Predicting Future Flood Risks in the Face of Climate Change: A Frequency Analysis Perspective
The frequency analysis of maximum flows represents a direct method to predict future flood risks in the face of climate change. Thus, the correct use of the tools (probability distributions and methods of estimating their parameters) necessary to carry out such analyzes is required to avoid possible...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/22/3883 |
| _version_ | 1797457488322232320 |
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| author | Cristian Gabriel Anghel Cornel Ilinca |
| author_facet | Cristian Gabriel Anghel Cornel Ilinca |
| author_sort | Cristian Gabriel Anghel |
| collection | DOAJ |
| description | The frequency analysis of maximum flows represents a direct method to predict future flood risks in the face of climate change. Thus, the correct use of the tools (probability distributions and methods of estimating their parameters) necessary to carry out such analyzes is required to avoid possible negative consequences. This article presents four probability distributions from the generalized Beta families, using the L- and LH-moments method as parameter estimation. New elements are presented regarding the applicability of Dagum, Paralogistic, Inverse Paralogistic and the four-parameter Burr distributions in the flood frequency analysis. The article represents the continuation of the research carried out in the Faculty of Hydrotechnics, being part of larger and more complex research with the aim of developing a normative regarding flood frequency analysis using these methods. According to the results obtained, among the four analyzed distributions, the Burr distribution was found to be the best fit model because the theoretical values of the statistical indicators calibrated the corresponding values of the observed data. Considering the existence of more rigorous selection criteria, it is recommended to use these methods in the frequency analysis. |
| first_indexed | 2024-03-09T16:22:53Z |
| format | Article |
| id | doaj.art-a1226fa5c85245e3a85f6aaa42865531 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T16:22:53Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-a1226fa5c85245e3a85f6aaa428655312023-11-24T15:11:08ZengMDPI AGWater2073-44412023-11-011522388310.3390/w15223883Predicting Future Flood Risks in the Face of Climate Change: A Frequency Analysis PerspectiveCristian Gabriel Anghel0Cornel Ilinca1Faculty of Hydrotechnics, Technical University of Civil Engineering Bucharest, Lacul Tei, Nr. 122-124, 020396 Bucharest, RomaniaFaculty of Hydrotechnics, Technical University of Civil Engineering Bucharest, Lacul Tei, Nr. 122-124, 020396 Bucharest, RomaniaThe frequency analysis of maximum flows represents a direct method to predict future flood risks in the face of climate change. Thus, the correct use of the tools (probability distributions and methods of estimating their parameters) necessary to carry out such analyzes is required to avoid possible negative consequences. This article presents four probability distributions from the generalized Beta families, using the L- and LH-moments method as parameter estimation. New elements are presented regarding the applicability of Dagum, Paralogistic, Inverse Paralogistic and the four-parameter Burr distributions in the flood frequency analysis. The article represents the continuation of the research carried out in the Faculty of Hydrotechnics, being part of larger and more complex research with the aim of developing a normative regarding flood frequency analysis using these methods. According to the results obtained, among the four analyzed distributions, the Burr distribution was found to be the best fit model because the theoretical values of the statistical indicators calibrated the corresponding values of the observed data. Considering the existence of more rigorous selection criteria, it is recommended to use these methods in the frequency analysis.https://www.mdpi.com/2073-4441/15/22/3883floodrisksfrequency analysislinear momentsstatistical distributions |
| spellingShingle | Cristian Gabriel Anghel Cornel Ilinca Predicting Future Flood Risks in the Face of Climate Change: A Frequency Analysis Perspective Water flood risks frequency analysis linear moments statistical distributions |
| title | Predicting Future Flood Risks in the Face of Climate Change: A Frequency Analysis Perspective |
| title_full | Predicting Future Flood Risks in the Face of Climate Change: A Frequency Analysis Perspective |
| title_fullStr | Predicting Future Flood Risks in the Face of Climate Change: A Frequency Analysis Perspective |
| title_full_unstemmed | Predicting Future Flood Risks in the Face of Climate Change: A Frequency Analysis Perspective |
| title_short | Predicting Future Flood Risks in the Face of Climate Change: A Frequency Analysis Perspective |
| title_sort | predicting future flood risks in the face of climate change a frequency analysis perspective |
| topic | flood risks frequency analysis linear moments statistical distributions |
| url | https://www.mdpi.com/2073-4441/15/22/3883 |
| work_keys_str_mv | AT cristiangabrielanghel predictingfuturefloodrisksinthefaceofclimatechangeafrequencyanalysisperspective AT cornelilinca predictingfuturefloodrisksinthefaceofclimatechangeafrequencyanalysisperspective |