| Autor: |
Ting-Xing Chen, Hai-Shen Lyu, Robert Horton, Yong-Hua Zhu, Ren-Sheng Chen, Ming-Yue Sun, Ming-Wen Liu, Yu Lin |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Advances in Climate Change Research, Vol 15, Iss 3, Pp 406-418 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1674-9278 |
| DOI: |
10.1016/j.accre.2024.04.006 |
| Popis: |
Flood frequency in river source regions is significantly affected by rainfall and snowmelt as part of climatic changes. A traditional univariate flood frequency analysis cannot reflect the complexity of floods, and when used in isolation, it can only underestimate flood risk. For effective flood prevention and mitigation, it is essential to consider the combined effects of precipitation and snowmelt. Copula functions can effectively quantify the joint distribution relationship between floods and their associated variables without restrictions on their distribution characteristics. This study uses copula functions to consider a multivariate probability distribution model of flood peak flow (Q) with cumulative snowmelt (CSm) and cumulative precipitation (CPr) for the Hutubi River basin located in northern Xinjiang, China. The joint frequencies of rainfall and snowmelt floods are predicted using copula models based on the Coupled Model Intercomparison Project Phase 6 data. The results show that Q has a significant positive correlation with 24-d CSm (r = 0.559, p = 0.002) and 23-d CPr (r = 0.965, p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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