Abstract
Abstract. Occurrences of tropical cyclones at a location are rare, and for many locations, only short periods of observations or hindcasts are available. Hence, estimation of return values (corresponding to a period considerably longer than that for which data are available) for cyclone-induced significant wave height (SWH) from small samples is challenging. The STM-E (space-time maximum and exposure) model was developed to provide reduced bias in estimates of return values compared to competitor approaches in such situations and realistic estimates of return value uncertainty. STM-E exploits data from a spatial neighbourhood satisfying certain conditions, rather than data from a single location, for return value estimation. This article provides critical assessment of the STM-E model for tropical cyclones in the Caribbean Sea near Guadeloupe for which a large database of synthetic cyclones is available, corresponding to more than 3000 years of observation. Results indicate that STM-E yields values for the 500-year return value of SWH and its variability, estimated from 200 years of cyclone data, consistent with direct empirical estimates obtained by sampling 500 years of data from the full synthetic cyclone database; similar results were found for estimation of the 100-year return value from samples corresponding to approximately 50 years of data. In general, STM-E also provides reduced bias and more realistic uncertainty estimates for return values relative to single-location analysis.
Highlights
Tropical cyclones are one of the deadliest and most devastating natural hazards that can significantly impact lives, economies, and the environment in coastal areas
We use the space-time maximum (STM)-E method to estimate the T = 500-year return value for significant wave height (SWH), and its uncertainty, based on random samples of tropical cyclones corresponding to T0 = 200 years of observation
This work considers the estimation of T -year return values for SWH over a geographic region, from small sets of T0 years of synthetic tropical cyclone data, using the STM-E methodology
Summary
Tropical cyclones ( named hurricanes or typhoons depending on the region of interest) are one of the deadliest and most devastating natural hazards that can significantly impact lives, economies, and the environment in coastal areas. For many locations, only short periods of observations or hindcasts of tropical cyclones are available, which can be challenging for estimation of return values (corresponding to a period considerably longer than that for which data are available) For this purpose, a widely used approach relies on the combination of synthetic cyclone track generation, wave modelling, and extreme value analysis. The hydrodynamic numerical model can be prohibitively costly to execute, limiting the number of model runs feasible, resulting in sparse, non-representative data for extreme value modelling To overcome this computational burden, possible solutions can either be based on parametric analytical models (like the ones used by Stephens and Ramsay, 2014, in the southwest Pacific Ocean) or on statistical predictive models (sometimes called meta- or surrogate models; Nadal-Caraballo et al, 2020). Statistical estimation is problematic, since inferences must be made concerning extreme quantiles of the distribution of quantities such as SWH, using a limited set of data
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