To design marine structures in deep water, currents must be modelled accurately as a function of depth. These models often take the form of T-year profiles, which assume the T-year extreme current speed occurs simultaneously at each depth. To better reflect the spatial correlation in the current speeds versus depth, we have recently introduced Turkstra current profiles. These assign the T-year speed at one depth, and “associated” speeds expected to occur simultaneously at other depths. Two essentially decoupled steps are required: (1) marginal analysis to estimate T-year extremes, and (2) some type of regression to find associated values. The result is a set of current profiles, each of which coincides with the T-year profile at a single depth and is reduced elsewhere. Our previous work with Turkstra profiles suggested that, when applied in an unbiased fashion, they could produce unconservative estimates of extreme loads. This is in direct contrast to the findings of Statoil, whose similar (“CCA”) current profiles have generally been found to yield conservative load estimates. This paper addresses this contradiction. In the process, we find considerable differences can arise in precisely how one performs steps 1 and 2 above. The net finding is to favor methods that properly emphasize the upper tails of the data—e.g., using peak-over-threshold (“POT”) data, and regression based on class means—rather than standard analyses that weigh all data equally. By applying such tail-sensitive methods to our dataset, we find the unconservative trend in Turkstra profiles to essentially vanish. For our data, these tail-fit results yield profiles with both larger marginal extremes, and broader profiles surrounding these extremes—hence the title of this paper.
New vulnerabilities of historic urban centers and archaeological sites: Extreme loads
