Integrated Hydrodynamic and Water Quality Modeling System to Support Nutrient Total Maximum Daily Load Development for Wissahickon Creek, Pennsylvania

Zou, Rui; Carter, Stephen; Shoemaker, Leslie; Parker, Andrew; Henry, Thomas

doi:10.1061/(asce)0733-9372(2006)132:4(555)

Cited by 67 publications

(33 citation statements)

References 4 publications

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“…The model has been applied to a variety of modelling studies. EFDC could provide the capability of internally linking four major modules: hydrodynamics, water quality, sediment transport, toxics [19], [20]. In our research, there are 380×241 grids in the simulation domain of model, the grid resolution is 150 metres, the physical domain is from (-9.71891E, 52.97371N) (left at the bottom) to (-8.87716E, 53.03773N) (right on the top).…”

Section: Numerical Modellingmentioning

confidence: 99%

Sensitivity Tests of Direct Insertion Data Assimilation with Pseudo Measurements

Liu¹,

Hartnett²

2014

IJCCE

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Abstract-Data Assimilation (DA) was a technique to improve the modelling forecasting ability. The measured data was blended with model background states in the DA process. Whether the model was robust to synthesize the measured data needs to be studied. In our research, Direct Insertion (DI) data assimilation was applied to update the model background states. In order to test the sensitivity of Direct Insertion data assimilation, five kinds of data assimilation model with different time interval were utilized. The influence of Direct Insertion data assimilation on surface layer was also studied. A small test area was chosen for data assimilation. Constant pseudo measurements were used to update the surface velocity components in data assimilation domain.

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Section: Numerical Modellingmentioning

confidence: 99%

Sensitivity Tests of Direct Insertion Data Assimilation with Pseudo Measurements

Liu¹,

Hartnett²

2014

IJCCE

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“…The EFDC model is one of the most extensively applied and technically defensible hydrodynamic models in the world (Wu and Xu, 2011). It has been successfully applied for modeling the flow circulation, thermal stratification, sediment transport, water quality, and eutrophication processes in numerous rivers, reservoirs, lakes and estuaries (Hamrick, 1992;Ji et al, 2001;Zou et al, 2006;Liu and Huang, 2009;Li et al, 2011;Wu and Xu, 2011). Model details are provided in Hamrick (1992) and Craig (2011).…”

Section: Numerical Simulationmentioning

confidence: 99%

Impact of revised thermal stability on pollutant transport time in a deep reservoir

Wang

Jiang

et al. 2016

Journal of Hydrology

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Keywords:Thermal stratification Revised Schmidt stability Water age Deep reservoir Three-dimensional model s u m m a r y Thermal stability (Schmidt stability) and water age, which are significantly related to water quality and algae bloom in deep reservoirs, are two crucial indicators of stratification strength and pollutant transport time, respectively. Here, the original Schmidt stability, which was derived from a onedimensional assumption, was theoretically extended to a three-dimensional water body. In addition, a three-dimensional model was verified for the case study of Hongfeng Reservoir in China based on data from 2009 and 2010. Although the revised stability was similar to the original stability of Hongfeng Reservoir, which occurred at a relatively low level, the greater stratification in other deep water bodies would enhance their difference. Air temperature and water depth were the most important factors of the temporal variation in stability and the spatial variation in stability, respectively. The pollutant transport processes in the Hongfeng Reservoir was very complex with alternate appearances of overflow, interflow and underflow, depending on the season. The spatial water age was primarily determined by the morphometry and the inflow/outflow (with the highest water age in North Lake), whereas the vertical difference in the water age among the layers was primarily controlled by thermal stratification. Negative linear relationships between the average stability and the water ages of the bottom layers in three representative sites during summer were observed. Positive linear relationships between the average stability and the water ages of the surface layers were also observed. These findings enable a better understanding of the hydrodynamic and pollutant transport processes in a deep reservoir.

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“…The model uses a sigma vertical coordinate system, and either regular, or curvilinear orthogonal horizontal coordinates. The model had been applied to a variety of modelling studies of rivers, lakes, estuaries, and coastal regions [33][34][35].…”

Section: Numerical Modelmentioning

confidence: 99%

Hindcasting and Forecasting of Surface Flow Fields through Assimilating High Frequency Remotely Sensing Radar Data

Ren

Hartnett

2017

Remote Sensing

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Abstract:In order to improve the forecasting ability of numerical models, a sequential data assimilation scheme, nudging, was applied to blend remotely sensing high-frequency (HF) radar surface currents with results from a three-dimensional numerical, EFDC (Environmental Fluid Dynamics Code) model. For the first time, this research presents the most appropriate nudging parameters, which were determined from sensitivity experiments. To examine the influence of data assimilation cycle lengths on forecasts and to extend forecasting improvements, the duration of data assimilation cycles was studied through assimilating linearly interpolated temporal radar data. Data assimilation nudging parameters have not been previously analyzed. Assimilation of HF radar measurements at each model computational timestep outperformed those assimilation models using longer data assimilation cycle lengths; root-mean-square error (RMSE) values of both surface velocity components during a 12 h model forecasting period indicated that surface flow fields were significantly improved when implementing nudging assimilation at each model computational timestep. The Data Assimilation Skill Score (DASS) technique was used to quantitatively evaluate forecast improvements. The averaged values of DASS over the data assimilation domain were 26% and 33% for east-west and north-south velocity components, respectively, over the half-day forecasting period. Correlation of Averaged Kinetic Energy (AKE) was improved by more than 10% in the best data assimilation model. Time series of velocity components and surface flow fields were presented to illustrate the improvement resulting from data assimilation application over time.

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Integrated Hydrodynamic and Water Quality Modeling System to Support Nutrient Total Maximum Daily Load Development for Wissahickon Creek, Pennsylvania

Cited by 67 publications

References 4 publications

Sensitivity Tests of Direct Insertion Data Assimilation with Pseudo Measurements

Sensitivity Tests of Direct Insertion Data Assimilation with Pseudo Measurements

Impact of revised thermal stability on pollutant transport time in a deep reservoir

Hindcasting and Forecasting of Surface Flow Fields through Assimilating High Frequency Remotely Sensing Radar Data

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