A Benchmarking Exercise on Estimating Extreme Environmental Conditions: Methodology and Baseline Results

Haselsteiner, Andreas F.; Coe, Ryan G.; Manuel, Lance; Nguyen, Phong T. T.; Martin, Nevin; Eckert-Gallup, Aubrey Celia

doi:10.1115/omae2019-96523

Cited by 16 publications

(15 citation statements)

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“…We note that this procedure bears many similarities to the approach introduced in Haselsteiner et al (2019) to characterise uncertainty of environmental contours. They also consider lines starting at a defined origin and the corresponding points of intersection for each line on an estimated contour and use bootstrapping to construct median and confidence interval estimates of the contour.…”

Section: Quantifying Uncertainty For Return Curve Estimatesmentioning

confidence: 95%

New estimation methods for extremal bivariate return curves

Murphy-Barltrop¹,

Wadsworth²,

Eastoe³

2021

Preprint

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In the multivariate setting, defining extremal risk measures is important in many contexts, such as finance, environmental planning and structural engineering. In this paper, we review the literature on extremal bivariate return curves, a risk measure that is the natural bivariate extension to a return level, and propose new estimation methods based on multivariate extreme value models that can account for both asymptotic dependence and asymptotic independence. We identify gaps in the existing literature and propose novel tools for testing and validating return curves and comparing estimates from a range of multivariate models. These tools are then used to compare a selection of models through simulation and case studies. We conclude with a discussion and list some of the challenges.

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Section: Quantifying Uncertainty For Return Curve Estimatesmentioning

confidence: 95%

New estimation methods for extremal bivariate return curves

Murphy-Barltrop¹,

Wadsworth²,

Eastoe³

2021

Preprint

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“…An alternative approach to characterizing temporal sensitivity could utilize the bootstrap method. This approach has been used in the EC Benchmark study [33] and in other previous contour studies; see, e.g., [23,27,31,40]. However, the forward chaining approach has been proposed in this work as a novel method of making comparisons temporally across various training and test sets with potentially fewer and faster calculations than might be required under the bootstrap method.…”

Section: Temporal Aggregationmentioning

confidence: 99%

“…Similarly, a study presented by Hiles et al [32] reviewed methods for analyzing extreme metocean data including environmental contour methods, and applied these methods to a case study of the Canadian Pacific coast to produce a spatial analysis of extreme wave conditions. Another ongoing effort is a cooperative benchmarking exercise in which researchers have come together to present their environmental contour methods [33]. 2 This effort employs wave datasets from three National Data Buoy Center (NDBC) buoys operated by the U.S. National Oceanic and Atmospheric Administration (NOAA), along with three wind and wave datasets from the coastDat-2 hindcast [34].…”

Section: Introductionmentioning

confidence: 99%

“…2 This effort employs wave datasets from three National Data Buoy Center (NDBC) buoys operated by the U.S. National Oceanic and Atmospheric Administration (NOAA), along with three wind and wave datasets from the coastDat-2 hindcast [34]. A set of common comparison methods for this benchmarking exercise, including qualitative approaches as well as quantitative approaches, was proposed in [33].…”

Section: Introductionmentioning

confidence: 99%

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Development of a Comparison Framework for Evaluating Environmental Contours of Extreme Sea States

Eckert

Martin

Coe

et al. 2020

JMSE

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Environmental contours of extreme sea states are often utilized for the purposes of reliability-based offshore design. Many methods have been proposed to estimate environmental contours of extreme sea states, including, but not limited to, the traditional inverse first-order reliability method (I-FORM) and subsequent modifications, copula methods, and Monte Carlo methods. These methods differ in terms of both the methodology selected for defining the joint distribution of sea state parameters and in the method used to construct the environmental contour from the joint distribution. It is often difficult to compare the results of proposed methods to determine which method should be used for a particular application or geographical region. The comparison of the predictions from various contour methods at a single site and across many sites is important to making environmental contours of extreme sea states useful in practice. The goal of this paper is to develop a comparison framework for evaluating methods for developing environmental contours of extreme sea states. This paper develops generalized metrics for comparing the performance of contour methods to one another across a collection of study sites, and applies these metrics and methods to develop conclusions about trends in the wave resource across geographic locations, as demonstrated for a pilot dataset. These proposed metrics and methods are intended to judge the environmental contours themselves relative to other contour methods, and are thus agnostic to a specific device, structure, or field of application. The metrics developed and applied in this paper include measures of predictive accuracy, physical validity, and aggregated temporal performance that can be used to both assess contour methods and provide recommendations for the use of certain methods in various geographical regions. The application and aggregation of the metrics proposed in this paper outline a comparison framework for environmental contour methods that can be applied to support design analysis workflows for offshore structures. This comparison framework could be extended in future work to include additional metrics of interest, potentially including those to address issues pertinent to a specific application area or analysis discipline, such as metrics related to structural response across contour methods or additional physics-based metrics based on wave dynamics.

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“…The real database measured from one wave buoy was selected 48 ; it is located off the Louisiana coast (28.508 N, 80.185 W) in the Gulf of Mexico. Here, a moored buoy (NDBC 42010) set by the U.S. National Oceanic and Atmospheric Administration (NOAA) provides 10 years of hourly measured wave data including a consistent time series of H s and T z .…”

Section: Evaluation Of Short-term Extreme Response Parametersmentioning

confidence: 99%

Long-term extreme response analysis for semi-submersible platform mooring systems

Zhao

Dong

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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The accurate assessment of long-term extreme responses of floating-structure mooring system designs is important because of small failure probabilities caused by long-term and complex ocean conditions. The most accurate assessment would involve considering all conceivable sea states in which each sea state is regarded as a stochastic process and performing nonlinear time-domain numerical simulations of mooring systems to estimate the extreme response from a long-term analysis. This procedure would be computationally intensive because of the numerous short-term sea states involved. Here, a more feasible approach to evaluate the long-term extreme response is presented through immediate integration combined with Monte Carlo simulations. A parameter fitting procedure of the short-term extreme response distribution under irregular wave conditions is employed to solve the long-term response integration. Case studies were conducted on a semi-submersible platform using environmental data measurements of the Gulf of Mexico and a joint distribution model of the environmental parameters was considered. This approach was observed to be effective and the results were compared with those of traditional methodologies (univariate extreme value design and environmental contour methods). The differences were reflected using a reliability analysis of mooring lines, which indicated that the design standards must be stricter when using long-term analysis.

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A Benchmarking Exercise on Estimating Extreme Environmental Conditions: Methodology and Baseline Results

Cited by 16 publications

References 0 publications

New estimation methods for extremal bivariate return curves

New estimation methods for extremal bivariate return curves

Development of a Comparison Framework for Evaluating Environmental Contours of Extreme Sea States

Long-term extreme response analysis for semi-submersible platform mooring systems

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