The US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory (CHL) expanded the Coastal Hazards System (CHS) to quantify storm surge and wave hazards for coastal Louisiana. The CHS Louisiana (CHS-LA) coastal study was sponsored by the Louisiana Coastal Protection and Restoration Authority (CPRA) and the New Orleans District (MVN), US Army Corps of Engineers (USACE) to support Louisiana’s critical coastal infrastructure and to ensure the effectiveness of coastal storm risk management projects. The CHS-LA applied the CHS Probabilistic Coastal Hazard Analysis (PCHA) framework to quantify tropical cyclone (TC) responses, leveraging new atmospheric and hydrodynamic numerical model simulations of synthetic TCs developed explicitly for the Louisiana region. This report focuses on documenting the PCHA conducted for the CHS-LA, including details related to the characterization of storm climate, storm sampling, storm recurrence rate estimation, marginal distributions, correlation and dependence structure of TC atmospheric-forcing parameters, development of augmented storm suites, and assignment of discrete storm weights to the synthetic TCs. As part of CHS-LA, coastal hazards were estimated within the study area for annual exceedance frequencies (AEFs) over the range of 10 yr-1 to 1×10-4 yr-1.

Surrogate models, also referenced as metamodels, have emerged as attractive data-driven, predictive models for storm surge estimation. They are calibrated based on an existing database of synthetic storm simulations and can provide fast-to-compute approximations of the expected storm surge, replacing the numerical model that was used to establish this database. This paper discusses specifically the development of a kriging metamodel for the prediction of peak storm surges. For nearshore nodes that have remained dry in some of the synthetic storm simulations, a necessary first step, before the metamodel calibration, is the imputation of the database to address the missing data corresponding to such dry instances to estimate the so-called pseudo-surge. This imputation is typically performed using a geospatial interpolation technique, with the k nearest-neighbor (kNN) interpolation being the one chosen for this purpose in this paper. The pseudo-surge estimates obtained from such an imputation may lead to an erroneous classification for some instances, with nodes classified as inundated (pseudo-surge greater than the node elevation), even though they were actually dry. The integration of a secondary node classification surrogate model was recently proposed to address the challenges associated with such erroneous information. This contribution further examines the above integration and offers several advances. The benefits of implementing the secondary surrogate model are carefully examined across nodes with different characteristics, revealing important trends for the necessity of integrating the classifier in the surge predictions. Additionally, the combination of the two surrogate models using a probabilistic characterization of the node classification, instead of a deterministic one, is considered. The synthetic storm database used to illustrate the surrogate model advances corresponds to 645 synthetic tropical cyclones (TCs) developed for a flood study in the Louisiana region. The fact that various flood protective measures are present in the region creates interesting scenarios with respect to the groups of nodes that remain dry for some storms behind these protected zones. Advances in the kNN interpolation methodology, used for the geospatial imputation, are also presented to address these unique features, considering the connectivity of nodes within the hydrodynamic simulation model.

In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly-proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in continental U. S.; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.

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