Case Study: Modeling of Sediment Transport and Wind-Wave Impact in Lake Okeechobee

Jin, Kang-Ren; Ji, Zhen‐Gang

doi:10.1061/(asce)0733-9429(2004)130:11(1055)

Cited by 97 publications

(64 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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: 98%

Sensitivity Tests of Direct Insertion Data Assimilation with Pseudo Measurements

Liu¹,

Hartnett²

2014

IJCCE

View full text Add to dashboard Cite

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.

show abstract

Section: Numerical Modellingmentioning

confidence: 98%

Sensitivity Tests of Direct Insertion Data Assimilation with Pseudo Measurements

Liu¹,

Hartnett²

2014

IJCCE

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Podsetchine and Huttula [50] addressed the correlation and time lag of wind direction/speed to resuspended sediment in Lake Karhijärvi, Finland. Jin and Ji [51] applied a three-dimensional model to investigate the effects of wind velocity and fetch and wind persistence on suspended-sediment concentrations in Lake Okeechobee. The mean circulation in the TFL would be one of the important factors to affect the transport and distribution of suspended sediment.…”

Section: Influence Of Wind Stress On Current and Suspended Sedimentmentioning

confidence: 99%

“…It implies that the water quality conditions in the lake may be affected by the wind stress. Jin and Ji [51] documented that the effects of wind-induced current and wind-induced resuspension on suspended-sediment transport were important factors affecting the suspended-sediment concentration in Lake Okeechobee. Cozar et al [52] presented empirical correlations between turbidity and wind speed based on the field measurements.…”

Section: Influence Of Wind Stress On Current and Suspended Sedimentmentioning

confidence: 99%

Investigating Suspended-Sediment Transport in a Shallow Lake Using a Three-Dimensional Model

Li¹,

Liu²,

Chiu³

2017

Preprint

View full text Add to dashboard Cite

A three-dimensional, unstructured grid, hydrodynamic and suspended-sediment transport model (i.e., SELFE-SED) was developed to simulate temporal and spatial variations of suspended sediment and was applied to the subtropical subalpine Tsuei-Feng Lake (TFL) of Taiwan. The model was validated with measured water level and suspended-sediment concentration in 2009, 2010, and 2011. The overall model simulation results are in quantitative agreement with the observational data. The validated model was then applied to explore the most important parameter that affects the suspended-sediment concentration and to investigate the effect of wind stress on the mean current and suspended-sediment distribution in this shallow lake. Modeling results of sensitivity analysis reveal that the settling velocity is a crucial parameter and erosion rate is less important in the suspended-sediment transport model. Remarkable lake circulation was found based on the strength of wind speed and wind direction. Strong wind would result in higher mean current in the top layer and suspended-sediment distribution in the top and bottom layers. This study demonstrated that the wind stress played a s ignificant influence on mean circulation and suspended-sediment transport in a shallow lake.

show abstract

Case Study: Modeling of Sediment Transport and Wind-Wave Impact in Lake Okeechobee

Cited by 97 publications

References 34 publications

Sensitivity Tests of Direct Insertion Data Assimilation with Pseudo Measurements

Sensitivity Tests of Direct Insertion Data Assimilation with Pseudo Measurements

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

Investigating Suspended-Sediment Transport in a Shallow Lake Using a Three-Dimensional Model

Contact Info

Product

Resources

About