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1000
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Abstract/Summary
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<jats:p>Abstract. The NW Mediterranean coast is highly susceptible to the impacts of extreme rainstorms and coastal storms, which often lead to flash floods, coastal erosion, and flooding across a highly urbanised territory. Often, these storms occur simultaneously, resulting in compound events that intensify local impacts when they happen in the same location or spread impacts across the territory when they occur in different areas. These multivariate and spatially compound events present significant challenges for risk management, potentially overwhelming emergency services. In this study, we analysed the prevailing atmospheric conditions during various types of extreme episodes, aiming to create the first classification of synoptic weather patterns (SWPs) conducive to compound events involving heavy rainfall and storm waves in the Spanish NW Mediterranean. To achieve this, we developed a methodological framework that combines an objective synoptic classification method based on principal component analysis and k-means clustering with a Bayesian network. This methodology was applied to a dataset comprising 562 storm events recorded over 30 years, including 112 compound events. First, we used the framework to determine the optimal combination of domain size, classification variables, and number of clusters based on the synoptic skill to replicate local-scale values of daily rainfall and significant wave height. Subsequently, we identified SWPs associated with extreme compound events, which are often characterised by upper-level lows and trough structures in conjunction with Mediterranean cyclones, resulting in severe to extreme coastal storms combined with convective systems. The obtained classification demonstrated strong skill, with scores exceeding 0.4 when considering factors like seasonality or the North Atlantic Oscillation. These findings contribute to a broader understanding of compound terrestrial–maritime extreme events in the study area and have the potential to aid in the development of effective risk management strategies.
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1000
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Hinweis
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DeepGreen-ID: 61ccabc3c2104e40a5955ad42dda4226 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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