English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Data assimilation has been applied to coastal hydrodynamic models to betterestimate system states or parameters by incorporating observed data into themodel. Kalman Filter (KF) is one of the most studied data assimilation methodswhose application is limited to linear systems. For nonlinear systems such ashydrodynamic 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) ofthe shallow water models. The sensitivity of the filter to 1) the quantity andprecision of the observations, and 2) the initial estimation of parameters isinvestigated in a 1-D shallow water problem located in the Gulf of Mexico.Results show that starting from an initial estimate of bottom roughness andbathymetry within a logical range and utilizing observations available at alimited number of gages the dual EnKF is able to improve the bottom roughnessand bathymetry fields. The performance of the filter is sensitive to theprecision of measured data, especially in the case of estimating Mannings n andbathymetry simultaneously.

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