A Dual EnKF for Estimating Water Level, Bottom Roughness, and Bathymetry in a 1-D Hydrodynamic Model

TL;DR

This paper introduces a dual Ensemble Kalman Filter approach for simultaneously estimating water surface elevation, bottom roughness, and bathymetry in a 1-D hydrodynamic model, demonstrating its effectiveness with limited observations.

Contribution

It develops and tests a dual EnKF method for joint state and parameter estimation in shallow water models, addressing nonlinear system challenges.

Findings

The dual EnKF improves bottom roughness and bathymetry estimates with limited data.

Estimation accuracy depends on the initial parameter guesses and data precision.

The method is sensitive to measurement accuracy, especially for multiple parameters.

Abstract

Data assimilation has been applied to coastal hydrodynamic models to better estimate system states or parameters by incorporating observed data into the model. Kalman Filter (KF) is one of the most studied data assimilation methods whose application is limited to linear systems. For nonlinear systems such as hydrodynamic models a variation of the KF called Ensemble Kalman Filter (EnKF) is applied to update the system state in the context of Monte Carlo simulation. In this research, a dual EnKF approach is used to simultaneously estimate state (water surface elevation) and parameters (bottom roughness and bathymetry) of the shallow water models. The sensitivity of the filter to 1) the quantity and precision of the observations, and 2) the initial estimation of parameters is investigated in a 1-D shallow water problem located in the Gulf of Mexico. Results show that starting from an initial estimate of bottom roughness and bathymetry within a logical range and utilizing observations available at a limited number of gages the dual EnKF is able to improve the bottom roughness and bathymetry fields. The performance of the filter is sensitive to the precision of measured data, especially in the case of estimating Mannings n and bathymetry simultaneously.