Twenty‐One‐Year Simulation of Chesapeake Bay Water Quality Using the CE‐QUAL‐ICM Eutrophication Model

Cerco, Carl F.; Noel, Mark R.

doi:10.1111/jawr.12107

Cited by 75 publications

(77 citation statements)

References 28 publications

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“…The IRL system is shallower (depths of 1.5-3 m) than the coastal systems used in previous studies by Moses et al [25] and Mishra and Mishra [22]. The Azov Sea and Delaware Bay used by Moses [24,27] had average depths of 9 m and 2.5-10 m, respectably [32] and 6-30 m in the Chesapeake Bay used by Mishra and Mishra [33].…”

Section: Meris Data Acquisition and Preprocessingmentioning

confidence: 59%

Monitoring of the 2011 Super Algal Bloom in Indian River Lagoon, FL, USA, Using MERIS

2015

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During the spring of 2011 an unprecedented "Super" algal bloom formed in the Indian River Lagoon (IRL), with Chlorophyll a (Chl a) concentrations over eight times the historical mean in some areas and lasted for seven months across the IRL. The European Space Agency's MEdium Resolution Imaging Spectrometer (MERIS) platform provided multispectral data at 665 and 708 nm, which was used to quantify the phytoplankton Chl a by fluorescence while minimizing the effects of other water column constituents. The three objectives were to: (1) calibrate and validate two Chl a algorithms using all available MERIS data of the IRL from 2002 to 2012; (2) determine the accuracy of the algorithms estimation of Chl a before, during, and after the 2011 super bloom; and (3) map the 2011 algal bloom using the Chl a algorithm that was proven to be effective in other similar estuaries. The chosen algorithm, Normalized Difference Chlorophyll Index (NDCI), was positively correlated with the in-situ measurements, with an R 2 value of 0.798. While there was a significant (62.9 ± 25%) underestimation of Chl a using MERIS NDCI, the underestimation appears to be consistent across the data and mostly in the estimations of lower concentrations, suggesting that a qualitative or ratio analysis is still valid. Analysis of the application of the NDCI processed MERIS data provided additional insights that the in-situ

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Section: Meris Data Acquisition and Preprocessingmentioning

confidence: 59%

Monitoring of the 2011 Super Algal Bloom in Indian River Lagoon, FL, USA, Using MERIS

2015

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“…It involves interactions between various algae and various nutrients, which takes into account the process of algal growth, metabolism, predation and settling. At present, CE-QUAL-ICM has been successfully applied to the study of eutrophication processes of various water bodies [72,73]. However, it is considered to be difficult to calibrate due to inevitable data noise and the high dimensionality of the parameter space.…”

Section: Case IVmentioning

confidence: 99%

Parameter Estimation of Water Quality Models Using an Improved Multi-Objective Particle Swarm Optimization

Wang

Hua

Wang

2018

Water

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Water quality models are of great importance for developing policies to control water pollution, with the model parameters playing a decisive role in the simulation results. It is necessary to introduce estimation through multi-objective parameters, which is often affected by noise in the data, into water quality models. This paper presents a multi-objective particle swarm optimization algorithm, which is based on the Mahalanobis distance operation, mechanism of cardinality preference and advection-diffusion operator. The Mahalanobis distance operation can effectively reduce the influence of noise in the data on model calibration. The mechanism of cardinality preference and the use of the advection-diffusion operator can prevent non-dominated solutions from falling into the local optimum. Four cases were used to test the proposed approach. The first two cases with true Pareto fronts show that this approach can accurately estimate the true Pareto front with a good distribution, even in the presence of noise. Furthermore, the application of the approach was tested by the O'Connor model and Crops of Engineers Integrated Compartment Water Quality Model. We show that our approach can produce satisfactory results for the multi-objective calibration of complex water quality models. In general, the proposed approach can provide accurate and efficient parameter estimation in water quality models.

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“…Modeling results revealed that high algae concentrations in estuaries are likely caused by excessive nutrient and algae supply in freshwater inflows. Cerco and Noel [43] applied the CE-QUAL-ICM (Corps of Engineers Integrated Compartment Water Quality Model) eutrophication model to simulate a 21-year (1985-2005) water quality model of Chesapeake Bay. The most significant finding was the influence of physical processes, notably stratification and associated effects (e.g., anoxic volume), on computed water quality.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Climate Change Impacts on Water Quality in a Tidal Estuarine System Using a Three-Dimensional Model

Liu

Chan

2016

Water

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Abstract:Climate change is one of the key factors affecting the future quality and quantity of water in rivers and tidal estuaries. A coupled three-dimensional hydrodynamic and water quality model has been developed and applied to the Danshuei River estuarine system in northern Taiwan to predict the influences of climate change on water quality. The water quality model considers state variables including nitrogen, phosphorus, organic carbon, and phytoplankton as well as dissolved oxygen, and is driven by a three-dimensional hydrodynamic model. The hydrodynamic water quality model was validated with observational salinity distribution and water quality state variables. According to the analyses of statistical error, predictions of salinity, dissolved oxygen, and nutrients from the model simulation quantitatively agreed with the observed data. The validated model was then applied to predict water quality conditions as a result of projected climate change effects. The simulated results indicated that the dissolved oxygen concentration was projected to significantly decrease whereas nutrients will increase because of climate change. Moreover, the dissolved oxygen concentration was lower than 2 mg/L in the main stream of the Danshuei River estuary and failed to meet the water quality standard. An appropriate strategy for effective water quality management for tidal estuaries is needed given the projected persistent climate trends.

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Twenty‐One‐Year Simulation of Chesapeake Bay Water Quality Using the CE‐QUAL‐ICM Eutrophication Model

Cited by 75 publications

References 28 publications

Monitoring of the 2011 Super Algal Bloom in Indian River Lagoon, FL, USA, Using MERIS

Monitoring of the 2011 Super Algal Bloom in Indian River Lagoon, FL, USA, Using MERIS

Parameter Estimation of Water Quality Models Using an Improved Multi-Objective Particle Swarm Optimization

Assessment of Climate Change Impacts on Water Quality in a Tidal Estuarine System Using a Three-Dimensional Model

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