transport Of Fib In The River Water (mentioning

confidence: 99%

“…Examples are the SWAT model that includes a pathogen transport model (Coffey et al, 2010a, Parajuli et al, 2009, Chin et al, 2009), the MIKE model that is coupled with a water quality module ECOLab (Bedri et al, 2014, Sokolova et al, 2013, Vijay et al, 2016, the model developed by Dorner et al (2006) that uses the WATFLOOD hydrological model, and the EG model that uses hydrology from the SimHyd model (Haydon and Deletic, 2006). Generally the microbial models simulate microbial concentrations or loads for a catchment (Coffey et al, 2010a, Ferguson et al, 2007 and water source (Sokolova et al, 2013, Menendez et al, 2013. The available models still have many limitations which include, for example, lack of available observed microbial and input data (Hofstra, 2011).…”

Section: Application Of Process-based Modelling and Scenario Analysismentioning

confidence: 99%

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Assessing the impact of socio-economic development and climate change on faecal indicator bacteria in the Betna River, Bangladesh

Islam¹

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Water quality assessment for a coastal zone through numerical modeling

Cited by 15 publications

References 21 publications

Adapting to life: ocean biogeochemical modelling and adaptive remeshing

Adapting to life: ocean biogeochemical modelling and adaptive remeshing

Numerical model for pollutant dispersion in the Dumai estuary

Assessing the impact of socio-economic development and climate change on faecal indicator bacteria in the Betna River, Bangladesh

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