The existing hydrodynamic models consider full physics approaches to calculate storm surge at coastal regions. However, due to the complexity of the equations that model these processes, the computational time and power required to run them can be large, compared to models that c
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The existing hydrodynamic models consider full physics approaches to calculate storm surge at coastal regions. However, due to the complexity of the equations that model these processes, the computational time and power required to run them can be large, compared to models that consider simplified equations. By contrast, simplified hydraulic models lack physical background, what leads to a minor accuracy in the surge estimations with respect to hydrodynamic models. In this project, a stochastic model has been developed with the objective to estimate surge at the coast of Mississippi (United States) at a reasonable accuracy and time, without solving equations that represent complex physical processes. The stochastic model needs to be trained by means of hurricane data, including surge levels. This information must be generated beforehand, by simulating a limited number of hurricanes with an hydrodynamic model. In this project, the hydrodynamic model Delft3D Flexible Mesh has been used for this purpose. The approach followed to build the stochastic model has been based on three main steps. The first step has been setting up and validating the hydrodynamic model in Delft3D FM. Hurricane Katrina (2005) has been simulated to calibrate the input parameters of the model, by comparing the maximum simulated water levels at 41 stations along the shoreline of Mississippi to the high water marks observed at the same locations during the event. Tide, surge and wave setup have been considered in the validation. The results of the validation show a line best fit slope from the origin of 0.912 and an R-squared of 0.996. At Gulfport, the absolute error of the surge estimation is 19 centimeters, equivalent to a relative error of 2.5%. The second step in the construction of the stochastic model has been the generation of a historical hurricane data base. The hurricane best tracks have been retrieved from the HURDAT2 data base. The variables considered have been the forward speed and the forward direction of the hurricane at landfall, the wind speed at landfall, the distance from landfall location to a reference point (Galveston Bay) and the maximum storm surge during the hurricane. In this case, only the hurricane forcing is considered as external action. The storm surge has been recorded at Gulfport Harbour (central coast of Mississippi). The values of the surge have been obtained by using the validated model to simulate the historical hurricanes making landfall in a rectangular domain of 600 kilometers, being Gulfport Harbour the center of the rectangle. Due to the scarce number of hurricanes making landfall in this region, the tracks of the hurricanes making landfall in the North of the Gulf of Mexico but outside the rectangular domain have been shifted inside the domain, in order to generate a sufficiently large data base to train the stochastic model. A data base with 140 hurricanes has been built, from which the 85% (119 hurricanes) have been used for the training of the stochastic model and the other 15% (21 hurricanes) have been used for the validation of the stochastic model. The third and last step has been the setup and validation of the stochastic model, by comparing the storm surge obtained from the stochastic model to the surge obtained from the hydrodynamic simulations. The stochastic model used to estimate storm surge has been a Bayesian Network that assumes normal copulas to represent the joint distribution between nodes of the network. The calculated slope of the best fit line for the mean surge values has been 0.861, with an R-squared of 0.885. Moreover, the average standard deviation of the estimations is 1.16 meters. These results indicate a reasonable estimation of the surge by means of the Bayesian Network. This estimation can be made in the order of seconds.
