Dina Vanessa Gomez Rave, Diego Urrea Méndez, Manuel del Jesus
{"title":"河口复合洪水分析的多变量设计事件","authors":"Dina Vanessa Gomez Rave, Diego Urrea Méndez, Manuel del Jesus","doi":"10.1016/j.coastaleng.2025.104850","DOIUrl":null,"url":null,"abstract":"<div><div>Understanding Compound Flood (CF) hazard in estuaries requires moving beyond univariate approaches toward multivariate frameworks that capture the joint behavior of multiple drivers. Although the relevance of such approaches is increasingly recognized, most existing methods remain limited to bivariate analyses. Extending to higher dimensions poses conceptual and computational challenges, particularly in estimating Joint Return Periods (JRP) and defining representative design events. This limitation is especially relevant in estuarine systems, where the hazard may result from the combined action of interacting drivers — including precipitation, river discharge, storm surge, and waves — that rarely occur in isolation. In this context, restricting the analysis to two variables may overlook relevant dependencies, reinforcing the need for models that account for higher-order interactions.</div><div>This study examines the role of multivariate dependence structures within a six-dimensional case-study, comparing different copula families to evaluate their suitability for CF hazard analysis. Focusing on the Santoña estuary, we assess how model choice influences the estimation of joint events and the selection of representative conditions for design. Among the models explored, vine constructions incorporating extreme-value copulas led to more coherent joint estimates, offering improved stability across dependence scenarios. Rather than seeking a universally optimal model, the analysis illustrates how the choice of dependence structure can influence the representation of joint extremes. The proposed framework supports physically interpretable and statistically consistent multivariate design events for compound hazard analysis in coastal settings.</div></div>","PeriodicalId":50996,"journal":{"name":"Coastal Engineering","volume":"202 ","pages":"Article 104850"},"PeriodicalIF":4.5000,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multivariate design events for compound flooding analysis in estuaries\",\"authors\":\"Dina Vanessa Gomez Rave, Diego Urrea Méndez, Manuel del Jesus\",\"doi\":\"10.1016/j.coastaleng.2025.104850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Understanding Compound Flood (CF) hazard in estuaries requires moving beyond univariate approaches toward multivariate frameworks that capture the joint behavior of multiple drivers. Although the relevance of such approaches is increasingly recognized, most existing methods remain limited to bivariate analyses. Extending to higher dimensions poses conceptual and computational challenges, particularly in estimating Joint Return Periods (JRP) and defining representative design events. This limitation is especially relevant in estuarine systems, where the hazard may result from the combined action of interacting drivers — including precipitation, river discharge, storm surge, and waves — that rarely occur in isolation. In this context, restricting the analysis to two variables may overlook relevant dependencies, reinforcing the need for models that account for higher-order interactions.</div><div>This study examines the role of multivariate dependence structures within a six-dimensional case-study, comparing different copula families to evaluate their suitability for CF hazard analysis. Focusing on the Santoña estuary, we assess how model choice influences the estimation of joint events and the selection of representative conditions for design. Among the models explored, vine constructions incorporating extreme-value copulas led to more coherent joint estimates, offering improved stability across dependence scenarios. Rather than seeking a universally optimal model, the analysis illustrates how the choice of dependence structure can influence the representation of joint extremes. The proposed framework supports physically interpretable and statistically consistent multivariate design events for compound hazard analysis in coastal settings.</div></div>\",\"PeriodicalId\":50996,\"journal\":{\"name\":\"Coastal Engineering\",\"volume\":\"202 \",\"pages\":\"Article 104850\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Coastal Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378383925001553\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Coastal Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378383925001553","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Multivariate design events for compound flooding analysis in estuaries
Understanding Compound Flood (CF) hazard in estuaries requires moving beyond univariate approaches toward multivariate frameworks that capture the joint behavior of multiple drivers. Although the relevance of such approaches is increasingly recognized, most existing methods remain limited to bivariate analyses. Extending to higher dimensions poses conceptual and computational challenges, particularly in estimating Joint Return Periods (JRP) and defining representative design events. This limitation is especially relevant in estuarine systems, where the hazard may result from the combined action of interacting drivers — including precipitation, river discharge, storm surge, and waves — that rarely occur in isolation. In this context, restricting the analysis to two variables may overlook relevant dependencies, reinforcing the need for models that account for higher-order interactions.
This study examines the role of multivariate dependence structures within a six-dimensional case-study, comparing different copula families to evaluate their suitability for CF hazard analysis. Focusing on the Santoña estuary, we assess how model choice influences the estimation of joint events and the selection of representative conditions for design. Among the models explored, vine constructions incorporating extreme-value copulas led to more coherent joint estimates, offering improved stability across dependence scenarios. Rather than seeking a universally optimal model, the analysis illustrates how the choice of dependence structure can influence the representation of joint extremes. The proposed framework supports physically interpretable and statistically consistent multivariate design events for compound hazard analysis in coastal settings.
期刊介绍:
Coastal Engineering is an international medium for coastal engineers and scientists. Combining practical applications with modern technological and scientific approaches, such as mathematical and numerical modelling, laboratory and field observations and experiments, it publishes fundamental studies as well as case studies on the following aspects of coastal, harbour and offshore engineering: waves, currents and sediment transport; coastal, estuarine and offshore morphology; technical and functional design of coastal and harbour structures; morphological and environmental impact of coastal, harbour and offshore structures.