Storm hazard analysis over extended geospatial grids utilizing surrogate models

Abstract The use of surrogate modeling techniques for storm surge estimation is providing unique opportunities for coastal hazard analysis and risk assessment. Specifically, surrogate models can support a comprehensive estimation of the coastal hazard/risk utilizing ensembles with large number of storm simulations. A critical challenge in this assessment is the high-dimensionality of the output, which needs to be handled efficiently both in terms of computational burden and, especially in the context of automated risk assessment tools, of memory requirements. Though dimensionality reduction techniques, like Principal Component Analysis (PCA), can address this challenge for the surrogate model calibration, the same does not apply for the hazard/risk estimation performed using the trained surrogate model. In this paper, the estimation of coastal risk using large storm ensemble predictions is examined for domains with hundreds of thousands of point locations (nodes of the grid for the storm surge computational model) for which the corresponding hazard curves need to be provided. This is achieved by exploiting their geospatial distribution and their surge response correlations within the high dimensional original output. K-means clustering is adopted to identify a small subset of nodes to serve as basis for the hazard estimation: instead of calculating the response for the storm ensemble over the entire grid, and then calculating the hazard curves, the hazard curves are first produced for this small subset, and then interpolated over the original grid. Kriging is examined for the latter geospatial interpolation, and its formulation is enhanced to support the desired application, with modifications established for both the calibration stage, as well as the efficient implementation of the interpolation. Additionally, different variants are examined for the clustering stage using information from: (i) the spatial structure of the grid, (ii) the characteristics of the response variability across the geographical domain, and (iii) the combination of the above two. A comprehensive framework is presented integrating both the surrogate model development and the hurricane hazard estimation, with the computational benefits offered in the latter estimation by the use of a small subset of nodes discussed in detail. A validation study is performed utilizing the Coastal Hazards System’s (CHS) North Atlantic Coast Comprehensive Study (NACCS) database. It is shown that the achieved numerical efficiency is significant, offering a 50 – 250-fold reduction of the computational burden with only a moderate impact on the accuracy of the estimated hazard curves.