Bottom Roughness and Bathymetry Estimation of 1-D Shallow Water Equations Model Using Ensemble Kalman Filter

Tuesday, 16 December 2014
Milad Hooshyar, Scott C Hagen and Dingbao Wang, University of Central Florida, Orlando, FL, United States
Hydrodynamic models are widely applied to coastal areas in order to predict water levels and flood inundation and typically involve solving a form of the Shallow Water Equations (SWE). The SWE are routinely discretized by applying numerical methods, such as the finite element method.

Like other numerical models, hydrodynamic models include uncertainty. Uncertainties are generated due to errors in the discrete approximation of coastal geometry, bathymetry, bottom friction and forcing functions such as tides and wind fields. Methods to counteract these uncertainties should always begin with improvements to physical characterization of: the geometric description through increased resolution, parameters that describe land cover variations in the natural and urban environment, parameters that enhance transfer of surface forcings to the water surface, open boundary forcings, and the wetting/drying brought upon by flood and ebb cycles. When the best possible physical representation is achieved, we are left with calibration and data assimilation to reduce model uncertainty.

Data assimilation has been applied to coastal hydrodynamic models to better estimate system states and/or system parameters by incorporating observed data into the model. Kalman Filter is one of the most studied data assimilation methods that minimizes the mean square errors between model state estimations and the observed data in linear systems (Kalman , 1960). For nonlinear systems, as with hydrodynamic models, a variation of Kalman filter called Ensemble Kalman Filter (EnKF), is applied to update the system state according to error statistics in the context of Monte Carlo simulations (Evensen , 2003) & (Hitoshi et. al, 2014).

In this research, Kalman Filter is incorporated to simultaneously estimate an influential parameter used in the shallow water equations, bottom roughness, and to adjust the physical feature of bathymetry. Starting from an initial estimate of bottom roughness and bathymetry, and applying EnKF, more accurate bottom roughness and bathymetry are obtained by utilizing measurements available in a limited number of gages. The procedure is examined along five typical near shore transects located in Gulf of Mexico.