{"title":"How to disentangle sea-level rise and a number of other processes influencing coastal floods?","authors":"Mirko Orlić, Miroslava Pasarić","doi":"10.1007/s12210-024-01242-z","DOIUrl":null,"url":null,"abstract":"<p>On 12 November 2019 at 21:50 UTC, about 85% of the city of Venice was flooded, due to the sea-level height reaching 189 cm—the second largest value ever recorded there. Both the operational modeling system and the machine learning system underestimated the event by about 40 cm. To explain the underestimation, the sea-level data recorded in the area were subjected to the decomposition method that had been gradually developed at the Andrija Mohorovičić Geophysical Institute over the last 40 or so years. The procedure revealed eight phenomena contributing to the sea level maximum: vertical land motion, sea-level rise, variable annual change, surge caused by planetary atmospheric waves, tide, storm surge, meteotsunami, and wind set-up inside the lagoon. It turned out that a combined contribution of the last two phenomena was almost equal to the difference between observed sea-level height and forecasted/hindcasted values. Consequently, the difference was related to a secondary atmospheric depression, which had caused both meteotsunami and wind set-up inside the lagoon but was not adequately captured by the operational modeling system nor was it allowed for by the machine learning system. Since the decomposition method proved to be useful in the Adriatic Sea, it is expected that the method could be applicable in other basins around the world if they are prone to strong and multifaceted atmospheric forcing.</p><h3 data-test=\"abstract-sub-heading\">Graphical abstract</h3>","PeriodicalId":54501,"journal":{"name":"Rendiconti Lincei-Scienze Fisiche E Naturali","volume":"23 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Rendiconti Lincei-Scienze Fisiche E Naturali","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1007/s12210-024-01242-z","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
On 12 November 2019 at 21:50 UTC, about 85% of the city of Venice was flooded, due to the sea-level height reaching 189 cm—the second largest value ever recorded there. Both the operational modeling system and the machine learning system underestimated the event by about 40 cm. To explain the underestimation, the sea-level data recorded in the area were subjected to the decomposition method that had been gradually developed at the Andrija Mohorovičić Geophysical Institute over the last 40 or so years. The procedure revealed eight phenomena contributing to the sea level maximum: vertical land motion, sea-level rise, variable annual change, surge caused by planetary atmospheric waves, tide, storm surge, meteotsunami, and wind set-up inside the lagoon. It turned out that a combined contribution of the last two phenomena was almost equal to the difference between observed sea-level height and forecasted/hindcasted values. Consequently, the difference was related to a secondary atmospheric depression, which had caused both meteotsunami and wind set-up inside the lagoon but was not adequately captured by the operational modeling system nor was it allowed for by the machine learning system. Since the decomposition method proved to be useful in the Adriatic Sea, it is expected that the method could be applicable in other basins around the world if they are prone to strong and multifaceted atmospheric forcing.
期刊介绍:
Rendiconti is the interdisciplinary scientific journal of the Accademia dei Lincei, the Italian National Academy, situated in Rome, which publishes original articles in the fi elds of geosciences, envi ronmental sciences, and biological and biomedi cal sciences. Particular interest is accorded to papers dealing with modern trends in the natural sciences, with interdisciplinary relationships and with the roots and historical development of these disciplines.