To inform the decision-making of coastal protection against sea level rise (SLR), we have to estimate the impact of shoreline alterations on the hydrodynamics. This task involves estimating of a large number of shoreline decision combinations. Here we present a sensitivity analysis based approach to understand how the variation in the shoreline, for example, due to construction of seawalls at different location along the shoreline, would impact the water levels over the coastal region under SLR. To facilitate efficient sensitivity analysis for expensive high-fidelity numerical models with high-dimensional outputs, we propose a Dimension REduced Surrogate based Sensitivity Analysis (DRESSA) method. DRESSA uses Principal Component Analysis (PCA) to exploit the correlation in the high-dimensional outputs to find a low-dimensional latent output representation, then builds a surrogate model for the latent outputs based on a small number of runs of the high-fidelity numerical model. In the end, DRESSA first establishes relevant covariance matrices for the low-dimensional latent outputs using the surrogate model, and then directly establishes sensitivity indexes for the high-dimensional outputs using these covariance matrices and the derived transformation between sensitivity in latent space and original space. We applied this method to generate sensitivity maps and investigate the impact of different containment strategies on peak water level (PWL) over the entire San Francisco Bay under SLR.
- Data-driven analysis
- Principal Component Analysis (PCA)
- Sea-level rise
- Surrogate assisted sensitivity analysis