Articles | Volume 10, issue 12
https://doi.org/10.5194/gmd-10-4511-2017
https://doi.org/10.5194/gmd-10-4511-2017
Methods for assessment of models
 | 
08 Dec 2017
Methods for assessment of models |  | 08 Dec 2017

Sensitivity analysis of a coupled hydrodynamic-vegetation model using the effectively subsampled quadratures method (ESQM v5.2)

Tarandeep S. Kalra, Alfredo Aretxabaleta, Pranay Seshadri, Neil K. Ganju, and Alexis Beudin

Abstract. Coastal hydrodynamics can be greatly affected by the presence of submerged aquatic vegetation. The effect of vegetation has been incorporated into the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system. The vegetation implementation includes the plant-induced three-dimensional drag, in-canopy wave-induced streaming, and the production of turbulent kinetic energy by the presence of vegetation. In this study, we evaluate the sensitivity of the flow and wave dynamics to vegetation parameters using Sobol' indices and a least squares polynomial approach referred to as the Effective Quadratures method. This method reduces the number of simulations needed for evaluating Sobol' indices and provides a robust, practical, and efficient approach for the parameter sensitivity analysis. The evaluation of Sobol' indices shows that kinetic energy, turbulent kinetic energy, and water level changes are affected by plant stem density, height, and, to a lesser degree, diameter. Wave dissipation is mostly dependent on the variation in plant stem density. Performing sensitivity analyses for the vegetation module in COAWST provides guidance to optimize efforts and reduce exploration of parameter space for future observational and modeling work.

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Short summary
The paper details the sensitivity of vegetation properties that are input to a 3-D submerged aquatic vegetation model within a coupled hydrodynamics and wave model. It describes a novel strategy to perform sensitivity analysis efficiently by using a combination of the Effective Quadratures method and Sobol' indices. This method reduces the number of simulations to understand the sensitivity patterns and also quantifies the amount of sensitivity.