Modelling phytoplankton dynamics in a complex estuarine system

Hartnett, Michael; Dabrowski, Tomasz

doi:10.1680/wama.800087

Cited by 18 publications

(18 citation statements)

References 5 publications

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“…In previous modelling studies (e.g. Nash and Hartnett (2010)) the Authors have found that spatial gradients of water quality parameter concentrations can show strong correlation with spatial gradients of residence times. In addition, spatial distributions of residence times can also clearly identify areas where waters are either well-mixed or poorly-mixed/stagnant.…”

Section: Sub-division Of the Waterbodymentioning

confidence: 93%

“…Data requirements for the hydrodynamic module included water depths, tidal forcings and freshwater inflows, as well as calibration and validation data in the form of flow and water level measurements. Dispersion and diffusion coefficients were required for the solute transport module; these were obtained by calibration with dye surveys (see Nash and Hartnett (2010) for more details).…”

Section: Model Development and Data Collectionmentioning

confidence: 99%

“…The composition of the industrial and domestic discharges and associated flow rates specified to the model are listed in Table 2; Figure 4 shows their respective locations. For more complete details of data collection and model development readers are referred to Nash and Hartnett (2010).…”

Section: Model Development and Data Collectionmentioning

confidence: 99%

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An integrated approach to trophic assessment of coastal waters incorporating measurement, modelling and water quality classification

Hartnett

Olbert

2012

Estuarine, Coastal and Shelf Science

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Section: Sub-division Of the Waterbodymentioning

confidence: 93%

Section: Model Development and Data Collectionmentioning

confidence: 99%

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An integrated approach to trophic assessment of coastal waters incorporating measurement, modelling and water quality classification

Hartnett

Olbert

2012

Estuarine, Coastal and Shelf Science

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“…MSN has been extensively tested and validated for experimental and natural test cases (see [21][22][23] for details). (Note: MSN was in turn developed from the hydro-biological model DIVAST (depth integrated velocity and solute transport) [24] which has also been widely used and validated (e.g., [25,26])). MSN_TT was created by modifying MSN to simulate the impacts of tidal turbines and using one-way nesting to resolve the flow between neighbouring turbines.…”

Section: Methodsmentioning

confidence: 99%

Towards a Low-Cost Modelling System for Optimising the Layout of Tidal Turbine Arrays

Olbert

Hartnett

2015

Energies

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Abstract:In the long-term, tidal turbines will most likely be deployed in farms/arrays where energy extraction by one turbine may significantly affect the energy available to another turbine. Given the prohibitive cost of experimental and/or field investigations of such turbine interactions, numerical models can play a significant role in determining the optimum layout of tidal turbine arrays with respect to energy capture. In the present research, a low-cost modelling solution for optimising turbine array layouts is presented and assessed. Nesting is used in a far-field model to telescope spatial resolution down to the scale of the turbines within the turbine array, allowing simulation of the interactions between adjacent turbines as well as the hydrodynamic impacts of individual turbines. The turbines are incorporated as momentum sinks. The results show that the model can compute turbine wakes with similar far-field spatial extents and velocity deficits to those measured in published experimental studies. The results show that optimum spacings for multi-row arrays with regard to power yield are 3-4 rotor diameters (RD) across-stream and 1-4 RD along-stream, and that turbines in downstream rows should be staggered to avoid wake effects of upstream turbines and to make use of the accelerated flows induced by adjacent upstream turbines.

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“…Increasingly, models are being extended to more complex systems in which the movement of the water is just one aspect of the system. The paper in this issue by Nash et al (2011) describes a generic methodology for modelling water quality within estuarine systems involving the modelling of nutrient dynamics and phytoplankton growth. Estuaries are increasingly subject to development pressures and it is important that methods are developed to model such systems so that the impact of future development can be simulated.…”

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confidence: 99%

Editorial

Bettess¹

2011

Proceedings of the Institution of Civil Engineers - Water Manag

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The models used to describe most water systems are complex and the development of water management as a discipline is a history of the development of modelling techniques. The papers in this issue illustrate some of the range of modelling problems that currently arise in water management.The first paper in this issue by Wang et al. (2011) on modelling of urban flooding due to a dike break adds to the literature on flood risk assessment. The importance of urban flood risk to society has been highlighted by recent floods in urban areas. It is difficult to overestimate the economic and social impact of such floods. In the UK, floods such as that in Carlisle in 2005 are still being studied (Horritt et al., 2010) because of their impact and as a means to develop and refine modelling techniques. As urban areas develop and with the expected future climate change it is likely that the risk of urban flooding will increase. A framework for such flood risk assessment was described by Sayers et al. (2008). One of the key problems of implementation, however, is to develop fast models of flood spreading or inundation over an irregular topography (Lamb et al., 2009 andWang et al., 2010). Modelling flood spreading in urban areas presents many challenges due to the nature of the topography. In this issue, the paper by Wang et al. (2011) demonstrates the importance of mesh resolution and the estimation of hydraulic roughness on the model predictions for such problems.The authors of the first paper illustrate the importance of accurate predictions of hydraulic roughness for flow modelling. Our ability to predict channel roughness has improved significantly recently; see for example, McGahey et al. (2009). Their approach relies, however, on having descriptions of the contributions to hydraulic roughness from different physical processes. The second paper in this issue (Kumar, 2011) adds to this information by providing a description of hydraulic roughness in mobile bed channels. The paper demonstrates the benefits that can be derived from collecting and using data that already exists in the literature. The paper represents an application of neural networks to a topic in water management. Although initially very suspicious of the use of neural networks over more traditional approaches, I was convinced after seeing a demonstration that neural networks can provide a better approximation to data than methods based on assuming some functional form for the relationship between the variables involved. Therefore although the use of neural networks may not seem to add to our understanding of the fundamental physics of a problem, they can provide improved tools for prediction.The next paper by Kangrang et al. (2011) represents an interesting application of the optimisation technique simulated annealing to a longstanding problem of the optimum use of reservoirs. The method of simulated annealing may be particularly suited to global optimisation problems in water management where noisy data makes the application of other optimisation techniq...

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Modelling phytoplankton dynamics in a complex estuarine system

Cited by 18 publications

References 5 publications

An integrated approach to trophic assessment of coastal waters incorporating measurement, modelling and water quality classification

An integrated approach to trophic assessment of coastal waters incorporating measurement, modelling and water quality classification

Towards a Low-Cost Modelling System for Optimising the Layout of Tidal Turbine Arrays

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