|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|6387990||1627745||2016||10 صفحه PDF||سفارش دهید||دانلود رایگان|
- A new climate-based approach for multivariate extreme events is presented.
- The model is based on a predictor-to-predictand weather types (WTs) classification.
- Non-stationarity is considered through changes in the occurrence probabilities of each WT.
- A Gaussian copula is used to account for the statistical dependence between variables.
- The model allows the identification of WTs related to extreme coastal flooding events.
Coastal floods often coincide with large waves, storm surge and tides. Thus, joint probability methods are needed to properly characterize extreme sea levels. This work introduces a statistical downscaling framework for multivariate extremes that relates the non-stationary behavior of coastal flooding events to the occurrence probability of daily weather patterns. The proposed method is based on recently-developed weather-type methods to predict extreme events (e.g., significant wave height, mean wave period, surge level) from large-scale sea-level pressure fields. For each weather type, variables of interest are modeled using Generalized Extreme Value (GEV) distributions and a Gaussian copula for modelling the interdependence between variables. The statistical dependence between consecutive days is addressed by defining a climate-based extremal index for each weather type. This work allows attribution of extreme events to specific weather conditions, enhancing the knowledge of climate-driven coastal flooding.
Journal: Ocean Modelling - Volume 104, August 2016, Pages 242-251