11 research outputs found
A Discontinuous Galerkin Finite Element Model for Compound Flood Simulations
Recent tropical cyclones, e.g., Hurricane Harvey (2017), have lead to
significant rainfall and resulting runoff with accompanying flooding. When the
runoff interacts with storm surge, the resulting floods can be greatly
amplified and lead to effects that cannot be modeled by simple superposition of
its distinctive sources. In an effort to develop accurate numerical simulations
of runoff, surge, and compounding floods, we develop a local discontinuous
Galerkin method for modified shallow water equations. In this modification,
nonzero sources to the continuity equation are included to incorporate rainfall
into the model using parametric rainfall models from literature as well as
hindcast data. The discontinuous Galerkin spatial discretization is accompanied
with a strong stability preserving explicit Runge Kutta time integrator. Hence,
temporal stability is ensured through the CFL condition and we exploit the
embarrassingly parallel nature of the developed method using MPI
parallelization. We demonstrate the capabilities of the developed method though
a sequence of physically relevant numerical tests, including small scale test
cases based on laboratory measurements and large scale experiments with
Hurricane Harvey in the Gulf of Mexico. The results highlight the conservation
properties and robustness of the developed method and show the potential of
compound flood modeling using our approach
State estimation of tidal hydrodynamics using ensemble Kalman filter
This paper presents a coupling of an ensemble Kalman filter (EnKF) with a discontinuous Galerkin-based, two-dimensional circulation model (DG ADCIRC-2DDI) to improve the state estimation of tidal hydrodynamics including water surface elevations and depth-integrated velocities. The methodology in this paper using EnKF perturbs the modeled hydrodynamics and bottom friction parameterization in the model while assimilating data with inherent error, and demonstrates a capability to apply EnKF within DG ADCIRC-2DDI for data assimilation. Parallel code development presents a unique aspect of the approach taken and is briefly described in the paper, followed by an application to a real estuarine system, the lower St. Johns River in north Florida, for the state estimation of tidal hydrodynamics. To test the value of gauge observations for improving state estimation, a tide modeling case study is performed for the lower St. Johns River successively using one of the four available tide gauging stations in model-data comparison. The results are improved simulations of water surface elevations and depth-integrated velocities using DG ADCIRC-2DDI with EnKF, both locally where data are available and non-locally where data are not available. The methodology, in general, is extensible to other modeling and data applications, for example, the use of remote sensing data, and specifically, can be readily applied as is to study other tidal systems. © 2013 Elsevier Ltd
State Estimation Of Tidal Hydrodynamics Using Ensemble Kalman Filter
This paper presents a coupling of an ensemble Kalman filter (EnKF) with a discontinuous Galerkin-based, two-dimensional circulation model (DG ADCIRC-2DDI) to improve the state estimation of tidal hydrodynamics including water surface elevations and depth-integrated velocities. The methodology in this paper using EnKF perturbs the modeled hydrodynamics and bottom friction parameterization in the model while assimilating data with inherent error, and demonstrates a capability to apply EnKF within DG ADCIRC-2DDI for data assimilation. Parallel code development presents a unique aspect of the approach taken and is briefly described in the paper, followed by an application to a real estuarine system, the lower St. Johns River in north Florida, for the state estimation of tidal hydrodynamics. To test the value of gauge observations for improving state estimation, a tide modeling case study is performed for the lower St. Johns River successively using one of the four available tide gauging stations in model-data comparison. The results are improved simulations of water surface elevations and depth-integrated velocities using DG ADCIRC-2DDI with EnKF, both locally where data are available and non-locally where data are not available. The methodology, in general, is extensible to other modeling and data applications, for example, the use of remote sensing data, and specifically, can be readily applied as is to study other tidal systems. © 2013 Elsevier Ltd
Modeling and data assessment of longitudinal salinity in a low-gradient estuarine river
Continuous data of vertical-profile salinity were analyzed for four stations located successively upriver in a macrotidal estuary, the lower St. Johns River (Northeast Florida, USA). The data analysis confirmed well-mixed salinity conditions in the river with at most 1.3 ppt of vertical variability at Dames Point (river km 20), where the main variations of salinity are along the longitudinal axis of the river. Given the well-mixed salinity conditions and dominant horizontal structure of salinity variations in the river, we present and apply a barotropic, two-dimensional modeling approach for hydrodynamic-salinity transport simulation in the lower St. Johns River. When properly forced by offshore surge, high-resolution wind fields and freshwater river inflows, the model replicated the salinity measurements remarkably well, including the separation into tidal and sub-tidal components. The data and model results show that, at times, offshore winds and surge can be more influential on longitudinal salinity variations than local winds over the river. We demonstrate the importance of using proper boundary conditions to force the model relative to the minimal sensitivity of the model to parameter adjustment of horizontal mixing and uncertainty-based perturbation of wind and inflow forcings