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Nonstationary flood coincidence risk analysis using time-varying copula functions
The coincidence of flood flows in a mainstream and its tributaries may lead to catastrophic floods. In this paper, we investigated the flood coincidence risk under nonstationary conditions arising from climate changes. The coincidence probabilities considering flood occurrence dates and flood magnit...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042327/ https://www.ncbi.nlm.nih.gov/pubmed/32099000 http://dx.doi.org/10.1038/s41598-020-60264-3 |
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author | Feng, Ying Shi, Peng Qu, Simin Mou, Shiyu Chen, Chen Dong, Fengcheng |
author_facet | Feng, Ying Shi, Peng Qu, Simin Mou, Shiyu Chen, Chen Dong, Fengcheng |
author_sort | Feng, Ying |
collection | PubMed |
description | The coincidence of flood flows in a mainstream and its tributaries may lead to catastrophic floods. In this paper, we investigated the flood coincidence risk under nonstationary conditions arising from climate changes. The coincidence probabilities considering flood occurrence dates and flood magnitudes were calculated using nonstationary multivariate models and compared with those from stationary models. In addition, the “most likely” design based on copula theory was used to provide the most likely flood coincidence scenarios. The Huai River and Hong River were selected as case studies. The results show that the highest probabilities of flood coincidence occur in mid-July. The marginal distributions for the flood magnitudes of the two rivers are nonstationary, and time-varying copulas provide a better fit than stationary copulas for the dependence structure of the flood magnitudes. Considering the annual coincidence probabilities for given flood magnitudes and the “most likely” design, the stationary model may underestimate the risk of flood coincidence in wet years or overestimate this risk in dry years. Therefore, it is necessary to use nonstationary models in climate change scenarios. |
format | Online Article Text |
id | pubmed-7042327 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70423272020-03-03 Nonstationary flood coincidence risk analysis using time-varying copula functions Feng, Ying Shi, Peng Qu, Simin Mou, Shiyu Chen, Chen Dong, Fengcheng Sci Rep Article The coincidence of flood flows in a mainstream and its tributaries may lead to catastrophic floods. In this paper, we investigated the flood coincidence risk under nonstationary conditions arising from climate changes. The coincidence probabilities considering flood occurrence dates and flood magnitudes were calculated using nonstationary multivariate models and compared with those from stationary models. In addition, the “most likely” design based on copula theory was used to provide the most likely flood coincidence scenarios. The Huai River and Hong River were selected as case studies. The results show that the highest probabilities of flood coincidence occur in mid-July. The marginal distributions for the flood magnitudes of the two rivers are nonstationary, and time-varying copulas provide a better fit than stationary copulas for the dependence structure of the flood magnitudes. Considering the annual coincidence probabilities for given flood magnitudes and the “most likely” design, the stationary model may underestimate the risk of flood coincidence in wet years or overestimate this risk in dry years. Therefore, it is necessary to use nonstationary models in climate change scenarios. Nature Publishing Group UK 2020-02-25 /pmc/articles/PMC7042327/ /pubmed/32099000 http://dx.doi.org/10.1038/s41598-020-60264-3 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Feng, Ying Shi, Peng Qu, Simin Mou, Shiyu Chen, Chen Dong, Fengcheng Nonstationary flood coincidence risk analysis using time-varying copula functions |
title | Nonstationary flood coincidence risk analysis using time-varying copula functions |
title_full | Nonstationary flood coincidence risk analysis using time-varying copula functions |
title_fullStr | Nonstationary flood coincidence risk analysis using time-varying copula functions |
title_full_unstemmed | Nonstationary flood coincidence risk analysis using time-varying copula functions |
title_short | Nonstationary flood coincidence risk analysis using time-varying copula functions |
title_sort | nonstationary flood coincidence risk analysis using time-varying copula functions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042327/ https://www.ncbi.nlm.nih.gov/pubmed/32099000 http://dx.doi.org/10.1038/s41598-020-60264-3 |
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